diff --git a/.gitignore b/.gitignore
index d2d6f36..8f35883 100644
--- a/.gitignore
+++ b/.gitignore
@@ -33,3 +33,13 @@ nosetests.xml
 .mr.developer.cfg
 .project
 .pydevproject
+
+# Mac files
+.DS_Store
+
+# IDE config
+.idea/
+
+# Notebooks checkpoints
+examples/.ipynb_checkpoints/
+
diff --git a/New_HexagonalTopology.ipynb b/New_HexagonalTopology.ipynb
new file mode 100644
index 0000000..a705d79
--- /dev/null
+++ b/New_HexagonalTopology.ipynb
@@ -0,0 +1,396 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example we will see how to plot an hexagonal map that reflects the results of the training process. This example is an extension of BasicUsage.ipynb. Only the plotting section has new code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'bokeh'",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-1-f1b6755161c7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlines\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mLine2D\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mbokeh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolors\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mRGB\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mbokeh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mio\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcurdoc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshow\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_notebook\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mbokeh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mfactor_mark\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfactor_cmap\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'bokeh'"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.patches import RegularPolygon, Ellipse\n",
+    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
+    "from matplotlib import cm, colorbar\n",
+    "from matplotlib.lines import Line2D\n",
+    "\n",
+    "from bokeh.colors import RGB\n",
+    "from bokeh.io import curdoc, show, output_notebook\n",
+    "from bokeh.transform import factor_mark, factor_cmap\n",
+    "from bokeh.models import ColumnDataSource, HoverTool\n",
+    "from bokeh.plotting import figure, output_file\n",
+    "\n",
+    "# display matplotlib plots in notebook\n",
+    "%matplotlib inline\n",
+    "# display bokeh plot in notebook\n",
+    "output_notebook()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Load, Preprocess and Training\n",
+    "We load the seeds dataset, normalise the data along the columns and then train our SOMs. \n",
+    "\n",
+    "> Note, we are training a hexagonal topology because we will be plotting this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      " [ 1000 / 1000 ] 100% - 0:00:00 left \n",
+      " quantization error: 0.45378615630601005\n",
+      "0.1523809523809524\n",
+      "0.45378615630601005\n",
+      "0.45378615630601005\n",
+      "0.15238095238095237\n"
+     ]
+    }
+   ],
+   "source": [
+    "from minisom import MiniSom\n",
+    "data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/00236/seeds_dataset.txt', \n",
+    "                   names=['area', 'perimeter', 'compactness', 'length_kernel', 'width_kernel',\n",
+    "                          'asymmetry_coefficient', 'length_kernel_groove', 'target'], \n",
+    "                   sep='\\t+')\n",
+    "t = data['target'].values\n",
+    "data = data[data.columns[:-1]]\n",
+    "\n",
+    "# data normalization\n",
+    "data = (data - np.mean(data, axis=0)) / np.std(data, axis=0)\n",
+    "data = data.values\n",
+    "\n",
+    "# initialization and training of 15x15 SOM\n",
+    "som = MiniSom(15, 15, data.shape[1], sigma=1.5, learning_rate=.7, activation_distance='euclidean',\n",
+    "              topology='hexagonal', neighborhood_function='gaussian', random_seed=10)\n",
+    "\n",
+    "som.train(data, 1000, verbose=True)\n",
+    "\n",
+    "# some_file.py\n",
+    "import sys\n",
+    "# insert at 1, 0 is the script path (or '' in REPL)\n",
+    "sys.path.insert(1, '/path/to/application/app/folder')\n",
+    "\n",
+    "print(som.calculate_hexa_topographical_error(data))\n",
+    "print(som.quantization_error(data))\n",
+    "print(som.partitioned_quant_error(data,20))\n",
+    "print(som.partitioned_topo_error(data,20))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "\n",
+    "## Plotting: matplotlib\n",
+    "Below, we are plotting using matplotlib to create a static hexagonal topology."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xx, yy = som.get_euclidean_coordinates()\n",
+    "umatrix = som.distance_map()\n",
+    "weights = som.get_weights()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = plt.figure(figsize=(10,10))\n",
+    "ax = f.add_subplot(111)\n",
+    "\n",
+    "ax.set_aspect('equal')\n",
+    "\n",
+    "# iteratively add hexagons\n",
+    "for i in range(weights.shape[0]):\n",
+    "    for j in range(weights.shape[1]):\n",
+    "        wy = yy[(i, j)] * 2 / np.sqrt(3) * 3 / 4\n",
+    "        hex = RegularPolygon((xx[(i, j)], wy), \n",
+    "                             numVertices=6, \n",
+    "                             radius=.95 / np.sqrt(3),\n",
+    "                             facecolor=cm.Blues(umatrix[i, j]), \n",
+    "                             alpha=.4, \n",
+    "                             edgecolor='gray')\n",
+    "        ax.add_patch(hex)\n",
+    "\n",
+    "markers = ['o', '+', 'x']\n",
+    "colors = ['C0', 'C1', 'C2']\n",
+    "for cnt, x in enumerate(data):\n",
+    "    # getting the winner\n",
+    "    w = som.winner(x)\n",
+    "    # place a marker on the winning position for the sample xx\n",
+    "    wx, wy = som.convert_map_to_euclidean(w) \n",
+    "    wy = wy * 2 / np.sqrt(3) * 3 / 4\n",
+    "    plt.plot(wx, wy, \n",
+    "             markers[t[cnt]-1], \n",
+    "             markerfacecolor='None',\n",
+    "             markeredgecolor=colors[t[cnt]-1], \n",
+    "             markersize=12, \n",
+    "             markeredgewidth=2)\n",
+    "\n",
+    "xrange = np.arange(weights.shape[0])\n",
+    "yrange = np.arange(weights.shape[1])\n",
+    "plt.xticks(xrange-.5, xrange)\n",
+    "plt.yticks(yrange * 2 / np.sqrt(3) * 3 / 4, yrange)\n",
+    "\n",
+    "divider = make_axes_locatable(plt.gca())\n",
+    "ax_cb = divider.new_horizontal(size=\"5%\", pad=0.05)    \n",
+    "cb1 = colorbar.ColorbarBase(ax_cb, cmap=cm.Blues, \n",
+    "                            orientation='vertical', alpha=.4)\n",
+    "cb1.ax.get_yaxis().labelpad = 16\n",
+    "cb1.ax.set_ylabel('distance from neurons in the neighbourhood',\n",
+    "                  rotation=270, fontsize=16)\n",
+    "plt.gcf().add_axes(ax_cb)\n",
+    "\n",
+    "legend_elements = [Line2D([0], [0], marker='o', color='C0', label='Kama',\n",
+    "                   markerfacecolor='w', markersize=14, linestyle='None', markeredgewidth=2),\n",
+    "                   Line2D([0], [0], marker='+', color='C1', label='Rosa',\n",
+    "                   markerfacecolor='w', markersize=14, linestyle='None', markeredgewidth=2),\n",
+    "                   Line2D([0], [0], marker='x', color='C2', label='Canadian',\n",
+    "                   markerfacecolor='w', markersize=14, linestyle='None', markeredgewidth=2)]\n",
+    "ax.legend(handles=legend_elements, bbox_to_anchor=(0.1, 1.08), loc='upper left', \n",
+    "          borderaxespad=0., ncol=3, fontsize=14)\n",
+    "\n",
+    "plt.savefig('resulting_images/som_seed_hex.png')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting: bokeh\n",
+    "Below, we are plotting using bokeh to create an interactive hexagonal topology.\n",
+    "\n",
+    "> Note: Compared to matplotlib plot, this is rotated 90 degrees."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hex_centre_col, hex_centre_row = [], []\n",
+    "hex_colour = []\n",
+    "label = []\n",
+    "\n",
+    "# define labels\n",
+    "SPECIES = ['Kama', 'Rosa', 'Canadian']\n",
+    "\n",
+    "for i in range(weights.shape[0]):\n",
+    "    for j in range(weights.shape[1]):\n",
+    "        wy = yy[(i, j)] * 2 / np.sqrt(3) * 3 / 4\n",
+    "        hex_centre_col.append(xx[(i, j)])\n",
+    "        hex_centre_row.append(wy)\n",
+    "        hex_colour.append(cm.Blues(umatrix[i, j]))\n",
+    "\n",
+    "weight_x, weight_y = [], []\n",
+    "for cnt, i in enumerate(data):\n",
+    "    w = som.winner(i)\n",
+    "    wx, wy = som.convert_map_to_euclidean(xy=w)\n",
+    "    wy = wy * 2 / np.sqrt(3) * 3 / 4\n",
+    "    weight_x.append(wx)\n",
+    "    weight_y.append(wy)\n",
+    "    label.append(SPECIES[t[cnt]-1])\n",
+    "    \n",
+    "# convert matplotlib colour palette to bokeh colour palette\n",
+    "hex_plt = [(255 * np.array(i)).astype(int) for i in hex_colour]\n",
+    "hex_bokeh = [RGB(*tuple(rgb)).to_hex() for rgb in hex_plt]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "  <div class=\"bk-root\" id=\"b7926dd9-1983-4107-964d-36c43895fa8c\" data-root-id=\"5651\"></div>\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function embed_document(root) {\n",
+       "    \n",
+       "  var docs_json = 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Hexagonal Topology\"},\"id\":\"5652\",\"type\":\"Title\"},{\"attributes\":{\"axis\":{\"id\":\"5662\"},\"ticker\":null},\"id\":\"5665\",\"type\":\"Grid\"},{\"attributes\":{\"source\":{\"id\":\"5677\"}},\"id\":\"5683\",\"type\":\"CDSView\"},{\"attributes\":{\"callback\":null,\"point_policy\":\"follow_mouse\",\"tooltips\":[[\"label\",\"@species\"],[\"(x,y)\",\"($x, $y)\"]]},\"id\":\"5702\",\"type\":\"HoverTool\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"field\":\"c\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"gray\"},\"size\":{\"units\":\"screen\",\"value\":54.84827557301445},\"x\":{\"field\":\"y\"},\"y\":{\"field\":\"x\"}},\"id\":\"5681\",\"type\":\"Hex\"},{\"attributes\":{\"fill_color\":{\"field\":\"species\",\"transform\":{\"id\":\"5685\"}},\"line_color\":{\"field\":\"species\",\"transform\":{\"id\":\"5685\"}},\"marker\":{\"field\":\"species\",\"transform\":{\"id\":\"5684\"}},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"wy\"},\"y\":{\"field\":\"wx\"}},\"id\":\"5687\",\"type\":\"Scatter\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.4},\"fill_color\":{\"field\":\"c\"},\"line_alpha\":{\"value\":0.4},\"line_color\":{\"value\":\"gray\"},\"size\":{\"units\":\"screen\",\"value\":54.84827557301445},\"x\":{\"field\":\"y\"},\"y\":{\"field\":\"x\"}},\"id\":\"5680\",\"type\":\"Hex\"},{\"attributes\":{},\"id\":\"5697\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"5658\",\"type\":\"LinearScale\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"5670\"},{\"id\":\"5671\"},{\"id\":\"5672\"},{\"id\":\"5702\"}]},\"id\":\"5673\",\"type\":\"Toolbar\"},{\"attributes\":{\"factors\":[\"Kama\",\"Rosa\",\"Canadian\"],\"markers\":[\"diamond\",\"cross\",\"x\"]},\"id\":\"5684\",\"type\":\"CategoricalMarkerMapper\"},{\"attributes\":{},\"id\":\"5663\",\"type\":\"BasicTicker\"}],\"root_ids\":[\"5651\"]},\"title\":\"Bokeh Application\",\"version\":\"2.2.3\"}};\n",
+       "  var render_items = [{\"docid\":\"f293967f-2312-4173-861b-1f53293fad9a\",\"root_ids\":[\"5651\"],\"roots\":{\"5651\":\"b7926dd9-1983-4107-964d-36c43895fa8c\"}}];\n",
+       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "\n",
+       "  }\n",
+       "  if (root.Bokeh !== undefined) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (root.Bokeh !== undefined) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else {\n",
+       "        attempts++;\n",
+       "        if (attempts > 100) {\n",
+       "          clearInterval(timer);\n",
+       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
+       "        }\n",
+       "      }\n",
+       "    }, 10, root)\n",
+       "  }\n",
+       "})(window);"
+      ],
+      "application/vnd.bokehjs_exec.v0+json": ""
+     },
+     "metadata": {
+      "application/vnd.bokehjs_exec.v0+json": {
+       "id": "5651"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "output_file(\"resulting_images/som_seed_hex.html\")\n",
+    "\n",
+    "# initialise figure/plot\n",
+    "fig = figure(title=\"SOM: Hexagonal Topology\",\n",
+    "             plot_height=800, plot_width=800,\n",
+    "             match_aspect=True,\n",
+    "             tools=\"wheel_zoom,save,reset\")\n",
+    "\n",
+    "# create data stream for plotting\n",
+    "source_hex = ColumnDataSource(\n",
+    "    data = dict(\n",
+    "        x=hex_centre_col,\n",
+    "        y=hex_centre_row,\n",
+    "        c=hex_bokeh\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "source_pages = ColumnDataSource(\n",
+    "    data=dict(\n",
+    "        wx=weight_x,\n",
+    "        wy=weight_y,\n",
+    "        species=label\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# define markers\n",
+    "MARKERS = ['diamond', 'cross', 'x']\n",
+    "\n",
+    "# add shapes to plot\n",
+    "fig.hex(x='y', y='x', source=source_hex,\n",
+    "        size=100 * (.95 / np.sqrt(3)),\n",
+    "        alpha=.4,\n",
+    "        line_color='gray',\n",
+    "        fill_color='c')\n",
+    "\n",
+    "fig.scatter(x='wy', y='wx', source=source_pages, \n",
+    "            legend_field='species',\n",
+    "            size=20, \n",
+    "            marker=factor_mark(field_name='species', markers=MARKERS, factors=SPECIES),\n",
+    "            color=factor_cmap(field_name='species', palette='Category10_3', factors=SPECIES))\n",
+    "\n",
+    "# add hover-over tooltip\n",
+    "fig.add_tools(HoverTool(\n",
+    "    tooltips=[\n",
+    "        (\"label\", '@species'),\n",
+    "        (\"(x,y)\", '($x, $y)')],\n",
+    "    mode=\"mouse\", \n",
+    "    point_policy=\"follow_mouse\"\n",
+    "))\n",
+    "\n",
+    "show(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![bokeh plot](https://htmlpreview.github.io/?https://github.com/JustGlowing/minisom/blob/master/examples/resulting_images/som_seed_hex.html)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.1-final"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/README.md b/README.md
deleted file mode 100644
index 5eb8b11..0000000
--- a/README.md
+++ /dev/null
@@ -1,4 +0,0 @@
-minisom
-=======
-
-MiniSom is minimalistic implementation of the Self Organizing Maps
diff --git a/Readme.md b/Readme.md
new file mode 100644
index 0000000..566f254
--- /dev/null
+++ b/Readme.md
@@ -0,0 +1,328 @@
+<h1>MiniSom<img src='https://3.bp.blogspot.com/-_6UDGEHzIrs/WSfiyjmoeRI/AAAAAAAABHw/3UQylcCBEhUfHNhf56WSHBBmQ6g_lXQhwCLcB/s320/minisom_logo.png' align='right'></h1>
+
+Self Organizing Maps
+--------------------
+
+MiniSom is a minimalistic and Numpy based implementation of the Self Organizing Maps (SOM). SOM is a type of Artificial Neural Network able to convert complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display. Minisom is designed to allow researchers to easily build on top of it and to give students the ability to quickly grasp its details.
+
+Updates about MiniSom are posted on <a href="https://twitter.com/JustGlowing">Twitter</a>.
+
+Installation
+---------------------
+
+Just use pip:
+
+    pip install minisom
+
+or download MiniSom to a directory of your choice and use the setup script:
+
+    git clone https://github.com/JustGlowing/minisom.git
+    python setup.py install
+
+How to use it
+---------------------
+
+In order to use MiniSom you need your data organized as a Numpy matrix where each row corresponds to an observation or as list of lists like the following:
+
+```python
+data = [[ 0.80,  0.55,  0.22,  0.03],
+        [ 0.82,  0.50,  0.23,  0.03],
+        [ 0.80,  0.54,  0.22,  0.03],
+        [ 0.80,  0.53,  0.26,  0.03],
+        [ 0.79,  0.56,  0.22,  0.03],
+        [ 0.75,  0.60,  0.25,  0.03],
+        [ 0.77,  0.59,  0.22,  0.03]]      
+```
+
+ Then you can train MiniSom just as follows:
+
+```python
+from minisom import MiniSom    
+som = MiniSom(6, 6, 4, sigma=0.3, learning_rate=0.5) # initialization of 6x6 SOM
+som.train(data, 100) # trains the SOM with 100 iterations
+```
+
+You can obtain the position of the winning neuron on the map for a given sample as follows:
+
+```
+som.winner(data[0])
+```
+
+For an overview of all the features implemented in minisom you can browse the following examples: https://github.com/JustGlowing/minisom/tree/master/examples
+
+#### Export a SOM and load it again
+
+A model can be saved using pickle as follows
+
+```python
+import pickle
+som = MiniSom(7, 7, 4)
+
+# ...train the som here
+
+# saving the som in the file som.p
+with open('som.p', 'wb') as outfile:
+    pickle.dump(som, outfile)
+```
+
+and can be loaded as follows
+
+```python
+with open('som.p', 'rb') as infile:
+    som = pickle.load(infile)
+```
+
+Note that if a lambda function is used to define the decay factor MiniSom will not be pickable anymore.
+
+Examples
+---------------------
+
+Here are some of the charts you'll see how to generate in the examples:
+
+| | |
+:-------------------------:|:-------------------------:
+Seeds map ![](https://github.com/JustGlowing/minisom/raw/master/examples/resulting_images/som_seed.png)  | Class assignment ![](https://github.com/JustGlowing/minisom/raw/master/examples/resulting_images/som_seed_pies.png)
+Handwritteng digits mapping ![](https://github.com/JustGlowing/minisom/raw/master/examples/resulting_images/som_digts.png)  |  Hexagonal Topology <img src="https://github.com/JustGlowing/minisom/raw/master/examples/resulting_images/som_seed_hex.png" alt="som hexagonal toplogy" width=450>
+Color quantization ![](https://github.com/JustGlowing/minisom/raw/master/examples/resulting_images/som_color_quantization.png)  |  Outliers detection ![](https://github.com/JustGlowing/minisom/raw/master/examples/resulting_images/som_outliers_detection_circle.png)
+
+Other tutorials
+------------
+- <a href="https://glowingpython.blogspot.com/2013/09/self-organizing-maps.html">Self Organizing Maps on the Glowing Python</a> by me ;-)
+- <a href="http://aa.ssdi.di.fct.unl.pt/files/AA-16_notes.pdf">Lecture notes from the Machine Learning course at the University of Lisbon</a>
+- <a href="https://heartbeat.fritz.ai/introduction-to-self-organizing-maps-soms-98e88b568f5d">Introduction to Self-Organizing</a> by Derrick Mwiti
+- <a href="http://inphronesys.com/?p=625">Self Organizing Maps on gapminder data</a> [in German]
+- <a href="https://medium.com/neuronio/discovering-som-an-unsupervised-neural-network-12e787f38f9">Discovering SOM, an Unsupervised Neural Network</a> by Gisely Alves
+- Video tutorials made by the GeoEngineerings School: <a href="https://www.youtube.com/watch?v=3osKNPyAxPM&list=PL-i8do33HJovC7xFKaYO21qT37vORJWXC&index=11">Part 1</a>; <a href="https://www.youtube.com/watch?v=uUpQ6MITlVs&list=PL-i8do33HJovC7xFKaYO21qT37vORJWXC&index=12">Part 2</a>; <a href="https://www.youtube.com/watch?v=mryFU0TEInk&list=PL-i8do33HJovC7xFKaYO21qT37vORJWXC&index=13">Part 3</a>; <a href="https://www.youtube.com/watch?v=9MzFOIoxxdk&index=14&list=PL-i8do33HJovC7xFKaYO21qT37vORJWXC">Part 4</a>
+- Video tutorial <a href="https://www.youtube.com/watch?v=0qtvb_Nx2tA">Self Organizing Maps: Introduction</a> by SuperDataScience
+- <a href="http://docs.unigrafia.fi/publications/kohonen_teuvo/">MATLAB Implementations and Applications of the Self-Organizing Map</a> by Teuvo Kohonen (Inventor of SOM)
+
+How to cite MiniSom
+------------
+```
+@misc{vettigliminisom,
+  title={MiniSom: minimalistic and NumPy-based implementation of the Self Organizing Map},
+  author={Giuseppe Vettigli},
+  year={2018},
+  url={https://github.com/JustGlowing/minisom/},
+}
+```
+
+Who uses Minisom?
+------------
+<ul>
+<li>
+Julianna C. Oliveira, Eduardo Zorita, Vimal Koul, Thomas Ludwig, Johanna Baehr.<a href="https://dl.acm.org/doi/abs/10.1145/3429309.3429319">Forecast opportunities for European summer climate ensemble predictions using Self-Organising Maps</a>. CI2020: Proceedings of the 10th International Conference on Climate Informatics. 2020.
+</li>
+<li>
+Gorgoglione, A., Castro, A., Gioia, A., & Iacobellis, V. <a href="https://link.springer.com/chapter/10.1007/978-3-030-58811-3_49">Application of the Self-organizing Map (SOM) to Characterize Nutrient Urban Runoff</a>. International Conference on Computational Science and Its Applications. Springer, Cham, 2020.
+</li>        
+<li>
+Bonelli Toro, A. G., and M. P. Gómez. <a href="https://www.researchgate.net/profile/Miguel_Zitto/publication/344397710_Energy_and_Shannon_entropy_in_acoustic_emission_signals_for_damage_evaluation_in_andesite_rock/links/5f70a7f7458515b7cf511057/Energy-and-Shannon-entropy-in-acoustic-emission-signals-for-damage-evaluation-in-andesite-rock.pdf#page=123">Machine Learning Applied to Acoustic Emission tor Tool Wear Classification during Milling of Composite Materials</a>. An International Forum For The AE Science and Technology. Vol. 37. 2020.
+</li>    
+<li>
+Mancini, R., Ritacco, A., Lanciano, G., & Cucinotta, T. <a href="http://retis.sssup.it/~tommaso/publications/IEEE-SBAC-PAD-2020.pdf">XPySom: High-Performance Self-Organizing Maps</a>. Proceedings of IEEE 32nd International Symposium on Computer Architecture and High Performance Computing. 2020.
+</li>
+<li>
+Chen, Yang, Nami Ashizawa, Seanglidet Yean, Chai Kiat Yeo, and Naoto Yanai. <a href="https://arxiv.org/pdf/2008.12686.pdf">Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion Detection</a>. arXiv preprint arXiv:2008.12686 2020.
+</li>
+<li>
+Athanasakis E, <a href="http://ikee.lib.auth.gr/record/320630/files/GRI-2020-28133.pdf">Data-Analysis in environmental and traffic data for Thessaloniki Greece</a>. Master Thesis at Aristotle University of Thessaloniki. 2020.
+</li>
+<li>
+Schillaci G, Ciria A, Lara B. <a href="https://arxiv.org/pdf/2007.14632.pdf">Tracking Emotions: Intrinsic Motivation Grounded on Multi-Level Prediction Error Dynamics</a>. IEEE ICDL-Epirob 2020. 2020.
+</li>
+<li>
+Massaro, Alessandro, Giuseppe Mastandrea, Luigi D'Oriano, Giuseppe Rocco Rana, Nicola Savino, and Angelo Galiano. <a href="https://ieeexplore.ieee.org/abstract/document/9138231">Systems for an intelligent application of Automated Processes in industry: a case study from “PMI IoT Industry 4.0” project</a>. 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT. 2020.
+</li>
+<li>
+Ko, Ili, Desmond Chambers, and Enda Barrett. <a href="https://arxiv.org/pdf/2003.10454">Feature dynamic deep learning approach for DDoS mitigation within the ISP domain</a>. International Journal of Information Security. 2020.
+</li>
+<li>
+Wesam Salah Alaloul and Abdul Hannan Qureshi. <a href="https://www.intechopen.com/online-first/data-processing-using-artificial-neural-networks">MData Processing Using Artificial Neural Networks</a>. In Dynamic Data Assimilation-Beating the Uncertainties. IntechOpen, 2020.
+</li>
+<li>
+Gennadi Lessin, Luca Polimene, Yuri Artioli, Momme Butenschön, Darren R. Clark, Ian Brown, Andrew P. Rees. <a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019JG005613">Modeling the seasonality and controls of nitrous oxide emissions on the northwest European continental shelf</a>. Journal of Geophysical Research: Biogeosciences. 2020.
+</li>
+<li>
+Jorge Amaya, Romain Dupuis, Maria Elena Innocenti, and Giovanni Lapenta. <a href="https://arxiv.org/pdf/2004.13430.pdf">Visualizing and Interpreting Unsupervised Solar Wind Classifications</a>. Frontiers in Astronomy and Space Sciences Space Physics. 2020.
+</li>
+<li>
+Sandipan Dey. <a href="https://www.amazon.com/Python-Image-Processing-Cookbook-processing-ebook/dp/B084ZN7Y5F">Python Image Processing Cookbook: Over 60 recipes to help you perform complex image processing and computer vision tasks with ease</a>. Packt Publishing Ltd, April 2020.
+</li>
+<li>
+Odestål, Oscar and Palmqvist Sjövall, Anna. <a href="https://lup.lub.lu.se/student-papers/search/publication/9007379">Adaptive Reference Images for Blood Cells using Variational Autoencoders and Self-Organizing Maps</a>. Master Thesis, Lund University. 2020.
+</li>
+<li>
+Hadleigh D. Thompson  Stephen J. Déry  Peter L. Jackson  Bernard E. Laval. <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.6560">A synoptic climatology of potential seiche‐inducing winds in a large intermontane lake: Quesnel Lake, British Columbia, Canada</a>. International Journal of Climatology. 2020.
+</li>
+<li>
+Benyamin Motevalli, Baichuan Sun, Amanda S. Barnard. <a href="https://pubs.acs.org/doi/abs/10.1021/acs.jpcc.9b10615">Understanding and Predicting the Cause of Defects in Graphene Oxide Nanostructures Using Machine Learning</a>. The Journal of Physical Chemistry C. 2020.
+</li>
+<li>
+Daniel L. Donaldson, Dilan Jayaweera. <a href="https://www.sciencedirect.com/science/article/abs/pii/S0142061519322859">Effective solar prosumer identification using net smart meter data</a>. International Journal of Electrical Power & Energy Systems
+Volume 118. 2020.
+</li>
+<li>
+Pauli Tikka, Moritz Mercker, Ilya Skovorodkin, Ulla Saarela, Seppo Vainio, Veli-Pekka Ronkainen, James P. Sluka, James A. Glazier, Anna Marciniak-Czochra, Franz Schaefer. <a href="https://www.biorxiv.org/content/biorxiv/early/2020/01/14/2020.01.14.905711.full.pdf">Computational Modelling of Nephron Progenitor Cell Movement and Aggregation during Kidney Organogenesis</a>. Pre-print on biorxiv.org. 2020.
+</li>    
+<li>
+Felix M. Riese, Sina Keller, Stefan Hinz. <a href="https://www.mdpi.com/2072-4292/12/1/7/pdf">Supervised and Semi-Supervised Self-Organizing Maps for Regression and Classification Focusing on Hyperspectral Data</a>. Remote Sensing, special Issue Advanced Machine Learning Approaches for Hyperspectral Data Analysis. 2020.
+</li>
+<li>
+Giobergia, Flavio, and Elena Baralis. <a href="https://ieeexplore.ieee.org/abstract/document/9006055">Fast Self-Organizing Maps Training</a>. 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019.
+</li>
+<li>
+Silva, Roberto F., Gustavo M. Mostaço, Fernando Xavier, Antonio Mauro Saraiva, and Carlos E. Cugnasca. <a href="https://efita-org.eu/wp-content/uploads/2020/02/24.-efita7.pdf">COMPARISON OF THE K-MEANS AND SELF-ORGANIZING MAPS TECHNIQUES TO LABEL AGRICULTURAL SUPPLY CHAIN DATA</a>. Digitizing Agriculture, 12th EFITA International Conference. 2019.
+</li>
+<li>
+Üstünkök, Tolga, Ozan Can Acar, and Murat Karakaya. <a href="https://ieeexplore.ieee.org/abstract/document/8965477/">Image Tag Refinement with Self Organizing Maps.</a>. 2019 1st International Informatics and Software Engineering Conference (UBMYK). IEEE, 2019.
+</li>
+<li>
+Rohana, N. A., Yusof, N., Uti, M. N., and Din, A. H. M. <a href="https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W16/543/2019/">Exploring spatio-temporal wave pattern using unsupervised technique</a>. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W16, 543–548. 2019.    
+</li>
+<li>
+Nguyen, Thanh Hai. <a href="https://link.springer.com/chapter/10.1007/978-3-030-35653-8_20">Metagenome-Based Disease Classification with Deep Learning and Visualizations Based on Self-organizing Maps</a>. International Conference on Future Data and Security Engineering. Springer, Cham, 2019.
+</li>
+<li>
+Ujjawal Kamal Panchal, Sanjay Verma. <a href="https://ieeexplore.ieee.org/abstract/document/8944605">Identification of Potential Future Credit Card Defaulters from Non Defaulters using Self Organizing Maps</a>. 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). 2019.    
+</li>
+<li>
+Mengxia Luo, Can Yang, Xiaorui Gong, Lei Yu. <a href="https://link.springer.com/chapter/10.1007/978-3-030-37228-6_16">FuncNet: A Euclidean Embedding Approach for Lightweight Cross-platform Binary Recognition</a>. Security and Privacy in Communication Networks, 15th EAI International Conference, SecureComm 2019.    
+</li>
+<li>
+Melvin Gelbard. <a href="https://project-archive.inf.ed.ac.uk/msc/20193475/msc_proj.pdf">A Data Mining Approach to the Study of Dynamic Changes in Brain White Matter</a>. Master Thesis, University of Edinburgh. 2019.    
+</li>
+<li>
+E Mele, C Elias, A Ktena. <a href="https://content.sciendo.com/configurable/contentpage/journals$002fecce$002f15$002f1$002farticle-p21.xml">Machine Learning Platform for Profiling and Forecasting at Microgrid Level</a>. Electrical, Control and Communication Engineering. 2019.    
+</li>
+<li>
+György Kovács. <a href="https://www.sciencedirect.com/science/article/pii/S0925231219311622">Smote-variants: A python implementation of 85 minority oversampling techniques</a>. Neurocomputing, 2019 - Elsevier.
+</li>
+<li>
+Catalin Stoean, Ruxandra Stoean, Roberto Antonio Becerra-García, Rodolfo García-Bermúdez, Miguel Atencia, Francisco García-Lagos, Luis Velázquez-Pérez, Gonzalo Joya. <a href="https://link.springer.com/chapter/10.1007/978-3-030-20518-8_3">Unsupervised Learning as a Complement to Convolutional Neural Network Classification in the Analysis of Saccadic Eye Movement in Spino-Cerebellar Ataxia Type 2</a>. IWANN 2019: Advances in Computational Intelligence pp 26-37, 2019.
+</li>
+<li>
+I Ko, D Chambers, E Barrett . <a href="http://ceur-ws.org/Vol-2421/MEX-A3T_paper_8.pdf">Feature dynamic deep learning approach for DDoS mitigation within the ISP domain</a>. International Journal of Information Security, 2019.
+</li>
+<li>
+Leonardo Barreto, Edjard Mota. <a href="https://arxiv.org/abs/1906.06761">Self-organized inductive reasoning with NeMuS</a>. June 2019.
+</li>
+<li>
+Casavantes, Marco, Roberto López, Luis Carlos González-Gurrola, and Manuel Montes-y-Gómez. <a href="http://ceur-ws.org/Vol-2517/T3-16.pdf">UACh-INAOE at HASOC 2019: Detecting Aggressive Tweets by Incorporating Authors' Traits as Descriptors</a>. In FIRE (Working Notes). 2019.
+</li>
+<li>
+Marco Casavantes, Roberto Lopez, and Luis Carlos Gonzalez. <a href="http://ceur-ws.org/Vol-2421/MEX-A3T_paper_8.pdf">UACh at MEX-A3T 2019: Preliminary Results on Detecting Aggressive Tweets by Adding Author Information Via an Unsupervised Strategy</a>. Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2019), 2019.
+</li>
+<li>
+G Roma, O Green, PA Tremblay. <a href="https://pure.hud.ac.uk/ws/files/17022863/adaptive_mapping.pdf">Adaptive Mapping of Sound Collections for Data-driven Musical Interfaces</a>. 19th edition of NIME, 2019.
+</li>
+<li>
+H. D. Thompson. Wind climatology of Quesnel Lake, British Columbia. Master Thesis, University of Northern British Columbia, Prince George, BC. 2019.
+</li>
+<li>
+Florent Forest, Mustapha Lebbah, Hanene Azzag and Jérôme Lacaille. <a href="http://florentfo.rest/files/LDRC-2019-DeepArchitecturesJointClusteringVisualization-full-paper.pdf">Deep Architectures for Joint Clustering and Visualization with Self-Organizing Maps</a>. LDRC@PAKDD 2019 (Learning Data Representation for Clustering@PAKDD) Macau China. 2019.
+</li>
+<li>
+Stephanie Kas. <a href="http://fb07-indico.physik.uni-giessen.de:8080/wiki/images/e/ea/Bachelor_Thesis_Stephanie_Kaes_2019-cmprd.pdf">Multiparameter Analysis of the Belle II Pixeldetector’s Data</a>. Bachelor Thesis, University of Giessen, July 2019.
+</li>
+<li>
+Katharina Dort. <a href="https://docs.belle2.org/record/1382/files/BELLE2-MTHESIS-2019-003.pdf">Search for Highly Ionizing Particles with the Pixel Detector in the Belle II Experiment</a>. Master Thesis, University of Giessen, May 2019.
+</li>
+<li>
+Rahul Kumar. <a href="https://www.amazon.co.uk/Machine-Learning-Quick-Reference-Essential/dp/1788830571">Machine Learning Quick Reference: Quick and essential machine learning hacks for training smart data models</a>. Packt Publishing Ltd, 31 Jan 2019.
+</li>
+<li>
+Michaela Vystrčilova. <a href="https://dspace.cuni.cz/bitstream/handle/20.500.11956/108348/130257629.pdf?sequence=1">Similarity methods for music recommender systems</a>. Bachelor Thesis in Computer Science, Charles University, 2019.
+</li>
+<li>
+Felix M. Riese, Sina Keller. <a href="https://arxiv.org/pdf/1903.11114.pdf">SUSI: Supervised Self-Organizing Maps for Regression and Classification in Python</a>.
+</li>
+<li>
+Dogo, E. M., et al. <a href="https://ieeexplore.ieee.org/abstract/document/8769276/">Sensed Outlier Detection for Water Monitoring Data and a Comparative Analysis of Quantization Error Using Kohonen Self-Organizing Maps</a>. 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS). IEEE, 2018.
+</li>
+<li>
+Y. Xie, L.Le, Y. Zhou, V. V. Raghavan. <a href="https://books.google.co.uk/books?id=gRJrDwAAQBAJ&pg">Deep Learning for Natural Language Processing</a>. Chapter of Computational Analysis and Understanding Natural Languages, Elsevier, 2018
+</li>
+<li>
+Enea Mele, Charalambos Elias, Aphrodite Ktena. <a href="https://ieeexplore.ieee.org/abstract/document/8659866">Electricity use profiling and forecasting at microgrid level</a>. IEEE 59th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), 2018.
+</li>
+<li>
+Chintan Shah, Anjali Jivani. <a href="https://ieeexplore.ieee.org/abstract/document/8554848">A Hybrid Approach of Text Summarization Using Latent Semantic Analysis and Deep Learning</a>. 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2018.
+</li>
+<li>
+Katsutoshi Masai. <a href="http://koara.lib.keio.ac.jp/xoonips/modules/xoonips/download.php/KO50002002-20184959-0003.pdf?file_id=137977">Facial Expression Classification Using Photo-reflective Sensors on Smart Eyewear</a>. Keio University, Doctoral Thesis, 2018.
+</li>
+<li>
+Katsutoshi Masai, Kai Kunze, Yuta Sugiura, Maki Sugimoto. <a href="https://dl.acm.org/citation.cfm?id=3267562">Mapping Natural Facial Expressions Using Unsupervised Learning and Optical Sensors on Smart Eyewear</a>. Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, 2018 ACM.
+</li>
+<li>
+Ili Ko, Desmond Chambers, Enda Barrett. <a href="https://ieeexplore.ieee.org/document/8343119/">A Lightweight DDoS Attack Mitigation System within the ISP Domain Utilising Self-organizing Map</a>. Proceedings of the Future Technologies, 2018 Springer.
+</li>
+<li>
+T. M. Nam et al. <a href="https://ieeexplore.ieee.org/document/8343119/">Self-organizing map-based approaches in DDoS flooding detection using SDN</a>. 2018 International Conference on Information Networking (ICOIN), 2018.
+</li>
+<li>
+Li Yuan <a href="http://digitalcommons.uri.edu/theses/1244/">Implementation of Self-Organizing Maps with Python</a>. Master Thesis, University of Rhode Island, 2018.
+</li>
+<li>
+Ying Xie, Linh Le, Yiyun Zhou, Vijay V.Raghavan. <a href="https://www.sciencedirect.com/science/article/pii/S0169716118300026">Deep Learning for Natural Language Processing</a>. Elsevier Handbook of Statistics, 2018.
+</li>
+<li>
+André de Vasconcelos Santos Silva. <a href="https://repositorio.iscte-iul.pt/bitstream/10071/18245/1/Master_Andre_Santos_Silva.pdf">Sparse Distributed Representations as Word
+Embeddings for Language Understanding</a>. Master Thesis, University Institute of Lisbon, 2018.
+</li>
+<li>
+Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, and Gunnar Rätsch. <a href="https://arxiv.org/pdf/1806.02199.pdf">Deep Self-Organization: Interpretable Discrete
+Representation Learning on Time Series</a>. 2018.
+</li>
+<li>
+John Mwangi Wandeto. <a href="https://publication-theses.unistra.fr/public/theses_doctorat/2018/Wandeto_John_2018_ED269.pdf">Self-Organizing Map Quantization Error Approach for Detecting Temporal Variations in Image Sets</a>. Doctoral Thesis, University of Strasbourg, 2018.
+</li>    
+<li>
+Birgitta Dresp-Langley, John Mwangi Wandeto, Henry Okola Nyongesa. <a href="https://www.openscience.fr/IMG/pdf/iste_fromd2d17v1n2.pdf">Using the quantization error from Self‐Organizing Map (SOM) output for fast detection of critical variations in image time series</a>. ISTE OpenScience, 2018.
+</li>
+<li>
+John M. Wandeto, Henry O. Nyongesa, Birgitta Dresp-Langley. <a href="https://arxiv.org/abs/1803.11125">Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the Years</a>. 2018.
+</li>
+<li>
+Denis Mayr Lima Martins, Gottfried Vossen, Fernando Buarque de Lima Neto. <a href="https://ieeexplore.ieee.org/abstract/document/8285698/">Learning database queries via intelligent semiotic machines</a>. IEEE Latin American Conference on Computational Intelligence (LA-CCI), 2017.
+</li>
+<li>
+Udemy online course. <a href="https://www.udemy.com/deeplearning/?utm_campaign=email&utm_source=sendgrid.com&utm_medium=email">Deep Learning A-Z™: Hands-On Artificial Neural Networks</a>
+<img src="https://www.udemy.com/staticx/udemy/images/v6/logo-coral.svg" height="20">
+</li>
+<li>
+Fredrik Broch Elgaaen, Nicholas Mowatt Larssen. <a href="https://www.duo.uio.no/bitstream/handle/10852/57507/master_nml.pdf?sequence=1&isAllowed=y">Data mining i banksektoren - Prediksjonsmodellering og analyse av kunder som sier opp boliglån</a>. University of Oslo, May 2017.
+</li>
+<li>
+Óscar Clavería González, Enric Monte Moreno, Salvador Torra Porras. <a href="http://www.sciencedirect.com/science/article/pii/S2110701715000694">A self-organizing map analysis of survey-based agents׳ expectations before impending shocks for model selection: The case of the 2008 financial crisis</a>. International Economics Volume 146, Pages 40–58. August 2016.
+</li>
+<li>
+Sameen Mansha, Faisal Kamiran, Asim Karim, Aizaz Anwar. <a href="http://link.springer.com/chapter/10.1007/978-3-319-46681-1_16">A Self-Organizing Map for Identifying InfluentialCommunities in Speech-based Networks</a>. Proceeding CIKM '16 Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pages 1965-1968. 2016.
+</li>
+<li>
+Sameen Mansha, Zaheer Babar, Faisal Kamiran, Asim Karim. <a href="http://link.springer.com/chapter/10.1007/978-3-319-46681-1_16">Neural Network Based Association Rule Mining from Uncertain Data</a>. Neural Information Processing Volume 9950 of the series Lecture Notes in Computer Science pp 129-136. 2016.
+</li>
+<li>
+Makiyama, Vitor Hirota, M. Jordan Raddick, and Rafael DC Santos. <a href="http://ceur-ws.org/Vol-1478/paper7.pdf">Text Mining Applied to SQL Queries: A Case Study for the SDSS SkyServer</a>. 2nd Annual International Symposium on Information Management and Big Data. 2015.
+</li>
+<li>
+Remi Domingues. <a href="http://www.diva-portal.org/smash/get/diva2:897808/FULLTEXT01.pdf">Machine Learning for
+Unsupervised Fraud Detection</a>. Royal Institute of Technology School of Computer Science and Communication KTH CSC. 2015.
+</li>
+<li>Ivana Kajić, Guido Schillaci, Saša Bodiroža, Verena V. Hafner, <a href="http://dl.acm.org/citation.cfm?id=2559816">Learning hand-eye coordination for a humanoid robot using SOMs</a>. Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
+Pages 192-193.</li>
+</ul>
+
+Compatibility notes
+---------------------
+Minisom has been tested under Python 3.8.0.
+
+License
+---------------------
+
+This program is distributed in the hope that it will be useful, but without warranty; without even the implied warranty of merchantability or fitness for a particular purpose.
+
+MiniSom by Giuseppe Vettigli is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit [http://creativecommons.org/licenses/by/3.0/](http://creativecommons.org/licenses/by/3.0/ "http://creativecommons.org/licenses/by/3.0/").
+
+![License]( http://i.creativecommons.org/l/by/3.0/88x31.png "Creative Commons Attribution 3.0 Unported License")
diff --git a/examples/AdvancedVisualization.ipynb b/examples/AdvancedVisualization.ipynb
new file mode 100644
index 0000000..7e88e37
--- /dev/null
+++ b/examples/AdvancedVisualization.ipynb
@@ -0,0 +1,15948 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook we will demonstrate some more advanced visualizations of MiniSom results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from minisom import MiniSom"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this example we will again load the <a href=\"https://archive.ics.uci.edu/ml/datasets/seeds\">seeds</a> dataset dataset using pandas:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "columns=['area', 'perimeter', 'compactness', 'length_kernel', 'width_kernel',\n",
+    "                   'asymmetry_coefficient', 'length_kernel_groove', 'target']\n",
+    "\n",
+    "data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/00236/seeds_dataset.txt', \n",
+    "                    names=columns, \n",
+    "                   sep='\\t+', engine='python')\n",
+    "target = data['target'].values\n",
+    "label_names = {1:'Kama', 2:'Rosa', 3:'Canadian'}\n",
+    "data = data[data.columns[:-1]]\n",
+    "# data normalization\n",
+    "data = (data - np.mean(data, axis=0)) / np.std(data, axis=0)\n",
+    "data = data.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can initialize and train MiniSom as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " [ 1000 / 1000 ] 100% - 0:00:00 left \n",
+      " quantization error: 0.6426435848319221\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialization and training\n",
+    "n_neurons = 9\n",
+    "m_neurons = 9\n",
+    "som = MiniSom(n_neurons, m_neurons, data.shape[1], sigma=1.5, learning_rate=.5, \n",
+    "              neighborhood_function='gaussian', random_seed=0)\n",
+    "\n",
+    "som.pca_weights_init(data)\n",
+    "som.train(data, 1000, verbose=True)  # random training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To visualize the quality of clustering of each neuron this map uses colors to show the mean difference of the values corresponding to a neuron and the weights of the neuron. Neurons that aren't winning neuron for any sample are left blank."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "        <script type=\"text/javascript\">\n",
+       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
+       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
+       "        if (typeof require !== 'undefined') {\n",
+       "        require.undef(\"plotly\");\n",
+       "        define('plotly', function(require, exports, module) {\n",
+       "            /**\n",
+       "* plotly.js v1.58.2\n",
+       "* Copyright 2012-2020, Plotly, Inc.\n",
+       "* All rights reserved.\n",
+       "* Licensed under the MIT license\n",
+       "*/\n",
+       "!function(t){if(\"object\"==typeof exports&&\"undefined\"!=typeof module)module.exports=t();else if(\"function\"==typeof define&&define.amd)define([],t);else{(\"undefined\"!=typeof window?window:\"undefined\"!=typeof global?global:\"undefined\"!=typeof self?self:this).Plotly=t()}}((function(){return function t(e,r,n){function i(o,s){if(!r[o]){if(!e[o]){var l=\"function\"==typeof require&&require;if(!s&&l)return l(o,!0);if(a)return a(o,!0);var c=new Error(\"Cannot find module '\"+o+\"'\");throw c.code=\"MODULE_NOT_FOUND\",c}var u=r[o]={exports:{}};e[o][0].call(u.exports,(function(t){return i(e[o][1][t]||t)}),u,u.exports,t,e,r,n)}return r[o].exports}for(var a=\"function\"==typeof require&&require,o=0;o<n.length;o++)i(n[o]);return i}({1:[function(t,e,r){\"use strict\";var n=t(\"../src/lib\"),i={\"X,X div\":\"direction:ltr;font-family:'Open Sans', verdana, arial, sans-serif;margin:0;padding:0;\",\"X input,X button\":\"font-family:'Open Sans', verdana, arial, sans-serif;\",\"X 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f=1;for(c=l(t,u,t.length,e),u=0;u<o;u++)f*=e;this.imuln(f),this.words[0]+c<67108864?this.words[0]+=c:this._iaddn(c)}},a.prototype.copy=function(t){t.words=new Array(this.length);for(var e=0;e<this.length;e++)t.words[e]=this.words[e];t.length=this.length,t.negative=this.negative,t.red=this.red},a.prototype.clone=function(){var t=new a(null);return this.copy(t),t},a.prototype._expand=function(t){for(;this.length<t;)this.words[this.length++]=0;return this},a.prototype.strip=function(){for(;this.length>1&&0===this.words[this.length-1];)this.length--;return this._normSign()},a.prototype._normSign=function(){return 1===this.length&&0===this.words[0]&&(this.negative=0),this},a.prototype.inspect=function(){return(this.red?\"<BN-R: \":\"<BN: \")+this.toString(16)+\">\"};var c=[\"\",\"0\",\"00\",\"000\",\"0000\",\"00000\",\"000000\",\"0000000\",\"00000000\",\"000000000\",\"0000000000\",\"00000000000\",\"000000000000\",\"0000000000000\",\"00000000000000\",\"000000000000000\",\"0000000000000000\",\"00000000000000000\",\"000000000000000000\",\"0000000000000000000\",\"00000000000000000000\",\"000000000000000000000\",\"0000000000000000000000\",\"00000000000000000000000\",\"000000000000000000000000\",\"0000000000000000000000000\"],u=[0,0,25,16,12,11,10,9,8,8,7,7,7,7,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5],f=[0,0,33554432,43046721,16777216,48828125,60466176,40353607,16777216,43046721,1e7,19487171,35831808,62748517,7529536,11390625,16777216,24137569,34012224,47045881,64e6,4084101,5153632,6436343,7962624,9765625,11881376,14348907,17210368,20511149,243e5,28629151,33554432,39135393,45435424,52521875,60466176];function h(t,e,r){r.negative=e.negative^t.negative;var n=t.length+e.length|0;r.length=n,n=n-1|0;var i=0|t.words[0],a=0|e.words[0],o=i*a,s=67108863&o,l=o/67108864|0;r.words[0]=s;for(var c=1;c<n;c++){for(var u=l>>>26,f=67108863&l,h=Math.min(c,e.length-1),p=Math.max(0,c-t.length+1);p<=h;p++){var d=c-p|0;u+=(o=(i=0|t.words[d])*(a=0|e.words[p])+f)/67108864|0,f=67108863&o}r.words[c]=0|f,l=0|u}return 0!==l?r.words[c]=0|l:r.length--,r.strip()}a.prototype.toString=function(t,e){var r;if(e=0|e||1,16===(t=t||10)||\"hex\"===t){r=\"\";for(var i=0,a=0,o=0;o<this.length;o++){var s=this.words[o],l=(16777215&(s<<i|a)).toString(16);r=0!==(a=s>>>24-i&16777215)||o!==this.length-1?c[6-l.length]+l+r:l+r,(i+=2)>=26&&(i-=26,o--)}for(0!==a&&(r=a.toString(16)+r);r.length%e!=0;)r=\"0\"+r;return 0!==this.negative&&(r=\"-\"+r),r}if(t===(0|t)&&t>=2&&t<=36){var h=u[t],p=f[t];r=\"\";var d=this.clone();for(d.negative=0;!d.isZero();){var g=d.modn(p).toString(t);r=(d=d.idivn(p)).isZero()?g+r:c[h-g.length]+g+r}for(this.isZero()&&(r=\"0\"+r);r.length%e!=0;)r=\"0\"+r;return 0!==this.negative&&(r=\"-\"+r),r}n(!1,\"Base should be between 2 and 36\")},a.prototype.toNumber=function(){var t=this.words[0];return 2===this.length?t+=67108864*this.words[1]:3===this.length&&1===this.words[2]?t+=4503599627370496+67108864*this.words[1]:this.length>2&&n(!1,\"Number can only safely store up to 53 bits\"),0!==this.negative?-t:t},a.prototype.toJSON=function(){return this.toString(16)},a.prototype.toBuffer=function(t,e){return n(\"undefined\"!=typeof o),this.toArrayLike(o,t,e)},a.prototype.toArray=function(t,e){return this.toArrayLike(Array,t,e)},a.prototype.toArrayLike=function(t,e,r){var i=this.byteLength(),a=r||Math.max(1,i);n(i<=a,\"byte array longer than desired length\"),n(a>0,\"Requested array length <= 0\"),this.strip();var o,s,l=\"le\"===e,c=new t(a),u=this.clone();if(l){for(s=0;!u.isZero();s++)o=u.andln(255),u.iushrn(8),c[s]=o;for(;s<a;s++)c[s]=0}else{for(s=0;s<a-i;s++)c[s]=0;for(s=0;!u.isZero();s++)o=u.andln(255),u.iushrn(8),c[a-s-1]=o}return c},Math.clz32?a.prototype._countBits=function(t){return 32-Math.clz32(t)}:a.prototype._countBits=function(t){var e=t,r=0;return e>=4096&&(r+=13,e>>>=13),e>=64&&(r+=7,e>>>=7),e>=8&&(r+=4,e>>>=4),e>=2&&(r+=2,e>>>=2),r+e},a.prototype._zeroBits=function(t){if(0===t)return 26;var e=t,r=0;return 0==(8191&e)&&(r+=13,e>>>=13),0==(127&e)&&(r+=7,e>>>=7),0==(15&e)&&(r+=4,e>>>=4),0==(3&e)&&(r+=2,e>>>=2),0==(1&e)&&r++,r},a.prototype.bitLength=function(){var t=this.words[this.length-1],e=this._countBits(t);return 26*(this.length-1)+e},a.prototype.zeroBits=function(){if(this.isZero())return 0;for(var t=0,e=0;e<this.length;e++){var r=this._zeroBits(this.words[e]);if(t+=r,26!==r)break}return t},a.prototype.byteLength=function(){return Math.ceil(this.bitLength()/8)},a.prototype.toTwos=function(t){return 0!==this.negative?this.abs().inotn(t).iaddn(1):this.clone()},a.prototype.fromTwos=function(t){return this.testn(t-1)?this.notn(t).iaddn(1).ineg():this.clone()},a.prototype.isNeg=function(){return 0!==this.negative},a.prototype.neg=function(){return this.clone().ineg()},a.prototype.ineg=function(){return this.isZero()||(this.negative^=1),this},a.prototype.iuor=function(t){for(;this.length<t.length;)this.words[this.length++]=0;for(var e=0;e<t.length;e++)this.words[e]=this.words[e]|t.words[e];return this.strip()},a.prototype.ior=function(t){return n(0==(this.negative|t.negative)),this.iuor(t)},a.prototype.or=function(t){return this.length>t.length?this.clone().ior(t):t.clone().ior(this)},a.prototype.uor=function(t){return this.length>t.length?this.clone().iuor(t):t.clone().iuor(this)},a.prototype.iuand=function(t){var e;e=this.length>t.length?t:this;for(var r=0;r<e.length;r++)this.words[r]=this.words[r]&t.words[r];return this.length=e.length,this.strip()},a.prototype.iand=function(t){return n(0==(this.negative|t.negative)),this.iuand(t)},a.prototype.and=function(t){return this.length>t.length?this.clone().iand(t):t.clone().iand(this)},a.prototype.uand=function(t){return this.length>t.length?this.clone().iuand(t):t.clone().iuand(this)},a.prototype.iuxor=function(t){var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n(\"number\"==typeof t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n(\"number\"==typeof t&&t>=0);var r=t/26|0,i=t%26;return 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,g=p>>>13,m=0|o[2],v=8191&m,y=m>>>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],C=8191&E,L=E>>>13,I=0|o[7],P=8191&I,z=I>>>13,O=0|o[8],D=8191&O,R=O>>>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,gt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var mt=(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)+(mt>>>26)|0,mt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(g,U)|0,a=Math.imul(g,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(g,H)|0,a=a+Math.imul(g,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(g,W)|0,a=a+Math.imul(g,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(g,J)|0,a=a+Math.imul(g,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,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(g,$)|0,a=a+Math.imul(g,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(C,U),i=(i=Math.imul(C,V))+Math.imul(L,U)|0,a=Math.imul(L,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(g,rt)|0,a=a+Math.imul(g,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(P,U),i=(i=Math.imul(P,V))+Math.imul(z,U)|0,a=Math.imul(z,V),n=n+Math.imul(C,H)|0,i=(i=i+Math.imul(C,G)|0)+Math.imul(L,H)|0,a=a+Math.imul(L,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(g,at)|0,a=a+Math.imul(g,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(P,H)|0,i=(i=i+Math.imul(P,G)|0)+Math.imul(z,H)|0,a=a+Math.imul(z,G)|0,n=n+Math.imul(C,W)|0,i=(i=i+Math.imul(C,X)|0)+Math.imul(L,W)|0,a=a+Math.imul(L,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(g,lt)|0,a=a+Math.imul(g,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(P,W)|0,i=(i=i+Math.imul(P,X)|0)+Math.imul(z,W)|0,a=a+Math.imul(z,X)|0,n=n+Math.imul(C,J)|0,i=(i=i+Math.imul(C,K)|0)+Math.imul(L,J)|0,a=a+Math.imul(L,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(g,ft)|0,a=a+Math.imul(g,ht)|0;var Mt=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,gt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,gt)|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(P,J)|0,i=(i=i+Math.imul(P,K)|0)+Math.imul(z,J)|0,a=a+Math.imul(z,K)|0,n=n+Math.imul(C,$)|0,i=(i=i+Math.imul(C,tt)|0)+Math.imul(L,$)|0,a=a+Math.imul(L,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,gt)|0)+Math.imul(g,dt)|0))<<13)|0;c=((a=a+Math.imul(g,gt)|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(P,$)|0,i=(i=i+Math.imul(P,tt)|0)+Math.imul(z,$)|0,a=a+Math.imul(z,tt)|0,n=n+Math.imul(C,rt)|0,i=(i=i+Math.imul(C,nt)|0)+Math.imul(L,rt)|0,a=a+Math.imul(L,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,gt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,gt)|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(P,rt)|0,i=(i=i+Math.imul(P,nt)|0)+Math.imul(z,rt)|0,a=a+Math.imul(z,nt)|0,n=n+Math.imul(C,at)|0,i=(i=i+Math.imul(C,ot)|0)+Math.imul(L,at)|0,a=a+Math.imul(L,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,gt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,gt)|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(P,at)|0,i=(i=i+Math.imul(P,ot)|0)+Math.imul(z,at)|0,a=a+Math.imul(z,ot)|0,n=n+Math.imul(C,lt)|0,i=(i=i+Math.imul(C,ct)|0)+Math.imul(L,lt)|0,a=a+Math.imul(L,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 Ct=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,gt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,gt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=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(P,lt)|0,i=(i=i+Math.imul(P,ct)|0)+Math.imul(z,lt)|0,a=a+Math.imul(z,ct)|0,n=n+Math.imul(C,ft)|0,i=(i=i+Math.imul(C,ht)|0)+Math.imul(L,ft)|0,a=a+Math.imul(L,ht)|0;var Lt=(c+(n=n+Math.imul(A,dt)|0)|0)+((8191&(i=(i=i+Math.imul(A,gt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,gt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=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(P,ft)|0,i=(i=i+Math.imul(P,ht)|0)+Math.imul(z,ft)|0,a=a+Math.imul(z,ht)|0;var It=(c+(n=n+Math.imul(C,dt)|0)|0)+((8191&(i=(i=i+Math.imul(C,gt)|0)+Math.imul(L,dt)|0))<<13)|0;c=((a=a+Math.imul(L,gt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=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 Pt=(c+(n=n+Math.imul(P,dt)|0)|0)+((8191&(i=(i=i+Math.imul(P,gt)|0)+Math.imul(z,dt)|0))<<13)|0;c=((a=a+Math.imul(z,gt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var zt=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,gt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,gt)|0)+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863;var Ot=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,gt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,gt))+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,l[0]=mt,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]=Ct,l[14]=Lt,l[15]=It,l[16]=Pt,l[17]=zt,l[18]=Ot,0!==c&&(l[19]=c,r.length++),r};function d(t,e,r){return(new g).mulp(t,e,r)}function g(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)},g.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},g.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},g.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]]},g.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],g=n[u+p],m=r[u+p+o],v=n[u+p+o],y=f*m-h*v;v=f*v+h*m,m=y,r[u+p]=d+m,n[u+p]=g+v,r[u+p+o]=d-m,n[u+p+o]=g-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},g.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},g.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}},g.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},g.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))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.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,g=1;0==(r.words[0]&g)&&d<26;++d,g<<=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 in reduction context\"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red 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 m={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 fffffffe fffffc2f\")}function x(){v.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function b(){v.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function _(){v.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function w(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function T(t){w.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}v.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},v.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):r.strip(),r},v.prototype.split=function(t,e){t.iushrn(this.n,0,e)},v.prototype.imulK=function(t){return t.imul(this.k)},i(y,v),y.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var i=t.words[9];for(e.words[e.length++]=4194303&i,n=10;n<t.length;n++){var a=0|t.words[n];t.words[n-10]=(4194303&a)<<4|i>>>22,i=a}i>>>=22,t.words[n-10]=i,0===i&&t.length>10?t.length-=10:t.length-=9},y.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 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(m[t])return m[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 m[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 t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},w.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},w.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},w.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},w.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},w.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},w.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},w.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},w.prototype.isqr=function(t){return this.imul(t,t.clone())},w.prototype.sqr=function(t){return this.mul(t,t)},w.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var f=this.pow(u,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var g=p,m=0;0!==g.cmp(s);m++)g=g.redSqr();n(m<d);var v=this.pow(f,new a(1).iushln(d-m-1));h=h.redMul(v),f=v.redSqr(),p=p.redMul(f),d=m}return h},w.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},w.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var 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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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+       "/*!\n",
+       " * @overview es6-promise - a tiny implementation of Promises/A+.\n",
+       " * @copyright Copyright (c) 2014 Yehuda Katz, Tom Dale, Stefan Penner and contributors (Conversion to ES6 API by Jake Archibald)\n",
+       " * @license   Licensed under MIT license\n",
+       " *            See https://raw.githubusercontent.com/stefanpenner/es6-promise/master/LICENSE\n",
+       " * @version   v4.2.8+1e68dce6\n",
+       " */\n",
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a=e(t,r.key);n.push(r),a<0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new h(this,n)},l.lt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new h(this,n)},l.le=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>=0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new h(this,n)},l.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var i=e(t,r.key);if(n.push(r),0===i)return new h(this,n);r=i<=0?r.left:r.right}return new h(this,[])},l.remove=function(t){var e=this.find(t);return e?e.remove():this},l.get=function(t){for(var e=this._compare,r=this.root;r;){var n=e(t,r.key);if(0===n)return r.value;r=n<=0?r.left:r.right}};var p=h.prototype;function d(t,e){t.key=e.key,t.value=e.value,t.left=e.left,t.right=e.right,t._color=e._color,t._count=e._count}function g(t,e){return 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}})},{}],248:[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},{}],249:[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}},{}],250:[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 g=l(\"labels\");l(\"labelFont\")&&(g=!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&&(g||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 g=[0,0,0],m={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||m;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=g;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 C=[0,0,0];h[M]>0?C[M]=a[1][M]:C[M]=a[0][M];for(var L=0;L<2;++L){var I=(M+1+L)%3,P=(M+1+(1^L))%3;this.gridEnable[I]&&this._lines.drawGrid(I,P,this.bounds,C,this.gridColor[I],this.gridWidth[I]*this.pixelRatio)}for(L=0;L<2;++L){I=(M+1+L)%3,P=(M+1+(1^L))%3;this.zeroEnable[P]&&Math.min(a[0][P],a[1][P])<=0&&Math.max(a[0][P],a[1][P])>=0&&this._lines.drawZero(I,P,this.bounds,C,this.zeroLineColor[P],this.zeroLineWidth[P]*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 z=c(v,A[M].primalMinor),O=c(y,A[M].mirrorMinor),D=this.lineTickLength;for(L=0;L<3;++L){var R=k/r[5*L];z[L]*=D[L]*R,O[L]*=D[L]*R}this.lineTickEnable[M]&&this._lines.drawAxisTicks(M,A[M].primalOffset,z,this.lineTickColor[M],this.lineTickWidth[M]*this.pixelRatio),this.lineTickMirror[M]&&this._lines.drawAxisTicks(M,A[M].mirrorOffset,O,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(L=0;L<3;++L)this.lineTickEnable[M]&&(q[L]+=k*U[L]*Math.max(this.lineTickLength[L],0)/r[5*L]);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(L=0;L<3;++L)q[L]+=k*U[L]*this.tickPad[L]/r[5*L];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(L=0;L<3;++L)q[L]+=k*U[L]*this.labelPad[L]/r[5*L];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\":251,\"./lib/cube.js\":252,\"./lib/lines.js\":253,\"./lib/text.js\":255,\"./lib/ticks.js\":256}],251:[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 g=-1;g<=1;g+=2)f[u]=g,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var m=c;c=u,u=m}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\":254,\"gl-buffer\":259,\"gl-vao\":358}],252:[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,C=-1,L=0;L<2;++L){var I=(z=L<<M)+(L<<A)+(1-L<<S),P=z+(1-L<<A)+(L<<S);o(c[z],c[I],c[P],f)<0||(L?E=1:C=1)}if(E<0||C<0)C>E&&(w|=1<<M);else{for(L=0;L<2;++L){I=(z=L<<M)+(L<<A)+(1-L<<S),P=z+(1-L<<A)+(L<<S);var z,O=d([l[z],l[I],l[P],l[z+(1<<A)+(1<<S)]]);L?E=O:C=O}C>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=g;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=m,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 g=[1,1,1],m=[0,0,0],v={cubeEdges:g,axis:m}},{\"bit-twiddle\":97,\"gl-mat4/multiply\":295,\"robust-orientation\":548,\"split-polygon\":566}],253:[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 g=+r[f][d].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var m=o.length/3|0;s[f]=h,l[f]=m-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){g=+r[f][v].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}m=o.length/3|0;c[f]=h,u[f]=m-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 g=f(l);g[t]=1,this.shader.uniforms.screenAxis=g,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\":254,\"gl-buffer\":259,\"gl-vao\":358}],254:[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:257}],255:[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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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 g=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],m=[0,0,0]},{\"./lib/cube.js\":252,\"extract-frustum-planes\":240,\"gl-mat4/multiply\":295,\"gl-mat4/transpose\":306,\"gl-vec4/transformMat4\":429,\"split-polygon\":566}],259:[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&&\"undefined\"!=typeof 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:495,\"ndarray-ops\":490,\"typedarray-pool\":595}],260:[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,g=[],m=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));_?(m=Math.min(m,_),v=!1):v=!0}v||(p=x,d=b),g.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(m)||(m=1),a.vectorScale=m;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],C=x[2],L=g[y],I=n.length(L)*k,P=0;P<8;P++){a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.vectors.push(L),a.vectors.push(L),a.vectors.push(L),a.vectors.push(L),a.vectors.push(L),a.vectors.push(L),a.vertexIntensity.push(I,I,I),a.vertexIntensity.push(I,I,I);var z=a.positions.length;a.cells.push([z-6,z-5,z-4],[z-3,z-2,z-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\":261,\"./lib/shaders\":262,\"gl-vec3\":377}],261:[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 g(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,g=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],g=+t.vertexIntensityBounds[1];else for(var m=0;m<p.length;++m){var v=p[m];d=Math.min(d,v),g=Math.max(g,v)}else for(m=0;m<n.length;++m){v=n[m][2];d=Math.min(d,v),g=Math.max(g,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(m=0;m<n.length;++m)for(var y=n[m],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(m=0;m<r.length;++m){var _=r[m];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)/(g-d),0]:[(y[2]-d)/(g-d),0],l.push(M[0],M[1]),f.push(m)}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 g=this.triShader;g.bind(),g.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=g(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),m=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:m,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,m,v,x,r.traceType||\"cone\");return b.update(e),b}},{colormap:131,\"gl-buffer\":259,\"gl-mat4/invert\":293,\"gl-mat4/multiply\":295,\"gl-shader\":335,\"gl-texture2d\":353,\"gl-vao\":358,ndarray:495}],262:[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:263}],263:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],264:[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\"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p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  gl_FragColor = opacity * fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},{\"gl-shader\":335,glslify:267}],269:[function(t,e,r){\"use strict\";var n=t(\"gl-texture2d\");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension(\"WEBGL_draw_buffers\");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new 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t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var g=this,m=[0|e,0|r];Object.defineProperties(m,{0:{get:function(){return g._shape[0]},set:function(t){return g.width=t}},1:{get:function(){return g._shape[1]},set:function(t){return g.height=t}}}),this._shapeVector=m,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,g=t._useDepth,m=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,m,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):g&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):g&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g?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 g=d.prototype;function m(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(g,{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 m(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return m(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,m(this,this._shape[0],t),t},enumerable:!1}}),g.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])}},g.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\":353}],270:[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 g=parseInt(d.split(\":\")[2]);if(isNaN(g))throw new Error(n(\"Could not parse error: %s\",d));h[g]=d}}var m=o(e).split(\"\\n\");for(p=0;p<m.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=m[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\":66,\"gl-constants/lookup\":265,\"glsl-shader-name\":431,\"sprintf-js\":567}],271:[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 g=i.attributes;return this.positionBuffer.bind(),g.position.pointer(),this.weightBuffer.bind(),g.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),g.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],g=t.colorValues||[0,0,0,1],m=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,C=l?_-1:_,L=0;L<C;++L){var I,P;l?(I=x*(o[L]-u),P=x*(o[L+1]-u)):(I=L<_-1?x*(o[L]-(o[L+1]-o[L])/2-u):x*(o[L]-(o[L]-o[L-1])/2-u),P=L<_-1?x*(o[L]+(o[L+1]-o[L])/2-u):x*(o[L]+(o[L]-o[L-1])/2-u));for(var z=0;z<E;++z){var O,D;l?(O=y*(r[z]-c),D=y*(r[z+1]-c)):(O=z<b-1?y*(r[z]-(r[z+1]-r[z])/2-c):y*(r[z]-(r[z]-r[z-1])/2-c),D=z<b-1?y*(r[z]+(r[z+1]-r[z])/2-c):y*(r[z]+(r[z]-r[z-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?(L+V)*b+(z+U):L*b+z],H=n.le(d,q);if(H<0)F=g[0],B=g[1],N=g[2],j=g[3];else if(H===m-1)F=g[4*m-4],B=g[4*m-3],N=g[4*m-2],j=g[4*m-1];else{var G=(q-d[H])/(d[H+1]-d[H]),Y=1-G,W=4*H,X=4*(H+1);F=Y*g[W]+G*g[X],B=Y*g[W+1]+G*g[X+1],N=Y*g[W+2]+G*g[X+2],j=Y*g[W+3]+G*g[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*O+.5*D,k[2*S+1]=.5*I+.5*P,M[2*S]=U,M[2*S+1]=V,A[S]=L*b+z,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\":272,\"binary-search-bounds\":96,\"gl-buffer\":259,\"gl-shader\":335,\"iota-array\":463,\"typedarray-pool\":595}],272:[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:273}],273:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],274:[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:276}],275:[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 g=new v(e,r,o,s,l,d);return g.update(t),g};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 g(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 m(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:g(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:g(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],g=t.lineWidth||1,m=!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,m=!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(g)?g.length>e-1?g[e-1]:g.length>0?g[g.length-1]:[0,0,0,1]:g;var 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t[0]=1-(p+g),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+g),t[6]=d+m,t[7]=0,t[8]=h+v,t[9]=d-m,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],285:[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}},{}],286:[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}},{}],287:[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}},{}],288:[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}},{}],289:[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}},{}],290:[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}},{}],291:[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}},{}],292:[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\":277,\"./clone\":278,\"./copy\":279,\"./create\":280,\"./determinant\":281,\"./fromQuat\":282,\"./fromRotation\":283,\"./fromRotationTranslation\":284,\"./fromScaling\":285,\"./fromTranslation\":286,\"./fromXRotation\":287,\"./fromYRotation\":288,\"./fromZRotation\":289,\"./frustum\":290,\"./identity\":291,\"./invert\":293,\"./lookAt\":294,\"./multiply\":295,\"./ortho\":296,\"./perspective\":297,\"./perspectiveFromFieldOfView\":298,\"./rotate\":299,\"./rotateX\":300,\"./rotateY\":301,\"./rotateZ\":302,\"./scale\":303,\"./str\":304,\"./translate\":305,\"./transpose\":306}],293:[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],g=e[13],m=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*g-f*d,M=u*m-h*d,A=u*v-p*d,S=f*m-h*g,E=f*v-p*g,C=h*v-p*m,L=y*C-x*E+b*S+_*A-w*M+T*k;if(!L)return null;return L=1/L,t[0]=(s*C-l*E+c*S)*L,t[1]=(i*E-n*C-a*S)*L,t[2]=(g*T-m*w+v*_)*L,t[3]=(h*w-f*T-p*_)*L,t[4]=(l*A-o*C-c*M)*L,t[5]=(r*C-i*A+a*M)*L,t[6]=(m*b-d*T-v*x)*L,t[7]=(u*T-h*b+p*x)*L,t[8]=(o*E-s*A+c*k)*L,t[9]=(n*A-r*E-a*k)*L,t[10]=(d*w-g*b+v*y)*L,t[11]=(f*b-u*w-p*y)*L,t[12]=(s*M-o*S-l*k)*L,t[13]=(r*S-n*M+i*k)*L,t[14]=(g*x-d*_-m*y)*L,t[15]=(u*_-f*x+h*y)*L,t}},{}],294:[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,g=e[0],m=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(g-_)<1e-6&&Math.abs(m-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=g-_,h=m-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*g+o*m+s*v),t[13]=-(l*g+c*m+u*v),t[14]=-(f*g+h*m+p*v),t[15]=1,t}},{\"./identity\":291}],295:[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],g=e[12],m=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*g,t[1]=x*i+b*l+_*h+w*m,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*g,t[5]=x*i+b*l+_*h+w*m,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*g,t[9]=x*i+b*l+_*h+w*m,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*g,t[13]=x*i+b*l+_*h+w*m,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],296:[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}},{}],297:[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}},{}],298:[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}},{}],299:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,M,A,S,E=n[0],C=n[1],L=n[2],I=Math.sqrt(E*E+C*C+L*L);if(Math.abs(I)<1e-6)return null;E*=I=1/I,C*=I,L*=I,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],g=e[8],m=e[9],v=e[10],y=e[11],x=E*E*o+a,b=C*E*o+L*i,_=L*E*o-C*i,w=E*C*o-L*i,T=C*C*o+a,k=L*C*o+E*i,M=E*L*o+C*i,A=C*L*o-E*i,S=L*L*o+a,t[0]=s*x+f*b+g*_,t[1]=l*x+h*b+m*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+g*k,t[5]=l*w+h*T+m*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*M+f*A+g*S,t[9]=l*M+h*A+m*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}},{}],300:[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}},{}],301:[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}},{}],302:[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}},{}],303:[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}},{}],304:[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]+\")\"}},{}],305:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,g=r[0],m=r[1],v=r[2];e===t?(t[12]=e[0]*g+e[4]*m+e[8]*v+e[12],t[13]=e[1]*g+e[5]*m+e[9]*v+e[13],t[14]=e[2]*g+e[6]*m+e[10]*v+e[14],t[15]=e[3]*g+e[7]*m+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*g+s*m+f*v+e[12],t[13]=i*g+l*m+h*v+e[13],t[14]=a*g+c*m+p*v+e[14],t[15]=o*g+u*m+d*v+e[15]);return t}},{}],306:[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}},{}],307:[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,g=0;g<2;++g)d+=Math.pow(u[f][g]-e[g],2);d<p&&(p=d,h=f)}var m=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(m[f]<-.001||m[f]>1.0001)return null;v+=m[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,m),m]}},{barycentric:78,\"polytope-closest-point/lib/closest_point_2d.js\":525}],308:[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:310}],309:[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\"),g=t(\"./lib/closest-point\"),m=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,g,m,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=m,this.edgeUVs=v,this.edgeIds=g,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,m.vertex,m.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 C(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function L(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 I(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],g=n[f][1],m=i[f],v=1-m,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=m*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=[],g=[],m=[],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,C=t.meshColor||[1,1,1,1],L=t.vertexUVs,I=t.vertexIntensity,P=t.cellUVs,z=t.cellIntensity,O=1/0,D=-1/0;if(!L&&!P)if(I)if(t.vertexIntensityBounds)O=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<I.length;++R){var F=I[R];O=Math.min(O,F),D=Math.max(D,F)}else if(z)if(t.cellIntensityBounds)O=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<z.length;++R){F=z[R];O=Math.min(O,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];O=Math.min(O,F),D=Math.max(D,F)}this.intensity=I||(z||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=!(z||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]:C,this.opacityscale&&I?a.push(X[0],X[1],X[2],this.opacity*M((I[W]-O)/(D-O),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=L?L[W]:I?[(I[W]-O)/(D-O),0]:P?P[R]:z?[(z[R]-O)/(D-O),0]:[(j[2]-O)/(D-O),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]:C,this.opacityscale&&I?a.push(X[0],X[1],X[2],this.opacity*M((I[W]-O)/(D-O),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=L?L[W]:I?[(I[W]-O)/(D-O),0]:P?P[R]:z?[(z[R]-O)/(D-O),0]:[(j[2]-O)/(D-O),0],g.push(Z[0],Z[1]),m.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]:C)?this.opacityscale&&I?a.push(X[0],X[1],X[2],this.opacity*M((I[W]-O)/(D-O),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=L?L[W]:I?[(I[W]-O)/(D-O),0]:P?P[R]:z?[(z[R]-O)/(D-O),0]:[(j[2]-O)/(D-O),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(g),this.edgeIds.update(new Uint32Array(m)),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=g(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=C(t),f=L(t),h=I(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),g=i(t),m=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:g,type:t.FLOAT,size:4},{buffer:m,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}]),P=i(t),z=i(t),O=i(t),D=i(t),R=i(t),F=a(t,[{buffer:P,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:z,type:t.FLOAT,size:4},{buffer:O,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,g,m,v,x,b,k,_,w,M,P,R,z,O,D,F,B,N);return j.update(e),j}},{\"./lib/closest-point\":307,\"./lib/shaders\":308,colormap:131,\"gl-buffer\":259,\"gl-mat4/invert\":293,\"gl-mat4/multiply\":295,\"gl-shader\":335,\"gl-texture2d\":353,\"gl-vao\":358,ndarray:495,normals:498,\"simplicial-complex-contour\":556,\"typedarray-pool\":595}],310:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],311:[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\":314,\"gl-buffer\":259,\"gl-shader\":335}],312:[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,g=0;g<2;++g){var m=a[g],v=a[g+2]-m,y=.5*(o[g+2]+o[g]),x=o[g+2]-o[g];u[g]=2*v/x,c[g]=2*(m-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(g=0;g<2;++g){f[0]=f[1]=0,f[g]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[g]/(s[g+2]-s[g])*d,r.uniforms.color=h[g];var _=6*n[g].length;p[g]&&_&&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,g=s.viewBox,m=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=g[2]-g[0],_=g[3]-g[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,C=s.tickMarkWidth,L=s.tickMarkColor,I=6*f[0].length,P=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),z=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),O=0+6*P,D=6*Math.max(0,z-P),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=I+6*R,N=6*Math.max(0,F-R);i[0]=2*(g[0]-E[1])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[1]*m/y,o[1]=C[1]*m/x,N&&(S.color=L[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2*(g[1]-E[0])/x-1,o[0]=C[0]*m/y,o[1]=E[0]*m/x,D&&(S.color=L[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,O,D)),i[0]=2*(g[2]+E[3])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[3]*m/y,o[1]=C[3]*m/x,N&&(S.color=L[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2*(g[3]+E[2])/x-1,o[0]=C[2]*m/y,o[1]=E[2]*m/x,D&&(S.color=L[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,O,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 g=0;g<6;++g)n[i++]=d,n[i++]=h[g],n[i++]=p[g]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":314,\"binary-search-bounds\":96,\"gl-buffer\":259,\"gl-shader\":335}],313:[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\":314,\"gl-buffer\":259,\"gl-shader\":335}],314:[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:316}],315:[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,g=l.prototype;g.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,g=e.tickPad,m=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*g[t],k<M&&i[M]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[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*g[t+2],k<M&&i[M]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[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))}),g.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)}}}(),g.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],g=a[o+2]-f,m=i[o],v=i[o+2]-m;p[o]=2*l/u*g/v,h[o]=2*(s-c)/u*g/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()}),g.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,g=p.text,m=p.font||\"sans-serif\";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=g.split(\"\\n\"),b=0;b<x.length;b++)for(n=a(m,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)},g.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":314,\"binary-search-bounds\":96,\"gl-buffer\":259,\"gl-shader\":335,\"text-cache\":575}],316:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],317:[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 g=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(g,e[1],g,e[3],p[d],h[d]):o.drawLine(e[0],g,e[2],g,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 m=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(m[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),m[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),m[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),m[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\":311,\"./lib/grid\":312,\"./lib/line\":313,\"./lib/text\":315,\"gl-select-static\":334}],318:[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,g=!!a.shift,m=!!(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&&m&&!h&&!p&&!g||m&&!h&&!p&&g)&&c.rotate(M,w*k*b,-T*k*_,0),(l&&m&&!h&&!p&&!g||v||m&&h&&!p&&!g)&&c.pan(M,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&m&&!h&&!p&&!g||y||m&&!h&&p&&!g)){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\":54,\"has-passive-events\":441,\"mouse-change\":483,\"mouse-event-offset\":484,\"mouse-wheel\":486,\"right-now\":542}],319:[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:320}],320:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],321:[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 g(){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 m(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 g,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=[],C=[],L=[],I=[],P=!0,z=!0,O=new Array(16),D=new Array(16),R={view:null,projection:O,model:D,_ortho:!1},F=(z=!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,z=!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\",P=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=I.length,n=0;n<e;++n)L[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(L[o]+a<255){C[n]=o,i.setPickBase(L[o]+1),L[o]+=a;continue t}var l=s(r,F);C[n]=e,I.push(l),L.push(a),i.setPickBase(1),e+=1}else C[n]=-1}for(;e>0&&0===L[e-1];)L.pop(),I.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||{},P=!0,z=!0)},N.add=function(t){N._stopped||(t.axes=M,E.push(t),C.push(-1),P=!0,z=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),C.pop(),P=!0,z=!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<I.length;++t)I[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=I.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&&(z=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=I[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(C[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),P=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),P=!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,P=P||t,z=z||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){(L=E[o]).pixelRatio=N.pixelRatio,L.axes=N.axes,P=P||!!L.dirty,z=z||!!L.dirty;var s=L.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 g=.05*(i[p]-n[p]);n[p]=n[p]-g,i[p]=i[p]+g}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(z=z||v,P=P||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=m((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 g=0;for(d=0;d<3;++d)g=Math.max(g,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/g,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(L=E[o]).axesBounds=d,N.clipToBounds&&(L.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),z&&(z=!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=I.length,n=0;n<e;++n){var i=I[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(C[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){(L=E[o]).axes=M,L.pixelRatio=N.pixelRatio,L.isOpaque&&L.isOpaque()&&L.draw(R),L.isTransparent&&L.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){(L=E[o]).isOpaque&&L.isOpaque()&&L.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 L;(L=E[o]).isTransparent&&L.isTransparent()&&L.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)}P=!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||(P=!0,Y())},N},createCamera:n}},{\"./camera.js\":318,\"./lib/shader\":319,\"a-big-triangle\":64,\"gl-axes3d\":250,\"gl-axes3d/properties\":258,\"gl-fbo\":269,\"gl-mat4/ortho\":296,\"gl-mat4/perspective\":297,\"gl-select-static\":334,\"gl-spikes3d\":345,\"is-mobile\":467,\"mouse-change\":483}],322:[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:323}],323:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],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\":322,\"gl-buffer\":259,\"gl-shader\":335,\"typedarray-pool\":595}],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],g=r[2],m=r[3];(a=c*p+u*d+f*g+h*m)<0&&(a=-a,p=-p,d=-d,g=-g,m=-m);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*g,t[3]=s*h+l*m,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\":600}],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},g={vertex:o,fragment:c,attributes:u},m={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,g)},r.createPickProject=function(t){return v(t,m)}},{\"gl-shader\":335,glslify:329}],329:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],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 g(t){return!0===t||t>1?1:t}function m(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),g=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 m(e,r,n,o,f,h,p,d,g,s,c,u);return v.update(t),v};var v=m.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 C(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,g=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=g,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var m=0;m<3;++m)if(a[m]){l.scale=e.projectScale[m],l.opacity=e.projectOpacity[m];for(var v=T,C=0;C<16;++C)v[C]=0;for(C=0;C<4;++C)v[5*C]=1;v[5*m]=0,i[m]<0?v[12+m]=d[0][m]:v[12+m]=d[1][m],s(v,c,v),l.model=v;var L=(m+1)%3,I=(m+2)%3,P=A(x),z=A(b);P[L]=1,z[I]=1;var O=p(0,0,0,S(_,P)),D=p(0,0,0,S(w,z));if(Math.abs(O[1])>Math.abs(D[1])){var R=O;O=D,D=R,R=P,P=z,z=R;var F=L;L=I,I=F}O[0]<0&&(P[L]=-1),D[1]>0&&(z[I]=-1);var B=0,N=0;for(C=0;C<4;++C)B+=Math.pow(c[4*L+C],2),N+=Math.pow(c[4*I+C],2);P[L]/=Math.sqrt(B),z[I]/=Math.sqrt(N),l.axes[0]=P,l.axes[1]=z,l.fragClipBounds[0]=E(k,g[0],m,-1e8),l.fragClipBounds[1]=E(k,g[1],m,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 L=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function I(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&C(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=L,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 P(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){I(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){I(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){I(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]=g(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=g(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,m=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=P(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),C=o.mallocFloat(4*S),L=o.mallocFloat(2*S),I=o.mallocUint32(S);if(S>0){var z=0,O=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=P(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(m)?q=n<m.length?+m[n]:0:m&&(q=+m);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;++$)E[3*z+$]=w[$];for($=0;$<4;++$)C[4*z+$]=D[$];I[z]=y;var tt=J[K[Q]];L[2*z]=V*(H*tt[0]-G*tt[1]+X[0]),L[2*z+1]=V*(G*tt[0]+H*tt[1]+X[1]),z+=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;++$)E[3*O+$]=w[$];for($=0;$<4;++$)C[4*O+$]=R[$];I[O]=y;tt=J[K[Q]];L[2*O]=V*(H*tt[0]-G*tt[1]+X[0]),L[2*O+1]=V*(G*tt[0]+H*tt[1]+X[1]),O+=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(C),this.glyphBuffer.update(L),this.idBuffer.update(I),o.free(E),o.free(C),o.free(L),o.free(I)},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\":259,\"gl-mat4/multiply\":295,\"gl-vao\":358,\"is-string-blank\":470,\"typedarray-pool\":595}],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][257][0].apply(r,arguments)},{dup:257}],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 g=s[2]-s[0],m=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,g,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,g,m,i),o.drawBox(p,h,g,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\":259,\"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\":270,\"weakmap-shim\":605}],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 i=n.prototype;i.update=function(t){t=t||{},this.enable=(t.enable||[!0,!0,!1,!1]).slice(),this.width=(t.width||[1,1,1,1]).slice(),this.color=(t.color||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]).map((function(t){return t.slice()})),this.center=(t.center||[1/0,1/0]).slice(),this.plot.setOverlayDirty()},i.draw=function(){var t=this.enable,e=this.width,r=this.color,n=this.center,i=this.plot,a=i.line,o=i.dataBox,s=i.viewBox;if(a.bind(),o[0]<=n[0]&&n[0]<=o[2]&&o[1]<=n[1]&&n[1]<=o[3]){var l=s[0]+(n[0]-o[0])/(o[2]-o[0])*(s[2]-s[0]),c=s[1]+(n[1]-o[1])/(o[3]-o[1])*(s[3]-s[1]);t[0]&&a.drawLine(l,c,s[0],c,e[0],r[0]),t[1]&&a.drawLine(l,c,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,c,s[2],c,e[2],r[2]),t[3]&&a.drawLine(l,c,l,s[3],e[3],r[3])}},i.dispose=function(){this.plot.removeOverlay(this)}},{}],343:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],344:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 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\":259,\"gl-vao\":358}],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][257][0].apply(r,arguments)},{dup:257}],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=[],g=0,m=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),m=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(g,m,m,m,g,g);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=g;g=m,m=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 g=u[s],m=f.length;f=f.concat(g.positions),p=p.concat(g.vectors),d=d.concat(g.vertexIntensity);for(c=0;c<g.cells.length;c++){var v=g.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+m)}}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),g=s(a[1],c),m=s(a[2],u),v=d+1,y=g+1,x=m+1;if(d=l(d,0,f-1),v=l(v,0,f-1),g=l(g,0,h-1),y=l(y,0,h-1),m=l(m,0,p-1),x=l(x,0,p-1),d<0||g<0||m<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][g],C=a[1][y],L=a[2][m],I=(o-A)/(S-A),P=(c-E)/(C-E),z=(u-L)/(a[2][x]-L);switch(isFinite(I)||(I=.5),isFinite(P)||(P=.5),isFinite(z)||(z=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(g=h-1-g,y=h-1-y),r.reversedZ&&(m=p-1-m,x=p-1-x),r.filled){case 5:k=m,M=x,w=g*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=m,M=x,b=d*p,_=v*p,w=g*p*f,T=y*p*f;break;case 3:w=g,T=y,k=m*h,M=x*h,b=d*h*p,_=v*h*p;break;case 2:w=g,T=y,b=d*h,_=v*h,k=m*h*f,M=x*h*f;break;case 1:b=d,_=v,k=m*f,M=x*f,w=g*f*p,T=y*f*p;break;default:b=d,_=v,w=g*f,T=y*f,k=m*f*h,M=x*f*h}var O=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,O,B,I),n.lerp(q,D,N,I),n.lerp(H,R,j,I),n.lerp(G,F,U,I);var Y=n.create(),W=n.create();n.lerp(Y,V,H,P),n.lerp(W,q,G,P);var X=n.create();return n.lerp(X,Y,W,z),X}(e,t,p)},g=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},m=[],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),g=c(r),m=c(n),v=Math.min(d,g,m);return isFinite(v)?v:1}(r));for(var C=0;C<E;C++){var L=n.create();n.copy(L,r[C]);var I=[L],P=[],z=d(L),O=L;P.push(z);var D=[],R=g(L,z),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),m.push({points:I,velocities:P,divergences:D});for(var B=0;B<100*i&&I.length<i&&T(L);){B++;var N=n.clone(z),j=n.squaredLength(N);if(0===j)break;if(j>M&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,L),z=d(N),n.squaredDistance(O,N)-M>-1e-4*M){I.push(N),O=N,P.push(z);R=g(N,z),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}L=N}}var U=o(m,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\":260,\"gl-vec3\":377,\"gl-vec4\":413}],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:350}],350:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],351:[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}]),g=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);g.minFilter=e.LINEAR,g.magFilter=e.LINEAR;var m=new A(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,g,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\",m.update(v),m};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\"),g=t(\"binary-search-bounds\"),m=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,g,m){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=m,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=g,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],C={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 L(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=C.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=C.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 C.showSurface=o,C.showContour=s,C}var I={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},P=w.slice(),z=[1,0,0,0,1,0,0,0,1];function O(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=I;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=z,n.vertexColor=this.vertexColor;var s=P;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=L(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 g=0;g<this.contourLevels[o].length;++g)g===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==g&&g-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][g]&&(f.uniforms.height=this.contourLevels[o][g],h.draw(r.LINES,this._contourCounts[o][g],this._contourOffsets[o][g]))}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 O.call(this,t,!1)},S.drawTransparent=function(t){return O.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=z;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=L(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,m=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*m;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=g.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 g=[0,0];g[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],g,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)m(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,C=A*w-S*_,L=_*M-w*k,I=Math.sqrt(E*E+C*C+L*L);I<1e-8?(I=Math.max(Math.abs(E),Math.abs(C),Math.abs(L)))<1e-8?(L=1,C=E=0,I=1):I=1/I:I=1/Math.sqrt(I),b.set(o,v,0,E*I),b.set(o,v,1,C*I),b.set(o,v,2,L*I)}s.free(x.data);var P=[1/0,1/0,1/0],z=[-1/0,-1/0,-1/0],O=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),C=b.get(Y+1,W+1,1),L=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++]=C,N[j++]=L,P[0]=Math.min(P[0],X+this.objectOffset[0]),P[1]=Math.min(P[1],Z+this.objectOffset[1]),P[2]=Math.min(P[2],G+this.objectOffset[2]),O=Math.min(O,J),z[0]=Math.max(z[0],X+this.objectOffset[0]),z[1]=Math.max(z[1],Z+this.objectOffset[1]),z[2]=Math.max(z[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(O=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-O)/(D-O);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[P,z],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===O&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[O,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[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(H=0;H<2;++H){var ot=it.positions[at[H]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var gt=($+dt+1)%3;for(V=0;V<2;++V){var mt=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[gt].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=mt*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]),g=d.cells,m=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<g.length;++e)for(var v=g[e],y=0;y<2;++y){var x=m[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,C=1-E,L=k*C,I=k*E,P=T*C,z=T*E,O=L*f.get(_,A)+I*f.get(_,S)+P*f.get(w,A)+z*f.get(w,S),D=L*p.get(_,A)+I*p.get(_,S)+P*p.get(w,A)+z*p.get(w,S);if(isNaN(O)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=O,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\":96,\"bit-twiddle\":97,colormap:131,\"gl-buffer\":259,\"gl-mat4/invert\":293,\"gl-mat4/multiply\":295,\"gl-texture2d\":353,\"gl-vao\":358,ndarray:495,\"ndarray-gradient\":488,\"ndarray-ops\":490,\"ndarray-pack\":491,\"surface-nets\":570,\"typedarray-pool\":595}],352:[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\"),g=t(\"detect-kerning\"),m=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);m(t.kerning,g(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 C=0;C<S;C++)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 L=u.mallocUint8(this.count),I=u.mallocFloat(2*this.count);this.textWidth=[];for(var P=0,z=0;P<this.counts.length;P++){for(var O=this.counts[P],D=this.font[P]||this.font[0],R=this.fontAtlas[P]||this.fontAtlas[0],F=0;F<O;F++){var B=this.text.charAt(z),N=this.text.charAt(z-1);if(L[z]=R.ids[B],I[2*z]=D.width[B],F){var j=I[2*z-2],U=I[2*z],V=I[2*z-1]+.5*j+.5*U;if(this.kerning){var q=D.kerning[N+B];q&&(V+=.001*q)}I[2*z+1]=V}else I[2*z+1]=.5*I[2*z];z++}this.textWidth.push(I.length?.5*I[2*z-2]+I[2*z-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:L,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:I,type:\"float\",usage:\"stream\"}),u.freeUint8(L),u.freeFloat(I),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\":97,\"color-normalize\":125,\"css-font\":144,\"detect-kerning\":172,\"es6-weak-map\":233,\"flatten-vertex-data\":244,\"font-atlas\":245,\"font-measure\":246,\"gl-util/context\":354,\"is-plain-obj\":469,\"object-assign\":499,\"parse-rect\":504,\"parse-unit\":506,\"pick-by-alias\":511,regl:540,\"to-px\":578,\"typedarray-pool\":595}],353:[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 g(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 m(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=m(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=m(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=g(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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strict\";e.exports=function(t,e){var r=t.length;if(0===r)throw new Error(\"Must have at least d+1 points\");var i=t[0].length;if(r<=i)throw new Error(\"Must input at least d+1 points\");var o=t.slice(0,i+1),s=n.apply(void 0,o);if(0===s)throw new Error(\"Input not in general position\");for(var l=new Array(i+1),u=0;u<=i;++u)l[u]=u;s<0&&(l[0]=1,l[1]=0);var f=new a(l,new Array(i+1),!1),h=f.adjacent,p=new Array(i+2);for(u=0;u<=i;++u){for(var d=l.slice(),g=0;g<=i;++g)g===u&&(d[g]=-1);var m=d[0];d[0]=d[1],d[1]=m;var v=new a(d,new Array(i+1),!0);h[u]=v,p[u]=v}p[i+1]=f;for(u=0;u<=i;++u){d=h[u].vertices;var y=h[u].adjacent;for(g=0;g<=i;++g){var x=d[g];if(x<0)y[g]=f;else for(var b=0;b<=i;++b)h[b].vertices.indexOf(x)<0&&(y[g]=h[b])}}var _=new c(i,o,p),w=!!e;for(u=i+1;u<r;++u)_.insert(t[u],w);return _.boundary()};var n=t(\"robust-orientation\"),i=t(\"simplicial-complex\").compareCells;function a(t,e,r){this.vertices=t,this.adjacent=e,this.boundary=r,this.lastVisited=-1}function o(t,e,r){this.vertices=t,this.cell=e,this.index=r}function s(t,e){return i(t.vertices,e.vertices)}a.prototype.flip=function(){var t=this.vertices[0];this.vertices[0]=this.vertices[1],this.vertices[1]=t;var e=this.adjacent[0];this.adjacent[0]=this.adjacent[1],this.adjacent[1]=e};var l=[];function c(t,e,r){this.dimension=t,this.vertices=e,this.simplices=r,this.interior=r.filter((function(t){return!t.boundary})),this.tuple=new Array(t+1);for(var i=0;i<=t;++i)this.tuple[i]=this.vertices[i];var a=l[t];a||(a=l[t]=function(t){for(var e=[\"function orient(){var tuple=this.tuple;return test(\"],r=0;r<=t;++r)r>0&&e.push(\",\"),e.push(\"tuple[\",r,\"]\");e.push(\")}return orient\");var i=new Function(\"test\",e.join(\"\")),a=n[t+1];return a||(a=n),i(a)}(t)),this.orient=a}var u=c.prototype;u.handleBoundaryDegeneracy=function(t,e){var r=this.dimension,n=this.vertices.length-1,i=this.tuple,a=this.vertices,o=[t];for(t.lastVisited=-n;o.length>0;){(t=o.pop()).vertices;for(var s=t.adjacent,l=0;l<=r;++l){var c=s[l];if(c.boundary&&!(c.lastVisited<=-n)){for(var u=c.vertices,f=0;f<=r;++f){var h=u[f];i[f]=h<0?e:a[h]}var p=this.orient();if(p>0)return c;c.lastVisited=-n,0===p&&o.push(c)}}}return null},u.walk=function(t,e){var r=this.vertices.length-1,n=this.dimension,i=this.vertices,a=this.tuple,o=e?this.interior.length*Math.random()|0:this.interior.length-1,s=this.interior[o];t:for(;!s.boundary;){for(var l=s.vertices,c=s.adjacent,u=0;u<=n;++u)a[u]=i[l[u]];s.lastVisited=r;for(u=0;u<=n;++u){var f=c[u];if(!(f.lastVisited>=r)){var h=a[u];a[u]=t;var p=this.orient();if(a[u]=h,p<0){s=f;continue t}f.boundary?f.lastVisited=-r:f.lastVisited=r}}return}return s},u.addPeaks=function(t,e){var r=this.vertices.length-1,n=this.dimension,i=this.vertices,l=this.tuple,c=this.interior,u=this.simplices,f=[e];e.lastVisited=r,e.vertices[e.vertices.indexOf(-1)]=r,e.boundary=!1,c.push(e);for(var h=[];f.length>0;){var p=(e=f.pop()).vertices,d=e.adjacent,g=p.indexOf(r);if(!(g<0))for(var m=0;m<=n;++m)if(m!==g){var v=d[m];if(v.boundary&&!(v.lastVisited>=r)){var y=v.vertices;if(v.lastVisited!==-r){for(var x=0,b=0;b<=n;++b)y[b]<0?(x=b,l[b]=t):l[b]=i[y[b]];if(this.orient()>0){y[x]=r,v.boundary=!1,c.push(v),f.push(v),v.lastVisited=r;continue}v.lastVisited=-r}var _=v.adjacent,w=p.slice(),T=d.slice(),k=new a(w,T,!0);u.push(k);var M=_.indexOf(e);if(!(M<0)){_[M]=k,T[g]=v,w[m]=-1,T[m]=e,d[m]=k,k.flip();for(b=0;b<=n;++b){var A=w[b];if(!(A<0||A===r)){for(var S=new Array(n-1),E=0,C=0;C<=n;++C){var L=w[C];L<0||C===b||(S[E++]=L)}h.push(new o(S,k,b))}}}}}}h.sort(s);for(m=0;m+1<h.length;m+=2){var I=h[m],P=h[m+1],z=I.index,O=P.index;z<0||O<0||(I.cell.adjacent[I.index]=P.cell,P.cell.adjacent[P.index]=I.cell)}},u.insert=function(t,e){var r=this.vertices;r.push(t);var n=this.walk(t,e);if(n){for(var i=this.dimension,a=this.tuple,o=0;o<=i;++o){var s=n.vertices[o];a[o]=s<0?t:r[s]}var l=this.orient(a);l<0||(0!==l||(n=this.handleBoundaryDegeneracy(n,t)))&&this.addPeaks(t,n)}},u.boundary=function(){for(var t=this.dimension,e=[],r=this.simplices,n=r.length,i=0;i<n;++i){var a=r[i];if(a.boundary){for(var o=new Array(t),s=a.vertices,l=0,c=0,u=0;u<=t;++u)s[u]>=0?o[l++]=s[u]:c=1&u;if(c===(1&t)){var f=o[0];o[0]=o[1],o[1]=f}e.push(o)}}return e}},{\"robust-orientation\":548,\"simplicial-complex\":558}],460:[function(t,e,r){\"use strict\";var n=t(\"binary-search-bounds\");function i(t,e,r,n,i){this.mid=t,this.left=e,this.right=r,this.leftPoints=n,this.rightPoints=i,this.count=(e?e.count:0)+(r?r.count:0)+n.length}e.exports=function(t){if(!t||0===t.length)return new v(null);return new v(m(t))};var a=i.prototype;function o(t,e){t.mid=e.mid,t.left=e.left,t.right=e.right,t.leftPoints=e.leftPoints,t.rightPoints=e.rightPoints,t.count=e.count}function s(t,e){var r=m(e);t.mid=r.mid,t.left=r.left,t.right=r.right,t.leftPoints=r.leftPoints,t.rightPoints=r.rightPoints,t.count=r.count}function l(t,e){var r=t.intervals([]);r.push(e),s(t,r)}function c(t,e){var r=t.intervals([]),n=r.indexOf(e);return n<0?0:(r.splice(n,1),s(t,r),1)}function u(t,e,r){for(var n=0;n<t.length&&t[n][0]<=e;++n){var i=r(t[n]);if(i)return i}}function f(t,e,r){for(var n=t.length-1;n>=0&&t[n][1]>=e;--n){var i=r(t[n]);if(i)return i}}function h(t,e){for(var r=0;r<t.length;++r){var n=e(t[r]);if(n)return n}}function p(t,e){return t-e}function d(t,e){var r=t[0]-e[0];return r||t[1]-e[1]}function g(t,e){var r=t[1]-e[1];return r||t[0]-e[0]}function m(t){if(0===t.length)return null;for(var e=[],r=0;r<t.length;++r)e.push(t[r][0],t[r][1]);e.sort(p);var n=e[e.length>>1],a=[],o=[],s=[];for(r=0;r<t.length;++r){var l=t[r];l[1]<n?a.push(l):n<l[0]?o.push(l):s.push(l)}var c=s,u=s.slice();return c.sort(d),u.sort(g),new i(n,m(a),m(o),c,u)}function v(t){this.root=t}a.intervals=function(t){return t.push.apply(t,this.leftPoints),this.left&&this.left.intervals(t),this.right&&this.right.intervals(t),t},a.insert=function(t){var e=this.count-this.leftPoints.length;if(this.count+=1,t[1]<this.mid)this.left?4*(this.left.count+1)>3*(e+1)?l(this,t):this.left.insert(t):this.left=m([t]);else if(t[0]>this.mid)this.right?4*(this.right.count+1)>3*(e+1)?l(this,t):this.right.insert(t):this.right=m([t]);else{var r=n.ge(this.leftPoints,t,d),i=n.ge(this.rightPoints,t,g);this.leftPoints.splice(r,0,t),this.rightPoints.splice(i,0,t)}},a.remove=function(t){var e=this.count-this.leftPoints;if(t[1]<this.mid)return this.left?4*(this.right?this.right.count:0)>3*(e-1)?c(this,t):2===(s=this.left.remove(t))?(this.left=null,this.count-=1,1):(1===s&&(this.count-=1),s):0;if(t[0]>this.mid)return this.right?4*(this.left?this.left.count:0)>3*(e-1)?c(this,t):2===(s=this.right.remove(t))?(this.right=null,this.count-=1,1):(1===s&&(this.count-=1),s):0;if(1===this.count)return this.leftPoints[0]===t?2:0;if(1===this.leftPoints.length&&this.leftPoints[0]===t){if(this.left&&this.right){for(var r=this,i=this.left;i.right;)r=i,i=i.right;if(r===this)i.right=this.right;else{var a=this.left,s=this.right;r.count-=i.count,r.right=i.left,i.left=a,i.right=s}o(this,i),this.count=(this.left?this.left.count:0)+(this.right?this.right.count:0)+this.leftPoints.length}else this.left?o(this,this.left):o(this,this.right);return 1}for(a=n.ge(this.leftPoints,t,d);a<this.leftPoints.length&&this.leftPoints[a][0]===t[0];++a)if(this.leftPoints[a]===t){this.count-=1,this.leftPoints.splice(a,1);for(s=n.ge(this.rightPoints,t,g);s<this.rightPoints.length&&this.rightPoints[s][1]===t[1];++s)if(this.rightPoints[s]===t)return this.rightPoints.splice(s,1),1}return 0},a.queryPoint=function(t,e){if(t<this.mid){if(this.left)if(r=this.left.queryPoint(t,e))return r;return u(this.leftPoints,t,e)}if(t>this.mid){var r;if(this.right)if(r=this.right.queryPoint(t,e))return r;return 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\":461}],461:[function(t,e,r){arguments[4][243][0].apply(r,arguments)},{dup:243}],462:[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}},{}],463:[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}},{}],464:[function(t,e,r){e.exports=!0},{}],465:[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)}},{}],466:[function(t,e,r){\"use strict\";e.exports=\"undefined\"!=typeof navigator&&(/MSIE/.test(navigator.userAgent)||/Trident\\//.test(navigator.appVersion))},{}],467:[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}},{}],468:[function(t,e,r){\"use strict\";e.exports=function(t){var e=typeof t;return null!==t&&(\"object\"===e||\"function\"===e)}},{}],469:[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({}))}},{}],470:[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}},{}],471:[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))}},{}],472:[function(t,e,r){e.exports=function(t,e,r){return t*(1-r)+e*r}},{}],473:[function(t,e,r){!function(t,n){\"object\"==typeof r&&\"undefined\"!=typeof 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 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r={transparent:[0,0,0,0],aliceblue:[240,248,255,1],antiquewhite:[250,235,215,1],aqua:[0,255,255,1],aquamarine:[127,255,212,1],azure:[240,255,255,1],beige:[245,245,220,1],bisque:[255,228,196,1],black:[0,0,0,1],blanchedalmond:[255,235,205,1],blue:[0,0,255,1],blueviolet:[138,43,226,1],brown:[165,42,42,1],burlywood:[222,184,135,1],cadetblue:[95,158,160,1],chartreuse:[127,255,0,1],chocolate:[210,105,30,1],coral:[255,127,80,1],cornflowerblue:[100,149,237,1],cornsilk:[255,248,220,1],crimson:[220,20,60,1],cyan:[0,255,255,1],darkblue:[0,0,139,1],darkcyan:[0,139,139,1],darkgoldenrod:[184,134,11,1],darkgray:[169,169,169,1],darkgreen:[0,100,0,1],darkgrey:[169,169,169,1],darkkhaki:[189,183,107,1],darkmagenta:[139,0,139,1],darkolivegreen:[85,107,47,1],darkorange:[255,140,0,1],darkorchid:[153,50,204,1],darkred:[139,0,0,1],darksalmon:[233,150,122,1],darkseagreen:[143,188,143,1],darkslateblue:[72,61,139,1],darkslategray:[47,79,79,1],darkslategrey:[47,79,79,1],darkturquoise:[0,206,209,1],darkviolet:[148,0,211,1],deeppink:[255,20,147,1],deepskyblue:[0,191,255,1],dimgray:[105,105,105,1],dimgrey:[105,105,105,1],dodgerblue:[30,144,255,1],firebrick:[178,34,34,1],floralwhite:[255,250,240,1],forestgreen:[34,139,34,1],fuchsia:[255,0,255,1],gainsboro:[220,220,220,1],ghostwhite:[248,248,255,1],gold:[255,215,0,1],goldenrod:[218,165,32,1],gray:[128,128,128,1],green:[0,128,0,1],greenyellow:[173,255,47,1],grey:[128,128,128,1],honeydew:[240,255,240,1],hotpink:[255,105,180,1],indianred:[205,92,92,1],indigo:[75,0,130,1],ivory:[255,255,240,1],khaki:[240,230,140,1],lavender:[230,230,250,1],lavenderblush:[255,240,245,1],lawngreen:[124,252,0,1],lemonchiffon:[255,250,205,1],lightblue:[173,216,230,1],lightcoral:[240,128,128,1],lightcyan:[224,255,255,1],lightgoldenrodyellow:[250,250,210,1],lightgray:[211,211,211,1],lightgreen:[144,238,144,1],lightgrey:[211,211,211,1],lightpink:[255,182,193,1],lightsalmon:[255,160,122,1],lightseagreen:[32,178,170,1],lightskyblue:[135,206,250,1],lightslategray:[119,136,153,1],lightslategrey:[119,136,153,1],lightsteelblue:[176,196,222,1],lightyellow:[255,255,224,1],lime:[0,255,0,1],limegreen:[50,205,50,1],linen:[250,240,230,1],magenta:[255,0,255,1],maroon:[128,0,0,1],mediumaquamarine:[102,205,170,1],mediumblue:[0,0,205,1],mediumorchid:[186,85,211,1],mediumpurple:[147,112,219,1],mediumseagreen:[60,179,113,1],mediumslateblue:[123,104,238,1],mediumspringgreen:[0,250,154,1],mediumturquoise:[72,209,204,1],mediumvioletred:[199,21,133,1],midnightblue:[25,25,112,1],mintcream:[245,255,250,1],mistyrose:[255,228,225,1],moccasin:[255,228,181,1],navajowhite:[255,222,173,1],navy:[0,0,128,1],oldlace:[253,245,230,1],olive:[128,128,0,1],olivedrab:[107,142,35,1],orange:[255,165,0,1],orangered:[255,69,0,1],orchid:[218,112,214,1],palegoldenrod:[238,232,170,1],palegreen:[152,251,152,1],paleturquoise:[175,238,238,1],palevioletred:[219,112,147,1],papayawhip:[255,239,213,1],peachpuff:[255,218,185,1],peru:[205,133,63,1],pink:[255,192,203,1],plum:[221,160,221,1],powderblue:[176,224,230,1],purple:[128,0,128,1],rebeccapurple:[102,51,153,1],red:[255,0,0,1],rosybrown:[188,143,143,1],royalblue:[65,105,225,1],saddlebrown:[139,69,19,1],salmon:[250,128,114,1],sandybrown:[244,164,96,1],seagreen:[46,139,87,1],seashell:[255,245,238,1],sienna:[160,82,45,1],silver:[192,192,192,1],skyblue:[135,206,235,1],slateblue:[106,90,205,1],slategray:[112,128,144,1],slategrey:[112,128,144,1],snow:[255,250,250,1],springgreen:[0,255,127,1],steelblue:[70,130,180,1],tan:[210,180,140,1],teal:[0,128,128,1],thistle:[216,191,216,1],tomato:[255,99,71,1],turquoise:[64,224,208,1],violet:[238,130,238,1],wheat:[245,222,179,1],white:[255,255,255,1],whitesmoke:[245,245,245,1],yellow:[255,255,0,1],yellowgreen:[154,205,50,1]};function n(t){return(t=Math.round(t))<0?0:t>255?255:t}function i(t){return n(\"%\"===t[t.length-1]?parseFloat(t)/100*255:parseInt(t))}function a(t){return(e=\"%\"===t[t.length-1]?parseFloat(t)/100:parseFloat(t))<0?0:e>1?1:e;var e}function o(t,e,r){return r<0?r+=1:r>1&&(r-=1),6*r<1?t+(e-t)*r*6:2*r<1?e:3*r<2?t+(e-t)*(2/3-r)*6:t}try{e.parseCSSColor=function(t){var e,s=t.replace(/ /g,\"\").toLowerCase();if(s in r)return r[s].slice();if(\"#\"===s[0])return 4===s.length?(e=parseInt(s.substr(1),16))>=0&&e<=4095?[(3840&e)>>4|(3840&e)>>8,240&e|(240&e)>>4,15&e|(15&e)<<4,1]:null:7===s.length&&(e=parseInt(s.substr(1),16))>=0&&e<=16777215?[(16711680&e)>>16,(65280&e)>>8,255&e,1]:null;var l=s.indexOf(\"(\"),c=s.indexOf(\")\");if(-1!==l&&c+1===s.length){var u=s.substr(0,l),f=s.substr(l+1,c-(l+1)).split(\",\"),h=1;switch(u){case\"rgba\":if(4!==f.length)return null;h=a(f.pop());case\"rgb\":return 3!==f.length?null:[i(f[0]),i(f[1]),i(f[2]),h];case\"hsla\":if(4!==f.length)return 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Qt=function(t,e,r){this.sensitivity=t?e?\"variant\":\"case\":e?\"accent\":\"base\",this.locale=r,this.collator=new Intl.Collator(this.locale?this.locale:[],{sensitivity:this.sensitivity,usage:\"search\"})};Qt.prototype.compare=function(t,e){return this.collator.compare(t,e)},Qt.prototype.resolvedLocale=function(){return new Intl.Collator(this.locale?this.locale:[]).resolvedOptions().locale};var $t=function(t,e,r,n,i){this.text=t,this.image=e,this.scale=r,this.fontStack=n,this.textColor=i},te=function(t){this.sections=t};te.fromString=function(t){return new te([new $t(t,null,null,null,null)])},te.prototype.isEmpty=function(){return 0===this.sections.length||!this.sections.some((function(t){return 0!==t.text.length||t.image&&0!==t.image.name.length}))},te.factory=function(t){return t instanceof te?t:te.fromString(t)},te.prototype.toString=function(){return 0===this.sections.length?\"\":this.sections.map((function(t){return t.text})).join(\"\")},te.prototype.serialize=function(){for(var t=[\"format\"],e=0,r=this.sections;e<r.length;e+=1){var n=r[e];if(n.image)t.push([\"image\",n.image.name]);else{t.push(n.text);var i={};n.fontStack&&(i[\"text-font\"]=[\"literal\",n.fontStack.split(\",\")]),n.scale&&(i[\"font-scale\"]=n.scale),n.textColor&&(i[\"text-color\"]=[\"rgba\"].concat(n.textColor.toArray())),t.push(i)}}return t};var ee=function(t){this.name=t.name,this.available=t.available};function re(t,e,r,n){return\"number\"==typeof t&&t>=0&&t<=255&&\"number\"==typeof e&&e>=0&&e<=255&&\"number\"==typeof r&&r>=0&&r<=255?void 0===n||\"number\"==typeof n&&n>=0&&n<=1?null:\"Invalid rgba value [\"+[t,e,r,n].join(\", \")+\"]: 'a' must be between 0 and 1.\":\"Invalid rgba value [\"+(\"number\"==typeof n?[t,e,r,n]:[t,e,r]).join(\", \")+\"]: 'r', 'g', and 'b' must be between 0 and 255.\"}function ne(t){if(null===t)return!0;if(\"string\"==typeof t)return!0;if(\"boolean\"==typeof t)return!0;if(\"number\"==typeof t)return!0;if(t instanceof Kt)return!0;if(t instanceof Qt)return!0;if(t instanceof te)return!0;if(t instanceof ee)return!0;if(Array.isArray(t)){for(var e=0,r=t;e<r.length;e+=1)if(!ne(r[e]))return!1;return!0}if(\"object\"==typeof t){for(var n in t)if(!ne(t[n]))return!1;return!0}return!1}function ie(t){if(null===t)return Ot;if(\"string\"==typeof t)return Rt;if(\"boolean\"==typeof t)return Ft;if(\"number\"==typeof t)return Dt;if(t instanceof Kt)return Bt;if(t instanceof Qt)return Ut;if(t instanceof te)return Vt;if(t instanceof ee)return qt;if(Array.isArray(t)){for(var e,r=t.length,n=0,i=t;n<i.length;n+=1){var a=ie(i[n]);if(e){if(e===a)continue;e=jt;break}e=a}return Ht(e||jt,r)}return Nt}function ae(t){var e=typeof t;return null===t?\"\":\"string\"===e||\"number\"===e||\"boolean\"===e?String(t):t instanceof Kt||t instanceof te||t instanceof ee?t.toString():JSON.stringify(t)}ee.prototype.toString=function(){return this.name},ee.fromString=function(t){return t?new ee({name:t,available:!1}):null},ee.prototype.serialize=function(){return[\"image\",this.name]};var oe=function(t,e){this.type=t,this.value=e};oe.parse=function(t,e){if(2!==t.length)return e.error(\"'literal' expression requires exactly one argument, but found \"+(t.length-1)+\" instead.\");if(!ne(t[1]))return e.error(\"invalid value\");var r=t[1],n=ie(r),i=e.expectedType;return\"array\"!==n.kind||0!==n.N||!i||\"array\"!==i.kind||\"number\"==typeof i.N&&0!==i.N||(n=i),new oe(n,r)},oe.prototype.evaluate=function(){return this.value},oe.prototype.eachChild=function(){},oe.prototype.outputDefined=function(){return!0},oe.prototype.serialize=function(){return\"array\"===this.type.kind||\"object\"===this.type.kind?[\"literal\",this.value]:this.value instanceof Kt?[\"rgba\"].concat(this.value.toArray()):this.value instanceof te?this.value.serialize():this.value};var se=function(t){this.name=\"ExpressionEvaluationError\",this.message=t};se.prototype.toJSON=function(){return this.message};var le={string:Rt,number:Dt,boolean:Ft,object:Nt},ce=function(t,e){this.type=t,this.args=e};ce.parse=function(t,e){if(t.length<2)return e.error(\"Expected at least one argument.\");var r,n=1,i=t[0];if(\"array\"===i){var a,o;if(t.length>2){var s=t[1];if(\"string\"!=typeof s||!(s in le)||\"object\"===s)return e.error('The item type argument of \"array\" must be one of string, number, boolean',1);a=le[s],n++}else a=jt;if(t.length>3){if(null!==t[2]&&(\"number\"!=typeof t[2]||t[2]<0||t[2]!==Math.floor(t[2])))return e.error('The length argument to \"array\" must be a positive integer literal',2);o=t[2],n++}r=Ht(a,o)}else r=le[i];for(var l=[];n<t.length;n++){var c=e.parse(t[n],n,jt);if(!c)return null;l.push(c)}return new ce(r,l)},ce.prototype.evaluate=function(t){for(var e=0;e<this.args.length;e++){var r=this.args[e].evaluate(t);if(!Wt(this.type,ie(r)))return r;if(e===this.args.length-1)throw new se(\"Expected value to be of type \"+Gt(this.type)+\", but found \"+Gt(ie(r))+\" instead.\")}return null},ce.prototype.eachChild=function(t){this.args.forEach(t)},ce.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},ce.prototype.serialize=function(){var t=this.type,e=[t.kind];if(\"array\"===t.kind){var r=t.itemType;if(\"string\"===r.kind||\"number\"===r.kind||\"boolean\"===r.kind){e.push(r.kind);var n=t.N;(\"number\"==typeof n||this.args.length>1)&&e.push(n)}}return e.concat(this.args.map((function(t){return t.serialize()})))};var ue=function(t){this.type=Vt,this.sections=t};ue.parse=function(t,e){if(t.length<2)return e.error(\"Expected at least one argument.\");var r=t[1];if(!Array.isArray(r)&&\"object\"==typeof r)return e.error(\"First argument must be an image or text section.\");for(var n=[],i=!1,a=1;a<=t.length-1;++a){var o=t[a];if(i&&\"object\"==typeof o&&!Array.isArray(o)){i=!1;var s=null;if(o[\"font-scale\"]&&!(s=e.parse(o[\"font-scale\"],1,Dt)))return null;var l=null;if(o[\"text-font\"]&&!(l=e.parse(o[\"text-font\"],1,Ht(Rt))))return null;var c=null;if(o[\"text-color\"]&&!(c=e.parse(o[\"text-color\"],1,Bt)))return null;var u=n[n.length-1];u.scale=s,u.font=l,u.textColor=c}else{var f=e.parse(t[a],1,jt);if(!f)return null;var h=f.type.kind;if(\"string\"!==h&&\"value\"!==h&&\"null\"!==h&&\"resolvedImage\"!==h)return e.error(\"Formatted text type must be 'string', 'value', 'image' or 'null'.\");i=!0,n.push({content:f,scale:null,font:null,textColor:null})}}return new ue(n)},ue.prototype.evaluate=function(t){return new te(this.sections.map((function(e){var r=e.content.evaluate(t);return ie(r)===qt?new $t(\"\",r,null,null,null):new $t(ae(r),null,e.scale?e.scale.evaluate(t):null,e.font?e.font.evaluate(t).join(\",\"):null,e.textColor?e.textColor.evaluate(t):null)})))},ue.prototype.eachChild=function(t){for(var e=0,r=this.sections;e<r.length;e+=1){var n=r[e];t(n.content),n.scale&&t(n.scale),n.font&&t(n.font),n.textColor&&t(n.textColor)}},ue.prototype.outputDefined=function(){return!1},ue.prototype.serialize=function(){for(var t=[\"format\"],e=0,r=this.sections;e<r.length;e+=1){var n=r[e];t.push(n.content.serialize());var i={};n.scale&&(i[\"font-scale\"]=n.scale.serialize()),n.font&&(i[\"text-font\"]=n.font.serialize()),n.textColor&&(i[\"text-color\"]=n.textColor.serialize()),t.push(i)}return t};var fe=function(t){this.type=qt,this.input=t};fe.parse=function(t,e){if(2!==t.length)return e.error(\"Expected two arguments.\");var r=e.parse(t[1],1,Rt);return r?new fe(r):e.error(\"No image name provided.\")},fe.prototype.evaluate=function(t){var e=this.input.evaluate(t),r=ee.fromString(e);return r&&t.availableImages&&(r.available=t.availableImages.indexOf(e)>-1),r},fe.prototype.eachChild=function(t){t(this.input)},fe.prototype.outputDefined=function(){return!1},fe.prototype.serialize=function(){return[\"image\",this.input.serialize()]};var he={\"to-boolean\":Ft,\"to-color\":Bt,\"to-number\":Dt,\"to-string\":Rt},pe=function(t,e){this.type=t,this.args=e};pe.parse=function(t,e){if(t.length<2)return e.error(\"Expected at least one argument.\");var r=t[0];if((\"to-boolean\"===r||\"to-string\"===r)&&2!==t.length)return e.error(\"Expected one argument.\");for(var n=he[r],i=[],a=1;a<t.length;a++){var o=e.parse(t[a],a,jt);if(!o)return null;i.push(o)}return new pe(n,i)},pe.prototype.evaluate=function(t){if(\"boolean\"===this.type.kind)return Boolean(this.args[0].evaluate(t));if(\"color\"===this.type.kind){for(var e,r,n=0,i=this.args;n<i.length;n+=1){if(r=null,(e=i[n].evaluate(t))instanceof Kt)return e;if(\"string\"==typeof e){var a=t.parseColor(e);if(a)return a}else if(Array.isArray(e)&&!(r=e.length<3||e.length>4?\"Invalid rbga value \"+JSON.stringify(e)+\": expected an array containing either three or four numeric values.\":re(e[0],e[1],e[2],e[3])))return new Kt(e[0]/255,e[1]/255,e[2]/255,e[3])}throw new se(r||\"Could not parse color from value '\"+(\"string\"==typeof e?e:String(JSON.stringify(e)))+\"'\")}if(\"number\"===this.type.kind){for(var o=null,s=0,l=this.args;s<l.length;s+=1){if(null===(o=l[s].evaluate(t)))return 0;var c=Number(o);if(!isNaN(c))return c}throw new se(\"Could not convert \"+JSON.stringify(o)+\" to number.\")}return\"formatted\"===this.type.kind?te.fromString(ae(this.args[0].evaluate(t))):\"resolvedImage\"===this.type.kind?ee.fromString(ae(this.args[0].evaluate(t))):ae(this.args[0].evaluate(t))},pe.prototype.eachChild=function(t){this.args.forEach(t)},pe.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},pe.prototype.serialize=function(){if(\"formatted\"===this.type.kind)return new ue([{content:this.args[0],scale:null,font:null,textColor:null}]).serialize();if(\"resolvedImage\"===this.type.kind)return new fe(this.args[0]).serialize();var t=[\"to-\"+this.type.kind];return this.eachChild((function(e){t.push(e.serialize())})),t};var de=[\"Unknown\",\"Point\",\"LineString\",\"Polygon\"],ge=function(){this.globals=null,this.feature=null,this.featureState=null,this.formattedSection=null,this._parseColorCache={},this.availableImages=null,this.canonical=null};ge.prototype.id=function(){return this.feature&&\"id\"in this.feature?this.feature.id:null},ge.prototype.geometryType=function(){return this.feature?\"number\"==typeof this.feature.type?de[this.feature.type]:this.feature.type:null},ge.prototype.geometry=function(){return this.feature&&\"geometry\"in this.feature?this.feature.geometry:null},ge.prototype.canonicalID=function(){return this.canonical},ge.prototype.properties=function(){return this.feature&&this.feature.properties||{}},ge.prototype.parseColor=function(t){var e=this._parseColorCache[t];return e||(e=this._parseColorCache[t]=Kt.parse(t)),e};var me=function(t,e,r,n){this.name=t,this.type=e,this._evaluate=r,this.args=n};me.prototype.evaluate=function(t){return 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a=Ne(e,n),o=rr.interpolationFactor(this.interpolation,n,e[a],e[a+1]),s=r[a].evaluate(t),l=r[a+1].evaluate(t);return\"interpolate\"===this.operator?Ve[this.type.kind.toLowerCase()](s,l,o):\"interpolate-hcl\"===this.operator?tr.reverse(tr.interpolate(tr.forward(s),tr.forward(l),o)):$e.reverse($e.interpolate($e.forward(s),$e.forward(l),o))},rr.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1)t(r[e])},rr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},rr.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 ir=function(t,e){this.type=t,this.args=e};ir.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=e.parse(o[a],1+i.length,r,void 0,{typeAnnotation:\"omit\"});if(!s)return null;r=r||s.type,i.push(s)}var l=n&&i.some((function(t){return Wt(n,t.type)}));return new ir(l?jt:r,i)},ir.prototype.evaluate=function(t){for(var e,r=null,n=0,i=0,a=this.args;i<a.length&&(n++,(r=a[i].evaluate(t))&&r instanceof ee&&!r.available&&(e||(e=r.name),r=null,n===this.args.length&&(r=e)),null===r);i+=1);return r},ir.prototype.eachChild=function(t){this.args.forEach(t)},ir.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},ir.prototype.serialize=function(){var t=[\"coalesce\"];return this.eachChild((function(e){t.push(e.serialize())})),t};var ar=function(t,e){this.type=e.type,this.bindings=[].concat(t),this.result=e};ar.prototype.evaluate=function(t){return this.result.evaluate(t)},ar.prototype.eachChild=function(t){for(var e=0,r=this.bindings;e<r.length;e+=1)t(r[e][1]);t(this.result)},ar.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 ar(r,o):null},ar.prototype.outputDefined=function(){return this.result.outputDefined()},ar.prototype.serialize=function(){for(var t=[\"let\"],e=0,r=this.bindings;e<r.length;e+=1){var n=r[e];t.push(n[0],n[1].serialize())}return t.push(this.result.serialize()),t};var or=function(t,e,r){this.type=t,this.index=e,this.input=r};or.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,Dt),n=e.parse(t[2],2,Ht(e.expectedType||jt));return r&&n?new or(n.type.itemType,r,n):null},or.prototype.evaluate=function(t){var e=this.index.evaluate(t),r=this.input.evaluate(t);if(e<0)throw new se(\"Array index out of bounds: \"+e+\" < 0.\");if(e>=r.length)throw new se(\"Array index out of bounds: \"+e+\" > \"+(r.length-1)+\".\");if(e!==Math.floor(e))throw new se(\"Array index must be an integer, but found \"+e+\" instead.\");return r[e]},or.prototype.eachChild=function(t){t(this.index),t(this.input)},or.prototype.outputDefined=function(){return!1},or.prototype.serialize=function(){return[\"at\",this.index.serialize(),this.input.serialize()]};var sr=function(t,e){this.type=Ft,this.needle=t,this.haystack=e};sr.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,jt),n=e.parse(t[2],2,jt);return r&&n?Xt(r.type,[Ft,Rt,Dt,Ot,jt])?new sr(r,n):e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Gt(r.type)+\" instead\"):null},sr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!r)return!1;if(!Zt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new se(\"Expected first argument to be of type boolean, string, number or null, but found \"+Gt(ie(e))+\" instead.\");if(!Zt(r,[\"string\",\"array\"]))throw new se(\"Expected second argument to be of type array or string, but found \"+Gt(ie(r))+\" instead.\");return r.indexOf(e)>=0},sr.prototype.eachChild=function(t){t(this.needle),t(this.haystack)},sr.prototype.outputDefined=function(){return!0},sr.prototype.serialize=function(){return[\"in\",this.needle.serialize(),this.haystack.serialize()]};var lr=function(t,e,r){this.type=Dt,this.needle=t,this.haystack=e,this.fromIndex=r};lr.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,jt),n=e.parse(t[2],2,jt);if(!r||!n)return null;if(!Xt(r.type,[Ft,Rt,Dt,Ot,jt]))return e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Gt(r.type)+\" instead\");if(4===t.length){var i=e.parse(t[3],3,Dt);return i?new lr(r,n,i):null}return new lr(r,n)},lr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!Zt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new se(\"Expected first argument to be of type boolean, string, number or null, but found \"+Gt(ie(e))+\" instead.\");if(!Zt(r,[\"string\",\"array\"]))throw new se(\"Expected second argument to be of type array or string, but found \"+Gt(ie(r))+\" instead.\");if(this.fromIndex){var n=this.fromIndex.evaluate(t);return r.indexOf(e,n)}return r.indexOf(e)},lr.prototype.eachChild=function(t){t(this.needle),t(this.haystack),this.fromIndex&&t(this.fromIndex)},lr.prototype.outputDefined=function(){return!1},lr.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 cr=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};cr.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 arguments.\");var r,n;e.expectedType&&\"value\"!==e.expectedType.kind&&(n=e.expectedType);for(var i={},a=[],o=2;o<t.length-1;o+=2){var s=t[o],l=t[o+1];Array.isArray(s)||(s=[s]);var 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e=this.input.evaluate(t);return(ie(e)===this.inputType&&this.outputs[this.cases[e]]||this.otherwise).evaluate(t)},cr.prototype.eachChild=function(t){t(this.input),this.outputs.forEach(t),t(this.otherwise)},cr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))&&this.otherwise.outputDefined()},cr.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];e.push(1===h.length?s(h[0]):h.map(s)),e.push(this.outputs[outputIndex$1].serialize())}return e.push(this.otherwise.serialize()),e};var ur=function(t,e,r){this.type=t,this.branches=e,this.otherwise=r};ur.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 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fr=function(t,e,r,n){this.type=t,this.input=e,this.beginIndex=r,this.endIndex=n};function hr(t,e){return\"==\"===t||\"!=\"===t?\"boolean\"===e.kind||\"string\"===e.kind||\"number\"===e.kind||\"null\"===e.kind||\"value\"===e.kind:\"string\"===e.kind||\"number\"===e.kind||\"value\"===e.kind}function pr(t,e,r,n){return 0===n.compare(e,r)}function dr(t,e,r){var n=\"==\"!==t&&\"!=\"!==t;return function(){function i(t,e,r){this.type=Ft,this.lhs=t,this.rhs=e,this.collator=r,this.hasUntypedArgument=\"value\"===t.type.kind||\"value\"===e.type.kind}return i.parse=function(t,e){if(3!==t.length&&4!==t.length)return e.error(\"Expected two or three arguments.\");var r=t[0],a=e.parse(t[1],1,jt);if(!a)return null;if(!hr(r,a.type))return e.concat(1).error('\"'+r+\"\\\" comparisons are not supported for type '\"+Gt(a.type)+\"'.\");var o=e.parse(t[2],2,jt);if(!o)return null;if(!hr(r,o.type))return e.concat(2).error('\"'+r+\"\\\" comparisons are not supported for type 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0,o={}),e=e.concat(Xr({key:s+\"[0]\",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:Jr,value:h}}))}else e=e.concat(h({key:s+\"[0]\",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Vr(It(a[1]))?e.concat([new St(s+\"[1]\",a[1],\"expressions are not allowed in function stops.\")]):e.concat(bn({key:s+\"[1]\",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=zr(t.value),l=Lt(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new St(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 St(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 Lr(i)&&void 0===a&&(u+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new St(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 St(t.key,c,\"stop domain values must appear in ascending order\")]:(r=l,\"categorical\"===a&&l in o?[new St(t.key,c,\"stop domain values must be unique\")]:(o[l]=!0,[])):[new St(t.key,c,\"integer expected, found \"+l)]}}function Qr(t){var e=(\"property\"===t.expressionContext?Yr:qr)(It(t.value),t.valueSpec);if(\"error\"===e.result)return e.value.map((function(e){return new St(\"\"+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 St(t.key,t.value,'Invalid data expression for \"'+t.propertyKey+'\". 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en={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function rn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};tn(t)||(t=an(t));var e=qr(t,en);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 nn(t,e){return t<e?-1:t>e?1:0}function an(t){if(!t)return!0;var e,r=t[0];return t.length<=1?\"any\"!==r:\"==\"===r?on(t[1],t[2],\"==\"):\"!=\"===r?cn(on(t[1],t[2],\"==\")):\"<\"===r||\">\"===r||\"<=\"===r||\">=\"===r?on(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(an))):\"all\"===r?[\"all\"].concat(t.slice(1).map(an)):\"none\"===r?[\"all\"].concat(t.slice(1).map(an).map(cn)):\"in\"===r?sn(t[1],t.slice(2)):\"!in\"===r?cn(sn(t[1],t.slice(2))):\"has\"===r?ln(t[1]):\"!has\"===r?cn(ln(t[1])):\"within\"!==r||t}function on(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function sn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(nn)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function ln(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function cn(t){return[\"!\",t]}function un(t){return tn(It(t.value))?Qr(Ct({},t,{expressionContext:\"filter\",valueSpec:{value:\"boolean\"}})):function t(e){var r=e.value,n=e.key;if(\"array\"!==zr(r))return[new St(n,r,\"array expected, \"+zr(r)+\" found\")];var i,a=e.styleSpec,o=[];if(r.length<1)return[new St(n,r,\"filter array must have at least 1 element\")];switch(o=o.concat($r({key:n+\"[0]\",value:r[0],valueSpec:a.filter_operator,style:e.style,styleSpec:e.styleSpec})),Lt(r[0])){case\"<\":case\"<=\":case\">\":case\">=\":r.length>=2&&\"$type\"===Lt(r[1])&&o.push(new St(n,r,'\"$type\" cannot be use with operator \"'+r[0]+'\"'));case\"==\":case\"!=\":3!==r.length&&o.push(new St(n,r,'filter array for operator \"'+r[0]+'\" must have 3 elements'));case\"in\":case\"!in\":r.length>=2&&\"string\"!==(i=zr(r[1]))&&o.push(new St(n+\"[1]\",r[1],\"string expected, \"+i+\" found\"));for(var s=2;s<r.length;s++)i=zr(r[s]),\"$type\"===Lt(r[1])?o=o.concat($r({key:n+\"[\"+s+\"]\",value:r[s],valueSpec:a.geometry_type,style:e.style,styleSpec:e.styleSpec})):\"string\"!==i&&\"number\"!==i&&\"boolean\"!==i&&o.push(new St(n+\"[\"+s+\"]\",r[s],\"string, number, or boolean expected, \"+i+\" found\"));break;case\"any\":case\"all\":case\"none\":for(var l=1;l<r.length;l++)o=o.concat(t({key:n+\"[\"+l+\"]\",value:r[l],style:e.style,styleSpec:e.styleSpec}));break;case\"has\":case\"!has\":i=zr(r[1]),2!==r.length?o.push(new St(n,r,'filter array for \"'+r[0]+'\" operator must have 2 elements')):\"string\"!==i&&o.push(new St(n+\"[1]\",r[1],\"string expected, \"+i+\" found\"));break;case\"within\":i=zr(r[1]),2!==r.length?o.push(new St(n,r,'filter array for \"'+r[0]+'\" operator must have 2 elements')):\"object\"!==i&&o.push(new St(n+\"[1]\",r[1],\"object expected, \"+i+\" found\"))}return o}(t)}function fn(t,e){var 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$/);if(\"paint\"===e&&l&&s[l[1]]&&s[l[1]].transition)return bn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new St(r,a,'unknown property \"'+o+'\"')];if(\"string\"===zr(a)&&Lr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new St(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 St(r,a,'use of \"text-field\" requires a style \"glyphs\" property')),\"text-font\"===o&&Or(It(a))&&\"identity\"===Lt(a.type)&&f.push(new St(r,a,'\"text-font\" does not support identity 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h=i.sources&&i.sources[r.source],p=h&&Lt(h.type);h?\"vector\"===p&&\"raster\"===s?e.push(new St(n,r.source,'layer \"'+r.id+'\" requires a raster source')):\"raster\"===p&&\"raster\"!==s?e.push(new St(n,r.source,'layer \"'+r.id+'\" requires a vector source')):\"vector\"!==p||r[\"source-layer\"]?\"raster-dem\"===p&&\"hillshade\"!==s?e.push(new St(n,r.source,\"raster-dem source can only be used with layer type 'hillshade'.\")):\"line\"!==s||!r.paint||!r.paint[\"line-gradient\"]||\"geojson\"===p&&h.lineMetrics||e.push(new St(n,r,'layer \"'+r.id+'\" specifies a line-gradient, which requires a GeoJSON source with `lineMetrics` enabled.')):e.push(new St(n,r,'layer \"'+r.id+'\" must specify a \"source-layer\"')):e.push(new St(n,r.source,'source \"'+r.source+'\" not found'))}else e.push(new St(n,r,'missing required property \"source\"'));return e=e.concat(Xr({key:n,value:r,valueSpec:a.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(){return[]},type:function(){return bn({key:n+\".type\",value:r.type,valueSpec:a.layer.type,style:t.style,styleSpec:t.styleSpec,object:r,objectKey:\"type\"})},filter:un,layout:function(t){return Xr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(t){return pn(Ct({layerType:s},t))}}})},paint:function(t){return Xr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(t){return hn(Ct({layerType:s},t))}}})}}}))}function gn(t){var e=t.value,r=t.key,n=zr(e);return\"string\"!==n?[new St(r,e,\"string expected, \"+n+\" found\")]:[]}var mn={promoteId:function(t){var e=t.key,r=t.value;if(\"string\"===zr(r))return gn({key:e,value:r});var n=[];for(var i in r)n.push.apply(n,gn({key:e+\".\"+i,value:r[i]}));return n}};function vn(t){var e=t.value,r=t.key,n=t.styleSpec,i=t.style;if(!e.type)return[new St(r,e,'\"type\" is required')];var a,o=Lt(e.type);switch(o){case\"vector\":case\"raster\":case\"raster-dem\":return Xr({key:r,value:e,valueSpec:n[\"source_\"+o.replace(\"-\",\"_\")],style:t.style,styleSpec:n,objectElementValidators:mn});case\"geojson\":if(a=Xr({key:r,value:e,valueSpec:n.source_geojson,style:i,styleSpec:n,objectElementValidators:mn}),e.cluster)for(var s in e.clusterProperties){var l=e.clusterProperties[s],c=l[0],u=\"string\"==typeof c?[c,[\"accumulated\"],[\"get\",s]]:c;a.push.apply(a,Qr({key:r+\".\"+s+\".map\",value:l[1],expressionContext:\"cluster-map\"})),a.push.apply(a,Qr({key:r+\".\"+s+\".reduce\",value:u,expressionContext:\"cluster-reduce\"}))}return a;case\"video\":return Xr({key:r,value:e,valueSpec:n.source_video,style:i,styleSpec:n});case\"image\":return Xr({key:r,value:e,valueSpec:n.source_image,style:i,styleSpec:n});case\"canvas\":return[new St(r,null,\"Please use runtime APIs to add canvas sources, rather than including them in stylesheets.\",\"source.canvas\")];default:return $r({key:r+\".type\",value:e.type,valueSpec:{values:[\"vector\",\"raster\",\"raster-dem\",\"geojson\",\"video\",\"image\"]},style:i,styleSpec:n})}}function yn(t){var e=t.value,r=t.styleSpec,n=r.light,i=t.style,a=[],o=zr(e);if(void 0===e)return a;if(\"object\"!==o)return a.concat([new St(\"light\",e,\"object expected, \"+o+\" found\")]);for(var s in e){var l=s.match(/^(.*)-transition$/);a=a.concat(l&&n[l[1]]&&n[l[1]].transition?bn({key:s,value:e[s],valueSpec:r.transition,style:i,styleSpec:r}):n[s]?bn({key:s,value:e[s],valueSpec:n[s],style:i,styleSpec:r}):[new St(s,e[s],'unknown property \"'+s+'\"')])}return a}var xn={\"*\":function(){return[]},array:Zr,boolean:function(t){var e=t.value,r=t.key,n=zr(e);return\"boolean\"!==n?[new St(r,e,\"boolean expected, \"+n+\" found\")]:[]},number:Jr,color:function(t){var e=t.key,r=t.value,n=zr(r);return\"string\"!==n?[new St(e,r,\"color expected, \"+n+\" 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r=r.concat(bn({key:\"\",value:t,valueSpec:e.$root,styleSpec:e,style:t,objectElementValidators:{glyphs:_n,\"*\":function(){return[]}}})),t.constants&&(r=r.concat(Et({key:\"constants\",value:t.constants,style:t,styleSpec:e}))),Tn(r)}function Tn(t){return[].concat(t).sort((function(t,e){return t.line-e.line}))}function kn(t){return function(){for(var e=[],r=arguments.length;r--;)e[r]=arguments[r];return Tn(t.apply(this,e))}}wn.source=kn(vn),wn.light=kn(yn),wn.layer=kn(dn),wn.filter=kn(un),wn.paintProperty=kn(hn),wn.layoutProperty=kn(pn);var Mn=wn,An=Mn.light,Sn=Mn.paintProperty,En=Mn.layoutProperty;function Cn(t,e){var r=!1;if(e&&e.length)for(var n=0,i=e;n<i.length;n+=1)t.fire(new kt(new Error(i[n].message))),r=!0;return r}var Ln=In;function In(t,e,r){var n=this.cells=[];if(t instanceof ArrayBuffer){this.arrayBuffer=t;var i=new Int32Array(this.arrayBuffer);t=i[0],this.d=(e=i[1])+2*(r=i[2]);for(var a=0;a<this.d*this.d;a++){var o=i[3+a],s=i[3+a+1];n.push(o===s?null:i.subarray(o,s))}var l=i[3+n.length+1];this.keys=i.subarray(i[3+n.length],l),this.bboxes=i.subarray(l),this.insert=this._insertReadonly}else{this.d=e+2*r;for(var c=0;c<this.d*this.d;c++)n.push([]);this.keys=[],this.bboxes=[]}this.n=e,this.extent=t,this.padding=r,this.scale=e/t,this.uid=0;var u=r/e*t;this.min=-u,this.max=t+u}In.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertCell,this.uid++),this.keys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},In.prototype._insertReadonly=function(){throw\"Cannot insert into a GridIndex created from an ArrayBuffer.\"},In.prototype._insertCell=function(t,e,r,n,i,a){this.cells[i].push(a)},In.prototype.query=function(t,e,r,n,i){var a=this.min,o=this.max;if(t<=a&&e<=a&&o<=r&&o<=n&&!i)return Array.prototype.slice.call(this.keys);var s=[];return this._forEachCell(t,e,r,n,this._queryCell,s,{},i),s},In.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=this.cells[i];if(null!==l)for(var c=this.keys,u=this.bboxes,f=0;f<l.length;f++){var h=l[f];if(void 0===o[h]){var p=4*h;(s?s(u[p+0],u[p+1],u[p+2],u[p+3]):t<=u[p+2]&&e<=u[p+3]&&r>=u[p+0]&&n>=u[p+1])?(o[h]=!0,a.push(c[h])):o[h]=!1}}},In.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToCellCoord(t),c=this._convertToCellCoord(e),u=this._convertToCellCoord(r),f=this._convertToCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.d*p+h;if((!s||s(this._convertFromCellCoord(h),this._convertFromCellCoord(p),this._convertFromCellCoord(h+1),this._convertFromCellCoord(p+1)))&&i.call(this,t,e,r,n,d,a,o,s))return}},In.prototype._convertFromCellCoord=function(t){return(t-this.padding)/this.scale},In.prototype._convertToCellCoord=function(t){return Math.max(0,Math.min(this.d-1,Math.floor(t*this.scale)+this.padding))},In.prototype.toArrayBuffer=function(){if(this.arrayBuffer)return this.arrayBuffer;for(var t=this.cells,e=3+this.cells.length+1+1,r=0,n=0;n<this.cells.length;n++)r+=this.cells[n].length;var i=new Int32Array(e+r+this.keys.length+this.bboxes.length);i[0]=this.extent,i[1]=this.n,i[2]=this.padding;for(var a=e,o=0;o<t.length;o++){var s=t[o];i[3+o]=a,i.set(s,a),a+=s.length}return i[3+t.length]=a,i.set(this.keys,a),i[3+t.length+1]=a+=this.keys.length,i.set(this.bboxes,a),a+=this.bboxes.length,i.buffer};var Pn=self.ImageData,zn=self.ImageBitmap,On={};function Dn(t,e,r){void 0===r&&(r={}),Object.defineProperty(e,\"_classRegistryKey\",{value:t,writeable:!1}),On[t]={klass:e,omit:r.omit||[],shallow:r.shallow||[]}}for(var Rn in Dn(\"Object\",Object),Ln.serialize=function(t,e){var r=t.toArrayBuffer();return e&&e.push(r),{buffer:r}},Ln.deserialize=function(t){return new Ln(t.buffer)},Dn(\"Grid\",Ln),Dn(\"Color\",Kt),Dn(\"Error\",Error),Dn(\"ResolvedImage\",ee),Dn(\"StylePropertyFunction\",Wr),Dn(\"StyleExpression\",Ur,{omit:[\"_evaluator\"]}),Dn(\"ZoomDependentExpression\",Gr),Dn(\"ZoomConstantExpression\",Hr),Dn(\"CompoundExpression\",me,{omit:[\"_evaluate\"]}),Tr)Tr[Rn]._classRegistryKey||Dn(\"Expression_\"+Rn,Tr[Rn]);function Fn(t){return t&&\"undefined\"!=typeof ArrayBuffer&&(t instanceof ArrayBuffer||t.constructor&&\"ArrayBuffer\"===t.constructor.name)}function Bn(t){return zn&&t instanceof zn}function Nn(t,e){if(null==t||\"boolean\"==typeof t||\"number\"==typeof t||\"string\"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp)return t;if(Fn(t)||Bn(t))return e&&e.push(t),t;if(ArrayBuffer.isView(t)){var r=t;return e&&e.push(r.buffer),r}if(t instanceof Pn)return e&&e.push(t.data.buffer),t;if(Array.isArray(t)){for(var n=[],i=0,a=t;i<a.length;i+=1)n.push(Nn(a[i],e));return n}if(\"object\"==typeof t){var o=t.constructor,s=o._classRegistryKey;if(!s)throw new Error(\"can't serialize object of unregistered class\");var l=o.serialize?o.serialize(t,e):{};if(!o.serialize){for(var c in t)if(t.hasOwnProperty(c)&&!(On[s].omit.indexOf(c)>=0)){var u=t[c];l[c]=On[s].shallow.indexOf(c)>=0?u:Nn(u,e)}t instanceof Error&&(l.message=t.message)}if(l.$name)throw new Error(\"$name property is reserved for worker serialization logic.\");return\"Object\"!==s&&(l.$name=s),l}throw new Error(\"can't serialize object of type \"+typeof t)}function jn(t){if(null==t||\"boolean\"==typeof t||\"number\"==typeof t||\"string\"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp||Fn(t)||Bn(t)||ArrayBuffer.isView(t)||t instanceof Pn)return t;if(Array.isArray(t))return t.map(jn);if(\"object\"==typeof t){var e=t.$name||\"Object\",r=On[e].klass;if(!r)throw new Error(\"can't deserialize unregistered class \"+e);if(r.deserialize)return r.deserialize(t);for(var n=Object.create(r.prototype),i=0,a=Object.keys(t);i<a.length;i+=1){var o=a[i];if(\"$name\"!==o){var s=t[o];n[o]=On[e].shallow.indexOf(o)>=0?s:jn(s)}}return n}throw new Error(\"can't deserialize object of type \"+typeof t)}var Un=function(){this.first=!0};Un.prototype.update=function(t,e){var r=Math.floor(t);return this.first?(this.first=!1,this.lastIntegerZoom=r,this.lastIntegerZoomTime=0,this.lastZoom=t,this.lastFloorZoom=r,!0):(this.lastFloorZoom>r?(this.lastIntegerZoom=r+1,this.lastIntegerZoomTime=e):this.lastFloorZoom<r&&(this.lastIntegerZoom=r,this.lastIntegerZoomTime=e),t!==this.lastZoom&&(this.lastZoom=t,this.lastFloorZoom=r,!0))};var Vn={\"Latin-1 Supplement\":function(t){return t>=128&&t<=255},Arabic:function(t){return t>=1536&&t<=1791},\"Arabic Supplement\":function(t){return t>=1872&&t<=1919},\"Arabic Extended-A\":function(t){return t>=2208&&t<=2303},\"Hangul Jamo\":function(t){return t>=4352&&t<=4607},\"Unified Canadian Aboriginal Syllabics\":function(t){return t>=5120&&t<=5759},Khmer:function(t){return t>=6016&&t<=6143},\"Unified Canadian Aboriginal Syllabics Extended\":function(t){return t>=6320&&t<=6399},\"General Punctuation\":function(t){return t>=8192&&t<=8303},\"Letterlike Symbols\":function(t){return t>=8448&&t<=8527},\"Number Forms\":function(t){return t>=8528&&t<=8591},\"Miscellaneous Technical\":function(t){return t>=8960&&t<=9215},\"Control Pictures\":function(t){return t>=9216&&t<=9279},\"Optical Character Recognition\":function(t){return t>=9280&&t<=9311},\"Enclosed Alphanumerics\":function(t){return t>=9312&&t<=9471},\"Geometric Shapes\":function(t){return t>=9632&&t<=9727},\"Miscellaneous Symbols\":function(t){return t>=9728&&t<=9983},\"Miscellaneous Symbols and Arrows\":function(t){return t>=11008&&t<=11263},\"CJK Radicals Supplement\":function(t){return t>=11904&&t<=12031},\"Kangxi Radicals\":function(t){return t>=12032&&t<=12255},\"Ideographic Description Characters\":function(t){return t>=12272&&t<=12287},\"CJK Symbols and Punctuation\":function(t){return t>=12288&&t<=12351},Hiragana:function(t){return t>=12352&&t<=12447},Katakana:function(t){return t>=12448&&t<=12543},Bopomofo:function(t){return t>=12544&&t<=12591},\"Hangul Compatibility Jamo\":function(t){return t>=12592&&t<=12687},Kanbun:function(t){return t>=12688&&t<=12703},\"Bopomofo Extended\":function(t){return t>=12704&&t<=12735},\"CJK Strokes\":function(t){return t>=12736&&t<=12783},\"Katakana Phonetic Extensions\":function(t){return t>=12784&&t<=12799},\"Enclosed CJK Letters and Months\":function(t){return t>=12800&&t<=13055},\"CJK Compatibility\":function(t){return t>=13056&&t<=13311},\"CJK Unified Ideographs Extension A\":function(t){return t>=13312&&t<=19903},\"Yijing Hexagram Symbols\":function(t){return t>=19904&&t<=19967},\"CJK Unified Ideographs\":function(t){return t>=19968&&t<=40959},\"Yi Syllables\":function(t){return t>=40960&&t<=42127},\"Yi Radicals\":function(t){return t>=42128&&t<=42191},\"Hangul Jamo Extended-A\":function(t){return t>=43360&&t<=43391},\"Hangul Syllables\":function(t){return t>=44032&&t<=55215},\"Hangul Jamo Extended-B\":function(t){return t>=55216&&t<=55295},\"Private Use Area\":function(t){return t>=57344&&t<=63743},\"CJK Compatibility Ideographs\":function(t){return t>=63744&&t<=64255},\"Arabic Presentation Forms-A\":function(t){return t>=64336&&t<=65023},\"Vertical Forms\":function(t){return t>=65040&&t<=65055},\"CJK Compatibility Forms\":function(t){return t>=65072&&t<=65103},\"Small Form Variants\":function(t){return t>=65104&&t<=65135},\"Arabic Presentation Forms-B\":function(t){return t>=65136&&t<=65279},\"Halfwidth and Fullwidth Forms\":function(t){return t>=65280&&t<=65519}};function qn(t){for(var e=0,r=t;e<r.length;e+=1)if(Hn(r[e].charCodeAt(0)))return!0;return!1}function Hn(t){return!(746!==t&&747!==t&&(t<4352||!(Vn[\"Bopomofo Extended\"](t)||Vn.Bopomofo(t)||Vn[\"CJK Compatibility 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0===this.fadeDuration?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)},ii.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 ai=function(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(Or(t))return new Wr(t,e);if(Vr(t)){var r=Yr(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=Kt.parse(t)),{kind:\"constant\",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};ai.prototype.isDataDriven=function(){return\"source\"===this.expression.kind||\"composite\"===this.expression.kind},ai.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var oi=function(t){this.property=t,this.value=new ai(t,void 0)};oi.prototype.transitioned=function(t,e){return new li(this.property,this.value,e,u({},t.transition,this.transition),t.now)},oi.prototype.untransitioned=function(){return new li(this.property,this.value,null,{},0)};var si=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};si.prototype.getValue=function(t){return x(this._values[t].value.value)},si.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new oi(this._values[t].property)),this._values[t].value=new ai(this._values[t].property,null===e?void 0:x(e))},si.prototype.getTransition=function(t){return x(this._values[t].transition)},si.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new oi(this._values[t].property)),this._values[t].transition=x(e)||void 0},si.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},si.prototype.transitioned=function(t,e){for(var r=new ci(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},si.prototype.untransitioned=function(){for(var t=new ci(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 li=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)};li.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 ci=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};ci.prototype.possiblyEvaluate=function(t,e,r){for(var n=new hi(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},ci.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1)if(this._values[e[t]].prior)return!0;return!1};var ui=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};ui.prototype.getValue=function(t){return x(this._values[t].value)},ui.prototype.setValue=function(t,e){this._values[t]=new ai(this._values[t].property,null===e?void 0:x(e))},ui.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},ui.prototype.possiblyEvaluate=function(t,e,r){for(var n=new hi(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 fi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};fi.prototype.isConstant=function(){return\"constant\"===this.value.kind},fi.prototype.constantOr=function(t){return\"constant\"===this.value.kind?this.value.value:t},fi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var hi=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};hi.prototype.get=function(t){return this._values[t]};var pi=function(t){this.specification=t};pi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},pi.prototype.interpolate=function(t,e,r){var n=Ve[this.specification.type];return n?n(t,e,r):t};var di=function(t,e){this.specification=t,this.overrides=e};di.prototype.possiblyEvaluate=function(t,e,r,n){return new fi(this,\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)}:t.expression,e)},di.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 fi(this,{kind:\"constant\",value:void 0},t.parameters);var n=Ve[this.specification.type];return n?new fi(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.parameters):t},di.prototype.evaluate=function(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new fi(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 fi(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 fi(this,{kind:\"constant\",value:s},e)}return new fi(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}(di),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 ii(Math.floor(e.zoom-1),e)),t.expression.evaluate(new ii(Math.floor(e.zoom),e)),t.expression.evaluate(new ii(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 vi=function(t){this.specification=t};vi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},vi.prototype.interpolate=function(){return!1};var yi=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 ai(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new oi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Dn(\"DataDrivenProperty\",di),Dn(\"DataConstantProperty\",pi),Dn(\"CrossFadedDataDrivenProperty\",gi),Dn(\"CrossFadedProperty\",mi),Dn(\"ColorRampProperty\",vi);var xi=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&&(this.metadata=(e=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 ui(r.layout)),r.paint)){for(var n in this._transitionablePaint=new si(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 hi(r.paint)}}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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){void 0===r&&(r={}),null!=e&&this._validate(En,\"layers.\"+this.id+\".layout.\"+t,t,e,r)||(\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e)},e.prototype.getPaintProperty=function(t){return m(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&&this._validate(Sn,\"layers.\"+this.id+\".paint.\"+t,t,e,r))return!1;if(m(t,\"-transition\"))return this._transitionablePaint.setTransition(t.slice(0,-\"-transition\".length),e||void 0),!1;var n=this._transitionablePaint._values[t],i=\"cross-faded-data-driven\"===n.property.specification[\"property-type\"],a=n.value.isDataDriven(),o=n.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var s=this._transitionablePaint._values[t].value;return s.isDataDriven()||a||i||this._handleOverridablePaintPropertyUpdate(t,o,s)},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)&&Cn(this,t.call(Mn,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:At,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 fi&&Lr(e.property.specification)&&(\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent)return!0}return!1},e}(Mt),bi={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},_i=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},wi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ti(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i=bi[t.type].BYTES_PER_ELEMENT,a=r=ki(r,Math.max(e,i)),o=t.components||1;return n=Math.max(n,i),r+=i*o,{name:t.name,type:t.type,components:o,offset:a}})),size:ki(r,Math.max(n,e)),alignment:e}}function ki(t,e){return Math.ceil(t/e)*e}wi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},wi.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},wi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},wi.prototype.clear=function(){this.length=0},wi.prototype.resize=function(t){this.reserve(t),this.length=t},wi.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)}},wi.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var Mi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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}(wi);Mi.prototype.bytesPerElement=4,Dn(\"StructArrayLayout2i4\",Mi);var Ai=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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}(wi);Ai.prototype.bytesPerElement=8,Dn(\"StructArrayLayout4i8\",Ai);var Si=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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}(wi);Si.prototype.bytesPerElement=12,Dn(\"StructArrayLayout2i4i12\",Si);var Ei=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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}(wi);Ei.prototype.bytesPerElement=8,Dn(\"StructArrayLayout2i4ub8\",Ei);var Ci=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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}(wi);Ci.prototype.bytesPerElement=18,Dn(\"StructArrayLayout8ui2ub18\",Ci);var Li=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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}(wi);Li.prototype.bytesPerElement=24,Dn(\"StructArrayLayout4i4ui4i24\",Li);var Ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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}(wi);Ii.prototype.bytesPerElement=12,Dn(\"StructArrayLayout3f12\",Ii);var Pi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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){return this.uint32[1*t+0]=e,t},e}(wi);Pi.prototype.bytesPerElement=4,Dn(\"StructArrayLayout1ul4\",Pi);var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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}(wi);zi.prototype.bytesPerElement=20,Dn(\"StructArrayLayout6i1ul2ui20\",zi);var Oi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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}(wi);Oi.prototype.bytesPerElement=12,Dn(\"StructArrayLayout2i2i2i12\",Oi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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}(wi);Di.prototype.bytesPerElement=16,Dn(\"StructArrayLayout2f1f2i16\",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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}(wi);Ri.prototype.bytesPerElement=12,Dn(\"StructArrayLayout2ub2f12\",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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}(wi);Fi.prototype.bytesPerElement=6,Dn(\"StructArrayLayout3ui6\",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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,g,m){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,g,m)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,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]=g,this.uint32[x+10]=m,this.int16[y+22]=v,t},e}(wi);Bi.prototype.bytesPerElement=48,Dn(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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,g,m,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,g,m,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,g,m,v,y,x,b,_,w,T,k,M,A,S,E){var C=34*t,L=17*t;return this.int16[C+0]=e,this.int16[C+1]=r,this.int16[C+2]=n,this.int16[C+3]=i,this.int16[C+4]=a,this.int16[C+5]=o,this.int16[C+6]=s,this.int16[C+7]=l,this.uint16[C+8]=c,this.uint16[C+9]=u,this.uint16[C+10]=f,this.uint16[C+11]=h,this.uint16[C+12]=p,this.uint16[C+13]=d,this.uint16[C+14]=g,this.uint16[C+15]=m,this.uint16[C+16]=v,this.uint16[C+17]=y,this.uint16[C+18]=x,this.uint16[C+19]=b,this.uint16[C+20]=_,this.uint16[C+21]=w,this.uint16[C+22]=T,this.uint32[L+12]=k,this.float32[L+13]=M,this.float32[L+14]=A,this.float32[L+15]=S,this.float32[L+16]=E,t},e}(wi);Ni.prototype.bytesPerElement=68,Dn(\"StructArrayLayout8i15ui1ul4f68\",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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){return this.float32[1*t+0]=e,t},e}(wi);ji.prototype.bytesPerElement=4,Dn(\"StructArrayLayout1f4\",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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}(wi);Ui.prototype.bytesPerElement=6,Dn(\"StructArrayLayout3i6\",Ui);var 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a=t;a<e;a++)this.paintVertexArray.emplace(a,r);this.maxValue=Math.max(this.maxValue,Math.abs(r))}},Sa.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},Sa.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()};var Ea=function(t,e,r,n,i,a){this.expression=t,this.uniformNames=e.map((function(t){return\"u_\"+t+\"_t\"})),this.type=r,this.useIntegerZoom=n,this.zoom=i,this.maxValue=0,this.paintVertexAttributes=e.map((function(t){return{name:\"a_\"+t,type:\"Float32\",components:\"color\"===r?4:2,offset:0}})),this.paintVertexArray=new a};Ea.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.expression.evaluate(new ii(this.zoom),e,{},n,[],i),o=this.expression.evaluate(new ii(this.zoom+1),e,{},n,[],i),s=this.paintVertexArray.length;this.paintVertexArray.resize(t),this._setPaintValue(s,t,a,o)},Ea.prototype.updatePaintArray=function(t,e,r,n){var i=this.expression.evaluate({zoom:this.zoom},r,n),a=this.expression.evaluate({zoom:this.zoom+1},r,n);this._setPaintValue(t,e,i,a)},Ea.prototype._setPaintValue=function(t,e,r,n){if(\"color\"===this.type)for(var i=ka(r),a=ka(n),o=t;o<e;o++)this.paintVertexArray.emplace(o,i[0],i[1],a[0],a[1]);else{for(var s=t;s<e;s++)this.paintVertexArray.emplace(s,r,n);this.maxValue=Math.max(this.maxValue,Math.abs(r),Math.abs(n))}},Ea.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},Ea.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()},Ea.prototype.setUniform=function(t,e){var r=this.useIntegerZoom?Math.floor(e.zoom):e.zoom,n=l(this.expression.interpolationFactor(r,this.zoom,this.zoom+1),0,1);t.set(n)},Ea.prototype.getBinding=function(t,e,r){return new va(t,e)};var Ca=function(t,e,r,n,i,a){this.expression=t,this.type=e,this.useIntegerZoom=r,this.zoom=n,this.layerId=a,this.zoomInPaintVertexArray=new i,this.zoomOutPaintVertexArray=new i};Ca.prototype.populatePaintArray=function(t,e,r){var n=this.zoomInPaintVertexArray.length;this.zoomInPaintVertexArray.resize(t),this.zoomOutPaintVertexArray.resize(t),this._setPaintValues(n,t,e.patterns&&e.patterns[this.layerId],r)},Ca.prototype.updatePaintArray=function(t,e,r,n,i){this._setPaintValues(t,e,r.patterns&&r.patterns[this.layerId],i)},Ca.prototype._setPaintValues=function(t,e,r,n){if(n&&r){var i=n[r.min],a=n[r.mid],o=n[r.max];if(i&&a&&o)for(var s=t;s<e;s++)this.zoomInPaintVertexArray.emplace(s,a.tl[0],a.tl[1],a.br[0],a.br[1],i.tl[0],i.tl[1],i.br[0],i.br[1],a.pixelRatio,i.pixelRatio),this.zoomOutPaintVertexArray.emplace(s,a.tl[0],a.tl[1],a.br[0],a.br[1],o.tl[0],o.tl[1],o.br[0],o.br[1],a.pixelRatio,o.pixelRatio)}},Ca.prototype.upload=function(t){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=t.createVertexBuffer(this.zoomInPaintVertexArray,oa.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=t.createVertexBuffer(this.zoomOutPaintVertexArray,oa.members,this.expression.isStateDependent))},Ca.prototype.destroy=function(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()};var La=function(t,e,r,n){this.binders={},this.layoutAttributes=n,this._buffers=[];var i=[];for(var a in t.paint._values)if(r(a)){var o=t.paint.get(a);if(o instanceof fi&&Lr(o.property.specification)){var s=Pa(a,t.type),l=o.value,c=o.property.specification.type,u=o.property.useIntegerZoom,f=o.property.specification[\"property-type\"],h=\"cross-faded\"===f||\"cross-faded-data-driven\"===f;if(\"constant\"===l.kind)this.binders[a]=h?new Aa(l.value,s):new Ma(l.value,s,c),i.push(\"/u_\"+a);else if(\"source\"===l.kind||h){var p=za(a,c,\"source\");this.binders[a]=h?new Ca(l,c,u,e,p,t.id):new Sa(l,s,c,p),i.push(\"/a_\"+a)}else{var d=za(a,c,\"composite\");this.binders[a]=new Ea(l,s,c,u,e,d),i.push(\"/z_\"+a)}}}this.cacheKey=i.sort().join(\"\")};La.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof Sa||e instanceof Ea?e.maxValue:0},La.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof Sa||o instanceof Ea||o instanceof Ca)&&o.populatePaintArray(t,e,r,n,i)}},La.prototype.setConstantPatternPositions=function(t,e){for(var r in this.binders){var n=this.binders[r];n instanceof Aa&&n.setConstantPatternPositions(t,e)}},La.prototype.updatePaintArrays=function(t,e,r,n,i){var a=!1;for(var o in t)for(var s=0,l=e.getPositions(o);s<l.length;s+=1){var c=l[s],u=r.feature(c.index);for(var f in this.binders){var h=this.binders[f];if((h instanceof Sa||h instanceof Ea||h instanceof Ca)&&!0===h.expression.isStateDependent){var p=n.paint.get(f);h.expression=p.value,h.updatePaintArray(c.start,c.end,u,t[o],i),a=!0}}}return a},La.prototype.defines=function(){var t=[];for(var e in this.binders){var r=this.binders[e];(r instanceof Ma||r instanceof Aa)&&t.push.apply(t,r.uniformNames.map((function(t){return\"#define HAS_UNIFORM_\"+t})))}return t},La.prototype.getPaintVertexBuffers=function(){return this._buffers},La.prototype.getUniforms=function(t,e){var r=[];for(var n in this.binders){var i=this.binders[n];if(i instanceof Ma||i instanceof Aa||i instanceof Ea)for(var a=0,o=i.uniformNames;a<o.length;a+=1){var s=o[a];if(e[s]){var l=i.getBinding(t,e[s],s);r.push({name:s,property:n,binding:l})}}}return r},La.prototype.setUniforms=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.name,l=o.property;this.binders[l].setUniform(o.binding,n,r.get(l),s)}},La.prototype.updatePaintBuffers=function(t){for(var e in this._buffers=[],this.binders){var r=this.binders[e];if(t&&r instanceof Ca){var n=2===t.fromScale?r.zoomInPaintVertexBuffer:r.zoomOutPaintVertexBuffer;n&&this._buffers.push(n)}else(r instanceof Sa||r instanceof Ea)&&r.paintVertexBuffer&&this._buffers.push(r.paintVertexBuffer)}},La.prototype.upload=function(t){for(var e in this.binders){var r=this.binders[e];(r instanceof Sa||r instanceof Ea||r instanceof Ca)&&r.upload(t)}this.updatePaintBuffers()},La.prototype.destroy=function(){for(var t in this.binders){var e=this.binders[t];(e instanceof Sa||e instanceof Ea||e instanceof Ca)&&e.destroy()}};var Ia=function(t,e,r,n){void 0===n&&(n=function(){return!0}),this.programConfigurations={};for(var i=0,a=e;i<a.length;i+=1){var o=a[i];this.programConfigurations[o.id]=new La(o,r,n,t)}this.needsUpload=!1,this._featureMap=new fa,this._bufferOffset=0};function Pa(t,e){return{\"text-opacity\":[\"opacity\"],\"icon-opacity\":[\"opacity\"],\"text-color\":[\"fill_color\"],\"icon-color\":[\"fill_color\"],\"text-halo-color\":[\"halo_color\"],\"icon-halo-color\":[\"halo_color\"],\"text-halo-blur\":[\"halo_blur\"],\"icon-halo-blur\":[\"halo_blur\"],\"text-halo-width\":[\"halo_width\"],\"icon-halo-width\":[\"halo_width\"],\"line-gap-width\":[\"gapwidth\"],\"line-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-extrusion-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"]}[t]||[t.replace(e+\"-\",\"\").replace(/-/g,\"_\")]}function za(t,e,r){var n={color:{source:Gi,composite:Yi},number:{source:ji,composite:Gi}},i=function(t){return{\"line-pattern\":{source:Ci,composite:Ci},\"fill-pattern\":{source:Ci,composite:Ci},\"fill-extrusion-pattern\":{source:Ci,composite:Ci}}[t]}(t);return i&&i[r]||n[e][r]}Ia.prototype.populatePaintArrays=function(t,e,r,n,i,a){for(var o in this.programConfigurations)this.programConfigurations[o].populatePaintArrays(t,e,n,i,a);void 0!==e.id&&this._featureMap.add(e.id,r,this._bufferOffset,t),this._bufferOffset=t,this.needsUpload=!0},Ia.prototype.updatePaintArrays=function(t,e,r,n){for(var i=0,a=r;i<a.length;i+=1){var o=a[i];this.needsUpload=this.programConfigurations[o.id].updatePaintArrays(t,this._featureMap,e,o,n)||this.needsUpload}},Ia.prototype.get=function(t){return this.programConfigurations[t]},Ia.prototype.upload=function(t){if(this.needsUpload){for(var e in this.programConfigurations)this.programConfigurations[e].upload(t);this.needsUpload=!1}},Ia.prototype.destroy=function(){for(var t in this.programConfigurations)this.programConfigurations[t].destroy()},Dn(\"ConstantBinder\",Ma),Dn(\"CrossFadedConstantBinder\",Aa),Dn(\"SourceExpressionBinder\",Sa),Dn(\"CrossFadedCompositeBinder\",Ca),Dn(\"CompositeExpressionBinder\",Ea),Dn(\"ProgramConfiguration\",La,{omit:[\"_buffers\"]}),Dn(\"ProgramConfigurationSet\",Ia);var Oa={min:-1*Math.pow(2,14),max:Math.pow(2,14)-1};function Da(t){for(var e=8192/t.extent,r=t.loadGeometry(),n=0;n<r.length;n++)for(var i=r[n],a=0;a<i.length;a++){var o=i[a];o.x=Math.round(o.x*e),o.y=Math.round(o.y*e),(o.x<Oa.min||o.x>Oa.max||o.y<Oa.min||o.y>Oa.max)&&(_(\"Geometry exceeds allowed extent, reduce your vector tile buffer size\"),o.x=l(o.x,Oa.min,Oa.max),o.y=l(o.y,Oa.min,Oa.max))}return r}function Ra(t,e,r,n,i){t.emplaceBack(2*e+(n+1)/2,2*r+(i+1)/2)}var Fa=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 Mi,this.indexArray=new Fi,this.segments=new ia,this.programConfigurations=new Ia(na,t.layers,t.zoom),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function Ba(t,e){for(var r=0;r<t.length;r++)if(Wa(e,t[r]))return!0;for(var n=0;n<e.length;n++)if(Wa(t,e[n]))return!0;return!!Va(t,e)}function Na(t,e,r){return!!Wa(t,e)||!!Ha(e,t,r)}function ja(t,e){if(1===t.length)return Ya(e,t[0]);for(var r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++)if(Wa(t,n[i]))return!0;for(var a=0;a<t.length;a++)if(Ya(e,t[a]))return!0;for(var o=0;o<e.length;o++)if(Va(t,e[o]))return!0;return!1}function Ua(t,e,r){if(t.length>1){if(Va(t,e))return!0;for(var n=0;n<e.length;n++)if(Ha(e[n],t,r))return!0}for(var i=0;i<t.length;i++)if(Ha(t[i],e,r))return!0;return!1}function Va(t,e){if(0===t.length||0===e.length)return!1;for(var r=0;r<t.length-1;r++)for(var n=t[r],i=t[r+1],a=0;a<e.length-1;a++)if(qa(n,i,e[a],e[a+1]))return!0;return!1}function qa(t,e,r,n){return w(t,r,n)!==w(e,r,n)&&w(t,e,r)!==w(t,e,n)}function Ha(t,e,r){var n=r*r;if(1===e.length)return t.distSqr(e[0])<n;for(var i=1;i<e.length;i++)if(Ga(t,e[i-1],e[i])<n)return!0;return!1}function Ga(t,e,r){var n=e.distSqr(r);if(0===n)return t.distSqr(e);var i=((t.x-e.x)*(r.x-e.x)+(t.y-e.y)*(r.y-e.y))/n;return t.distSqr(i<0?e:i>1?r:r.sub(e)._mult(i)._add(e))}function Ya(t,e){for(var r,n,i,a=!1,o=0;o<t.length;o++)for(var s=0,l=(r=t[o]).length-1;s<r.length;l=s++)(n=r[s]).y>e.y!=(i=r[l]).y>e.y&&e.x<(i.x-n.x)*(e.y-n.y)/(i.y-n.y)+n.x&&(a=!a);return a}function Wa(t,e){for(var r=!1,n=0,i=t.length-1;n<t.length;i=n++){var a=t[n],o=t[i];a.y>e.y!=o.y>e.y&&e.x<(o.x-a.x)*(e.y-a.y)/(o.y-a.y)+a.x&&(r=!r)}return r}function Xa(t,e,r){var n=r[0],i=r[2];if(t.x<n.x&&e.x<n.x||t.x>i.x&&e.x>i.x||t.y<n.y&&e.y<n.y||t.y>i.y&&e.y>i.y)return!1;var a=w(t,e,r[0]);return a!==w(t,e,r[1])||a!==w(t,e,r[2])||a!==w(t,e,r[3])}function Za(t,e,r){var n=e.paint.get(t).value;return\"constant\"===n.kind?n.value:r.programConfigurations.get(e.id).getMaxValue(t)}function Ja(t){return Math.sqrt(t[0]*t[0]+t[1]*t[1])}function Ka(t,e,r,n,a){if(!e[0]&&!e[1])return t;var o=i.convert(e)._mult(a);\"viewport\"===r&&o._rotate(-n);for(var s=[],l=0;l<t.length;l++)s.push(t[l].sub(o));return s}Fa.prototype.populate=function(t,e,r){var n=this.layers[0],i=[],a=null;\"circle\"===n.type&&(a=n.layout.get(\"circle-sort-key\"));for(var o=0,s=t;o<s.length;o+=1){var l=s[o],c=l.feature,u=l.id,f=l.index,h=l.sourceLayerIndex,p=this.layers[0]._featureFilter.needGeometry,d={type:c.type,id:u,properties:c.properties,geometry:p?Da(c):[]};if(this.layers[0]._featureFilter.filter(new ii(this.zoom),d,r)){p||(d.geometry=Da(c));var g=a?a.evaluate(d,{},r):void 0;i.push({id:u,properties:c.properties,type:c.type,sourceLayerIndex:h,index:f,geometry:d.geometry,patterns:{},sortKey:g})}}a&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var m=0,v=i;m<v.length;m+=1){var y=v[m],x=y.geometry,b=y.index,_=y.sourceLayerIndex,w=t[b].feature;this.addFeature(y,x,b,r),e.featureIndex.insert(w,x,b,_,this.index)}},Fa.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Fa.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Fa.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Fa.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,na),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Fa.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Fa.prototype.addFeature=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1)for(var o=0,s=a[i];o<s.length;o+=1){var l=s[o],c=l.x,u=l.y;if(!(c<0||c>=8192||u<0||u>=8192)){var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray,t.sortKey),h=f.vertexLength;Ra(this.layoutVertexArray,c,u,-1,-1),Ra(this.layoutVertexArray,c,u,1,-1),Ra(this.layoutVertexArray,c,u,1,1),Ra(this.layoutVertexArray,c,u,-1,1),this.indexArray.emplaceBack(h,h+1,h+2),this.indexArray.emplaceBack(h,h+3,h+2),f.vertexLength+=4,f.primitiveLength+=2}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,{},n)},Dn(\"CircleBucket\",Fa,{omit:[\"layers\"]});var Qa=new yi({\"circle-sort-key\":new di(At.layout_circle[\"circle-sort-key\"])}),$a={paint:new yi({\"circle-radius\":new di(At.paint_circle[\"circle-radius\"]),\"circle-color\":new di(At.paint_circle[\"circle-color\"]),\"circle-blur\":new di(At.paint_circle[\"circle-blur\"]),\"circle-opacity\":new di(At.paint_circle[\"circle-opacity\"]),\"circle-translate\":new pi(At.paint_circle[\"circle-translate\"]),\"circle-translate-anchor\":new 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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=us(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}return r&&i.push(r),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 fs=hs;function hs(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(ps,this,e),this.length=this._features.length}function ps(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){for(var e=null,r=t.readVarint()+t.pos;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 ds(t,e,r){if(3===t){var n=new fs(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}hs.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 ss(this._pbf,e,this.extent,this._keys,this._values)};var gs={VectorTile:function(t,e){this.layers=t.readFields(ds,{},e)},VectorTileFeature:ss,VectorTileLayer:fs},ms=gs.VectorTileFeature.types,vs=Math.pow(2,13);function ys(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*vs)+o,i*vs*2,a*vs*2,Math.round(s))}var xs=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 Si,this.indexArray=new Fi,this.programConfigurations=new Ia(os,t.layers,t.zoom),this.segments=new ia,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function bs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}xs.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ts(\"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?Da(o):[]};if(this.layers[0]._featureFilter.filter(new ii(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Da(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(es(\"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)}}},xs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},xs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},xs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},xs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},xs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,os),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},xs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},xs.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=Qo(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&&!((P=d).every((function(t){return t.x<0}))||P.every((function(t){return t.x>8192}))||P.every((function(t){return t.y<0}))||P.every((function(t){return t.y>8192}))))for(var g=0,m=0;m<d.length;m++){var v=d[m];if(m>=1){var y=d[m-1];if(!bs(v,y)){f.vertexLength+4>ia.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);g+b>32768&&(g=0),ys(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,g),ys(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,g),ys(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,g+=b),ys(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,g);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>ia.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),\"Polygon\"===ms[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 C=S[E];ys(this.layoutVertexArray,C.x,C.y,0,0,1,1,0),w.push(C.x),w.push(C.y)}}}for(var 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a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Ts(s,s),f=Ts(s,l),h=Ts(l,l),p=Ts(c,s),d=Ts(c,l),g=u*h-f*f,m=(h*p-f*d)/g,v=(u*d-f*p)/g,y=i.z*(1-m-v)+r.z*m+a.z*v;if(isFinite(y))return y}return 1/0}for(var x=1/0,b=0,_=e;b<_.length;b+=1)x=Math.min(x,_[b].z);return x}var Ms=Ti([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4).members,As=gs.VectorTileFeature.types,Ss=Math.cos(Math.PI/180*37.5),Es=Math.pow(2,14)/.5,Cs=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 Ei,this.indexArray=new Fi,this.programConfigurations=new Ia(Ms,t.layers,t.zoom),this.segments=new ia,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return 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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)this.addLine(f[u],t,o,s,l,c);this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Cs.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\"===As[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 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o=e.y*n-e.x,s=-e.y-e.x*n;this.addHalfVertex(t,e.x+e.y*r,e.y-e.x*r,a,!1,r,i),this.addHalfVertex(t,o,s,a,!0,-n,i),this.distance>Es/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},Cs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((t.x<<1)+(n?1:0),(t.y<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&s)<<2,s>>6);var l=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,l),o.primitiveLength++),i?this.e2=l:this.e1=l},Cs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Es-1):this.distance},Cs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Dn(\"LineBucket\",Cs,{omit:[\"layers\",\"patternFeatures\"]});var Ls=new yi({\"line-cap\":new 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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&&Zs(e,r,this),this.pos=e-1,this.writeVarint(r),this.pos+=r},writeFloat:function(t){this.realloc(4),qs(this.buf,t,this.pos,!0,23,4),this.pos+=4},writeDouble:function(t){this.realloc(8),qs(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&&Zs(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,Gs.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,Js,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,Ks,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,tl,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,Qs,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,$s,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,el,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,rl,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,nl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,il,e)},writeBytesField:function(t,e){this.writeTag(t,Gs.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,Gs.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,Gs.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,Gs.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,Gs.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,Gs.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,Gs.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,Gs.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,Gs.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,Gs.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};var hl=function(t,e){var r=e.pixelRatio,n=e.version,i=e.stretchX,a=e.stretchY,o=e.content;this.paddedRect=t,this.pixelRatio=r,this.stretchX=i,this.stretchY=a,this.content=o,this.version=n},pl={tl:{configurable:!0},br:{configurable:!0},tlbr:{configurable:!0},displaySize:{configurable:!0}};pl.tl.get=function(){return[this.paddedRect.x+1,this.paddedRect.y+1]},pl.br.get=function(){return[this.paddedRect.x+this.paddedRect.w-1,this.paddedRect.y+this.paddedRect.h-1]},pl.tlbr.get=function(){return this.tl.concat(this.br)},pl.displaySize.get=function(){return[(this.paddedRect.w-2)/this.pixelRatio,(this.paddedRect.h-2)/this.pixelRatio]},Object.defineProperties(hl.prototype,pl);var dl=function(t,e){var r={},n={};this.haveRenderCallbacks=[];var i=[];this.addImages(t,r,i),this.addImages(e,n,i);var a=fl(i),o=new po({width:a.w||1,height:a.h||1});for(var s in t){var l=t[s],c=r[s].paddedRect;po.copy(l.data,o,{x:0,y:0},{x:c.x+1,y:c.y+1},l.data)}for(var u in e){var f=e[u],h=n[u].paddedRect,p=h.x+1,d=h.y+1,g=f.data.width,m=f.data.height;po.copy(f.data,o,{x:0,y:0},{x:p,y:d},f.data),po.copy(f.data,o,{x:0,y:m-1},{x:p,y:d-1},{width:g,height:1}),po.copy(f.data,o,{x:0,y:0},{x:p,y:d+m},{width:g,height:1}),po.copy(f.data,o,{x:g-1,y:0},{x:p-1,y:d},{width:1,height:m}),po.copy(f.data,o,{x:0,y:0},{x:p+g,y:d},{width:1,height:m})}this.image=o,this.iconPositions=r,this.patternPositions=n};dl.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 hl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},dl.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)},dl.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl;r.update(e.data,void 0,{x:n[0],y:n[1]})}},Dn(\"ImagePosition\",hl),Dn(\"ImageAtlas\",dl);var gl={horizontal:1,vertical:2,horizontalOnly:3},ml=function(){this.scale=1,this.fontStack=\"\",this.imageName=null};ml.forText=function(t,e){var r=new ml;return r.scale=t||1,r.fontStack=e,r},ml.forImage=function(t){var e=new ml;return e.imageName=t,e};var vl=function(){this.text=\"\",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function yl(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g){var m,v=vl.fromFeature(t,i);f===gl.vertical&&v.verticalizePunctuation();var y=ni.processBidirectionalText,x=ni.processStyledBidirectionalText;if(y&&1===v.sections.length){m=[];for(var b=0,_=y(v.toString(),Ml(v,c,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new vl;T.text=w,T.sections=v.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);m.push(T)}}else if(x){m=[];for(var M=0,A=x(v.text,v.sectionIndex,Ml(v,c,a,e,n,p,d));M<A.length;M+=1){var S=A[M],E=new vl;E.text=S[0],E.sectionIndex=S[1],E.sections=v.sections,m.push(E)}}else m=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,Ml(v,c,a,e,n,p,d));var C=[],L={positionedLines:C,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,g=0,m=\"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),C=b.getCharCode(A),L=0,I=null,P=null,z=null,O=24,D=!(l===gl.horizontal||!u&&!Hn(C)||u&&(xl[C]||(H=C,Vn.Arabic(H)||Vn[\"Arabic Supplement\"](H)||Vn[\"Arabic Extended-A\"](H)||Vn[\"Arabic Presentation Forms-A\"](H)||Vn[\"Arabic Presentation Forms-B\"](H))));if(S.imageName){var R=n[S.imageName];if(!R)continue;z=S.imageName,t.iconsInText=t.iconsInText||!0,P=R.paddedRect;var F=R.displaySize;S.scale=24*S.scale/f,L=w+(24-F[1]*S.scale),O=(I={width:F[0],height:F[1],left:1,top:-3,advance:D?F[1]:F[0]}).advance;var B=D?F[0]*S.scale-24*_:F[1]*S.scale-24*_;B>0&&B>M&&(M=B)}else{var N=r[S.fontStack],j=N&&N[C];if(j&&j.rect)P=j.rect,I=j.metrics;else{var U=e[S.fontStack],V=U&&U[C];if(!V)continue;I=V.metrics}L=24*(_-S.scale)}D?(t.verticalizable=!0,k.push({glyph:C,imageName:z,x:h,y:p+L,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:I,rect:P}),h+=O*S.scale+c):(k.push({glyph:C,imageName:z,x:h,y:p+L,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:I,rect:P}),h+=I.advance*S.scale+c)}0!==k.length&&(d=Math.max(h-c,d),Sl(k,0,k.length-1,m,M)),h=0;var q=a*_+M;T.lineOffset=Math.max(M,w),p+=q,g=Math.max(q,g),++v}else p+=a,++v}var H,G=p- -17,Y=Al(o),W=Y.horizontalAlign,X=Y.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var c,u=(e-r)*i;c=a!==o?-s*n- -17:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=0,d=h[f].positionedGlyphs;p<d.length;p+=1){var g=d[p];g.x+=u,g.y+=c}})(t.positionedLines,m,W,X,d,g,a,G,i.length),t.top+=-X*G,t.bottom=t.top+G,t.left+=-W*d,t.right=t.left+d}(L,e,r,n,m,o,s,l,f,c,h,g),!function(t){for(var e=0,r=t;e<r.length;e+=1)if(0!==r[e].positionedGlyphs.length)return!1;return!0}(C)&&L}vl.fromFeature=function(t,e){for(var r=new vl,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},vl.prototype.length=function(){return this.text.length},vl.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},vl.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},vl.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},vl.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;e+=n&&Gn(n)&&!Us[t[r+1]]||i&&Gn(i)&&!Us[t[r-1]]||!Us[t[r]]?t[r]:Us[t[r]]}return e}(this.text)},vl.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&xl[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&xl[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},vl.prototype.substring=function(t,e){var r=new vl;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},vl.prototype.toString=function(){return this.text},vl.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return Math.max(e,t.sections[r].scale)}),0)},vl.prototype.addTextSection=function(t,e){this.text+=t.text,this.sections.push(ml.forText(t.scale,t.fontStack||e));for(var r=this.sections.length-1,n=0;n<t.text.length;++n)this.sectionIndex.push(r)},vl.prototype.addImageSection=function(t){var e=t.image?t.image.name:\"\";if(0!==e.length){var r=this.getNextImageSectionCharCode();r?(this.text+=String.fromCharCode(r),this.sections.push(ml.forImage(e)),this.sectionIndex.push(this.sections.length-1)):_(\"Reached maximum number of images 6401\")}else _(\"Can't add FormattedSection with an empty image.\")},vl.prototype.getNextImageSectionCharCode=function(){return this.imageSectionID?this.imageSectionID>=63743?null:++this.imageSectionID:(this.imageSectionID=57344,this.imageSectionID)};var xl={9:!0,10:!0,11:!0,12:!0,13:!0,32:!0},bl={};function _l(t,e,r,n,i,a){if(e.imageName){var o=n[e.imageName];return o?o.displaySize[0]*e.scale*24/a+i:0}var s=r[e.fontStack],l=s&&s[t];return l?l.metrics.advance*e.scale+i:0}function wl(t,e,r,n){var i=Math.pow(t-e,2);return n?t<e?i/2:2*i:i+Math.abs(r)*r}function Tl(t,e,r){var n=0;return 10===t&&(n-=1e4),r&&(n+=150),40!==t&&65288!==t||(n+=50),41!==e&&65289!==e||(n+=50),n}function kl(t,e,r,n,i,a){for(var o=null,s=wl(e,r,i,a),l=0,c=n;l<c.length;l+=1){var u=c[l],f=wl(e-u.x,r,i,a)+u.badness;f<=s&&(o=u,s=f)}return{index:t,x:e,priorBreak:o,badness:s}}function Ml(t,e,r,n,i,a,o){if(\"point\"!==a)return[];if(!t)return[];for(var s,l=[],c=function(t,e,r,n,i,a){for(var o=0,s=0;s<t.length();s++){var l=t.getSection(s);o+=_l(t.getCharCode(s),l,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,o),u=t.text.indexOf(\"\\u200b\")>=0,f=0,h=0;h<t.length();h++){var p=t.getSection(h),d=t.getCharCode(h);if(xl[d]||(f+=_l(d,p,n,i,e,o)),h<t.length()-1){var g=!((s=d)<11904||!(Vn[\"Bopomofo Extended\"](s)||Vn.Bopomofo(s)||Vn[\"CJK Compatibility Forms\"](s)||Vn[\"CJK Compatibility Ideographs\"](s)||Vn[\"CJK Compatibility\"](s)||Vn[\"CJK Radicals Supplement\"](s)||Vn[\"CJK Strokes\"](s)||Vn[\"CJK Symbols and Punctuation\"](s)||Vn[\"CJK Unified Ideographs Extension A\"](s)||Vn[\"CJK Unified 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o,s=t.image;if(s.content){var l=s.content,c=s.pixelRatio||1;o=[l[0]/c,l[1]/c,s.displaySize[0]-l[2]/c,s.displaySize[1]-l[3]/c]}var u,f,h,p,d=e.left*a,g=e.right*a;\"width\"===r||\"both\"===r?(p=i[0]+d-n[3],f=i[0]+g+n[1]):f=(p=i[0]+(d+g-s.displaySize[0])/2)+s.displaySize[0];var m=e.top*a,v=e.bottom*a;return\"height\"===r||\"both\"===r?(u=i[1]+m-n[0],h=i[1]+v+n[2]):h=(u=i[1]+(m+v-s.displaySize[1])/2)+s.displaySize[1],{image:s,top:u,right:f,bottom:h,left:p,collisionPadding:o}}bl[10]=!0,bl[32]=!0,bl[38]=!0,bl[40]=!0,bl[41]=!0,bl[43]=!0,bl[45]=!0,bl[47]=!0,bl[173]=!0,bl[183]=!0,bl[8203]=!0,bl[8208]=!0,bl[8211]=!0,bl[8231]=!0;var Cl=function(t){function e(e,r,n,i){t.call(this,e,r),this.angle=n,void 0!==i&&(this.segment=i)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.clone=function(){return new e(this.x,this.y,this.angle,this.segment)},e}(i);function Ll(t,e){var r=e.expression;if(\"constant\"===r.kind)return{kind:\"constant\",layoutSize:r.evaluate(new ii(t+1))};if(\"source\"===r.kind)return{kind:\"source\"};for(var n=r.zoomStops,i=r.interpolationType,a=0;a<n.length&&n[a]<=t;)a++;for(var o=a=Math.max(0,a-1);o<n.length&&n[o]<t+1;)o++;o=Math.min(n.length-1,o);var s=n[a],l=n[o];return\"composite\"===r.kind?{kind:\"composite\",minZoom:s,maxZoom:l,interpolationType:i}:{kind:\"camera\",minZoom:s,maxZoom:l,minSize:r.evaluate(new ii(s)),maxSize:r.evaluate(new ii(l)),interpolationType:i}}function Il(t,e,r){var n=e.uSize,i=r.lowerSize;return\"source\"===t.kind?i/128:\"composite\"===t.kind?Ue(i/128,r.upperSize/128,e.uSizeT):n}function Pl(t,e){var r=0,n=0;if(\"constant\"===t.kind)n=t.layoutSize;else if(\"source\"!==t.kind){var i=t.interpolationType,a=i?l(rr.interpolationFactor(i,e,t.minZoom,t.maxZoom),0,1):0;\"camera\"===t.kind?n=Ue(t.minSize,t.maxSize,a):r=a}return{uSizeT:r,uSize:n}}Dn(\"Anchor\",Cl);var 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h=Hl(n.stretch-x,b,u,t.left),p=Gl(n.fixed-T,k,n.stretch,g),d=Hl(a.stretch-_,w,f,t.top),v=Gl(a.fixed-M,A,a.stretch,m),y=Hl(l.stretch-x,b,u,t.left),S=Gl(l.fixed-T,k,l.stretch,g),E=Hl(c.stretch-_,w,f,t.top),C=Gl(c.fixed-M,A,c.stretch,m),L=new i(h,d),I=new i(y,d),P=new i(y,E),z=new i(h,E),O=new i(p/s,v/s),D=new i(S/s,C/s),R=e*Math.PI/180;if(R){var F=Math.sin(R),B=Math.cos(R),N=[B,-F,F,B];L._matMult(N),I._matMult(N),z._matMult(N),P._matMult(N)}var j=n.stretch+n.fixed,U=a.stretch+a.fixed;return{tl:L,tr:I,bl:z,br:P,tex:{x:o.paddedRect.x+1+j,y:o.paddedRect.y+1+U,w:l.stretch+l.fixed-j,h:c.stretch+c.fixed-U},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:O,pixelOffsetBR:D,minFontScaleX:k/s/u,minFontScaleY:A/s/f,isSDF:r}};if(n&&(o.stretchX||o.stretchY))for(var C=ql(h,v,g),L=ql(p,y,m),I=0;I<C.length-1;I++)for(var P=C[I],z=C[I+1],O=0;O<L.length-1;O++)a.push(E(P,L[O],z,L[O+1]));else 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k=s[m.imageName];x=k.sdf,y=1/(b=k.pixelRatio)}var M=a?[m.x+T,m.y]:[0,0],A=a?[0,0]:[m.x+T+r[0],m.y+r[1]-_],S=[0,0];w&&(S=A,A=[0,0]);var E=(m.metrics.left-y)*m.scale-T+A[0],C=(-m.metrics.top-y)*m.scale+A[1],L=E+v.w*m.scale/b,I=C+v.h*m.scale/b,P=new i(E,C),z=new i(L,C),O=new i(E,I),D=new i(L,I);if(w){var R=new i(-T,T- -17),F=-Math.PI/2,B=12-T,N=new i(22-B,-(m.imageName?B:0)),j=new(Function.prototype.bind.apply(i,[null].concat(S)));P._rotateAround(F,R)._add(N)._add(j),z._rotateAround(F,R)._add(N)._add(j),O._rotateAround(F,R)._add(N)._add(j),D._rotateAround(F,R)._add(N)._add(j)}if(c){var U=Math.sin(c),V=Math.cos(c),q=[V,-U,U,V];P._matMult(q),z._matMult(q),O._matMult(q),D._matMult(q)}var H=new i(0,0),G=new i(0,0);u.push({tl:P,tr:z,bl:O,br:D,tex:v,writingMode:e.writingMode,glyphOffset:M,sectionIndex:m.sectionIndex,isSDF:x,pixelOffsetTL:H,pixelOffsetBR:G,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. 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Fi,this.programConfigurations=t,this.segments=new ia,this.dynamicLayoutVertexArray=new Ii,this.opacityVertexArray=new Pi,this.placedSymbolArray=new Ji};cc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},cc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Ds.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Rs.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,ac,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},cc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Dn(\"SymbolBuffers\",cc);var uc=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new ia,this.collisionVertexArray=new Ri};uc.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Fs.members,!0)},uc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Dn(\"CollisionBuffers\",uc);var fc=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=eo([]),this.placementViewportMatrix=eo([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Ll(this.zoom,e[\"text-size\"]),this.iconSizeData=Ll(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),this.sortFeaturesByY=(\"viewport-y\"===i||\"auto\"===i&&!this.sortFeaturesByKey)&&(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 gl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};fc.prototype.createArrays=function(){this.text=new cc(new Ia(Ds.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new cc(new Ia(Ds.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new $i,this.lineVertexArray=new ta,this.symbolInstances=new Qi},fc.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=Us[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},fc.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 te&&!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 ii(this.zoom),g=0,m=t;g<m.length;g+=1){var v=m[g],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?Da(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Da(y));var k=void 0;if(l){var M=n.getValueAndResolveTokens(\"text-field\",T,r,p),A=te.factory(M);lc(A)&&(this.hasRTLText=!0),(!this.hasRTLText||\"unavailable\"===ei()||this.hasRTLText&&ni.isParsed())&&(k=js(A,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens(\"icon-image\",T,r,p);S=E instanceof ee?E:ee.fromString(E)}if(k||S){var C=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,L={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Da(y),properties:y.properties,type:ic[y.type],sortKey:C};if(this.features.push(L),S&&(f[S.name]=!0),k){var I=a.evaluate(T,{},r).join(\",\"),P=\"map\"===i.get(\"text-rotation-alignment\")&&\"point\"!==i.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(gl.vertical)>=0;for(var z=0,O=k.sections;z<O.length;z+=1){var D=O[z];if(D.image)f[D.image.name]=!0;else{var R=qn(k.toString()),F=D.fontStack||I,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,P,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 g=s(p,d,f),m=o(p,d,n[g].geometry);delete e[p],delete r[d],r[l(h,n[m].geometry,!0)]=m,n[g].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}))}},fc.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))},fc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},fc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},fc.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},fc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},fc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},fc.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}},fc.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),g=this.glyphOffsetArray.length,m=d.vertexLength,v=this.allowVerticalPlacement&&o===gl.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,C=b.minFontScaleY,L=b.glyphOffset,I=b.isSDF,P=b.sectionIndex,z=d.vertexLength,O=L[1];oc(p,s.x,s.y,_.x,O+_.y,M.x,M.y,r,I,A.x,A.y,E,C),oc(p,s.x,s.y,w.x,O+w.y,M.x+M.w,M.y,r,I,S.x,A.y,E,C),oc(p,s.x,s.y,T.x,O+T.y,M.x,M.y+M.h,r,I,A.x,S.y,E,C),oc(p,s.x,s.y,k.x,O+k.y,M.x+M.w,M.y+M.h,r,I,S.x,S.y,E,C),sc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(z,z+1,z+2),h.emplaceBack(z+1,z+2,z+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(L[0]),x!==e.length-1&&P===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[P])}t.placedSymbolArray.emplaceBack(s.x,s.y,g,this.glyphOffsetArray.length-g,m,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},fc.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))},fc.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},fc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i);this.addCollisionDebugVertices(a.x1,a.y1,a.x2,a.y2,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},fc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new uc(Oi,Bs.members,qi),this.iconCollisionBox=new uc(Oi,Bs.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)}},fc.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 g=t.get(d);c.iconBox={x1:g.x1,y1:g.y1,x2:g.x2,y2:g.y2,anchorPointX:g.anchorPointX,anchorPointY:g.anchorPointY},c.iconFeatureIndex=g.featureIndex;break}for(var m=s;m<l;m++){var v=t.get(m);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},fc.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))}},fc.prototype.hasTextData=function(){return this.text.segments.get().length>0},fc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},fc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},fc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},fc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},fc.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)},fc.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},fc.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})},fc.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=this.symbolInstances.get(n[r]);this.featureSortOrder.push(i.featureIndex),[i.rightJustifiedTextSymbolIndex,i.centerJustifiedTextSymbolIndex,i.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),i.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,i.verticalPlacedTextSymbolIndex),i.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,i.placedIconSymbolIndex),i.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,i.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Dn(\"SymbolBucket\",fc,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),fc.MAX_GLYPHS=65535,fc.addDynamicAttributes=sc;var hc=new yi({\"symbol-placement\":new pi(At.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new pi(At.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new pi(At.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new di(At.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new pi(At.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new pi(At.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new pi(At.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new pi(At.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new pi(At.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new di(At.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new pi(At.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new pi(At.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new di(At.layout_symbol[\"icon-image\"]),\"icon-rotate\":new di(At.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new pi(At.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new pi(At.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new di(At.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new di(At.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new pi(At.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new pi(At.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new pi(At.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new di(At.layout_symbol[\"text-field\"]),\"text-font\":new di(At.layout_symbol[\"text-font\"]),\"text-size\":new di(At.layout_symbol[\"text-size\"]),\"text-max-width\":new di(At.layout_symbol[\"text-max-width\"]),\"text-line-height\":new pi(At.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new di(At.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new di(At.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new di(At.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new pi(At.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new di(At.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new pi(At.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new pi(At.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new di(At.layout_symbol[\"text-rotate\"]),\"text-padding\":new pi(At.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new pi(At.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new di(At.layout_symbol[\"text-transform\"]),\"text-offset\":new di(At.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new pi(At.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new pi(At.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new pi(At.layout_symbol[\"text-optional\"])}),pc={paint:new yi({\"icon-opacity\":new di(At.paint_symbol[\"icon-opacity\"]),\"icon-color\":new di(At.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new di(At.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new di(At.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new di(At.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new pi(At.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new pi(At.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new di(At.paint_symbol[\"text-opacity\"]),\"text-color\":new di(At.paint_symbol[\"text-color\"],{runtimeType:Bt,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new di(At.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new di(At.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new di(At.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new pi(At.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new pi(At.paint_symbol[\"text-translate-anchor\"])}),layout:hc},dc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ot,this.defaultValue=t};dc.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},dc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},dc.prototype.outputDefined=function(){return!1},dc.prototype.serialize=function(){return null},Dn(\"FormatSectionOverride\",dc,{omit:[\"defaultValue\"]});var gc=function(t){function e(e){t.call(this,e,pc)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(this.layout._values[\"icon-rotation-alignment\"]=\"point\"!==this.layout.get(\"symbol-placement\")?\"map\":\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(this.layout._values[\"text-rotation-alignment\"]=\"point\"!==this.layout.get(\"symbol-placement\")?\"map\":\"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()||Vr(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 fc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=pc.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i,a=this.paint.get(n),o=new dc(a),s=new Ur(o,a.property.specification);i=\"constant\"===a.value.kind||\"source\"===a.value.kind?new Hr(\"source\",s):new Gr(\"composite\",s,a.value.zoomStops,a.value._interpolationType),this.paint._values[n]=new fi(a.property,i,a.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=pc.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1)if(n.overrides&&n.overrides.hasOverride(r[e]))return void(i=!0)};if(\"constant\"===r.value.kind&&r.value.value instanceof te)a(r.value.value.sections);else if(\"source\"===r.value.kind){var o=function(t){i||(t instanceof oe&&ie(t.value)===Vt?a(t.value.sections):t instanceof ue?a(t.sections):t.eachChild(o))},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(xi),mc={paint:new yi({\"background-color\":new pi(At.paint_background[\"background-color\"]),\"background-pattern\":new mi(At.paint_background[\"background-pattern\"]),\"background-opacity\":new pi(At.paint_background[\"background-opacity\"])})},vc=function(t){function e(e){t.call(this,e,mc)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e}(xi),yc={paint:new yi({\"raster-opacity\":new pi(At.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new pi(At.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new pi(At.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new pi(At.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new pi(At.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new pi(At.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new pi(At.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new pi(At.paint_raster[\"raster-fade-duration\"])})},xc=function(t){function e(e){t.call(this,e,yc)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e}(xi),bc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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}(xi),_c={circle:oo,heatmap:vo,hillshade:xo,fill:as,\"fill-extrusion\":ws,line:zs,symbol:gc,background:vc,raster:xc},wc=self.HTMLImageElement,Tc=self.HTMLCanvasElement,kc=self.HTMLVideoElement,Mc=self.ImageData,Ac=self.ImageBitmap,Sc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Sc.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 wc||t instanceof Tc||t instanceof kc||t instanceof Mc||Ac&&t instanceof Ac?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 wc||t instanceof Tc||t instanceof kc||t instanceof Mc||Ac&&t instanceof Ac?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)},Sc.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)},Sc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Sc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Ec=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()})};Ec.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))},Ec.prototype.remove=function(){delete this._channel,this._callback=function(){}};var Cc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},g([\"receive\",\"process\"],this),this.invoker=new Ec(this.process),this.target.addEventListener(\"message\",this.receive,!1),this.globalScope=k()?t:self};function Lc(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]}Cc.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:Nn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:\"<cancel>\",targetMapId:n,sourceMapId:a.mapId})}}},Cc.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)},Cc.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)}},Cc.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(jn(e.error)):n(null,jn(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?Nn(e):null,data:Nn(n,a)},a)}:function(t){i=!0},s=null,l=jn(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)}},Cc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Ic=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]))};Ic.prototype.setNorthEast=function(t){return this._ne=t instanceof Pc?new Pc(t.lng,t.lat):Pc.convert(t),this},Ic.prototype.setSouthWest=function(t){return this._sw=t instanceof Pc?new Pc(t.lng,t.lat):Pc.convert(t),this},Ic.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Pc)e=t,r=t;else{if(!(t instanceof Ic))return Array.isArray(t)?4===t.length||t.every(Array.isArray)?this.extend(Ic.convert(t)):this.extend(Pc.convert(t)):this;if(r=t._ne,!(e=t._sw)||!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 Pc(e.lng,e.lat),this._ne=new Pc(r.lng,r.lat)),this},Ic.prototype.getCenter=function(){return new Pc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Ic.prototype.getSouthWest=function(){return this._sw},Ic.prototype.getNorthEast=function(){return this._ne},Ic.prototype.getNorthWest=function(){return new Pc(this.getWest(),this.getNorth())},Ic.prototype.getSouthEast=function(){return new Pc(this.getEast(),this.getSouth())},Ic.prototype.getWest=function(){return this._sw.lng},Ic.prototype.getSouth=function(){return this._sw.lat},Ic.prototype.getEast=function(){return this._ne.lng},Ic.prototype.getNorth=function(){return this._ne.lat},Ic.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Ic.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Ic.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Ic.prototype.contains=function(t){var e=Pc.convert(t),r=e.lng,n=e.lat,i=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(i=this._sw.lng>=r&&r>=this._ne.lng),this._sw.lat<=n&&n<=this._ne.lat&&i},Ic.convert=function(t){return!t||t instanceof Ic?t:new Ic(t)};var Pc=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\")};Pc.prototype.wrap=function(){return new Pc(c(this.lng,-180,180),this.lat)},Pc.prototype.toArray=function(){return[this.lng,this.lat]},Pc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Pc.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))},Pc.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 Ic(new Pc(this.lng-r,this.lat-e),new Pc(this.lng+r,this.lat+e))},Pc.convert=function(t){if(t instanceof Pc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Pc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Pc(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 zc=2*Math.PI*6371008.8;function Oc(t){return zc*Math.cos(t*Math.PI/180)}function Dc(t){return(180+t)/360}function Rc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Fc(t,e){return t/Oc(e)}function Bc(t){return 360/Math.PI*Math.atan(Math.exp((180-360*t)*Math.PI/180))-90}var Nc=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};Nc.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Pc.convert(t);return new Nc(Dc(r.lng),Rc(r.lat),Fc(e,r.lat))},Nc.prototype.toLngLat=function(){return new Pc(360*this.x-180,Bc(this.y))},Nc.prototype.toAltitude=function(){return this.z*Oc(Bc(this.y))},Nc.prototype.meterInMercatorCoordinateUnits=function(){return 1/zc*(t=Bc(this.y),1/Math.cos(t*Math.PI/180));var t};var jc=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=qc(0,t,t,e,r)};jc.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},jc.prototype.url=function(t,e){var r,n,i,a,o,s=(n=this.y,i=this.z,a=Lc(256*(r=this.x),256*(n=Math.pow(2,i)-n-1),i),o=Lc(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)},jc.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))},jc.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y};var Uc=function(t,e){this.wrap=t,this.canonical=e,this.key=qc(t,e.z,e.z,e.x,e.y)},Vc=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new jc(r,+n,+i),this.key=qc(e,t,r,n,i)};function qc(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)}Vc.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},Vc.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new Vc(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Vc(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},Vc.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?qc(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):qc(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},Vc.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},Vc.prototype.children=function(t){if(this.overscaledZ>=t)return[new Vc(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 Vc(e,this.wrap,e,r,n),new Vc(e,this.wrap,e,r+1,n),new Vc(e,this.wrap,e,r,n+1),new Vc(e,this.wrap,e,r+1,n+1)]},Vc.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))},Vc.prototype.wrapped=function(){return new Vc(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},Vc.prototype.unwrapTo=function(t){return new Vc(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},Vc.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},Vc.prototype.toUnwrapped=function(){return new Uc(this.wrap,this.canonical)},Vc.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},Vc.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new Nc(t.x-this.wrap,t.y))},Dn(\"CanonicalTileID\",jc),Dn(\"OverscaledTileID\",Vc,{omit:[\"posMatrix\"]});var Hc=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)]};Hc.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])},Hc.prototype.getUnpackVector=function(){return\"terrarium\"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},Hc.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)},Hc.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},Hc.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},Hc.prototype.getPixels=function(){return new po({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},Hc.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)]},Dn(\"DEMData\",Hc);var Gc=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}};Gc.prototype.encode=function(t){return this._stringToNumber[t]},Gc.prototype.decode=function(t){return this._numberToString[t]};var Yc=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},Wc={geometry:{configurable:!0}};Wc.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},Wc.geometry.set=function(t){this._geometry=t},Yc.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(Yc.prototype,Wc);var Xc=function(){this.state={},this.stateChanges={},this.deletedStates={}};Xc.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]},Xc.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}},Xc.prototype.getState=function(t,e){var r=String(e),n=u({},(this.state[t]||{})[r],(this.stateChanges[t]||{})[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var i=this.deletedStates[t][e];if(null===i)return{};for(var a in i)delete n[a]}return n},Xc.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},Xc.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)delete this.state[o][c][h[f]];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 p in t)t[p].setFeatureState(r,e)};var Zc=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new Ln(8192,16,0),this.grid3D=new Ln(8192,16,0),this.featureIndexArray=new ra,this.promoteId=e};function Jc(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof hi?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function Kc(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 Qc(t,e){return e-t}Zc.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])}},Zc.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new gs.VectorTile(new Hs(this.rawTileData)).layers,this.sourceLayerCoder=new Gc(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},Zc.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=rn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=Kc(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=Kc(t.cameraQueryGeometry),d=0,g=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(Wa(t,f[u]))return!0;for(var h=0;h<t.length-1;h++)if(Xa(t[h],t[h+1],c))return!0;return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)}));d<g.length;d+=1)h.push(g[d]);h.sort(Qc);for(var m,v={},y=function(i){var u=h[i];if(u!==m){m=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(v,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Da(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},x=0;x<h.length;x++)y(x);return v},Zc.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 ii(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),g=0;g<f.length;g++){var m=f[g];if(!(a&&a.indexOf(m)<0)){var v=s[m];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||\"_geojsonTileLayer\",d));var x=l[m];x.paint=Jc(x.paint,v.paint,p,y,o),x.layout=Jc(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new Yc(p,this.z,this.x,this.y,d);_.layer=x;var w=t[m];void 0===w&&(w=t[m]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},Zc.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=rn(i),u=0,f=t;u<f.length;u+=1)this.loadMatchingFeature(l,r,n,f[u],c,a,o,s,e);return l},Zc.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},Zc.prototype.getId=function(t,e){var r=t.id;return this.promoteId&&\"boolean\"==typeof(r=t.properties[\"string\"==typeof this.promoteId?this.promoteId:this.promoteId[e]])&&(r=Number(r)),r},Dn(\"FeatureIndex\",Zc,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});var $c=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};$c.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},$c.prototype.wasRequested=function(){return\"errored\"===this.state||\"loaded\"===this.state||\"reloading\"===this.state},$c.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)r[s[o].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 fc){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 fc&&o.hasRTLText){this.hasRTLText=!0,ni.isLoading()||ni.isLoaded()||\"deferred\"!==ei()||ri();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 Xi},$c.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},$c.prototype.getBucket=function(t){return this.buckets[t.id]},$c.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Sc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Sc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},$c.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},$c.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},$c.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=rn(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 ii(this.tileID.overscaledZ),p)){var d=r.getId(p,i),g=new Yc(p,l,c,u,d);g.tile=f,t.push(g)}}}},$c.prototype.hasData=function(){return\"loaded\"===this.state||\"reloading\"===this.state||\"expired\"===this.state},$c.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},$c.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=M(t.cacheControl);r[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*r[\"max-age\"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},$c.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)},$c.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||\"_geojsonTileLayer\",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},$c.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},$c.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},$c.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},$c.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},$c.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},$c.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=this.dependencies[n[r]];if(i)for(var a=0,o=e;a<o.length;a+=1)if(i[o[a]])return!0}return!1};var tu=self.performance,eu=function(t){this._marks={start:[t.url,\"start\"].join(\"#\"),end:[t.url,\"end\"].join(\"#\"),measure:t.url.toString()},tu.mark(this._marks.start)};eu.prototype.finish=function(){tu.mark(this._marks.end);var t=tu.getEntriesByName(this._marks.measure);return 0===t.length&&(tu.measure(this._marks.measure,this._marks.start,this._marks.end),t=tu.getEntriesByName(this._marks.measure),tu.clearMarks(this._marks.start),tu.clearMarks(this._marks.end),tu.clearMeasures(this._marks.measure)),t},t.Actor=Cc,t.AlphaImage=ho,t.CanonicalTileID=jc,t.CollisionBoxArray=Xi,t.Color=Kt,t.DEMData=Hc,t.DataConstantProperty=pi,t.DictionaryCoder=Gc,t.EXTENT=8192,t.ErrorEvent=kt,t.EvaluationParameters=ii,t.Event=Tt,t.Evented=Mt,t.FeatureIndex=Zc,t.FillBucket=rs,t.FillExtrusionBucket=xs,t.ImageAtlas=dl,t.ImagePosition=hl,t.LineBucket=Cs,t.LngLat=Pc,t.LngLatBounds=Ic,t.MercatorCoordinate=Nc,t.ONE_EM=24,t.OverscaledTileID=Vc,t.Point=i,t.Point$1=i,t.Properties=yi,t.Protobuf=Hs,t.RGBAImage=po,t.RequestManager=q,t.RequestPerformance=eu,t.ResourceType=ht,t.SegmentVector=ia,t.SourceFeatureState=Xc,t.StructArrayLayout1ui2=Hi,t.StructArrayLayout2f1f2i16=Di,t.StructArrayLayout2i4=Mi,t.StructArrayLayout3ui6=Fi,t.StructArrayLayout4i8=Ai,t.SymbolBucket=fc,t.Texture=Sc,t.Tile=$c,t.Transitionable=si,t.Uniform1f=va,t.Uniform1i=ma,t.Uniform2f=ya,t.Uniform3f=xa,t.Uniform4f=ba,t.UniformColor=_a,t.UniformMatrix4f=Ta,t.UnwrappedTileID=Uc,t.ValidationError=St,t.WritingMode=gl,t.ZoomHistory=Un,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=sc,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=o,t.bindAll=g,t.browser=R,t.cacheEntryPossiblyAdded=function(t){++ut>ot&&(t.getActor().send(\"enforceCacheSizeLimit\",at),ut=0)},t.clamp=l,t.clearTileCache=function(t){var e=self.caches.delete(\"mapbox-tiles\");t&&e.catch(t).then((function(){return t()}))},t.clipLine=jl,t.clone=function(t){var e=new to(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=x,t.clone$2=function(t){var e=new to(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=Ns,t.config=F,t.create=function(){var t=new to(16);return to!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0),t[0]=1,t[5]=1,t[10]=1,t[15]=1,t},t.create$1=function(){var t=new to(9);return to!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0),t[0]=1,t[4]=1,t[8]=1,t},t.create$2=function(){var t=new to(4);return to!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t},t.createCommonjsModule=e,t.createExpression=qr,t.createLayout=Ti,t.createStyleLayer=function(t){return\"custom\"===t.type?new bc(t):new _c[t.type](t)},t.cross=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t},t.deepEqual=function t(e,r){if(Array.isArray(e)){if(!Array.isArray(r)||e.length!==r.length)return!1;for(var n=0;n<e.length;n++)if(!t(e[n],r[n]))return!1;return!0}if(\"object\"==typeof e&&null!==e&&null!==r){if(\"object\"!=typeof r)return!1;if(Object.keys(e).length!==Object.keys(r).length)return!1;for(var i in e)if(!t(e[i],r[i]))return!1;return!0}return e===r},t.dot=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]},t.dot$1=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]+t[3]*e[3]},t.ease=s,t.emitValidationErrors=Cn,t.endsWith=m,t.enforceCacheSizeLimit=function(t){st(),Q&&Q.then((function(e){e.keys().then((function(r){for(var n=0;n<r.length-t;n++)e.delete(r[n])}))}))},t.evaluateSizeForFeature=Il,t.evaluateSizeForZoom=Pl,t.evaluateVariableOffset=$l,t.evented=ti,t.extend=u,t.featureFilter=rn,t.filterObject=y,t.fromRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=-r,t[4]=n,t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},t.getAnchorAlignment=Al,t.getAnchorJustification=tc,t.getArrayBuffer=yt,t.getImage=bt,t.getJSON=function(t,e){return vt(u(t,{type:\"json\"}),e)},t.getRTLTextPluginStatus=ei,t.getReferrer=mt,t.getVideo=function(t,e){var r,n,i=self.document.createElement(\"video\");i.muted=!0,i.onloadstart=function(){e(null,i)};for(var a=0;a<t.length;a++){var o=self.document.createElement(\"source\");r=t[a],n=void 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r}function ft(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 ht(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(pt(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(pt(c[n][i],c[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function pt(t,e,r,n,i,a){return[Math.round(r*(t*n-i)),Math.round(r*(e*n-a))]}function dt(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)*i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,gt(o,t[s],a,i);var l=t[s].minX,c=t[s].minY,u=t[s].maxX,f=t[s].maxY;l<o.minX&&(o.minX=l),c<o.minY&&(o.minY=c),u>o.maxX&&(o.maxX=u),f>o.maxY&&(o.maxY=f)}return o}function gt(t,e,r,n){var i=e.geometry,a=e.type,o=[];if(\"Point\"===a||\"MultiPoint\"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if(\"LineString\"===a)mt(o,i,t,r,!1,!1);else if(\"MultiLineString\"===a||\"Polygon\"===a)for(s=0;s<i.length;s++)mt(o,i[s],t,r,\"Polygon\"===a,0===s);else if(\"MultiPolygon\"===a)for(var l=0;l<i.length;l++){var c=i[l];for(s=0;s<c.length;s++)mt(o,c[s],t,r,!0,0===s)}if(o.length){var u=e.tags||null;if(\"LineString\"===a&&n.lineMetrics){for(var f in 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 mt(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 vt(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++)J(r,t.features[n],e,n);else J(r,\"Feature\"===t.type?t:{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=rt(t,1,-1-r,r,0,-1,2,e),a=rt(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=rt(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=ut(i,1).concat(n)),a&&(n=n.concat(ut(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 yt(t,e,r){return 32*((1<<t)*r+e)+t}function xt(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=b(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}F.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(N(t[l],l));this.trees[i+1]=new D(s,G,Y,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 D(s,G,Y,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",c,s.length,+Date.now()-u)}return r&&console.timeEnd(\"total time\"),this},F.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(V(r),q(a),V(i),q(n));u<f.length;u+=1){var h=l.points[f[u]];c.push(h.numPoints?j(h):this.points[h.index])}return c},F.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=i.points[c[l]];u.parentId===t&&s.push(u.numPoints?j(u):this.points[u.index])}if(0===s.length)throw new Error(n);return s},F.prototype.getLeaves=function(t,e,r){var n=[];return this._appendLeaves(n,t,e=e||10,r=r||0,0),n},F.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.radius/a.extent,s=(r-o)/i,l=(r+1+o)/i,c={features:[]};return this._addTileFeatures(n.range((e-o)/i,s,(e+1+o)/i,l),n.points,e,r,i,c),0===e&&this._addTileFeatures(n.range(1-o/i,s,1,l),n.points,i,r,i,c),e===i-1&&this._addTileFeatures(n.range(0,s,o/i,l),n.points,-1,r,i,c),c.features.length?c:null},F.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},F.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},F.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?U(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)}},F.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},F.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.reduce,a=n.radius/(n.extent*Math.pow(2,e)),o=0;o<t.length;o++){var s=t[o];if(!(s.zoom<=e)){s.zoom=e;for(var l=this.trees[e+1],c=l.within(s.x,s.y,a),u=s.numPoints||1,f=s.x*u,h=s.y*u,p=i&&u>1?this._map(s,!0):null,d=(o<<5)+(e+1)+this.points.length,g=0,m=c;g<m.length;g+=1){var v=l.points[m[g]];if(!(v.zoom<=e)){v.zoom=e;var y=v.numPoints||1;f+=v.x*y,h+=v.y*y,u+=y,v.parentId=d,i&&(p||(p=this._map(s,!0)),i(p,this._map(v)))}}1===u?r.push(s):(s.parentId=d,r.push(B(f/u,h/u,d,u,p)))}}return r},F.prototype._getOriginId=function(t){return t-this.points.length>>5},F.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},F.prototype._map=function(t,e){if(t.numPoints)return e?H({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?H({},n):n},vt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},vt.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=yt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time(\"creation\"),h=this.tiles[f]=dt(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 g,m,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;g=m=v=y=null,x=rt(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=rt(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(g=rt(x,u,n-_,n+T,1,h.minY,h.maxY,l),m=rt(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=rt(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=rt(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd(\"clipping\"),s.push(g||[],e+1,2*r,2*n),s.push(m||[],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)}}},vt.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=yt(t,e=(e%o+o)%o,r);if(this.tiles[s])return ht(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[yt(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]?ht(this.tiles[s],i):null):null};var bt=function(e){function r(t,r,n,i){e.call(this,t,r,n,xt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.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.\"));!function t(e,r){var n,i=e&&e.type;if(\"FeatureCollection\"===i)for(n=0;n<e.features.length;n++)t(e.features[n],r);else if(\"GeometryCollection\"===i)for(n=0;n<e.geometries.length;n++)t(e.geometries[n],r);else if(\"Feature\"===i)t(e.geometry,r);else if(\"Polygon\"===i)f(e.coordinates,r);else if(\"MultiPolygon\"===i)for(n=0;n<e.coordinates.length;n++)f(e.coordinates[n],r);return e}(o,!0);try{e._geoJSONIndex=n.cluster?new F(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=t.createExpression(h[1]),g=t.createExpression(\"string\"==typeof p?[p,[\"accumulated\"],[\"get\",f]]:p);i[f]=d.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 vt(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;return n&&n[t.uid]?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 object.\"))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l),_t=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:bt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name \"'+t+'\" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error(\"RTL text plugin already registered.\");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return _t.prototype.setReferrer=function(t,e){this.referrer=e},_t.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 i)i[a].availableImages=e}r()},_t.prototype.setLayers=function(t,e,r){this.getLayerIndex(t).replace(e),r()},_t.prototype.updateLayers=function(t,e,r){this.getLayerIndex(t).update(e.layers,e.removedIds),r()},_t.prototype.loadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).loadTile(e,r)},_t.prototype.loadDEMTile=function(t,e,r){this.getDEMWorkerSource(t,e.source).loadTile(e,r)},_t.prototype.reloadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).reloadTile(e,r)},_t.prototype.abortTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).abortTile(e,r)},_t.prototype.removeTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).removeTile(e,r)},_t.prototype.removeDEMTile=function(t,e){this.getDEMWorkerSource(t,e.source).removeTile(e)},_t.prototype.removeSource=function(t,e,r){if(this.workerSources[t]&&this.workerSources[t][e.type]&&this.workerSources[t][e.type][e.source]){var n=this.workerSources[t][e.type][e.source];delete this.workerSources[t][e.type][e.source],void 0!==n.removeSource?n.removeSource(e,r):r()}},_t.prototype.loadWorkerSource=function(t,e,r){try{this.self.importScripts(e.url),r()}catch(t){r(t.toString())}},_t.prototype.syncRTLPluginState=function(e,r,n){try{t.plugin.setState(r);var i=t.plugin.getPluginURL();if(t.plugin.isLoaded()&&!t.plugin.isParsed()&&null!=i){this.self.importScripts(i);var a=t.plugin.isParsed();n(a?void 0:new Error(\"RTL Text Plugin failed to import scripts from \"+i),a)}}catch(t){n(t.toString())}},_t.prototype.getAvailableImages=function(t){var e=this.availableImages[t];return e||(e=[]),e},_t.prototype.getLayerIndex=function(t){var e=this.layerIndexes[t];return e||(e=this.layerIndexes[t]=new n),e},_t.prototype.getWorkerSource=function(t,e,r){var n=this;return this.workerSources[t]||(this.workerSources[t]={}),this.workerSources[t][e]||(this.workerSources[t][e]={}),this.workerSources[t][e][r]||(this.workerSources[t][e][r]=new this.workerSourceTypes[e]({send:function(e,r,i){n.actor.send(e,r,i,t)}},this.getLayerIndex(t),this.getAvailableImages(t))),this.workerSources[t][e][r]},_t.prototype.getDEMWorkerSource=function(t,e){return this.demWorkerSources[t]||(this.demWorkerSources[t]={}),this.demWorkerSources[t][e]||(this.demWorkerSources[t][e]=new u),this.demWorkerSources[t][e]},_t.prototype.enforceCacheSizeLimit=function(e,r){t.enforceCacheSizeLimit(r)},\"undefined\"!=typeof WorkerGlobalScope&&void 0!==t.window&&t.window instanceof WorkerGlobalScope&&(t.window.worker=new _t(t.window)),_t})),n(0,(function(t){var e=t.createCommonjsModule((function(t){function e(t){return!r(t)}function r(t){return\"undefined\"==typeof window||\"undefined\"==typeof document?\"not a browser\":Array.prototype&&Array.prototype.every&&Array.prototype.filter&&Array.prototype.forEach&&Array.prototype.indexOf&&Array.prototype.lastIndexOf&&Array.prototype.map&&Array.prototype.some&&Array.prototype.reduce&&Array.prototype.reduceRight&&Array.isArray?Function.prototype&&Function.prototype.bind?Object.keys&&Object.create&&Object.getPrototypeOf&&Object.getOwnPropertyNames&&Object.isSealed&&Object.isFrozen&&Object.isExtensible&&Object.getOwnPropertyDescriptor&&Object.defineProperty&&Object.defineProperties&&Object.seal&&Object.freeze&&Object.preventExtensions?\"JSON\"in window&&\"parse\"in JSON&&\"stringify\"in JSON?function(){if(!(\"Worker\"in window&&\"Blob\"in window&&\"URL\"in window))return!1;var t,e,r=new Blob([\"\"],{type:\"text/javascript\"}),n=URL.createObjectURL(r);try{e=new Worker(n),t=!0}catch(e){t=!1}return e&&e.terminate(),URL.revokeObjectURL(n),t}()?\"Uint8ClampedArray\"in window?ArrayBuffer.isView?function(){var t=document.createElement(\"canvas\");t.width=t.height=1;var e=t.getContext(\"2d\");if(!e)return!1;var r=e.getImageData(0,0,1,1);return r&&r.width===t.width}()?(void 0===n[r=t&&t.failIfMajorPerformanceCaveat]&&(n[r]=function(t){var r=function(t){var r=document.createElement(\"canvas\"),n=Object.create(e.webGLContextAttributes);return n.failIfMajorPerformanceCaveat=t,r.probablySupportsContext?r.probablySupportsContext(\"webgl\",n)||r.probablySupportsContext(\"experimental-webgl\",n):r.supportsContext?r.supportsContext(\"webgl\",n)||r.supportsContext(\"experimental-webgl\",n):r.getContext(\"webgl\",n)||r.getContext(\"experimental-webgl\",n)}(t);if(!r)return!1;var n=r.createShader(r.VERTEX_SHADER);return!(!n||r.isContextLost())&&(r.shaderSource(n,\"void main() {}\"),r.compileShader(n),!0===r.getShaderParameter(n,r.COMPILE_STATUS))}(r)),n[r]?void 0:\"insufficient WebGL support\"):\"insufficient Canvas/getImageData support\":\"insufficient ArrayBuffer support\":\"insufficient Uint8ClampedArray support\":\"insufficient worker support\":\"insufficient JSON support\":\"insufficient Object support\":\"insufficient Function support\":\"insufficent Array support\";var r}t.exports?t.exports=e:window&&(window.mapboxgl=window.mapboxgl||{},window.mapboxgl.supported=e,window.mapboxgl.notSupportedReason=r);var n={};e.webGLContextAttributes={antialias:!1,alpha:!0,stencil:!0,depth:!0}})),r={create:function(e,r,n){var i=t.window.document.createElement(e);return void 0!==r&&(i.className=r),n&&n.appendChild(i),i},createNS:function(e,r){return t.window.document.createElementNS(e,r)}},n=t.window.document.documentElement.style;function i(t){if(!n)return t[0];for(var e=0;e<t.length;e++)if(t[e]in n)return t[e];return t[0]}var a,o=i([\"userSelect\",\"MozUserSelect\",\"WebkitUserSelect\",\"msUserSelect\"]);r.disableDrag=function(){n&&o&&(a=n[o],n[o]=\"none\")},r.enableDrag=function(){n&&o&&(n[o]=a)};var s=i([\"transform\",\"WebkitTransform\"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var c=Object.defineProperty({},\"passive\",{get:function(){l=!0}});t.window.addEventListener(\"test\",c,c),t.window.removeEventListener(\"test\",c,c)}catch(t){l=!1}r.addEventListener=function(t,e,r,n){void 0===n&&(n={}),t.addEventListener(e,r,\"passive\"in n&&l?n:n.capture)},r.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),t.removeEventListener(e,r,\"passive\"in n&&l?n:n.capture)};var u=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener(\"click\",u,!0)};function f(t){var e=t.userImage;return!!(e&&e.render&&e.render())&&(t.data.replace(new Uint8Array(e.data.buffer)),!0)}r.suppressClick=function(){t.window.addEventListener(\"click\",u,!0),t.window.setTimeout((function(){t.window.removeEventListener(\"click\",u,!0)}),0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect();return new 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)).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];this._notify(n.ids,n.callback)}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){e.version=this.images[t].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)this.images[i[n]]||(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. 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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),p=m,d=m,g=1e20;function m(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]=-g,n[1]=+g;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]=+g}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]]}}m.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?g:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?g: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++)r[n]=Math.max(0,Math.min(255,Math.round(255-255*((this.gridOuter[n]-this.gridInner[n])/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)i.push({stack:a,id:s[o]});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)).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++){var c=t[l];n.push({left:i=s*r,right:a=(s+=c)*r,isDash:o=!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.width*(this.nextRow+r+i),o=0,s=t[o],l=0;l<this.width;l++){l/s.right>1&&(s=t[++o]);var c=Math.abs(l-s.left),u=Math.abs(l-s.right),f=Math.min(c,u),h=void 0,p=i/r*(n+1);if(s.isDash){var d=n-Math.abs(p);h=Math.sqrt(f*f+d*d)}else h=n-Math.sqrt(f*f+p*p);this.data[a+l]=Math.max(0,Math.min(255,h+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);this.data[o+c]=Math.max(0,Math.min(255,(l.isDash?h:-h)+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=new e.Actor(i[a],n,this.id);o.name=\"Worker \"+a,this.actors.push(o)}};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){t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n=n||function(){})},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)).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)).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),C=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)).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),L=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)).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,e.request=this.actor.send(i,{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},(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),I=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),P=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)).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=Math.max(i-r,a-n),u=Math.max(0,Math.floor(-Math.log(c)/Math.LN2)),f=Math.pow(2,u);return new t.CanonicalTileID(u,Math.floor((r+i)/2*f),Math.floor((n+a)/2*f))}(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,I.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),z=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)).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)this.urls.push(this.map._requestManager.transformRequest(i[n],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,I.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}(P),O=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)).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,I.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 r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(P),D={vector:S,raster:E,\"raster-dem\":C,geojson:L,video:z,image:P,canvas:O};function R(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function F(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&\"fill-extrusion\"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&\"fill-extrusion\"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(B);for(var c=[],u=0,f=l;u<f.length;u+=1){var h=f[u];c.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,R(t.transform,h.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var c in o)for(var u=o[c],f=l[c]=l[c]||{},h=e[c]=e[c]||[],p=0,d=u;p<d.length;p+=1){var g=d[p];f[g.featureIndex]||(f[g.featureIndex]=!0,h.push(g))}}return e}(c);for(var d in p)p[d].forEach((function(e){var r=e.feature,n=t.getFeatureState(r.layer[\"source-layer\"],r.id);r.source=r.layer.source,r.layer[\"source-layer\"]&&(r.sourceLayer=r.layer[\"source-layer\"]),r.state=n}));return p}function B(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}var N=function(t,e){this.max=t,this.onRemove=e,this.reset()};N.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},N.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout((function(){n.remove(t,a)}),r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},N.prototype.has=function(t){return t.wrapped().key in this.data},N.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},N.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},N.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},N.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},N.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},N.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},N.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n<i.length;n+=1){var a=i[n];t(a.value)||e.push(a)}for(var o=0,s=e;o<s.length;o+=1){var l=s[o];this.remove(l.value.tileID,l)}};var j=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};j.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},j.prototype.updateData=function(t){var e=this.context.gl;this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},j.prototype.destroy=function(){this.buffer&&(this.context.gl.deleteBuffer(this.buffer),delete this.buffer)};var U={Int8:\"BYTE\",Uint8:\"UNSIGNED_BYTE\",Int16:\"SHORT\",Uint16:\"UNSIGNED_SHORT\",Int32:\"INT\",Uint32:\"UNSIGNED_INT\",Float32:\"FLOAT\"},V=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};V.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},V.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},V.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=e.attributes[this.attributes[r].name];void 0!==n&&t.enableVertexAttribArray(n)}},V.prototype.setVertexAttribPointers=function(t,e,r){for(var n=0;n<this.attributes.length;n++){var i=this.attributes[n],a=e.attributes[i.name];void 0!==a&&t.vertexAttribPointer(a,i.components,t[U[i.type]],!1,this.itemSize,i.offset+this.itemSize*(r||0))}},V.prototype.destroy=function(){this.buffer&&(this.context.gl.deleteBuffer(this.buffer),delete this.buffer)};var q=function(t){this.gl=t.gl,this.default=this.getDefault(),this.current=this.default,this.dirty=!1};q.prototype.get=function(){return this.current},q.prototype.set=function(t){},q.prototype.getDefault=function(){return this.default},q.prototype.setDefault=function(){this.set(this.default)};var H=function(e){function r(){e.apply(this,arguments)}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.getDefault=function(){return t.Color.transparent},r.prototype.set=function(t){var 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e=this.gl;e.bindFramebuffer(e.FRAMEBUFFER,t),this.current=t,this.dirty=!1}},e}(q),pt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindRenderbuffer(e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(q),dt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindTexture(e.TEXTURE_2D,t),this.current=t,this.dirty=!1}},e}(q),gt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var 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e=this.gl;e.pixelStorei(e.UNPACK_ALIGNMENT,t),this.current=t,this.dirty=!1}},e}(q),xt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_PREMULTIPLY_ALPHA_WEBGL,t),this.current=t,this.dirty=!1}},e}(q),bt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_FLIP_Y_WEBGL,t),this.current=t,this.dirty=!1}},e}(q),_t=function(t){function e(e,r){t.call(this,e),this.context=e,this.parent=r}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return null},e}(q),wt=function(t){function 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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 Ct=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 gt(this),this.bindElementBuffer=new mt(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\")};Ct.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()},Ct.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},Ct.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Ct.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Ct.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},Ct.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Ct.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)},Ct.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))},Ct.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)},Ct.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)},Ct.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)},Ct.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Lt=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)).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(It).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(It).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,r=Math.ceil(t.height/this._source.tileSize)+1,n=Math.floor(e*r*5),i=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,n):n;this._cache.setMaxSize(i)},r.prototype.handleWrapJump=function(t){var e=Math.round((t-(void 0===this._prevLng?t:this._prevLng))/360);if(this._prevLng=t,e){var r={};for(var n in this._tiles){var i=this._tiles[n];i.tileID=i.tileID.unwrapTo(i.tileID.wrap+e),r[i.tileID.key]=i}for(var a in this._tiles=r,this._timers)clearTimeout(this._timers[a]),delete this._timers[a];for(var o in this._tiles)this._setTileReloadTimer(o,this._tiles[o])}},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(Pt(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],g=this._tiles[p];if(g&&!(g.fadeEndTime&&g.fadeEndTime<=t.browser.now())){var m=this.findLoadedParent(d,o);m&&(this._addTile(m.tileID),c[m.tileID.key]=m.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],g=this._tiles[d.key];if(!g.hasData()){if(e+1>this._source.maxzoom){var m=d.children(this._source.maxzoom)[0],v=this.getTile(m);if(v&&v.hasData()){n[m.key]=m;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=g.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(g=this.getTile(_))&&x&&(g=this._addTile(_)),g&&(n[_.key]=_,x=g.wasRequested(),g.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)this._loadedParentTiles[o[a]]=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,g=0,m=c;g<m.length;g+=1){var v=m[g];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,g=Math.pow(2,o.zoom-n.tileID.overscaledZ),m=r*n.queryPadding*t.EXTENT/n.tileSize/g,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-m<t.EXTENT&&v[0].y-m<t.EXTENT&&v[1].x+m>=0&&v[1].y+m>=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:g})}}},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(Pt(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){this._state.updateState(t=t||\"_geojsonTileLayer\",e,r)},r.prototype.removeFeatureState=function(t,e,r){this._state.removeFeatureState(t=t||\"_geojsonTileLayer\",e,r)},r.prototype.getFeatureState=function(t,e){return this._state.getState(t=t||\"_geojsonTileLayer\",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 It(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 Pt(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function zt(){return new t.window.Worker(Yi.workerUrl)}Lt.maxOverzooming=10,Lt.maxUnderzooming=3;var Ot=\"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 zt);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[Ot]},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}var Zt=function(t,e){this.reset(t,e)};Zt.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},Zt.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 Jt=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 Kt(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 Qt(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 $t(e,r){var n=[e.x,e.y,0,1];ue(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function te(t,e){return.5+t/e*.5}function ee(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function re(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,g=n.transform.width/n.transform.height,m=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!m)ce(y.numGlyphs,h);else{m=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),ee(x,f)){var b=te(n.transform.cameraToCenterDistance,x[3]),_=t.evaluateSizeForFeature(c,u,y),w=s?_/b:_*b,T=new t.Point(y.anchorX,y.anchorY),k=$t(T,a).point,M={},A=ae(y,w,!1,l,r,a,o,e.glyphOffsetArray,p,h,k,T,M,g);m=A.useVertical,(A.notEnoughRoom||m||A.needsFlipping&&ae(y,w,!0,l,r,a,o,e.glyphOffsetArray,p,h,k,T,M,g).notEnoughRoom)&&ce(y.numGlyphs,h)}else ce(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ne(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),g=e.getoffsetX(f-1),m=se(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!m)return null;var v=se(t*g,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:m,last:v}:null}function ie(e,r,n,i){return e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i?{useVertical:!0}:(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function ae(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var g,m=r/24,v=e.lineOffsetX*m,y=e.lineOffsetY*m;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ne(m,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=$t(w.first.point,s).point,k=$t(w.last.point,s).point;if(i&&!n){var M=ie(e.writingMode,T,k,d);if(M)return M}g=[w.first];for(var A=e.glyphStartIndex+1;A<x-1;A++)g.push(se(m*l.getoffsetX(A),v,y,n,f,h,e.segment,b,_,c,o,p));g.push(w.last)}else{if(i&&!n){var S=$t(h,a).point,E=e.lineStartIndex+e.segment+1,C=new t.Point(c.getx(E),c.gety(E)),L=$t(C,a),I=L.signedDistanceFromCamera>0?L.point:oe(h,C,S,1,a),P=ie(e.writingMode,S,I,d);if(P)return P}var z=se(m*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!z)return{notEnoughRoom:!0};g=[z]}for(var O=0,D=g;O<D.length;O+=1){var R=D[O];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function oe(t,e,r,n,i){var a=$t(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function se(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,g=0;i&&(d*=-1,g=Math.PI),d<0&&(g+=Math.PI);for(var m=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((m+=d)<l||m>=c)return null;if(y=v,w.push(v),void 0===(v=h[m])){var T=new t.Point(u.getx(m),u.gety(m)),k=$t(T,f);if(k.signedDistanceFromCamera>0)v=h[m]=k.point;else{var M=m-d;v=oe(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 C=g+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:C,path:w}}Jt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Jt.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)},Jt.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)},Jt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Jt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Jt.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}return this._forEachCell(t,e,r,n,this._queryCell,o,{hitTest:i,seenUids:{box:{},circle:{}}},a),i?o.length>0:o},Jt.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=[];return this._forEachCell(a,s,o,l,this._queryCellCircle,c,{hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}},i),n?c.length>0:c},Jt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Jt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Jt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Jt.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 g=this.circleCells[i];if(null!==g)for(var m=this.circles,v=0,y=g;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(m[b],m[b+1],m[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=m[b],w=m[b+1],T=m[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Jt.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 g=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[g+0],f[g+1],f[g+2],f[g+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var m=this.circleCells[i];if(null!==m)for(var v=this.circles,y=0,x=m;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}}},Jt.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++)if(i.call(this,t,e,r,n,this.xCellCount*p+h,a,o,s))return},Jt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Jt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Jt.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},Jt.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 le=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ce(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(le,3*n)}}function ue(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 fe=function(t,e,r){void 0===e&&(e=new Jt(t.width+200,t.height+200,25)),void 0===r&&(r=new Jt(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 he(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}fe.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)}},fe.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],g=new t.Point(r.anchorX,r.anchorY),m=$t(g,o),v=te(this.transform.cameraToCenterDistance,m.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=$t(g,s).point,b=ne(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,g,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 Zt,E=b.first,C=b.last,L=[],I=E.path.length-1;I>=1;I--)L.push(E.path[I]);for(var P=1;P<C.path.length;P++)L.push(C.path[P]);var z=2.5*k;if(l){var O=L.map((function(t){return $t(t,l)}));L=O.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:O.map((function(t){return t.point}))}var D=[];if(L.length>0){for(var R=L[0].clone(),F=L[0].clone(),B=1;B<L.length;B++)R.x=Math.min(R.x,L[B].x),R.y=Math.min(R.y,L[B].y),F.x=Math.max(F.x,L[B].x),F.y=Math.max(F.y,L[B].y);D=R.x>=M.x&&F.x<=A.x&&R.y>=M.y&&F.y<=A.y?[L]:F.x<M.x||R.x>A.x||F.y<M.y||R.y>A.y?[]:t.clipLine([L],M.x,M.y,A.x,A.y)}for(var N=0,j=D;N<j.length;N+=1){var U;S.reset(j[N],.25*k),U=S.length<=.5*k?1:Math.ceil(S.paddedLength/z)+1;for(var V=0;V<U;V++){var q=V/Math.max(U-1,1),H=S.lerp(q),G=H.x+100,Y=H.y+100;d.push(G,Y,k,0);var W=G-k,X=Y-k,Z=G+k,J=Y+k;if(T=T&&this.isOffscreen(W,X,Z,J),w=w||this.isInsideGrid(W,X,Z,J),!e&&this.grid.hitTestCircle(G,Y,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},fe.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 g=d[p],m=g.key;if(void 0===f[m.bucketInstanceId]&&(f[m.bucketInstanceId]={}),!f[m.bucketInstanceId][m.featureIndex]){var v=[new t.Point(g.x1,g.y1),new t.Point(g.x2,g.y1),new t.Point(g.x2,g.y2),new t.Point(g.x1,g.y2)];t.polygonIntersectsPolygon(r,v)&&(f[m.bucketInstanceId][m.featureIndex]=!0,void 0===h[m.bucketInstanceId]&&(h[m.bucketInstanceId]=[]),h[m.bucketInstanceId].push(m.featureIndex))}}return h},fe.prototype.insertCollisionBox=function(t,e,r,n,i){(e?this.ignoredGrid:this.grid).insert({bucketInstanceId:r,featureIndex:n,collisionGroupID:i},t[0],t[1],t[2],t[3])},fe.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])},fe.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return ue(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}},fe.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},fe.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},fe.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var pe=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};pe.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var de=function(t,e,r,n,i){this.text=new pe(t?t.text:null,e,r,i),this.icon=new pe(t?t.icon:null,e,n,i)};de.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},me=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ve=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},ye=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function xe(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 be(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}}ye.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 _e=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new fe(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new ye(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function we(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)}_e.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=he(n,1,this.transform.zoom),g=Kt(f,h,p,this.transform,d),m=null;if(h){var v=Qt(f,h,p,this.transform,d);m=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ve(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:g,labelToScreenMatrix:m,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];e.push({sortKey:_.sortKey,symbolInstanceStart:_.symbolInstanceStart,symbolInstanceEnd:_.symbolInstanceEnd,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},_e.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var g,m=[f.textOffset0,f.textOffset1],v=xe(t,r,n,m,i),y=this.collisionIndex.placeCollisionBox(be(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(!d||0!==this.collisionIndex.placeCollisionBox(be(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return y.box.length>0?(this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(g=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:m,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:g},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}):void 0},_e.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,g=a.collisionGroup,m=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()||m);!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,C={box:null,offscreen:null},L={box:null,offscreen:null},I=null,P=null,z=0,O=0,D=0;a.textFeatureIndex?z=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(z=e.featureIndex),a.verticalTextFeatureIndex&&(O=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,i.markUsedOrientation(o,n=a,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&&(s[i]===t.WritingMode.vertical?(C=n(),L=C):C=r(),!(C&&C.box&&C.box.length));i+=1);else C=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 m=i.attemptAnchorPlacement(N[d%N.length],t,a,s,c,b,_,f,l,g,d>=N.length,e,o,n,u);if(m&&(h=m.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=m.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&!(C&&C.box&&C.box.length)&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),C&&(T=C.box,S=C.offscreen);var V=F(C&&C.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,g.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(C&&C.box&&C.box.length)}}if(T=(p=C)&&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\");I=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,g.predicate,e.collisionCircleDiameter,W),T=y||I.circles.length>0&&!I.collisionDetected,S=S&&I.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var X=function(t){var e=w&&E?be(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,g.predicate)};A=L&&L.box&&L.box.length&&a.verticalIconBox?(P=X(a.verticalIconBox)).box.length>0:(P=X(a.iconBox)).box.length>0,S=S&&P.offscreen}var Z=m||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,J=v||0===e.numIconVertices;if(Z||J?J?Z||(A=A&&T):T=A&&T:A=T=A&&T,T&&p&&p.box&&i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,L&&L.box&&O?O:z,g.ID),A&&P&&i.collisionIndex.insertCollisionBox(P.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,D,g.ID),I&&(T&&i.collisionIndex.insertCollisionCircles(I.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,g.ID),n)){var K=o.bucketInstanceId,Q=i.collisionCircleArrays[K];void 0===Q&&(Q=i.collisionCircleArrays[K]=new me);for(var $=0;$<I.circles.length;$+=4)Q.circles.push(I.circles[$+0]),Q.circles.push(I.circles[$+1]),Q.circles.push(I.circles[$+2]),Q.circles.push(I.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 C=S[E];A(o.symbolInstances.get(C),o.collisionArrays[C])}else for(var L=e.symbolInstanceStart;L<e.symbolInstanceEnd;L++)A(o.symbolInstances.get(L),o.collisionArrays[L]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var I=this.collisionCircleArrays[o.bucketInstanceId];t.invert(I.invProjMatrix,l),I.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},_e.prototype.markUsedJustification=function(e,r,n,i){var a;a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:{left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex}[t.getAnchorJustification(r)];for(var o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];o<s.length;o+=1){var l=s[o];l>=0&&(e.text.placedSymbolArray.get(l).crossTileID=a>=0&&l!==a?0:n.crossTileID)}},_e.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)e.text.placedSymbolArray.get(s[o]).placedOrientation=i;n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},_e.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 de(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new de(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 de(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)},_e.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)}},_e.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 de(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 de(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)},g=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,g=a.crossTileID,m=i.opacities[g];r[g]?m=o:m||(i.opacities[g]=m=p),r[g]=!0;var v=a.numIconVertices>0,y=i.placedOrientations[a.crossTileID],x=y===t.WritingMode.vertical,b=y===t.WritingMode.horizontal||y===t.WritingMode.horizontalOnly;if(s>0||l>0){var _=Le(m.text);d(e.text,s,x?Ie:_),d(e.text,l,b?Ie:_);var w=m.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=w||x?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=w||b?1:0);var T=i.variableOffsets[a.crossTileID];T&&i.markUsedJustification(e,T.anchor,a,y);var k=i.placedOrientations[a.crossTileID];k&&(i.markUsedJustification(e,\"left\",a,k),i.markUsedOrientation(e,k,a))}if(v){var M=Le(m.icon),A=!(h&&a.verticalPlacedIconSymbolIndex&&x);a.placedIconSymbolIndex>=0&&(d(e.icon,a.numIconVertices,A?M:Ie),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=m.icon.isHidden()),a.verticalPlacedIconSymbolIndex>=0&&(d(e.icon,a.numVerticalIconVertices,A?Ie:M),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=m.icon.isHidden())}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var S=e.collisionArrays[n];if(S){var E=new t.Point(0,0);if(S.textBox||S.verticalTextBox){var C=!0;if(c){var L=i.variableOffsets[g];L?(E=xe(L.anchor,L.width,L.height,L.textOffset,L.textBoxScale),u&&E._rotate(f?i.transform.angle:-i.transform.angle)):C=!1}S.textBox&&we(e.textCollisionBox.collisionVertexArray,m.text.placed,!C||x,E.x,E.y),S.verticalTextBox&&we(e.textCollisionBox.collisionVertexArray,m.text.placed,!C||b,E.x,E.y)}var I=Boolean(!b&&S.verticalIconBox);S.iconBox&&we(e.iconCollisionBox.collisionVertexArray,m.icon.placed,I,h?E.x:0,h?E.y:0),S.verticalIconBox&&we(e.iconCollisionBox.collisionVertexArray,m.icon.placed,!I,h?E.x:0,h?E.y:0)}}},m=0;m<e.symbolInstances.length;m++)g(m);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]}},_e.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},_e.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},_e.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},_e.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},_e.prototype.setStale=function(){this.stale=!0};var Te=Math.pow(2,25),ke=Math.pow(2,24),Me=Math.pow(2,17),Ae=Math.pow(2,16),Se=Math.pow(2,9),Ee=Math.pow(2,8),Ce=Math.pow(2,1);function Le(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*Te+e*ke+r*Me+e*Ae+r*Se+e*Ee+r*Ce+e}var Ie=0,Pe=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=[]};Pe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;)if(e.getBucketParts(a,n,t[this._currentTileIndex],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;)if(e.placeLayerBucketPart(a[this._currentPartIndex],this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0;return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new _e(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 Pe(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 Oe=512/t.EXTENT/2,De=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)})}};De.prototype.getScaledCoordinates=function(e,r){var n=Oe/Math.pow(2,r.canonical.z-this.tileID.canonical.z);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*n),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*n)}},De.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 Re=function(){this.maxCrossTileID=0};Re.prototype.generate=function(){return++this.maxCrossTileID};var Fe=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Fe.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},Fe.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 De(t,e.symbolInstances,e.bucketInstanceId),!0},Fe.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1)delete this.usedCrossTileIDs[t][i[n].crossTileID]},Fe.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 Be=function(){this.layerIndexes={},this.crossTileIDs=new Re,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Be.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Fe);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},Be.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 Ne=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return\"source.canvas\"!==t.identifier})))},je=t.pick(Ut,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Ue=t.pick(Ut,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),Ve=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i,a=t.styleSpec.$root[n];a.required&&null!=(i=\"version\"===n?r:\"array\"===a.type?[]:{})&&(e[n]=i)}return e}(),qe=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 Be,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){o.dispatcher.broadcast(\"syncRTLPluginState\",{pluginStatus:e.pluginStatus,pluginURL:e.pluginURL},(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)).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(Ve,!1)},r.prototype._load=function(e,r){if(!r||!Ne(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,g=l.stretchX,m=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:g,stretchY:m,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=this._layers[n[r]];\"custom\"!==i.type&&e.push(i.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=this._layers[u[c]];f.recalculate(e,this._availableImages),!f.isHidden(e.zoom)&&f.source&&(this.sourceCaches[f.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(),Ne(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=function(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,{}),g=f.slice(),m=Object.create(null);for(i=0,a=0;i<f.length;i++)d.hasOwnProperty(o=f[i])?a++:(n.push({command:Ut.removeLayer,args:[o]}),g.splice(g.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)g[g.length-1-i]!==(o=h[h.length-1-i])&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),g.splice(g.lastIndexOf(o,g.length-a),1)):a++,n.push({command:Ut.addLayer,args:[d[o],c=g[g.length-i]]}),g.splice(g.length-i,0,o),m[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!m[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=g[g.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}(this.serialize(),e).filter((function(t){return!(t.command in Ue)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in je)}));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 Lt(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(Ne(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)i.push(f[u])}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var h=[],p=this._order.length-1;p>=0;p--){var d=this._order[p];if(r(d))for(var g=i.length-1;g>=0;g--){var m=i[g].feature;if(n[m.layer.id]<p)break;h.push(m),i.pop()}else for(var v=0,y=t;v<y.length;v+=1){var x=y[v][d];if(x)for(var b=0,_=x;b<_.length;b+=1)h.push(_[b].feature)}}return h},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)c.push(o[f[u]]);c.sort(B);for(var h=function(){var r=d[p],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)o.push(f[u])}},p=0,d=c;p<d.length;p+=1)h();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=r[t[e].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=a}))};for(var m in s)g(m);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)&&Ne(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=this._layers[u[c]];if(\"symbol\"===f.type){if(!l[f.source]){var h=this.sourceCaches[f.source];l[f.source]=h.getRenderableIds(!0).map((function(t){return h.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var p=this.crossTileSymbolIndex.addLayer(f,l[f.source],e.center.lng);o=o||p}}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 d=0,g=this._order;d<g.length;d+=1){var m=this._layers[g[d]];\"symbol\"===m.type&&this.placement.updateLayerOpacities(m,l[m.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);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var He=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Ge=vr(\"#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;}\"),Ye=vr(\"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);}\"),We=vr(\"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);}\"),Xe=vr(\"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);}\"),Ze=vr(\"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);}\"),Je=vr(\"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;}\"),Ke=vr(\"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;}\"),Qe=vr(\"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;}\"),$e=vr(\"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);}\"),tr=vr(\"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);}\"),er=vr(\"#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);}\"),rr=vr(\"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;}\"),nr=vr(\"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;}\"),ir=vr(\"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);}\"),ar=vr(\"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;}\"),or=vr(\"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;}\"),sr=vr(\"#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;}\"),lr=vr(\"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;}\"),cr=vr(\"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);}\"),ur=vr(\"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);}\"),fr=vr(\"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;}\"),hr=vr(\"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);}\"),pr=vr(\"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;}\"),dr=vr(\"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=vr(\"#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);}\"),mr=vr(\"#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 vr(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 yr=Object.freeze({__proto__:null,prelude:Ge,background:Ye,backgroundPattern:We,circle:Xe,clippingMask:Ze,heatmap:Je,heatmapTexture:Ke,collisionBox:Qe,collisionCircle:$e,debug:tr,fill:er,fillOutline:rr,fillOutlinePattern:nr,fillPattern:ir,fillExtrusion:ar,fillExtrusionPattern:or,hillshadePrepare:sr,hillshade:lr,line:cr,lineGradient:ur,linePattern:fr,lineSDF:hr,raster:pr,symbolIcon:dr,symbolSDF:gr,symbolTextAndIcon:mr}),xr=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};xr.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);t.extVertexArrayObject&&this.vao&&this.boundProgram===e&&this.boundLayoutVertexBuffer===r&&!l&&this.boundIndexBuffer===i&&this.boundVertexOffset===a&&this.boundDynamicVertexBuffer===o&&this.boundDynamicVertexBuffer2===s?(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind()):this.freshBind(e,r,n,i,a,o,s)},xr.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,g=r;d<g.length;d+=1){var m=g[d];m.bind(),m.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},xr.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var br=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(Ge.fragmentSource,e.fragmentSource).join(\"\\n\"),l=o.concat(Ge.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 g=a.getActiveAttrib(this.program,d);g&&(this.attributes[g.name]=a.getAttribLocation(this.program,g.name))}for(var m=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<m;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 _r(t,e,r){var n=1/he(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]}}br.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g){var m,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=(m={},m[v.LINES]=2,m[v.TRIANGLES]=3,m[v.LINE_STRIP]=1,m)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new xr)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,g),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var wr=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}},Tr=function(e,r,n,i,a,o,s){return t.extend(wr(e,r,n,i),_r(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},kr=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(kr(e),_r(n,r,i))},Ar=function(t,e){return{u_matrix:t,u_world:e}},Sr=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Er=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=he(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=he(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}},Lr=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}},Pr=function(t){return{u_matrix:t}},zr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:he(e,1,r),u_intensity:n}},Or=function(e,r,n){var i=e.transform;return{u_matrix:Nr(e,r,n),u_ratio:1/he(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Dr=function(e,r,n){return t.extend(Or(e,r,n),{u_image:0})},Rr=function(e,r,n,i){var a=e.transform,o=Br(r,a);return{u_matrix:Nr(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/he(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]]}},Fr=function(e,r,n,i,a){var o=e.lineAtlas,s=Br(r,e.transform),l=\"round\"===n.layout.get(\"line-cap\"),c=o.getDash(i.from,l),u=o.getDash(i.to,l),f=c.width*a.fromScale,h=u.width*a.toScale;return t.extend(Or(e,r,n),{u_patternscale_a:[s/f,-c.height/2],u_patternscale_b:[s/h,-u.height/2],u_sdfgamma:o.width/(256*Math.min(f,h)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:c.y,u_tex_y_b:u.y,u_mix:a.t})};function Br(t,e){return 1/he(t,1,e.tileZoom)}function Nr(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var jr=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:Ur(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Ur(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 Vr,qr=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}},Hr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(qr(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})},Gr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Hr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Yr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Wr=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/he(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})},Xr={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 Zr(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 g=i[d],m=r.getTile(g),v=m.getBucket(n);if(v){var y=g.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(g.posMatrix,m,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,m),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],C=0;C<E.circleArray.length/4;C++){var L=4*C,I=E.circleArray[L+0],P=E.circleArray[L+1],z=E.circleArray[L+2],O=E.circleArray[L+3];k.emplace(M++,I,P,z,O,0),k.emplace(M++,I,P,z,O,1),k.emplace(M++,I,P,z,O,2),k.emplace(M++,I,P,z,O,3)}(!Vr||Vr.length<2*h)&&(Vr=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(Vr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Lr(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 Jr=t.identity(new Float32Array(16));function Kr(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 Qr(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,g={};p.clear();for(var m=0;m<h.length;m++){var v=h.get(m),y=v.hidden||!v.crossTileID||e.allowVerticalPlacement&&!v.placedOrientation?null:i[v.crossTileID];if(y){var x=new t.Point(v.anchorX,v.anchorY),b=$t(x,n?l:s),_=te(o.cameraToCenterDistance,b.signedDistanceFromCamera),w=a.evaluateSizeForFeature(e.textSizeData,u,v)*_/t.ONE_EM;n&&(w*=e.tilePixelRatio/c);for(var T=Kr(y.anchor,y.width,y.height,y.textOffset,y.textBoxScale,w),k=n?$t(x.add(T),s).point:b.point.add(r?T.rotate(-o.angle):T),M=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,A=0;A<v.numGlyphs;A++)t.addDynamicAttributes(p,k,M);f&&v.associatedIconIndex>=0&&(g[v.associatedIconIndex]={shiftedAnchor:k,angle:M})}else ce(v.numGlyphs,p)}if(f){d.clear();for(var S=e.icon.placedSymbolArray,E=0;E<S.length;E++){var C=S.get(E);if(C.hidden)ce(C.numGlyphs,d);else{var L=g[E];if(L)for(var I=0;I<C.numGlyphs;I++)t.addDynamicAttributes(d,L.shiftedAnchor,L.angle);else ce(C.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function $r(t,e,r){return r.iconsInText&&e?\"symbolTextAndIcon\":t?\"symbolSDF\":\"symbolIcon\"}function tn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,g=e.transform,m=\"map\"===l,v=\"map\"===c,y=m&&\"point\"!==n.layout.get(\"symbol-placement\"),x=m&&!v&&!y,b=void 0!==n.layout.get(\"symbol-sort-key\").constantOr(1),_=e.depthModeForSublayer(0,Mt.ReadOnly),w=n.layout.get(\"text-variable-anchor\"),T=[],k=0,M=i;k<M.length;k+=1){var A=M[k],S=r.getTile(A),E=S.getBucket(n);if(E){var C=a?E.text:E.icon;if(C&&C.segments.get().length){var L=C.programConfigurations.get(n.id),I=a||E.sdfIcons,P=a?E.textSizeData:E.iconSizeData,z=v||0!==g.pitch,O=e.useProgram($r(I,a,E),L),D=t.evaluateSizeForZoom(P,g.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a)B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText&&(F=S.imageAtlasTexture.size,j=S.imageAtlasTexture,U=z||e.options.rotating||e.options.zooming||\"composite\"===P.kind||\"camera\"===P.kind?d.LINEAR:d.NEAREST);else{var V=1!==n.layout.get(\"icon-size\").constantOr(0)||E.iconsNeedLinear;B=S.imageAtlasTexture,N=I||e.options.rotating||e.options.zooming||V||z?d.LINEAR:d.NEAREST,R=S.imageAtlasTexture.size}var q=he(S,1,e.transform.zoom),H=Kt(A.posMatrix,v,m,e.transform,q),G=Qt(A.posMatrix,v,m,e.transform,q),Y=w&&E.hasTextData(),W=\"none\"!==n.layout.get(\"icon-text-fit\")&&Y&&E.hasIconData();y&&re(E,A.posMatrix,e,a,H,G,v,u);var X=e.translatePosMatrix(A.posMatrix,S,o,s),Z=y||a&&w||W?Jr:H,J=e.translatePosMatrix(G,S,o,s,!0),K=I&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1),Q={program:O,buffers:C,uniformValues:I?E.iconsInText?Gr(P.kind,D,x,v,e,X,Z,J,R,F):Hr(P.kind,D,x,v,e,X,Z,J,a,R,!0):qr(P.kind,D,x,v,e,X,Z,J,a,R),atlasTexture:B,atlasTextureIcon:j,atlasInterpolation:N,atlasInterpolationIcon:U,isSDF:I,hasHalo:K};if(b)for(var $=0,tt=C.segments.get();$<tt.length;$+=1){var et=tt[$];T.push({segments:new t.SegmentVector([et]),sortKey:et.sortKey,state:Q})}else T.push({segments:C.segments,sortKey:0,state:Q})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var rt=0,nt=T;rt<nt.length;rt+=1){var it=nt[rt],at=it.state;if(p.activeTexture.set(d.TEXTURE0),at.atlasTexture.bind(at.atlasInterpolation,d.CLAMP_TO_EDGE),at.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),at.atlasTextureIcon&&at.atlasTextureIcon.bind(at.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),at.isSDF){var ot=at.uniformValues;at.hasHalo&&(ot.u_is_halo=1,en(at.buffers,it.segments,n,e,at.program,_,f,h,ot)),ot.u_is_halo=0}en(at.buffers,it.segments,n,e,at.program,_,f,h,at.uniformValues)}}function en(t,e,r,n,i,a,o,s,l){var c=n.context;i.draw(c,c.gl.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 rn(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),g=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",s=h.LINES):(l=d?\"fillPattern\":\"fill\",s=h.TRIANGLES);for(var m=0,v=n;m<v.length;m+=1){var y=v[m],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(g));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?Sr(S,t,g,x,E):Ar(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,g,x):kr(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 nn(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 g=d[p],m=e.getTile(g),v=m.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),m.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&m.imageAtlas){var _=m.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(g.posMatrix,m,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\")),M=r.paint.get(\"fill-extrusion-vertical-gradient\"),A=u?Tr(k,t,M,h,g,f,m):wr(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 an(e,r,n,i,a,o){var s=e.context,l=s.gl,c=r.fbo;if(c){var u=e.useProgram(\"hillshade\");s.activeTexture.set(l.TEXTURE0),l.bindTexture(l.TEXTURE_2D,c.colorAttachment.get());var f=function(e,r,n){var i=n.paint.get(\"hillshade-shadow-color\"),a=n.paint.get(\"hillshade-highlight-color\"),o=n.paint.get(\"hillshade-accent-color\"),s=n.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===n.paint.get(\"hillshade-illumination-anchor\")&&(s-=e.transform.angle);var l,c,u,f=!e.options.moving;return{u_matrix:e.transform.calculatePosMatrix(r.tileID.toUnwrapped(),f),u_image:0,u_latrange:(l=r.tileID,c=Math.pow(2,l.canonical.z),u=l.canonical.y,[new t.MercatorCoordinate(0,u/c).toLngLat().lat,new t.MercatorCoordinate(0,(u+1)/c).toLngLat().lat]),u_light:[n.paint.get(\"hillshade-exaggeration\"),s],u_shadow:i,u_highlight:a,u_accent:o}}(e,r,n);u.draw(s,l.TRIANGLES,i,a,o,Et.disabled,f,n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments)}}function on(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 g=r.fbo;if(!g){var m=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);m.bind(c.LINEAR,c.CLAMP_TO_EDGE),(g=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(m.texture)}l.bindFramebuffer.set(g.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 sn(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 ln=new t.Color(1,0,0,1),cn=new t.Color(0,1,0,1),un=new t.Color(0,0,1,1),fn=new t.Color(1,0,1,1),hn=new t.Color(0,1,1,1);function pn(t,e,r,n){gn(t,0,e+r/2,t.transform.width,r,n)}function dn(t,e,r,n){gn(t,e-r/2,0,r,t.transform.height,n)}function gn(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 mn(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),\"$debug\",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=Math.floor((f&&f.byteLength||0)/1024),p=r.getTile(n).tileSize,d=512/Math.min(p,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+\" \"+h+\"kb\"),s.draw(i,a.TRIANGLES,l,c,St.alphaBlended,Et.disabled,Ir(o,t.Color.transparent,d),\"$debug\",e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var vn={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),g=d.getBucket(n);if(g&&g.text&&g.text.segments.get().length){var m=t.evaluateSizeForZoom(g.textSizeData,l.zoom),v=he(d,1,r.transform.zoom),y=Kt(p.posMatrix,u,c,r.transform,v),x=\"none\"!==n.layout.get(\"icon-text-fit\")&&g.hasIconData();if(m){var b=Math.pow(2,l.zoom-d.tileID.overscaledZ);Qr(g,c,u,s,t.symbolSize,l,y,p.posMatrix,b,m,x)}}}}(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)&&tn(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)&&tn(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&&(Zr(e,r,n,i,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),!0),Zr(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=[],g=0;g<i.length;g++){var m=i[g],v=r.getTile(m),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:Er(e,m,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;S.program.draw(c,u.TRIANGLES,f,h,p,Et.disabled,S.uniformValues,n.id,S.layoutVertexBuffer,S.indexBuffer,A.segments,n.paint,e.transform.zoom,S.programConfiguration)}}}},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;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE,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);e.useProgram(\"heatmap\",p).draw(a,o.TRIANGLES,Mt.disabled,s,l,Et.disabled,zr(u.posMatrix,f,e.transform.zoom,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){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:0,u_color_ramp:1,u_opacity:r.paint.get(\"heatmap-opacity\")}}(e,r),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\",g=e.context,m=g.gl,v=!0;if(h){g.activeTexture.set(m.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(g,n.gradient,m.RGBA)),y.bind(m.LINEAR,m.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 C=w.imageAtlas,L=C.patternPositions[E.to.toString()],I=C.patternPositions[E.from.toString()];L&&I&&k.setConstantPatternPositions(L,I)}var P=f?Rr(e,w,n,p):c?Fr(e,w,n,c,p):h?Dr(e,w,n):Or(e,w,n);f?(g.activeTexture.set(m.TEXTURE0),w.imageAtlasTexture.bind(m.LINEAR,m.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(g.activeTexture.set(m.TEXTURE0),e.lineAtlas.bind(g)),A.draw(g,m.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,P,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);rn(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);rn(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))nn(t,e,r,n,a,At.disabled,St.disabled),nn(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();nn(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?on(t,p,r,a,o,At.disabled,s):\"translucent\"===t.renderPass&&an(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 P?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,g=f;d<g.length;d+=1){var m=g[d],v=t.depthModeForSublayer(m.overscaledZ-h,1===r.paint.get(\"raster-opacity\")?Mt.ReadWrite:Mt.ReadOnly,a.LESS),y=e.getTile(m),x=t.transform.calculatePosMatrix(m.toUnwrapped(),p);y.registerFadeDuration(r.paint.get(\"raster-fade-duration\"));var b=e.findLoadedParent(m,0),_=sn(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=jr(x,T||[0,0],w||1,_,r);o instanceof P?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[m.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\"),g=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var m=r.getCrossfadeParameters(),v=0,y=g;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Wr(b,i,t,c,{tileID:x,tileSize:l},m):Yr(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++)mn(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)}}},yn=function(t,e){this.context=new Ct(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Lt.maxUnderzooming+Lt.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Be,this.gpuTimers={}};yn.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)this.style._layers[i[n]].resize()},yn.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,He.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,He.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,I.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,He.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)},yn.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,Pr(n),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},yn.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(Mt.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,Mt.disabled,new At({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Pr(s.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},yn.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)},yn.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)},yn.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]},yn.prototype.colorModeForRenderPass=function(){var e=this.context.gl;return this._showOverdrawInspector?new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0]):\"opaque\"===this.renderPass?St.unblended:St.alphaBlended},yn.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])},yn.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},yn.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 g=0;g<i.length;g++)if(this.style._layers[i[g]].is3D()){this.opaquePassCutoff=g;break}this.renderPass=\"offscreen\";for(var m=0,v=i;m<v.length;m+=1){var y=this.style._layers[v[m]];if(y.hasOffscreenPass()&&!y.isHidden(this.transform.zoom)){var x=f[y.source];(\"custom\"===y.type||x.length)&&this.renderLayer(this,a[y.source],y,x)}}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 b=this.style._layers[i[this.currentLayer]],_=a[b.source],w=u[b.source];this._renderTileClippingMasks(b,w),this.renderLayer(this,_,b,w)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var T=this.style._layers[i[this.currentLayer]],k=a[T.source],M=(\"symbol\"===T.type?h:f)[T.source];this._renderTileClippingMasks(T,u[T.source]),this.renderLayer(this,k,T,M)}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&&vn.debug(this,l,l.getVisibleCoordinates())),this.options.showPadding&&function(t){var e=t.transform.padding;pn(t,t.transform.height-(e.top||0),3,ln),pn(t,e.bottom||0,3,cn),dn(t,e.left||0,3,un),dn(t,t.transform.width-(e.right||0),3,fn);var r=t.transform.centerPoint;!function(t,e,r,n){gn(t,e-1,r-10,2,20,n),gn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,hn)}(this),this.context.setDefault()},yn.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),vn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},yn.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)}},yn.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},yn.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},yn.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},yn.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]:he(r,n[0],this.transform.zoom),a?n[1]:he(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},yn.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},yn.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},yn.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},yn.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 br(this.context,yr[t],e,Xr[t],this._showOverdrawInspector)),this.cache[r]},yn.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()},yn.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)},yn.prototype.initDebugOverlayCanvas=function(){null==this.debugOverlayCanvas&&(this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512,this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,this.context.gl.RGBA))},yn.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var xn=function(t,e){this.points=t,this.planes=e};xn.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 xn(a,o)};var bn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};bn.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 bn(n,i)},bn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},bn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},bn.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 _n=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};_n.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},_n.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)},_n.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},_n.prototype.clone=function(){return new _n(this.top,this.bottom,this.left,this.right)},_n.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var wn=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 _n,this._posMatrixCache={},this._alignedPosMatrixCache={}},Tn={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}};wn.prototype.clone=function(){var t=new wn(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},Tn.minZoom.get=function(){return this._minZoom},Tn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Tn.maxZoom.get=function(){return this._maxZoom},Tn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Tn.minPitch.get=function(){return this._minPitch},Tn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Tn.maxPitch.get=function(){return this._maxPitch},Tn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Tn.renderWorldCopies.get=function(){return this._renderWorldCopies},Tn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Tn.worldSize.get=function(){return this.tileSize*this.scale},Tn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Tn.size.get=function(){return new t.Point(this.width,this.height)},Tn.bearing.get=function(){return-this.angle/Math.PI*180},Tn.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))},Tn.pitch.get=function(){return this._pitch/Math.PI*180},Tn.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())},Tn.fov.get=function(){return this._fov/Math.PI*180},Tn.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())},Tn.zoom.get=function(){return this._zoom},Tn.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())},Tn.center.get=function(){return this._center},Tn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Tn.padding.get=function(){return this._edgeInsets.toJSON()},Tn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Tn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},wn.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},wn.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},wn.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)},wn.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},wn.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=xn.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 bn([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 g=u.pop(),m=g.x,v=g.y,y=g.fullyVisible;if(!y){var x=g.aabb.intersects(s);if(0===x)continue;y=2===x}var b=g.aabb.distanceX(o),_=g.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_));if(g.zoom===h||w>3+(1<<h-g.zoom)-2&&g.zoom>=l)f.push({tileID:new t.OverscaledTileID(g.zoom===h?p:g.zoom,g.wrap,g.zoom,m,v),distanceSq:t.sqrLen([o[0]-.5-m,o[1]-.5-v])});else for(var T=0;T<4;T++){var k=(m<<1)+T%2,M=(v<<1)+(T>>1);u.push({aabb:g.aabb.quadrant(T),zoom:g.zoom+1,x:k,y:M,wrap:g.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},wn.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Tn.unmodified.get=function(){return this._unmodified},wn.prototype.zoomScale=function(t){return Math.pow(2,t)},wn.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},wn.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)},wn.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Tn.point.get=function(){return this.project(this.center)},wn.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())},wn.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},wn.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},wn.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},wn.prototype.coordinateLocation=function(t){return t.toLngLat()},wn.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[1]/i,s=n[1]/a,l=r[2]/i,c=n[2]/a,u=l===c?0:(0-l)/(c-l);return new t.MercatorCoordinate(t.number(r[0]/i,n[0]/a,u)/this.worldSize,t.number(o,s,u)/this.worldSize)},wn.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])},wn.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)))},wn.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},wn.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])},wn.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]},wn.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},wn.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 g=p.y,m=c.y/2;g-m<a&&(i=a+m),g+m>o&&(i=o-m)}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}},wn.prototype._calcMatrices=function(){if(this.height){var e=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(this._fov/2)*this.height;var r=Math.PI/2+this._pitch,n=this._fov*(.5+e.y/this.height),i=Math.sin(n)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-r-n,.01,Math.PI-.01)),a=this.point,o=a.x,s=a.y,l=1.01*(Math.cos(Math.PI/2-this._pitch)*i+this.cameraToCenterDistance),c=this.height/50,u=new Float64Array(16);t.perspective(u,this._fov,this.width/this.height,c,l),u[8]=2*-e.x/this.width,u[9]=2*e.y/this.height,t.scale(u,u,[1,-1,1]),t.translate(u,u,[0,0,-this.cameraToCenterDistance]),t.rotateX(u,u,this._pitch),t.rotateZ(u,u,this.angle),t.translate(u,u,[-o,-s,0]),this.mercatorMatrix=t.scale([],u,[this.worldSize,this.worldSize,this.worldSize]),t.scale(u,u,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=u,this.invProjMatrix=t.invert([],this.projMatrix);var f=this.width%2/2,h=this.height%2/2,p=Math.cos(this.angle),d=Math.sin(this.angle),g=o-Math.round(o)+p*f+d*h,m=s-Math.round(s)+p*h+d*f,v=new Float64Array(u);if(t.translate(v,v,[g>.5?g-1:g,m>.5?m-1:m,0]),this.alignedProjMatrix=v,u=t.create(),t.scale(u,u,[this.width/2,-this.height/2,1]),t.translate(u,u,[1,-1,0]),this.labelPlaneMatrix=u,u=t.create(),t.scale(u,u,[1,-1,1]),t.translate(u,u,[-1,-1,0]),t.scale(u,u,[2/this.width,2/this.height,1]),this.glCoordMatrix=u,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(u=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=u,this._posMatrixCache={},this._alignedPosMatrixCache={}}},wn.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},wn.prototype.getCameraPoint=function(){var e=Math.tan(this._pitch)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,e))},wn.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(wn.prototype,Tn);var kn=function(e){var r,n,i,a;this._hashName=e&&encodeURIComponent(e),t.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=!1,i=null,a=function(){i=null,n&&(r(),i=setTimeout(a,300),n=!1)},function(){return n=!0,i||a(),i})};kn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},kn.prototype.remove=function(){return t.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},kn.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},kn.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(\"/\")},kn.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},kn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,\"\",e)}catch(t){}};var Mn={linearity:.3,easing:t.bezier(0,0,.3,1)},An=t.extend({deceleration:2500,maxSpeed:1400},Mn),Sn=t.extend({deceleration:20,maxSpeed:1400},Mn),En=t.extend({deceleration:1e3,maxSpeed:360},Mn),Cn=t.extend({deceleration:1e3,maxSpeed:90},Mn),Ln=function(t){this._map=t,this.clear()};function In(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Pn(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)}}Ln.prototype.clear=function(){this._inertiaBuffer=[]},Ln.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},Ln.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},Ln.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=Pn(r.pan.mag(),o,t.extend({},An,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,In(s,l)}if(r.zoom){var c=Pn(r.zoom,o,Sn);s.zoom=this._map.transform.zoom+c.amount,In(s,c)}if(r.bearing){var u=Pn(r.bearing,o,En);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),In(s,u)}if(r.pitch){var f=Pn(r.pitch,o,Cn);s.pitch=this._map.transform.pitch+f.amount,In(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 zn=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)).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),On=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)).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),Dn=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)).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),Rn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Rn.prototype.reset=function(){delete this._mousedownPos},Rn.prototype.wheel=function(t){return this._firePreventable(new Dn(t.type,this._map,t))},Rn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new zn(t.type,this._map,t))},Rn.prototype.mouseup=function(t){this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.dblclick=function(t){return this._firePreventable(new zn(t.type,this._map,t))},Rn.prototype.mouseover=function(t){this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.mouseout=function(t){this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.touchstart=function(t){return this._firePreventable(new On(t.type,this._map,t))},Rn.prototype.touchmove=function(t){this._map.fire(new On(t.type,this._map,t))},Rn.prototype.touchend=function(t){this._map.fire(new On(t.type,this._map,t))},Rn.prototype.touchcancel=function(t){this._map.fire(new On(t.type,this._map,t))},Rn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Rn.prototype.isEnabled=function(){return!0},Rn.prototype.isActive=function(){return!1},Rn.prototype.enable=function(){},Rn.prototype.disable=function(){};var Fn=function(t){this._map=t};Fn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Fn.prototype.mousemove=function(t){this._map.fire(new zn(t.type,this._map,t))},Fn.prototype.mousedown=function(){this._delayContextMenu=!0},Fn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new zn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Fn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new zn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},Fn.prototype.isEnabled=function(){return!0},Fn.prototype.isActive=function(){return!1},Fn.prototype.enable=function(){},Fn.prototype.disable=function(){};var Bn=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Nn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Bn.prototype.isEnabled=function(){return!!this._enabled},Bn.prototype.isActive=function(){return!!this._active},Bn.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Bn.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Bn.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Bn.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\"}}},Bn.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)}},Bn.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))},Bn.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},Bn.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var jn=function(t){this.reset(),this.numTouches=t.numTouches};jn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},jn.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)r._add(i[n]);return r.div(e.length)}(r),this.touches=Nn(n,r)))},jn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Nn(r,e);for(var i in this.touches){var a=n[i];(!a||a.dist(this.touches[i])>30)&&(this.aborted=!0)}}},jn.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 Un=function(t){this.singleTap=new jn(t),this.numTaps=t.numTaps,this.reset()};Un.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},Un.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},Un.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},Un.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 Vn=function(){this._zoomIn=new Un({numTouches:1,numTaps:2}),this._zoomOut=new Un({numTouches:2,numTaps:1}),this.reset()};Vn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Vn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Vn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Vn.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},Vn.prototype.touchcancel=function(){this.reset()},Vn.prototype.enable=function(){this._enabled=!0},Vn.prototype.disable=function(){this._enabled=!1,this.reset()},Vn.prototype.isEnabled=function(){return this._enabled},Vn.prototype.isActive=function(){return this._active};var qn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};qn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},qn.prototype._correctButton=function(t,e){return!1},qn.prototype._move=function(t,e){return{}},qn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},qn.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)},qn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},qn.prototype.enable=function(){this._enabled=!0},qn.prototype.disable=function(){this._enabled=!1,this.reset()},qn.prototype.isEnabled=function(){return this._enabled},qn.prototype.isActive=function(){return this._active};var Hn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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}(qn),Gn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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}(qn),Yn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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}(qn),Wn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Wn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Wn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Wn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Wn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Wn.prototype.touchcancel=function(){this.reset()},Wn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Nn(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}}},Wn.prototype.enable=function(){this._enabled=!0},Wn.prototype.disable=function(){this._enabled=!1,this.reset()},Wn.prototype.isEnabled=function(){return this._enabled},Wn.prototype.isActive=function(){return this._active};var Xn=function(){this.reset()};function Zn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}function Jn(t,e){return Math.log(t/e)/Math.LN2}Xn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Xn.prototype._start=function(t){},Xn.prototype._move=function(t,e,r){return{}},Xn.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]]))},Xn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[1],a=Zn(r,e,n[0]),o=Zn(r,e,i);if(a&&o){var s=this._aroundCenter?null:a.add(o).div(2);return this._move([a,o],s,t)}}},Xn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[1],o=Zn(n,e,i[0]),s=Zn(n,e,a);o&&s||(this._active&&r.suppressClick(),this.reset())}},Xn.prototype.touchcancel=function(){this.reset()},Xn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around},Xn.prototype.disable=function(){this._enabled=!1,this.reset()},Xn.prototype.isEnabled=function(){return this._enabled},Xn.prototype.isActive=function(){return this._active};var Kn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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(Jn(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:Jn(this._distance,r),pinchAround:e}},e}(Xn);function Qn(t,e){return 180*t.angleWith(e)/Math.PI}var $n=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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:Qn(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=Qn(t,this._startVector);return Math.abs(r)<e},e}(Xn);function ti(t){return Math.abs(t.y)>Math.abs(t.x)}var ei=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.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,ti(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)return this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}},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 ti(t)&&ti(e)&&a}},e}(Xn),ri={panStep:100,bearingStep:15,pitchStep:10},ni=function(){var t=ri;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function ii(t){return t*(2-t)}ni.prototype.reset=function(){this._active=!1},ni.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:ii,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})}}}},ni.prototype.enable=function(){this._enabled=!0},ni.prototype.disable=function(){this._enabled=!1,this.reset()},ni.prototype.isEnabled=function(){return this._enabled},ni.prototype.isActive=function(){return this._active};var ai=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)};ai.prototype.setZoomRate=function(t){this._defaultZoomRate=t},ai.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},ai.prototype.isEnabled=function(){return!!this._enabled},ai.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},ai.prototype.isZooming=function(){return!!this._zooming},ai.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},ai.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},ai.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()}},ai.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},ai.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())}},ai.prototype.renderFrame=function(){return this._onScrollFrame()},ai.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}}},ai.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},ai.prototype.reset=function(){this._active=!1};var oi=function(t,e){this._clickZoom=t,this._tapZoom=e};oi.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},oi.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},oi.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},oi.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var si=function(){this.reset()};si.prototype.reset=function(){this._active=!1},si.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})}}},si.prototype.enable=function(){this._enabled=!0},si.prototype.disable=function(){this._enabled=!1,this.reset()},si.prototype.isEnabled=function(){return this._enabled},si.prototype.isActive=function(){return this._active};var li=function(){this._tap=new Un({numTouches:1,numTaps:1}),this.reset()};li.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},li.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))},li.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)},li.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)},li.prototype.touchcancel=function(){this.reset()},li.prototype.enable=function(){this._enabled=!0},li.prototype.disable=function(){this._enabled=!1,this.reset()},li.prototype.isEnabled=function(){return this._enabled},li.prototype.isActive=function(){return this._active};var ci=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};ci.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},ci.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},ci.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},ci.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var ui=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};ui.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},ui.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},ui.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},ui.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var fi=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};fi.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\")},fi.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},fi.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},fi.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},fi.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},fi.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var hi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},pi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e}(t.Event);function di(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var gi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Ln(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];r.addEventListener(l,s[1],l===t.window.document?this.handleWindowEvent:this.handleEvent,s[2])}};gi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0];r.removeEventListener(a,i[1],a===t.window.document?this.handleWindowEvent:this.handleEvent,i[2])}},gi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new Rn(e,t));var n=e.boxZoom=new Bn(e,t);this._add(\"boxZoom\",n);var i=new Vn,a=new si;e.doubleClickZoom=new oi(a,i),this._add(\"tapZoom\",i),this._add(\"clickZoom\",a);var o=new li;this._add(\"tapDragZoom\",o);var s=e.touchPitch=new ei;this._add(\"touchPitch\",s);var l=new Gn(t),c=new Yn(t);e.dragRotate=new ui(t,l,c),this._add(\"mouseRotate\",l,[\"mousePitch\"]),this._add(\"mousePitch\",c,[\"mouseRotate\"]);var u=new Hn(t),f=new Wn(t);e.dragPan=new ci(r,u,f),this._add(\"mousePan\",u),this._add(\"touchPan\",f,[\"touchZoom\",\"touchRotate\"]);var h=new $n,p=new Kn;e.touchZoomRotate=new fi(r,p,h,o),this._add(\"touchRotate\",h,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",p,[\"touchPan\",\"touchRotate\"]);var d=e.scrollZoom=new ai(e,this);this._add(\"scrollZoom\",d,[\"mousePan\"]);var g=e.keyboard=new ni;this._add(\"keyboard\",g),this._add(\"blockableMapEvent\",new Fn(e));for(var m=0,v=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];m<v.length;m+=1){var y=v[m];t.interactive&&t[y]&&e[y].enable(t[y])}},gi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},gi.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=[]}},gi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1)if(e[t].handler.isActive())return!0;return!1},gi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},gi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},gi.prototype.isMoving=function(){return Boolean(hi(this._eventsInProgress))||this.isZooming()},gi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},gi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+\"Window\")},gi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r];this._el.contains(i.target)&&e.push(i)}return e},gi.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 g=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(g=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,g,h,n),g&&g.needsRenderFrame&&this._triggerRenderFrame()),(g||p.isActive())&&(o[h]=p)}}var m={};for(var v in this._previousActiveHandlers)o[v]||(m[v]=n);this._previousActiveHandlers=o,(Object.keys(m).length||di(i))&&(this._changes.push([i,a,m]),this._triggerRenderFrame()),(Object.keys(o).length||di(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()},gi.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)}},gi.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=[]},gi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!di(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)},gi.prototype._fireEvents=function(e,r){var n=this,i=hi(this._eventsInProgress),a=hi(e),o={};for(var s in e)this._eventsInProgress[s]||(o[s+\"start\"]=e[s].originalEvent),this._eventsInProgress[s]=e[s];for(var l in!i&&a&&this._fireEvent(\"movestart\",a.originalEvent),o)this._fireEvent(l,o[l]);for(var c in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent(\"move\",a.originalEvent),e)this._fireEvent(c,e[c].originalEvent);var u,f={};for(var h in this._eventsInProgress){var p=this._eventsInProgress[h],d=p.handlerName,g=p.originalEvent;this._handlersById[d].isActive()||(delete this._eventsInProgress[h],f[h+\"end\"]=u=r[d]||g)}for(var m in f)this._fireEvent(m,f[m]);var v=hi(this._eventsInProgress);if((i||a)&&!v){this._updatingCamera=!0;var y=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),x=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};y?(x(y.bearing||this._map.getBearing())&&(y.bearing=0),this._map.easeTo(y,{originalEvent:u})):(this._map.fire(new t.Event(\"moveend\",{originalEvent:u})),x(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},gi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},gi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new pi(\"renderFrame\",{timeStamp:e})),t._applyChanges()})))};var mi=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)).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)),g=p.sub(d),m=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/g.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/g.y;if(!(v<0||m<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(m,v)),i.maxZoom),x=t.Point.convert(i.offset),b=new t.Point(x.x+(i.padding.left-i.padding.right)/2,x.y+(i.padding.top-i.padding.bottom)/2).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(b)),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),g=i.pointLocation(d),m=t.LngLat.convert(e.center||g);this._normalizeCenter(m);var v,y,x=i.project(g),b=i.project(m).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 g=i.zoomScale(i.zoom-a),m=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(m,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(g));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),g=t.Point.convert(e.offset),m=a.centerPoint.add(g),v=a.pointLocation(m),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 C(t){return(Math.exp(t)-Math.exp(-t))/2}function L(t){return(Math.exp(t)+Math.exp(-t))/2}var I=E(0),P=function(t){return L(I)/L(I+_*t)},z=function(t){return w*((L(I)*(C(e=I+_*t)/L(e))-C(I))/S)/k;var e},O=(E(1)-I)/_;if(Math.abs(k)<1e-6||!isFinite(O)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;O=Math.abs(Math.log(T/w))/_,z=function(){return 0},P=function(t){return Math.exp(D*_*t)}}return e.duration=\"duration\"in e?+e.duration:1e3*O/(\"screenSpeed\"in e?+e.screenSpeed/_:+e.speed),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*O,d=1/P(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),m=a.centerPoint.add(g));var v=1===e?y:a.unproject(x.add(b.mult(z(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,m),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),vi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};vi.prototype.getDefaultPosition=function(){return\"bottom-right\"},vi.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},vi.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},vi.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\"}},vi.prototype._updateData=function(t){!t||\"metadata\"!==t.sourceDataType&&\"style\"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},vi.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)}},vi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var yi=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};yi.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},yi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},yi.prototype.getDefaultPosition=function(){return\"bottom-left\"},yi.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},yi.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}},yi.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 xi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};xi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},xi.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)}},xi.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},xi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var bi={\"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\"},_i=t.window.HTMLImageElement,wi=t.window.HTMLElement,Ti=t.window.ImageBitmap,ki={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},Mi=function(n){function i(e){var r=this;if(null!=(e=t.extend({},ki,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 wn(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 xi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},bi,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 wi))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 gi(this,e),this._hash=e.hash&&new kn(\"string\"==typeof e.hash&&e.hash||void 0).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 vi({customAttribution:e.customAttribution})),this.addControl(new yi,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)).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 zn(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 zn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new zn(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;return void 0===r?n.prototype.off.call(this,t,e):(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 qe(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 qe(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;if(this._lazyInitEmptyStyle(),r instanceof _i||Ti&&r instanceof Ti){var c=t.browser.getImageData(r);this.style.addImage(e,{data:new t.RGBAImage({width:c.width,height:c.height},c.data),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 u=r;this.style.addImage(e,{data:new t.RGBAImage({width:r.width,height:r.height},new Uint8Array(r.data)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:u}),u.onAdd&&u.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 _i||Ti&&r instanceof Ti?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;return void 0===a||void 0===o?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`\"))):a!==n.data.width||o!==n.data.height?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\"))):(n.data.replace(s,!(r instanceof _i||Ti&&r instanceof Ti)),void 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 yn(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(),Ai(this._canvasContainer),Ai(this._controlContainer),Ai(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}(mi);function Ai(t){t.parentNode&&t.parentNode.removeChild(t)}var Si={showCompass:!0,showZoom:!0,visualizePitch:!1},Ei=function(e){var n=this;this.options=t.extend({},Si,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))};Ei.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Ei.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},Ei.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 Ci(this._map,this._compass,this.options.visualizePitch)),this._container},Ei.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},Ei.prototype._createButton=function(t,e){var n=r.create(\"button\",t,this._container);return n.type=\"button\",n.addEventListener(\"click\",e),n},Ei.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString(\"NavigationControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)};var Ci=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Gn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Yn({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 Li(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}Ci.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ci.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)}},Ci.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()},Ci.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ci.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)},Ci.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ci.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ci.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))},Ci.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))},Ci.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ci.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var Ii={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 Pi(t,e,r){var n=t.classList;for(var i in Ii)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var zi,Oi=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 g=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");g.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(g);var m=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");m.setAttributeNS(null,\"opacity\",\"0.25\"),m.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\"),m.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(m),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})),Pi(this._element,this._anchor,\"marker\"),this._popup=null}return e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.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=Li(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,Ii[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),Di={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0},Ri=0,Fi=!1,Bi=function(e){function n(r){e.call(this),this.options=t.extend({},Di,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.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!==zi?n(zi):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){n(zi=\"denied\"!==t.state)})):n(zi=!!t.window.navigator.geolocation),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,Ri=0,Fi=!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&&Fi)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 Oi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Oi({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){e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||e.originalEvent&&\"resize\"===e.originalEvent.type||(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\":Ri--,Fi=!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\"),++Ri>1?(e={maximumAge:6e5,timeout:0},Fi=!0):(e=this.options.positionOptions,Fi=!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),Ni={maxWidth:100,unit:\"metric\"},ji=function(e){this.options=t.extend({},Ni,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function Ui(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;l>5280?Vi(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\")):Vi(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else r&&\"nautical\"===r.unit?Vi(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\")):s>=1e3?Vi(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):Vi(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function Vi(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)));t.style.width=e*(s/r)+\"px\",t.innerHTML=s+\"&nbsp;\"+n}ji.prototype.getDefaultPosition=function(){return\"bottom-left\"},ji.prototype._onMove=function(){Ui(this._map,this._container,this.options)},ji.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},ji.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},ji.prototype.setUnit=function(t){this.options.unit=t,Ui(this._map,this._container,this.options)};var qi=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\")};qi.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},qi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},qi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},qi.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)},qi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},qi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},qi.prototype._isFullscreen=function(){return this._fullscreen},qi.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())},qi.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 Hi={closeButton:!0,closeOnClick:!0,className:\"\",maxWidth:\"240px\"},Gi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Hi),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.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;if(this._map&&(this._lngLat||this._trackPointer)&&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=Li(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var i=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),a=this.options.anchor,o=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(!a){var s,l=this._container.offsetWidth,c=this._container.offsetHeight;s=i.y+o.bottom.y<c?[\"top\"]:i.y>this._map.transform.height-c?[\"bottom\"]:[],i.x<l/2?s.push(\"left\"):i.x>this._map.transform.width-l/2&&s.push(\"right\"),a=0===s.length?\"bottom\":s.join(\"-\")}var u=i.add(o[a]).round();r.setTransform(this._container,Ii[a]+\" translate(\"+u.x+\"px,\"+u.y+\"px)\"),Pi(this._container,a,\"popup\")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented),Yi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Mi,NavigationControl:Ei,GeolocateControl:Bi,AttributionControl:vi,ScaleControl:ji,FullscreenControl:qi,Popup:Gi,Marker:Oi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(Ot)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(Ot),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 Yi})),r}))},{}],474:[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\":135}],475:[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 g(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,m,v){if(e||(e=[0,0,0]),r||(r=[0,0,0]),i||(i=[0,0,0]),m||(m=[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],C=u[13],L=u[14],I=u[15];if(0!==M||0!==A||0!==S){if(h[0]=M,h[1]=A,h[2]=S,h[3]=I,!s(f,f))return!1;l(f,f),y=m,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 m[0]=m[1]=m[2]=0,m[3]=1;if(e[0]=E,e[1]=C,e[2]=L,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]),g(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]),g(p[2],p[2],p[0],1,-i[1]),i[2]=c.dot(p[1],p[2]),g(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 P=0;P<3;P++)r[P]*=-1,p[P][0]*=-1,p[P][1]*=-1,p[P][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\":476,\"gl-mat4/clone\":278,\"gl-mat4/create\":280,\"gl-mat4/determinant\":281,\"gl-mat4/invert\":293,\"gl-mat4/transpose\":306,\"gl-vec3/cross\":365,\"gl-vec3/dot\":370,\"gl-vec3/length\":380,\"gl-vec3/normalize\":387}],476:[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}},{}],477:[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 h(t){return[t||0,t||0,t||0]}e.exports=function(t,e,r,f){if(0===o(e)||0===o(r))return!1;var h=a(e,l.translate,l.scale,l.skew,l.perspective,l.quaternion),p=a(r,c.translate,c.scale,c.skew,c.perspective,c.quaternion);return!(!h||!p)&&(n(u.translate,l.translate,c.translate,f),n(u.skew,l.skew,c.skew,f),n(u.scale,l.scale,c.scale,f),n(u.perspective,l.perspective,c.perspective,f),s(u.quaternion,l.quaternion,c.quaternion,f),i(t,u.translate,u.scale,u.skew,u.perspective,u.quaternion),!0)}},{\"gl-mat4/determinant\":281,\"gl-vec3/lerp\":381,\"mat4-decompose\":475,\"mat4-recompose\":478,\"quat-slerp\":527}],478:[function(t,e,r){var n={identity:t(\"gl-mat4/identity\"),translate:t(\"gl-mat4/translate\"),multiply:t(\"gl-mat4/multiply\"),create:t(\"gl-mat4/create\"),scale:t(\"gl-mat4/scale\"),fromRotationTranslation:t(\"gl-mat4/fromRotationTranslation\")},i=(n.create(),n.create());e.exports=function(t,e,r,a,o,s){return 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\":280,\"gl-mat4/fromRotationTranslation\":284,\"gl-mat4/identity\":291,\"gl-mat4/multiply\":295,\"gl-mat4/scale\":303,\"gl-mat4/translate\":305}],479:[function(t,e,r){\"use strict\";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],480:[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 p(t){this._components=t.slice(),this._time=[0],this.prevMatrix=t.slice(),this.nextMatrix=t.slice(),this.computedMatrix=t.slice(),this.computedInverse=t.slice(),this.computedEye=[0,0,0],this.computedUp=[0,0,0],this.computedCenter=[0,0,0],this.computedRadius=[0],this._limits=[-1/0,1/0]}e.exports=function(t){return new p((t=t||{}).matrix||[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1])};var d=p.prototype;d.recalcMatrix=function(t){var e=this._time,r=n.le(e,t),o=this.computedMatrix;if(!(r<0)){var s=this._components;if(r===e.length-1)for(var l=16*r,c=0;c<16;++c)o[c]=s[l++];else{var u=e[r+1]-e[r],h=(l=16*r,this.prevMatrix),p=!0;for(c=0;c<16;++c)h[c]=s[l++];var d=this.nextMatrix;for(c=0;c<16;++c)d[c]=s[l++],p=p&&h[c]===d[c];if(u<1e-6||p)for(c=0;c<16;++c)o[c]=h[c];else i(o,h,d,(t-e[r])/u)}var g=this.computedUp;g[0]=o[1],g[1]=o[5],g[2]=o[9],f(g,g);var m=this.computedInverse;a(m,o);var v=this.computedEye,y=m[15];v[0]=m[12]/y,v[1]=m[13]/y,v[2]=m[14]/y;var 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\"+r+\"(data,\"+o.map((function(t){return\"shape[\"+t+\"]\"})).join(\",\")+\",\"+o.map((function(t){return\"stride[\"+t+\"]\"})).join(\",\")+\",offset)}\"),new Function(\"CTOR_LIST\",\"ORDER\",a.join(\"\\n\"))(c[t],s)}var c={float32:[],float64:[],int8:[],int16:[],int32:[],uint8:[],uint16:[],uint32:[],array:[],uint8_clamped:[],bigint64:[],biguint64:[],buffer:[],generic:[]};e.exports=function(t,e,r,n){if(void 0===t)return(0,c.array[0])([]);\"number\"==typeof t&&(t=[t]),void 0===e&&(e=[t.length]);var o=e.length;if(void 0===r){r=new Array(o);for(var s=o-1,u=1;s>=0;--s)r[s]=u,u*=e[s]}if(void 0===n){n=0;for(s=0;s<o;++s)r[s]<0&&(n-=(e[s]-1)*r[s])}for(var f=function(t){if(i(t))return\"buffer\";if(a)switch(Object.prototype.toString.call(t)){case\"[object Float64Array]\":return\"float64\";case\"[object Float32Array]\":return\"float32\";case\"[object Int8Array]\":return\"int8\";case\"[object Int16Array]\":return\"int16\";case\"[object Int32Array]\":return\"int32\";case\"[object 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h=e[l],p=e[c],d=e[u],g=new Array(3),m=0,v=new Array(3),y=0,x=0;x<3;++x)g[x]=h[x]-p[x],m+=g[x]*g[x],v[x]=d[x]-p[x],y+=v[x]*v[x];if(m*y>a){var b=i[c],_=1/Math.sqrt(m*y);for(x=0;x<3;++x){var w=(x+1)%3,T=(x+2)%3;b[x]+=_*(v[w]*g[T]-v[T]*g[w])}}}for(o=0;o<n;++o){b=i[o];var k=0;for(x=0;x<3;++x)k+=b[x]*b[x];if(k>a)for(_=1/Math.sqrt(k),x=0;x<3;++x)b[x]*=_;else for(x=0;x<3;++x)b[x]=0}return i},r.faceNormals=function(t,e,r){for(var n=t.length,i=new Array(n),a=void 0===r?1e-6:r,o=0;o<n;++o){for(var s=t[o],l=new Array(3),c=0;c<3;++c)l[c]=e[s[c]];var u=new Array(3),f=new Array(3);for(c=0;c<3;++c)u[c]=l[1][c]-l[0][c],f[c]=l[2][c]-l[0][c];var h=new Array(3),p=0;for(c=0;c<3;++c){var d=(c+1)%3,g=(c+2)%3;h[c]=u[d]*f[g]-u[g]*f[d],p+=h[c]*h[c]}p=p>a?1/Math.sqrt(p):0;for(c=0;c<3;++c)h[c]*=p;i[o]=h}return i}},{}],499:[function(t,e,r){\n",
+       "/*\n",
+       "object-assign\n",
+       "(c) Sindre Sorhus\n",
+       "@license MIT\n",
+       "*/\n",
+       "\"use strict\";var n=Object.getOwnPropertySymbols,i=Object.prototype.hasOwnProperty,a=Object.prototype.propertyIsEnumerable;function o(t){if(null==t)throw new TypeError(\"Object.assign cannot be called with null or undefined\");return Object(t)}e.exports=function(){try{if(!Object.assign)return!1;var t=new String(\"abc\");if(t[5]=\"de\",\"5\"===Object.getOwnPropertyNames(t)[0])return!1;for(var e={},r=0;r<10;r++)e[\"_\"+String.fromCharCode(r)]=r;if(\"0123456789\"!==Object.getOwnPropertyNames(e).map((function(t){return e[t]})).join(\"\"))return!1;var n={};return\"abcdefghijklmnopqrst\".split(\"\").forEach((function(t){n[t]=t})),\"abcdefghijklmnopqrst\"===Object.keys(Object.assign({},n)).join(\"\")}catch(t){return!1}}()?Object.assign:function(t,e){for(var r,s,l=o(t),c=1;c<arguments.length;c++){for(var u in r=Object(arguments[c]))i.call(r,u)&&(l[u]=r[u]);if(n){s=n(r);for(var f=0;f<s.length;f++)a.call(r,s[f])&&(l[s[f]]=r[s[f]])}}return l}},{}],500:[function(t,e,r){\"use 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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",
+       "var n,i=t(\"./lib/build-log\"),a=t(\"./lib/epsilon\"),o=t(\"./lib/intersecter\"),s=t(\"./lib/segment-chainer\"),l=t(\"./lib/segment-selector\"),c=t(\"./lib/geojson\"),u=!1,f=a();function h(t,e,r){var i=n.segments(t),a=n.segments(e),o=r(n.combine(i,a));return n.polygon(o)}n={buildLog:function(t){return!0===t?u=i():!1===t&&(u=!1),!1!==u&&u.list},epsilon:function(t){return f.epsilon(t)},segments:function(t){var e=o(!0,f,u);return 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h=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]]},{\"array-bounds\":70,\"color-normalize\":125,\"flatten-vertex-data\":244,\"object-assign\":499,\"pick-by-alias\":511,\"to-float32\":577,\"update-diff\":599}],535:[function(t,e,r){\"use strict\";var 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\"),g=t(\"parse-rect\");function m(t,e){if(!(this instanceof m))return new m(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=m.shaders.has(t)?m.shaders.get(t):m.shaders.set(t,m.createShaders(t)).get(t),this.update(e)}e.exports=m,m.dashMult=2,m.maxPatternLength=256,m.precisionThreshold=3e6,m.maxPoints=1e4,m.maxLines=2048,m.shaders=new d,m.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}},m.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},m.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()},m.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>m.precisionThreshold||e.scale[1]*e.viewport.height>m.precisionThreshold||\"rect\"===e.join||!e.join&&(e.thickness<=2||e.count>=m.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},m.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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Uint8Array})},t=a({},m.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<m.maxLines&&(d.depth=2*(m.maxLines-1-f%m.maxLines)/m.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,\"uint8\"):null),null!=t.viewport&&(d.viewport=g(t.viewport)),d.viewport||(d.viewport=g([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var v,y;if(t.positions.x&&t.positions.y){var x=t.positions.x,b=t.positions.y;y=d.count=Math.max(x.length,b.length),v=new Float64Array(2*y);for(var _=0;_<y;_++)v[2*_]=x[_],v[2*_+1]=b[_]}else v=l(t.positions,\"float64\"),y=d.count=Math.floor(v.length/2);var w=d.bounds=i(v,2);if(d.fill){for(var T=[],k={},M=0,A=0,S=0,E=d.count;A<E;A++){var C=v[2*A],L=v[2*A+1];isNaN(C)||isNaN(L)||null==C||null==L?(C=v[2*M],L=v[2*M+1],k[A]=M):M=A,T[S++]=C,T[S++]=L}for(var I=c(T,d.hole||[]),P=0,z=I.length;P<z;P++)null!=k[I[P]]&&(I[P]=k[I[P]]);d.triangles=I}var O=new Float64Array(v);u(O,2,w);var D=new Float64Array(2*y+6);d.close?v[0]===v[2*y-2]&&v[1]===v[2*y-1]?(D[0]=O[2*y-4],D[1]=O[2*y-3]):(D[0]=O[2*y-2],D[1]=O[2*y-1]):(D[0]=O[0],D[1]=O[1]),D.set(O,2),d.close?v[0]===v[2*y-2]&&v[1]===v[2*y-1]?(D[2*y+2]=O[2],D[2*y+3]=O[3],d.count-=1):(D[2*y+2]=O[0],D[2*y+3]=O[1],D[2*y+4]=O[2],D[2*y+5]=O[3]):(D[2*y+2]=O[2*y-2],D[2*y+3]=O[2*y-1],D[2*y+4]=O[2*y-2],D[2*y+5]=O[2*y-1]),d.positionBuffer(h(D)),d.positionFractBuffer(p(D))}if(t.range?d.range=t.range:d.range||(d.range=d.bounds),(t.range||t.positions)&&d.count){var R=d.bounds,F=R[2]-R[0],B=R[3]-R[1],N=d.range[2]-d.range[0],j=d.range[3]-d.range[1];d.scale=[F/N,B/j],d.translate=[-d.range[0]/N+R[0]/N||0,-d.range[1]/j+R[1]/j||0],d.scaleFract=p(d.scale),d.translateFract=p(d.translate)}if(t.dashes){var U,V=0;if(!t.dashes||t.dashes.length<2)V=1,U=new Uint8Array([255,255,255,255,255,255,255,255]);else{V=0;for(var q=0;q<t.dashes.length;++q)V+=t.dashes[q];U=new Uint8Array(V*m.dashMult);for(var H=0,G=255,Y=0;Y<2;Y++)for(var W=0;W<t.dashes.length;++W){for(var X=0,Z=t.dashes[W]*m.dashMult*.5;X<Z;++X)U[H++]=G;G^=255}}d.dashLength=V,d.dashTexture({channels:1,data:U,width:U.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(t.color){var J=d.count,K=t.color;K||(K=\"transparent\");var Q=new Uint8Array(4*J+4);if(Array.isArray(K)&&\"number\"!=typeof K[0]){for(var $=0;$<J;$++){var tt=n(K[$],\"uint8\");Q.set(tt,4*$)}Q.set(n(K[0],\"uint8\"),4*J)}else for(var et=n(K,\"uint8\"),rt=0;rt<J+1;rt++)Q.set(et,4*rt);d.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:Q})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var v=[],y=0;y<this.passes.length;y++)null!==this.passes[y]&&v.push(this.passes[y]);return this.passes=v,this}},m.prototype.destroy=function(){return this.passes.forEach((function(t){t.colorBuffer.destroy(),t.positionBuffer.destroy(),t.dashTexture.destroy()})),this.passes.length=0,this}},{\"array-bounds\":70,\"array-normalize\":71,\"color-normalize\":125,earcut:177,\"es6-weak-map\":233,\"flatten-vertex-data\":244,glslify:536,\"object-assign\":499,\"parse-rect\":504,\"pick-by-alias\":511,\"to-float32\":577}],536:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],537:[function(t,e,r){\"use strict\";function n(t,e){return function(t){if(Array.isArray(t))return t}(t)||function(t,e){if(\"undefined\"==typeof Symbol||!(Symbol.iterator in Object(t)))return;var r=[],n=!0,i=!1,a=void 0;try{for(var o,s=t[Symbol.iterator]();!(n=(o=s.next()).done)&&(r.push(o.value),!e||r.length!==e);n=!0);}catch(t){i=!0,a=t}finally{try{n||null==s.return||s.return()}finally{if(i)throw a}}return r}(t,e)||a(t,e)||function(){throw new TypeError(\"Invalid attempt to destructure non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\")}()}function i(t){return function(t){if(Array.isArray(t))return o(t)}(t)||function(t){if(\"undefined\"!=typeof Symbol&&Symbol.iterator in Object(t))return Array.from(t)}(t)||a(t)||function(){throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\")}()}function a(t,e){if(t){if(\"string\"==typeof t)return o(t,e);var r=Object.prototype.toString.call(t).slice(8,-1);return\"Object\"===r&&t.constructor&&(r=t.constructor.name),\"Map\"===r||\"Set\"===r?Array.from(t):\"Arguments\"===r||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(r)?o(t,e):void 0}}function o(t,e){(null==e||e>t.length)&&(e=t.length);for(var r=0,n=new Array(e);r<e;r++)n[r]=t[r];return n}var s=t(\"color-normalize\"),l=t(\"array-bounds\"),c=t(\"color-id\"),u=t(\"@plotly/point-cluster\"),f=t(\"object-assign\"),h=t(\"glslify\"),p=t(\"pick-by-alias\"),d=t(\"update-diff\"),g=t(\"flatten-vertex-data\"),m=t(\"is-iexplorer\"),v=t(\"to-float32\"),y=t(\"parse-rect\"),x=b;function b(t,e){var r=this;if(!(this instanceof b))return new b(t,e);\"function\"==typeof t?(e||(e={}),e.regl=t):(e=t,t=null),e&&e.length&&(e.positions=e);var n,i=(t=e.regl)._gl,a=[];this.tooManyColors=m,n=t.texture({data:new Uint8Array(1020),width:255,height:1,type:\"uint8\",format:\"rgba\",wrapS:\"clamp\",wrapT:\"clamp\",mag:\"nearest\",min:\"nearest\"}),f(this,{regl:t,gl:i,groups:[],markerCache:[null],markerTextures:[null],palette:a,paletteIds:{},paletteTexture:n,maxColors:255,maxSize:100,canvas:i.canvas}),this.update(e);var o={uniforms:{constPointSize:!!e.constPointSize,pixelRatio:t.context(\"pixelRatio\"),palette:n,paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translate:t.prop(\"translate\"),translateFract:t.prop(\"translateFract\"),opacity:t.prop(\"opacity\"),marker:t.prop(\"markerTexture\")},attributes:{x:function(t,e){return e.xAttr||{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return e.borderSize.length?{buffer:e.sizeBuffer,stride:2,offset:1}:{constant:[Math.round(255*e.borderSize/r.maxSize)]}},colorId:function(t,e){return e.color.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:0}:{constant:r.tooManyColors?a.slice(4*e.color,4*e.color+4):[e.color]}},borderColorId:function(t,e){return e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4*e.borderColor,4*e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop(\"elements\"),count:t.prop(\"count\"),offset:t.prop(\"offset\"),primitive:\"points\"},s=f({},o);s.frag=h([\"precision 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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\"]),m&&(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),g=0;g<r.length;g++){var m=r[g];d[m]=p?p[m]: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 points\",snap:\"snap cluster lod tree\",size:\"sizes size radius\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",color:\"colors color fill fill-color fillColor\",borderColor:\"borderColors borderColor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range dataBox databox\",viewport:\"viewport viewPort viewBox viewbox\",opacity:\"opacity alpha transparency\",bounds:\"bound bounds boundaries limits\",tooManyColors:\"tooManyColors palette paletteMode optimizePalette enablePalette\"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:\"stream\",type:\"uint8\"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),colorBuffer:o.buffer({data:new 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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=g(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])}}]),m){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,C=n,L=C.count,I=C.color,P=C.borderColor,z=C.colorBuffer;if(t.tooManyColors){if(I.length||P.length){E=new Uint8Array(8*L);for(var O=0;O<L;O++){var D=I[O];E[8*O]=h[4*D],E[8*O+1]=h[4*D+1],E[8*O+2]=h[4*D+2],E[8*O+3]=h[4*D+3];var R=P[O];E[8*O+4]=h[4*R],E[8*O+5]=h[4*R+1],E[8*O+6]=h[4*R+2],E[8*O+7]=h[4*R+3]}}}else if(I.length||P.length){E=new Uint8Array(4*L+2);for(var F=0;F<L;F++)null!=I[F]&&(E[4*F]=I[F]%c,E[4*F+1]=Math.floor(I[F]/c)),null!=P[F]&&(E[4*F+2]=P[F]%c,E[4*F+3]=Math.floor(P[F]/c))}z({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\":57,\"array-bounds\":70,\"color-id\":123,\"color-normalize\":125,\"flatten-vertex-data\":244,glslify:538,\"is-iexplorer\":466,\"object-assign\":499,\"parse-rect\":504,\"pick-by-alias\":511,\"to-float32\":577,\"update-diff\":599}],538:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],539:[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 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u=0;u<s.columns;u++)s.bounds[u]=a(o.data[u],1)}o.range&&(s.range=o.range,n=s.range&&\"number\"!=typeof s.range[0]),o.domain&&(s.domain=o.domain);var d=!1;null!=o.padding&&(Array.isArray(o.padding)&&o.padding.length===s.columns&&\"number\"==typeof o.padding[o.padding.length-1]?(s.padding=o.padding.map(p),d=!0):s.padding=p(o.padding));var g=s.columns,m=s.count,v=s.viewport.width,y=s.viewport.height,x=s.viewport.x,b=s.viewport.y,_=v/g,w=y/g;s.passes=[];for(var T=0;T<g;T++)for(var k=0;k<g;k++)if((s.diagonal||k!==T)&&(s.upper||!(T>k))&&(s.lower||!(T<k))){var M=f(s.id,T,k),A=this.passes[M]||(this.passes[M]={});if(o.data&&(o.transpose?A.positions={x:{buffer:s.buffer,offset:k,count:m,stride:g},y:{buffer:s.buffer,offset:T,count:m,stride:g}}:A.positions={x:{buffer:s.buffer,offset:k*m,count:m},y:{buffer:s.buffer,offset:T*m,count:m}},A.bounds=h(s.bounds,T,k)),o.domain||o.viewport||o.data){var S=d?h(s.padding,T,k):s.padding;if(s.domain){var E=h(s.domain,T,k),C=E[0],L=E[1],I=E[2],P=E[3];A.viewport=[x+C*v+S[0],b+L*y+S[1],x+I*v-S[2],b+P*y-S[3]]}else 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\":70,\"array-range\":72,\"flatten-vertex-data\":244,\"parse-rect\":504,\"pick-by-alias\":511,raf:528,\"regl-scatter2d\":537}],540:[function(t,e,r){!function(t,n){\"object\"==typeof r&&\"undefined\"!=typeof 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",
+       " * repeat-string <https://github.com/jonschlinkert/repeat-string>\n",
+       " *\n",
+       " * Copyright (c) 2014-2015, Jon Schlinkert.\n",
+       " * Licensed under the MIT License.\n",
+       " */\n",
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r,n=parseInt(e);if(isNaN(n))\"\\u95f0\"===e[0]&&(r=!0,e=e.substring(1)),\"\\u6708\"===e[e.length-1]&&(e=e.substring(0,e.length-1)),n=1+[\"\\u4e00\",\"\\u4e8c\",\"\\u4e09\",\"\\u56db\",\"\\u4e94\",\"\\u516d\",\"\\u4e03\",\"\\u516b\",\"\\u4e5d\",\"\\u5341\",\"\\u5341\\u4e00\",\"\\u5341\\u4e8c\"].indexOf(e);else{var i=e[e.length-1];r=\"i\"===i||\"I\"===i}return this.toMonthIndex(t,n,r)},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:\"yyyy/mm/dd\",firstDay:1,isRTL:!1}},_validateYear:function(t,e){if(t.year&&(t=t.year()),\"number\"!=typeof t||t<1888||t>2111)throw e.replace(/\\{0\\}/,this.local.name);return t},toMonthIndex:function(t,e,r){var i=this.intercalaryMonth(t);if(r&&e!==i||e<1||e>12)throw n.local.invalidMonth.replace(/\\{0\\}/,this.local.name);return i?!r&&e<=i?e-1:e:e-1},toChineseMonth:function(t,e){t.year&&(e=(t=t.year()).month());var r=this.intercalaryMonth(t);if(e<0||e>(r?12:11))throw n.local.invalidMonth.replace(/\\{0\\}/,this.local.name);return r?e<r?e+1:e:e+1},intercalaryMonth:function(t){return t=this._validateYear(t),f[t-f[0]]>>13},isIntercalaryMonth:function(t,e){t.year&&(e=(t=t.year()).month());var r=this.intercalaryMonth(t);return!!r&&r===e},leapYear:function(t){return 0!==this.intercalaryMonth(t)},weekOfYear:function(t,e,r){var i,o=this._validateYear(t,n.local.invalidyear),s=h[o-h[0]],l=s>>9&4095,c=s>>5&15,u=31&s;(i=a.newDate(l,c,u)).add(4-(i.dayOfWeek()||7),\"d\");var f=this.toJD(t,e,r)-i.toJD();return 1+Math.floor(f/7)},monthsInYear:function(t){return this.leapYear(t)?13:12},daysInMonth:function(t,e){t.year&&(e=t.month(),t=t.year()),t=this._validateYear(t);var r=f[t-f[0]];if(e>(r>>13?12:11))throw n.local.invalidMonth.replace(/\\{0\\}/,this.local.name);return r&1<<12-e?30:29},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,s,r,n.local.invalidDate);t=this._validateYear(i.year()),e=i.month(),r=i.day();var o=this.isIntercalaryMonth(t,e),s=this.toChineseMonth(t,e),l=function(t,e,r,n,i){var a,o,s;if(\"object\"==typeof t)o=t,a=e||{};else{var l;if(!(\"number\"==typeof t&&t>=1888&&t<=2111))throw new Error(\"Lunar year outside range 1888-2111\");if(!(\"number\"==typeof e&&e>=1&&e<=12))throw new Error(\"Lunar month outside range 1 - 12\");if(!(\"number\"==typeof r&&r>=1&&r<=30))throw new Error(\"Lunar day outside range 1 - 30\");\"object\"==typeof n?(l=!1,a=n):(l=!!n,a=i||{}),o={year:t,month:e,day:r,isIntercalary:l}}s=o.day-1;var c,u=f[o.year-f[0]],p=u>>13;c=p&&(o.month>p||o.isIntercalary)?o.month:o.month-1;for(var d=0;d<c;d++){s+=u&1<<12-d?30:29}var g=h[o.year-h[0]],m=new Date(g>>9&4095,(g>>5&15)-1,(31&g)+s);return a.year=m.getFullYear(),a.month=1+m.getMonth(),a.day=m.getDate(),a}(t,s,r,o);return a.toJD(l.year,l.month,l.day)},fromJD:function(t){var e=a.fromJD(t),r=function(t,e,r,n){var i,a;if(\"object\"==typeof t)i=t,a=e||{};else{if(!(\"number\"==typeof t&&t>=1888&&t<=2111))throw new Error(\"Solar year outside range 1888-2111\");if(!(\"number\"==typeof e&&e>=1&&e<=12))throw new Error(\"Solar month outside range 1 - 12\");if(!(\"number\"==typeof r&&r>=1&&r<=31))throw new Error(\"Solar day outside range 1 - 31\");i={year:t,month:e,day:r},a=n||{}}var o=h[i.year-h[0]],s=i.year<<9|i.month<<5|i.day;a.year=s>=o?i.year:i.year-1,o=h[a.year-h[0]];var l,c=new Date(o>>9&4095,(o>>5&15)-1,31&o),u=new Date(i.year,i.month-1,i.day);l=Math.round((u-c)/864e5);var p,d=f[a.year-f[0]];for(p=0;p<13;p++){var g=d&1<<12-p?30:29;if(l<g)break;l-=g}var m=d>>13;!m||p<m?(a.isIntercalary=!1,a.month=1+p):p===m?(a.isIntercalary=!0,a.month=p):(a.isIntercalary=!1,a.month=p);return 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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:\"Coptic\",jdEpoch:1825029.5,daysPerMonth:[30,30,30,30,30,30,30,30,30,30,30,30,5],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Coptic\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Thout\",\"Paopi\",\"Hathor\",\"Koiak\",\"Tobi\",\"Meshir\",\"Paremhat\",\"Paremoude\",\"Pashons\",\"Paoni\",\"Epip\",\"Mesori\",\"Pi Kogi Enavot\"],monthNamesShort:[\"Tho\",\"Pao\",\"Hath\",\"Koi\",\"Tob\",\"Mesh\",\"Pat\",\"Pad\",\"Pash\",\"Pao\",\"Epi\",\"Meso\",\"PiK\"],dayNames:[\"Tkyriaka\",\"Pesnau\",\"Pshoment\",\"Peftoou\",\"Ptiou\",\"Psoou\",\"Psabbaton\"],dayNamesShort:[\"Tky\",\"Pes\",\"Psh\",\"Pef\",\"Pti\",\"Pso\",\"Psa\"],dayNamesMin:[\"Tk\",\"Pes\",\"Psh\",\"Pef\",\"Pt\",\"Pso\",\"Psa\"],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.coptic=a},{\"../main\":621,\"object-assign\":499}],609:[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:\"Discworld\",jdEpoch:1721425.5,daysPerMonth:[16,32,32,32,32,32,32,32,32,32,32,32,32],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Discworld\",epochs:[\"BUC\",\"UC\"],monthNames:[\"Ick\",\"Offle\",\"February\",\"March\",\"April\",\"May\",\"June\",\"Grune\",\"August\",\"Spune\",\"Sektober\",\"Ember\",\"December\"],monthNamesShort:[\"Ick\",\"Off\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Gru\",\"Aug\",\"Spu\",\"Sek\",\"Emb\",\"Dec\"],dayNames:[\"Sunday\",\"Octeday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Oct\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Oc\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:2,isRTL:!1}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),13},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),400},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/8)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]},daysInWeek:function(){return 8},dayOfWeek:function(t,e,r){return(this._validate(t,e,r,n.local.invalidDate).day()+1)%8},weekDay:function(t,e,r){var n=this.dayOfWeek(t,e,r);return n>=2&&n<=6},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{century:o[Math.floor((i.year()-1)/100)+1]||\"\"}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year()+(i.year()<0?1:0),e=i.month(),(r=i.day())+(e>1?16:0)+(e>2?32*(e-2):0)+400*(t-1)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t+.5)-Math.floor(this.jdEpoch)-1;var e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=a},{\"../main\":621,\"object-assign\":499}],610:[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 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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\":621,\"object-assign\":499}],611:[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\":621,\"object-assign\":499}],612:[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\":621,\"object-assign\":499}],613:[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\":621,\"object-assign\":499}],614:[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\":621,\"object-assign\":499}],615:[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\":621,\"object-assign\":499}],616:[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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c=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 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++)\"undefined\"==typeof 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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\":621,\"object-assign\":499}],619:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\"),a=n.instance();function 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(e.year());return a.leapYear(t)},weekOfYear:function(t,e,r){var 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>=1&&t<=this.yearsOffset?1:0)},_g2tYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)}}),n.calendars.thai=o},{\"../main\":621,\"object-assign\":499}],620:[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:\"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\":778,\"../../plot_api/plot_template\":816,\"../../registry\":910}],629:[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\"),g=r.hoverlabel||{};if(d){var m=a(\"hoverlabel.bgcolor\",g.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a(\"hoverlabel.bordercolor\",g.bordercolor||i.contrast(m));n.coerceFont(a,\"hoverlabel.font\",{family:g.font.family,size:g.font.size,color:g.font.color||v})}a(\"captureevents\",!!d)}},{\"../../lib\":778,\"../color\":643}],630:[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\":804,\"fast-isnumeric\":241}],631:[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 g=f[d],m=i.coerceRef(t,e,p,g,\"\",\"paper\");if(\"paper\"!==m)i.getFromId(p,m)._annIndices.push(e._index);if(i.coercePosition(e,p,a,m,g,.5),u){var v=\"a\"+g,y=i.coerceRef(t,e,p,v,\"pixel\",[\"pixel\",\"paper\"]);\"pixel\"!==y&&y!==m&&(y=e[v]=\"pixel\");var x=\"pixel\"===y?h[d]:.4;i.coercePosition(e,p,a,y,v,x)}a(g+\"anchor\"),a(g+\"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\":778,\"../../plots/array_container_defaults\":822,\"../../plots/cartesian/axes\":827,\"./attributes\":626,\"./common_defaults\":629}],632:[function(t,e,r){\"use strict\";var n=t(\"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\"),g=t(\"../../plot_api/plot_template\").arrayEditor,m=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=g(t.layout,_,e),A=M.modifyBase,S=M.modifyItem,E=M.getUpdateObj;w._infolayer.selectAll(\".\"+b+'[data-index=\"'+r+'\"]').remove();var C=\"clip\"+w._uid+\"_ann\"+r;if(e._input&&!1!==e.visible){var L={x:{},y:{}},I=+e.textangle||0,P=w._infolayer.append(\"g\").classed(b,!0).attr(\"data-index\",String(r)).style(\"opacity\",e.opacity),z=P.append(\"g\").classed(\"annotation-text-g\",!0),O=k[e.showarrow?\"annotationTail\":\"annotationPosition\"],D=e.captureevents||k.annotationText||O,R=z.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(\"#\"+C).data(U?[0]:[]);V.enter().append(\"clipPath\").classed(\"annclip\",!0).attr(\"id\",C).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(\"#\"+C).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(),g=u.bBox((f?n:G).node()),b=g.width,_=g.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,$,tt,et=X[Z],rt=e[et+\"ref\"]||et,nt=e[\"a\"+et+\"ref\"],it={x:v,y:x}[et],at=(I+(\"x\"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=q*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+\"anchor\"],ut=e[et+\"shift\"]*(\"x\"===et?1:-1),ft=L[et],ht=l.getRefType(rt);if(it&&\"domain\"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e[\"a\"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.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),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var gt=e[\"a\"+et];if(tt=ot*H(.5,e.xanchor)-st*H(.5,e.yanchor),nt===rt){var mt=l.getRefType(nt);\"domain\"===mt?(\"y\"===et&&(gt=1-gt),ft.tail=it._offset+it._length*gt):\"paper\"===mt?\"y\"===et?(gt=1-gt,ft.tail=T.t+T.h*gt):ft.tail=T.l+T.w*gt:ft.tail=it._offset+it.r2p(gt),K=tt}else ft.tail=J+gt,K=tt+gt;ft.text=ft.tail+tt;var vt=w[\"x\"===et?\"width\":\"height\"];if(\"paper\"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),\"pixel\"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*H($,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?C:null,t);else{var wt=N+_t-g.top,Tt=N+bt-g.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?C: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(L.x.text-B/2),Math.round(L.y.text-q/2)),z.attr({transform:\"rotate(\"+I+\",\"+L.x.text+\",\"+L.y.text+\")\"});var kt,Mt=function(r,n){P.selectAll(\".annotation-arrow-g\").remove();var l=L.x.head,f=L.y.head,h=L.x.tail+r,p=L.y.tail+n,g=L.x.text+r,b=L.y.text+n,_=o.rotationXYMatrix(I,g,b),w=o.apply2DTransform(_),M=o.apply2DTransform2(_),C=+j.attr(\"width\"),O=+j.attr(\"height\"),D=g-.5*C,F=D+C,B=b-.5*O,N=B+O,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=P.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(m(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\":809,\"../../plot_api/plot_template\":816,\"../annotations/attributes\":626}],636:[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\":778,\"../../plots/array_container_defaults\":822,\"../../plots/cartesian/axes\":827,\"../annotations/common_defaults\":629,\"./attributes\":635}],638:[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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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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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\":768,\"../../plot_api/edit_types\":809,\"../../plot_api/plot_template\":816,\"../../plots/animation_attributes\":821,\"../../plots/font_attributes\":855,\"../../plots/pad_attributes\":889,\"./constants\":734}],734:[function(t,e,r){\"use 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T={y:e.y,b:u.height*g[w],t:u.height*d[w]};\"fraction\"===e.lenmode?(T.l=0,T.xl=e.x-e.len*d[b],T.r=0,T.xr=e.x+e.len*g[b]):(T.x=e.x,T.l=u.outerLength*d[b],T.r=u.outerLength*g[b]),i.autoMargin(t,m(e),T)}function x(t,e,r){if(e.currentvalue.visible){var n,i,a=e._dims;switch(e.currentvalue.xanchor){case\"right\":n=a.inputAreaLength-f.currentValueInset-a.currentValueMaxWidth,i=\"left\";break;case\"center\":n=.5*a.inputAreaLength,i=\"middle\";break;default:n=f.currentValueInset,i=\"left\"}var l=s.ensureSingle(t,\"text\",f.labelClass,(function(t){t.attr({\"text-anchor\":i,\"data-notex\":1})})),u=e.currentvalue.prefix?e.currentvalue.prefix:\"\";if(\"string\"==typeof r)u+=r;else{var h=e.steps[e.active].label,d=e._gd._fullLayout._meta;d&&(h=s.templateString(h,d)),u+=h}e.currentvalue.suffix&&(u+=e.currentvalue.suffix),l.call(o.font,e.currentvalue.font).text(u).call(c.convertToTspans,e._gd);var g=c.lineCount(l),m=(a.currentValueMaxLines+1-g)*e.currentvalue.font.size*p;return 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w=e._anchorAxis;w&&w.autorange&&-1!==(e.ticklabelposition||\"\").indexOf(\"inside\")&&!V(a,e._id)&&(a._insideTickLabelsAutorange||(a._insideTickLabelsAutorange={}),a._insideTickLabelsAutorange[w._name+\".autorange\"]=w.autorange,_.push((function(){v.each((function(t,r){var n=dt(this);e._vals[r].bb=h.bBox(n.node())}))})));var T=s.syncOrAsync(_);return T&&T.then&&t._promises.push(T),T},B.getPxPosition=function(t,e){var r,n=t._fullLayout._size,i=e._id.charAt(0),a=e.side;return\"free\"!==e.anchor?r=e._anchorAxis:\"x\"===i?r={_offset:n.t+(1-(e.position||0))*n.h,_length:0}:\"y\"===i&&(r={_offset:n.l+(e.position||0)*n.w,_length:0}),\"top\"===a||\"left\"===a?r._offset:\"bottom\"===a||\"right\"===a?r._offset+r._length:void 0},B.shouldShowZeroLine=function(t,e,r){var n=s.simpleMap(e.range,e.r2l);return n[0]*n[1]<=0&&e.zeroline&&(\"linear\"===e.type||\"-\"===e.type)&&!(e.rangebreaks&&e.maskBreaks(0)===P)&&(pt(e,0)||!function(t,e,r,n){var i=r._mainAxis;if(!i)return;var 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r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchmove\",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchend\",(function(t){f(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),c.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),c}},{\"../cartesian/constants\":833,\"has-passive-events\":441,\"mouse-change\":483,\"mouse-event-offset\":484,\"mouse-wheel\":486}],866:[function(t,e,r){\"use strict\";var n=t(\"../cartesian/axes\"),i=t(\"../../lib/str2rgbarray\");function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],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.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=[\"xaxis\",\"yaxis\"];o.merge=function(t){var e,r,n,a,o,l,c,u,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?\"\":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]=\"auto\"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf(\"all\"):!!a,f=c?\"allticks\"===a:-1!==String(a).indexOf(\"ticks\"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var 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\":801,\"../cartesian/axes\":827}],867:[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\":684,\"../../constants/xmlns_namespaces\":754,\"../../plot_api/edit_types\":809,\"../cartesian\":840,\"../cartesian/attributes\":825,\"../cartesian/constants\":833,\"../get_data\":864,\"../layout_attributes\":881,\"./scene2d\":868}],868:[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\"),g=t(\"../cartesian/constraints\"),m=g.enforce,v=g.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:this.graphDiv._fullLayout._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis}};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();m(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 g=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),g.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 m=this.fullData[g.trace.index]||{},v=g.pointIndex,y=s.castHoverinfo(m,i,v);if(y&&\"all\"!==y){var x=y.split(\"+\");-1===x.indexOf(\"x\")&&(g.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(g.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(g.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(g.textLabel=void 0),-1===x.indexOf(\"name\")&&(g.name=void 0)}s.loneHover({x:g.screenCoord[0],y:g.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",g.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",g.traceCoord[1]),zLabel:g.traceCoord[2],text:g.textLabel,name:g.name,color:s.castHoverOption(m,v,\"bgcolor\")||g.color,borderColor:s.castHoverOption(m,v,\"bordercolor\"),fontFamily:s.castHoverOption(m,v,\"font.family\"),fontSize:s.castHoverOption(m,v,\"font.size\"),fontColor:s.castHoverOption(m,v,\"font.color\"),nameLength:s.castHoverOption(m,v,\"namelength\"),textAlign:s.castHoverOption(m,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\":661,\"../../components/fx\":683,\"../../lib/show_no_webgl_msg\":799,\"../../plots/cartesian/axes\":827,\"../../registry\":910,\"../cartesian/autorange\":826,\"../cartesian/constants\":833,\"../cartesian/constraints\":834,\"./camera\":865,\"./convert\":866,\"gl-plot2d\":317,\"gl-select-box\":333,\"gl-spikes2d\":342,\"webgl-context\":606}],869:[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\":684,\"../../constants/xmlns_namespaces\":754,\"../../lib\":778,\"../../plot_api/edit_types\":809,\"../get_data\":864,\"./layout/attributes\":870,\"./layout/defaults\":874,\"./layout/layout_attributes\":875,\"./scene\":879}],870:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],871:[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\":643,\"../../../lib/extend\":768,\"../../../plot_api/edit_types\":809,\"../../cartesian/layout_attributes\":841}],872:[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,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\":778,\"../../../plot_api/plot_template\":816,\"../../cartesian/axis_defaults\":829,\"../../cartesian/type_defaults\":852,\"./axis_attributes\":871,tinycolor2:576}],873:[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\":778,\"../../../lib/str2rgbarray\":801}],874:[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 g=n.getDfltFromLayout(\"dragmode\");if(!1!==g&&!g)if(g=\"orbit\",t.camera&&t.camera.up){var m=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(m&&v&&y?y/Math.sqrt(m*m+v*v+y*y)>.999&&(g=\"turntable\"):g=\"turntable\")}else g=\"turntable\";r(\"dragmode\",g),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\":643,\"../../../lib\":778,\"../../../registry\":910,\"../../get_data\":864,\"../../subplot_defaults\":904,\"./axis_defaults\":872,\"./layout_attributes\":875}],875:[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\":778,\"../../../lib/extend\":768,\"../../domain\":854,\"./axis_attributes\":871}],876:[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\":801}],877:[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\":778,\"../../cartesian/axes\":827}],878:[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])))}},{}],879:[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=t(\"../../plots/cartesian/axes\"),p=t(\"../../components/fx\"),d=t(\"../../lib/str2rgbarray\"),g=t(\"../../lib/show_no_webgl_msg\"),m=t(\"./project\"),v=t(\"./layout/convert\"),y=t(\"./layout/spikes\"),x=t(\"./layout/tick_marks\"),b=t(\"is-mobile\")({tablet:!0,featureDetect:!0});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=v(e,e[this.id]),this.spikeOptions=y(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:b,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)e=!1;else try{f.warn([\"webgl setup failed possibly due to\",b?\"disabling\":\"enabling\",\"preserveDrawingBuffer config.\",\"The device may not be supported by is-mobile module!\",\"Inverting preserveDrawingBuffer option in second attempt to create webgl scene.\"].join(\" \")),b=t.glOptions.preserveDrawingBuffer=!t.glOptions.preserveDrawingBuffer,this.glplot=s(t)}catch(r){b=t.glOptions.preserveDrawingBuffer=!t.glOptions.preserveDrawingBuffer,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),x(e),e.glplot.axes.update(e.axesOptions);for(var c,u=Object.keys(e.traces),d=null,g=e.glplot.selection,v=0;v<u.length;++v)\"skip\"!==(t=e.traces[u[v]]).data.hoverinfo&&t.handlePick(g)&&(d=t),t.setContourLevels&&t.setContourLevels();function y(t,r){var n=e.fullSceneLayout[t];return h.tickText(n,n.d2l(r),\"hover\").text}if(null!==d){var b=m(e.glplot.cameraParams,g.dataCoordinate);t=d.data;var _,w=r._fullData[t.index],T=g.index,k={xLabel:y(\"xaxis\",g.traceCoordinate[0]),yLabel:y(\"yaxis\",g.traceCoordinate[1]),zLabel:y(\"zaxis\",g.traceCoordinate[2])},M=p.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 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C={x:g.traceCoordinate[0],y:g.traceCoordinate[1],z:g.traceCoordinate[2],data:w._input,fullData:w,curveNumber:w.index,pointNumber:T};p.appendArrayPointValue(C,w,T),t._module.eventData&&(C=w._module.eventData(C,g,w,{},T));var L={points:[C]};e.fullSceneLayout.hovermode&&p.loneHover({trace:w,x:(.5+.5*b[0]/b[3])*s,y:(.5-.5*b[1]/b[3])*l,xLabel:k.xLabel,yLabel:k.yLabel,zLabel:k.zLabel,text:_,name:d.name,color:p.castHoverOption(w,T,\"bgcolor\")||d.color,borderColor:p.castHoverOption(w,T,\"bordercolor\"),fontFamily:p.castHoverOption(w,T,\"font.family\"),fontSize:p.castHoverOption(w,T,\"font.size\"),fontColor:p.castHoverOption(w,T,\"font.color\"),nameLength:p.castHoverOption(w,T,\"namelength\"),textAlign:p.castHoverOption(w,T,\"align\"),hovertemplate:f.castOption(w,T,\"hovertemplate\"),hovertemplateLabels:f.extendFlat({},C,k),eventData:[C]},{container:n,gd:r}),g.buttons&&g.distance<5?r.emit(\"plotly_click\",L):r.emit(\"plotly_hover\",L),c=L}else 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n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(d(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&M(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var g,m=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){m[0][a]=1/0,m[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,A=w._trace.data._pad||0;\"ErrorBars\"===w.constructor.name&&s._lowerLogErrorBound?m[0][a]=Math.min(m[0][a],s._lowerLogErrorBound):m[0][a]=Math.min(m[0][a],T[0][a]/h[a]-A),m[1][a]=Math.max(m[1][a],T[1][a]/h[a]+A)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);m[0][a]=Math.min(m[0][a],E),m[1][a]=Math.max(m[1][a],E)}}if(\"rangemode\"in s&&\"tozero\"===s.rangemode&&(m[0][a]=Math.min(m[0][a],0),m[1][a]=Math.max(m[1][a],0)),m[0][a]>m[1][a])m[0][a]=-1,m[1][a]=1;else{var C=m[1][a]-m[0][a];m[0][a]-=C/32,m[1][a]+=C/32}if(\"reversed\"===s.autorange){var L=m[0][a];m[0][a]=m[1][a],m[1][a]=L}}else{var I=s.range;m[0][a]=s.r2l(I[0]),m[1][a]=s.r2l(I[1])}m[0][a]===m[1][a]&&(m[0][a]-=1,m[1][a]+=1),v[a]=m[1][a]-m[0][a],this.glplot.setBounds(a,{min:m[0][a]*h[a],max:m[1][a]*h[a]})}var P=c.aspectmode;if(\"cube\"===P)g=[1,1,1];else if(\"manual\"===P){var z=c.aspectratio;g=[z.x,z.y,z.z]}else{if(\"auto\"!==P&&\"data\"!==P)throw new Error(\"scene.js aspectRatio was not one 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r=t.visible;return\"undefined\"==typeof r||!0===r})),d=!1,g=p.map((function(t,e){return d=d||\"undefined\"!=typeof t.groupId,t}));if(d){var m=n.nest().key((function(t,e){return\"undefined\"!=typeof t.groupId?t.groupId:\"unstacked\"})).entries(g),v=[],y=m.map((function(t,e){if(\"unstacked\"===t.key)return t.values;var r=t.values[0].r.map((function(t,e){return 0}));return t.values.forEach((function(t,e,n){t.yStack=[r],v.push(r),r=o.util.sumArrays(t.r,r)})),t.values}));p=n.merge(y)}p.forEach((function(t,e){t.t=Array.isArray(t.t[0])?t.t:[t.t],t.r=Array.isArray(t.r[0])?t.r:[t.r]}));var x=Math.min(h.width-h.margin.left-h.margin.right,h.height-h.margin.top-h.margin.bottom)/2;x=Math.max(10,x);var b,_=[h.margin.left+x,h.margin.top+x];d?b=[0,n.max(o.util.sumArrays(o.util.arrayLast(p).r[0],o.util.arrayLast(v)))]:b=n.extent(o.util.flattenArray(p.map((function(t,e){return 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P=n.range.apply(this,C);if(P=P.map((function(t,e){return parseFloat(t.toPrecision(12))})),s=n.scale.linear().domain(C.slice(0,2)).range(\"clockwise\"===h.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=s.domain(),u.layout.angularAxis.endPadding=A?E:0,\"undefined\"==typeof(t=n.select(this).select(\"svg.chart-root\"))||t.empty()){var z=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g class='legend-group'></g>' + '<g class='tooltips-group'></g>' + '<g class='title-group'><text></text></g>' + '</g>' + '</svg>\",\"application/xml\"),O=this.appendChild(this.ownerDocument.importNode(z.documentElement,!0));t=n.select(O)}t.select(\".guides-group\").style({\"pointer-events\":\"none\"}),t.select(\".angular.axis-group\").style({\"pointer-events\":\"none\"}),t.select(\".radial.axis-group\").style({\"pointer-events\":\"none\"});var D,R=t.select(\".chart-group\"),F={fill:\"none\",stroke:h.tickColor},B={\"font-size\":h.font.size,\"font-family\":h.font.family,fill:h.font.color,\"text-shadow\":[\"-1px 0px\",\"1px -1px\",\"-1px 1px\",\"1px 1px\"].map((function(t,e){return\" \"+t+\" 0 \"+h.font.outlineColor})).join(\",\")};if(h.showLegend){D=t.select(\".legend-group\").attr({transform:\"translate(\"+[x,h.margin.top]+\")\"}).style({display:\"block\"});var N=p.map((function(t,e){var r=o.util.cloneJson(t);return r.symbol=\"DotPlot\"===t.geometry?t.dotType||\"circle\":\"LinePlot\"!=t.geometry?\"square\":\"line\",r.visibleInLegend=\"undefined\"==typeof 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U=[(h.width-(h.margin.left+h.margin.right+2*x+(j?j.width:0)))/2,(h.height-(h.margin.top+h.margin.bottom+2*x))/2];if(U[0]=Math.max(0,U[0]),U[1]=Math.max(0,U[1]),t.select(\".outer-group\").attr(\"transform\",\"translate(\"+U+\")\"),h.title&&h.title.text){var V=t.select(\"g.title-group text\").style(B).text(h.title.text),q=V.node().getBBox();V.attr({x:_[0]-q.width/2,y:_[1]-x-20})}var H=t.select(\".radial.axis-group\");if(h.radialAxis.gridLinesVisible){var G=H.selectAll(\"circle.grid-circle\").data(r.ticks(5));G.enter().append(\"circle\").attr({class:\"grid-circle\"}).style(F),G.attr(\"r\",r),G.exit().remove()}H.select(\"circle.outside-circle\").attr({r:x}).style(F);var Y=t.select(\"circle.background-circle\").attr({r:x}).style({fill:h.backgroundColor,stroke:h.stroke});function W(t,e){return s(t)%360+h.orientation}if(h.radialAxis.visible){var 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i=o.util.convertToCartesian(x+12,r+180);lt.text(o.util.round(it)).move([i[0]+_[0],i[1]+_[1]])})).on(\"mouseout.angular-guide\",(function(t,e){ot.select(\"line\").style({opacity:0})}))}var ht=ot.select(\"circle\").style({stroke:\"grey\",fill:\"none\"});R.on(\"mousemove.radial-guide\",(function(t,e){var n=o.util.getMousePos(Y).radius;ht.attr({r:n}).style({opacity:.5}),at=r.invert(o.util.getMousePos(Y).radius);var i=o.util.convertToCartesian(n,h.radialAxis.orientation);ct.text(o.util.round(at)).move([i[0]+_[0],i[1]+_[1]])})).on(\"mouseout.radial-guide\",(function(t,e){ht.style({opacity:0}),ut.hide(),lt.hide(),ct.hide()})),t.selectAll(\".geometry-group .mark\").on(\"mouseover.tooltip\",(function(e,r){var i=n.select(this),a=this.style.fill,s=\"black\",l=this.style.opacity||1;if(i.attr({\"data-opacity\":l}),a&&\"none\"!==a){i.attr({\"data-fill\":a}),s=n.hsl(a).darker().toString(),i.style({fill:s,opacity:1});var c={t:o.util.round(e[0]),r:o.util.round(e[1])};k&&(c.t=w[e[0]]);var u=\"t: 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o=n.hsl(a.color).l,s=o>=.5?\"#aaa\":\"white\",u=o>=.5?\"black\":\"white\",f=i||\"\";e.style({fill:u,\"font-size\":a.fontSize+\"px\"}).text(f);var h=a.padding,p=e.node().getBBox(),d={fill:a.color,stroke:s,\"stroke-width\":\"2px\"},g=p.width+2*h+l,m=p.height+2*h;return r.attr({d:\"M\"+[[l,-m/2],[l,-m/4],[a.hasTick?0:l,0],[l,m/4],[l,m/2],[g,m/2],[g,-m/2]].join(\"L\")+\"Z\"}).style(d),t.attr({transform:\"translate(\"+[l,-m/2+2*h]+\")\"}),t.style({display:\"block\"}),c},c.move=function(e){if(t)return t.attr({transform:\"translate(\"+[e[0],e[1]]+\")\"}).style({display:\"block\"}),c},c.hide=function(){if(t)return t.style({display:\"none\"}),c},c.show=function(){if(t)return t.style({display:\"block\"}),c},c.config=function(t){return i(a,t),c},c},o.tooltipPanel.uid=1,o.adapter={},o.adapter.plotly=function(){var t={convert:function(t,e){var r={};if(t.data&&(r.data=t.data.map((function(t,r){var 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s=i({},t.layout);if([[s,[\"plot_bgcolor\"],[\"backgroundColor\"]],[s,[\"showlegend\"],[\"showLegend\"]],[s,[\"radialaxis\"],[\"radialAxis\"]],[s,[\"angularaxis\"],[\"angularAxis\"]],[s.angularaxis,[\"showline\"],[\"gridLinesVisible\"]],[s.angularaxis,[\"showticklabels\"],[\"labelsVisible\"]],[s.angularaxis,[\"nticks\"],[\"ticksCount\"]],[s.angularaxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.angularaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.angularaxis,[\"range\"],[\"domain\"]],[s.angularaxis,[\"endpadding\"],[\"endPadding\"]],[s.radialaxis,[\"showline\"],[\"gridLinesVisible\"]],[s.radialaxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.radialaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.radialaxis,[\"range\"],[\"domain\"]],[s.angularAxis,[\"showline\"],[\"gridLinesVisible\"]],[s.angularAxis,[\"showticklabels\"],[\"labelsVisible\"]],[s.angularAxis,[\"nticks\"],[\"ticksCount\"]],[s.angularAxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.angularAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.angularAxis,[\"range\"],[\"domain\"]],[s.angularAxis,[\"endpadding\"],[\"endPadding\"]],[s.radialAxis,[\"showline\"],[\"gridLinesVisible\"]],[s.radialAxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.radialAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.radialAxis,[\"range\"],[\"domain\"]],[s.font,[\"outlinecolor\"],[\"outlineColor\"]],[s.legend,[\"traceorder\"],[\"reverseOrder\"]],[s,[\"labeloffset\"],[\"labelOffset\"]],[s,[\"defaultcolorrange\"],[\"defaultColorRange\"]]].forEach((function(t,r){o.util.translator.apply(null,t.concat(e))})),e?(\"undefined\"!=typeof 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e,r,n,i,a=t[0],o=t[1]-a,s=L(a,360),l=s+o,c=Math.cos(I(s)),u=Math.sin(I(s)),f=Math.cos(I(l)),h=Math.sin(I(l));i=s<=90&&l>=90||s>90&&l>=450?1:u<=0&&h<=0?0:Math.max(u,h);e=s<=180&&l>=180||s>180&&l>=540?-1:c>=0&&f>=0?0:Math.min(c,f);r=s<=270&&l>=270||s>270&&l>=630?-1:u>=0&&h>=0?0:Math.min(u,h);n=l>=360?1:c<=0&&f<=0?0:Math.max(c,f);return[e,r,n,i]}(p),b=x[2]-x[0],_=x[3]-x[1],w=h/f,T=Math.abs(_/b);w>T?(d=f,y=(h-(g=f*T))/n.h/2,m=[o[0],o[1]],v=[s[0]+y,s[1]-y]):(g=h,y=(f-(d=h/T))/n.w/2,m=[o[0]+y,o[1]-y],v=[s[0],s[1]]),this.xLength2=d,this.yLength2=g,this.xDomain2=m,this.yDomain2=v;var 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e=m(t),r=Math.cos(e),n=Math.sin(e);return\"M\"+[f+u*r,p-u*n]+\"L\"+[f+a*r,p-a*n]},transFn:o.noop,crisp:!1}),h.drawLabels(n,g,{vals:w,layer:i[\"angular-axis\"],repositionOnUpdate:!0,transFn:function(t){return v(m(t))},labelFns:x})}F(i[\"angular-line\"].select(\"path\"),d.showline,{d:r.pathSubplot(),transform:l(f,p)}).attr(\"stroke-width\",d.linewidth).call(c.stroke,d.linecolor)},O.updateFx=function(t,e){this.gd._context.staticPlot||(this.updateAngularDrag(t),this.updateRadialDrag(t,e,0),this.updateRadialDrag(t,e,1),this.updateMainDrag(t))},O.updateMainDrag=function(t){var e,r,s=this,c=s.gd,u=s.layers,f=t._zoomlayer,h=S.MINZOOM,p=S.OFFEDGE,d=s.radius,g=s.innerRadius,x=s.cx,T=s.cy,k=s.cxx,M=s.cyy,A=s.sectorInRad,C=s.vangles,L=s.radialAxis,I=E.clampTiny,P=E.findXYatLength,z=E.findEnclosingVertexAngles,O=S.cornerHalfWidth,D=S.cornerLen/2,R=m.makeDragger(u,\"path\",\"maindrag\",\"crosshair\");n.select(R).attr(\"d\",s.pathSubplot()).attr(\"transform\",l(x,T));var F,B,N,j,U,V,q,H,G,Y={element:R,gd:c,subplot:s.id,plotinfo:{id:s.id,xaxis:s.xaxis,yaxis:s.yaxis},xaxes:[s.xaxis],yaxes:[s.yaxis]};function W(t,e){return Math.sqrt(t*t+e*e)}function X(t,e){return W(t-k,e-M)}function Z(t,e){return Math.atan2(M-e,t-k)}function J(t,e){return[t*Math.cos(e),t*Math.sin(-e)]}function K(t,e){if(0===t)return s.pathSector(2*O);var r=D/t,n=e-r,i=e+r,a=Math.max(0,Math.min(t,d)),o=a-O,l=a+O;return\"M\"+J(o,n)+\"A\"+[o,o]+\" 0,0,0 \"+J(o,i)+\"L\"+J(l,i)+\"A\"+[l,l]+\" 0,0,1 \"+J(l,n)+\"Z\"}function Q(t,e,r){if(0===t)return s.pathSector(2*O);var n,i,a=J(t,e),o=J(t,r),l=I((a[0]+o[0])/2),c=I((a[1]+o[1])/2);if(l&&c){var u=c/l,f=-1/u,h=P(O,u,l,c);n=P(D,f,h[0][0],h[0][1]),i=P(D,f,h[1][0],h[1][1])}else{var p,d;c?(p=D,d=O):(p=O,d=D),n=[[l-p,c-d],[l+p,c-d]],i=[[l-p,c+d],[l+p,c+d]]}return\"M\"+n.join(\"L\")+\"L\"+i.reverse().join(\"L\")+\"Z\"}function $(t,e){return e=Math.max(Math.min(e,d),g),t<p?t=0:d-t<p?t=d:e<p?e=0:d-e<p&&(e=d),Math.abs(e-t)>h?(t<e?(N=t,j=e):(N=e,j=t),!0):(N=null,j=null,!1)}function tt(t,e){t=t||U,e=e||\"M0,0Z\",H.attr(\"d\",t),G.attr(\"d\",e),m.transitionZoombox(H,G,V,q),V=!0;var r={};at(r),c.emit(\"plotly_relayouting\",r)}function et(t,n){var i,a,o=F+(t*=e),l=B+(n*=r),c=X(F,B),u=Math.min(X(o,l),d),f=Z(F,B);$(c,u)&&(i=U+s.pathSector(j),N&&(i+=s.pathSector(N)),a=K(N,f)+K(j,f)),tt(i,a)}function rt(t,e,r,n){var i=E.findIntersectionXY(r,n,r,[t-k,M-e]);return W(i[0],i[1])}function nt(t,e){var r,n,i=F+t,a=B+e,o=Z(F,B),l=Z(i,a),c=z(o,C),u=z(l,C);$(rt(F,B,c[0],c[1]),Math.min(rt(i,a,u[0],u[1]),d))&&(r=U+s.pathSector(j),N&&(r+=s.pathSector(N)),n=[Q(N,c[0],c[1]),Q(j,c[0],c[1])].join(\" \")),tt(r,n)}function it(){if(m.removeZoombox(c),null!==N&&null!==j){var t={};at(t),m.showDoubleClickNotifier(c),a.call(\"_guiRelayout\",c,t)}}function at(t){var e=L._rl,r=(e[1]-e[0])/(1-g/d)/d,n=[e[0]+(N-g)*r,e[0]+(j-g)*r];t[s.id+\".radialaxis.range\"]=n}function ot(t,e){var r=c._fullLayout.clickmode;if(m.removeZoombox(c),2===t){var n={};for(var i in s.viewInitial)n[s.id+\".\"+i]=s.viewInitial[i];c.emit(\"plotly_doubleclick\",null),a.call(\"_guiRelayout\",c,n)}r.indexOf(\"select\")>-1&&1===t&&_(e,c,[s.xaxis],[s.yaxis],s.id,Y),r.indexOf(\"event\")>-1&&y.click(c,e,s.id)}Y.prepFn=function(t,n,a){var l=c._fullLayout.dragmode,u=R.getBoundingClientRect();c._fullLayout._calcInverseTransform(c);var h=c._fullLayout._invTransform;e=c._fullLayout._invScaleX,r=c._fullLayout._invScaleY;var p=o.apply3DTransform(h)(n-u.left,a-u.top);if(F=p[0],B=p[1],C){var g=E.findPolygonOffset(d,A[0],A[1],C);F+=k+g[0],B+=M+g[1]}switch(l){case\"zoom\":Y.moveFn=C?nt:et,Y.clickFn=ot,Y.doneFn=it,function(){N=null,j=null,U=s.pathSubplot(),V=!1;var t=c._fullLayout[s.id];q=i(t.bgcolor).getLuminance(),(H=m.makeZoombox(f,q,x,T,U)).attr(\"fill-rule\",\"evenodd\"),G=m.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)},O.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,g=i.radialAxis,y=S.radialDragBoxSize,x=y/2;if(g.visible){var b,_,T,A=I(i.radialAxisAngle),E=g._rl,C=E[0],L=E[1],z=E[r],O=.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=m.makeRectDragger(u,T,\"crosshair\",-x,-x,y,y),U={element:j,gd:c};F(n.select(j),g.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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v=e.gd._fullLayout,y=v[e.id],x=_(A+c*t._invScaleX,E+m*t._invScaleY),b=P(x-z);if(L=C+b,i.frontplot.attr(\"transform\",l(e.xOffset2,e.yOffset2)+s([-b,d,g])),e.vangles){I=e.radialAxisAngle+b;var w=l(h,p)+s(-b),T=l(h,p)+s(-I);i.bg.attr(\"transform\",w),i[\"radial-grid\"].attr(\"transform\",w),i[\"radial-axis\"].attr(\"transform\",T),i[\"radial-line\"].select(\"line\").attr(\"transform\",T),e.updateRadialAxisTitle(v,y,I)}else e.clipPaths.forTraces.select(\"path\").attr(\"transform\",l(d,g)+s(b));D.each((function(){var t=n.select(this),e=u.getTranslate(t);t.attr(\"transform\",l(e.x,e.y)+s([b]))})),R.each((function(){var t=n.select(this),e=t.select(\"text\"),r=u.getTranslate(t);t.attr(\"transform\",s([b,e.attr(\"x\"),e.attr(\"y\")])+l(r.x,r.y))})),f.rotation=o.modHalf(L,360),e.updateAngularAxis(v,y),e._hasClipOnAxisFalse&&!o.isFullCircle(e.sectorInRad)&&O.call(u.hideOutsideRangePoints,e);var S=!1;for(var F in e.traceHash)if(a.traceIs(F,\"gl\")){var 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x={};x[h]=[v/d,(v+1)/d],a(l,e,m,x),o.id=y,f(s,l,m,o)}}},{\"../lib\":778,\"../plot_api/plot_template\":816,\"./domain\":854}],905:[function(t,e,r){\"use strict\";['Variables are inserted using %{variable}, for example \"y: %{y}\".','Numbers are formatted using d3-format\\'s syntax %{variable:d3-format}, for example \"Price: %{y:$.2f}\".',t(\"../constants/docs\").FORMAT_LINK,\"for details on the formatting syntax.\",'Dates are formatted using d3-time-format\\'s syntax %{variable|d3-time-format}, for example \"Day: %{2019-01-01|%A}\".',t(\"../constants/docs\").DATE_FORMAT_LINK,\"for details on the date formatting syntax.\"].join(\" \");function n(t){var e=t.description?\" \"+t.description:\"\",r=t.keys||[];if(r.length>0){for(var n=[],i=0;i<r.length;i++)n[i]=\"`\"+r[i]+\"`\";e+=\"Finally, the template string has access to \",e=1===r.length?\"variable \"+n[0]:\"variables \"+n.slice(0,-1).join(\", \")+\" and \"+n.slice(-1)+\".\"}return e}r.hovertemplateAttrs=function(t,e){t=t||{};n(e=e||{});var r={valType:\"string\",dflt:\"\",editType:t.editType||\"none\"};return!1!==t.arrayOk&&(r.arrayOk=!0),r},r.texttemplateAttrs=function(t,e){t=t||{};n(e=e||{});var r={valType:\"string\",dflt:\"\",editType:t.editType||\"calc\"};return!1!==t.arrayOk&&(r.arrayOk=!0),r}},{\"../constants/docs\":748}],906:[function(t,e,r){\"use strict\";var n=t(\"./ternary\"),i=t(\"../../plots/get_data\").getSubplotCalcData,a=t(\"../../lib\").counterRegex;r.name=\"ternary\";var o=r.attr=\"subplot\";r.idRoot=\"ternary\",r.idRegex=r.attrRegex=a(\"ternary\"),(r.attributes={})[o]={valType:\"subplotid\",dflt:\"ternary\",editType:\"calc\"},r.layoutAttributes=t(\"./layout_attributes\"),r.supplyLayoutDefaults=t(\"./layout_defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t.calcdata,a=e._subplots.ternary,o=0;o<a.length;o++){var s=a[o],l=i(r,\"ternary\",s),c=e[s]._subplot;c||(c=new 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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\":642,\"../../lib/extend\":768,\"../../plot_api/edit_types\":809,\"../cartesian/layout_attributes\":841,\"../domain\":854}],908:[function(t,e,r){\"use 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n=t(\"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\"),g=t(\"../../components/dragelement\"),m=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 r=e[this.id],n=e._size;this._hasClipOnAxisFalse=!1;for(var i=0;i<t.length;i++){if(!1===t[i][0].trace.cliponaxis){this._hasClipOnAxisFalse=!0;break}}this.updateLayers(r),this.adjustLayout(r,n),p.generalUpdatePerTraceModule(this.graphDiv,this,t,r),this.layers.plotbg.select(\"path\").call(c.fill,r.bgcolor)},S.makeFramework=function(t){var e=this.graphDiv,r=t[this.id],n=this.clipId=\"clip\"+this.layoutId+this.id,i=this.clipIdRelative=\"clip-relative\"+this.layoutId+this.id;this.clipDef=o.ensureSingleById(t._clips,\"clipPath\",n,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.clipDefRelative=o.ensureSingleById(t._clips,\"clipPath\",i,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.plotContainer=o.ensureSingle(this.container,\"g\",this.id),this.updateLayers(r),u.setClipUrl(this.layers.backplot,n,e),u.setClipUrl(this.layers.grids,n,e)},S.updateLayers=function(t){var e=this.layers,r=[\"draglayer\",\"plotbg\",\"backplot\",\"grids\"];\"below 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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,g=(d.x[0]+d.x[1])/2,m=(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*g-i/2,n=e.t+e.h*(1-m)-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:[g-o/2,g+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:[m-l/2,m+l/2],_id:\"y\"},f(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var 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z=s(r-S._offset,n+a);p.layers.baxis.attr(\"transform\",z),p.layers.bgrid.attr(\"transform\",z);var O=s(r+i/2,n)+\"rotate(30)\"+s(0,-A._offset);p.layers.aaxis.attr(\"transform\",O),p.layers.agrid.attr(\"transform\",O);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,g=d?l:i.isPtInsideSector,m=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 g(y,x,[t.rp0,t.rp1],[t.thetag0,t.thetag1],d)?m+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(\"../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,g=c(\"text\"),m=c(\"texttemplate\");if(m||(d=c(\"textinfo\",Array.isArray(g)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),m||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 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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\":1167}],1061:[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\":778,\"./layout_attributes\":1060}],1062:[function(t,e,r){\"use strict\";var 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0===d._deltaLastValue&&(d._deltaLastValue=a(r[0]));var l=C.select(\"text.delta\");function p(){l.text(o(a(r[0]),i)).call(m.fill,s(r[0])).call(f.convertToTspans,t)}return l.call(c.font,d.delta.font).call(m.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=P(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(m.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}(),O+=d.delta.position+d.delta.font.size+d.delta.font.family+d.delta.valueformat,O+=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=C.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=P(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)}(),O+=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),O,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,O,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}),z.attr({dx:D,dy:R})}(d._hasNumber||d._hasDelta)&&C.attr(\"transform\",(function(){var t=s.numbersScaler(p);O+=t[2];var e,r=M(d,\"numbersScale\",1,t[0],O,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=g-r*n;return i=M(d,\"numbersTranslate\",0,i,O,Math.max),o(i,e)+a(r)}))}(t,I,e,{numbersX:p,numbersY:A,numbersScaler:S,transitionOpts:r,onComplete:d}),P&&(E={range:L.gauge.axis.range,color:L.gauge.bgcolor,line:{color:L.gauge.bordercolor,width:0},thickness:1},C={range:L.gauge.axis.range,color:\"rgba(0, 0, 0, 0)\",line:{color:L.gauge.bordercolor,width:L.gauge.borderwidth},thickness:1});var H=I.selectAll(\"g.angular\").data(z?e:[]);H.exit().remove();var G=I.selectAll(\"g.angularaxis\").data(z?e:[]);G.exit().remove(),z&&function(t,e,r,i){var a,c,u,f,p=r[0].trace,d=i.size,g=i.radius,m=i.innerRadius,v=i.gaugeBg,y=i.gaugeOutline,x=[d.l+d.w/2,d.t+d.h/2+g/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 C(t){return n.svg.arc().innerRadius((m+g)/2-t/2*(g-m)).outerRadius((m+g)/2+t/2*(g-m)).startAngle(-S)}function L(t){t.attr(\"d\",(function(t){return C(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.setScale();var I=function(t){return(a.range[0]-t.x)/(a.range[1]-a.range[0])*Math.PI+Math.PI},P={},z=h.makeLabelFns(a,0).labelStandoff;P.xFn=function(t){var e=I(t);return Math.cos(e)*z},P.yFn=function(t){var e=I(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(z+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*l)},P.anchorFn=function(t){var e=I(t),r=Math.cos(e);return Math.abs(r)<.1?\"middle\":r>0?\"start\":\"end\"},P.heightFn=function(t,e,r){var n=I(t);return-.5*(1+Math.sin(n))*r};var O=function(t){return o(x[0]+g*Math.cos(t),x[1]-g*Math.sin(t))};u=function(t){return O(I(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=I(t);return O(e)+\"rotate(\"+-s(e)+\")\"}}),h.drawLabels(t,a,{vals:c,layer:k,transFn:u,labelFns:P})}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(L).call(_),F.exit().remove();var B=C(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&&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(L).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(L).call(_),Y.exit().remove()}(t,0,e,{radius:N,innerRadius:j,gauge:H,layer:G,size:R,gaugeBg:E,gaugeOutline:C,transitionOpts:r,onComplete:d});var Y=I.selectAll(\"g.bullet\").data(O?e:[]);Y.exit().remove();var W=I.selectAll(\"g.bulletaxis\").data(O?e:[]);W.exit().remove(),O&&function(t,e,r,n){var i,a,s,l,c,f=r[0].trace,p=n.gauge,d=n.layer,g=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 C(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 L=[g].concat(f.gauge.steps),I=p.selectAll(\"g.bg-bullet\").data(L);I.enter().append(\"g\").classed(\"bg-bullet\",!0).append(\"rect\"),I.select(\"rect\").call(C).call(_),I.exit().remove();var P=p.selectAll(\"g.value-bullet\").data([f.gauge.bar]);P.enter().append(\"g\").classed(\"value-bullet\",!0).append(\"rect\"),P.select(\"rect\").attr(\"height\",A).attr(\"y\",(M-A)/2).call(_),b(T)?P.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)))):P.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);P.exit().remove();var z=r.filter((function(){return f.gauge.threshold.value})),O=p.selectAll(\"g.threshold-bullet\").data(z);O.enter().append(\"g\").classed(\"threshold-bullet\",!0).append(\"line\"),O.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(m.stroke,f.gauge.threshold.line.color).style(\"stroke-width\",f.gauge.threshold.line.width),O.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(C).call(_),D.exit().remove()}(t,0,e,{gauge:Y,layer:W,size:R,gaugeBg:E,gaugeOutline:C,transitionOpts:r,onComplete:d});var X=I.selectAll(\"text.title\").data(e);X.exit().remove(),X.enter().append(\"text\").classed(\"title\",!0),X.attr(\"text-anchor\",(function(){return O?v.right:v[L.title.align]})).text(L.title.text).call(c.font,L.title.font).call(f.convertToTspans,t),X.attr(\"transform\",(function(){var t,e=R.l+R.w*y[L.title.align],r=u.titlePadding,n=c.bBox(X.node());if(P){if(z)if(L.gauge.axis.visible)t=c.bBox(G.node()).top-r-n.bottom;else t=R.t+R.h/2-N/2-n.bottom-r;O&&(t=A-(n.top+n.bottom)/2,e=R.l-u.bulletPadding*R.w)}else t=L._numbersTop-r-n.bottom;return o(e,t)}))}))}},{\"../../components/color\":643,\"../../components/drawing\":665,\"../../constants/alignment\":745,\"../../lib\":778,\"../../lib/svg_text_utils\":802,\"../../plots/cartesian/axes\":827,\"../../plots/cartesian/axis_defaults\":829,\"../../plots/cartesian/layout_attributes\":841,\"../../plots/cartesian/position_defaults\":844,\"./constants\":1118,d3:169}],1122:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/template_attributes\").hovertemplateAttrs,a=t(\"../mesh3d/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll;var c=e.exports=l(s({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:i(),showlegend:s({},o.showlegend,{dflt:!1})},n(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{opacity:a.opacity,lightposition:a.lightposition,lighting:a.lighting,flatshading:a.flatshading,contour:a.contour,hoverinfo:s({},o.hoverinfo)}),\"calc\",\"nested\");c.flatshading.dflt=!0,c.lighting.facenormalsepsilon.dflt=0,c.x.editType=c.y.editType=c.z.editType=c.value.editType=\"calc+clearAxisTypes\",c.transforms=void 0},{\"../../components/colorscale/attributes\":650,\"../../lib/extend\":768,\"../../plot_api/edit_types\":809,\"../../plots/attributes\":823,\"../../plots/template_attributes\":905,\"../mesh3d/attributes\":1127}],1123:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/calc\"),i=t(\"../streamtube/calc\").processGrid,a=t(\"../streamtube/calc\").filter;e.exports=function(t,e){e._len=Math.min(e.x.length,e.y.length,e.z.length,e.value.length),e._x=a(e.x,e._len),e._y=a(e.y,e._len),e._z=a(e.z,e._len),e._value=a(e.value,e._len);var r=i(e);e._gridFill=r.fill,e._Xs=r.Xs,e._Ys=r.Ys,e._Zs=r.Zs,e._len=r.len;for(var o=1/0,s=-1/0,l=0;l<e._len;l++){var c=e._value[l];o=Math.min(o,c),s=Math.max(s,c)}e._minValues=o,e._maxValues=s,e._vMin=void 0===e.isomin||null===e.isomin?o:e.isomin,e._vMax=void 0===e.isomax||null===e.isomin?s:e.isomax,n(t,e,{vals:[e._vMin,e._vMax],containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":651,\"../streamtube/calc\":1294}],1124:[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 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0),\"category descending\"===t.categoryorder&&(e=e.reverse())}return function(t,e){e=null==e?[]:e.map((function(t){return t}));var r={},n={},i=[];e.forEach((function(t,e){r[t]=0,n[t]=e}));for(var a=0;a<t.length;a++){var o,s=t[a];void 0===r[s]?(r[s]=1,o=e.push(s)-1,n[s]=o):(r[s]++,o=n[s]),i.push(o)}var l=e.map((function(t){return r[t]}));return{uniqueValues:e,uniqueCounts:l,inds:i}}(t.values,e)}));p=l.isArrayOrTypedArray(e.counts)?e.counts:[e.counts],function(t){var e;if(function(t){for(var e=new Array(t.length),r=0;r<t.length;r++){if(t[r]<0||t[r]>=t.length)return!1;if(void 0!==e[t[r]])return!1;e[t[r]]=!0}return!0}(t.map((function(t){return t.displayindex}))))for(e=0;e<t.length;e++)t[e]._displayindex=t[e].displayindex;else for(e=0;e<t.length;e++)t[e]._displayindex=e}(r),r.forEach((function(t,e){!function(t,e){t._categoryarray=e.uniqueValues,null===t.ticktext||void 0===t.ticktext?t._ticktext=[]:t._ticktext=t.ticktext.slice();for(var r=t._ticktext.length;r<e.uniqueValues.length;r++)t._ticktext.push(e.uniqueValues[r])}(t,m[e])}));var v,y=e.line;y?(i(e,\"line\")&&a(t,e,{vals:e.line.color,containerStr:\"line\",cLetter:\"c\"}),v=s.tryColorscale(y)):v=l.identity;var x,b,_,w,T,k=r[0].values.length,M={},A=m.map((function(t){return t.inds}));for(g=0,x=0;x<k;x++){var S=[];for(b=0;b<A.length;b++)S.push(A[b][x]);d=p[x%p.length],g+=d;var E=(_=x,w=void 0,T=void 0,l.isArrayOrTypedArray(y.color)?T=w=y.color[_%y.color.length]:w=y.color,{color:v(w),rawColor:T}),C=S+\"-\"+E.rawColor;void 0===M[C]&&(M[C]=f(S,E.color,E.rawColor)),h(M[C],x,d)}var L,I=r.map((function(t,e){return function(t,e,r,n,i){return{dimensionInd:t,containerInd:e,displayInd:r,dimensionLabel:n,count:i,categories:[],dragX:null}}(e,t._index,t._displayindex,t.label,g)}));for(x=0;x<k;x++)for(d=p[x%p.length],b=0;b<I.length;b++){var P=I[b].containerInd,z=m[b].inds[x],O=I[b].categories;if(void 0===O[z]){var D=e.dimensions[P]._categoryarray[z],R=e.dimensions[P]._ticktext[z];O[z]={dimensionInd:b,categoryInd:L=z,categoryValue:D,displayInd:L,categoryLabel:R,valueInds:[],count:0,dragY:null}}u(O[z],x,d)}return n(function(t,e,r){var n=t.map((function(t){return t.categories.length})).reduce((function(t,e){return Math.max(t,e)}));return{dimensions:t,paths:e,trace:void 0,maxCats:n,count:r}}(I,M,g))}},{\"../../components/colorscale/calc\":651,\"../../components/colorscale/helpers\":654,\"../../components/drawing\":665,\"../../lib\":778,\"../../lib/filter_unique.js\":769,\"../../lib/gup\":775,\"fast-isnumeric\":241}],1144:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/colorscale/helpers\").hasColorscale,a=t(\"../../components/colorscale/defaults\"),o=t(\"../../plots/domain\").defaults,s=t(\"../../plots/array_container_defaults\"),l=t(\"./attributes\"),c=t(\"../parcoords/merge_length\");function u(t,e){function r(r,i){return n.coerce(t,e,l.dimensions,r,i)}var 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 g={family:f.font.family,size:Math.round(f.font.size),color:f.font.color};n.coerceFont(h,\"labelfont\",g);var m={family:f.font.family,size:Math.round(f.font.size/1.2),color:f.font.color};n.coerceFont(h,\"tickfont\",m)}},{\"../../components/colorscale/defaults\":653,\"../../components/colorscale/helpers\":654,\"../../lib\":778,\"../../plots/array_container_defaults\":822,\"../../plots/domain\":854,\"../parcoords/merge_length\":1157,\"./attributes\":1141}],1145:[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:\"parcats\",basePlotModule:t(\"./base_plot\"),categories:[\"noOpacity\"],meta:{}}},{\"./attributes\":1141,\"./base_plot\":1142,\"./calc\":1143,\"./defaults\":1144,\"./plot\":1147}],1146:[function(t,e,r){\"use strict\";var n=t(\"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\",g).on(\"mouseout\",m).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 z=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(z),S.exit().remove(),A.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var O=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\",O+\" -1px  1px 2px, \"+O+\" 1px  1px 2px, \"+O+\"  1px -1px 2px, \"+O+\" -1px -1px 2px\").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\",C),M.exit().remove(),k.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",L).on(\"drag\",I).on(\"dragend\",P)),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 g(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(),g=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 m=t.parcatsViewModel.dimensions[l],x=t.parcatsViewModel.dimensions[l+1];i=(m.x+m.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>\"),C=n.mouse(f)[0];a.loneHover({trace:h,x:b-d.left+g.left,y:w-d.top+g.top,text:E,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:T,idealAlign:C<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 m(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=z(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 g=u.model.count,m=u.model.categoryLabel,v=g/u.parcatsViewModel.model.count,y={countLabel:g,categoryLabel:m,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\", 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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,g=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 m=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:m,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 \"+m+\"): \"+k.probabilityLabel),M.push(\"P(\"+m+\" | color): \"+w.toFixed(3)),M.push(\"P(color | \"+m+\"): \"+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*(g-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:m,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 C(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 L(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 I(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),O(t.parcatsViewModel)}}function P(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=z(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),O(t.parcatsViewModel,!0)})).each(\"end\",(function(){(o||s)&&i.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function z(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 O(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 g={trace:o,key:o.uid,model:i,x:h,y:p,width:u,height:f,hoveron:o.hoveron,hoverinfoItems:n,arrangement:o.arrangement,bundlecolors:o.bundlecolors,sortpaths:o.sortpaths,labelfont:o.labelfont,categorylabelfont:o.tickfont,pathShape:d,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};return i.dimensions&&(N(g),B(g)),g}function F(t,e,r,i,a){var o,s,l=[],c=[];for(s=0;s<r.length-1;s++)o=n.interpolateNumber(r[s]+t[s],t[s+1]),l.push(o(a)),c.push(o(1-a));var u=\"M \"+t[0]+\",\"+e[0];for(u+=\"l\"+r[0]+\",0 \",s=1;s<r.length;s++)u+=\"C\"+l[s-1]+\",\"+e[s-1]+\" \"+c[s-1]+\",\"+e[s]+\" \"+t[s]+\",\"+e[s],u+=\"l\"+r[s]+\",0 \";for(u+=\"l0,\"+i+\" \",u+=\"l -\"+r[r.length-1]+\",0 \",s=r.length-2;s>=0;s--)u+=\"C\"+c[s]+\",\"+(e[s+1]+i)+\" \"+l[s]+\",\"+(e[s]+i)+\" \"+(t[s]+r[s])+\",\"+(e[s]+i),u+=\"l-\"+r[s]+\",0 \";return u+=\"Z\"}function B(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return t.categories.map((function(t){return t.displayInd}))})),a=t.model.dimensions.map((function(t){return t.displayInd})),o=t.dimensions.map((function(t){return t.model.dimensionInd})),s=e.map((function(t){return t.x})),l=e.map((function(t){return t.width})),c=[];for(var u in r.paths)r.paths.hasOwnProperty(u)&&c.push(r.paths[u]);function f(t){var e=t.categoryInds.map((function(t,e){return i[e][t]}));return o.map((function(t){return e[t]}))}c.sort((function(e,r){var n=f(e),i=f(r);return\"backward\"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),n<i?-1:n>i?1:0}));for(var h=new Array(c.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),g=0;g<c.length;g++){var m,v=c[g];m=p>0?d*(v.count/p):0;for(var y,x=new Array(n.length),b=0;b<v.categoryInds.length;b++){var _=v.categoryInds[b],w=i[b][_],T=a[b];x[T]=n[T][w],n[T][w]+=m;var k=t.dimensions[T].categories[w],M=k.bands.length,A=k.bands[M-1];if(void 0===A||v.rawColor!==A.rawColor){var S=void 0===A?0:A.y+A.height;k.bands.push({key:S,color:v.color,rawColor:v.rawColor,height:m,width:k.width,count:v.count,y:S,categoryViewModel:k,parcatsViewModel:t})}else{var E=k.bands[M-1];E.height+=m,E.count+=v.count}}y=\"hspline\"===t.pathShape?F(s,x,l,m,.5):F(s,x,l,m,0),h[g]={key:v.valueInds[0],model:v,height:m,leftXs:s,topYs:x,dimWidths:l,svgD:y,parcatsViewModel:t}}t.paths=h}function N(t){var e=t.model.dimensions.map((function(t){return{displayInd:t.displayInd,dimensionInd:t.dimensionInd}}));e.sort((function(t,e){return t.displayInd-e.displayInd}));var r=[];for(var n in e){var i=e[n].dimensionInd,a=t.model.dimensions[i];r.push(j(t,a))}t.dimensions=r}function j(t,e){var r,n=t.model.dimensions.length,i=e.displayInd;r=40+(n>1?(t.width-80-16)/(n-1):0)*i;var a,o,s,l,c,u=[],f=t.model.maxCats,h=e.categories.length,p=e.count,d=t.height-8*(f-1),g=8*(f-h)/2,m=e.categories.map((function(t){return{displayInd:t.displayInd,categoryInd:t.categoryInd}}));for(m.sort((function(t,e){return t.displayInd-e.displayInd})),c=0;c<h;c++)l=m[c].categoryInd,o=e.categories[l],a=p>0?o.count/p*d:0,s={key:o.valueInds[0],model:o,width:16,height:a,y:null!==o.dragY?o.dragY:g,bands:[],parcatsViewModel:t},g=g+a+8,u.push(s);return{key:e.dimensionInd,x:null!==e.dragX?e.dragX:r,y:0,width:16,model:e,categories:u,parcatsViewModel:t,dragCategoryDisplayInd:null,dragDimensionDisplayInd:null,initialDragDimensionDisplayInds:null,initialDragCategoryDisplayInds:null,dragHasMoved:null,potentialClickBand:null}}e.exports=function(t,e,r,n){f(r,t,n,e)}},{\"../../components/drawing\":665,\"../../components/fx\":683,\"../../lib\":778,\"../../lib/svg_text_utils\":802,\"../../plot_api/plot_api\":813,d3:169,tinycolor2:576}],1147:[function(t,e,r){\"use strict\";var 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\":1146}],1148:[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\":650,\"../../lib/extend\":768,\"../../plot_api/plot_template\":816,\"../../plots/cartesian/layout_attributes\":841,\"../../plots/domain\":854,\"../../plots/font_attributes\":855}],1149:[function(t,e,r){\"use strict\";var n=t(\"./constants\"),i=t(\"d3\"),a=t(\"../../lib/gup\").keyFun,o=t(\"../../lib/gup\").repeat,s=t(\"../../lib\").sorterAsc,l=t(\"../../lib\").strTranslate,c=n.bar.snapRatio;function u(t,e){return t*(1-c)+e*c}var f=n.bar.snapClose;function h(t,e){return t*(1-f)+e*f}function p(t,e,r,n){if(function(t,e){for(var r=0;r<e.length;r++)if(t>=e[r][0]&&t<=e[r][1])return!0;return!1}(r,n))return r;var i=t?-1:1,a=0,o=e.length-1;if(i<0){var s=a;a=o,o=s}for(var l=e[a],c=l,f=a;i*f<i*o;f+=i){var p=f+i,d=e[p];if(i*r<i*h(l,d))return u(l,c);if(i*r<i*d||p===o)return u(d,l);c=l,l=d}}function d(t){t.attr(\"x\",-n.bar.captureWidth/2).attr(\"width\",n.bar.captureWidth)}function g(t){t.attr(\"visibility\",\"visible\").style(\"visibility\",\"visible\").attr(\"fill\",\"yellow\").attr(\"opacity\",0)}function m(t){if(!t.brush.filterSpecified)return\"0,\"+t.height;for(var 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k(t){t.on(\"mousemove\",(function(t){i.event.preventDefault(),t.parent.inBrushDrag||T(this,t)})).on(\"mouseleave\",(function(t){t.parent.inBrushDrag||y()})).call(i.behavior.drag().on(\"dragstart\",(function(t){!function(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.unitToPaddedPx.invert(r),o=e.brush,s=_(e,r),l=s.interval,c=o.svgBrush;if(c.wasDragged=!1,c.grabbingBar=\"ns\"===s.region,c.grabbingBar){var u=l.map(e.unitToPaddedPx);c.grabPoint=r-u[0]-n.verticalPadding,c.barLength=u[1]-u[0]}c.clickableOrdinalRange=s.clickableOrdinalRange,c.stayingIntervals=e.multiselect&&o.filterSpecified?o.filter.getConsolidated():[],l&&(c.stayingIntervals=c.stayingIntervals.filter((function(t){return t[0]!==l[0]&&t[1]!==l[1]}))),c.startExtent=s.region?l[\"s\"===s.region?1:0]:a,e.parent.inBrushDrag=!0,c.brushStartCallback()}(this,t)})).on(\"drag\",(function(t){w(this,t)})).on(\"dragend\",(function(t){!function(t,e){var 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 s();a.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}(this,t)})))}function M(t,e){return t[0]-e[0]}function A(t){t.filterSpecified=!1,t.svgBrush.extent=[[-1/0,1/0]]}function S(t){for(var e,r=t.slice(),n=[],i=r.shift();i;){for(e=i.slice();(i=r.shift())&&i[0]<=e[1];)e[1]=Math.max(e[1],i[1]);n.push(e)}return 1===n.length&&n[0][0]>n[0][1]&&(n=[]),n}e.exports={makeBrush:function(t,e,r,n,i,a){var o,l=function(){var t,e,r=[];return{set:function(n){1===(r=n.map((function(t){return t.slice().sort(s)})).sort(M)).length&&r[0][0]===-1/0&&r[0][1]===1/0&&(r=[[0,-1]]),t=S(r),e=r.reduce((function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]}),[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return l.set(r),{filter:l,filterSpecified:e,svgBrush:{extent:[],brushStartCallback:n,brushCallback:(o=i,function(t){var e=t.brush,r=function(t){return t.svgBrush.extent.map((function(t){return t.slice()}))}(e).slice();e.filter.set(r),o()}),brushEndCallback:a}}},ensureAxisBrush:function(t){var e=t.selectAll(\".\"+n.cn.axisBrush).data(o,a);e.enter().append(\"g\").classed(n.cn.axisBrush,!0),function(t){var e=t.selectAll(\".background\").data(o);e.enter().append(\"rect\").classed(\"background\",!0).call(d).call(g).style(\"pointer-events\",\"auto\").attr(\"transform\",l(0,n.verticalPadding)),e.call(k).attr(\"height\",(function(t){return t.height-n.verticalPadding}));var r=t.selectAll(\".highlight-shadow\").data(o);r.enter().append(\"line\").classed(\"highlight-shadow\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width+n.bar.strokeWidth).attr(\"stroke\",n.bar.strokeColor).attr(\"opacity\",n.bar.strokeOpacity).attr(\"stroke-linecap\",\"butt\"),r.attr(\"y1\",(function(t){return t.height})).call(x);var i=t.selectAll(\".highlight\").data(o);i.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\"),i.attr(\"y1\",(function(t){return t.height})).call(x)}(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\":778,\"../../lib/gup\":775,\"./constants\":1152,d3:169}],1150:[function(t,e,r){\"use strict\";var n=t(\"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\":754,\"../../plots/get_data\":864,\"./plot\":1159,d3:169}],1151:[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\":655,\"../../lib\":778,\"../../lib/gup\":775}],1152:[function(t,e,r){\"use strict\";e.exports={maxDimensionCount:60,overdrag:45,verticalPadding:2,tickDistance:50,canvasPixelRatio:1,blockLineCount:5e3,layers:[\"contextLineLayer\",\"focusLineLayer\",\"pickLineLayer\"],axisTitleOffset:28,axisExtentOffset:10,deselectedLineColor:\"#777\",bar:{width:4,captureWidth:10,fillColor:\"magenta\",fillOpacity:1,snapDuration:150,snapRatio:.25,snapClose:.01,strokeColor:\"white\",strokeOpacity:1,strokeWidth:1,handleHeight:8,handleOpacity:1,handleOverlap:0},cn:{axisExtentText:\"axis-extent-text\",parcoordsLineLayers:\"parcoords-line-layers\",parcoordsLineLayer:\"parcoords-lines\",parcoords:\"parcoords\",parcoordsControlView:\"parcoords-control-view\",yAxis:\"y-axis\",axisOverlays:\"axis-overlays\",axis:\"axis\",axisHeading:\"axis-heading\",axisTitle:\"axis-title\",axisExtent:\"axis-extent\",axisExtentTop:\"axis-extent-top\",axisExtentTopText:\"axis-extent-top-text\",axisExtentBottom:\"axis-extent-bottom\",axisExtentBottomText:\"axis-extent-bottom-text\",axisBrush:\"axis-brush\"},id:{filterBarPattern:\"filter-bar-pattern\"}}},{}],1153:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/colorscale/helpers\").hasColorscale,a=t(\"../../components/colorscale/defaults\"),o=t(\"../../plots/domain\").defaults,s=t(\"../../plots/array_container_defaults\"),l=t(\"../../plots/cartesian/axes\"),c=t(\"./attributes\"),u=t(\"./axisbrush\"),f=t(\"./constants\").maxDimensionCount,h=t(\"./merge_length\");function p(t,e,r,i){function a(r,i){return n.coerce(t,e,c.dimensions,r,i)}var o=a(\"values\"),s=a(\"visible\");if(o&&o.length||(s=e.visible=!1),s){a(\"label\"),a(\"tickvals\"),a(\"ticktext\"),a(\"tickformat\");var f=a(\"range\");e._ax={_id:\"y\",type:\"linear\",showexponent:\"all\",exponentformat:\"B\",range:f},l.setConvert(e._ax,i.layout),a(\"multiselect\");var h=a(\"constraintrange\");h&&(e.constraintrange=u.cleanRanges(h,e))}}e.exports=function(t,e,r,l){function u(r,i){return n.coerce(t,e,c,r,i)}var d=t.dimensions;Array.isArray(d)&&d.length>f&&(n.log(\"parcoords traces support up to \"+f+\" dimensions at the moment\"),d.splice(f));var g=s(t,e,{name:\"dimensions\",layout:l,handleItemDefaults:p}),m=function(t,e,r,o,s){var l=s(\"line.color\",r);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,l,u);o(e,l,u),Array.isArray(g)&&g.length||(e.visible=!1),h(e,g,\"values\",m);var v={family:l.font.family,size:Math.round(l.font.size/1.2),color:l.font.color};n.coerceFont(u,\"labelfont\",v),n.coerceFont(u,\"tickfont\",v),n.coerceFont(u,\"rangefont\",v),u(\"labelangle\"),u(\"labelside\")}},{\"../../components/colorscale/defaults\":653,\"../../components/colorscale/helpers\":654,\"../../lib\":778,\"../../plots/array_container_defaults\":822,\"../../plots/cartesian/axes\":827,\"../../plots/domain\":854,\"./attributes\":1148,\"./axisbrush\":1149,\"./constants\":1152,\"./merge_length\":1157}],1154:[function(t,e,r){\"use strict\";var 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\":778}],1155:[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\":1148,\"./base_plot\":1150,\"./calc\":1151,\"./defaults\":1153,\"./plot\":1159}],1156:[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, p57_60, colors;\\n\\nuniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\\n             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\\n\\nuniform vec2 resolution, viewBoxPos, viewBoxSize;\\nuniform sampler2D mask, palette;\\nuniform float maskHeight;\\nuniform float drwLayer; // 0: context, 1: focus, 2: pick\\nuniform vec4 contextColor;\\n\\nbool isPick    = (drwLayer > 1.5);\\nbool isContext = (drwLayer < 0.5);\\n\\nconst vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\\nconst vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\\n\\nfloat val(mat4 p, mat4 v) {\\n    return dot(matrixCompMult(p, v) * UNITS, UNITS);\\n}\\n\\nfloat axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\\n    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\\n    return y1 * (1.0 - ratio) + y2 * ratio;\\n}\\n\\nint iMod(int a, int b) {\\n    return a - b * (a / b);\\n}\\n\\nbool fOutside(float p, float lo, float 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 g(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 m(t){var e=\"0\"+t;return e.substr(e.length-2)}function v(t){return t<o?\"p\"+m(t+1)+\"_\"+m(t+4):\"colors\"}function y(t,e,r,n,i,a,o,l,c,u,f,h,p){for(var d=[[],[]],g=0;g<64;g++)d[0][g]=g===i?1:0,d[1][g]=g===a?1:0;var m=t.lines.canvasOverdrag,v=t.domain,y=t.canvasWidth,x=t.canvasHeight,b=t.deselectedLines.color;return s.extendFlat({key:f,resolution:[y,x],viewBoxPos:[o+m,l],viewBoxSize:[c,u],i0:i,i1:a,dim0A:d[0].slice(0,16),dim0B:d[0].slice(16,32),dim0C:d[0].slice(32,48),dim0D:d[0].slice(48,64),dim1A:d[1].slice(0,16),dim1B:d[1].slice(16,32),dim1C:d[1].slice(32,48),dim1D:d[1].slice(48,64),drwLayer:h,contextColor:[b[0]/255,b[1]/255,b[2]/255,b[3]<1?b[3]:Math.max(1/255,Math.pow(1/t.lines.color.length,1/3))],scissorX:(n===e?0:o+m)+(t.pad.l-m)+t.layoutWidth*v.x[0],scissorWidth:(n===r?y-o+m:c+.5)+(n===e?o+m:0),scissorY:l+t.pad.b+t.layoutHeight*v.y[0],scissorHeight:u,viewportX:t.pad.l-m+t.layoutWidth*v.x[0],viewportY:t.pad.b+t.layoutHeight*v.y[0],viewportWidth:y,viewportHeight:x},p)}function x(t){var e=Math.max(0,Math.floor(2047*t[0]),0),r=Math.min(2047,Math.ceil(2047*t[1]),2047);return[Math.min(e,r),Math.max(e,r)]}e.exports=function(t,e){var r,n,l,m,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=[];C(e);var E=T({profile:!1,blend:{enable:_,func:{srcRGB:\"src alpha\",dstRGB:\"one minus src 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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 left\",\"top center\",\"top right\",\"middle center\",\"bottom left\",\"bottom center\",\"bottom right\"],editType:\"plot\"},editType:\"plot\"},domain:i({name:\"pie\",trace:!0,editType:\"calc\"}),hole:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},sort:{valType:\"boolean\",dflt:!0,editType:\"calc\"},direction:{valType:\"enumerated\",values:[\"clockwise\",\"counterclockwise\"],dflt:\"counterclockwise\",editType:\"calc\"},rotation:{valType:\"number\",min:-360,max:360,dflt:0,editType:\"calc\"},pull:{valType:\"number\",min:0,max:1,dflt:0,arrayOk:!0,editType:\"calc\"},_deprecated:{title:{valType:\"string\",dflt:\"\",editType:\"calc\"},titlefont:c({},u,{}),titleposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle center\",\"bottom left\",\"bottom center\",\"bottom right\"],editType:\"calc\"}}}},{\"../../components/color/attributes\":642,\"../../lib/extend\":768,\"../../plots/attributes\":823,\"../../plots/domain\":854,\"../../plots/font_attributes\":855,\"../../plots/template_attributes\":905}],1161:[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\":890}],1162:[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={},g=s(o[\"_\"+e.type+\"colormap\"]),m=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))||(m+=y),a.push({v:y,label:x,color:g(u[r],x),i:r,pts:[r],hidden:b})):(v=!0,(i=a[_]).v+=y,i.pts.push(r),i.hidden||(m+=y),!1===i.color&&u[r]&&(i.color=g(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=m),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\":643,\"fast-isnumeric\":241,tinycolor2:576}],1163:[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 g=c(\"textposition\");s(t,e,n,c,g,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(g)||\"auto\"===g||\"outside\"===g)&&c(\"automargin\"),(\"inside\"===g||\"auto\"===g||Array.isArray(g))&&c(\"insidetextorientation\")}o(e,n,c);var m=c(\"hole\");if(c(\"title.text\")){var v=c(\"title.position\",m?\"middle center\":\"top center\");m||\"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\":778,\"../../plots/domain\":854,\"../bar/defaults\":924,\"./attributes\":1160,\"fast-isnumeric\":241}],1164:[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\":679}],1165:[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\":778}],1166:[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\":1160,\"./base_plot\":1161,\"./calc\":1162,\"./defaults\":1163,\"./layout_attributes\":1167,\"./layout_defaults\":1168,\"./plot\":1169,\"./style\":1170,\"./style_one\":1171}],1167:[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\"}}},{}],1168:[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\":778,\"./layout_attributes\":1167}],1169:[function(t,e,r){\"use strict\";var n=t(\"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,g=t(\"../bar/constants\").TEXTPAD,m=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:[m.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),g=r.separators,y=[];if(f&&-1!==f.indexOf(\"label\")&&y.push(t.label),t.text=m.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=m.formatPieValue(t.v,g),f&&-1!==f.indexOf(\"value\")&&y.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=m.formatPiePercent(t.percent,g),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:m.castOption(b.bgcolor,t.pts)||t.color,borderColor:m.castOption(b.bordercolor,t.pts),fontFamily:m.castOption(_.family,t.pts),fontSize:m.castOption(_.size,t.pts),fontColor:m.castOption(_.color,t.pts),nameLength:m.castOption(b.namelength,t.pts),textAlign:m.castOption(b.align,t.pts),hovertemplate:m.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=m.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=m.castOption(t._input.textfont.color,e.pts));var i=m.castOption(t.insidetextfont.family,e.pts)||m.castOption(t.textfont.family,e.pts)||r.family,a=m.castOption(t.insidetextfont.size,e.pts)||m.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,g=[];if(!d){var m,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,g.push(a)}};if(f||h){for(m=4;m>=-4;m-=2)v(Math.PI*m,\"tan\");for(m=4;m>=-4;m-=2)v(Math.PI*(m+1),\"tan\")}if(f||p){for(m=4;m>=-4;m-=2)v(Math.PI*(m+1.5),\"rad\");for(m=4;m>=-4;m-=2)v(Math.PI*(m+.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;g.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,g.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,g.push(a));for(var x=0,b=0,_=0;_<g.length;_++){var w=g[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return g[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*g);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*g);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 C(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 L(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=P(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=I(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function I(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 P(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 z(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-=I(i,e));var c=s/2,u=l/2;\"funnelarea\"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+P(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-=I(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 O(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\"),g=n.separators;if(s=f?[e.label]:[],h){var v=m.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(m.formatPieValue(e.v,g)),d&&s.push(m.formatPiePercent(e.v/r.vTotal,g)),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:m.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:m.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=m.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),z(e,a);var h=l.makeTraceGroups(r._pielayer,e,\"trace\").each((function(e){var h=n.select(this),d=e[0],g=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=m.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=O(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=O(a,s),r.midangle=s,s+=l*r.v/2,n=O(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=g.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),g.pull){var v=+m.castOption(g.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=g.hole;if(i.v===d.vTotal){var k=\"M\"+(o+i.px0[0])+\",\"+(c+i.px0[1])+L(i.px0,i.pxmid,!0,1)+L(i.pxmid,i.px0,!0,1)+\"Z\";T?h.attr(\"d\",\"M\"+(o+T*i.px0[0])+\",\"+(c+T*i.px0[1])+L(i.px0,i.pxmid,!1,T)+L(i.pxmid,i.px0,!1,T)+\"Z\"+k):h.attr(\"d\",k)}else{var M=L(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])+L(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=m.castOption(g.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=m.castOption(t.outsidetextfont.color,e.pts)||m.castOption(t.textfont.color,e.pts)||r.color,i=m.castOption(t.outsidetextfont.family,e.pts)||m.castOption(t.textfont.family,e.pts)||r.family,a=m.castOption(t.outsidetextfont.size,e.pts)||m.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(g,i,r.font):b(g,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=C(y,i);else if(v=w(y,i,d),\"auto\"===S&&v.scale<1){var x=l.ensureUniformFontSize(t,g.outsidetextfont);u.call(s.font,x),v=C(y=s.bBox(u.node()),i)}var T=v.textPosAngle,k=void 0===T?i.pxmid:O(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(g.type,v,r),e[a].transform=v,u.attr(\"transform\",l.getTextTransform(v))}))}function L(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(g.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=g.title.text;g._meta&&(i=l.templateString(i,g._meta)),r.text(i).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,g.title.font).call(f.convertToTspans,t),e=\"middle center\"===g.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):L(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 g(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 i,c,u,h,p=r.labelExtraY+(n?r.yLabelMax:r.yLabelMin),d=n?t.yLabelMin:t.yLabelMax,g=n?t.yLabelMax:t.yLabelMin,v=t.cyFinal+o(t.px0[1],t.px1[1]),y=p-d;if(y*l>0&&(t.labelExtraY=y),Array.isArray(e.pull))for(c=0;c<f.length;c++)(u=f[c])===t||(m.castOption(e.pull,t.pts)||0)>=(m.castOption(e.pull,u.pts)||0)||((t.pxmid[1]-u.pxmid[1])*l>0?(y=u.cyFinal+o(u.px0[1],u.px1[1])-d-t.labelExtraY)*l>0&&(t.labelExtraY+=y):(g+t.labelExtraY-v)*l>0&&(i=3*s*Math.abs(c-f.indexOf(t)),(h=u.cxFinal+a(u.px0[0],u.px1[0])+i-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*s>0&&(t.labelExtraX+=h)))}for(n=0;n<2;n++)for(i=n?g:v,o=n?Math.max:Math.min,l=n?1:-1,r=0;r<2;r++){for(a=r?Math.max:Math.min,s=r?1:-1,(c=t[n][r]).sort(i),u=t[1-n][r],f=u.concat(c),p=[],h=0;h<c.length;h++)void 0!==c[h].yLabelMid&&p.push(c[h]);for(d=!1,h=0;n&&h<u.length;h++)if(void 0!==u[h].yLabelMid){d=u[h];break}for(h=0;h<p.length;h++){var x=h&&p[h-1];d&&!h&&(x=d),y(p[h],x)}}}(y,g),function(t,e){t.each((function(t){var r=n.select(this);if(t.labelExtraX||t.labelExtraY){var i=r.select(\"g.slicetext text\");t.transform.targetX+=t.labelExtraX,t.transform.targetY+=t.labelExtraY,i.attr(\"transform\",l.getTextTransform(t.transform));var a=t.cxFinal+t.pxmid[0],s=\"M\"+a+\",\"+(t.cyFinal+t.pxmid[1]),c=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var u=t.labelExtraX*t.pxmid[1]/t.pxmid[0],f=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(u)>Math.abs(f)?s+=\"l\"+f*t.pxmid[0]/t.pxmid[1]+\",\"+f+\"H\"+(a+t.labelExtraX+c):s+=\"l\"+t.labelExtraX+\",\"+u+\"v\"+(f-u)+\"h\"+c}else s+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+c;l.ensureSingle(r,\"path\",\"textline\").call(o.stroke,e.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,e.outsidetextfont.size/8),d:s,fill:\"none\"})}else r.select(\"path.textline\").remove()}))}(v,g),_&&g.automargin){var k=s.bBox(h.node()),M=g.domain,A=a.w*(M.x[1]-M.x[0]),S=a.h*(M.y[1]-M.y[0]),E=(.5*A-d.r)/a.w,I=(.5*S-d.r)/a.h;i.autoMargin(t,\"pie.\"+g.uid+\".automargin\",{xl:M.x[0]-E,xr:M.x[1]+E,yb:M.y[0]-I,yt:M.y[1]+I,l:Math.max(d.cx-d.r-k.left,0),r:Math.max(k.right-(d.cx+d.r),0),b:Math.max(k.bottom-(d.cy+d.r),0),t:Math.max(d.cy-d.r-k.top,0),pad:5})}}))}));setTimeout((function(){h.selectAll(\"tspan\").each((function(){var t=n.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))}))}),0)},formatSliceLabel:D,transformInsideText:w,determineInsideTextFont:b,positionTitleOutside:L,prerenderTitles:_,layoutAreas:z,attachFxHandlers:x,computeTransform:R}},{\"../../components/color\":643,\"../../components/drawing\":665,\"../../components/fx\":683,\"../../lib\":778,\"../../lib/svg_text_utils\":802,\"../../plots/plots\":890,\"../bar/constants\":922,\"../bar/uniform_text\":936,\"./event_data\":1164,\"./helpers\":1165,d3:169}],1170:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"./style_one\"),a=t(\"../bar/uniform_text\").resizeText;e.exports=function(t){var e=t._fullLayout._pielayer.selectAll(\".trace\");a(t,e,\"pie\"),e.each((function(t){var e=t[0].trace,r=n.select(this);r.style({opacity:e.opacity}),r.selectAll(\"path.surface\").each((function(t){n.select(this).call(i,t,e)}))}))}},{\"../bar/uniform_text\":936,\"./style_one\":1171,d3:169}],1171:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"./helpers\").castOption;e.exports=function(t,e,r){var a=r.marker.line,o=i(a.color,e.pts)||n.defaultLine,s=i(a.width,e.pts)||0;t.style(\"stroke-width\",s).call(n.fill,e.color).call(n.stroke,o)}},{\"../../components/color\":643,\"./helpers\":1165}],1172:[function(t,e,r){\"use strict\";var 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\":1186}],1173:[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 Int32Array(e),l=0;l<e;l++)o=c[l],s=u[l],r[l]=l,n[2*l]=o,n[2*l+1]=s,o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);this.idToIndex=r,this.pointcloudOptions.idToIndex=r,this.pointcloudOptions.positions=n;var g=i(t.marker.color),m=i(t.marker.border.color),v=t.opacity*t.marker.opacity;g[3]*=v,this.pointcloudOptions.color=g;var y=t.marker.blend;if(null===y){y=c.length<100||u.length<100}this.pointcloudOptions.blend=y,m[3]*=v,this.pointcloudOptions.borderColor=m;var x=t.marker.sizemin,b=Math.max(t.marker.sizemax,t.marker.sizemin);this.pointcloudOptions.sizeMin=x,this.pointcloudOptions.sizeMax=b,this.pointcloudOptions.areaRatio=t.marker.border.arearatio,this.pointcloud.update(this.pointcloudOptions);var _=this.scene.xaxis,w=this.scene.yaxis,T=b/2||.5;t._extremes[_._id]=a(_,[d[0],d[2]],{ppad:T}),t._extremes[w._id]=a(w,[d[1],d[3]],{ppad:T})},l.dispose=function(){this.pointcloud.dispose()},e.exports=function(t,e){var r=new s(t,e.uid);return r.update(e),r}},{\"../../lib/str2rgbarray\":801,\"../../plots/cartesian/autorange\":826,\"../scatter/get_trace_color\":1196,\"gl-pointcloud2d\":324}],1174:[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\":778,\"./attributes\":1172}],1175:[function(t,e,r){\"use 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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(\"../../lib/extend\").extendFlat,h=t(\"../../plot_api/edit_types\").overrideAll;t(\"../../constants/docs\").FORMAT_LINK;(e.exports=h({hoverinfo:f({},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\"},valuesuffix:{valType:\"string\",dflt:\"\"},arrangement:{valType:\"enumerated\",values:[\"snap\",\"perpendicular\",\"freeform\",\"fixed\"],dflt:\"snap\"},textfont:n({}),customdata:void 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o=n[t=i[w]],s=0,l={};for(T=0;T<o.length;T++)l[(e=o[T]).label]||(l[e.label]=0),l[e.label]+=e.value,s+=e.value;for(T=0;T<o.length;T++)(e=o[T]).flow={value:s,labelConcentration:l[e.label]/s,concentration:e.value/s,links:o},e.concentrationscale&&(e.color=a(e.concentrationscale(e.flow.labelConcentration)))}var c=0;for(w=0;w<r.sourceLinks.length;w++)c+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/c;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}}(),s.node.x.length&&s.node.y.length){for(_=0;_<Math.min(s.node.x.length,s.node.y.length,k.nodes.length);_++)if(s.node.x[_]&&s.node.y[_]){var C=[s.node.x[_]*g,s.node.y[_]*v];k.nodes[_].x0=C[0]-d/2,k.nodes[_].x1=C[0]+d/2;var L=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=C[1]-L/2,k.nodes[_].y1=C[1]+L/2}if(\"snap\"===s.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 i}(y=k.nodes));i.update(k)}return{circular:b,key:r,trace:s,guid:f.randstr(),horizontal:h,width:g,height:v,nodePad:s.node.pad,nodeLineColor:s.node.line.color,nodeLineWidth:s.node.line.width,linkLineColor:s.link.line.color,linkLineWidth:s.link.line.width,valueFormat:s.valueformat,valueSuffix:s.valuesuffix,textFont:s.textfont,translateX:u.x[0]*t.width+t.margin.l,translateY:t.height-u.y[1]*t.height+t.margin.t,dragParallel:h?v:g,dragPerpendicular:h?g:v,arrangement:s.arrangement,sankey:i,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function b(t,e,r){var n=a(e.color),i=e.source.label+\"|\"+e.target.label+\"__\"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:o.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:_,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function _(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,\"top\"===e.circularLinkType?\"M \"+n.targetX+\" \"+(n.targetY+r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 1 \"+(n.rightFullExtent-r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 1 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 0 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 0 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"L\"+n.targetX+\" \"+(n.targetY-r)+\"Z\":\"M \"+n.targetX+\" \"+(n.targetY-r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 0 \"+(n.rightFullExtent-r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 0 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 1 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 1 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"L\"+n.targetX+\" \"+(n.targetY+r)+\"Z\";var e,r,n,i=t.link.source.x1,a=t.link.target.x0,o=v(i,a),s=o(.5),l=o(.5),c=t.link.y0-t.link.width/2,u=t.link.y0+t.link.width/2,f=t.link.y1-t.link.width/2,h=t.link.y1+t.link.width/2;return\"M\"+i+\",\"+c+\"C\"+s+\",\"+c+\" \"+l+\",\"+f+\" \"+a+\",\"+f+\"L\"+a+\",\"+h+\"C\"+l+\",\"+h+\" \"+s+\",\"+u+\" \"+i+\",\"+u+\"Z\"}}function w(t,e){var r=a(e.color),i=n.nodePadAcross,s=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var l=e.dx,c=Math.max(.5,e.dy),u=\"node_\"+e.pointNumber;return e.group&&(u=f.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:u,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(l),visibleHeight:c,zoneX:-i,zoneY:-s,zoneWidth:l+2*i,zoneHeight:c+2*s,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:o.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,u].join(\"_\"),interactionState:t.interactionState,figure:t}}function T(t){t.attr(\"transform\",(function(t){return h(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function k(t){t.call(T)}function M(t,e){t.call(k),e.attr(\"d\",_())}function A(t){t.attr(\"width\",(function(t){return t.node.x1-t.node.x0})).attr(\"height\",(function(t){return t.visibleHeight}))}function S(t){return t.link.width>1||t.linkLineWidth>0}function E(t){return h(t.translateX,t.translateY)+(t.horizontal?\"matrix(1 0 0 1 0 0)\":\"matrix(0 1 1 0 0 0)\")}function C(t){return h(t.horizontal?0:t.labelY,t.horizontal?t.labelY:0)}function L(t){return i.svg.line()([[t.horizontal?t.left?-t.sizeAcross:t.visibleWidth+n.nodeTextOffsetHorizontal:n.nodeTextOffsetHorizontal,0],[t.horizontal?t.left?-n.nodeTextOffsetHorizontal:t.sizeAcross:t.visibleHeight-n.nodeTextOffsetHorizontal,0]])}function I(t){return t.horizontal?\"matrix(1 0 0 1 0 0)\":\"matrix(0 1 1 0 0 0)\"}function P(t){return t.horizontal?\"scale(1 1)\":\"scale(-1 1)\"}function z(t){return t.darkBackground&&!t.horizontal?\"rgb(255,255,255)\":\"rgb(0,0,0)\"}function O(t){return t.horizontal&&t.left?\"100%\":\"0%\"}function D(t,e,r){t.on(\".basic\",null).on(\"mouseover.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.hover(this,t,e),t.interactionState.hovered=[this,t])})).on(\"mousemove.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.follow(this,t),t.interactionState.hovered=[this,t])})).on(\"mouseout.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.unhover(this,t,e),t.interactionState.hovered=!1)})).on(\"click.basic\",(function(t){t.interactionState.hovered&&(r.unhover(this,t,e),t.interactionState.hovered=!1),t.interactionState.dragInProgress||t.partOfGroup||r.select(this,t,e)}))}function R(t,e,r,a){var o=i.behavior.drag().origin((function(t){return{x:t.node.x0+t.visibleWidth/2,y:t.node.y0+t.visibleHeight/2}})).on(\"dragstart\",(function(i){if(\"fixed\"!==i.arrangement&&(f.ensureSingle(a._fullLayout._infolayer,\"g\",\"dragcover\",(function(t){a._fullLayout._dragCover=t})),f.raiseToTop(this),i.interactionState.dragInProgress=i.node,B(i.node),i.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,i.interactionState.hovered),i.interactionState.hovered=!1),\"snap\"===i.arrangement)){var o=i.traceId+\"|\"+i.key;i.forceLayouts[o]?i.forceLayouts[o].alpha(1):function(t,e,r,i){!function(t){for(var e=0;e<t.length;e++)t[e].y=(t[e].y0+t[e].y1)/2,t[e].x=(t[e].x0+t[e].x1)/2}(r.graph.nodes);var a=r.graph.nodes.filter((function(t){return t.originalX===r.node.originalX})).filter((function(t){return!t.partOfGroup}));r.forceLayouts[e]=u.forceSimulation(a).alphaDecay(0).force(\"collide\",u.forceCollide().radius((function(t){return t.dy/2+r.nodePad/2})).strength(1).iterations(n.forceIterations)).force(\"constrain\",function(t,e,r,i){return function(){for(var t=0,a=0;a<r.length;a++){var o=r[a];o===i.interactionState.dragInProgress?(o.x=o.lastDraggedX,o.y=o.lastDraggedY):(o.vx=(o.originalX-o.x)/n.forceTicksPerFrame,o.y=Math.min(i.size-o.dy/2,Math.max(o.dy/2,o.y))),t=Math.max(t,Math.abs(o.vx),Math.abs(o.vy))}!i.interactionState.dragInProgress&&t<.1&&i.forceLayouts[e].alpha()>0&&i.forceLayouts[e].alpha(0)}}(0,e,a,r)).stop()}(0,o,i),function(t,e,r,i,a){window.requestAnimationFrame((function o(){var s;for(s=0;s<n.forceTicksPerFrame;s++)r.forceLayouts[i].tick();if(function(t){for(var e=0;e<t.length;e++)t[e].y0=t[e].y-t[e].dy/2,t[e].y1=t[e].y0+t[e].dy,t[e].x0=t[e].x-t[e].dx/2,t[e].x1=t[e].x0+t[e].dx}(r.graph.nodes),r.sankey.update(r.graph),M(t.filter(N(r)),e),r.forceLayouts[i].alpha()>0)window.requestAnimationFrame(o);else{var l=r.node.originalX;r.node.x0=l-r.visibleWidth/2,r.node.x1=l+r.visibleWidth/2,F(r,a)}}))}(t,e,i,o,a)}})).on(\"drag\",(function(r){if(\"fixed\"!==r.arrangement){var n=i.event.x,a=i.event.y;\"snap\"===r.arrangement?(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2,r.node.y0=a-r.visibleHeight/2,r.node.y1=a+r.visibleHeight/2):(\"freeform\"===r.arrangement&&(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2),a=Math.max(0,Math.min(r.size-r.visibleHeight/2,a)),r.node.y0=a-r.visibleHeight/2,r.node.y1=a+r.visibleHeight/2),B(r.node),\"snap\"!==r.arrangement&&(r.sankey.update(r.graph),M(t.filter(N(r)),e))}})).on(\"dragend\",(function(t){if(\"fixed\"!==t.arrangement){t.interactionState.dragInProgress=!1;for(var e=0;e<t.node.childrenNodes.length;e++)t.node.childrenNodes[e].x=t.node.x,t.node.childrenNodes[e].y=t.node.y;\"snap\"!==t.arrangement&&F(t,a)}}));t.on(\".drag\",null).call(o)}function F(t,e){for(var r=[],n=[],i=0;i<t.graph.nodes.length;i++){var a=(t.graph.nodes[i].x0+t.graph.nodes[i].x1)/2,o=(t.graph.nodes[i].y0+t.graph.nodes[i].y1)/2;r.push(a/t.figure.width),n.push(o/t.figure.height)}y.call(\"_guiRestyle\",e,{\"node.x\":[r],\"node.y\":[n]},t.trace.index).then((function(){e._fullLayout._dragCover&&e._fullLayout._dragCover.remove()}))}function B(t){t.lastDraggedX=t.x0+t.dx/2,t.lastDraggedY=t.y0+t.dy/2}function N(t){return function(e){return e.node.originalX===t.node.originalX}}e.exports=function(t,e,r,l,c){var u=!1;f.ensureSingle(t._fullLayout._infolayer,\"g\",\"first-render\",(function(){u=!0}));var h=t._fullLayout._dragCover,p=r.filter((function(t){return m(t).trace.visible})).map(x.bind(null,l)),v=e.selectAll(\".\"+n.cn.sankey).data(p,d);v.exit().remove(),v.enter().append(\"g\").classed(n.cn.sankey,!0).style(\"box-sizing\",\"content-box\").style(\"position\",\"absolute\").style(\"left\",0).style(\"shape-rendering\",\"geometricPrecision\").style(\"pointer-events\",\"auto\").attr(\"transform\",E),v.each((function(e,r){t._fullData[r]._sankey=e;var n=\"bgsankey-\"+e.trace.uid+\"-\"+r;f.ensureSingle(t._fullLayout._draggers,\"rect\",n),t._fullData[r]._bgRect=i.select(\".\"+n),t._fullData[r]._bgRect.style(\"pointer-events\",\"all\").attr(\"width\",e.width).attr(\"height\",e.height).attr(\"x\",e.translateX).attr(\"y\",e.translateY).classed(\"bgsankey\",!0).style({fill:\"transparent\",\"stroke-width\":0})})),v.transition().ease(n.ease).duration(n.duration).attr(\"transform\",E);var y=v.selectAll(\".\"+n.cn.sankeyLinks).data(g,d);y.enter().append(\"g\").classed(n.cn.sankeyLinks,!0).style(\"fill\",\"none\");var k=y.selectAll(\".\"+n.cn.sankeyLink).data((function(t){return t.graph.links.filter((function(t){return t.value})).map(b.bind(null,t))}),d);k.enter().append(\"path\").classed(n.cn.sankeyLink,!0).call(D,v,c.linkEvents),k.style(\"stroke\",(function(t){return S(t)?o.tinyRGB(a(t.linkLineColor)):t.tinyColorHue})).style(\"stroke-opacity\",(function(t){return S(t)?o.opacity(t.linkLineColor):t.tinyColorAlpha})).style(\"fill\",(function(t){return t.tinyColorHue})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})).style(\"stroke-width\",(function(t){return S(t)?t.linkLineWidth:1})).attr(\"d\",_()),k.style(\"opacity\",(function(){return t._context.staticPlot||u||h?1:0})).transition().ease(n.ease).duration(n.duration).style(\"opacity\",1),k.exit().transition().ease(n.ease).duration(n.duration).style(\"opacity\",0).remove();var M=v.selectAll(\".\"+n.cn.sankeyNodeSet).data(g,d);M.enter().append(\"g\").classed(n.cn.sankeyNodeSet,!0),M.style(\"cursor\",(function(t){switch(t.arrangement){case\"fixed\":return\"default\";case\"perpendicular\":return\"ns-resize\";default:return\"move\"}}));var F=M.selectAll(\".\"+n.cn.sankeyNode).data((function(t){var e=t.graph.nodes;return function(t){var e,r=[];for(e=0;e<t.length;e++)t[e].originalX=(t[e].x0+t[e].x1)/2,t[e].originalY=(t[e].y0+t[e].y1)/2,-1===r.indexOf(t[e].originalX)&&r.push(t[e].originalX);for(r.sort((function(t,e){return t-e})),e=0;e<t.length;e++)t[e].originalLayerIndex=r.indexOf(t[e].originalX),t[e].originalLayer=t[e].originalLayerIndex/(r.length-1)}(e),e.map(w.bind(null,t))}),d);F.enter().append(\"g\").classed(n.cn.sankeyNode,!0).call(T).style(\"opacity\",(function(e){return!t._context.staticPlot&&!u||e.partOfGroup?0:1})),F.call(D,v,c.nodeEvents).call(R,k,c,t),F.transition().ease(n.ease).duration(n.duration).call(T).style(\"opacity\",(function(t){return t.partOfGroup?0:1})),F.exit().transition().ease(n.ease).duration(n.duration).style(\"opacity\",0).remove();var B=F.selectAll(\".\"+n.cn.nodeRect).data(g);B.enter().append(\"rect\").classed(n.cn.nodeRect,!0).call(A),B.style(\"stroke-width\",(function(t){return t.nodeLineWidth})).style(\"stroke\",(function(t){return o.tinyRGB(a(t.nodeLineColor))})).style(\"stroke-opacity\",(function(t){return o.opacity(t.nodeLineColor)})).style(\"fill\",(function(t){return t.tinyColorHue})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})),B.transition().ease(n.ease).duration(n.duration).call(A);var N=F.selectAll(\".\"+n.cn.nodeCapture).data(g);N.enter().append(\"rect\").classed(n.cn.nodeCapture,!0).style(\"fill-opacity\",0),N.attr(\"x\",(function(t){return t.zoneX})).attr(\"y\",(function(t){return t.zoneY})).attr(\"width\",(function(t){return t.zoneWidth})).attr(\"height\",(function(t){return t.zoneHeight}));var 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n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../../plots/template_attributes\").texttemplateAttrs,s=t(\"../../components/colorscale/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=n.marker,u=n.line,f=c.line;e.exports={carpet:{valType:\"string\",editType:\"calc\"},a:{valType:\"data_array\",editType:\"calc\"},b:{valType:\"data_array\",editType:\"calc\"},mode:l({},n.mode,{dflt:\"markers\"}),text:l({},n.text,{}),texttemplate:o({editType:\"plot\"},{keys:[\"a\",\"b\",\"text\"]}),hovertext:l({},n.hovertext,{}),line:{color:u.color,width:u.width,dash:u.dash,shape:l({},u.shape,{values:[\"linear\",\"spline\"]}),smoothing:u.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,fill:l({},n.fill,{values:[\"none\",\"toself\",\"tonext\"],dflt:\"none\"}),fillcolor:n.fillcolor,marker:l({symbol:c.symbol,opacity:c.opacity,maxdisplayed:c.maxdisplayed,size:c.size,sizeref:c.sizeref,sizemin:c.sizemin,sizemode:c.sizemode,line:l({width:f.width,editType:\"calc\"},s(\"marker.line\")),gradient:c.gradient,editType:\"calc\"},s(\"marker\")),textfont:n.textfont,textposition:n.textposition,selected:n.selected,unselected:n.unselected,hoverinfo:l({},i.hoverinfo,{flags:[\"a\",\"b\",\"text\",\"name\"]}),hoveron:n.hoveron,hovertemplate:a()}},{\"../../components/colorscale/attributes\":650,\"../../lib/extend\":768,\"../../plots/attributes\":823,\"../../plots/template_attributes\":905,\"../scatter/attributes\":1186}],1221:[function(t,e,r){\"use 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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\"),g=p(\"b\"),m=Math.min(d.length,g.length);if(m){e._length=m,p(\"text\"),p(\"texttemplate\"),p(\"hovertext\"),p(\"mode\",m<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\":778,\"../scatter/constants\":1190,\"../scatter/fillcolor_defaults\":1194,\"../scatter/line_defaults\":1199,\"../scatter/line_shape_defaults\":1201,\"../scatter/marker_defaults\":1205,\"../scatter/subtypes\":1211,\"../scatter/text_defaults\":1212,\"./attributes\":1220}],1223:[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}},{}],1224:[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}},{}],1225:[function(t,e,r){\"use strict\";var n=t(\"../scatter/hover\"),i=t(\"../../lib\").fillText;e.exports=function(t,e,r,a){var o=n(t,e,r,a);if(o&&!1!==o[0].index){var s=o[0];if(void 0===s.index){var l=1-s.y0/t.ya._length,c=t.xa._length,u=c*l/2,f=c-u;return s.x0=Math.max(Math.min(s.x0,f),u),s.x1=Math.max(Math.min(s.x1,f),u),o}var h=s.cd[s.index];s.a=h.a,s.b=h.b,s.xLabelVal=void 0,s.yLabelVal=void 0;var p=s.trace,d=p._carpet,g=p._module.formatLabels(h,p);s.yLabel=g.yLabel,delete s.text;var m=[];if(!p.hovertemplate){var v=(h.hi||p.hoverinfo).split(\"+\");-1!==v.indexOf(\"all\")&&(v=[\"a\",\"b\",\"text\"]),-1!==v.indexOf(\"a\")&&y(d.aaxis,h.a),-1!==v.indexOf(\"b\")&&y(d.baxis,h.b),m.push(\"y: \"+s.yLabel),-1!==v.indexOf(\"text\")&&i(h,p,m),s.extraText=m.join(\"<br>\")}return o}function y(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,\"\"):t._hovertitle,m.push(r+\": \"+e.toFixed(3)+t.labelsuffix)}}},{\"../../lib\":778,\"../scatter/hover\":1197}],1226:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),colorbar:t(\"../scatter/marker_colorbar\"),formatLabels:t(\"./format_labels\"),calc:t(\"./calc\"),plot:t(\"./plot\"),style:t(\"../scatter/style\").style,styleOnSelect:t(\"../scatter/style\").styleOnSelect,hoverPoints:t(\"./hover\"),selectPoints:t(\"../scatter/select\"),eventData:t(\"./event_data\"),moduleType:\"trace\",name:\"scattercarpet\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"svg\",\"carpet\",\"symbols\",\"showLegend\",\"carpetDependent\",\"zoomScale\"],meta:{}}},{\"../../plots/cartesian\":840,\"../scatter/marker_colorbar\":1204,\"../scatter/select\":1208,\"../scatter/style\":1210,\"./attributes\":1220,\"./calc\":1221,\"./defaults\":1222,\"./event_data\":1223,\"./format_labels\":1224,\"./hover\":1225,\"./plot\":1227}],1227:[function(t,e,r){\"use strict\";var 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\":665,\"../../plots/cartesian/axes\":827,\"../scatter/plot\":1207}],1228:[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,g=f(\"geojson\");(\"string\"==typeof g&&\"\"!==g||n.isPlainObject(g))&&(d=\"geojson-id\"),\"geojson-id\"===f(\"locationmode\",d)&&f(\"featureidkey\"),h=p.length}else{var m=f(\"lon\")||[],v=f(\"lat\")||[];h=Math.min(m.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\":778,\"../scatter/fillcolor_defaults\":1194,\"../scatter/line_defaults\":1199,\"../scatter/marker_defaults\":1205,\"../scatter/subtypes\":1211,\"../scatter/text_defaults\":1212,\"./attributes\":1228}],1231:[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}},{}],1232:[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\":827}],1233:[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 g=l[t.index],m=g.lonlat,v=[u.c2p(m),f.c2p(m)],y=g.mrc||1;t.x0=v[0]-y,t.x1=v[0]+y,t.y0=v[1]-y,t.y1=v[1]+y,t.loc=g.loc,t.lon=m[0],t.lat=m[1];var x={};x[c.geo]={_subplot:h};var b=c._module.formatLabels(g,c,x);return 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T=y-_,k=y+w;p[v+u[0]]=x-i.c2l(T),p[v+u[1]]=i.c2l(k)-x,p[v+u[2]]=0,p[v+u[3]]=0,d=Math.min(d,y-_),g=Math.max(g,y+w)}}}h[a]={positions:r,errors:p,_bnds:[d,g]}}}return p(i,l),p(a,u),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(f.hasMarkers(e)){var s=r.font,l=r.align,c=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var u=n.sizes?n.sizes[i]:n.size,h=Array.isArray(s)?s[i].size:s.size,p=Array.isArray(l)?l.length>1?l[i]:l[0]:l,d=Array.isArray(c)?c.length>1?c[i]:c[0]:c,g=m[p],v=m[d],y=u?u/.8+1:0,x=-v*y-.5*v;o.offset[i]=[g*y/h,x/h]}}return o}}},{\"../../components/drawing\":665,\"../../components/fx/helpers\":679,\"../../constants/interactions\":752,\"../../lib\":778,\"../../lib/gl_format_color\":774,\"../../plots/cartesian/axis_ids\":830,\"../../registry\":910,\"../scatter/make_bubble_size_func\":1203,\"../scatter/subtypes\":1211,\"./constants\":1240,\"./helpers\":1245,\"color-normalize\":125,\"fast-isnumeric\":241,\"svg-path-sdf\":574}],1242:[function(t,e,r){\"use strict\";var 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,g){function m(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,g,m);if(x){u(t,e,g,m);var b=x<s.PTS_LINESONLY?\"lines+markers\":\"lines\";m(\"text\"),m(\"hovertext\"),m(\"hovertemplate\"),m(\"mode\",b),l.hasLines(e)&&(m(\"connectgaps\"),h(t,e,r,g,m),m(\"line.shape\")),l.hasMarkers(e)&&(f(t,e,r,g,m),m(\"marker.line.width\",v||y?1:0)),l.hasText(e)&&(m(\"texttemplate\"),d(t,e,g,m));var _=(e.line||{}).color,w=(e.marker||{}).color;m(\"fill\"),\"none\"!==e.fill&&p(t,e,r,m);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,m)}else e.visible=!1}},{\"../../lib\":778,\"../../registry\":910,\"../scatter/constants\":1190,\"../scatter/fillcolor_defaults\":1194,\"../scatter/line_defaults\":1199,\"../scatter/marker_defaults\":1205,\"../scatter/period_defaults\":1206,\"../scatter/subtypes\":1211,\"../scatter/text_defaults\":1212,\"../scatter/xy_defaults\":1213,\"./attributes\":1238,\"./helpers\":1245}],1243:[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]||{},g=c.textUnselectedOptions[u]||{},m=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=g.color,x=p.color,b=Array.isArray(x);for(m.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],m.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;m.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(m)}}},{\"../../components/color\":643,\"../../constants/interactions\":752,\"../../lib\":778}],1244:[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\":1195}],1245:[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\":1240}],1246:[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 g=d&&d.line;g&&(h.mlc=Array.isArray(g.color)?g.color[f]:g.color,h.mlw=i.isArrayOrTypedArray(g.width)?g.width[f]:g.width);var m=d&&d.gradient;m&&\"none\"!==m.type&&(h.mgt=Array.isArray(m.type)?m.type[f]:m.type,h.mgc=Array.isArray(m.color)?m.color[f]:m.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,g=d[0].t,m=d[0].trace,v=t.xa,y=t.ya,x=g.x,b=g.y,_=v.c2p(e),w=y.c2p(r),T=t.distance;if(g.tree){var k=v.p2c(_-T),M=v.p2c(_+T),A=y.p2c(w-T),S=y.p2c(w+T);i=\"x\"===n?g.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])):g.tree.range(Math.min(k,M),Math.min(A,S),Math.max(k,M),Math.max(A,S))}else i=g.ids;var E=T;if(\"x\"===n)for(c=0;c<i.length;c++)s=x[i[c]],(u=Math.abs(v.c2p(s)-_))<E&&(E=u,f=y.c2p(b[i[c]])-w,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,m)]},calcHover:o}},{\"../../lib\":778,\"../../registry\":910,\"../scatter/get_trace_color\":1196}],1247:[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\":840,\"../scatter/cross_trace_defaults\":1192,\"../scatter/marker_colorbar\":1204,\"./attributes\":1238,\"./calc\":1239,\"./defaults\":1242,\"./format_labels\":1244,\"./hover\":1246,\"./plot\":1248,\"./select\":1250}],1248:[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,g,m=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,_=m._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,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]),i=a+2);p=p.concat(d.slice(i)),i&&p.push(d[i],d[i+1])}else{var g=s._nexttrace;if(g){var m=v.lineOptions[e+1];if(m){var y=m.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=g.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=m.dragmode,A=l(M),S=m.clickmode.indexOf(\"select\")>-1;for(d=0;d<b;d++){var E=r[d][0],C=E.trace,L=E.t,I=L.index,P=C._length,z=L.x,O=L.y;if(C.selectedpoints||A||S){if(A||(A=!0),C.selectedpoints){var D=v.selectBatch[I]=s.selIndices2selPoints(C),R={};for(g=0;g<D.length;g++)R[D[g]]=1;var F=[];for(g=0;g<P;g++)R[g]||F.push(g);v.unselectBatch[I]=F}var B=L.xpx=new Array(P),N=L.ypx=new Array(P);for(g=0;g<P;g++)B[g]=y.c2p(z[g]),N[g]=x.c2p(O[g])}else L.xpx=L.ypx=null}if(A){if(v.select2d||(v.select2d=n(m._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(m,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\":661,\"../../lib\":778,\"../../lib/prepare_regl\":791,\"../scatter/link_traces\":1202,\"../scatter/subtypes\":1211,\"./edit_style\":1243,\"gl-text\":352,\"regl-error2d\":534,\"regl-line2d\":535,\"regl-scatter2d\":537}],1249:[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 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e,r,i,a,s,l,c,u,f,h,p,d,g=t._x,m=t._y,v=t._z,y=t._len,x=-1/0,b=1/0,_=-1/0,w=1/0,T=-1/0,k=1/0,M=\"\";for(y&&(c=g[0],f=m[0],p=v[0]),y>1&&(u=g[y-1],h=m[y-1],d=v[y-1]),e=0;e<y;e++)x=Math.max(x,g[e]),b=Math.min(b,g[e]),_=Math.max(_,m[e]),w=Math.min(w,m[e]),T=Math.max(T,v[e]),k=Math.min(k,v[e]),a||g[e]===c||(a=!0,M+=\"x\"),s||m[e]===f||(s=!0,M+=\"y\"),l||v[e]===p||(l=!0,M+=\"z\");a||(M+=\"x\"),s||(M+=\"y\"),l||(M+=\"z\");var A=o(t._x),S=o(t._y),E=o(t._z);M=(M=(M=M.replace(\"x\",(c>u?\"-\":\"+\")+\"x\")).replace(\"y\",(f>h?\"-\":\"+\")+\"y\")).replace(\"z\",(p>d?\"-\":\"+\")+\"z\");var C=function(){y=0,A=[],S=[],E=[]};(!y||y<A.length*S.length*E.length)&&C();var L=function(t){return\"x\"===t?g:\"y\"===t?m:v},I=function(t){return\"x\"===t?A:\"y\"===t?S:E},P=function(t){return t[y-1]<t[0]?-1:1},z=L(M[1]),O=L(M[3]),D=L(M[5]),R=I(M[1]).length,F=I(M[3]).length,B=I(M[5]).length,N=!1,j=function(t,e,r){return 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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\":665,\"../../lib\":778,\"../../lib/svg_text_utils\":802,\"../bar/style\":934,\"../bar/uniform_text\":936,\"../pie/helpers\":1165,\"../pie/plot\":1169,\"./constants\":1301,\"./fx\":1303,\"./helpers\":1304,\"./style\":1309,d3:169,\"d3-hierarchy\":161}],1309:[function(t,e,r){\"use strict\";var n=t(\"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\":643,\"../../lib\":778,\"../bar/uniform_text\":936,d3:169}],1310:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"../../components/colorscale/attributes\"),a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../../plots/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll;function c(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 u=e.exports=l(s({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:a(),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or surfacecolor\",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:\"calc\"}),{contours:{x:c(),y:c(),z:c()},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:s({},i.zauto,{}),zmin:s({},i.zmin,{}),zmax:s({},i.zmax,{})},hoverinfo:s({},o.hoverinfo),showlegend:s({},o.showlegend,{dflt:!1})}),\"calc\",\"nested\");u.x.editType=u.y.editType=u.z.editType=\"calc+clearAxisTypes\",u.transforms=void 0},{\"../../components/color\":643,\"../../components/colorscale/attributes\":650,\"../../lib/extend\":768,\"../../plot_api/edit_types\":809,\"../../plots/attributes\":823,\"../../plots/template_attributes\":905}],1311:[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\":651}],1312:[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 g(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function m(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(g(t,n))}return e}function v(t){for(var e=m(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=m(t),n=m(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),g=l.dataScale,m=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<m;r++)x[e][r]=[];for(r=0;r<m;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<m;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<m;r++)for(n=0;n<v;n++)null==(a=x[e][r][n])?x[e][r][n]=NaN:a=x[e][r][n]*=g[e];for(e=0;e<3;e++)for(r=0;r<m;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<m;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(m*v),[m,v]),i(new Float32Array(m*v),[m,v]),i(new Float32Array(m*v),[m,v])];for(e=0;e<3;e++)for(r=0;r<m;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(m*v),[m,v]);for(r=0;r<m;r++)for(n=0;n<v;n++)k.set(r,n,t.surfacecolor[n][r]);b.push(k)}else w.intensityBounds[0]*=g[2],w.intensityBounds[1]*=g[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\":655,\"../../lib\":778,\"../../lib/gl_format_color\":774,\"../../lib/str2rgbarray\":801,\"../heatmap/find_empties\":1070,\"../heatmap/interp2d\":1073,\"gl-surface3d\":351,ndarray:495,\"ndarray-linear-interpolate\":489}],1313:[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 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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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d=i.ensureSingle(h,\"g\",\"slicetext\"),T=i.ensureSingle(d,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),E=i.ensureUniformFontSize(t,u.determineTextFont(C,s,S.font,{onPathbar:!0}));T.text(s._text||\" \").classed(\"slicetext\",!0).attr(\"text-anchor\",\"start\").call(a.font,E).call(o.convertToTspans,t),s.textBB=a.bBox(T.node()),s.transform=b(s,{fontSize:E.size,onPathbar:!0}),s.transform.fontSize=E.size,w?T.transition().attrTween(\"transform\",(function(t){var e=M(t,!0,A,[g,m]);return function(t){return _(e(t))}})):T.attr(\"transform\",_(s))}))}},{\"../../components/drawing\":665,\"../../lib\":778,\"../../lib/svg_text_utils\":802,\"../sunburst/fx\":1303,\"../sunburst/helpers\":1304,\"./constants\":1327,\"./partition\":1334,\"./style\":1336,d3:169}],1330:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../lib\"),a=t(\"../../components/drawing\"),o=t(\"../../lib/svg_text_utils\"),s=t(\"./partition\"),l=t(\"./style\").styleOne,c=t(\"./constants\"),u=t(\"../sunburst/helpers\"),f=t(\"../sunburst/fx\"),h=t(\"../sunburst/plot\").formatSliceLabel;e.exports=function(t,e,r,p,d){var 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strict\";e.exports={attributes:t(\"./attributes\"),layoutAttributes:t(\"./layout_attributes\"),supplyDefaults:t(\"./defaults\"),crossTraceDefaults:t(\"../box/defaults\").crossTraceDefaults,supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),crossTraceCalc:t(\"./cross_trace_calc\"),plot:t(\"./plot\"),style:t(\"./style\"),styleOnSelect:t(\"../scatter/style\").styleOnSelect,hoverPoints:t(\"./hover\"),selectPoints:t(\"../box/select\"),moduleType:\"trace\",name:\"violin\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"cartesian\",\"svg\",\"symbols\",\"oriented\",\"box-violin\",\"showLegend\",\"violinLayout\",\"zoomScale\"],meta:{}}},{\"../../plots/cartesian\":840,\"../box/defaults\":948,\"../box/select\":955,\"../scatter/style\":1210,\"./attributes\":1337,\"./calc\":1338,\"./cross_trace_calc\":1339,\"./defaults\":1340,\"./hover\":1342,\"./layout_attributes\":1344,\"./layout_defaults\":1345,\"./plot\":1346,\"./style\":1347}],1344:[function(t,e,r){\"use strict\";var 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f={\"stroke-width\":u+\"px\",\"stroke-dasharray\":2*u+\"px,\"+u+\"px\"};o.selectAll(\"path.mean\").style(f).call(i.stroke,c.color),o.selectAll(\"path.meanline\").style(f).call(i.stroke,c.color),a(o,r,t)}))}},{\"../../components/color\":643,\"../scatter/style\":1210,d3:169}],1348:[function(t,e,r){\"use strict\";var 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,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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\"zerolinewidth\": 2}}}, \"title\": {\"text\": \"quality plot\"}},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('8a210be8-4c30-4dfb-af12-becb35fb4633');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.graph_objects as go\n",
+    "\n",
+    "win_map = som.win_map(data)\n",
+    "size=som.distance_map().shape[0]\n",
+    "qualities=np.empty((size,size))\n",
+    "qualities[:]=np.NaN\n",
+    "for position, values in win_map.items():\n",
+    "    qualities[position[0], position[1]] = np.mean(abs(values-som.get_weights()[position[0], position[1]]))\n",
+    "\n",
+    "layout = go.Layout(title='quality plot')\n",
+    "fig = go.Figure(layout=layout)\n",
+    "fig.add_trace(go.Heatmap(z=qualities, colorscale='Viridis'))\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The property plot shows the mean values of each neuron seperately plotted for each property. This view can be used to explore the \"correlation\" of properties."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
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true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('8a6f276f-e0e2-4c47-8056-4ef97afbf566');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from plotly.subplots import make_subplots\n",
+    "import math\n",
+    "def showPropertyPlot(som, data, columns):\n",
+    "# plots the distances for each different property\n",
+    "    win_map = som.win_map(data)\n",
+    "    size=som.distance_map().shape[0]\n",
+    "    properties=np.empty((size*size,2+data.shape[1]))\n",
+    "    properties[:]=np.NaN\n",
+    "    i=0\n",
+    "    for row in range(0,size):\n",
+    "        for col in range(0,size):\n",
+    "            properties[size*row+col,0]=row\n",
+    "            properties[size*row+col,1]=col\n",
+    "\n",
+    "    for position, values in win_map.items():\n",
+    "        properties[size*position[0]+position[1],0]=position[0]\n",
+    "        properties[size*position[0]+position[1],1]=position[1]\n",
+    "        properties[size*position[0]+position[1],2:] = np.mean(values, axis=0)\n",
+    "        i=i+1\n",
+    "\n",
+    "    B = ['row', 'col']\n",
+    "    B.extend(columns)\n",
+    "    properties = pd.DataFrame(data=properties, columns=B)\n",
+    "    \n",
+    "    fig = make_subplots(rows=math.ceil(math.sqrt(data.shape[1])), cols=math.ceil(math.sqrt(data.shape[1])), shared_xaxes=False, horizontal_spacing=0.1, vertical_spacing=0.05, subplot_titles=columns, column_widths=None, row_heights=None)\n",
+    "    i=0\n",
+    "    zmin=min(np.min(properties.iloc[:,2:]))\n",
+    "    zmax=max(np.max(properties.iloc[:,2:]))\n",
+    "    for property in columns:\n",
+    "        fig.add_traces(\n",
+    "            [go.Heatmap(z=properties.sort_values(by=['row', 'col'])[property].values.reshape(size,size), zmax=zmax, zmin=zmin, coloraxis = 'coloraxis2')],\n",
+    "            rows=[i // math.ceil(math.sqrt(data.shape[1])) + 1 ],\n",
+    "            cols=[i % math.ceil(math.sqrt(data.shape[1])) + 1 ]\n",
+    "            )\n",
+    "        i=i+1\n",
+    "\n",
+    "    for layout in fig.layout:\n",
+    "        if layout.startswith('xaxis') or layout.startswith('yaxis'):\n",
+    "            fig.layout[layout].visible=False\n",
+    "            fig.layout[layout].visible=False\n",
+    "        if layout.startswith('coloraxis'):\n",
+    "            fig.layout[layout].cmax=zmax\n",
+    "            fig.layout[layout].cmin=zmin\n",
+    "        if layout.startswith('colorscale'):\n",
+    "            fig.layout[layout]={'diverging':'viridis'}\n",
+    "\n",
+    "    fig.update_layout(\n",
+    "        height=800\n",
+    "    )\n",
+    "    fig.show()\n",
+    "    \n",
+    "showPropertyPlot(som, data, columns[:-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The distribution map shows the min, max and mean of the values of samples corresponding with a neuron. Each neuron's data is shown in an own subplot. This plot can be used to examine similarities or differences in the neuron weights.\n",
+    "In this example of supervised learning, I have shown the main class of each neuron (as major class of all samples of a neuron) via background color of the subplots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def distributionMap(data, clusters, size, columns, minimum, maximum, plottype='barpolar'):\n",
+    "    spec={\"type\": \"polar\"}\n",
+    "    fig = make_subplots(rows=size, cols=size, specs=np.full((size,size),spec).tolist(), shared_yaxes=True, shared_xaxes=True,horizontal_spacing=0, vertical_spacing=0, subplot_titles=None, column_widths=None, row_heights=None)\n",
+    "    categories=columns\n",
+    "    if plottype=='spider':\n",
+    "        for index, row in data.iterrows():\n",
+    "            fig.add_traces(\n",
+    "                [go.Scatterpolargl(\n",
+    "                    r=row['max'],\n",
+    "                    name='max',\n",
+    "                    fillcolor='green',\n",
+    "                    line=dict(color='green'),\n",
+    "                    theta=categories,           \n",
+    "                    opacity=0.5),\n",
+    "                go.Scatterpolargl(\n",
+    "                    r=row['mean'],\n",
+    "                    name='mean',\n",
+    "                    fillcolor='blue',\n",
+    "                    line=dict(color='blue'),\n",
+    "                    theta=categories,           \n",
+    "                    opacity=0.5),\n",
+    "                go.Scatterpolargl(\n",
+    "                    r=row['min'],\n",
+    "                    name='min',\n",
+    "                    fillcolor='red',\n",
+    "                    line=dict(color='red'),\n",
+    "                    theta=categories,           \n",
+    "                    opacity=0.5)],\n",
+    "                rows=[row['row'], row['row'], row['row']],\n",
+    "                cols=[row['col'], row['col'], row['col']]\n",
+    "                )\n",
+    "    else:\n",
+    "        for index, row in data.iterrows():\n",
+    "            fig.add_traces(\n",
+    "                [\n",
+    "                go.Barpolar(                   \n",
+    "                    base=minimum,\n",
+    "                    r=row['max']-minimum,\n",
+    "                    name='max'+str(index),\n",
+    "                    marker_color=\"green\",\n",
+    "                    theta=categories,           \n",
+    "                    #opacity=1\n",
+    "                 ),\n",
+    "                go.Barpolar(           \n",
+    "                    base=minimum,\n",
+    "                    r=row['mean']-minimum,\n",
+    "                    name='mean'+str(index),\n",
+    "                    marker_color=\"blue\",\n",
+    "                    theta=categories,           \n",
+    "                    #opacity=1\n",
+    "                 ),\n",
+    "                go.Barpolar(           \n",
+    "                    base=minimum,\n",
+    "                    r=row['min']-minimum,\n",
+    "                    name='min'+str(index),\n",
+    "                    marker_color=\"darkred\",\n",
+    "                    theta=categories,  \n",
+    "                    #opacity=1\n",
+    "                ) \n",
+    "                ],\n",
+    "                rows=[row['row'], row['row'], row['row']],\n",
+    "                cols=[row['col'], row['col'], row['col']]\n",
+    "                )        \n",
+    "\n",
+    "    if plottype=='spider':\n",
+    "        fig.update_traces(mode='lines', fill='toself')\n",
+    "    for layout in fig.layout:\n",
+    "        if layout.startswith('polar'):\n",
+    "            fig.layout[layout].angularaxis.visible=False\n",
+    "            fig.layout[layout].angularaxis.tickfont.size = 7\n",
+    "            fig.layout[layout].radialaxis.visible=True\n",
+    "            fig.layout[layout].radialaxis.tickfont.size = 7\n",
+    "            fig.layout[layout].barmode='overlay'\n",
+    "            fig.layout[layout].radialaxis.range = [minimum, maximum+1]\n",
+    "\n",
+    "    for index, row in data.iterrows():\n",
+    "            color=row['bgcolor']\n",
+    "            if row['row']==0 and row['col']==0:\n",
+    "                # row needs to be switched because all the other plots (heatmaps) are shown with row 0 at bottom \n",
+    "                fig.layout['polar'].bgcolor=\"rgb(\"+\",\".join(str(i) for i in [color]*3) +\")\"\n",
+    "            else:\n",
+    "                # row needs to be switched because all the other plots (heatmaps) are shown with row 0 at bottom \n",
+    "                fig.layout['polar'+str((row['row']-1)*size+row['col'])].bgcolor=\"rgb(\"+\",\".join(str(i) for i in [color]*3) +\")\"\n",
+    "            \n",
+    "    fig.update_layout(\n",
+    "        width=900,\n",
+    "        height=900,\n",
+    "        showlegend=False\n",
+    "    )\n",
+    "\n",
+    "    fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
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+         "subplot": "polar24",
+         "theta": [
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+          "perimeter",
+          "compactness",
+          "length_kernel",
+          "width_kernel",
+          "asymmetry_coefficient",
+          "length_kernel_groove"
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+         "type": "barpolar"
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+         "theta": [
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+          "length_kernel",
+          "width_kernel",
+          "asymmetry_coefficient",
+          "length_kernel_groove"
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+         "type": "barpolar"
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+         "marker": {
+          "color": "darkred"
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+          "compactness",
+          "length_kernel",
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+       "                            \n",
+       "var gd = document.getElementById('243c75d0-4c4f-495b-bc78-c70e00483377');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
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+       "\n",
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plotDistributionOfDataInClusters(som, data, columns, plottype='barpolar'): \n",
+    "    size=som.distance_map().shape[0]\n",
+    "    clusters= np.array(np.arange(0,size*size)).reshape(size,size)\n",
+    "    distributionMapData=pd.DataFrame(columns=['col', 'row', 'min', 'mean', 'max', 'bgcolor'])\n",
+    "    labels_map = som.labels_map(data, [label_names[t] for t in target])\n",
+    "    win_map=som.win_map(data)\n",
+    "    for position in win_map.keys():\n",
+    "        label_fracs = [labels_map[position][l] for l in label_names.values()]\n",
+    "        bgcolor = label_fracs.index(np.max(label_fracs))*255//len(label_fracs) \n",
+    "        winner=win_map[position]\n",
+    "        minima=np.min(winner, axis=0)\n",
+    "        means=np.mean(winner, axis=0)\n",
+    "        maxima=np.max(winner, axis=0)\n",
+    "        row=int(position[1]+1)\n",
+    "        col=int(position[0]+1)\n",
+    "        distributionMapData=distributionMapData.append(\n",
+    "            {'col': col,\n",
+    "             # row needs to be shifted because all the other plots (heatmaps) are shown with row 0 at bottom \n",
+    "             'row': size-row+1, \n",
+    "             'min': minima, \n",
+    "             'mean': means, \n",
+    "             'max': maxima,\n",
+    "             'bgcolor' : bgcolor}, \n",
+    "             verify_integrity=True, ignore_index=True)\n",
+    "\n",
+    "    noClusters=np.max(clusters).item()+1\n",
+    "    clusterData=pd.DataFrame(columns=['col', 'row', 'min', 'mean', 'max'])\n",
+    "    \n",
+    "    maximum=max([l.max() for l in distributionMapData['max'].values])\n",
+    "    minimum=min([l.min() for l in distributionMapData['min'].values])\n",
+    "    \n",
+    "    distributionMap(distributionMapData, clusters, size, columns, minimum, maximum, plottype)\n",
+    "\n",
+    "plotDistributionOfDataInClusters(som, data, columns[:-1], plottype='barpolar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Starburst Gradient visualization\n",
+    "This visualization can be used to find similaraties between neurons resp. between the samples that are represented. the black lines follow the maximum gradient between nodes, the stars separate regions of neurons with smaller distance towards each other from others.\n",
+    "\n",
+    "If you image the UMAT (distance values) being plotted as 3 dimensional plot lile mountains and valleys the starburst map shows the at the center of the stars the position a ball would end up when it started rolling at the begin of one of the corresponding lines. \n",
+    "\n",
+    "From Hamel, Brown: Improved Interpretability of the Unified Distance Matrix with Connected Components (https://homepage.cs.uri.edu/faculty/hamel/pubs/improved-umat-dmin11.pdf)\n",
+    "\n",
+    "\"At the core of the implementation is the function find.internal.node which, given the map coordinates of a neural element, will find the corresponding internal node of the associated star. [...] Given a position on the map this function first searches the adjacent nodes for the minimal UMAT value using the function find.min. If an adjacent node with a smaller UMAT value than the value of our current node exists and if this UMAT value is smaller than the UMAT values of all other adjacent nodes, then that node lies along the maximum gradient of the surface and we make this node our new current position. If no such node exists, then the gradient at our current position is zero and we are at an internal node.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def findMin(x, y, umat):\n",
+    "    newxmin=max(0,x-1)\n",
+    "    newxmax=min(umat.shape[0],x+2)\n",
+    "    newymin=max(0,y-1)\n",
+    "    newymax=min(umat.shape[1],y+2)\n",
+    "    minx, miny = np.where(umat[newxmin:newxmax,newymin:newymax] == umat[newxmin:newxmax,newymin:newymax].min())\n",
+    "    return newxmin+minx[0], newymin+miny[0]\n",
+    "\n",
+    "def findInternalNode(x, y, umat):\n",
+    "    minx, miny = findMin(x,y,umat)\n",
+    "    if (minx == x and miny == y):\n",
+    "        cx = minx\n",
+    "        cy = miny\n",
+    "    else:\n",
+    "        cx,cy = findInternalNode(minx,miny,umat)\n",
+    "    return cx, cy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "from matplotlib import cm\n",
+    "import numpy as np\n",
+    "\n",
+    "def matplotlib_cmap_to_plotly(cmap, entries):\n",
+    "    h = 1.0/(entries-1)\n",
+    "    colorscale = []\n",
+    "\n",
+    "    for k in range(entries):\n",
+    "        C = (np.array(cmap(k*h)[:3])*255)\n",
+    "        colorscale.append([k*h, 'rgb'+str((C[0], C[1], C[2]))])\n",
+    "\n",
+    "    return colorscale"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
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[0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 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\"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"autotypenumbers\": \"strict\", \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"starburstMap\"}, \"width\": 500},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('4bb50441-0208-428d-acd5-bbd1333c1a93');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plotStarburstMap(som):\n",
+    "    boner_rgb = []\n",
+    "    norm = matplotlib.colors.Normalize(vmin=0, vmax=255)\n",
+    "    bone_r_cmap = matplotlib.cm.get_cmap('bone_r')\n",
+    "\n",
+    "    bone_r = matplotlib_cmap_to_plotly(bone_r_cmap, 255)\n",
+    "\n",
+    "    layout = go.Layout(title='starburstMap')\n",
+    "    fig = go.Figure(layout=layout)\n",
+    "    fig.add_trace(go.Heatmap(z=som.distance_map().T, colorscale=bone_r))\n",
+    "    shapes=[]\n",
+    "\n",
+    "    for row in np.arange(som.distance_map().shape[0]):\n",
+    "        for col in np.arange(som.distance_map().shape[1]):\n",
+    "            cx,cy = findInternalNode(row, col, som.distance_map().T)\n",
+    "            shape=go.layout.Shape(\n",
+    "                    type=\"line\",\n",
+    "                    x0=row,\n",
+    "                    y0=col,\n",
+    "                    x1=cx,\n",
+    "                    y1=cy,\n",
+    "                    line=dict(\n",
+    "                        color=\"Black\",\n",
+    "                        width=1\n",
+    "                    )\n",
+    "                )\n",
+    "            shapes=np.append(shapes, shape)\n",
+    "\n",
+    "    fig.update_layout(shapes=shapes.tolist(), \n",
+    "        width=500,\n",
+    "        height=500) \n",
+    "    fig.show()\n",
+    "    \n",
+    "plotStarburstMap(som)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here you can see that the samples at (4,4) belong to the same cluster like (8,6). They are in some sense more similar than (4,4) and (8,8).\n",
+    "\n",
+    "Compare it with the distance plot that is also given in the basic example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "plt.figure(figsize=(9, 9))\n",
+    "\n",
+    "plt.pcolor(som.distance_map().T, cmap='bone_r')  # plotting the distance map as background\n",
+    "plt.colorbar()\n",
+    "\n",
+    "# Plotting the response for each pattern in the iris dataset\n",
+    "# different colors and markers for each label\n",
+    "markers = ['o', 's', 'D']\n",
+    "colors = ['C0', 'C1', 'C2']\n",
+    "for cnt, xx in enumerate(data):\n",
+    "    w = som.winner(xx)  # getting the winner\n",
+    "    # palce a marker on the winning position for the sample xx\n",
+    "    plt.plot(w[0]+.5, w[1]+.5, markers[target[cnt]-1], markerfacecolor='None',\n",
+    "             markeredgecolor=colors[target[cnt]-1], markersize=12, markeredgewidth=2)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/BasicUsage.ipynb b/examples/BasicUsage.ipynb
new file mode 100644
index 0000000..c82ffa9
--- /dev/null
+++ b/examples/BasicUsage.ipynb
@@ -0,0 +1,323 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook we will see the basics of how to use MiniSom.\n",
+    "\n",
+    "Let's start importing MiniSom:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from minisom import MiniSom"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "MiniSom relies on the Python ecosystem to import and preprocess the data. For this example we will load the <a href=\"https://archive.ics.uci.edu/ml/datasets/seeds\">seeds</a> dataset dataset using pandas:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "columns=['area', 'perimeter', 'compactness', 'length_kernel', 'width_kernel',\n",
+    "                   'asymmetry_coefficient', 'length_kernel_groove', 'target']\n",
+    "data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/00236/seeds_dataset.txt', \n",
+    "                    names=columns, \n",
+    "                   sep='\\t+', engine='python')\n",
+    "target = data['target'].values\n",
+    "label_names = {1:'Kama', 2:'Rosa', 3:'Canadian'}\n",
+    "data = data[data.columns[:-1]]\n",
+    "# data normalization\n",
+    "data = (data - np.mean(data, axis=0)) / np.std(data, axis=0)\n",
+    "data = data.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can initialize and train MiniSom as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " [ 1000 / 1000 ] 100% - 0:00:00 left \n",
+      " quantization error: 0.6426435848319221\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialization and training\n",
+    "n_neurons = 9\n",
+    "m_neurons = 9\n",
+    "som = MiniSom(n_neurons, m_neurons, data.shape[1], sigma=1.5, learning_rate=.5, \n",
+    "              neighborhood_function='gaussian', random_seed=0)\n",
+    "\n",
+    "som.pca_weights_init(data)\n",
+    "som.train(data, 1000, verbose=True)  # random training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To visualize the result of the training we can plot the distance map (U-Matrix) using a pseudocolor where the neurons of the maps are displayed as an array of cells and the color represents the (weights) distance from the neighbour neurons. On top of the pseudo color we can add markers that repesent the samples mapped in the specific cells:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "plt.figure(figsize=(9, 9))\n",
+    "\n",
+    "plt.pcolor(som.distance_map().T, cmap='bone_r')  # plotting the distance map as background\n",
+    "plt.colorbar()\n",
+    "\n",
+    "# Plotting the response for each pattern in the iris dataset\n",
+    "# different colors and markers for each label\n",
+    "markers = ['o', 's', 'D']\n",
+    "colors = ['C0', 'C1', 'C2']\n",
+    "for cnt, xx in enumerate(data):\n",
+    "    w = som.winner(xx)  # getting the winner\n",
+    "    # palce a marker on the winning position for the sample xx\n",
+    "    plt.plot(w[0]+.5, w[1]+.5, markers[target[cnt]-1], markerfacecolor='None',\n",
+    "             markeredgecolor=colors[target[cnt]-1], markersize=12, markeredgewidth=2)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To have an overview of how the samples are distributed across the map a scatter chart can be used where each dot represents the coordinates of the winning neuron. A random offset is added to avoid overlaps between points within the same cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "w_x, w_y = zip(*[som.winner(d) for d in data])\n",
+    "w_x = np.array(w_x)\n",
+    "w_y = np.array(w_y)\n",
+    "\n",
+    "plt.figure(figsize=(10, 9))\n",
+    "plt.pcolor(som.distance_map().T, cmap='bone_r', alpha=.2)\n",
+    "plt.colorbar()\n",
+    "\n",
+    "for c in np.unique(target):\n",
+    "    idx_target = target==c\n",
+    "    plt.scatter(w_x[idx_target]+.5+(np.random.rand(np.sum(idx_target))-.5)*.8,\n",
+    "                w_y[idx_target]+.5+(np.random.rand(np.sum(idx_target))-.5)*.8, \n",
+    "                s=50, c=colors[c-1], label=label_names[c])\n",
+    "plt.legend(loc='upper right')\n",
+    "plt.grid()\n",
+    "plt.savefig('resulting_images/som_seed.png')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To have an idea of which neurons of the map are activated more often we can create another pseudocolor plot that reflects the activation frequencies:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(7, 7))\n",
+    "frequencies = som.activation_response(data)\n",
+    "plt.pcolor(frequencies.T, cmap='Blues') \n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When dealing with a supervised problem, one can visualize the proportion of samples per class falling in a specific neuron using a pie chart per neuron:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x648 with 74 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.gridspec as gridspec\n",
+    "\n",
+    "labels_map = som.labels_map(data, [label_names[t] for t in target])\n",
+    "\n",
+    "fig = plt.figure(figsize=(9, 9))\n",
+    "the_grid = gridspec.GridSpec(n_neurons, m_neurons, fig)\n",
+    "for position in labels_map.keys():\n",
+    "    label_fracs = [labels_map[position][l] for l in label_names.values()]\n",
+    "    plt.subplot(the_grid[n_neurons-1-position[1],\n",
+    "                         position[0]], aspect=1)\n",
+    "    patches, texts = plt.pie(label_fracs)\n",
+    "\n",
+    "plt.legend(patches, label_names.values(), bbox_to_anchor=(3.5, 6.5), ncol=3)\n",
+    "plt.savefig('resulting_images/som_seed_pies.png')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To understand how the training evolves we can plot the quantization and topographic error of the SOM at each step. This is particularly important when estimating the number of iterations to run:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "som = MiniSom(10, 20, data.shape[1], sigma=3., learning_rate=.7,\n",
+    "              neighborhood_function='gaussian', random_seed=10)\n",
+    "\n",
+    "max_iter = 1000\n",
+    "q_error = []\n",
+    "t_error = []\n",
+    "\n",
+    "for i in range(max_iter):\n",
+    "    rand_i = np.random.randint(len(data))\n",
+    "    som.update(data[rand_i], som.winner(data[rand_i]), i, max_iter)\n",
+    "    q_error.append(som.quantization_error(data))\n",
+    "    t_error.append(som.topographic_error(data))\n",
+    "\n",
+    "plt.plot(np.arange(max_iter), q_error, label='quantization error')\n",
+    "plt.plot(np.arange(max_iter), t_error, label='topographic error')\n",
+    "plt.ylabel('quantization error')\n",
+    "plt.xlabel('iteration index')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that in the snippet above we have to run each learning iteration in a for loop and saved the errors in separated lists."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/Classification.ipynb b/examples/Classification.ipynb
new file mode 100644
index 0000000..005379d
--- /dev/null
+++ b/examples/Classification.ipynb
@@ -0,0 +1,122 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This examples show how to use MiniSom to solve a classification problem. The classification mechanism will be implemented with MiniSom and the evaluation will make use of sklearn.\n",
+    "\n",
+    "First, let's load a dataset (in this case the famous Iris dataset) and apply normalization:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from minisom import MiniSom\n",
+    "import numpy as np\n",
+    "\n",
+    "data = np.genfromtxt('iris.csv', delimiter=',', usecols=(0, 1, 2, 3))\n",
+    "data = np.apply_along_axis(lambda x: x/np.linalg.norm(x), 1, data)\n",
+    "labels = np.genfromtxt('iris.csv', delimiter=',', usecols=(4), dtype=str)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's naive classification function that classifies a sample in `data` using the label assigned to the associated winning neuron. A label $c$ is associated to a neuron if the majority of samples mapped in that neuron have label $c$. The function will assign the most common label in the dataset in case that a sample is mapped to a neuron for which no class is assigned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def classify(som, data):\n",
+    "    \"\"\"Classifies each sample in data in one of the classes definited\n",
+    "    using the method labels_map.\n",
+    "    Returns a list of the same length of data where the i-th element\n",
+    "    is the class assigned to data[i].\n",
+    "    \"\"\"\n",
+    "    winmap = som.labels_map(X_train, y_train)\n",
+    "    default_class = np.sum(list(winmap.values())).most_common()[0][0]\n",
+    "    result = []\n",
+    "    for d in data:\n",
+    "        win_position = som.winner(d)\n",
+    "        if win_position in winmap:\n",
+    "            result.append(winmap[win_position].most_common()[0][0])\n",
+    "        else:\n",
+    "            result.append(default_class)\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can 1) split the data in train and test set, 2) train the som, 3) print the classification report that contains all the metrics to evaluate the results of the classification."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "      setosa       1.00      1.00      1.00        13\n",
+      "  versicolor       0.92      1.00      0.96        12\n",
+      "   virginica       1.00      0.92      0.96        13\n",
+      "\n",
+      "    accuracy                           0.97        38\n",
+      "   macro avg       0.97      0.97      0.97        38\n",
+      "weighted avg       0.98      0.97      0.97        38\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import classification_report\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(data, labels, stratify=labels)\n",
+    "\n",
+    "som = MiniSom(7, 7, 4, sigma=3, learning_rate=0.5, \n",
+    "              neighborhood_function='triangle', random_seed=10)\n",
+    "som.pca_weights_init(X_train)\n",
+    "som.train_random(X_train, 500, verbose=False)\n",
+    "\n",
+    "print(classification_report(y_test, classify(som, X_test)))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/Clustering.ipynb b/examples/Clustering.ipynb
new file mode 100644
index 0000000..b244730
--- /dev/null
+++ b/examples/Clustering.ipynb
@@ -0,0 +1,632 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example we will see how to use MiniSom to cluster the iris dataset.\n",
+    "\n",
+    "First, let's load the data and train our SOM:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
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+      " quantization error: 0.864828807271489\n"
+     ]
+    }
+   ],
+   "source": [
+    "from minisom import MiniSom\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/00236/seeds_dataset.txt', \n",
+    "                    names=['area', 'perimeter', 'compactness', 'length_kernel', 'width_kernel',\n",
+    "                   'asymmetry_coefficient', 'length_kernel_groove', 'target'], usecols=[0, 5], \n",
+    "                   sep='\\t+', engine='python')\n",
+    "# data normalization\n",
+    "data = (data - np.mean(data, axis=0)) / np.std(data, axis=0)\n",
+    "data = data.values\n",
+    "\n",
+    "# Initialization and training\n",
+    "som_shape = (1, 3)\n",
+    "som = MiniSom(som_shape[0], som_shape[1], data.shape[1], sigma=.5, learning_rate=.5,\n",
+    "              neighborhood_function='gaussian', random_seed=10)\n",
+    "\n",
+    "som.train_batch(data, 500, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we will consider all the sample mapped into a specific neuron as a cluster. To identify each cluster more easily we will translate the bidimensional indexes of the neurons on the SOM into a monodimentional indexes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# each neuron represents a cluster\n",
+    "winner_coordinates = np.array([som.winner(x) for x in data]).T\n",
+    "# with np.ravel_multi_index we convert the bidimensional\n",
+    "# coordinates to a monodimensional index\n",
+    "cluster_index = np.ravel_multi_index(winner_coordinates, som_shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot each cluster with a different color:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# plotting the clusters using the first 2 dimentions of the data\n",
+    "for c in np.unique(cluster_index):\n",
+    "    plt.scatter(data[cluster_index == c, 0],\n",
+    "                data[cluster_index == c, 1], label='cluster='+str(c), alpha=.7)\n",
+    "\n",
+    "# plotting centroids\n",
+    "for centroid in som.get_weights():\n",
+    "    plt.scatter(centroid[:, 0], centroid[:, 1], marker='x', \n",
+    "                s=80, linewidths=35, color='k', label='centroid')\n",
+    "plt.legend();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/ColorQuantization.ipynb b/examples/ColorQuantization.ipynb
new file mode 100644
index 0000000..fc7c0ec
--- /dev/null
+++ b/examples/ColorQuantization.ipynb
@@ -0,0 +1,10201 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example we will load an image and then buil a matrix that we will call `pixels` and will look like this:\n",
+    "\n",
+    "\\begin{bmatrix}\n",
+    "r_1 & g_1 & b_1\\\\\n",
+    "\\vdots & \\vdots & \\vdots\\\\\n",
+    "r_n & g_n & b_n\n",
+    "\\end{bmatrix}\n",
+    "\n",
+    "In this matrix each row represent the color of a given pixel in the image in the RGB space and the columns represent the intensity in a specific color.\n",
+    "\n",
+    "We will use this matrix to train our SOM and then we will quantize the original set of colors to obtain a smaller set of colors that will segment the image into uniform areas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "training...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
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+      " quantization error: 0.17677845009409074\n",
+      "quantization...\n",
+      "building new image...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-14-e67273d42f27>:20: DeprecationWarning: 'shape' argument should be used instead of 'dims'\n",
+      "  clustered[np.unravel_index(i, dims=(img.shape[0], img.shape[1]))] = q\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "done.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from minisom import MiniSom\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# read the image\n",
+    "img = plt.imread('tree.jpg')\n",
+    "\n",
+    "# reshaping the pixels matrix\n",
+    "pixels = np.reshape(img, (img.shape[0]*img.shape[1], 3)) / 255.\n",
+    "\n",
+    "# SOM initialization and training\n",
+    "print('training...')\n",
+    "som = MiniSom(2, 3, 3, sigma=1.,\n",
+    "              learning_rate=0.2, neighborhood_function='bubble')  # 3x3 = 9 final colors\n",
+    "som.random_weights_init(pixels)\n",
+    "starting_weights = som.get_weights().copy()  # saving the starting weights\n",
+    "som.train(pixels, 10000, random_order=True, verbose=True)\n",
+    "\n",
+    "print('quantization...')\n",
+    "qnt = som.quantization(pixels)  # quantize each pixels of the image\n",
+    "print('building new image...')\n",
+    "clustered = np.zeros(img.shape)\n",
+    "for i, q in enumerate(qnt):  # place the quantized values into a new image\n",
+    "    clustered[np.unravel_index(i, dims=(img.shape[0], img.shape[1]))] = q\n",
+    "print('done.')\n",
+    "\n",
+    "# show the result\n",
+    "plt.figure(figsize=(7, 7))\n",
+    "plt.figure(1)\n",
+    "plt.subplot(221)\n",
+    "plt.title('original')\n",
+    "plt.imshow(img)\n",
+    "plt.subplot(222)\n",
+    "plt.title('result')\n",
+    "plt.imshow(clustered)\n",
+    "\n",
+    "plt.subplot(223)\n",
+    "plt.title('initial colors')\n",
+    "plt.imshow(starting_weights, interpolation='none')\n",
+    "plt.subplot(224)\n",
+    "plt.title('learned colors')\n",
+    "plt.imshow(som.get_weights(), interpolation='none')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('resulting_images/som_color_quantization.png')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/ColorSpaceMapping.ipynb b/examples/ColorSpaceMapping.ipynb
new file mode 100644
index 0000000..75ba737
--- /dev/null
+++ b/examples/ColorSpaceMapping.ipynb
@@ -0,0 +1,130 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example we will take a small set of colors in the RGB space and use the intensity values to train a SOM. After the som is trained we will plot the map coloring each neuron using its weights as RGB coordinates.\n",
+    "\n",
+    "Let's first define our colors:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from minisom import MiniSom\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "\n",
+    "#Training inputs for RGBcolors\n",
+    "colors = [[0., 0., 0.],\n",
+    "      [0., 0., 1.],\n",
+    "      [0., 0., 0.5],\n",
+    "      [0.125, 0.529, 1.0],\n",
+    "      [0.33, 0.4, 0.67],\n",
+    "      [0.6, 0.5, 1.0],\n",
+    "      [0., 1., 0.],\n",
+    "      [1., 0., 0.],\n",
+    "      [0., 1., 1.],\n",
+    "      [1., 0., 1.],\n",
+    "      [1., 1., 0.],\n",
+    "      [1., 1., 1.],\n",
+    "      [.33, .33, .33],\n",
+    "      [.5, .5, .5],\n",
+    "      [.66, .66, .66]]\n",
+    "color_names = \\\n",
+    "    ['black', 'blue', 'darkblue', 'skyblue',\n",
+    "     'greyblue', 'lilac', 'green', 'red',\n",
+    "     'cyan', 'violet', 'yellow', 'white',\n",
+    "     'darkgrey', 'mediumgrey', 'lightgrey']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's initialize our SOM and plot the colors without training:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "som = MiniSom(30, 30, 3, sigma=3., \n",
+    "              learning_rate=2.5, \n",
+    "              neighborhood_function='gaussian')\n",
+    "\n",
+    "plt.imshow(abs(som.get_weights()), interpolation='none')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now train the SOM and check how the organization of the weights has changed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "som.train(colors, 500, random_order=True, verbose=True)\n",
+    "\n",
+    "plt.imshow(abs(som.get_weights()), interpolation='none')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using a different neighborhood function the weights be less smooth across the map:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "som = MiniSom(30, 30, 3, sigma=8., \n",
+    "              learning_rate=.5, \n",
+    "              neighborhood_function='bubble')\n",
+    "som.train_random(colors, 500, verbose=True)\n",
+    "\n",
+    "plt.imshow(abs(som.get_weights()), interpolation='none')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/DemocracyIndex.ipynb b/examples/DemocracyIndex.ipynb
new file mode 100644
index 0000000..ae4759d
--- /dev/null
+++ b/examples/DemocracyIndex.ipynb
@@ -0,0 +1,1594 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "In this example we will see how to use MiniSom to draw some insights from the <a href=\"https://en.wikipedia.org/wiki/Democracy_Index\">Democracy Index data</a> from Wikipedia.\n",
+    "\n",
+    "First, let's load the dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.patches import Patch\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from minisom import MiniSom\n",
+    "from sklearn.preprocessing import minmax_scale, scale\n",
+    "\n",
+    "# download from wikipedia and reorganization\n",
+    "import os.path\n",
+    "if not os.path.isfile('democracy_index.csv'):\n",
+    "    wikitables = pd.read_html('https://en.wikipedia.org/wiki/Democracy_Index',\n",
+    "                              attrs={\"class\":\"sortable\"}, header=0)\n",
+    "    democracy_index = wikitables[0]\n",
+    "    democracy_index.columns = [c.lower().replace(' ', '_') for c in democracy_index.columns]\n",
+    "    democracy_index.rename(columns={'score': 'democracy_index', \n",
+    "                                    'functioning_ofgovernment': 'functioning_of_government',\n",
+    "                                    'politicalparticipation': 'political_participation',\n",
+    "                                    'politicalculture': 'political_culture',\n",
+    "                                    'civilliberties': 'civil_liberties'}, inplace=True)\n",
+    "    democracy_index.category = democracy_index.category.replace('Flawed democracy[a]', 'Flawed democracy')\n",
+    "    democracy_index = democracy_index[:-1]\n",
+    "    democracy_index.to_csv('democracy_index.csv')\n",
+    "    print('data downloaded from Wikipedia')\n",
+    "else:\n",
+    "    # pre-downloaded file\n",
+    "    democracy_index = pd.read_csv('democracy_index.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>rank</th>\n",
+       "      <th>country</th>\n",
+       "      <th>democracy_index</th>\n",
+       "      <th>electoral_processand_pluralism</th>\n",
+       "      <th>functioning_of_government</th>\n",
+       "      <th>political_participation</th>\n",
+       "      <th>political_culture</th>\n",
+       "      <th>civil_liberties</th>\n",
+       "      <th>category</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Norway</td>\n",
+       "      <td>9.87</td>\n",
+       "      <td>10.00</td>\n",
+       "      <td>9.64</td>\n",
+       "      <td>10.00</td>\n",
+       "      <td>10.00</td>\n",
+       "      <td>9.71</td>\n",
+       "      <td>Full democracy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Iceland</td>\n",
+       "      <td>9.58</td>\n",
+       "      <td>10.00</td>\n",
+       "      <td>9.29</td>\n",
+       "      <td>8.89</td>\n",
+       "      <td>10.00</td>\n",
+       "      <td>9.71</td>\n",
+       "      <td>Full democracy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Sweden</td>\n",
+       "      <td>9.39</td>\n",
+       "      <td>9.58</td>\n",
+       "      <td>9.64</td>\n",
+       "      <td>8.33</td>\n",
+       "      <td>10.00</td>\n",
+       "      <td>9.41</td>\n",
+       "      <td>Full democracy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>New Zealand</td>\n",
+       "      <td>9.26</td>\n",
+       "      <td>10.00</td>\n",
+       "      <td>9.29</td>\n",
+       "      <td>8.89</td>\n",
+       "      <td>8.13</td>\n",
+       "      <td>10.00</td>\n",
+       "      <td>Full democracy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Denmark</td>\n",
+       "      <td>9.22</td>\n",
+       "      <td>10.00</td>\n",
+       "      <td>9.29</td>\n",
+       "      <td>8.33</td>\n",
+       "      <td>9.38</td>\n",
+       "      <td>9.12</td>\n",
+       "      <td>Full democracy</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0 rank      country  democracy_index  \\\n",
+       "0           0    1       Norway             9.87   \n",
+       "1           1    2      Iceland             9.58   \n",
+       "2           2    3       Sweden             9.39   \n",
+       "3           3    4  New Zealand             9.26   \n",
+       "4           4    5      Denmark             9.22   \n",
+       "\n",
+       "   electoral_processand_pluralism  functioning_of_government  \\\n",
+       "0                           10.00                       9.64   \n",
+       "1                           10.00                       9.29   \n",
+       "2                            9.58                       9.64   \n",
+       "3                           10.00                       9.29   \n",
+       "4                           10.00                       9.29   \n",
+       "\n",
+       "   political_participation  political_culture  civil_liberties        category  \n",
+       "0                    10.00              10.00             9.71  Full democracy  \n",
+       "1                     8.89              10.00             9.71  Full democracy  \n",
+       "2                     8.33              10.00             9.41  Full democracy  \n",
+       "3                     8.89               8.13            10.00  Full democracy  \n",
+       "4                     8.33               9.38             9.12  Full democracy  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "democracy_index.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see the dataset contains a set of of social metrics related to the democracy level for each country. The goal is to use these metrics as features for our SOM so that we can create a bidimensional space where each country is mapped according to their democracity level.\n",
+    "\n",
+    "Let's define a set of color for the categories in which the countries are classified and also a country codes for each country. These will become handy when we will visualize the results on the map."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "category_color = {'Full democracy': 'darkgreen',\n",
+    "                  'Flawed democracy': 'limegreen',\n",
+    "                  'Hybrid regime': 'darkorange',\n",
+    "                  'Authoritarian': 'crimson'}\n",
+    "colors_dict = {c: category_color[dm] for c, dm in zip(democracy_index.country,\n",
+    "                                                      democracy_index.category)}\n",
+    "\n",
+    "country_codes = {'Afghanistan': 'AF',\n",
+    " 'Albania': 'AL',\n",
+    " 'Algeria': 'DZ',\n",
+    " 'Angola': 'AO',\n",
+    " 'Argentina': 'AR',\n",
+    " 'Armenia': 'AM',\n",
+    " 'Australia': 'AU',\n",
+    " 'Austria': 'AT',\n",
+    " 'Azerbaijan': 'AZ',\n",
+    " 'Bahrain': 'BH',\n",
+    " 'Bangladesh': 'BD',\n",
+    " 'Belarus': 'BY',\n",
+    " 'Belgium': 'BE',\n",
+    " 'Benin': 'BJ',\n",
+    " 'Bhutan': 'BT',\n",
+    " 'Bolivia': 'BO',\n",
+    " 'Bosnia and Herzegovina': 'BA',\n",
+    " 'Botswana': 'BW',\n",
+    " 'Brazil': 'BR',\n",
+    " 'Bulgaria': 'BG',\n",
+    " 'Burkina Faso': 'BF',\n",
+    " 'Burundi': 'BI',\n",
+    " 'Cambodia': 'KH',\n",
+    " 'Cameroon': 'CM',\n",
+    " 'Canada': 'CA',\n",
+    " 'Cape Verde': 'CV',\n",
+    " 'Central African Republic': 'CF',\n",
+    " 'Chad': 'TD',\n",
+    " 'Chile': 'CL',\n",
+    " 'China': 'CN',\n",
+    " 'Colombia': 'CO',\n",
+    " 'Comoros': 'KM',\n",
+    " 'Costa Rica': 'CR',\n",
+    " 'Croatia': 'HR',\n",
+    " 'Cuba': 'CU',\n",
+    " 'Cyprus': 'CY',\n",
+    " 'Czech Republic': 'CZ',\n",
+    " 'Democratic Republic of the Congo': 'CD',\n",
+    " 'Denmark': 'DK',\n",
+    " 'Djibouti': 'DJ',\n",
+    " 'Dominican Republic': 'DO',\n",
+    " 'Ecuador': 'EC',\n",
+    " 'Egypt': 'EG',\n",
+    " 'El Salvador': 'SV',\n",
+    " 'Equatorial Guinea': 'GQ',\n",
+    " 'Eritrea': 'ER',\n",
+    " 'Estonia': 'EE',\n",
+    " 'Ethiopia': 'ET',\n",
+    " 'Fiji': 'FJ',\n",
+    " 'Finland': 'FI',\n",
+    " 'France': 'FR',\n",
+    " 'Gabon': 'GA',\n",
+    " 'Gambia': 'GM',\n",
+    " 'Georgia': 'GE',\n",
+    " 'Germany': 'DE',\n",
+    " 'Ghana': 'GH',\n",
+    " 'Greece': 'GR',\n",
+    " 'Guatemala': 'GT',\n",
+    " 'Guinea': 'GN',\n",
+    " 'Guinea-Bissau': 'GW',\n",
+    " 'Guyana': 'GY',\n",
+    " 'Haiti': 'HT',\n",
+    " 'Honduras': 'HN',\n",
+    " 'Hong Kong': 'HK',\n",
+    " 'Hungary': 'HU',\n",
+    " 'Iceland': 'IS',\n",
+    " 'India': 'IN',\n",
+    " 'Indonesia': 'ID',\n",
+    " 'Iran': 'IR',\n",
+    " 'Iraq': 'IQ',\n",
+    " 'Ireland': 'IE',\n",
+    " 'Israel': 'IL',\n",
+    " 'Italy': 'IT',\n",
+    " 'Ivory Coast': 'IC',\n",
+    " 'Jamaica': 'JM',\n",
+    " 'Japan': 'JP',\n",
+    " 'Jordan': 'JO',\n",
+    " 'Kazakhstan': 'KZ',\n",
+    " 'Kenya': 'KE',\n",
+    " 'Kuwait': 'KW',\n",
+    " 'Kyrgyzstan': 'KG',\n",
+    " 'Laos': 'LA',\n",
+    " 'Latvia': 'LV',\n",
+    " 'Lebanon': 'LB',\n",
+    " 'Lesotho': 'LS',\n",
+    " 'Liberia': 'LR',\n",
+    " 'Libya': 'LY',\n",
+    " 'Lithuania': 'LT',\n",
+    " 'Luxembourg': 'LU',\n",
+    " 'Macedonia': 'MK',\n",
+    " 'Madagascar': 'MG',\n",
+    " 'Malawi': 'MW',\n",
+    " 'Malaysia': 'MY',\n",
+    " 'Mali': 'ML',\n",
+    " 'Malta': 'MT',\n",
+    " 'Mauritania': 'MR',\n",
+    " 'Mauritius': 'MU',\n",
+    " 'Mexico': 'MX',\n",
+    " 'Moldova': 'MD',\n",
+    " 'Mongolia': 'MN',\n",
+    " 'Montenegro': 'ME',\n",
+    " 'Morocco': 'MA',\n",
+    " 'Mozambique': 'MZ',\n",
+    " 'Myanmar': 'MM',\n",
+    " 'Namibia': 'NA',\n",
+    " 'Nepal': 'NP',\n",
+    " 'Netherlands': 'NL',\n",
+    " 'New Zealand': 'NZ',\n",
+    " 'North Macedonia': 'NM',\n",
+    " 'Nicaragua': 'NI',\n",
+    " 'Niger': 'NE',\n",
+    " 'Nigeria': 'NG',\n",
+    " 'North Korea': 'KP',\n",
+    " 'Norway': 'NO',\n",
+    " 'Oman': 'OM',\n",
+    " 'Pakistan': 'PK',\n",
+    " 'Palestine': 'PS',\n",
+    " 'Panama': 'PA',\n",
+    " 'Papua New Guinea': 'PG',\n",
+    " 'Paraguay': 'PY',\n",
+    " 'Peru': 'PE',\n",
+    " 'Philippines': 'PH',\n",
+    " 'Poland': 'PL',\n",
+    " 'Portugal': 'PT',\n",
+    " 'Qatar': 'QA',\n",
+    " 'Republic of China (Taiwan)': 'TW',\n",
+    " 'Republic of the Congo': 'CG',\n",
+    " 'Romania': 'RO',\n",
+    " 'Russia': 'RU',\n",
+    " 'Rwanda': 'RW',\n",
+    " 'Saudi Arabia': 'SA',\n",
+    " 'Senegal': 'SN',\n",
+    " 'Serbia': 'RS',\n",
+    " 'Sierra Leone': 'SL',\n",
+    " 'Singapore': 'SG',\n",
+    " 'Slovakia': 'SK',\n",
+    " 'Slovenia': 'SI',\n",
+    " 'South Africa': 'ZA',\n",
+    " 'South Korea': 'KR',\n",
+    " 'Spain': 'ES',\n",
+    " 'Sri Lanka': 'LK',\n",
+    " 'Sudan': 'SD',\n",
+    " 'Suriname': 'SR',\n",
+    " 'Swaziland': 'SZ',\n",
+    " 'Sweden': 'SE',\n",
+    " 'Switzerland': 'CH',\n",
+    " 'Syria': 'SY',\n",
+    " 'Tajikistan': 'TJ',\n",
+    " 'Tanzania': 'TZ',\n",
+    " 'Thailand': 'TH',\n",
+    " 'Timor-Leste': 'TL',\n",
+    " 'Togo': 'TG',\n",
+    " 'Trinidad and Tobago': 'TT',\n",
+    " 'Tunisia': 'TN',\n",
+    " 'Turkey': 'TR',\n",
+    " 'Turkmenistan': 'TM',\n",
+    " 'Uganda': 'UG',\n",
+    " 'Ukraine': 'UA',\n",
+    " 'United Arab Emirates': 'AE',\n",
+    " 'United Kingdom': 'GB',\n",
+    " 'United States': 'US',\n",
+    " 'Uruguay': 'UY',\n",
+    " 'Uzbekistan': 'UZ',\n",
+    " 'Venezuela': 'VE',\n",
+    " 'Vietnam': 'VN',\n",
+    " 'Yemen': 'YE',\n",
+    " 'Zambia': 'ZM',\n",
+    " 'Zimbabwe': 'ZW'}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are now ready 1) isolate our features, 2) scale the feature and train our SOM:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+    {
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+     "output_type": "stream",
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+      " quantization error: 0.4245385409165895\n"
+     ]
+    }
+   ],
+   "source": [
+    "feature_names = ['democracy_index', 'electoral_processand_pluralism', 'functioning_of_government',\n",
+    "                 'political_participation', 'political_culture', 'civil_liberties']\n",
+    "\n",
+    "X = democracy_index[feature_names].values\n",
+    "X = scale(X)\n",
+    "\n",
+    "size = 15\n",
+    "som = MiniSom(size, size, len(X[0]),\n",
+    "              neighborhood_function='gaussian', sigma=1.5,\n",
+    "              random_seed=1)\n",
+    "\n",
+    "som.pca_weights_init(X)\n",
+    "som.train_random(X, 1000, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A social map of countries\n",
+    "----\n",
+    "\n",
+    "Here we will plot each country in a cell that represent the winning neuron on the SOM. The country codes will be used instead of the full names of the countries to make the map more readable. The name of the countries will be colored according to their democracy status."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x1008 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def shorten_country(c):\n",
+    "    if len(c) > 6:\n",
+    "        return country_codes[c]\n",
+    "    else:\n",
+    "        return c\n",
+    "\n",
+    "country_map = som.labels_map(X, democracy_index.country)\n",
+    "    \n",
+    "plt.figure(figsize=(14, 14))\n",
+    "for p, countries in country_map.items():\n",
+    "    countries = list(countries)\n",
+    "    x = p[0] + .1\n",
+    "    y = p[1] - .3\n",
+    "    for i, c in enumerate(countries):\n",
+    "        off_set = (i+1)/len(countries) - 0.05\n",
+    "        plt.text(x, y+off_set, shorten_country(c), color=colors_dict[c], fontsize=10)\n",
+    "plt.pcolor(som.distance_map().T, cmap='gray_r', alpha=.2)\n",
+    "plt.xticks(np.arange(size+1))\n",
+    "plt.yticks(np.arange(size+1))\n",
+    "plt.grid()\n",
+    "\n",
+    "legend_elements = [Patch(facecolor=clr,\n",
+    "                         edgecolor='w',\n",
+    "                         label=l) for l, clr in category_color.items()]\n",
+    "plt.legend(handles=legend_elements, loc='center left', bbox_to_anchor=(1, .95))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Features planes\n",
+    "----\n",
+    "\n",
+    "Here we will create a map for each feature used that reflects the magnitude of the weights associated to it for each neuron."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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oOI36HR66x/I5wFci4t8j4n6KFeS5qYFhbZSWoRdS9Ou9mWJ5PZfigo3ycLcBp1Ec7buX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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "W = som.get_weights()\n",
+    "plt.figure(figsize=(10, 10))\n",
+    "for i, f in enumerate(feature_names):\n",
+    "    plt.subplot(3, 3, i+1)\n",
+    "    plt.title(f)\n",
+    "    plt.pcolor(W[:,:,i].T, cmap='coolwarm')\n",
+    "    plt.xticks(np.arange(size+1))\n",
+    "    plt.yticks(np.arange(size+1))\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most relevant feature plane\n",
+    "----\n",
+    "\n",
+    "In this map we associate each neuron to the feature with the maximum weight. This segments our map in regions where specific features have high values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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BRUVFl90LsWLFimPN5cAxKKYAAOAn47HHHit77LHHyjpy7Pbt21u9DxOOx6V8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\nMATBFACAIQimAAAM4X8BfhQZGnuyB4UAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Z = np.zeros((size, size))\n",
+    "plt.figure(figsize=(8, 8))\n",
+    "for i in np.arange(som._weights.shape[0]):\n",
+    "    for j in np.arange(som._weights.shape[1]):\n",
+    "        feature = np.argmax(W[i, j , :])\n",
+    "        plt.plot([j+.5], [i+.5], 'o', color='C'+str(feature),\n",
+    "                 marker='s', markersize=24)\n",
+    "\n",
+    "legend_elements = [Patch(facecolor='C'+str(i),\n",
+    "                         edgecolor='w',\n",
+    "                         label=f) for i, f in enumerate(feature_names)]\n",
+    "\n",
+    "plt.legend(handles=legend_elements,\n",
+    "           loc='center left',\n",
+    "           bbox_to_anchor=(1, .95))\n",
+    "        \n",
+    "plt.xlim([0, size])\n",
+    "plt.ylim([0, size])\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/FeatureSelection.ipynb b/examples/FeatureSelection.ipynb
new file mode 100644
index 0000000..25c4093
--- /dev/null
+++ b/examples/FeatureSelection.ipynb
@@ -0,0 +1,361 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Selection based on self organizing maps\n",
+    "This notebook presents a way to achieve feature selection through self organizing maps. The code is based in an algorithm proposed in this [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-89935-0_6). **_The selection process strongly depends on two parameters_**:\n",
+    "1. *How well the trained SOM represents the data (since the weights are used during the selection).*\n",
+    "2. *A parameter \"a\" which is arbitarily defined by the user. Values between 0.03 and 0.06 have been proved to work well.*\n",
+    "\n",
+    "For reasons of simplicity and complementarity this notebook builds on the [democracy-index example](https://github.com/JustGlowing/minisom/blob/master/examples/DemocracyIndex.ipynb). So first we load the data by exactly the same way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(0, '../')\n",
+    "%load_ext autoreload\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.patches import Patch\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from minisom import MiniSom\n",
+    "from sklearn.preprocessing import minmax_scale, scale\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# download from wikipedia and reorganization\n",
+    "import os.path\n",
+    "if not os.path.isfile('democracy_index.csv'):\n",
+    "    wikitables = pd.read_html('https://en.wikipedia.org/wiki/Democracy_Index',\n",
+    "                              attrs={\"class\":\"sortable\"}, header=0)\n",
+    "    democracy_index = wikitables[0]\n",
+    "    democracy_index.columns = [c.lower().replace(' ', '_') for c in democracy_index.columns]\n",
+    "    democracy_index.rename(columns={'score': 'democracy_index', \n",
+    "                                    'functioning_ofgovernment': 'functioning_of_government',\n",
+    "                                    'politicalparticipation': 'political_participation',\n",
+    "                                    'politicalculture': 'political_culture',\n",
+    "                                    'civilliberties': 'civil_liberties'}, inplace=True)\n",
+    "    democracy_index.category = democracy_index.category.replace('Flawed democracy[a]', 'Flawed democracy')\n",
+    "    democracy_index = democracy_index[:-1]\n",
+    "    democracy_index.to_csv('democracy_index.csv')\n",
+    "    print('data downloaded from Wikipedia')\n",
+    "else:\n",
+    "    # pre-downloaded file\n",
+    "    democracy_index = pd.read_csv('democracy_index.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To demonstrate the method, we choose some of the variables that have numeric values and set \"democracy index\" as the target variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feature_names = ['democracy_index', 'electoral_processand_pluralism', 'functioning_of_government',\n",
+    "                 'political_participation', 'political_culture', 'civil_liberties']\n",
+    "\n",
+    "X = democracy_index[feature_names].values\n",
+    "X = scale(X)\n",
+    "\n",
+    "feature_df = pd.DataFrame(X, columns=feature_names)\n",
+    "target = feature_df.iloc[:,0]\n",
+    "Features = feature_df.iloc[:,1:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A simple method to select variables is through correlation. By plotting a correlation matrix we see that all the variables are highly correlated with democracy index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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dk3G1bJcBKyjtTp8DvAn8bNVf0p1udYePAXJU9TWrnn8D+1X1XyJyK/CRqoqIXAI8C1zk3p1urfMmMBy4XVV/PMX+LgQeV9U1IpKjqnWs+X8DrlDVkVbX+Neq+rmI3AO8qqp1rO70scCFqpojIo1wnQikWev8F2gBNFDVCu/bKUxPqlUf2KIV0/wdQtXk+/4aenUrmr/M3yFU2fzv6lVc6DQyaNY1/g6hykK6DfvDgyvyV3xV6e+bkF4j/DKYw3Sn/w6qug74CtiAq0W7xFp0A3C7iGzE1U0+tAp1pgCjgAXARmCdqv6gqodxXQ/+zqr3q5NU8Q5wi4isANpitWpV9Sdc3f1rrG5+91vhvsDVaEusbJyn8BBwn4isBkr6cFU1EVdX/XKrq/8bIEJEbgaKVPVLYDzQQ0QGeKnXMAzDP9RZ+ZefmJb4X5h1b3uUqv7T37FUlmmJ+5hpidcI0xL3vWppiS/5rPIt8fNu8ktL3FwT/4sSke+BVpSOnjcMwzDc+PP+78oySfwvSlWvLDvPSuwtysx+UlXn1ExUhmEYpxHz7HSjNvGW2A3DMP6yasHodJPEDcMwDMMb0xI3DMMwjFrKtMQNwzAMo5by4zPRK8skccMwDMPwxnSnG4ZhGEYtZZK4YRiGYdRS5pq4YRiGYdRSpiVuGIZhGLWUGdhmGIZhGLWU6U43jOpV235QJKDXMH+HUCVpg+/wdwhVlpTsw98e95HhGYv8HUKVHNtW9mnMtUC3avi/Z7rTDcMwDKOWMkncMAzDMGqpWvBT3SaJG4ZhGIY3piVuGIZhGLVULRidbvN3AIZhGIZxWnI6K/+qBBG5RER+FZGdIvKUl+XNRGSeiGwSkYUi0riiOk0SNwzDMAxvVCv/qoCI2IG3gUuB9sB1ItK+TLHXgE9VtTPwPDCuonpNEjcMwzAMb6q3Jd4T2KmqSap6AvgvMLRMmfbAPOv9Ai/LyzFJ3DAMwzC8qd4k3gjY5za935rnbiMw3Hp/JRAhIjGnqtQkccMwDMPwQh2OSr9E5C4RWeP2uqtMdeJtE2WmHwf6ich6oB9wADjl6DozOt0wDMMwvKnCLWaqOgmYdIoi+4EmbtONgYNl6jgIXAUgInWA4aqadartmpa4YRiGYXijzsq/KrYaaCMiLUQkCLgWmO5eQETqi0hxXh4FfFRRpSaJG4ZhGIY3Tq38qwKqWgTcD8wBtgFfq+pWEXleRIZYxfoDv4rIDiAeeLGiek13umEYhmF4U81PbFPV2cDsMvOedXv/DfBNVeo0Sdz4U1u2fS+vTPsZp1O58px23HZhV4/lBzOOMearhRzNzScyLJiXrh9AfN06HMw4xmOfJOJwKkUOJ9f17cjVfcre0lnznnlpAouXrSK6Xl2mff6ev8PxKrhXD6Ievh+x28idPpucz6Z4LLfHx1Hvn08iEXUQm42sdz6gYPnKGo2x3gVdaDn2VsRu49AX89j/luev47V8biRR53YAwBYaTFD9KJafcQsAfQ98Re62vQAUHEjnl1terpGYB13cnwkTnsdus/HRx1N45dW3PZbffNM1vDz+GQ4cPATAO+98zEcfT+Gsszrw9pvjiIisg8PhYNz4N5k6dbq3TVS7ZTsP8cqcDThVubJrC247t53H8oOZuYyZsYajeSeIDA3kpWE9iY8MA+DeL5ewaX8GXZvG8Oa1fWsk3nL+zI9dFZHdQHdVTa/iev2BE6r68+/dtpf6HlfVK6qjvtrkD/wNFuI6ZmuqIYb+Vl1XWF1C7VV1/B+ttzo4nE7GfbeM9/5+OfFR4dzw+nf069CcVgn1SspMmLGCK7q3ZUiPM1j12wEmzl7Fi9cPIDYyjE8eGEZQgJ28gkKGv/o1/To0Iy4q3I97BMMuG8j1w4fw9NjX/BrHSdls1H3sIdIfegJH2mHiPnqX/CU/U7R7T0mRiJE3cnzeInK/n05A82bETBhH6lXX12iMrcbdwZZrnqcgJYMuP40nI3ENeTv2lxRJGj255H3D2y8lvGPpT3E680+w/qInai5ewGazMfGNF7nksuvYvz+FFctnM2NmItu2/eZR7uup03no4Wc85uXlHWfkbQ+xc2cyDRrEs2rFjyQmLiQrK9unMTucyrif1vPeDecRHxnGDR/Mo1/bhrSKjSwpM2HuJq7o3IwhZzVnVXIaE+dv4cVhPQG4pXdb8gsdfLMuyadxnpLD4b9tV5I/ron3B/pUZQUR8WmPgfUkHeMkKnv8VXX66ZLAAbbsTaNJTCSNYyIJDLAzqGtrFm7d7VEmKfUo57Rx3arZo3VDFm5xLQ8MsBMU4PpYnChynDY/ZtS9SyeiIiP8HcZJBbVvR9H+AzgOpkBREXlz5xNyftn/7oqEu1pbtjrhONKP1GiMEV1bk598iPy9aWhhEYenLSN6UI+Tlo8d1pfD3y+twQjL69mjK7t27SY5eS+FhYV8/fUPDBk8qFLr/vZbEjt3JgOQkpJK2uEjxMae8tbjarHlYAZN6tWhcb06BNptDOrQhIW/egzGJunwMc5pEQdAj+axHsvPaRFPWJCfO4ur8Zq4r1QqiYvIjSKySkQ2iMh/yia9ky23nhO7TkQ2Ws+DbQ7cDTxilT2vzLNi54lIU2vdySIyQUQWAC+LSE8R+VlE1lv/nlHJ2MeIyGciMl9EfhORO635/UVkgYh8CWy25j0qIlus18NuddxsxbdRRD6z5sWKyLcistp6nWvN72ft2wYr1ggRaSAii615W0TkPKvsu9b9hFtF5Dm37e0WkeesY7dZRNpZ82NEJNGq9z94v++wuI7mIrJdRD6xYv9GRMK8lMtxe/83EZn8e4+/iIwUkbes91db+7pRRBa7LZ8mIjNEJFlE7reO+XoRWSEi0ZX5m1ZWWlYeCXXrlEzHR4WTlpXrUaZtwxjmbnJ9wc3fnExuQSGZufkAHDqaw9WvTeWSsV8w8oKz/N4Krw1ssfVxpKWVTDvS0rHHxnqUyf7gE8IuuYiEH74i5l/jyPrXxBqNMbhBNAUHSzuvTqQcIbiB949ecOP6hDSNI3PplpJ5tuAgusx5mbNmvUTMJSdP/tWpYaME9u0vTXD7D6TQsGFCuXJXXXkZ69b+j6/+O4nGjRuWW96jexeCggLZtWu3L8MFIC37OAmRoSXT8ZGhpB077lGmbXwUc7cdAGD+9oPknigiM6/A57FVWvWOTveJCpO4iJwJjADOVdUugAO4oaLlIhILvI/rPrezgKtVdTfwHvBvVe2iqkuAtyh9VuwXgPv/6LbARar6GLAdOF9VuwLPAi9VYT87A5cDvYFnRaT4090T+IeqtheRbsCtwDlAL+BOEekqIh2AfwADrP14yFr3DWs/euB6ws4H1vzHgfusY3EecBy4HphjzTsL2GCV/Yeqdrfi6ycind1iTlfVs4F3rToBRgNLrWMwHWhawX6fAUyyjm02cG9lDpabP3L8nwUGWcdsiNv8jriOR09cIy/zrDqXAzd7q0jcHqLw4U/LKx28lnuOQvmznkcH92Jt0kFG/Osb1iSlEBcVjt3mKpVQrw5TH7+a6aOuZcaaHRw5llfpbf9liZfzyjLdGGEDB5A3aw6Hho7gyGOjqDd6lPf1fKUSMRaLHdaX9JnLPa6Nrup2NxsGPcmv97xOy7G3EtIs3leRlhAvMWuZmGfO+h+t2vTi7G4DmTdvCR9/+LrH8oSEOCZPnsgddzxabl1f8LaFsrvx6MDOrN1zmBGT5rJm72HiIkKx206jm6ZqQUu8Mn0VFwLdgNXWBykUSKvE8l7AYlVNBlDVjJPU3xvr5nbgM+AVt2VTVbX4okQU8ImItMH1+QisROzFflDV48Bxq2XZE8gEVhXHB/QFvlfVXAAR+Q5XElbgm+Lrzm77cRHQ3u0/V6SIRADLgAki8gXwnaruF5HVwEciEghMU9XiJH6NuJ7qEwA0wPXc3E3Wsu+sf9e6HZ/zi9+r6iwROVrBfu9T1WXW+8+BB3E9YL+y/sjxXwZMFpGvKd0XgAWqegw4JiJZwAxr/mZcJzPluD9E4fjMCZX+3xIfFc6hzJKOBlKzcokt05qOiwpnwkhXt2ReQSHzNiUTERpcrkyrhHqsSzrEwLNaVnbzf0nOtMPY4+JKpu1x9XGkew7ZCBt8GUceeRKAE1t+QYKCsNWNwnk0s0ZiLDh4hOCG9UumgxrEUHDI+3+l2KHnsnPUBx7zTqS6yubvTSPr563U6dSC/D2pvgsYOLA/hSZuLevGjRqQkuK5zYyM0n344MMvGPfS0yXTERF1mP7Dpzw7+hVWrlrn01iLxUeGcii7tOWdmn2c2DqhHmXiIkKZcI3rckveiSLmbTtAREhVvtp9S2vBwLbKnPII8InVcu6iqmeo6phKLBe8n4xVxH0d977PsbgSQEdgMBDyO+t0n3av/2RNgZPthw3o7bbfjVT1mHVN+A5cJzMrRKSdqi7GlYAPAJ9Z3fMtcLWwL7RayrPK7FNxn5IDz5OtqhzTk+33yeaVPaa/+/ir6t3AM7ieULRBSp//695X5nSbdlLNd0t0aBLH3vQsDhzJprDIwZz1O+nXoZlHmaM5x3FaZ9EfzlvPsJ6uqwSpmTnkF7qedpidV8CG5FSax0VVZ3h/Sie2bSegSSPsDRIgIICwiwaQv8Sz98SRmkpw97MBCGjWFAkKqrEEDnBsw05CWjYguGkcEhhA7LBzyUhcXa5caKuGBNQN59iaX0vmBUSFI9Z12oDoCCJ7tPMYEOcrq9dsoHXrFjRv3oTAwECuuWYoM2YmepRJSCg9eRo8+GK2b98JQGBgIN9O/ZDPP/+Gb7+d6fNYi3VoWI+9GTkcOJpLocPJnK376Ne2gUeZo3kFOK1egQ+XbmdYl+Y1Fl+l/Ela4vOAH0Tk36qaZl23jKjE8uXA2yLSQlWTRSTaasUeAyLd1v8Z15NrPsPVTX+yESRRuJIgwMhK7l+xoSIyDgjHNbDuKVxdxe4W42o5jseVuK8EbgJOAN9b+3fEbT8Scd24/yqAiHRR1Q0i0kpVNwObRaQ30E5EjgMHVPV9EQkHzsb1oPtcIEtE4nH9PN3CCvZjMa5j9IKIXArUq6B8UxHprarLgevwfmxTrUsiv1r7fOwkdVXp+FvHYSWwUkQG4/m4wRoRYLfx1FV9uWfSbJyqDO15Bq0Tonnnp9W0bxxL/47NWbMrhYmzVyII3Vo2YNRw160sSamZTJixvOQM7ub+nWnTwPeDgSryxOjxrF6/iczMbC4cdiP33n4Twys5wKlGOJxk/utN6r/+Mtjs5M78kaLk3UTcOZLCbTvIX/ozWRPfo+6ox6hz7d9QVY6+8ErF9VZzjLue/oCOU55B7DZSp8wn79f9NPu/ERzbsIuMRNdNG7FX9uXwtGUeq4a2aUybV+9CnYrYhH1vfl8jSdzhcPDQw88we9aX2G02Jn/yFb/8soMxox9nzdqNzJz5Px64/zauuOJiioocHM3I5LY7XMN6rr56MOeddw7RMfW4+eZrALj9jkfYuHGrT2MOsNl46pIu3PPlEtf/v7Oa0zouincWbqV9g3r0P6Mha3YfZuKCLQjQrWl9Rl1aegvorZMXsPvIMfJOFHHx67MYM7gbfVqVHwfgU7VgdLpU5tqIiIzA9Qg4G1AI3IfrZ9S6q2q6t+WqusJKNC9Z89NUdaCItMV1M7sTeADXr7p8BNQHDgO3qupea4DVTOvmd6yE+IlVZj5wk6o2lwpuMRORMUBDoBWua8ivWMm03Hoi8ihwmzX5gaq+bs2/BXgCV6t4vaqOFJH6uH4b9kxcJ0OLVfVuEXkTuMAq+wuuhHettX4hkAPcbJ3YTMZ1DT4JV4t0uqpOFrdbx0SkO/Caqva3WrNTrGO1CFfXejdvt5iJaxDhbFyJvw/wm3XM8sTtFjMR+RvwsvV32ALUsfavysdfREZacd9vXY5og+uEaB7wMHBL8XKrTvf9HOm+7GSq0p1+OgjoNczfIVRJ2uA7/B1ClSUlV+t4yBpxQUblx3acDo59NNLfIVRZ6I0v/uGBFrljrqv09034mCk1OLCjVKWSeG1mJfEcVT1Nb6z1DSuJz7S6v/80TBL3LZPEa4ZJ4r5XLUn82Wsrn8Sf/69fkrh5YpthGIZheOPHW8cq60+TxEXkVvxPCJsAACAASURBVEpv/yq2TFXv80c8NcXqYp/nZdGFf7ZWuGEYRo3y44C1yvrTJHFV/Rj42N9x1DRVPQJ08XcchmEYfzZadPoPbPvTJHHDMAzDqFamJW4YhmEYtZS5Jm4YhmEYtZRpiRuGYRhG7aQmiRuGYRhGLWWSuGEYhmHUUmZ0umEYhmHUUqYlbhiGYRi1U214LLlJ4oZhGIbhjWmJG0Y1y8/zdwRVUtt+UCRuxgf+DqHKlnb6p79DqLLnGvT3dwhV4tj4i79DqLobq6EOk8QNwzAMo3Yyt5gZhmEYRm1VZJK4YRiGYdRKpiVuGIZhGLWVSeKGYRiGUUud/r9/YpK4YRiGYXhjutMNwzAMo5ZSM7DNMAzDMGop051uGIZhGLWTmiRuGIZhGLWUSeKGYRiGUTuZlrhhGIZh1FYmiRuGYRhG7eQs8ncEFTNJ3PhTW7bjAK/MWoPTqVzZvTW39evosfzg0RzGfLeco7n5RIYF89LV5xIfFc72gxm8NH0lOQWF2EW4o38nBnVuXuPxB/fqQdTD9yN2G7nTZ5Pz2RSP5fb4OOr980kkog5is5H1zgcULF9Z43GeyjMvTWDxslVE16vLtM/f83c4ACRc0Jmuz9+E2G0kfbmQ7W/N8Fje5bkbievTHoCA0CCC60fyfbu7qNuhGd3G30pgRCjqcPLLGz+wb/qKGom5Zb/OXDzaFfOG/y5k+bszypU58/JzOO+R4aBK6ra9/PDg2wAMGHUdrQd0QWxC8pItJI75tEZitp/RleAht4PNRuGquRQu+M5judStT/CIB5HQcLDZODH7Mxzb1wFga9CM4OH3QHAoqHJ84hNQVFgjcRcz3emG4UcOp5NxM1bx3q0XER8Zxg3v/ki/MxvTKq5uSZkJP63liq4tGXJ2K1btSmFi4npevLovoUEBjP3buTSrH0ladh7Xvz2b3m0aEhkaVHM7YLNR97GHSH/oCRxph4n76F3yl/xM0e49JUUiRt7I8XmLyP1+OgHNmxEzYRypV11fczFWwrDLBnL98CE8PfY1f4cCgNiEbi+NZOGIcRxPyWDgj2M5mLiO7B0HSspsGP15yfs2t11M3Y7NACg6XsDKB98lJzmVkPi6XDznBQ4t3ERhtm9/IldswiVjR/LlDePIPpTBbdPH8tvcdaT/Vhpzvebx9LlvCJ9eNYb87DzCYiIBaNStDY27t+X9QU8BcPO3o2na60z2rtjm05gRG8FX3sXxSWPQrCOEPvgKRVtXoWn7S4oEXXg1RZuWUbR8DhLXmNDb/0neuL+DzUbwdQ9TMOUNnCm7ISwCHA7fxuuNSrVWJyKXAG8AduADVR1fZnlT4BOgrlXmKVWdfao6bdUaYSWJyIMisk1Evqim+pqLyPVu091FZOIfqO/n6oirEtsJFpG5IrJBREbUxDZPRyLysIiEVXe9W/YfoUl0BI2jIwgMsDOoczMWbtvnUSYpLYtzWiUA0KNlAgu3ub5gmtWPpFl915dgXGQY0XVCOJqbX90hnlJQ+3YU7T+A42AKFBWRN3c+Ief3KVNKkXDXobPVCceRfqRGY6yM7l06ERUZ4e8wSkR3bcWx3ank7j2Ms9DB3h9W0GhQt5OWbzqsN3unLQcgJ+kQOcmpAOSnZlKQnk1wjO/3rWGXVmTsTiVznyvmX2asoO1Az5i7XjeAtZ/+j3zrhCLvSLZrgSoBwYHYAwOwBwViD7CTm57l85htTdvgTE9BM1LBUUTRhqUEdOhZppQiwa7Pr4SGo9kZANjbdsGZsseVwAHyjvmlWazOyr8qIiJ24G3gUqA9cJ2ItC9T7Bnga1XtClwLvFNRvf5qid8LXKqqydVUX3PgeuBLAFVdA6z5vZWpatlvSl/pCgSqapca2t4piUiAqvrjKtDDwOdAtTZn0rLzSIgKL5mOjwxn8750jzJtE+oxd+tebuhzJvN/2UduQSGZeQXUDQsuKbN5XzqFDgdNoms2Edli6+NISyuZdqSlE9ThTI8y2R98Qv03XqHO1VciISGkP/h4jcZYG4UmRHP8QOnJTl5KBjFdW3ktG9a4PuFNY0lburXcsuguLbEFBZCzO83LmtUrIiGaYymlMWenZNCoTMzRLVwnozd/Oxqbzcbi178ladEmDqzbyZ7lv/DQ6rdBhLWfJnJk50GfxyyR0Whm6f83zTqCrWlbjzInEr8i5M7RBJ57GRIUwvFJowGw1W8IqoTc8SwSHknRxqUULpzm85jLUme1tsR7AjtVNQlARP4LDAV+cd8kEGm9jwIq/EPVeEtcRN4DWgLTRSRLRB53W7bFalU3t1rq74vIVhFJFJFQq0xrq/W6UUTWiUgrYDxwntWifURE+ovITKt8tIhME5FNIrJCRDpb88eIyEcislBEkkTkQbc4cqx/+1vLvxGR7SLyhYiItewya95SEZlYvL2T7HO5GEQkDlfi6mLF7fVb5GTbOUmdNhHZLSJ13dbfKSLxIhIrIt+KyGrrda7bcZgkIonApyIyUkS+E5GfROQ3EXnF/biIyMsistb6G/R0O35DrDJ2EXnV2sYmEfn7qY6lddwbAgtEZEElP0aVol6emChl/k8+emk31ianMuKtmaxJTiUuMgy7rbTQ4ew8nvlmGc9d1QebrXq71ipUNlgot1NhAweQN2sOh4aO4Mhjo6g3epT39YxS3g6Ptw8L0HRoL/bPXFXuGdohcXU55817WPXwpJOu62taZru2ADvRzeP5fMQLfP/gW1z+8p0ER4ZRr1k89Vs3ZGKvB5h4zv0069OBJj3b+T7ASnx+A7qeR9Ga+eS9eCfHP3qBkOsedq1nt2NvcSb5X/6b4+88TUDHXthbd/J9zGXDrcaWONAIcO8K3G/NczcGuFFE9gOzgQcqqrTGk7iq3o3r7OIC4N+nKNoGeFtVOwCZwHBr/hfW/LOAPkAK8BSwRFW7qGrZOp8D1qtqZ+BpwH1ERztgEK4zpNEiEugljq64WortcZ18nCsiIcB/cPUm9AViK9jtcjGoahpwh1vcu8quVMF2vNXpBH4ArrTWPwfYraqpuK7D/FtVe+A6lh+41dUNGKqqxZckugAjgE7ACBFpYs0PBxaqajfgGPACMNDa3vNWmduBLGs7PYA7RaTFyY6lqk7E+jyo6gXeDp6I3CUia0RkzYf/W+31AHsTHxXGoazckunU7FxiI0M9ysRFhjHhhv58df8VPDDQ1SESEeK67p2Tf4IHPl3AfRd1oXPTiv7E1c+Zdhh7XFzJtD2uPo50z56EsMGXcXzeQgBObPkFCQrCVjeqJsOsdY6nZBDaKKZkOqxBNMdTM72WbTq0N3usrvRiAXVCOf/zx9n88lSOrNvp01iLHTuUQUSD0pgjG0STUybmYykZ7PjfOpxFDrL2HeZI0kGimydwxiXdObB+J4V5BRTmFbBrwUYadW3t85g16whSt37JtETFlHSXFwvocSFFG5cB4NzzKwQEImGRaOYRHElbXd3ohSco2r4WWyPvvSW+5HRIpV/u31PW664y1Xk9fSwzfR0wWVUbA5cBn4nIKfO0X66JV1Kyqm6w3q8FmotIBNBIVb8HUNV8Va2oC7Yv8JlVfj4QIyLF33KzVLVAVdOBNCDey/qrVHW/lSA34Oq6bwckuV0OmOJlvcrGcCqn2s7J6vwKVwIG1zWVr6z3FwFvicgGYDoQaR1PgOmqetyt7nmqmqWq+bi6eppZ808AP1nvNwOLVLXQet/cmn8xcLO1nZVADK4TMvB+LCukqpNUtbuqdr99YI/KrAJAh0Yx7D1yjAMZxygscjBn0x76tWviUeZobj5Oq5X14aItDOvm+qIoLHLw6BeLuKJrSy7u1Kxc3TXhxLbtBDRphL1BAgQEEHbRAPKXeCYUR2oqwd3PBiCgWVMkKAjnUe8JyXDJ2JBERIsEwpvEYgu003RoLw7MWVuuXESrBgTVDefImt9K5tkC7fT96GF2T13K/pmraizmgxuTiG6RQJQVc/vBvdjxP8+Yf01cQ7PersstofXqENOiAZl708g6cISm55yJ2G3YAuw07dWOIzsPeNtMtXLu+w1b/QZIvTiwBxDQpS+OXzxPwjUzHXubzgBIXGMICEJzsyjasR5bQjMIDAKbDXvLDjhT93nbjE+pUyr/cvuesl6TylS3H3D/AmpM+e7y24GvAVR1ORAC1OcU/D06vQjPE4kQt/cFbu8dQCjez2Qqcqqzn7Lb8HY8vJWpahyVOQOr7HoV1bkcaC0iscAwXK1lcB3n3mWSNdbVgVw8ney4FGppH56zuJyqOkWkuIwAD6jqnDLb6X+Ken0iwG7jqcE9uWfyPJyqDD27Na3j6/LO3A20bxRD/zObsCY5lYmJ6xGgW/N4Rg1xDbxJ3LKHdbtTycwrYPo6VyfJ88P70K5htC9D9uRwkvmvN6n/+stgs5M780eKkncTcedICrftIH/pz2RNfI+6ox6jzrV/Q1U5+sIrFddbw54YPZ7V6zeRmZnNhcNu5N7bb2L44EF+i0cdTtY9PZl+U5503WL230Vk7zhAxyeGk7ExmYOJrluc3Ae0FWsypBexvdoRVC+C5tecD8Cqh/9D5tY95bZT3THPeXYy1336JDa7jY1fLyL9twOc/+hwUjYl89vcdSQt2kTL8ztx19xXUIeTeS99yfHMHLbPXknzPu25K3E8qpC0aCO/zVvv03gBcDopmPY+oXeOtm4xm4czdR9BF1+HY/9OHL+spmDGx4RcfS+B5w0GoOBrazzy8VwKl8wg9MFXAXBsX4tje/kTLV+r5islq4E2Vs/kAVyNrLK3kuwFLgQmi8iZuHLi4VNV6u8kvhu4AkBEzgZanKqwqmaLyH4RGaaq00QkGNcw/GPAyUYdLQZuAMZaiSTdquePxL0daCkizVV1N6Ut35P5vTGcajte6wQQke+BCcA2VS0eDZMI3A+8apXp4tbTUZ3mAPeIyHxVLRSRtrg+sKdS/PdLr6BclZ13RiPOO8PzstO9F5WOIxzYsRkDO5ZvaV/epSWXd2lZ3eFUWcHylaSWue/72PuTS94X7d5D+t8f5HT26nNP+TuEclLmbyRl/kaPeVte/dZjeuu/PO9pBtjz7TL2fLvMp7GdzK4FG9m1wDPmxRM8Y5479gsY63nTjzqVH5/+yOfxeePYvo48677vYicSSzsUNW0/x99+2uu6ResWUbRukU/jq0h1DmxT1SIRuR/Xd6Qd+EhVt4rI88AaVZ0OPAa8LyKP4GqUjXRrOHnl7yT+LaVdr6uBHZVY5ybgP9aOFwJXA5uAIhHZCEwG3E8zxwAfi8gmXKOfb/mjQavqcRG5F/hJRNKBivrVflcMFWznVHV+het4jnSb9yDwtlU+ANdJwN2ViaOKPsDVTb7OGgR4GFePwKlMAn4UkZSTXRc3DMOoadU8Oh3rnu/ZZeY96/b+F+DcqtQpFSR54yREpI6q5liJ6m3gNy+D6mrNdmqL49+8UKs+sBn/mu/vEKokbsYHFRc6zXzX6Z/+DqHKdtbgM4Oqw0PX+PZhNr5Q59Xv/3AGTup0caW/b1puTvTLbSGn88C2092dVg/CVlz38/2nlm/HMAzDcKMqlX75i7+702stqzXs0SIWkVuBh8oUXaaq91VUn3Udu+yYgCe9bccwDMPwPfPs9L8YVf0Y+Ph3rntlNYdjGIZh/AFOP7awK8skccMwDMPwwp/d5JVlkrhhGIZheFHdo9N9wSRxwzAMw/DC6TBJ3DAMwzBqJXNN3DAMwzBqKXNN3DAMwzBqqdrwLDSTxA3DMAzDC9OdbhiGYRi1lOlON4xqVjTfP78g9XslJdfgT5dWg6W18DnkV20e6+8QqqxT+4p++PD08rcZp/xJ69PSGa/+8Toc5hYzwzAMw6idTEvcMAzDMGopc03cMAzDMGqpWjA43SRxwzAMw/DGtMQNwzAMo5ZymCRuGIZhGLWTYpK4YRiGYdRKzlpwUdwkccMwDMPwwmla4oZhGIZRO5nudMMwDMOopZz+DqASTBI3DMMwDC8cpiVuGIZhGLWTaYkbhmEYRi1lrokbhp/Z23cj5Oq7QWwU/vwTJxKneiyXerGE3PIYEloHbDYKpn2MY+tqJDqO8Gcn4UzdD4Bj93YKprxVIzHXu6ALLcfeithtHPpiHvvfmuaxvOVzI4k6twMAttBggupHsfyMWwDoe+ArcrftBaDgQDq/3PJyjcSccEFnuj5/E2K3kfTlQra/NcNjeZfnbiSuT3sAAkKDCK4fyfft7qJuh2Z0G38rgRGhqMPJL2/8wL7pK2ok5lN55qUJLF62iuh6dZn2+Xv+DgeAvhf05h8vPobNbuObz3/g/Tc/8Vh+5YgreGL0g6QeOgzAFx9+zTdf/MA553bjqbGPlpRr2boZj/79H8z7cZHPYw7r2434f9wNNhtZ3/xExvue//9in7qLsHM6A67Psj26Ljt7Xu1a9vhthPfrCTYh7+f1pL1Y83+HWvAjZiaJG39iYiNkxH3kTXwazUwn7Mk3KNq0EuehvSVFgi69jqK1SyhcMgtbQlNC73ue3H+OBMCZnkLeuPtrNmabjVbj7mDLNc9TkJJBl5/Gk5G4hrwd+0uKJI2eXPK+4e2XEt6xRcm0M/8E6y96oiYjRmxCt5dGsnDEOI6nZDDwx7EcTFxH9o4DJWU2jP685H2b2y6mbsdmABQdL2Dlg++Sk5xKSHxdLp7zAocWbqIwO69G96GsYZcN5PrhQ3h67Gt+jaOYzWbj2Zf/j9uuvp/Ug6lMTfyE+XMWs2tHske5H3/4H2NHef4G58pla7lywA0ARNWNZM7K71i2sAZOlGw24p+9j/23PU1hajrNpr5BzvyVnNhV+v/v8PhJJe/r3jiEkDNbARDS9UxCz27P7qH3AtD0y9cI7dmJ46s2+z5uN7XhFjObvwM4XYnIQhHpbr2fLSJ1rde9bmUaisg3v7P+ySLyt+qK10v9XUTkMrfpISLyVAXr/PwHtvewiIS5Tc8Wkbq/t77qYGveFufhg+iRQ+AoomjtIgLO6uVZSBUJscIODUOzjtR8oG4iurYmP/kQ+XvT0MIiDk9bRvSgHictHzusL4e/X1qDEZYX3bUVx3ankrv3MM5CB3t/WEGjQd1OWr7psN7snbYcgJykQ+QkpwKQn5pJQXo2wTERNRL3qXTv0omoSP/HUazz2R3Ym7yP/XsOUFhYxOzv/8eFl/Srcj2DBl/IkvnLyT9e4IMoPYV0bkvh3oMU7j8EhUUcm72IOhf2Omn5yMv7kT1roWtCFQkOQgIDkKBAJMCOIz3T5zGX5ajCy19MEq8EVb1MVTOBusC9bvMPqqrPEvHvJSIBQBegJImr6nRVHX+q9VS1zx/Y7MNASRJ3O2Z+Y6tbH+fRwyXTzqPpSFSMR5kTsz4noOcFhL/4GWH3PU/+V++Wrh+TQNiotwh95BXsrTrUSMzBDaIpOJheGl/KEYIbRHsv27g+IU3jyFy6pWSeLTiILnNe5qxZLxFzycmTf3UKTYjm+IHSk5+8lAxCE+p5LRvWuD7hTWNJW7q13LLoLi2xBQWQszvNZ7HWVvEJsaQcSC2ZPpSSSnyD2HLlBl4xgB8WfskbH44noWF8ueWXDRvIrO/m+DTWYgHx9SlMKf3/V3QonYD4GO9lG8YR2CiBvBUbAcjfsJ28lZtoteQLWi35gtyl6ziRtK9G4nbnFKn0y1/+MklcRJqLyHYR+URENonINyISJiIXish6EdksIh+JSLCXdXeLSH1gPNBKRDaIyKtWnVusMnYRec2qZ5OIPGDNf1ZEVovIFhGZJFK5v7a1zZdFZJX1am3NHywiK62Y54pIvDV/jFV/IvAp8Dwwwop1hIiMFJG3rLLxIvK9iGy0Xn2s+TnWv/1FZLFV5hcReU9EbNayd0VkjYhsFZHnrHkPAg2BBSKyoMwxQ0QetfZ/i4g87Pb32CYi71t1JYpI6O/40/4hAd37U7hiLrn/uIm8t58lZOQTIIJmHyXnmZvJG3c/Bd9MIuS2JyEkrOIK/yhvHw/1/uzH2GF9SZ+5HJylY2hXdbubDYOe5Nd7Xqfl2FsJaVb+i7zaeftEnyTmpkN7sX/mKrTM8yxD4upyzpv3sOrhSSdd9y/Ny+dCyxynBYlLuLDbEIb2v56fF69i/JujPZbHxsXQ9szWLF2w3KehntJJ/rSRl/XjWOLSks9yYNMGBLVswq7+N7Gr342E9TqL0O4dazBQF63Cy1/+MknccgYwSVU7A9nAo8BkYISqdsI1RuCeU6z/FLBLVbuoatkLj3cBLYCuVv1fWPPfUtUeqtoRCAWuqEK82araE3gLeN2atxTopapdgf8C/+dWvhswVFWvB54FvrJi/apMvROBRap6FnA2UL5ZBD2Bx4BOQCvgKmv+P1S1O9AZ6CcinVV1InAQuEBVL3CvRES6AbcC5wC9gDtFpKu1uA3wtqp2ADKB4d4OgojcZZ04rPn4l8qfjTsz07HVK22t2OrVL9ddHthnEEXrFrvKJ29HAgOR8EgoKoTcY675+3biPJyCLa5Rpbf9exUcPEJww/ol00ENYig4dNRr2dih55L2/TKPeSdSXWXz96aR9fNW6nRq4W3VanU8JYPQRqUtrLAG0RxP9d4J03Rob/ZM80wiAXVCOf/zx9n88lSOrNvp01hrq9SUNBo0Kj0hS2gQT9qhdI8ymUezKDxRCMDUz6bR4awzPZZfMnQgc2cvpKioZjp/i1LTCXTrLQhIqE9RmvfLVRGXuXWlA3Uu6kP+xu1oXj6al0/u4jWEntXO1yGX46zCy1/+akl8n6oWf+t9DlwIJKvqDmveJ8D5v7Pui4D3VLUIQFUzrPkXWC3nzcAAoCr9slPc/u1tvW8MzLHqe6JMfdNV9Xgl6h0AvGvF6VDVLC9lVqlqkqo6rO33teZfIyLrgPXWtttXsK2+wPeqmquqOcB3wHnWsmRV3WC9Xws091aBqk5S1e6q2v3W9k0qsXsuzj07sMU1RGLiwR5AQLd+FG3yHNCjR9Own9EFAFtCEwgIQnOykDpR4Op8QGISsMU1xJmeUult/17HNuwkpGUDgpvGIYEBxA47l4zE1eXKhbZqSEDdcI6t+bVkXkBUOBLkGqsaEB1BZI92HgPifCVjQxIRLRIIbxKLLdBO06G9ODBnbblyEa0aEFQ3nCNrfiuZZwu00/ejh9k9dSn7Z67yeay11eb1v9CsZVMaNW1IYGAAl105kPlzFnuUiY0rPZEacMn55Qa9XX7lxcz6vma60gHyN+8gsFlDAhvFQ2AAEZf1I2d++QF1gS0aYY+qQ/76bSXzilIOE9qjE9htEGAntEcnCvzSnV75l7/81Uan+7LXQ8rWLyIhwDtAd1XdJyJjgJAq1Kle3r8JTFDV6SLSHxjjVia3ijFXdtsAKiItgMeBHqp6VEQmU/H+nOrj7T66xoGrp6L6OJ3kf/UuYfe/ADY7hcsTcabsJeiKm3Ds2YFj80oKvv2AkBseJGjAlaBK/mcTALC37kjQFTeB0+GqZ8pbkJdTreF55XCy6+kP6DjlGcRuI3XKfPJ+3U+z/xvBsQ27yEhcA0DslX05PM2zFR7apjFtXr0LdSpiE/a9+X2NJHF1OFn39GT6TXnSdYvZfxeRveMAHZ8YTsbGZA4mrgM8B7QVazKkF7G92hFUL4Lm17jOn1c9/B8yt+7xedyn8sTo8axev4nMzGwuHHYj995+E8MHD/JbPA6Hg7FPvcKHX03EZrfz7ZfT2flrEg88+Xe2bNjGgjmLuenOa7lg0Pk4HEVkHc1m1IPPlazfqEkDGjSKZ9XP62owaCdpY9+l8Yeu/39Z3yZyYudeYh64ifwtO8hdsBKAyMv7kz3L83a3Y3OWEtbrLJpPfxcUcpeuKSlfk2rD6HQpe13lz0pEmgPJQB9VXS4i7wO7gb8DA1R1p5WU1qvqGyKyEHhcVdeIyG6gO67Etk5Vm7nVOVNVO4rI3bha49eqapGIROPqZfkVVwvTDqwAvlHVMda2Zqqq19Ht1jbfU9XxInIjri7/wSKyHrhDVdeKyMdAC1Xtb50g5Kjqa9b6w4EhqnqLNT0S18nE/SLyX2CFqr4uInYgXFWzRSRHVetYJwc/4mpl77HeTwJ24rre3hWIBTYBT6rqZKtnYIiqJrvF3x1oiuuSRS9cCX0lcBNwtPjYWeUfB+qo6phT/R2P3XtprfrAbvgu3N8hVMnB8kNCTntXbR7r7xCqrFP7Ef4OoUp+iK5fcaHTzBnbf/zDGfjTRjdW+vvm5gOf+yXj/9W607cBt4jIJiAa+Deu67VTrSTkBE76RAFVPQIsswZovVpm8QfAXmCTiGwErrdGZ78PbAamAeX7RU8tWERWAg8Bj1jzxljxLgHST7YisABoXzywrcyyh3B182/G1Y3trYt/Oa6BfFtwnfx8r6obcXWjbwU+AtybgpOAH4sHthVT1XW4kvgqXAn8A1Vdf6qdNgzDOB3Uhmvif7WWeEnL73RX3JJV1VMlal9tuz+uXoiqDMKrEaYl7lumJV4zTEvc96qjJf7x/7N33/FV1fcfx1/vhC0gOyyRoaKCgihuBbWO4sIF7rqtWGdrq9WfuFdbW7RWRa3W1Ypb1Aqo4Ja9BLSyBGQvQXaSz++Pcy7chJulSc45yefp4z7IGffmzZXkc8/3fEcZrsQvLMWVuKTjgEEELbNPFh72K+mvQKpzcD2ghZkVO99Gdbsn7pxzzpVKeXZYC29dPgIcDSwAxkp6y8ymp84xs+vSzr+K4NZlsapNETezuUDsrsIlvU4wNC3dH8ysfQRxADCzUcCoqL6/c87FQTk3k+8PzDSz2QBh36STgelFnH8WMLCIY1tVmyIeV2Z2StQZnHPOba+ci3gbIH2c3AKC+TO2I2lngou7D0t6US/izjnnXAZ5ZWhOl3QZwaRfKYPNbHD6KRmeVtQ99zMJRjKVODOPF3HnnHMug7JciYcFe3AxpywA0meraksw02UmGWtKfAAAIABJREFUZwJXlub7VrchZs4551yplPPc6WOBXSV1kFSLoFC/VfgkSZ2BxgTDfEvkV+LOOedcBuXZOz2cBOw3wDCCIWb/NLNpku4AxplZqqCfBfzHSjn+24u4c845l0F5T+JiZu8C7xbad2uh7dvK8ppexJ1zzrkMKme9t5/Hi7hzzjmXQZSrk5WWF3HnnHMugyjnRC8tL+IuUT58rXHUEcrktJUflXxSjNzeqnfUEcosafOQA0yd/lLUEcrk8v1+H3WEMvtnObxGEhZq8CLunHPOZZCfgDLuRdw555zLwJvTnXPOuYTy3unOOedcQnnvdOeccy6h/J64c845l1DxL+FexJ1zzrmMvGObc845l1DenO6cc84llPdOd8455xLKr8Sdc865hIp/Cfci7pxzzmXkHducc865hLIEXIt7EXdVWosj9mavO8+H7CzmvTCSb/8+tMDxrrefS7ND9gQgu25tajdryLudL6Vu22bs/9R1KFuoZg3mPDWMuc9+UCmZjz2mNw8+eAfZWVn88+l/88CfHilw/Pzz+nH/fbfw/cLFAPzjH0/zz6f/TbduXXjk4Xtp0LA+eXl53Hvfw7z88luVkrljr705ZuB5KDuLSf8ZxRePDt3unD2OP4DDrjsNzFgyYx5vXh38vY686Sx2ObI7yhJzPvmK4bc9W+F5Dz3iIG6++7dkZWfxyvNv8sTD/ypw/JT+J3DDwKtZsngZAC88NYRXXniTAw7ZlxvvvH7reR132ZnrL7+ZD/4b7Wp1t9zzIB9/NoYmjRvxxvOPRZolXdde3Tn71gtRdhafvPQB7z76xnbn9Dz+IE6+th9mMH/GXAZfM2jrsTr163L3+39jwrAxvDDwqcqMDkCuF3HnIpQl9r73Qj7vdy8bFq2g13t3sXj4BNb+7/utp3w18PmtX3e4+Bh27NoegI1LVvHJiQPJ35xLdr3aHPnRAyweNp6NS1ZXbOSsLB4adDfH9TmLBQsW8eUX7zL07eHMmPFtgfOGvPwW11x7S4F969dv4IKLrmHmzDm0apXDmC//y/Dho/jhhzUVmllZ4rg7L+DFc+5lzeKVXPTWnXz7/gSWf7vtfW7cPoeDrzyJZ0+9jY1r1lOvaUMA2uy7K233240njr0RgPNfHUi7A/dg3pczKixvVlYWt97/ey464zcsWbiEl4f/iw+Hfcys/80pcN5/3xzBnTf9qcC+0Z+N55QjzwFgx0YNGTb6NT4b9WWFZS2tvn2O5uzTTuKPd/456ihbKSuLc++4hL+cewcrF6/k1rfuY9KIcSycuWDrOS3at6TPgFO557RbWL9mHQ3Cfxcpp/z2TL4ZPb2yo28V/xIOWVEHcAFJoyTtF379rqRG4WNA2jmtJb3yE1//GUmnl2PeuZKaFc4YJ4332YV1c5awft5SbEse37/xBS2P3bfI89v2PZjvX/8cANuSR/7mXACyatcEVc4kyvv33IdZs+YyZ848tmzZwpAhb3LSiceW6rnffjubmTODQrRo0RKWLltB8+ZNKzIuAK27d2Ll3CWsnr+M/C15TB/6JbsdXfB93uesIxn/7Ag2rlkPwPoV4QcLM2rUrkl2zRpk16pJdo1s1i3/oULz7t2jC/PmzGfBd9+zZUsu774+gqOO61Xm1zn2xKP45MMv2LhhUwWkLJv9uu/Fjg0bRB2jgI7dd2Hpd4tZNn8peVtyGT30M7of07PAOb3O/AUfPvse69esA2Dtim0fOHfu2pGGzRox7ZPJlZo7XT5W6kdUvIjHkJn1MbPVQCNgQNr+hWZWboW4nBTIWFqSsisgSwF1WjVmw8IVW7c3LFpJnVZNMp5bt20z6rVrzrJPp217fusm9P7wPo4Z/zAzHxla4VfhAK3btGT+goVbtxd8v4jWrVtud96pp/RhwvgRvPSfwbRt23q74z33606tWjWZNWtuRcYFoEHLJqxdtO19XrNoJQ1aNi5wTpMOLWnSoRXnvzqQC16/nY699gbg+wkz+e6L6Vwz9hGuGfsIsz+ewoqZC6lIOS2bs+j7JVu3Fy9aQk6r5tudd/QJR/LmqBcZ9NR9tGyds93xPn2P5p3XhlVo1iRrlNOElQuXb91etWgFjXMK/vzldGxNyw6tuOmVu7j59Xvo2qs7AJLof8uvGHJPxd9aKU5+GR5R8SJeQSS1l/S1pH9JmiLpFUn1JB0laaKkqZL+Kal2hufOldQMuA/oJGmSpD+Fr/lVeE62pD+HrzNF0lXh/lsljZX0laTBUukuISX1lPS5pMmSxkhqIOkCSX9PO+dtSb0LPbVwxt6S3k57zt8lXZD297pV0qfAGZI6SXpP0nhJn0javUxvcsl/p+13WuZPzG36HsTCt8dA/rbjGxeuZNSRN/LBQdexU7/Dqd2sYcbnlqdMma1Q5rffGUGnXQ+kx75H88EHn/D0U38rcLxlyxY888xDXHLJ9ds9t7IU/r5ZNbJp0j6H5/vfxetX/53j77+U2g3r0XjnHJrt0pqHDryKhw74DTsf3IWd9i/XfwbbK8V7PHL4Jxy170mc3PtsPv94DPc9PLDA8eYtmrLbHrvw6cgvKjRqkpXm33J2djY5HVrxwJkDefyqv3HBfVdQt2E9jjjvWKaMnMCqtA+HUbAy/BcVL+IVqzMw2Mz2BtYA1wPPAP3NbC+CPglXFPP8G4FZZtbdzG4odOwyoAOwT/j6L4T7/25mPc2sK1AXOKGkkJJqAS8B15hZN+AXwIZS/h2Ly5jJRjM71Mz+AwwGrjKzfYHfAf8oIt9lksZJGjds/cxSxoINC1dSt/W25uS6rZqwcfGqjOe2OfkgFoRN6dsFXrKatd8soMmBFVxcgO8XLGKntCvrtm1asWjRkgLnrFy5is2bNwPw5FMv0KPHXluPNWhQn7fefJZbBz7A6DETKjwvwNrFK2nQatv73LBVE34s1GqxdtFK/jdiAvm5efwwfxkrZi+kSfuWdD5uP76fOJMt6zexZf0mZo2cTJt9dqnQvEsWLaVVm21X1i1b5bB08fIC56xe9QNbNm8B4OXn3qBLtz0KHD/u5KN5/91R5OYmYU6vaKxavIImrZtt3W7cqimrlxb8+Vu5eAUTR4wlLzeP5QuWsnj2QnLat6JTj84cdf5xPPDpP+j3x/M5+NRenP6Hcyr7r+BX4o75ZvZZ+PXzwFHAHDP7X7jvX8DhP/G1fwE8Zma5AGa2Mtx/hKTRkqYCRwJdSvFanYFFZjY2fK01qdetAC8BSKoPHAy8LGkS8DjQKtMTzGywme1nZvsdW6/0v+BXT5rFDh1bUq9dc1QzmzZ9D2Lx8PHbnVe/UytqNdqBVeO2dR6r06oJWXVqAlBzxx1o0nM3fpy5qPR/y59o7LhJ7LJLB9q334maNWvSr9/JDH17eIFzWrZssfXrE088hq+/Dj7Y1KxZk1dffornn3+FV199m8qycPJsmnRoyY47NSerZjZ7nngg/xtR8H3+Zvg4dj4oKIR1G9enaYdWrJ63lB++X0G7A/ZA2Vlk1cim3YG7s2Lm95m+TbmZOnE6O3dsR5t2ralZswZ9TjmaD4d9XOCc5i22fSg58rjDt+v0dvwpx/DO696UXpw5k2eS074Vzdq2ILtmDQ448RAmjRhb4JyJw8ew+0FdAajfuAEtO7Ri2bwlPHHtIG445Ap+f+gAhtzzLJ+/9hGv3P9Cpm9TofKwUj+i4r3TK1ZF/p9V4deXVIfganY/M5sv6Tagzk95rVAuBT/olea1SnrOuvDPLGC1mXUvxWv+JJaXz5Q/PsNB/74RZWcx79+jWPvN9+z++9NZPWk2i4cHV6ptTjmY798o2CzaYNfWdLnt3KD5XWLmo++w9uv5FRV1q7y8PK659hbefedFsrOyeOZfLzF9+v+4beDvGDd+Mm+/PYKrfnMRJ5xwDLm5eaxauZqLLrkWgDPOOJHDDjuAJk0bc/75/QC4+JLrmDx5WnHf8mezvHyG3foMZz37B7Kys5g85COWf/s9h19/GoumzOHb9ycw+6MpdDx8Ly57/wEsL58P7nmRDat/5Ot3R9P+4D25bPh9mMHsjybz7QcTKzRvXl4ed974AE+99BBZ2dm8+uJbzPxmNlf94XK+mjSDkcM+5rxLz+SIYw8nLy+XH1at4aarb9/6/DY7taJVmxzGfF45LR2lccPA+xg7cQqrV6/hqL7nMuDi8zitlB0iK0p+Xj7P3/ok1z97C1nZWXw65EMWfruAvtf1Z+7UWUx6fxxffTSJLod1464RfyU/L58h9z7HutU/Rpo7XX5Et6PKQlHdM6vqJLUH5gAHm9kXkp4A5gKXA0ea2UxJzwATzWyQpFHA78xsnKS5wH4EhXWCme2c9ppvm1lXSb8muBo/08xyJTUhaNX5BmgPZANfAq+Y2W3h93rbzLbr3R42p39N0Mw/VlIDgub0A4EHgEOBNsA04CQzG1VMxp2ATwiu7usAk4DbzeyZ1HPMbHl47ufAX83s5fDe/d5mVmxX1Ddbnp2of7CnrYx2/HBZ3d6qd9QRyuy5Df8r+aSYmTr9pagjlMnl+/0+6ghl9s+5r/zsISXn7nxqqX/fPP/da5UzhKUQb06vWDOAX0maAjQB/gpcSNCEPJWg6BY5M4OZrQA+Czup/anQ4SeBecAUSZOBs8Me7U8AU4E3gLGUgpltBvoDD4evNYKgAH9G8EFkKvBnYLtLj8IZzWw+MASYQnCfvrjLqnOAi8PvOQ04uTR5nXOuMiRhiJk3p1esfDP7daF9HwD7FD7RzHqnfd0+7euzC53aNdyfS9BR7vr0g2Z2C3BLoedgZhcUFzS8H35ghkMZe5MUl9HMfg9s99E9/Tnh9hzguOJyOedcVHzaVeeccy6hfAGUaszM5hJeNceJpNcJhqal+4OZeVdb55xLk5eAMu5FvJoxs1OizuCcc0kQ/xLuRdw555zLKAmjt7yIO+eccxlE2eu8tLyIO+eccxl4c7pzzjmXUN6xzTnnnEuoJNwT9xnbnHPOuQzKexUzScdJ+kbSTEk3FnFOP0nTJU2T9GJJr+lX4s4551wG5Tljm6Rs4BHgaGABMFbSW2Y2Pe2cXYGbgEPMbJWkFplfbRu/EnfOOecyKOe50/cHZprZ7HC9iv+w/XoRlwKPmNkqADNbWtKLehF3zjnnMjCzUj9KoQ2Qvp7xgnBfut2A3SR9JulLSSWuLeHN6S5Rjn2nX9QRymTtjMIz3MZb3uTpJZ8UM6cPbRZ1hDJL2tKej497IOoIkShL73RJlwGXpe0abGaD00/J8LTC1b8GsCvQG2gLfCKpa7hCZUZexJ1zzrkM8svQOz0s2IOLOWUBsFPadltgYYZzvjSzLcAcSd8QFPUil5X25nTnnHMuAyvDoxTGArtK6iCpFnAm8Fahc94AjgCQ1IygeX12cS/qV+LOOedcBuU57aqZ5Ur6DTAMyAb+aWbTJN0BjDOzt8Jjx0iaDuQBN5jZiuJe14u4c845l0F5z51uZu8C7xbad2va1wZcHz5KxYu4c845l0Ge+bSrzjnnXCKV52QvFcWLuHPOOZdBEuZO9yLunHPOZeDriTvnnHMJ5VfizjnnXEL5lbhzzjmXUN473TnnnEso753unHPOJVRZ5k6PihdxV6V9Nvkb7n/2LfLzjVOO6MnFJx1R4PjCZasYOPhlVq1Zx47163HPgP7kNG0EwKLlq7jtiVdZsmI1kvj77y+kTfMmFZ955mIeGDaJfDNO2acDFx2ye8HMq9dx29BxrFq/mYZ1a3JP3/3JaVgPgAEvfsKUBSvZp11THj7z0ArPmpLdeR9qn3QxZGWxZcz7bBn5WoHjatSM2v2vRnV3gKwsNr/7HHlfTwAgq9XO1D7tCqhdF8zY8NANkLulQvPWO3Rfcm7+NWRl8cMr77HyiZcLHG9+42XUO2DvIF/d2mQ3acTM/c8Ijv3uInbotT9kifWfT2Tp3Y9VaNaUrr26c/atF6LsLD556QPeffSN7c7pefxBnHxtP8xg/oy5DL5m0NZjderX5e73/8aEYWN4YeBTlZK5OLfc8yAffzaGJo0b8cbzlfMelpVfibtyIenXwHoze7aYc54EHjSz6ZLmAvuZ2fIizv3RzOpLag08ZGanS7ogfM5vfkbOawmW31sfbr8LnF3cMnoVKS8/n3uefoPHb7qEnKY7cvYtf6d3jz3p1DZn6zkPvvAOJx62Lycdvi+jp81k0Evvcc+AMwG45dEhXNL3CA7aazfWb9yElGklwfLObNz73kQeO+cwchrW45wnP6DXbq3p1LzhtszvT+GEvXfmpG7tGTNnKQ99+BV3990fgF8dtBsbt+TxyoRi10woX8qi9imXsWHwbdgPK6h79QPkThuDLV2w9ZRaR51B7pTPyP1iGGrRlroX/x/r770csrKofda1bPr3IPIXzYV6DSAvr2LzZmWRc+uVLLjoj2xZspydXx7Ejx+OZvOseVtPWXbftsWoGp17EnX26ARAnX32oG6PPZl78gAA2r34Z+ruvxcbxkyt0MjKyuLcOy7hL+fewcrFK7n1rfuYNGIcC2due49btG9JnwGncs9pt7B+zToaNG1Y4DVO+e2ZfDM6PkvN9u1zNGefdhJ/vPPPUUcpUhKuxH0VswQws8eKK+DhOZeYWZl+Qs1soZmd/vPSBSRlA9cC9dJev09UBRzgq5nz2SmnKW1zmlKzRg2OO6gbo8YXfItmfb+EA7oEv6D337PT1uOzFiwhNy+fg/baDYB6dWpTt3atis+8cCU7Na5P28b1qZmdxbFddmLUNwVXK5y9bC0HdGgBQM/2zQscP6BDDvVqVe5n86x2u5K/fBG2cgnk5ZI76VNqdNm/0FmGagf/NFR3B2zNSgCyd+tO/qLvggIOsH4tVHBnojp778aWeQvZsmAxbMll7bsfUf+oA4s8v+HxvVjzzqhgwwzVroVq1kC1aqIa2eQtr/h/4h2778LS7xazbP5S8rbkMnroZ3Q/pmeBc3qd+Qs+fPY91q9ZB8DaFWu2Htu5a0caNmvEtE8mV3jW0tqv+17s2LBB1DGKZWX4LypexGNI0vmSpkiaLOk5SbdJ+p2kPSSNSTuvvaQp4dejJO1Xxu/TXtJXabt2kvSepG8kDUw771xJYyRNkvR4WLCR9KOkOySNBm4GWgMjJY0Mj88Nl9PL+Brh4xlJX0maKum6n/qeZbJ01Q+0DJvGAVo02ZElK38ocE7nnVvz/pjgLfhg7DTWbdjE6rXr+G7RchrsUIfr/vos/W4axIMvvENefsX3VF26ZgMtG9bdup3TsC5L124ocM5uOTvy/ozvAfjw64Ws25zL6vWbKjxbUdSwCbZ6W6OP/bAC7di0wDmbh79EjR69qHfzE9S96BY2vfEEAFnNWoMZdS65lbrX/JmavftWeN4aOc3YsmjZ1u3cxcupkdM087mtW1CzTUvWfxkUv42Tvmb96Cl0+uQFOn3yAus+ncDm2fMrPHOjnCasXLjtPV61aAWNcwre2snp2JqWHVpx0yt3cfPr99C1V3cAJNH/ll8x5J5irwNcBnmWX+pHVLyIx4ykLgQF8Ugz6wZckzpmZjOAWpI6hrv6A0PK8dvvD5wDdAfOkLSfpD3C73OImXUnWB7vnPD8HYCvzOwAM7uDYIH7I8yswI3nYl6jO9DGzLqa2V7A05lCSbpM0jhJ4556bXip/zKZWsIKN4lff87xjPt6Nv1uGsT4GbNp0aQh2dlZ5OXnMfHrOfz27ON58a7fsGDpSt78aFypv/dPlenzfOFW/OuP3pvx3y2j/+D3GTdvGS0a1CU7K8If5Uy3GQq9+TX2OYzccR+y/u5L2fDPu6hz1rXB87Kzye6wBxtf/Csb/vFHanQ9kOxd9qqk4Ol5M+9u2KcXa4d/CuEHuJrtWlGr407M6n0es3qdS70Du1F3v64VHi/TrZzCE5FkZ2eT06EVD5w5kMev+hsX3HcFdRvW44jzjmXKyAmsWlTsipYuA7P8Uj+i4vfE4+dI4JXU/WwzW1noB3gI0A+4j6Aw9i/H7z0itXatpNeAQ4FcYF9gbJijLrA0PD8PeLUUr3tUEa8xFOgo6WHgHSBjhTazwcBggI3j3yh1u1VOkx1ZvGJbU+fSlT/QonHB+4QtGjfkr9edD8D6jZt4f+xUGtSrS06THdm9fRvahldoR+zXhakz51HRchrWZfGabVfeS9ZsoHn9ugXOadGgLg/2OzjIvDmXD2Z8T4M6NSs8W1HshxWoUbOt29qx6dbm8pQaPY9i45N3AJD/3TdQoyaq1xBbvYK82dOCZnQg9+vxZLXpRN7MirvHnLtkOTVbNd+WrWUzcpdmLnAN+vRiyZ2PbN2u/4uD2Tj5a2z9RgDWfTyOut12Z8O4rzI+v7ysWryCJq23vceNWzVl9dJVBc5ZuXgFsyf+j7zcPJYvWMri2QvJad+KTj06s1vP3TnyvGOpXa8ONWrWYNP6jbxy/wsVmrkqSMJkL34lHj+iyOsCAF4C+knajWD52W/L8XsX/r4W5vmXmXUPH53N7Lbw+EYzK00vpIyvYWargG7AKOBK4Mny+WsEunRqy7zFK1iwdCVbcnN574vJ9Np3jwLnrFqzjvzwKuupN0fSt1fP8Lk7sWbdBlau+RGAMdNm0rFNi/KMlzlz68bMW/kj369ax5a8fIZNm0+v3VoVzLx+09YON099+jV9u7ev8FzFyZ//LVnNWqHGLSC7BjW6H0re9LEFzrHVy8neNejtrRZtoUYtbN0P5P5vIlktd4aatSAri+yOXchfUrHN0xun/o+aO7emZpscqFmDBn168eOHX253Xs0ObcjesT4bJ87Yui930TLq9twLsrOgRjZ1e+7FpkpoTp8zeSY57VvRrG0LsmvW4IATD2HSiILv8cThY9j9oKBVoH7jBrTs0Ipl85bwxLWDuOGQK/j9oQMYcs+zfP7aR17AS8nMSv2Iil+Jx88HwOuS/mpmKyQVuPFlZrMk5QH/R1DQy9PR4ffbAPQFLgLWA2+GeZaGxxuY2XcZnr8WaAAU7hX/QabXANYBm83sVUmzgGfK8y9TIzubmy44mSvue4r8/Hz69u7JLm1b8sjLw+nSsS29992TcTNm8dB/3gOJfXfvwB8vDO7JZmdlcf05fbjs7icwYM8ObTjtyMKdtcpfjawsbjyuO1e8+An5ZpzcrT27tNiRf4yaxp6tGtO7c2vGzV3GQyO/QsC+7Zpx0y/32fr8C58ZydwVa1m/OZdj/vYOt524Lwd3almxofPz2fTGE9S9dGA4xOwD8pfMp9YxZ5G3YCZ508eyaejT1DljADUPOxGATUMeCp67YR1bPhlK3av/BEDe1+PJ+3p8xebNy2fpnY/S9qm7ICubH14dzuaZ82h61Xls/Op/rBs5GoCGx/dmzTsfFXjq2mGfUu/AbrR/61EwWPfpuK3nV6T8vHyev/VJrn/2FrKys/h0yIcs/HYBfa/rz9yps5j0/ji++mgSXQ7rxl0j/kp+Xj5D7n2Odat/rPBsP9UNA+9j7MQprF69hqP6nsuAi8/jtBOPjTpWAUm4ElcSJnivbiT9CriBoLl6IjAX+NHM/hwe/x3wJ6CDmc0N940Cfmdm48owxKw98LaZdQ2HmPUhuM+9C/Cimd0ent8fuImg5WYLcKWZfZl6nbTXvYrginqRmR2RniPTaxB8WHiabS1CN5nZf4t7b8rSnB4HNmNsySfFSN7k+AxBKq3vh26MOkKZ3b9xh6gjlMnj4x6IOkKZ1WzW8WePCW3VaM9S/75ZtHp6xY9BzcCLuEsUL+IVy4t45fAiXvHKo4i3bLRHqX/fLF49I5Ii7s3pzjnnXAZJuMj1Il5FSWpKcC+6sKNSPdCdc84VLQn3xL2IV1Fhoe4edQ7nnEsqvxJ3zjnnEioJc6d7EXfOOecyiHI61dLyIu6cc85l4M3pzjnnXEJ5c7pzzjmXUFEuMVpaXsSdc865DPxK3DnnnEsovyfunHPOJVS+9053zjnnkikJV+K+AIpzgKTLzGxw1DnKImmZk5YXkpc5aXkhmZnjJKvkU5yrFi6LOsBPkLTMScsLycuctLyQzMyx4UXcOeecSygv4s4551xCeRF3LpDEe3JJy5y0vJC8zEnLC8nMHBvesc0555xLKL8Sd8455xLKi7hzzjmXUF7EnXPOuYTyIu6cc84llE+76qotSXua2fRC+3qb2aiIIhVLUiPgfKA9aT+7ZnZ1VJlKIukDMzuqpH1xIqk2cBrbv893RJWpJJJ2Ax4Fcsysq6S9gZPM7K6Io2Uk6RrgaWAt8CSwD3CjmQ2PNFgC+ZW4q86GSPqDAnUlPQzcG3WoYrxLUFimAuPTHrEjqY6kJkAzSY0lNQkf7YHW0aYr0ZvAyUAusC7tEWdPADcBWwDMbApwZqSJineRma0BjgGaAxcC90UbKZn8StxVZwcA9wOfAw2AF4BDIk1UvDpmdn3UIUrpcuBagoI9HlC4fw3wSFShSqmtmR0XdYgyqmdmYySl78uNKkwppIL2AZ42s8kqFN6VjhdxV51tATYAdYE6wByzWK89+JykS4G3gU2pnWa2MrpImZnZIGCQpKvM7OGo85TR55L2MrOpUQcpg+WSOgEGIOl0YFG0kYo1XtJwoANwk6QGQJx/9mLLJ3tx1ZakyQRNp3cCTYHHgS1mdnqkwYog6UrgbmA14S9rwMysY3SpSibpYLa/v/xsZIFKIGk6sAswh+DDkgje570jDVYMSR0JZj47GFhFkP0cM/su0mBFkJQFdAdmm9lqSU2BNuFtAFcGfiXuqrOLzWxc+PVi4GRJ50UZqATXA7uY2fKog5SWpOeATsAkIC/cbUBsizjwy6gDlEVYEPczs19I2gHIMrO1UecqgQF7AicAdwA7ELSGuTLyK3FXrUk6FNjVzJ6W1AxoYGZzos6ViaS3gDPNbH3UWUpL0gxgT0vYLxpJ3YDDws1PzGxylHlKIuljMzs86hylJelRguY6r3QNAAAfVElEQVTzI81sD0mNgeFm1jPiaInjV+Ku2pI0ENgP6Eww3KUW8Dzx7dyWB0ySNJKC98RjO8QM+ApoSbzvzxYQDn+6FHgt3PW8pMExv7c/QtLvgJdI60kfx/4SoQPMrIekiQBmtkpSrahDJZEXcVednUIwPnUCgJktDDvYxNUb4SNJmgHTJY2h4AePk6KLVKKLCYrMOgBJ9wNfAHEu4heFf16Zts+AuPaX2CIpm20d8ZrjHdt+Ei/irjrbbGYmKfWLZIeoAxXHzP6V+jpsftwpAR2Bbos6wE8gtt2/J/w61sOfzKxD1BnK6CHgdaCFpLuB04Fboo2UTF7EXXU2RNLjQKNw6NZFBJNmxJKkUcBJBD+3k4Blkj6K89hxM/tI0s4E/Q7el1QPyI46VwmeBkZLej3c7gs8FWGeEkk6P9P+uI4CMLMXJI0HjiL4gNTXzGZEHCuRvGObq9YkHU0wa5SAYWY2IuJIRZI00cz2kXQJwVX4QElTYj706VLgMqCJmXWStCvwWJynXQWQ1AM4lODfxcdmNjHiSMUKZxtMqUNQHCfEbbikpIZmtiaczW87Mb6HH1t+Je6qtbBox7ZwF1JDUiugH3Bz1GFK6Upgf2A0gJl9K6lFtJEyK1Rg5oaP1LEmcS4wZnZV+rakHYHnIopTnBcJhpWNZ9tcBxCOxSe+9/Bjy4u4q3YkraXgL5ACzKxhJcYpizuAYcCnZjY2nODj24gzlWSTmW1OzagpqQbFvPcRq0oFZj2wa9QhCjOzE8I/k3YPP7a8Od1VW5LuIJjk5TmCX9TnEIwTfyDSYFWIpAcIZpg7H7gKGABMN7OktCQkgqShbPvgkUUwkcrLZvaH6FIVLYmr28WVF3FXbUkabWYHlLQvapJ+b2YPhPc9t/uBjfM48XA2sYtJ63cAPBnnyV+SWGAk9UrbzAW+M7MFUeUpiqQ6QD1gJNCbbb3+GwL/NbM9IoqWWN6c7qqzPEnnAP8hKI5nUXBoUVykeu2OK/asGAoXlHmCGPf6T0krMM3CIXzpBSbuy6f2KXzVLen+GF6JJ3l1u1jyK3FXbYVrWw8imKHNgM+Aa81sbnSpqhZJJxAsMLMzwUVDajGR2PU7CGdqSxWY7ylYYJ4ws79Hla0kkiaYWY9C+2I5ciGc5OWPZnZn1FmqAi/izsVcofud24nz7GeSZgKnAlPj3ISeLknLp0q6gqCfQUdgVtqhBsBnZnZuJMFKIOkLMzso6hxVgRdxV22FUz1eyvbLZF5U1HOiUOh+53bM7KPKylJW4TzvR8V8nfbtSOpK0Dls68pacZw4JRxK1hi4F7gx7dDaOA+Jk3Q7MAV4LSkf7uLKi7irtiR9DnxCcG9u671wM3s1slBVjKSeBM3pH1Fw7vQHIwtVgnBhnN4ERfxdgqVJP43bxCkQjF8v7nhcC3k4zHMHgp+7DcT4Nkvcecc2V53Vi2HHnyKFs53dy/ZXiHEev3w38CNB3qSsUnU60A2YaGYXSsoBnow4U1HSx7QXnt89tmPbzSzOCw0lihdxV529LamPmb0bdZBSehoYCPwVOAK4kJgvzEEw3eoxUYcoow1mli8pV1JDYCnxLYaJnDRFwew/5wAdzOxOSTsBrcxsTMTREseb0121ldaktwnYQsyb9CSNN7N9JU01s73CfZ+Y2WFRZyuKpPuAD81seNRZSkvSP4A/AmcCvyVoSZhkZhdGGqwYkg7PtN/MPq7sLKUh6VGCpUePNLM9wiF9w82sZ8TREseLuHMJIekz4DDgFeBDgmFQ95lZ50iDFSNpH5QKC4chNoz7kq/hCIaUOgTz1Y83syMjilSs1JC41KI+4b7JZtYt6mxJ483prtqRtLuZfR2uVLUdM5tQ2ZlK6VqCyUiuJugsdgTwq0gTlSCp9z4lnUqwipkBnxL0pI4tMzsxfTtsno7z9MFbwvHiBltHiiRqBENc+JW4q3YkDTazy8LhT4VZHK9ewl9495nZDVFnKStJbdg22QsQ32Ze2Nqcvgvw73BXf2CWmV0ZXaqyCe85T0nddombcKbE/sC+wDMEnQlvMbOXo8yVRF7EnSuCpKPjtL64pA8Jxlwn5odW0v0Ev6yns20Yn8V8gpppQNfU+xzO/z7VzLpEm6xohebVzwK6A3PjOtkLBC1iBOueQ9BvYkZx57vMvDnduaLdT7zWGp8IvCnpZWBdaqeZvRZdpBL1BTqb2aYSz4yPb4B2wHfh9k7EvDmdgvPq5wL/NrPPogpTSvWAVJN63YizJJYXceeKFrfhW02AFUB6c78BcS7is4GapE30kgBNgRmSUsOdegJfSHoLYjvN7SvARjPLg+D2i6R6ZrY+4lwZSboVOAN4leDn7GlJL5vZXdEmSx5vTneuCJkWlXBlI+lVgolTPqDgjG1xXj41cdPcSvoS+IWZ/Rhu1ycYsnVwtMkykzQD2MfMNobbdYEJvhRp2fmVuHMJIelpMq8nHqu53gt5K3wkRhyLdCnUSRVwADP7UVK9KAOVYC7BULiN4XZtCi7g4krJi7hzRZsbdYBC3k77ug5wCrAwoiwlCnvUHx3nzlXpJH1qZoeGY9vTPywlYWz7Okk9UsMjJe1LMCd5rKR1wNsETJM0Itw+mmAonysjb0531ZakcQRTmb5oZquizlNWYa/p9+M4JC5F0jDgRDPbHHWWqixcaOY/bPtQ1wrob2bjo0u1PUnFzmtgZv+qrCxVhRdxV21J2oVg/vH+BL17nya4j5iIHwpJnYF3zGyXqLMURdLjQA+CJvX0HvVxXsXsQGCama0Nt+sDXcxsdLTJiiepJtCZoOXgazPbknYsVsMlXfnxIu6qvfCK9gQgNZ/zP4FBcVvGMa2ZV+Gfi4Gb4rx0aris53bM7PbKzlJakiYCPQqNEx+X5E6OcemkKWmImfWTNJXM/Tv2jiBWovk9cVetSdqb4Gq8D8FwlxcIptv8kGDCjNhI4hSmqWItaQczW1fS+TGh9NaYcEWzpP+ujMtwyWvCP0+INEUVkvR/mM79ZJLGA6uBp4Ab0yYkGS3pkOiSFVTUHO8pMZ7rHUkHEby/9YF2kroBl5vZgGiTFWu2pKsJWmYABhCMd0+yWDS5mtmi8M/vSjrXlY43p7tqS1JHM4v9L+ci5nhPieVc7ymSRhPMi/1W2mpVX5lZ12iTFU1SC+Ahgkl1jGCM+7VmtjTSYD9DjJrTC/f833qI+I8AiCW/EnfV2SWSHjCz1QDhmsa/NbNbIs5VgJkdEXWGn8PM5gfrcWyVV9S5cRAW6zOjzlHO5kYdAEp/S0hS4ySOGImCF3FXnf3SzP6Y2jCzVZL6ALEq4imS6hA07aaWyPwEeCw161VMzZd0MGCSahEsoxrLhS4k/d7MHii0mMhWcZxlLlwytUipefXNrNjzYugDglENrgRexF11li2pdupeeDj1Y+2IMxXnWWAt8HC4fRbwHMEc1HH1a2AQ0AZYAAwH4rqkZ+rDxbhiz4qXE4s5Fvd59YsTl454sedF3FVnzwMfpE1nehEQ58kmOptZt7TtkZImR5amdGRm50QdojTMbGj45frC61pLiuUHJTO7MOoMFcQ7a5WSd2xz1ZqkXxKsaSyCiV6GRRypSJKeIWg+/zLcPgD4VZx7ekv6FpgDvAS8mup/EGeZOoHFpWNYcSQdD3QhmJIXADO7I7pEP10S3u+48CLuXEKEKz91BuaFu9oRNAHnE/TsjeVEGZL2J+go1heYDvzHzJ6PNtX2wg90fYB+BB86UhoCe5rZ/pEEKwVJjxGsz30E8CTBiIAxZnZxpMF+IkkTU6MZXPG8iLtqK5xe82FgD6AWkA2si+swF0k7l3DKmjj36JXUDHgQOMfMsqPOU1g4hr07cAdwa9qhtcDImL+3U8xs77Q/6wOvmdkxUWdLJ6lJccdTsyRKahK3GRPjyu+Ju+rs7wRXiC8D+wHnA7Gdh7ykCTIkTSBmPXolNSRYbe1MoBPwOhDLK1ozmyzpK+CYBC7EkVqxbL2k1sAKoEOEeYoynm1TBxdmQEfYVsxdybyIu2rNzGZKyjazPOBpSZ9HnelniGOP3snAG8AdZvZF1GFKYmZ5kppKqpWwldfeltQI+BMwgaAgPhltpO2ZWRw/WCSaF3FXna0Pxy5PkvQAsAjYIeJMP0cc7411NDOT1EBSfTP7MepApfAd8JmkxKy8ZmZ3hl++KultoI6Z/RBlpkwk7W5mXxc1lXCcpxCOKy/irjo7D8gCfgNcB+wEnBZpoqqni6TngCaAJC0j6FH/VcS5irMwfGQBiVh0RtKVwAtmttrMNkmqJ2mAmf0j6myFXA9cBvwlwzEjmOrWlYF3bHPVkqRs4F9mdm7UWcpLHHv0hrcnbjazkeF2b+AeMzs40mBVjKRJZta90L7Y/Xtw5c+vxF21FN77bJ6Ee5+l7dFLMN49bnZIFXAAMxslKda3LCQ1B37P9mOu43yVmCVp6xKq4YfUWhFnKlI4SdF/gCFmNivqPEnmRdxVZ3NJxr3P9B697YBV4deNCMaMd4DY9uidLen/CKaHBTiXYPKXOHuBYJz4CQTTxv4KWBZpopINA4aE48WNIPd70UYq1klAf4LM+QTv9xAzm1f801xh3pzuqi1JAzPtN7PbKztLaYS/oN8ys3fD7V8CvzCz30abrGjhynC3EyzaIuBj4LaYj7keb2b7psZch/s+MrNeUWcriqQs4HLSZh8EngxHXcSapF2B/yOm8wfEnRdx5xIiVVwK7RtnZvtFlakqkvSlmR0oaRjBuuILgVfMrFPE0aoUSe0JZsfrT7A87UtmlqnDmyuGN6e7akvSCOCMQuuJ/8fMjo02WZGWS7qFYOEWI2iaXhFtpOJJGsr2Q99+IFgp7PGYLqN6l6Qdgd8SzOjXkGD0QuxIGmJm/SRNJfPyqXGdinc0UJNgoqUzzGx2xJESy6/EXbWVtB69YQe3gcDh4a6Pgdtjei8cAEmDgObAv8Nd/YHFQF2goZmdF1W2qkBSKzNbVNSUvCXN8heV1HjxqHNUBX4l7qqzPEntUp1pwl+Esf1UGxbra6LOUUb7mNnhadtDJX1sZodLmhZZqmJI6kiwBvpBBIvLfAFcF8erRTNbFH45wMz+kH5M0v3AH7Z/VnQknRsuftNHUp/Cx2PYqTT2sqIO4FyEbgY+lfRcOCHJx8BNEWcqkqTdJA2WNFzSh6lH1LlK0FxSu9RG+HWzcDOuQ/teBIYALYHWBE2+/y72GdE7OsO+X1Z6ipKlhhc2yPCoH1WoJPMrcVdtmdl74fSPB4a7rjOz5VFmKsHLwGMEc2LHvtdx6LcEH5RmEfSa7gAMCMeKx3WREZnZc2nbz0v6TWRpiiHpCmAA0FHSlLRDDYDPoklVNDN7PPyyI3BNof4o3qntJ/B74q5ak3QS2+4xjzKzt6PMU5xMvdOTQFJtYHeCIv51emc2SUeb2YjIwmUg6T5gNcFkJEZwH7828AjEazx+2AGvMXAvcGPaobVxyllYpr4nce6PEmdexF21Ff6y7kkwuQfAWcA4M4tlk7qk24ClBMt5bkrtj/Mv65JImmBmcVs+tbjJaMzMOlZamBJIamhma4qa1S+u/zbCGdt6p+YLCPN/ZGZ7RZssebyIu2orbH7sbmb54XY2MDHGw3IyFZdYFZWySuLVV5xaDyS9bWYnhP82Cq/THdt/G5LOJ+h/8gpB7n7A3YVuY7hS8CLuqq2wiPdOXa2EVwOj4lrEq6I4XomXJImZ40jSngSrlgn4wMymRxwpkbxjm6vO7gUmShpJ8IvkcGLcOx1AUldgTwouzPFsdImqJZV8SuUoal3ulDivzx0WbS/cP5NfibtqTVIrgvviAkab2eKIIxUpnOu9N0ERf5dgCNGnZnZ6lLkykXSGmb0sqYOZFXmPWdJrZnZqZWb7ueJ0JR5+AC2KxXzlNVcOvIi7ak3S3kB70lqlzOy1yAIVI5xasxvBfftuknIIFrk4MeJo20kVujgVvPJSFf9OLrm8Od1VW5L+CewNTCOYmQuCTjaxLOLABjPLl5QrqSFBT/VYdlwCVoRXiR3CpV4LMLOTIshUXuZGHaAwSTWBK0gbLkkwN/2WyEK5SuFF3FVnB5rZnlGHKINxkhoBTxCsMf4jMCbaSEU6HuhBsI54IibxkFRss36qhSamzf+PEiwo8o9w+7xw3yWRJXKVwpvTXbUl6SngL0nsFRsu49jQzKaUcGqkJDU3s2WSGhDco/0x6kxFkfR0MYfNzC6qtDBlJGmymXUraZ+revxK3FVn/wK+kLSYYPIUEfyyjuUQM0mHAJPMbB1wKNBD0qC4rlQVypE0HGgCSNIy4Fdm9lXEubZjZhdGneFnyJPUycxmwdZFXJIyNa/7GfxK3FVbkmYC1wNT2XZPPM7LN04h6Ni2N0Ez9VPAqWbWK9JgxZD0OXCzmY0Mt3sD95jZwZEGK4Gk44EuFBzKd0d0iYon6SjgaSC10lp74MLU++6qLl/FzFVn88zsLTObY2bfpR5RhypGrgWfuk8GBpnZIIKFLuJsh/RCYmaj2LaSVSxJeoxgvvSrCFpnzgAyrtcdI58BjxN8GM0Pv/4i0kSuUviVuKu2JP0DaAQMpeBc5LHsnS7pI+A94EKCXsjLCJrXYzvftKTXgQkELQcA5wL7mVnf6FIVT9IUM9s77c/6wGtmdkzU2YoiaQiwhoLrADQ2szOiS+Uqg98Td9VZXYLinf7LOc5DzPoDZwMXm9nicG3uP0WcqSQXAbez7T39mOBDSJxtCP9cL6k1sIJgCdU461yoE9vIcJERV8V5EXfVVgI7Mq0laEbPk7QbwfKe/444U7HCVaquLuq4pIfN7KpKjFQab4dD+f5E0IpgBGu4x9lESQea2ZcAkg4ghuuJu/Lnzemu2goL4aNAjpl1DWdvO8nM7oo4WkaSxgOHEawf/SUwDlhvZudEGuxniPvsZ+Fa6HXM7IeosxRH0gygMzAv3NUOmEFwfzy2Iy7cz+cd21x19gTBgidbAMIx12dGmqh4MrP1wKnAw2Z2CkEPaleOJF0ZXoljZpuALEkDIo5VkuMImvx7hY8OQB/gBCB20/K68uPN6a46q2dmY6QCi1LlRhWmFCTpIOAc4OJwX3aEeaqqS83skdSGma2SdCnbZkOLnZiPqnAVyK/EXXW2XFIngnueSDodWBRtpGJdS9By8LqZTQsn9Ej6OODYLOuZJktpn+wkZQO1IszjXJH8nrirtsIiOBg4GFgFzAHOiftVjaQdwlnbEk/SBWb2TNQ50kn6E8FkKY8RfMD7NTDfzH4bZS7nMvEi7qodSdcX2lWXoFVqHYCZPVjpoUohbEp/CqhvZu0kdQMuN7PY3q+VNJSwpSPNDwSd8h43s42Vn6p4krKAy4GjCFoKhhMs+erTmLrY8SLuqh1JA8MvOwM9gTcJflmfCHxsZrFc+UnSaOB04C0z2yfc95WZdY02WdEkDQKas20oXH9gMcEHp4Zmdl5U2ZyrCrxjm6t2zOx2gHBhjh5mtjbcvg14OcJoJTKz+YU64sX96nAfMzs8bXuopI/N7HBJ0yJLlYGkIWbWT9JUtm89wIdpuTjyIu6qs3bA5rTtzQT3QuNqvqSDAZNUi2ASlRkRZypJc0ntzGweQDjLXLPw2OainxaJa8I/T4g0hXNl4EXcVWfPAWPC+b0NOIVgedK4+jUwCGgDLCC4V3tlpIlK9lvgU0mzCG5ZdAAGSNqBmL3XZpYamTDAzP6QfkzS/cAftn+Wc9Hye+KuWpPUg2AWNAjuh0+MMk9VFM56tjtBEf86jp3Z0mWaRS61GEpUmZwrihdx5xJC0r+Aa8xsdbjdGPiLmV0UbbLihbcA2pPW8mdmz0YWqAiSrgAGAB2BWWmHGgCfmdm5kQRzrhhexJ1LCEkTU73Si9sXJ5KeAzoBk9jWCc/MrMhFUaIiaUeCeenvBW5MO7TWzFZGk8q54vk9ceeSI0tS43BlMCQ1If4/w/sBe1oyrhbMzOZK2q6fgaQmXshdHMX9F4Bzbpu/AJ9LeiXcPgO4O8I8pfEV0JJ4T2eb8iJBz/TxBB0d08fyGUEzu3Ox4s3pziWIpD2BI8PND81sepR5SiJpJNAdGANsSu03s5MiC+VcFeJX4s4lS02CK0QLv46726IOUFrhSIUimdmEysriXGn5lbhzCSHpGuBS4FWCQn4KMNjMHo40WBURthoUxczsyGKOOxcJL+LOJYSkKcBBqRXMwglTvojj+GVJn5rZoZLWUnAKUxEUxIYRRXOuSvHmdOeSQxScKz2PeK7HjZkdGv7ZIOosZSWpJnAFkJrzfRTBimtbIgvlXBG8iDuXHE8Do8NpYgH6EixNGmuSsoEcCk72Mi+6RCV6lKC/wT/C7fPCfbFc3c5Vb96c7lyChJ2vDiW4Ao/9NLGSrgIGAkuA/HC3xfEWQIqkyWbWraR9zsWBX4k7lwCSsoAp4drhSeolfQ3Q2cxWRB2kDPIkdTKzWQCSOhL/JV9dNeVF3LkEMLN8SZPTl/VMiPnAD1GHKKMbgJGSZofb7YELo4vjXNG8iDuXHK2AaZLGAOtSO2M+ccpsYJSkdyg42cuD0UUq0WfA48BR4fbjwBfRxXGuaF7EnUuO26MO8BPMCx+1wkcSPAusAe4Mt88iWHv+jMgSOVcE79jmXIJIagnsTzD2eqyZLY44UqlIakDQoe3HqLOUxDu2uSTJijqAc650JF1CMAf5qcDpwJeS4r6WeFdJEwkWQpkmabykLlHnKsFESQemNiQdQNDE7lzs+JW4cwkh6Rvg4FRPb0lNgc/NrHO0yYom6XPgZjMbGW73Bu4xs4MjDVYMSTOAzgS3AQDaATMIhsjFenicq378nrhzybEAWJu2vZag93ec7ZAq4ABmNiqcLjbOjos6gHOl5UXcueT4nmDGtjcJ7omfDIyRdD3Etsf3bEn/R9AxDOBcYE6EeUpkZt9FncG50vIi7lxyzAofKW+Gf8Z5fvKLCHrVv0Y4yxw+5tq5cuP3xJ2rIiQ9bGZXRZ3DOVd5/ErcuarjkKgDpEj6m5ldK2koBZciBWI/QY1zieFF3DlXEVL3wP8caQrnqjgv4s65cmdm48Mvu5vZoPRjkq4BPqr8VM5VPT7Zi3NVh6IOkMGvMuy7oLJDOFdV+ZW4c1XHoJJPqRySzgLOBjpIeivtUAMgScuSOhdrXsSdi7miOoelpDqJmdkzlZWpFD4HFgHNgL+k7V8LTIkkkXNVkA8xcy7mJPUq7riZxfb+sqSOwEIz2xhu1wVyzGxupMGcqyK8iDvnKoykcQTzvW8Ot2sBn5lZz2iTOVc1eHO6cwkhaVfgXmBPoE5qv5l1jCxUyWqkCjiAmW0OC7lzrhx473TnkuNp4FEgFzgCeJZt47HjapmkrRO7SDoZWB5hHueqFG9Ody4hJI03s30lTTWzvcJ9n5jZYVFnK4qkTsALQGuCIXDzgfPNbGakwZyrIrw53bnk2CgpC/hW0m8IVjVrEXGmYpnZLOBASfUJLhrWlvQc51zp+ZW4cwkhqScwA2gE3Ak0BP5kZl9GGqwYkmoDpwHtSbtoMLM7osrkXFXiV+LOJYCkbKCfmd0A/EhylvN8E/gBGA9sijiLc1WOF3HnEsDM8iTtK0mWrOaztmZ2XNQhnKuqvIg7lxwTgTclvQysS+00s9eii1SizyXtZWZTow7iXFXk98SdSwhJT2fYbWZ2UaWHKSVJ04FdgDkEzekiyLx3pMGcqyK8iDvnKoyknTPtN7PvKjuLc1WRT/biXEJIaivpdUlLJS2R9KqktlHnKoEV8XDOlQO/EncuISSNAF5k2yxt5wLnmNnR0aUqnqSpBEVbBFPFdgC+MbMukQZzrorwIu5cQkiaZGbdS9oXZ5J6AJeb2eVRZ3GuKvDmdOeSY7mkcyVlh49zgRVRhyoLM5sA+ApmzpUTvxJ3LiEktQP+DhxE0ET9OXC1mc2LNFgxJF2ftpkF7As0MbNjI4rkXJXiV+LOJcdOZnaSmTU3sxZm1hfYKepQmUhK3be/FWgQPmoDbwMnR5XLuarGr8SdSwhJE8ysR0n74iAcH/5LYCjQu/BxM1tZ2Zmcq4p8xjbnYk7SQcDBQPNCzdMNgexoUpXoMeA9gt7o49L2i+BWQMcoQjlX1XgRdy7+agH1CX5eG6TtXwOcHkmiEpjZQ8BDkh41syuizuNcVeXN6c4lhKSdfaYz51w679jmXHI8KalRakNSY0nDogzknIuWF3HnkqOZma1ObZjZKqBFhHmccxHzIu5ccuSHY8WBrYuL+P0w56ox79jmXHLcDHwq6aNw+3DgsgjzOOci5h3bnEsQSc2AAwmGan1hZssjjuSci5A3pzuXEJIEHAf0MLOhQD1J+0ccyzkXIb8Sdy4hJD0K5ANHmtkekhoDw83MFxRxrprye+LOJccBZtZD0kQIeqdLqhV1KOdcdLw53bnk2CIpm7BHuqTmBFfmzrlqyou4c8nxEPA60ELS3cCnwD3RRnLORcnviTuXIJJ2B44i6J3+gZnNiDiScy5CXsSdizlJTYo77st6Old9eRF3LuYkzeH/27tzI4RhKAigK7ogdF10Q+OfgGOwGzA7vJcqUbajY6XnOfj7G8/PUJKZGd96wp9yOx1+3MxsSbLWuiS5Jdlm5v56gvV66uSAU1mJQwk9ceDIShx66IkDOypm0ENPHNgR4tBDTxzYcSYORfTEgW9CHABK2U4HgFJCHABKCXEAKCXEAaCUEAeAUg90HnuaIuhjVgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sn\n",
+    "\n",
+    "feat_num = len(feature_names)\n",
+    "\n",
+    "# plt.figure(figsize=(feat_num,feat_num/2))\n",
+    "corrMatrix = feature_df.corr().round(2)\n",
+    "sn.heatmap(corrMatrix, annot=True)\n",
+    "\n",
+    "# plt.tight_layout()\n",
+    "    \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another way is to rank the importance of the variables in relevance with the target variable using a regression tree. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.08210176 0.04062965 0.00936197 0.02779484 0.84011178]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "model = DecisionTreeRegressor(random_state=0)\n",
+    "model.fit(Features, target)\n",
+    "print(model.feature_importances_) #use inbuilt class feature_importances of tree based classifiers\n",
+    "# #plot graph of feature importances for better visualization\n",
+    "feat_importances = pd.Series(model.feature_importances_, index=Features.columns)\n",
+    "feat_importances.nlargest(Features.shape[1]).plot(kind='barh')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In both of the cases above, \"civil liberties\" and \"electoral processand pluralism\" appear to be the most related variables. Let's train a self organised map."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "category_color = {'Full democracy': 'darkgreen',\n",
+    "                  'Flawed democracy': 'limegreen',\n",
+    "                  'Hybrid regime': 'darkorange',\n",
+    "                  'Authoritarian': 'crimson'}\n",
+    "colors_dict = {c: category_color[dm] for c, dm in zip(democracy_index.country,\n",
+    "                                                      democracy_index.category)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " [ 1000 / 1000 ] 100% - 0:00:00 left \n",
+      " quantization error: 0.03822093702335934\n",
+      " topographic error: 0.041916167664670656\n"
+     ]
+    }
+   ],
+   "source": [
+    "size = 15\n",
+    "som = MiniSom(size, size, len(X[0]),\n",
+    "              neighborhood_function='gaussian', sigma=1.5,\n",
+    "              random_seed=1)\n",
+    "\n",
+    "som.pca_weights_init(X)\n",
+    "som.train_random(X, 1000, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The topographic error is small so the map represents the data well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "W = som.get_weights()\n",
+    "plt.figure(figsize=(10, 10))\n",
+    "for i, f in enumerate(feature_names):\n",
+    "    plt.subplot(3, 3, i+1)\n",
+    "    plt.title(f)\n",
+    "    plt.pcolor(W[:,:,i].T, cmap='coolwarm')\n",
+    "    plt.xticks(np.arange(size+1))\n",
+    "    plt.yticks(np.arange(size+1))\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that the two varibles that ranked first in the previous methods have almost identcal planes. This is a hint that they are strongly dependent, so having both of them in a model is redundant. We are hoping that a SOM based selection can prevent that. Let's define the algorithm, and perform the selection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def som_feature_selection(W, labels, target_index = 0, a = 0.04):\n",
+    "    \"\"\" Performs feature selection based on a self organised map trained with the desired variables\n",
+    "\n",
+    "    INPUTS: W = numpy array, the weights of the map (X*Y*N) where X = map's rows, Y = map's columns, N = number of variables\n",
+    "            labels = list, holds the names of the variables in same order as in W\n",
+    "            target_index = int, the position of the target variable in W and labels\n",
+    "            a = float, an arbitary parameter in which the selection depends, values between 0.03 and 0.06 work well\n",
+    "\n",
+    "    OUTPUTS: selected_labels = list of strings, holds the names of the selected features in order of selection\n",
+    "             target_name = string, the name of the target variable so that user is sure he gave the correct input\n",
+    "    \"\"\"\n",
+    "\n",
+    "\n",
+    "    W_2d = np.reshape(W, (W.shape[0]*W.shape[1], W.shape[2])) #reshapes W into MxN assuming M neurons and N features\n",
+    "    target_name = labels[target_index]\n",
+    "\n",
+    "\n",
+    "    Rand_feat = np.random.uniform(low=0, high=1, size=(W_2d.shape[0], W_2d.shape[1] - 1)) # create N -1 random features\n",
+    "    W_with_rand = np.concatenate((W_2d,Rand_feat), axis=1) # add them to the N regular ones\n",
+    "    W_normed = (W_with_rand - W_with_rand.min(0)) / W_with_rand.ptp(0) # normalize each feature between 0 and 1\n",
+    "\n",
+    "    Target_feat = W_normed[:,target_index] # column of target feature\n",
+    "\n",
+    "    # Two conditions to check against a\n",
+    "    Check_matrix1 = abs(np.vstack(Target_feat) - W_normed)\n",
+    "    Check_matrix2 = abs(np.vstack(Target_feat) + W_normed - 1)\n",
+    "    S = np.logical_or(Check_matrix1 <= a, Check_matrix2 <= a).astype(int) # applie \"or\" element-wise in two matrices\n",
+    "\n",
+    "    S[:,target_index] = 0 #ignore the target feature so that it is not picked\n",
+    "\n",
+    "    selected_labels = []\n",
+    "    while True:\n",
+    "\n",
+    "        S2 = np.sum(S, axis=0) # add all rows for each column (feature)\n",
+    "\n",
+    "        if not np.any(S2 > 0): # if all features add to 0 kill\n",
+    "            break\n",
+    "\n",
+    "        selected_feature_index = np.argmax(S2) # feature with the highest sum gets selected first\n",
+    "\n",
+    "        if selected_feature_index > (S.shape[1] - (Rand_feat.shape[1] + 1)): # if random feature is selected kill\n",
+    "            break\n",
+    "\n",
+    "\n",
+    "        selected_labels.append(labels[selected_feature_index])\n",
+    "\n",
+    "        # delete all rows where selected feature evaluates to 1, thus avoid selecting complementary features\n",
+    "        rows_to_delete = np.where(S[:,selected_feature_index] == 1)\n",
+    "        S[rows_to_delete, :] = 0\n",
+    "\n",
+    "#     selected_labels = [label for i, label in enumerate(labels) if i in feature_indeces]\n",
+    "    return selected_labels, target_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Target variable: democracy_index\n",
+      "Selected features ['functioning_of_government', 'political_participation', 'civil_liberties', 'political_culture']\n"
+     ]
+    }
+   ],
+   "source": [
+    "selected_features, target_name = som_feature_selection(W, feature_names, 0, 0.04)\n",
+    "print(\"Target variable: {}\\nSelected features {}\".format(target_name, selected_features))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/HandwrittenDigits.ipynb b/examples/HandwrittenDigits.ipynb
new file mode 100644
index 0000000..d4f0552
--- /dev/null
+++ b/examples/HandwrittenDigits.ipynb
@@ -0,0 +1,113 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example we will see how to train a SOM to create a map of hand-written digits using the <a href=\"https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\">UCI ML hand-written digits datasets</a>.\n",
+    "\n",
+    "First, we'll 1) load the data using the sklearn wrapper, 2) scale the data, 3) train the som."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from minisom import MiniSom\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from sklearn import datasets\n",
+    "from sklearn.preprocessing import scale\n",
+    "\n",
+    "# load the digits dataset from scikit-learn\n",
+    "digits = datasets.load_digits(n_class=10)\n",
+    "data = digits.data  # matrix where each row is a vector that represent a digit.\n",
+    "data = scale(data)\n",
+    "num = digits.target  # num[i] is the digit represented by data[i]\n",
+    "\n",
+    "som = MiniSom(30, 30, 64, sigma=4,\n",
+    "              learning_rate=0.5, neighborhood_function='triangle')\n",
+    "som.pca_weights_init(data)\n",
+    "som.train(data, 5000, random_order=True, verbose=True)  # random training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Not that each input vector for the SOM represents the entire image obtained reshaping the original image of dimension 8-by-8 into a vector of 64 elements. The images in input are gray scale.\n",
+    "\n",
+    "We can now place each digit on the map represented by the SOM:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(8, 8))\n",
+    "wmap = {}\n",
+    "im = 0\n",
+    "for x, t in zip(data, num):  # scatterplot\n",
+    "    w = som.winner(x)\n",
+    "    wmap[w] = im\n",
+    "    plt. text(w[0]+.5,  w[1]+.5,  str(t),\n",
+    "              color=plt.cm.rainbow(t / 10.), fontdict={'weight': 'bold',  'size': 11})\n",
+    "    im = im + 1\n",
+    "plt.axis([0, som.get_weights().shape[0], 0,  som.get_weights().shape[1]])\n",
+    "plt.savefig('resulting_images/som_digts.png')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10, 10), facecolor='white')\n",
+    "cnt = 0\n",
+    "for j in reversed(range(20)):  # images mosaic\n",
+    "    for i in range(20):\n",
+    "        plt.subplot(20, 20, cnt+1, frameon=False,  xticks=[],  yticks=[])\n",
+    "        if (i, j) in wmap:\n",
+    "            plt.imshow(digits.images[wmap[(i, j)]],\n",
+    "                       cmap='Greys', interpolation='nearest')\n",
+    "        else:\n",
+    "            plt.imshow(np.zeros((8, 8)),  cmap='Greys')\n",
+    "        cnt = cnt + 1\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('resulting_images/som_digts_imgs.png')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/HexagonalTopology.ipynb b/examples/HexagonalTopology.ipynb
new file mode 100644
index 0000000..cd6e5b1
--- /dev/null
+++ b/examples/HexagonalTopology.ipynb
@@ -0,0 +1,696 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example we will see how to plot an hexagonal map that reflects the results of the training process. This example is an extension of BasicUsage.ipynb. Only the plotting section has new code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div class=\"bk-root\">\n",
+       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
+       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
+       "    </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "\n",
+       "(function(root) {\n",
+       "  function now() {\n",
+       "    return new Date();\n",
+       "  }\n",
+       "\n",
+       "  var force = true;\n",
+       "\n",
+       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
+       "    root._bokeh_onload_callbacks = [];\n",
+       "    root._bokeh_is_loading = undefined;\n",
+       "  }\n",
+       "\n",
+       "  var JS_MIME_TYPE = 'application/javascript';\n",
+       "  var HTML_MIME_TYPE = 'text/html';\n",
+       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
+       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
+       "\n",
+       "  /**\n",
+       "   * Render data to the DOM node\n",
+       "   */\n",
+       "  function render(props, node) {\n",
+       "    var script = document.createElement(\"script\");\n",
+       "    node.appendChild(script);\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when an output is cleared or removed\n",
+       "   */\n",
+       "  function handleClearOutput(event, handle) {\n",
+       "    var cell = handle.cell;\n",
+       "\n",
+       "    var id = cell.output_area._bokeh_element_id;\n",
+       "    var server_id = cell.output_area._bokeh_server_id;\n",
+       "    // Clean up Bokeh references\n",
+       "    if (id != null && id in Bokeh.index) {\n",
+       "      Bokeh.index[id].model.document.clear();\n",
+       "      delete Bokeh.index[id];\n",
+       "    }\n",
+       "\n",
+       "    if (server_id !== undefined) {\n",
+       "      // Clean up Bokeh references\n",
+       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
+       "      cell.notebook.kernel.execute(cmd, {\n",
+       "        iopub: {\n",
+       "          output: function(msg) {\n",
+       "            var id = msg.content.text.trim();\n",
+       "            if (id in Bokeh.index) {\n",
+       "              Bokeh.index[id].model.document.clear();\n",
+       "              delete Bokeh.index[id];\n",
+       "            }\n",
+       "          }\n",
+       "        }\n",
+       "      });\n",
+       "      // Destroy server and session\n",
+       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
+       "      cell.notebook.kernel.execute(cmd);\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when a new output is added\n",
+       "   */\n",
+       "  function handleAddOutput(event, handle) {\n",
+       "    var output_area = handle.output_area;\n",
+       "    var output = handle.output;\n",
+       "\n",
+       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
+       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
+       "      return\n",
+       "    }\n",
+       "\n",
+       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
+       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
+       "      // store reference to embed id on output_area\n",
+       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "    }\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "      var bk_div = document.createElement(\"div\");\n",
+       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "      var script_attrs = bk_div.children[0].attributes;\n",
+       "      for (var i = 0; i < script_attrs.length; i++) {\n",
+       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
+       "      }\n",
+       "      // store reference to server id on output_area\n",
+       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function register_renderer(events, OutputArea) {\n",
+       "\n",
+       "    function append_mime(data, metadata, element) {\n",
+       "      // create a DOM node to render to\n",
+       "      var toinsert = this.create_output_subarea(\n",
+       "        metadata,\n",
+       "        CLASS_NAME,\n",
+       "        EXEC_MIME_TYPE\n",
+       "      );\n",
+       "      this.keyboard_manager.register_events(toinsert);\n",
+       "      // Render to node\n",
+       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+       "      render(props, toinsert[toinsert.length - 1]);\n",
+       "      element.append(toinsert);\n",
+       "      return toinsert\n",
+       "    }\n",
+       "\n",
+       "    /* Handle when an output is cleared or removed */\n",
+       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
+       "    events.on('delete.Cell', handleClearOutput);\n",
+       "\n",
+       "    /* Handle when a new output is added */\n",
+       "    events.on('output_added.OutputArea', handleAddOutput);\n",
+       "\n",
+       "    /**\n",
+       "     * Register the mime type and append_mime function with output_area\n",
+       "     */\n",
+       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+       "      /* Is output safe? */\n",
+       "      safe: true,\n",
+       "      /* Index of renderer in `output_area.display_order` */\n",
+       "      index: 0\n",
+       "    });\n",
+       "  }\n",
+       "\n",
+       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
+       "  if (root.Jupyter !== undefined) {\n",
+       "    var events = require('base/js/events');\n",
+       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+       "\n",
+       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+       "      register_renderer(events, OutputArea);\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  \n",
+       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
+       "    root._bokeh_timeout = Date.now() + 5000;\n",
+       "    root._bokeh_failed_load = false;\n",
+       "  }\n",
+       "\n",
+       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
+       "     \"<div style='background-color: #fdd'>\\n\"+\n",
+       "     \"<p>\\n\"+\n",
+       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
+       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
+       "     \"</p>\\n\"+\n",
+       "     \"<ul>\\n\"+\n",
+       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
+       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
+       "     \"</ul>\\n\"+\n",
+       "     \"<code>\\n\"+\n",
+       "     \"from bokeh.resources import INLINE\\n\"+\n",
+       "     \"output_notebook(resources=INLINE)\\n\"+\n",
+       "     \"</code>\\n\"+\n",
+       "     \"</div>\"}};\n",
+       "\n",
+       "  function display_loaded() {\n",
+       "    var el = document.getElementById(\"1001\");\n",
+       "    if (el != null) {\n",
+       "      el.textContent = \"BokehJS is loading...\";\n",
+       "    }\n",
+       "    if (root.Bokeh !== undefined) {\n",
+       "      if (el != null) {\n",
+       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
+       "      }\n",
+       "    } else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(display_loaded, 100)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "\n",
+       "  function run_callbacks() {\n",
+       "    try {\n",
+       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+       "        if (callback != null)\n",
+       "          callback();\n",
+       "      });\n",
+       "    } finally {\n",
+       "      delete root._bokeh_onload_callbacks\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
+       "  }\n",
+       "\n",
+       "  function load_libs(css_urls, js_urls, callback) {\n",
+       "    if (css_urls == null) css_urls = [];\n",
+       "    if (js_urls == null) js_urls = [];\n",
+       "\n",
+       "    root._bokeh_onload_callbacks.push(callback);\n",
+       "    if (root._bokeh_is_loading > 0) {\n",
+       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+       "      return null;\n",
+       "    }\n",
+       "    if (js_urls == null || js_urls.length === 0) {\n",
+       "      run_callbacks();\n",
+       "      return null;\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
+       "\n",
+       "    function on_load() {\n",
+       "      root._bokeh_is_loading--;\n",
+       "      if (root._bokeh_is_loading === 0) {\n",
+       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+       "        run_callbacks()\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    function on_error() {\n",
+       "      console.error(\"failed to load \" + url);\n",
+       "    }\n",
+       "\n",
+       "    for (var i = 0; i < css_urls.length; i++) {\n",
+       "      var url = css_urls[i];\n",
+       "      const element = document.createElement(\"link\");\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error;\n",
+       "      element.rel = \"stylesheet\";\n",
+       "      element.type = \"text/css\";\n",
+       "      element.href = url;\n",
+       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+       "      document.body.appendChild(element);\n",
+       "    }\n",
+       "\n",
+       "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\": \"T2yuo9Oe71Cz/I4X9Ac5+gpEa5a8PpJCDlqKYO0CfAuEszu1JrXLl8YugMqYe3sM\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\": \"98GDGJ0kOMCUMUePhksaQ/GYgB3+NH9h996V88sh3aOiUNX3N+fLXAtry6xctSZ6\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\": \"89bArO+nlbP3sgakeHjCo1JYxYR5wufVgA3IbUvDY+K7w4zyxJqssu7wVnfeKCq8\"};\n",
+       "\n",
+       "    for (var i = 0; i < js_urls.length; i++) {\n",
+       "      var url = js_urls[i];\n",
+       "      var element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error;\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      if (url in hashes) {\n",
+       "        element.crossOrigin = \"anonymous\";\n",
+       "        element.integrity = \"sha384-\" + hashes[url];\n",
+       "      }\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "  };\n",
+       "\n",
+       "  function inject_raw_css(css) {\n",
+       "    const element = document.createElement(\"style\");\n",
+       "    element.appendChild(document.createTextNode(css));\n",
+       "    document.body.appendChild(element);\n",
+       "  }\n",
+       "\n",
+       "  \n",
+       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\"];\n",
+       "  var css_urls = [];\n",
+       "  \n",
+       "\n",
+       "  var inline_js = [\n",
+       "    function(Bokeh) {\n",
+       "      Bokeh.set_log_level(\"info\");\n",
+       "    },\n",
+       "    function(Bokeh) {\n",
+       "    \n",
+       "    \n",
+       "    }\n",
+       "  ];\n",
+       "\n",
+       "  function run_inline_js() {\n",
+       "    \n",
+       "    if (root.Bokeh !== undefined || force === true) {\n",
+       "      \n",
+       "    for (var i = 0; i < inline_js.length; i++) {\n",
+       "      inline_js[i].call(root, root.Bokeh);\n",
+       "    }\n",
+       "    if (force === true) {\n",
+       "        display_loaded();\n",
+       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(run_inline_js, 100);\n",
+       "    } else if (!root._bokeh_failed_load) {\n",
+       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+       "      root._bokeh_failed_load = true;\n",
+       "    } else if (force !== true) {\n",
+       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
+       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
+       "    }\n",
+       "\n",
+       "  }\n",
+       "\n",
+       "  if (root._bokeh_is_loading === 0) {\n",
+       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
+       "    run_inline_js();\n",
+       "  } else {\n",
+       "    load_libs(css_urls, js_urls, function() {\n",
+       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+       "      run_inline_js();\n",
+       "    });\n",
+       "  }\n",
+       "}(window));"
+      ],
+      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\": \"T2yuo9Oe71Cz/I4X9Ac5+gpEa5a8PpJCDlqKYO0CfAuEszu1JrXLl8YugMqYe3sM\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\": \"98GDGJ0kOMCUMUePhksaQ/GYgB3+NH9h996V88sh3aOiUNX3N+fLXAtry6xctSZ6\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\": \"89bArO+nlbP3sgakeHjCo1JYxYR5wufVgA3IbUvDY+K7w4zyxJqssu7wVnfeKCq8\"};\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from minisom import MiniSom\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.patches import RegularPolygon, Ellipse\n",
+    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
+    "from matplotlib import cm, colorbar\n",
+    "from matplotlib.lines import Line2D\n",
+    "\n",
+    "from bokeh.colors import RGB\n",
+    "from bokeh.io import curdoc, show, output_notebook\n",
+    "from bokeh.transform import factor_mark, factor_cmap\n",
+    "from bokeh.models import ColumnDataSource, HoverTool\n",
+    "from bokeh.plotting import figure, output_file\n",
+    "\n",
+    "# display matplotlib plots in notebook\n",
+    "%matplotlib inline\n",
+    "# display bokeh plot in notebook\n",
+    "output_notebook()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Load, Preprocess and Training\n",
+    "We load the seeds dataset, normalise the data along the columns and then train our SOMs. \n",
+    "\n",
+    "> Note, we are training a hexagonal topology because we will be plotting this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-2-fe54c6b94a38>:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
+      "  data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/00236/seeds_dataset.txt',\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " [ 1000 / 1000 ] 100% - 0:00:00 left \n",
+      " quantization error: 0.45378615630601005\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/00236/seeds_dataset.txt', \n",
+    "                   names=['area', 'perimeter', 'compactness', 'length_kernel', 'width_kernel',\n",
+    "                          'asymmetry_coefficient', 'length_kernel_groove', 'target'], \n",
+    "                   sep='\\t+')\n",
+    "t = data['target'].values\n",
+    "data = data[data.columns[:-1]]\n",
+    "\n",
+    "# data normalization\n",
+    "data = (data - np.mean(data, axis=0)) / np.std(data, axis=0)\n",
+    "data = data.values\n",
+    "\n",
+    "# initialization and training of 15x15 SOM\n",
+    "som = MiniSom(15, 15, data.shape[1], sigma=1.5, learning_rate=.7, activation_distance='euclidean',\n",
+    "              topology='hexagonal', neighborhood_function='gaussian', random_seed=10)\n",
+    "\n",
+    "som.train(data, 1000, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "\n",
+    "## Plotting: matplotlib\n",
+    "Below, we are plotting using matplotlib to create a static hexagonal topology."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xx, yy = som.get_euclidean_coordinates()\n",
+    "umatrix = som.distance_map()\n",
+    "weights = som.get_weights()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = plt.figure(figsize=(10,10))\n",
+    "ax = f.add_subplot(111)\n",
+    "\n",
+    "ax.set_aspect('equal')\n",
+    "\n",
+    "# iteratively add hexagons\n",
+    "for i in range(weights.shape[0]):\n",
+    "    for j in range(weights.shape[1]):\n",
+    "        wy = yy[(i, j)] * 2 / np.sqrt(3) * 3 / 4\n",
+    "        hex = RegularPolygon((xx[(i, j)], wy), \n",
+    "                             numVertices=6, \n",
+    "                             radius=.95 / np.sqrt(3),\n",
+    "                             facecolor=cm.Blues(umatrix[i, j]), \n",
+    "                             alpha=.4, \n",
+    "                             edgecolor='gray')\n",
+    "        ax.add_patch(hex)\n",
+    "\n",
+    "markers = ['o', '+', 'x']\n",
+    "colors = ['C0', 'C1', 'C2']\n",
+    "for cnt, x in enumerate(data):\n",
+    "    # getting the winner\n",
+    "    w = som.winner(x)\n",
+    "    # place a marker on the winning position for the sample xx\n",
+    "    wx, wy = som.convert_map_to_euclidean(w) \n",
+    "    wy = wy * 2 / np.sqrt(3) * 3 / 4\n",
+    "    plt.plot(wx, wy, \n",
+    "             markers[t[cnt]-1], \n",
+    "             markerfacecolor='None',\n",
+    "             markeredgecolor=colors[t[cnt]-1], \n",
+    "             markersize=12, \n",
+    "             markeredgewidth=2)\n",
+    "\n",
+    "xrange = np.arange(weights.shape[0])\n",
+    "yrange = np.arange(weights.shape[1])\n",
+    "plt.xticks(xrange-.5, xrange)\n",
+    "plt.yticks(yrange * 2 / np.sqrt(3) * 3 / 4, yrange)\n",
+    "\n",
+    "divider = make_axes_locatable(plt.gca())\n",
+    "ax_cb = divider.new_horizontal(size=\"5%\", pad=0.05)    \n",
+    "cb1 = colorbar.ColorbarBase(ax_cb, cmap=cm.Blues, \n",
+    "                            orientation='vertical', alpha=.4)\n",
+    "cb1.ax.get_yaxis().labelpad = 16\n",
+    "cb1.ax.set_ylabel('distance from neurons in the neighbourhood',\n",
+    "                  rotation=270, fontsize=16)\n",
+    "plt.gcf().add_axes(ax_cb)\n",
+    "\n",
+    "legend_elements = [Line2D([0], [0], marker='o', color='C0', label='Kama',\n",
+    "                   markerfacecolor='w', markersize=14, linestyle='None', markeredgewidth=2),\n",
+    "                   Line2D([0], [0], marker='+', color='C1', label='Rosa',\n",
+    "                   markerfacecolor='w', markersize=14, linestyle='None', markeredgewidth=2),\n",
+    "                   Line2D([0], [0], marker='x', color='C2', label='Canadian',\n",
+    "                   markerfacecolor='w', markersize=14, linestyle='None', markeredgewidth=2)]\n",
+    "ax.legend(handles=legend_elements, bbox_to_anchor=(0.1, 1.08), loc='upper left', \n",
+    "          borderaxespad=0., ncol=3, fontsize=14)\n",
+    "\n",
+    "plt.savefig('resulting_images/som_seed_hex.png')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting: bokeh\n",
+    "Below, we are plotting using bokeh to create an interactive hexagonal topology.\n",
+    "\n",
+    "> Note: Compared to matplotlib plot, this is rotated 90 degrees."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hex_centre_col, hex_centre_row = [], []\n",
+    "hex_colour = []\n",
+    "label = []\n",
+    "\n",
+    "# define labels\n",
+    "SPECIES = ['Kama', 'Rosa', 'Canadian']\n",
+    "\n",
+    "for i in range(weights.shape[0]):\n",
+    "    for j in range(weights.shape[1]):\n",
+    "        wy = yy[(i, j)] * 2 / np.sqrt(3) * 3 / 4\n",
+    "        hex_centre_col.append(xx[(i, j)])\n",
+    "        hex_centre_row.append(wy)\n",
+    "        hex_colour.append(cm.Blues(umatrix[i, j]))\n",
+    "\n",
+    "weight_x, weight_y = [], []\n",
+    "for cnt, i in enumerate(data):\n",
+    "    w = som.winner(i)\n",
+    "    wx, wy = som.convert_map_to_euclidean(xy=w)\n",
+    "    wy = wy * 2 / np.sqrt(3) * 3 / 4\n",
+    "    weight_x.append(wx)\n",
+    "    weight_y.append(wy)\n",
+    "    label.append(SPECIES[t[cnt]-1])\n",
+    "    \n",
+    "# convert matplotlib colour palette to bokeh colour palette\n",
+    "hex_plt = [(255 * np.array(i)).astype(int) for i in hex_colour]\n",
+    "hex_bokeh = [RGB(*tuple(rgb)).to_hex() for rgb in hex_plt]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "  <div class=\"bk-root\" id=\"b7926dd9-1983-4107-964d-36c43895fa8c\" data-root-id=\"5651\"></div>\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function embed_document(root) {\n",
+       "    \n",
+       "  var docs_json = 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Hexagonal Topology\"},\"id\":\"5652\",\"type\":\"Title\"},{\"attributes\":{\"axis\":{\"id\":\"5662\"},\"ticker\":null},\"id\":\"5665\",\"type\":\"Grid\"},{\"attributes\":{\"source\":{\"id\":\"5677\"}},\"id\":\"5683\",\"type\":\"CDSView\"},{\"attributes\":{\"callback\":null,\"point_policy\":\"follow_mouse\",\"tooltips\":[[\"label\",\"@species\"],[\"(x,y)\",\"($x, $y)\"]]},\"id\":\"5702\",\"type\":\"HoverTool\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"field\":\"c\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"gray\"},\"size\":{\"units\":\"screen\",\"value\":54.84827557301445},\"x\":{\"field\":\"y\"},\"y\":{\"field\":\"x\"}},\"id\":\"5681\",\"type\":\"Hex\"},{\"attributes\":{\"fill_color\":{\"field\":\"species\",\"transform\":{\"id\":\"5685\"}},\"line_color\":{\"field\":\"species\",\"transform\":{\"id\":\"5685\"}},\"marker\":{\"field\":\"species\",\"transform\":{\"id\":\"5684\"}},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"wy\"},\"y\":{\"field\":\"wx\"}},\"id\":\"5687\",\"type\":\"Scatter\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.4},\"fill_color\":{\"field\":\"c\"},\"line_alpha\":{\"value\":0.4},\"line_color\":{\"value\":\"gray\"},\"size\":{\"units\":\"screen\",\"value\":54.84827557301445},\"x\":{\"field\":\"y\"},\"y\":{\"field\":\"x\"}},\"id\":\"5680\",\"type\":\"Hex\"},{\"attributes\":{},\"id\":\"5697\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"5658\",\"type\":\"LinearScale\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"5670\"},{\"id\":\"5671\"},{\"id\":\"5672\"},{\"id\":\"5702\"}]},\"id\":\"5673\",\"type\":\"Toolbar\"},{\"attributes\":{\"factors\":[\"Kama\",\"Rosa\",\"Canadian\"],\"markers\":[\"diamond\",\"cross\",\"x\"]},\"id\":\"5684\",\"type\":\"CategoricalMarkerMapper\"},{\"attributes\":{},\"id\":\"5663\",\"type\":\"BasicTicker\"}],\"root_ids\":[\"5651\"]},\"title\":\"Bokeh Application\",\"version\":\"2.2.3\"}};\n",
+       "  var render_items = [{\"docid\":\"f293967f-2312-4173-861b-1f53293fad9a\",\"root_ids\":[\"5651\"],\"roots\":{\"5651\":\"b7926dd9-1983-4107-964d-36c43895fa8c\"}}];\n",
+       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "\n",
+       "  }\n",
+       "  if (root.Bokeh !== undefined) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (root.Bokeh !== undefined) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else {\n",
+       "        attempts++;\n",
+       "        if (attempts > 100) {\n",
+       "          clearInterval(timer);\n",
+       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
+       "        }\n",
+       "      }\n",
+       "    }, 10, root)\n",
+       "  }\n",
+       "})(window);"
+      ],
+      "application/vnd.bokehjs_exec.v0+json": ""
+     },
+     "metadata": {
+      "application/vnd.bokehjs_exec.v0+json": {
+       "id": "5651"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "output_file(\"resulting_images/som_seed_hex.html\")\n",
+    "\n",
+    "# initialise figure/plot\n",
+    "fig = figure(title=\"SOM: Hexagonal Topology\",\n",
+    "             plot_height=800, plot_width=800,\n",
+    "             match_aspect=True,\n",
+    "             tools=\"wheel_zoom,save,reset\")\n",
+    "\n",
+    "# create data stream for plotting\n",
+    "source_hex = ColumnDataSource(\n",
+    "    data = dict(\n",
+    "        x=hex_centre_col,\n",
+    "        y=hex_centre_row,\n",
+    "        c=hex_bokeh\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "source_pages = ColumnDataSource(\n",
+    "    data=dict(\n",
+    "        wx=weight_x,\n",
+    "        wy=weight_y,\n",
+    "        species=label\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# define markers\n",
+    "MARKERS = ['diamond', 'cross', 'x']\n",
+    "\n",
+    "# add shapes to plot\n",
+    "fig.hex(x='y', y='x', source=source_hex,\n",
+    "        size=100 * (.95 / np.sqrt(3)),\n",
+    "        alpha=.4,\n",
+    "        line_color='gray',\n",
+    "        fill_color='c')\n",
+    "\n",
+    "fig.scatter(x='wy', y='wx', source=source_pages, \n",
+    "            legend_field='species',\n",
+    "            size=20, \n",
+    "            marker=factor_mark(field_name='species', markers=MARKERS, factors=SPECIES),\n",
+    "            color=factor_cmap(field_name='species', palette='Category10_3', factors=SPECIES))\n",
+    "\n",
+    "# add hover-over tooltip\n",
+    "fig.add_tools(HoverTool(\n",
+    "    tooltips=[\n",
+    "        (\"label\", '@species'),\n",
+    "        (\"(x,y)\", '($x, $y)')],\n",
+    "    mode=\"mouse\", \n",
+    "    point_policy=\"follow_mouse\"\n",
+    "))\n",
+    "\n",
+    "show(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![bokeh plot](https://htmlpreview.github.io/?https://github.com/JustGlowing/minisom/blob/master/examples/resulting_images/som_seed_hex.html)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/MovieCovers.ipynb b/examples/MovieCovers.ipynb
new file mode 100644
index 0000000..3496523
--- /dev/null
+++ b/examples/MovieCovers.ipynb
@@ -0,0 +1,127 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(0, '../')\n",
+    "%load_ext autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import imageio\n",
+    "from glob import glob\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "# covers of the top 100 movies on www.imdb.com/chart/top the 13th of August 2019\n",
+    "# images downloaded from www.themoviedb.org\n",
+    "data = []\n",
+    "all_covers = glob('movie_covers/*.jpg')\n",
+    "for cover_jpg in all_covers:\n",
+    "    cover = imageio.imread(cover_jpg)\n",
+    "    data.append(cover.reshape(np.prod(cover.shape)))\n",
+    "    \n",
+    "original_shape = imageio.imread(all_covers[0]).shape\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "data = scaler.fit_transform(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from minisom import MiniSom\n",
+    "\n",
+    "w = 10\n",
+    "h = 10\n",
+    "som = MiniSom(h, w, len(data[0]), learning_rate=0.5,\n",
+    "              sigma=3, neighborhood_function='triangle')\n",
+    "\n",
+    "som.train_random(data, 2500, verbose=True)\n",
+    "win_map = som.win_map(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "som.activation_response(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.axes_grid1 import ImageGrid\n",
+    "%matplotlib inline\n",
+    "\n",
+    "fig = plt.figure(figsize=(30, 20))\n",
+    "grid = ImageGrid(fig, 111,\n",
+    "                 nrows_ncols=(h, w), axes_pad=0)\n",
+    "\n",
+    "def place_image(i, img):\n",
+    "    img = (scaler.inverse_transform(img)).astype(int)\n",
+    "    grid[i].imshow(img.reshape(original_shape))\n",
+    "    grid[i].axis('off')\n",
+    "\n",
+    "to_fill = []\n",
+    "collided = []\n",
+    "\n",
+    "for i in range(w*h):\n",
+    "    position = np.unravel_index(i, (h, w))\n",
+    "    if position in win_map:\n",
+    "        img = win_map[position][0]\n",
+    "        collided += win_map[position][1:]\n",
+    "        place_image(i, img)\n",
+    "    else:\n",
+    "        to_fill.append(i)\n",
+    "\n",
+    "collided = collided[::-1]\n",
+    "for i in to_fill:\n",
+    "    position = np.unravel_index(i, (h, w))\n",
+    "    img = collided.pop()\n",
+    "    place_image(i, img)\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/OutliersDetection.ipynb b/examples/OutliersDetection.ipynb
new file mode 100644
index 0000000..5f0c6f7
--- /dev/null
+++ b/examples/OutliersDetection.ipynb
@@ -0,0 +1,487 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this post we will see how to perform Outlier Detection using MiniSom."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from minisom import MiniSom\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from sklearn.datasets import make_blobs\n",
+    "from sklearn.preprocessing import scale"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First let's create a datast with two clusters of data a 35% percento of outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "outliers_percentage = 0.35\n",
+    "inliers = 300\n",
+    "outliers = int(inliers * outliers_percentage)\n",
+    "\n",
+    "\n",
+    "data = make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[.3, .3],\n",
+    "                  n_samples=inliers, random_state=0)[0]\n",
+    "\n",
+    "data = scale(data)\n",
+    "data = np.concatenate([data, \n",
+    "                       (np.random.rand(outliers, 2)-.5)*4.])\n",
+    "\n",
+    "plt.figure(figsize=(8, 8))\n",
+    "plt.scatter(data[:, 0], data[:, 1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What we expect from a good outlier algorithm is that all the samples far away from the two main clusters are labeled as outliers. This can be obtained considering as outliers the samples with a high quantization error.\n",
+    "\n",
+    "To test this idea we have to 1) train a SOM, 2) compute the quantization error, 3) set a treshold for the quantization error:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " [   0 / 100 ]   0% - ? it/s\r",
+      " [   0 / 100 ]   0% - ? it/s\r",
+      " [   1 / 100 ]   1% - 0:00:00 left \r",
+      " [   2 / 100 ]   2% - 0:00:00 left \r",
+      " [   3 / 100 ]   3% - 0:00:00 left \r",
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+      " [   5 / 100 ]   5% - 0:00:00 left \r",
+      " [   6 / 100 ]   6% - 0:00:00 left \r",
+      " [   7 / 100 ]   7% - 0:00:00 left \r",
+      " [   8 / 100 ]   8% - 0:00:00 left \r",
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+      " quantization error: 0.4655965288224112\n",
+      "Error treshold: 0.3430036362711185\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/giuseppe.vettigli/venv3/lib/python3.8/site-packages/minisom.py:154: UserWarning: Warning: sigma is too high for the dimension of the map.\n",
+      "  warn('Warning: sigma is too high for the dimension of the map.')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'frequency')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "som = MiniSom(2, 1, data.shape[1], sigma=1, learning_rate=0.5,\n",
+    "              neighborhood_function='triangle', random_seed=10)\n",
+    "\n",
+    "\n",
+    "som.train(data, 100, random_order=False, verbose=True)  # random training\n",
+    "\n",
+    "quantization_errors = np.linalg.norm(som.quantization(data) - data, axis=1)\n",
+    "error_treshold = np.percentile(quantization_errors, \n",
+    "                               100*(1-outliers_percentage)+5)\n",
+    "\n",
+    "print('Error treshold:', error_treshold)\n",
+    "\n",
+    "is_outlier = quantization_errors > error_treshold\n",
+    "\n",
+    "plt.hist(quantization_errors)\n",
+    "plt.axvline(error_treshold, color='k', linestyle='--')\n",
+    "plt.xlabel('error')\n",
+    "plt.ylabel('frequency')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This histogram shows the frequency of the quantization error. The dashed line represents the treshold picked to label the outliers. We can see that most of the samples have a low quantization error and the errors higher than the treshold are much more rare. (Notice that we were able to set a good treshold as we knew the percentage of outliers in our data. This is usually a parameter to tune experimentally.)\n",
+    "\n",
+    "We are now ready to plot our dataset again highlighting the outliers with a different color:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 8))\n",
+    "plt.scatter(data[~is_outlier, 0], data[~is_outlier, 1],\n",
+    "            label='inlier')\n",
+    "plt.scatter(data[is_outlier, 0], data[is_outlier, 1],\n",
+    "            label='outlier')\n",
+    "plt.legend()\n",
+    "plt.savefig('resulting_images/som_outliers_detection.png')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also try the same on a different dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      " quantization error: 0.3696783769236724\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_circles\n",
+    "data = make_circles(noise=.1, n_samples=inliers, random_state=0)[0]\n",
+    "data = scale(data)\n",
+    "data = np.concatenate([data, \n",
+    "                       (np.random.rand(outliers, 2)-.5)*4.])\n",
+    "\n",
+    "\n",
+    "som = MiniSom(5, 5, data.shape[1], sigma=1, learning_rate=0.5,\n",
+    "              neighborhood_function='triangle', random_seed=10)\n",
+    "\n",
+    "\n",
+    "som.train_batch(data, 100, verbose=True)  \n",
+    "quantization_errors = np.linalg.norm(som.quantization(data) - data, axis=1)\n",
+    "error_treshold = np.percentile(quantization_errors, \n",
+    "                               100*(1-outliers_percentage)+5)\n",
+    "is_outlier = quantization_errors > error_treshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 8))\n",
+    "plt.scatter(data[~is_outlier, 0], data[~is_outlier, 1],\n",
+    "            label='inlier')\n",
+    "plt.scatter(data[is_outlier, 0], data[is_outlier, 1],\n",
+    "            label='outlier')\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.savefig('resulting_images/som_outliers_detection_circle.png')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/PoemsAnalysis.ipynb b/examples/PoemsAnalysis.ipynb
new file mode 100644
index 0000000..8721d2e
--- /dev/null
+++ b/examples/PoemsAnalysis.ipynb
@@ -0,0 +1,695 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Poems analysis\n",
+    "----\n",
+    "\n",
+    "In this notebook we will use Minisom to cluster poems from three different authors.\n",
+    "\n",
+    "Requirements:\n",
+    "- Glove vectors, https://nlp.stanford.edu/projects/glove/ glove.6B.50d.txt\n",
+    "- Beautiful soup\n",
+    "- An internet connection as the poems will be downlaoded from www.poemhunter.com"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Retrieving the poems from poemhunter.com\n",
+    "----\n",
+    "\n",
+    "***Warning***: this may take a while."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from urllib.request import urlopen\n",
+    "from bs4 import BeautifulSoup\n",
+    "\n",
+    "#-------------------------------------------------------------------------------\n",
+    "\n",
+    "def scrape_poem(poem_url):\n",
+    "    poem_page = urlopen(poem_url).read()\n",
+    "    soup = BeautifulSoup(poem_page)\n",
+    "    poem = ''\n",
+    "    poem_string = soup.find_all(\"div\", \n",
+    "                                {\"class\": \"KonaBody\" })[0].find_all('p')[0]\n",
+    "    poem_string = str(poem_string)[3:-4].replace('<br/>', ' ')\n",
+    "    return poem_string\n",
+    "\n",
+    "def scrape_poems_index(poems_index_url):\n",
+    "    poems_index = urlopen(poems_index_url).read()    \n",
+    "    soup = BeautifulSoup(poems_index)\n",
+    "    pages = soup.find_all(\"div\", {\"class\": \"pgbluev1\"})\n",
+    "    if len(pages) == 0:\n",
+    "        return get_all_links(soup)\n",
+    "    \n",
+    "    pages = pages[0].find_all('a')\n",
+    "    \n",
+    "    result = {}\n",
+    "    cnt = 0\n",
+    "    for page in pages:\n",
+    "        page_link = 'https://www.poemhunter.com/'+page['href']\n",
+    "        page_soup = BeautifulSoup(urlopen(page_link))\n",
+    "        result.update(get_all_links(page_soup))\n",
+    "    return result\n",
+    "\n",
+    "def get_all_links(page_soup):\n",
+    "    result = {}    \n",
+    "    for link in page_soup.find_all(\"p\", {\"class\": \"cl333\"}):\n",
+    "        link = link.find_all('a')[0]\n",
+    "        result[link['title']] = 'https://www.poemhunter.com/'+link['href']\n",
+    "    return result\n",
+    "\n",
+    "def get_poems(poems_index, max_poems=None):\n",
+    "    poems = {}\n",
+    "    for i, (title, poem_url) in enumerate(poems_index.items()):\n",
+    "        print('fetching', title, '...')\n",
+    "        try:\n",
+    "            poems[title] = scrape_poem(poem_url)\n",
+    "            print('OK')\n",
+    "        except:\n",
+    "            print('impossible to fetch')\n",
+    "        if i == max_poems-1:\n",
+    "            return poems\n",
+    "    return poems"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "poems_index_neruda = scrape_poems_index('https://www.poemhunter.com/pablo-neruda/poems/')\n",
+    "poems_index_bukowski = scrape_poems_index('https://www.poemhunter.com/charles-bukowski/poems/')\n",
+    "poems_index_poe = scrape_poems_index('https://www.poemhunter.com/edgar-allan-poe/poems/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fetching Still Another Day: XVII/Men poem ...\n",
+      "OK\n",
+      "fetching Still Another Day: I poem ...\n",
+      "OK\n",
+      "fetching Ode to Hope poem ...\n",
+      "OK\n",
+      "fetching Unity poem ...\n",
+      "OK\n",
+      "fetching Epithalamium poem ...\n",
+      "OK\n",
+      "fetching Ode To Ironing poem ...\n",
+      "OK\n",
+      "fetching Ode To The Cat poem ...\n",
+      "OK\n",
+      "fetching Ode To Age poem ...\n",
+      "OK\n",
+      "fetching The Men poem ...\n",
+      "OK\n",
+      "fetching Lxxxiv From: ‘cien Sonetos De Amor’ poem ...\n",
+      "OK\n",
+      "fetching Come With Me, I Said, And No One Knew (VII) poem ...\n",
+      "OK\n",
+      "fetching The Portrait In The Rock poem ...\n",
+      "OK\n",
+      "fetching Walking Around (Original Spanish) poem ...\n",
+      "OK\n",
+      "fetching Sonnet Xiii:The Light That Rises From Your Feet To Your Hair poem ...\n",
+      "OK\n",
+      "fetching Waltz poem ...\n",
+      "OK\n",
+      "fetching Triangles poem ...\n",
+      "OK\n",
+      "fetching Soneto Xvii poem ...\n",
+      "OK\n",
+      "fetching The Old Women Of The Ocean poem ...\n",
+      "OK\n",
+      "fetching Oda Al Tomate poem ...\n",
+      "OK\n",
+      "fetching Sonnet Ix: There Where The Waves Shatter poem ...\n",
+      "OK\n",
+      "fetching The House Of Odes poem ...\n",
+      "OK\n",
+      "fetching Ode To Clothes poem ...\n",
+      "OK\n",
+      "fetching Lone Gentleman poem ...\n",
+      "OK\n",
+      "fetching Leave Me A Place Underground poem ...\n",
+      "OK\n",
+      "fetching Poor Creatures poem ...\n",
+      "OK\n",
+      "fetching The Eighth Of September poem ...\n",
+      "OK\n",
+      "fetching Gautama Christ poem ...\n",
+      "OK\n",
+      "fetching Luminous Mind, Bright Devil poem ...\n",
+      "OK\n",
+      "fetching Finale poem ...\n",
+      "OK\n",
+      "fetching Sonnet Viii: If Your Eyes Were Not  The Color Of The Moon poem ...\n",
+      "OK\n",
+      "fetching Potter poem ...\n",
+      "OK\n",
+      "fetching From The Heights Of Maccho Picchu poem ...\n",
+      "OK\n",
+      "fetching Song Of Despair poem ...\n",
+      "OK\n",
+      "fetching The Tree Is Here, Still, In Pure Stone poem ...\n",
+      "OK\n",
+      "fetching Lost In The Forest poem ...\n",
+      "OK\n",
+      "fetching What Spain Was Like poem ...\n",
+      "OK\n",
+      "fetching Sonnet Lxxiii: Maybe You'Ll Remember poem ...\n",
+      "OK\n",
+      "fetching Ode To Tomatoes poem ...\n",
+      "OK\n",
+      "fetching The United Fruit Co. poem ...\n",
+      "OK\n",
+      "fetching La Muerta poem ...\n",
+      "OK\n",
+      "fetching The Insect poem ...\n",
+      "OK\n",
+      "fetching The People poem ...\n",
+      "OK\n",
+      "fetching Sonnet Xlii: I Hunt For A Sign Of You poem ...\n",
+      "OK\n",
+      "fetching The Fear poem ...\n",
+      "OK\n",
+      "fetching Sonnet Xcv:Who Ever Desired Each Other As We Do poem ...\n",
+      "OK\n",
+      "fetching The Fickle One poem ...\n",
+      "OK\n",
+      "fetching The Queen poem ...\n",
+      "OK\n",
+      "fetching Love, We'Re Going Home Now poem ...\n",
+      "OK\n",
+      "fetching Sonnet Lxxxi: Rest With Your Dream Inside My Dream poem ...\n",
+      "OK\n",
+      "fetching Poesia poem ...\n",
+      "OK\n",
+      "fetching So That You Will Hear Me poem ...\n",
+      "OK\n",
+      "fetching Castro Alves From Brazil poem ...\n",
+      "OK\n",
+      "fetching Entrance Of The Rivers poem ...\n",
+      "OK\n",
+      "fetching Tell Me, Is The Rose Naked? poem ...\n",
+      "OK\n",
+      "fetching Ode To Bird Watching poem ...\n",
+      "OK\n",
+      "fetching Algunas Bestias poem ...\n",
+      "OK\n",
+      "fetching Tie Your Heart At Night To Mine, Love, poem ...\n",
+      "OK\n",
+      "fetching Enigma With Flower poem ...\n",
+      "OK\n",
+      "fetching Poet's Obligation poem ...\n",
+      "OK\n",
+      "fetching Sonnet Xxvii: Naked You Are As Simple As One Of Your Hands poem ...\n",
+      "OK\n",
+      "fetching A 340 Dollar Horse And A Hundred Dollar Whore poem ...\n",
+      "OK\n",
+      "fetching Beasts Bounding Through Time poem ...\n",
+      "OK\n",
+      "fetching For The Foxes poem ...\n",
+      "OK\n",
+      "fetching The Great Escape poem ...\n",
+      "OK\n",
+      "fetching On The Fire Suicides Of The Buddhists poem ...\n",
+      "OK\n",
+      "fetching My Cats poem ...\n",
+      "OK\n",
+      "fetching No help for that poem ...\n",
+      "OK\n",
+      "fetching The Last Days Of The Suicide Kid poem ...\n",
+      "OK\n",
+      "fetching Trollius And Trellises poem ...\n",
+      "OK\n",
+      "fetching Hell Is A Lonely Place poem ...\n",
+      "OK\n",
+      "fetching The Trash Men poem ...\n",
+      "OK\n",
+      "fetching German poem ...\n",
+      "OK\n",
+      "fetching I Am Visited By An Editor And A Poet poem ...\n",
+      "OK\n",
+      "fetching Goading The Muse poem ...\n",
+      "OK\n",
+      "fetching So You Want To Be A Writer poem ...\n",
+      "OK\n",
+      "fetching New Mexico poem ...\n",
+      "OK\n",
+      "fetching air and light and time and space poem ...\n",
+      "OK\n",
+      "fetching The Japanese Wife poem ...\n",
+      "OK\n",
+      "fetching Gas poem ...\n",
+      "OK\n",
+      "fetching Marina poem ...\n",
+      "OK\n",
+      "fetching The Retreat poem ...\n",
+      "OK\n",
+      "fetching The Blackbirds Are Rough Today poem ...\n",
+      "OK\n",
+      "fetching Rain Or Shine poem ...\n",
+      "OK\n",
+      "fetching Magical Mystery Tour poem ...\n",
+      "OK\n",
+      "fetching This poem ...\n",
+      "OK\n",
+      "fetching One Thirty-Six A.M. poem ...\n",
+      "OK\n",
+      "fetching The German Hotel poem ...\n",
+      "OK\n",
+      "fetching Out Of The Arm Of One Love... poem ...\n",
+      "OK\n",
+      "fetching Now poem ...\n",
+      "OK\n",
+      "fetching The Worst And The Best poem ...\n",
+      "OK\n",
+      "fetching Hemingway Never Did This poem ...\n",
+      "OK\n",
+      "fetching True Story poem ...\n",
+      "OK\n",
+      "fetching Somebody poem ...\n",
+      "OK\n",
+      "fetching Revolt In The Ranks poem ...\n",
+      "OK\n",
+      "fetching Mama poem ...\n",
+      "OK\n",
+      "fetching The Sun Wields Mercy poem ...\n",
+      "OK\n",
+      "fetching The Great Slob poem ...\n",
+      "OK\n",
+      "fetching The Meek Shall Inherit The Earth poem ...\n",
+      "OK\n",
+      "fetching The Laughing Heart poem ...\n",
+      "OK\n",
+      "fetching Small Conversation In The Afternoon With John Fante poem ...\n",
+      "OK\n",
+      "fetching Hooray Say The Roses poem ...\n",
+      "OK\n",
+      "fetching What Can We Do? poem ...\n",
+      "OK\n",
+      "fetching Layover poem ...\n",
+      "OK\n",
+      "fetching Poetry Reading poem ...\n",
+      "OK\n",
+      "fetching On Going Back To The Street After Viewing An Art Show poem ...\n",
+      "OK\n",
+      "fetching The House poem ...\n",
+      "OK\n",
+      "fetching No. 6 poem ...\n",
+      "OK\n",
+      "fetching Something For The Touts, The Nuns, The Grocery Clerks, And You . . . poem ...\n",
+      "OK\n",
+      "fetching Show Biz poem ...\n",
+      "OK\n",
+      "fetching Short Order poem ...\n",
+      "OK\n",
+      "fetching The Shower poem ...\n",
+      "OK\n",
+      "fetching Luck poem ...\n",
+      "OK\n",
+      "fetching Crucifix In A Deathhand poem ...\n",
+      "OK\n",
+      "fetching Three Oranges poem ...\n",
+      "OK\n",
+      "fetching Sleep poem ...\n",
+      "OK\n",
+      "fetching The Night I Was Going To Die poem ...\n",
+      "OK\n",
+      "fetching The Shoelace poem ...\n",
+      "OK\n",
+      "fetching These Things poem ...\n",
+      "OK\n",
+      "fetching Trashcan Lives poem ...\n",
+      "OK\n",
+      "fetching Splash poem ...\n",
+      "OK\n",
+      "fetching Ulalume poem ...\n",
+      "OK\n",
+      "fetching To Isadore poem ...\n",
+      "OK\n",
+      "fetching The City Of Sin poem ...\n",
+      "OK\n",
+      "fetching To Marie Louise (Shew) poem ...\n",
+      "OK\n",
+      "fetching The Village Street poem ...\n",
+      "OK\n",
+      "fetching Impromptu - To Kate Carol poem ...\n",
+      "OK\n",
+      "fetching The Bells - A Collaboration poem ...\n",
+      "OK\n",
+      "fetching The Divine Right Of Kings poem ...\n",
+      "OK\n",
+      "fetching To M-- poem ...\n",
+      "OK\n",
+      "fetching Stanzas poem ...\n",
+      "OK\n",
+      "fetching To -- -- poem ...\n",
+      "OK\n",
+      "fetching To M.L.S. poem ...\n",
+      "OK\n",
+      "fetching To -- poem ...\n",
+      "OK\n",
+      "fetching Sonnet- To Zante poem ...\n",
+      "OK\n",
+      "fetching To F--S S. O--D poem ...\n",
+      "OK\n",
+      "fetching To F-- poem ...\n",
+      "OK\n",
+      "fetching In Youth I Have Known One poem ...\n",
+      "OK\n",
+      "fetching Israfel poem ...\n",
+      "OK\n",
+      "fetching The Forest Reverie poem ...\n",
+      "OK\n",
+      "fetching Sancta Maria poem ...\n",
+      "OK\n",
+      "fetching Song poem ...\n",
+      "OK\n",
+      "fetching To One Departed poem ...\n",
+      "OK\n",
+      "fetching To Helen - 1848 poem ...\n",
+      "OK\n",
+      "fetching Epigram For Wall Street poem ...\n",
+      "OK\n",
+      "fetching Sonnet- To Science poem ...\n",
+      "OK\n",
+      "fetching Hymn poem ...\n",
+      "OK\n",
+      "fetching Hymn To Aristogeiton And Harmodius poem ...\n",
+      "OK\n",
+      "fetching In The Greenest Of Our Valleys poem ...\n",
+      "OK\n",
+      "fetching Serenade poem ...\n",
+      "OK\n",
+      "fetching To -- -- --. Ulalume: A Ballad poem ...\n",
+      "OK\n",
+      "fetching Tamerlane poem ...\n",
+      "OK\n",
+      "fetching The Valley Of Unrest poem ...\n",
+      "OK\n",
+      "fetching To One In Paradise poem ...\n",
+      "OK\n",
+      "fetching Sonnet- Silence poem ...\n",
+      "OK\n",
+      "fetching Enigma poem ...\n",
+      "OK\n",
+      "fetching The Lake poem ...\n",
+      "OK\n",
+      "fetching The Coliseum poem ...\n",
+      "OK\n",
+      "fetching The Sleeper poem ...\n",
+      "OK\n",
+      "fetching The Happiest Day, The Happiest Hour poem ...\n",
+      "OK\n",
+      "fetching For Annie poem ...\n",
+      "OK\n",
+      "fetching The Haunted Palace poem ...\n",
+      "OK\n",
+      "fetching A Paean poem ...\n",
+      "OK\n",
+      "fetching To My Mother poem ...\n",
+      "OK\n",
+      "fetching An Acrostic poem ...\n",
+      "OK\n",
+      "fetching The Conqueror Worm poem ...\n",
+      "OK\n",
+      "fetching To Helen poem ...\n",
+      "OK\n",
+      "fetching Imitation poem ...\n",
+      "OK\n",
+      "fetching Al Aaraaf poem ...\n",
+      "OK\n",
+      "fetching Fairy-Land poem ...\n",
+      "OK\n",
+      "fetching Romance poem ...\n",
+      "OK\n",
+      "fetching Lenore poem ...\n",
+      "OK\n",
+      "fetching Spirits Of The Dead poem ...\n",
+      "OK\n",
+      "fetching Dreams poem ...\n",
+      "OK\n",
+      "fetching The City In The Sea poem ...\n",
+      "OK\n",
+      "fetching A Valentine poem ...\n",
+      "OK\n",
+      "fetching An Enigma poem ...\n",
+      "OK\n",
+      "fetching Eulalie poem ...\n",
+      "OK\n",
+      "fetching Elizabeth poem ...\n",
+      "OK\n",
+      "fetching The Bells poem ...\n",
+      "OK\n",
+      "fetching Dreamland poem ...\n",
+      "OK\n"
+     ]
+    }
+   ],
+   "source": [
+    "poems_neruda = get_poems(poems_index_neruda, max_poems=60)\n",
+    "poems_bukowski = get_poems(poems_index_bukowski, max_poems=60)\n",
+    "poems_poe = get_poems(poems_index_poe, max_poems=60)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_poems = [poems_neruda, poems_bukowski, poems_poe]\n",
+    "titles = np.concatenate([list(title_list.keys()) for title_list in all_poems])\n",
+    "y = np.concatenate([[i]*len(p) for i, p in enumerate(all_poems)])\n",
+    "all_poems = np.concatenate([list(p.values()) for p in all_poems])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Preprocessing of the poems\n",
+    "---\n",
+    "\n",
+    "The following operations are applied:\n",
+    "\n",
+    "1. stopwords removal\n",
+    "2. tokenization\n",
+    "3. conversion in Glove vectors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from string import punctuation\n",
+    "import stop_words\n",
+    "stopwords = stop_words.get_stop_words('english')\n",
+    "\n",
+    "def tokenize_poem(poem):\n",
+    "    poem = poem.lower().replace('\\n', ' ')\n",
+    "    for sign in punctuation:\n",
+    "        poem = poem.replace(sign, '')\n",
+    "    tokens = poem.split()\n",
+    "    tokens = [t for t in tokens if t not in stopwords and t != '']\n",
+    "    return tokens\n",
+    "\n",
+    "tokenized_poems = [tokenize_poem(poem) for poem in all_poems]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gimme_glove():\n",
+    "    with open('glove.6B/glove.6B.50d.txt', encoding='utf-8') as glove_raw:\n",
+    "        for line in glove_raw.readlines():\n",
+    "            splitted = line.split(' ')\n",
+    "            yield splitted[0], np.array(splitted[1:], dtype=np.float)\n",
+    "            \n",
+    "glove = {w: x for w, x in gimme_glove()}\n",
+    "\n",
+    "def closest_word(in_vector, top_n=1):\n",
+    "    vectors = glove.values()\n",
+    "    idx = np.argsort([np.linalg.norm(vec-in_vector) for vec in vectors])\n",
+    "    return [glove.keys()[i] for i in idx[:top_n]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def poem_to_vec(tokens):\n",
+    "    words = [w for w in np.unique(tokens) if w in glove]\n",
+    "    return np.array([glove[w] for w in words])\n",
+    "\n",
+    "W = [poem_to_vec(tokenized).mean(axis=0) for tokenized in tokenized_poems]\n",
+    "W = np.array(W)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Running minisom and visualizing the result\n",
+    "----\n",
+    "***Warning***: This may take a while."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " [ 90000 / 90000 ] 100% - 0:00:00 left \n",
+      " quantization error: 0.28209285828168446\n"
+     ]
+    }
+   ],
+   "source": [
+    "from minisom import MiniSom\n",
+    "map_dim = 16\n",
+    "som = MiniSom(map_dim, map_dim, 50, sigma=1.0, random_seed=1)\n",
+    "#som.random_weights_init(W)\n",
+    "som.train_batch(W, num_iteration=len(W)*500, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "author_to_color = {0: 'chocolate', # neruda\n",
+    "                   1: 'steelblue', # bukowski\n",
+    "                   2: 'dimgray'}   # poe \n",
+    "color = [author_to_color[yy] for yy in y]\n",
+    "\n",
+    "from matplotlib.patches import Patch\n",
+    "legend_elements = [Patch(facecolor=author_to_color[0], edgecolor='white',label='neruda'),\n",
+    "                   Patch(facecolor=author_to_color[1], edgecolor='white',label='bukowski'),\n",
+    "                   Patch(facecolor=author_to_color[2], edgecolor='white',label='poe'),]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 20))\n",
+    "texts = []\n",
+    "for i, (t, c, vec) in enumerate(zip(titles, color, W)):\n",
+    "    winnin_position = som.winner(vec)\n",
+    "    texts.append(plt.text(winnin_position[0], \n",
+    "                 winnin_position[1]+np.random.rand()*.9, \n",
+    "                 t[:-5],\n",
+    "                 color=c))\n",
+    "\n",
+    "plt.legend(handles=legend_elements, loc='upper left')\n",
+    "plt.xticks(range(map_dim))\n",
+    "plt.yticks(range(map_dim))\n",
+    "plt.grid()\n",
+    "plt.xlim([0, map_dim])\n",
+    "plt.ylim([0, map_dim])\n",
+    "plt.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/TimeSeries.ipynb b/examples/TimeSeries.ipynb
new file mode 100644
index 0000000..8576083
--- /dev/null
+++ b/examples/TimeSeries.ipynb
@@ -0,0 +1,90 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Requirements: data from https://archive.ics.uci.edu/ml/machine-learning-databases/00396/Sales_Transactions_Dataset_Weekly.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(0, '../')\n",
+    "\n",
+    "from minisom import MiniSom\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.gridspec import GridSpec\n",
+    "%matplotlib inline\n",
+    "\n",
+    "%load_ext autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%autoreload 2\n",
+    "import pandas as pd\n",
+    "# data from\n",
+    "# https://archive.ics.uci.edu/ml/datasets/Sales_Transactions_Dataset_Weekly\n",
+    "sales_transaction = pd.read_csv('Sales_Transactions_Dataset_Weekly.csv')\n",
+    "data = sales_transaction[[f'Normalized {i}' for i in range(52)]].values\n",
+    "som = MiniSom(8, 8, data.shape[1], sigma=2., learning_rate=0.5, \n",
+    "              neighborhood_function='gaussian', random_seed=10)\n",
+    "som.pca_weights_init(data)\n",
+    "print(\"Training...\")\n",
+    "som.train_batch(data, 50000, verbose=True)  # random training\n",
+    "print(\"\\n...ready!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "win_map = som.win_map(data)\n",
+    "\n",
+    "plt.figure(figsize=(16, 16))\n",
+    "the_grid = GridSpec(8, 8)\n",
+    "for position in win_map.keys():\n",
+    "    plt.subplot(the_grid[6-position[1], position[0]])\n",
+    "    plt.plot(np.min(win_map[position], axis=0), color='gray', alpha=.5)\n",
+    "    plt.plot(np.mean(win_map[position], axis=0))\n",
+    "    plt.plot(np.max(win_map[position], axis=0), color='gray', alpha=.5)\n",
+    "plt.savefig('resulting_images/time_series.png')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/TopicModeling.ipynb b/examples/TopicModeling.ipynb
new file mode 100644
index 0000000..b1dd8d2
--- /dev/null
+++ b/examples/TopicModeling.ipynb
@@ -0,0 +1,135 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example we will see how to perform topic extraction using MiniSom. The goal is to extract the main topics (represented as a set of words) that occur in a collection of documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "from minisom import MiniSom\n",
+    "from sklearn.datasets import fetch_20newsgroups\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The colloction of documents that we will work with is the famous `20newsgroups` dataset. It contains more than 10000 newsgroups posts. We will download the dataset using sklearn and will transform the textual documents into a matrix `D` where each row represents a post using <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html#sklearn.feature_extraction.text.TfidfTransformer\">TF-IDF representation</a>:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = fetch_20newsgroups(shuffle=True, random_state=1,\n",
+    "                             remove=('headers', 'footers', 'quotes'))\n",
+    "documents = dataset.data\n",
+    "\n",
+    "no_features = 1000\n",
+    "\n",
+    "tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=2,\n",
+    "                                   max_features=no_features,\n",
+    "                                   stop_words='english')\n",
+    "tfidf = tfidf_vectorizer.fit_transform(documents)\n",
+    "tfidf_feature_names = tfidf_vectorizer.get_feature_names()\n",
+    "D = tfidf.todense().tolist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we have to train a SOM that clusters the documents, the total number of neurons in the SOM will be also the number of topics to extract:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "n_neurons = 2\n",
+    "m_neurons = 4\n",
+    "som = MiniSom(n_neurons, m_neurons, no_features)\n",
+    "som.pca_weights_init(D)\n",
+    "som.train(D, 40000, random_order=False, verbose=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will consider as topic the list of first `top_keywords` associated with the biggest weights of each neuron. With the following for loop we will inspect all the weights and recover the words associated with the weights using the feature names saved by the TfidfVectorizer:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Topic 1 : steve low reported truth want knowledge shall right people don\n",
+      "Topic 2 : use don just armenians turkey people like os turkish armenia\n",
+      "Topic 3 : used help new buy x11 mail info thanks appreciated advance\n",
+      "Topic 4 : read study event ideas writing religious learn ed religion alt\n",
+      "Topic 5 : learn files point includes board email sound games home pc\n",
+      "Topic 6 : light matter expect final deleted sure administration clinton stuff like\n",
+      "Topic 7 : words generally clinton lots dod machines encryption like money jesus\n",
+      "Topic 8 : report use cards clipper cable air 17 space 19 mode\n"
+     ]
+    }
+   ],
+   "source": [
+    "top_keywords = 10\n",
+    "\n",
+    "weights = som.get_weights()\n",
+    "cnt = 1\n",
+    "for i in range(n_neurons):\n",
+    "    for j in range(m_neurons):\n",
+    "        keywords_idx = np.argsort(weights[i,j,:])[-top_keywords:]\n",
+    "        keywords = ' '.join([tfidf_feature_names[k] for k in keywords_idx])\n",
+    "        print('Topic', cnt, ':', keywords)\n",
+    "        cnt += 1"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/TravellingSalesmanProblem.ipynb b/examples/TravellingSalesmanProblem.ipynb
new file mode 100644
index 0000000..cf622ac
--- /dev/null
+++ b/examples/TravellingSalesmanProblem.ipynb
@@ -0,0 +1,106 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more details about the problems and an introduction to the idea behind the code see: https://glowingpython.blogspot.com/2020/06/solving-travelling-salesman-problem.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from minisom import MiniSom\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.RandomState(10)\n",
+    "N_points = 40\n",
+    "N_neurons = N_points*2\n",
+    "t = np.linspace(0, np.pi*2, N_points)\n",
+    "x = np.cos(t)+(np.random.rand(N_points)-.5)*.3\n",
+    "y = np.sin(t)+(np.random.rand(N_points)-.5)*.3\n",
+    "\n",
+    "som = MiniSom(1, N_neurons, 2, sigma=8, learning_rate=.4,\n",
+    "              neighborhood_function='gaussian', random_seed=0)\n",
+    "points = np.array([x,y]).T\n",
+    "som.random_weights_init(points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x648 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 9))\n",
+    "for i, iterations in enumerate(range(5, 61, 5)):\n",
+    "    som.train(points, iterations, verbose=False, random_order=False)\n",
+    "    plt.subplot(3, 4, i+1)\n",
+    "    plt.scatter(x,y)\n",
+    "    visit_order = np.argsort([som.winner(p)[1] for p in points])\n",
+    "    visit_order = np.concatenate((visit_order, [visit_order[0]]))\n",
+    "    plt.plot(points[visit_order][:,0], points[visit_order][:,1])\n",
+    "    plt.title(\"iterations: {i};\\nerror: {e:.3f}\".format(i=iterations, \n",
+    "                                                        e=som.quantization_error(points)))\n",
+    "    plt.xticks([])\n",
+    "    plt.yticks([])\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/democracy_index.csv b/examples/democracy_index.csv
new file mode 100644
index 0000000..bc080fc
--- /dev/null
+++ b/examples/democracy_index.csv
@@ -0,0 +1,168 @@
+,rank,country,democracy_index,electoral_processand_pluralism,functioning_of_government,political_participation,political_culture,civil_liberties,category
+0,1,Norway,9.87,10.00,9.64,10.00,10.00,9.71,Full democracy
+1,2,Iceland,9.58,10.00,9.29,8.89,10.00,9.71,Full democracy
+2,3,Sweden,9.39,9.58,9.64,8.33,10.00,9.41,Full democracy
+3,4,New Zealand,9.26,10.00,9.29,8.89,8.13,10.00,Full democracy
+4,5,Denmark,9.22,10.00,9.29,8.33,9.38,9.12,Full democracy
+5,=6,Ireland,9.15,9.58,7.86,8.33,10.00,10.00,Full democracy
+6,=6,Canada,9.15,9.58,9.64,7.78,8.75,10.00,Full democracy
+7,8,Finland,9.14,10.00,8.93,8.33,8.75,9.71,Full democracy
+8,9,Australia,9.09,10.00,8.93,7.78,8.75,10.00,Full democracy
+9,10,Switzerland,9.03,9.58,9.29,7.78,9.38,9.12,Full democracy
+10,11,Netherlands,8.89,9.58,9.29,8.33,8.13,9.12,Full democracy
+11,12,Luxembourg,8.81,10.00,8.93,6.67,8.75,9.71,Full democracy
+12,13,Germany,8.68,9.58,8.57,8.33,7.50,9.41,Full democracy
+13,14,United Kingdom,8.53,9.58,7.50,8.33,8.13,9.12,Full democracy
+14,15,Uruguay,8.38,10.00,8.57,6.11,7.50,9.71,Full democracy
+15,16,Austria,8.29,9.58,7.86,8.33,6.88,8.82,Full democracy
+16,17,Mauritius,8.22,9.17,8.21,5.56,8.75,9.41,Full democracy
+17,18,Malta,8.21,9.17,8.21,6.11,8.75,8.82,Full democracy
+18,19,Spain,8.08,9.17,7.14,7.78,7.50,8.82,Full democracy
+19,20,Costa Rica,8.07,9.58,7.50,6.67,7.50,9.12,Full democracy
+20,21,South Korea,8.00,9.17,7.86,7.22,7.50,8.24,Flawed democracy
+21,22,Japan,7.99,8.75,8.21,6.67,7.50,8.82,Flawed democracy
+22,=23,Chile,7.97,9.58,8.57,4.44,8.13,9.12,Flawed democracy
+23,=23,Estonia,7.97,9.58,8.21,6.67,6.88,8.53,Flawed democracy
+24,25,United States,7.96,9.17,7.14,7.78,7.50,8.24,Flawed democracy
+25,26,Cape Verde,7.88,9.17,7.86,6.67,6.88,8.82,Flawed democracy
+26,27,Portugal,7.84,9.58,7.50,6.11,6.88,9.12,Flawed democracy
+27,28,Botswana,7.81,9.17,7.14,6.11,7.50,9.12,Flawed democracy
+28,29,France,7.80,9.58,7.50,7.78,5.63,8.53,Flawed democracy
+29,30,Israel,7.79,9.17,7.50,8.89,7.50,5.88,Flawed democracy
+30,31,Belgium,7.78,9.58,8.93,5.00,6.88,8.53,Flawed democracy
+31,32,Republic of China (Taiwan),7.73,9.58,8.21,6.11,5.63,9.12,Flawed democracy
+32,33,Italy,7.71,9.58,6.07,7.78,6.88,8.24,Flawed democracy
+33,34,Czech Republic,7.69,9.58,6.79,6.67,6.88,8.53,Flawed democracy
+34,35,Cyprus,7.59,9.17,6.43,6.67,6.88,8.82,Flawed democracy
+35,=36,Slovenia,7.50,9.58,6.79,6.67,6.25,8.24,Flawed democracy
+36,=36,Lithuania,7.50,9.58,6.43,6.11,6.25,9.12,Flawed democracy
+37,38,Latvia,7.38,9.58,6.07,5.56,6.88,8.82,Flawed democracy
+38,39,Greece,7.29,9.58,5.36,6.11,6.88,8.53,Flawed democracy
+39,40,South Africa,7.24,7.42,7.50,8.33,5.00,7.94,Flawed democracy
+40,41,India,7.23,9.17,6.79,7.22,5.63,7.35,Flawed democracy
+41,42,Timor-Leste,7.19,9.08,6.79,5.56,6.88,7.65,Flawed democracy
+42,43,Trinidad and Tobago,7.16,9.58,7.14,6.11,5.63,7.35,Flawed democracy
+43,44,Slovakia,7.10,9.58,6.79,5.56,5.63,7.94,Flawed democracy
+44,45,Panama,7.05,9.58,6.07,6.67,5.00,7.94,Flawed democracy
+45,46,Bulgaria,7.03,9.17,6.43,7.22,4.38,7.94,Flawed democracy
+46,=47,Argentina,7.02,9.17,5.36,6.11,6.25,8.24,Flawed democracy
+47,=47,Jamaica,7.02,8.75,7.14,4.44,6.25,8.53,Flawed democracy
+48,49,Suriname,6.98,9.17,6.43,6.67,5.00,7.65,Flawed democracy
+49,50,Brazil,6.97,9.58,5.36,6.67,5.00,8.24,Flawed democracy
+50,51,Colombia,6.96,9.17,6.79,5.00,5.63,8.24,Flawed democracy
+51,52,Malaysia,6.88,7.75,7.86,6.67,6.25,5.88,Flawed democracy
+52,53,Philippines,6.71,9.17,5.71,7.22,4.38,7.06,Flawed democracy
+53,=54,Poland,6.67,9.17,6.07,6.11,4.38,7.65,Flawed democracy
+54,=54,Guyana,6.67,9.17,5.71,6.11,5.00,7.35,Flawed democracy
+55,56,Lesotho,6.64,9.17,5.00,6.67,5.63,6.76,Flawed democracy
+56,=57,Ghana,6.63,8.33,5.71,6.67,6.25,6.18,Flawed democracy
+57,=57,Hungary,6.63,8.75,6.07,5.00,6.25,7.06,Flawed democracy
+58,59,Peru,6.60,9.17,5.00,5.56,5.63,7.65,Flawed democracy
+59,60,Croatia,6.57,9.17,6.07,5.56,5.00,7.06,Flawed democracy
+60,61,Dominican Republic,6.54,9.17,5.36,6.11,5.00,7.06,Flawed democracy
+61,62,Mongolia,6.50,9.17,5.71,5.56,5.00,7.06,Flawed democracy
+62,=63,Serbia,6.41,8.25,5.36,6.11,5.00,7.35,Flawed democracy
+63,=63,Tunisia,6.41,6.42,5.71,7.78,6.25,5.88,Flawed democracy
+64,65,Indonesia,6.39,6.92,7.14,6.67,5.63,5.59,Flawed democracy
+65,=66,Singapore,6.38,4.33,7.86,6.11,6.25,7.35,Flawed democracy
+66,=66,Romania,6.38,9.17,5.71,5.00,4.38,7.65,Flawed democracy
+67,68,Ecuador,6.27,8.75,5.36,6.11,4.38,6.76,Flawed democracy
+68,69,Namibia,6.25,5.67,5.36,6.67,5.63,7.94,Flawed democracy
+69,70,Paraguay,6.24,8.75,5.71,5.00,4.38,7.35,Flawed democracy
+70,=71,Sri Lanka,6.19,7.83,5.71,5.00,6.25,6.18,Flawed democracy
+71,=71,Mexico,6.19,8.33,6.07,7.22,3.13,6.18,Flawed democracy
+72,=73,Hong Kong,6.15,3.08,6.07,5.56,7.50,8.53,Flawed democracy
+73,=73,Senegal,6.15,7.50,6.07,4.44,6.25,6.47,Flawed democracy
+74,75,Papua New Guinea,6.03,6.92,6.07,3.89,5.63,7.65,Flawed democracy
+75,76,Albania,5.98,7.00,4.71,5.56,5.00,7.65,Hybrid regime
+76,77,El Salvador,5.96,9.17,4.29,5.56,3.75,7.06,Hybrid regime
+77,78,North Macedonia,5.87,6.50,5.36,6.67,3.75,7.06,Hybrid regime
+78,=79,Moldova,5.85,7.08,4.64,6.11,4.38,7.06,Hybrid regime
+79,=79,Fiji,5.85,6.58,5.36,6.11,5.63,5.59,Hybrid regime
+80,=81,Montenegro,5.74,6.08,5.36,6.11,4.38,6.76,Hybrid regime
+81,=81,Benin,5.74,6.50,5.71,5.00,5.63,5.88,Hybrid regime
+82,83,Bolivia,5.70,7.50,4.64,5.56,3.75,7.06,Hybrid regime
+83,84,Ukraine,5.69,6.17,3.21,6.67,6.25,6.18,Hybrid regime
+84,85,Honduras,5.63,8.50,4.64,4.44,4.38,6.18,Hybrid regime
+85,86,Zambia,5.61,6.17,4.64,3.89,6.88,6.47,Hybrid regime
+86,87,Guatemala,5.60,7.92,5.36,3.89,4.38,6.47,Hybrid regime
+87,88,Bangladesh,5.57,7.83,5.07,5.56,4.38,5.00,Hybrid regime
+88,89,Georgia,5.50,7.83,3.57,6.11,4.38,5.59,Hybrid regime
+89,90,Malawi,5.49,6.58,4.29,4.44,6.25,5.88,Hybrid regime
+90,=91,Tanzania,5.41,7.00,5.00,5.00,5.63,4.41,Hybrid regime
+91,=91,Mali,5.41,7.42,3.93,3.89,5.63,6.18,Hybrid regime
+92,93,Liberia,5.35,7.42,2.57,5.56,5.63,5.59,Hybrid regime
+93,94,Bhutan,5.30,8.75,6.79,2.78,4.38,3.82,Hybrid regime
+94,95,Madagascar,5.22,6.08,3.57,6.11,5.63,4.71,Hybrid regime
+95,96,Uganda,5.20,5.25,3.57,4.44,6.88,5.88,Hybrid regime
+96,97,Nepal,5.18,4.33,5.36,5.00,5.63,5.59,Hybrid regime
+97,=98,Kenya,5.11,3.50,5.36,6.67,5.63,4.41,Hybrid regime
+98,=98,Kyrgyzstan,5.11,6.58,2.93,6.67,4.38,5.00,Hybrid regime
+99,100,Morocco,4.99,5.25,4.64,5.00,5.63,4.41,Hybrid regime
+100,101,Bosnia and Herzegovina,4.98,6.50,2.93,5.56,3.75,6.18,Hybrid regime
+101,102,Haiti,4.91,5.58,2.93,3.89,6.25,5.88,Hybrid regime
+102,103,Armenia,4.79,5.67,4.64,5.56,2.50,5.59,Hybrid regime
+103,104,Burkina Faso,4.75,4.42,4.29,4.44,5.63,5.00,Hybrid regime
+104,105,Sierra Leone,4.66,6.58,1.86,3.33,6.25,5.29,Hybrid regime
+105,=106,Lebanon,4.63,3.92,2.21,6.67,5.63,4.71,Hybrid regime
+106,=106,Thailand,4.63,3.00,4.29,5.00,5.00,5.88,Hybrid regime
+107,108,Nigeria,4.44,6.08,4.64,3.33,3.75,4.41,Hybrid regime
+108,109,Palestine,4.39,3.83,2.14,7.78,4.38,3.82,Hybrid regime
+109,110,Turkey,4.37,4.50,5.00,5.00,5.00,2.35,Hybrid regime
+110,111,Gambia,4.31,4.48,4.29,3.33,5.63,3.82,Hybrid regime
+111,112,Pakistan,4.17,6.08,5.36,2.22,2.50,4.71,Hybrid regime
+112,113,Ivory Coast,4.15,4.83,2.86,3.33,5.63,4.12,Hybrid regime
+113,114,Iraq,4.06,4.75,0.07,6.67,5.00,3.82,Hybrid regime
+114,115,Jordan,3.93,3.58,4.29,3.89,4.38,3.53,Authoritarian
+115,=116,Mozambique,3.85,3.58,2.14,5.00,5.00,3.53,Authoritarian
+116,=116,Kuwait,3.85,3.17,4.29,3.89,4.38,3.53,Authoritarian
+117,118,Myanmar,3.83,3.67,3.93,3.89,5.63,2.06,Authoritarian
+118,119,Mauritania,3.82,3.00,3.57,5.00,3.13,4.41,Authoritarian
+119,120,Niger,3.76,5.25,1.14,3.33,4.38,4.71,Authoritarian
+120,121,Comoros,3.71,4.33,2.21,4.44,3.75,3.82,Authoritarian
+121,122,Nicaragua,3.63,2.67,1.86,3.89,5.63,4.12,Authoritarian
+122,123,Angola,3.62,1.75,2.86,5.56,5.00,2.94,Authoritarian
+123,124,Gabon,3.61,2.58,2.21,4.44,5.00,3.82,Authoritarian
+124,125,Cambodia,3.59,1.33,5.00,2.78,5.63,3.24,Authoritarian
+125,126,Algeria,3.50,2.58,2.21,3.89,5.00,3.82,Authoritarian
+126,127,Egypt,3.36,3.58,3.21,3.33,3.75,2.94,Authoritarian
+127,=128,Ethiopia,3.35,0.00,3.57,5.56,5.00,2.65,Authoritarian
+128,=128,Rwanda,3.35,1.67,5.00,2.78,4.38,2.94,Authoritarian
+129,130,China,3.32,0.00,5.00,3.89,6.25,1.47,Authoritarian
+130,131,Republic of the Congo,3.31,3.17,2.50,3.89,3.75,3.24,Authoritarian
+131,132,Cameroon,3.28,3.17,2.86,3.33,4.38,2.65,Authoritarian
+132,133,Qatar,3.19,0.00,4.29,2.22,5.63,3.82,Authoritarian
+133,=134,Zimbabwe,3.16,0.50,2.00,4.44,5.63,3.24,Authoritarian
+134,=134,Venezuela,3.16,1.67,1.79,4.44,4.38,3.53,Authoritarian
+135,136,Guinea,3.14,3.50,0.43,4.44,4.38,2.94,Authoritarian
+136,137,Belarus,3.13,0.92,2.86,3.89,5.63,2.35,Authoritarian
+137,138,Togo,3.10,3.17,0.79,3.33,5.00,3.24,Authoritarian
+138,139,Vietnam,3.08,0.00,3.21,3.89,5.63,2.65,Authoritarian
+139,140,Oman,3.04,0.00,3.93,2.78,4.38,4.12,Authoritarian
+140,141,Swaziland,3.03,0.92,2.86,2.22,5.63,3.53,Authoritarian
+141,142,Cuba,3.00,1.08,3.57,3.33,4.38,2.65,Authoritarian
+142,143,Afghanistan,2.97,2.92,1.14,4.44,2.50,3.82,Authoritarian
+143,=144,Kazakhstan,2.94,0.50,2.14,4.44,4.38,3.24,Authoritarian
+144,=144,Russia,2.94,2.17,1.79,5.00,2.50,3.24,Authoritarian
+145,146,Djibouti,2.87,0.42,1.79,3.89,5.63,2.65,Authoritarian
+146,147,United Arab Emirates,2.76,0.00,3.93,2.22,5.00,2.65,Authoritarian
+147,148,Bahrain,2.71,0.83,3.21,2.78,4.38,2.35,Authoritarian
+148,149,Azerbaijan,2.65,0.50,2.14,3.33,3.75,3.53,Authoritarian
+149,150,Iran,2.45,0.00,3.21,4.44,3.13,1.47,Authoritarian
+150,=151,Eritrea,2.37,0.00,2.14,1.67,6.88,1.18,Authoritarian
+151,=151,Laos,2.37,0.83,2.86,1.67,5.00,1.47,Authoritarian
+152,153,Burundi,2.33,0.00,0.43,3.89,5.00,2.35,Authoritarian
+153,154,Libya,2.19,1.00,0.36,1.67,5.00,2.94,Authoritarian
+154,155,Sudan,2.15,0.00,1.79,2.78,5.00,1.18,Authoritarian
+155,156,Uzbekistan,2.01,0.08,1.86,2.22,5.00,0.88,Authoritarian
+156,157,Guinea-Bissau,1.98,1.67,0.00,2.78,3.13,2.35,Authoritarian
+157,158,Yemen,1.95,0.00,0.00,3.89,5.00,0.88,Authoritarian
+158,=159,Saudi Arabia,1.93,0.00,2.86,2.22,3.13,1.47,Authoritarian
+159,=159,Tajikistan,1.93,0.08,0.79,1.67,6.25,0.88,Authoritarian
+160,161,Equatorial Guinea,1.92,0.00,0.43,3.33,4.38,1.47,Authoritarian
+161,162,Turkmenistan,1.72,0.00,0.79,2.22,5.00,0.59,Authoritarian
+162,163,Chad,1.61,0.00,0.00,1.67,3.75,2.65,Authoritarian
+163,164,Central African Republic,1.52,2.25,0.00,1.11,1.88,2.35,Authoritarian
+164,165,Democratic Republic of the Congo,1.49,0.50,0.71,2.22,3.13,0.88,Authoritarian
+165,166,Syria,1.43,0.00,0.00,2.78,4.38,0.00,Authoritarian
+166,167,North Korea,1.08,0.00,2.50,1.67,1.25,0.00,Authoritarian
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+        </script>
+        <script type="text/javascript">
+          (function() {
+            var fn = function() {
+              Bokeh.safely(function() {
+                (function(root) {
+                  function embed_document(root) {
+                    
+                  var docs_json = document.getElementById('5992').textContent;
+                  var render_items = [{"docid":"7580911d-4901-4d52-aac9-288b5c69481f","root_ids":["5651"],"roots":{"5651":"75c7b8a9-82fb-4d61-893a-18704f46af03"}}];
+                  root.Bokeh.embed.embed_items(docs_json, render_items);
+                
+                  }
+                  if (root.Bokeh !== undefined) {
+                    embed_document(root);
+                  } else {
+                    var attempts = 0;
+                    var timer = setInterval(function(root) {
+                      if (root.Bokeh !== undefined) {
+                        clearInterval(timer);
+                        embed_document(root);
+                      } else {
+                        attempts++;
+                        if (attempts > 100) {
+                          clearInterval(timer);
+                          console.log("Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing");
+                        }
+                      }
+                    }, 10, root)
+                  }
+                })(window);
+              });
+            };
+            if (document.readyState != "loading") fn();
+            else document.addEventListener("DOMContentLoaded", fn);
+          })();
+        </script>
+    
+  </body>
+  
+</html>
\ No newline at end of file
diff --git a/examples/resulting_images/som_seed_hex.png b/examples/resulting_images/som_seed_hex.png
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diff --git a/examples/resulting_images/som_seed_pies.png b/examples/resulting_images/som_seed_pies.png
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index 0000000..6ded9d9
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diff --git a/examples/resulting_images/time_series.png b/examples/resulting_images/time_series.png
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diff --git a/examples/tree.jpg b/examples/tree.jpg
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diff --git a/minisom.py b/minisom.py
new file mode 100644
index 0000000..0d15bbf
--- /dev/null
+++ b/minisom.py
@@ -0,0 +1,818 @@
+from math import sqrt
+
+from numpy import (array, unravel_index, nditer, linalg, random, subtract, max,
+                   power, exp, pi, zeros, ones, arange, outer, meshgrid, dot,
+                   logical_and, mean, std, cov, argsort, linspace, transpose,
+                   einsum, prod, nan, sqrt, hstack, diff, argmin, multiply)
+from numpy import sum as npsum
+from numpy.linalg import norm
+from collections import defaultdict, Counter
+from warnings import warn
+from sys import stdout
+from time import time
+from datetime import timedelta
+import pickle
+import os
+
+# for unit tests
+from numpy.testing import assert_almost_equal, assert_array_almost_equal
+from numpy.testing import assert_array_equal
+import unittest
+
+"""
+    Minimalistic implementation of the Self Organizing Maps (SOM).
+"""
+
+
+def _build_iteration_indexes(data_len, num_iterations,
+                             verbose=False, random_generator=None):
+    """Returns an iterable with the indexes of the samples
+    to pick at each iteration of the training.
+
+    If random_generator is not None, it must be an instalce
+    of numpy.random.RandomState and it will be used
+    to randomize the order of the samples."""
+    iterations = arange(num_iterations) % data_len
+    if random_generator:
+        random_generator.shuffle(iterations)
+    if verbose:
+        return _wrap_index__in_verbose(iterations)
+    else:
+        return iterations
+
+
+def _wrap_index__in_verbose(iterations):
+    """Yields the values in iterations printing the status on the stdout."""
+    m = len(iterations)
+    digits = len(str(m))
+    progress = '\r [ {s:{d}} / {m} ] {s:3.0f}% - ? it/s'
+    progress = progress.format(m=m, d=digits, s=0)
+    stdout.write(progress)
+    beginning = time()
+    stdout.write(progress)
+    for i, it in enumerate(iterations):
+        yield it
+        sec_left = ((m-i+1) * (time() - beginning)) / (i+1)
+        time_left = str(timedelta(seconds=sec_left))[:7]
+        progress = '\r [ {i:{d}} / {m} ]'.format(i=i+1, d=digits, m=m)
+        progress += ' {p:3.0f}%'.format(p=100*(i+1)/m)
+        progress += ' - {time_left} left '.format(time_left=time_left)
+        stdout.write(progress)
+
+
+def fast_norm(x):
+    """Returns norm-2 of a 1-D numpy array.
+
+    * faster than linalg.norm in case of 1-D arrays (numpy 1.9.2rc1).
+    """
+    return sqrt(dot(x, x.T))
+
+
+def asymptotic_decay(learning_rate, t, max_iter):
+    """Decay function of the learning process.
+    Parameters
+    ----------
+    learning_rate : float
+        current learning rate.
+
+    t : int
+        current iteration.
+
+    max_iter : int
+        maximum number of iterations for the training.
+    """
+    return learning_rate / (1+t/(max_iter/2))
+
+def get_coordinates_from_index(n, x, y):
+    if n<0:
+        return(-1,-1)
+    return(n%y, int(n/y))
+
+def get_index_from_coordinates(c, x, y):
+    if c[0]<0 or c[1]<0 or c[0]>y or c[1]>x:
+        return(-1)
+    return(c[1]*y + c[0])
+
+def get_neighbors_from_index(n, x, y):
+    c = get_coordinates_from_index(n, x, y)
+    offset_x = -1 if c[1]%2==0 else 1
+    neighbors_c =[(c[0]-1,c[1]), (c[0]+1,c[1]), (c[0],c[1]-1), (c[0]+offset_x,c[1]-1), (c[0],c[1]+1), 
+                (c[0]+offset_x,c[1]+1)] 
+    neighbors_c = list(filter(lambda c: (c[0]>=0) and (c[1]>=0) and (c[1]<x) and (c[0]<y), neighbors_c))
+    neighbors = list(map(lambda c: get_index_from_coordinates(c, x, y), neighbors_c))
+    return list(set(neighbors))
+
+
+class MiniSom(object):
+    def __init__(self, x, y, input_len, sigma=1.0, learning_rate=0.5,
+                 decay_function=asymptotic_decay,
+                 neighborhood_function='gaussian', topology='rectangular',
+                 activation_distance='euclidean', random_seed=None):
+        """Initializes a Self Organizing Maps.
+
+        A rule of thumb to set the size of the grid for a dimensionality
+        reduction task is that it should contain 5*sqrt(N) neurons
+        where N is the number of samples in the dataset to analyze.
+
+        E.g. if your dataset has 150 samples, 5*sqrt(150) = 61.23
+        hence a map 8-by-8 should perform well.
+
+        Parameters
+        ----------
+        x : int
+            x dimension of the SOM.
+
+        y : int
+            y dimension of the SOM.
+
+        input_len : int
+            Number of the elements of the vectors in input.
+
+        sigma : float, optional (default=1.0)
+            Spread of the neighborhood function, needs to be adequate
+            to the dimensions of the map.
+            (at the iteration t we have sigma(t) = sigma / (1 + t/T)
+            where T is #num_iteration/2)
+        learning_rate : initial learning rate
+            (at the iteration t we have
+            learning_rate(t) = learning_rate / (1 + t/T)
+            where T is #num_iteration/2)
+
+        decay_function : function (default=None)
+            Function that reduces learning_rate and sigma at each iteration
+            the default function is:
+                        learning_rate / (1+t/(max_iterarations/2))
+
+            A custom decay function will need to to take in input
+            three parameters in the following order:
+
+            1. learning rate
+            2. current iteration
+            3. maximum number of iterations allowed
+
+
+            Note that if a lambda function is used to define the decay
+            MiniSom will not be pickable anymore.
+
+        neighborhood_function : string, optional (default='gaussian')
+            Function that weights the neighborhood of a position in the map.
+            Possible values: 'gaussian', 'mexican_hat', 'bubble', 'triangle'
+
+        topology : string, optional (default='rectangular')
+            Topology of the map.
+            Possible values: 'rectangular', 'hexagonal'
+
+        activation_distance : string, optional (default='euclidean')
+            Distance used to activate the map.
+            Possible values: 'euclidean', 'cosine', 'manhattan', 'chebyshev'
+
+        random_seed : int, optional (default=None)
+            Random seed to use.
+        """
+        if sigma >= x or sigma >= y:
+            warn('Warning: sigma is too high for the dimension of the map.')
+
+        self._random_generator = random.RandomState(random_seed)
+
+        self._learning_rate = learning_rate
+        self._sigma = sigma
+        self._input_len = input_len
+        # random initialization
+        self._weights = self._random_generator.rand(x, y, input_len)*2-1
+        self._weights /= linalg.norm(self._weights, axis=-1, keepdims=True)
+
+        self._activation_map = zeros((x, y))
+        self._neigx = arange(x)
+        self._neigy = arange(y)  # used to evaluate the neighborhood function
+
+        if topology not in ['hexagonal', 'rectangular']:
+            msg = '%s not supported only hexagonal and rectangular available'
+            raise ValueError(msg % topology)
+        self.topology = topology
+        self._xx, self._yy = meshgrid(self._neigx, self._neigy)
+        self._xx = self._xx.astype(float)
+        self._yy = self._yy.astype(float)
+        if topology == 'hexagonal':
+            self._xx[::-2] -= 0.5
+            if neighborhood_function in ['triangle']:
+                warn('triangle neighborhood function does not ' +
+                     'take in account hexagonal topology')
+
+        self._decay_function = decay_function
+
+        neig_functions = {'gaussian': self._gaussian,
+                          'mexican_hat': self._mexican_hat,
+                          'bubble': self._bubble,
+                          'triangle': self._triangle}
+
+        if neighborhood_function not in neig_functions:
+            msg = '%s not supported. Functions available: %s'
+            raise ValueError(msg % (neighborhood_function,
+                                    ', '.join(neig_functions.keys())))
+
+        if neighborhood_function in ['triangle',
+                                     'bubble'] and (divmod(sigma, 1)[1] != 0
+                                                    or sigma < 1):
+            warn('sigma should be an integer >=1 when triangle or bubble' +
+                 'are used as neighborhood function')
+
+        self.neighborhood = neig_functions[neighborhood_function]
+
+        distance_functions = {'euclidean': self._euclidean_distance,
+                              'cosine': self._cosine_distance,
+                              'manhattan': self._manhattan_distance,
+                              'chebyshev': self._chebyshev_distance}
+
+        if activation_distance not in distance_functions:
+            msg = '%s not supported. Distances available: %s'
+            raise ValueError(msg % (activation_distance,
+                                    ', '.join(distance_functions.keys())))
+
+        self._activation_distance = distance_functions[activation_distance]
+
+    def get_weights(self):
+        """Returns the weights of the neural network."""
+        return self._weights
+
+    def get_euclidean_coordinates(self):
+        """Returns the position of the neurons on an euclidean
+        plane that reflects the chosen topology in two meshgrids xx and yy.
+        Neuron with map coordinates (1, 4) has coordinate (xx[1, 4], yy[1, 4])
+        in the euclidean plane.
+
+        Only useful if the topology chosen is not rectangular.
+        """
+        return self._xx.T, self._yy.T
+
+    def convert_map_to_euclidean(self, xy):
+        """Converts map coordinates into euclidean coordinates
+        that reflects the chosen topology.
+
+        Only useful if the topology chosen is not rectangular.
+        """
+        return self._xx.T[xy], self._yy.T[xy]
+
+    def _activate(self, x):
+        """Updates matrix activation_map, in this matrix
+           the element i,j is the response of the neuron i,j to x."""
+        self._activation_map = self._activation_distance(x, self._weights)
+
+    def activate(self, x):
+        """Returns the activation map to x."""
+        self._activate(x)
+        return self._activation_map
+
+    def _gaussian(self, c, sigma):
+        """Returns a Gaussian centered in c."""
+        d = 2*pi*sigma*sigma
+        ax = exp(-power(self._xx-self._xx.T[c], 2)/d)
+        ay = exp(-power(self._yy-self._yy.T[c], 2)/d)
+        return (ax * ay).T  # the external product gives a matrix
+
+    def _mexican_hat(self, c, sigma):
+        """Mexican hat centered in c."""
+        p = power(self._xx-self._xx.T[c], 2) + power(self._yy-self._yy.T[c], 2)
+        d = 2*pi*sigma*sigma
+        return (exp(-p/d)*(1-2/d*p)).T
+
+    def _bubble(self, c, sigma):
+        """Constant function centered in c with spread sigma.
+        sigma should be an odd value.
+        """
+        ax = logical_and(self._neigx > c[0]-sigma,
+                         self._neigx < c[0]+sigma)
+        ay = logical_and(self._neigy > c[1]-sigma,
+                         self._neigy < c[1]+sigma)
+        return outer(ax, ay)*1.
+
+    def _triangle(self, c, sigma):
+        """Triangular function centered in c with spread sigma."""
+        triangle_x = (-abs(c[0] - self._neigx)) + sigma
+        triangle_y = (-abs(c[1] - self._neigy)) + sigma
+        triangle_x[triangle_x < 0] = 0.
+        triangle_y[triangle_y < 0] = 0.
+        return outer(triangle_x, triangle_y)
+
+    def _cosine_distance(self, x, w):
+        num = (w * x).sum(axis=2)
+        denum = multiply(linalg.norm(w, axis=2), linalg.norm(x))
+        return 1 - num / (denum+1e-8)
+
+    def _euclidean_distance(self, x, w):
+        return linalg.norm(subtract(x, w), axis=-1)
+
+    def _manhattan_distance(self, x, w):
+        return linalg.norm(subtract(x, w), ord=1, axis=-1)
+
+    def _chebyshev_distance(self, x, w):
+        return max(subtract(x, w), axis=-1)
+
+    def _check_iteration_number(self, num_iteration):
+        if num_iteration < 1:
+            raise ValueError('num_iteration must be > 1')
+
+    def _check_input_len(self, data):
+        """Checks that the data in input is of the correct shape."""
+        data_len = len(data[0])
+        if self._input_len != data_len:
+            msg = 'Received %d features, expected %d.' % (data_len,
+                                                          self._input_len)
+            raise ValueError(msg)
+
+    def winner(self, x):
+        """Computes the coordinates of the winning neuron for the sample x."""
+        self._activate(x)
+        return unravel_index(self._activation_map.argmin(),
+                             self._activation_map.shape)
+
+    def update(self, x, win, t, max_iteration):
+        """Updates the weights of the neurons.
+
+        Parameters
+        ----------
+        x : np.array
+            Current pattern to learn.
+        win : tuple
+            Position of the winning neuron for x (array or tuple).
+        t : int
+            Iteration index
+        max_iteration : int
+            Maximum number of training itarations.
+        """
+        eta = self._decay_function(self._learning_rate, t, max_iteration)
+        # sigma and learning rate decrease with the same rule
+        sig = self._decay_function(self._sigma, t, max_iteration)
+        # improves the performances
+        g = self.neighborhood(win, sig)*eta
+        # w_new = eta * neighborhood_function * (x-w)
+        self._weights += einsum('ij, ijk->ijk', g, x-self._weights)
+
+    def quantization(self, data):
+        """Assigns a code book (weights vector of the winning neuron)
+        to each sample in data."""
+        self._check_input_len(data)
+        winners_coords = argmin(self._distance_from_weights(data), axis=1)
+        return self._weights[unravel_index(winners_coords,
+                                           self._weights.shape[:2])]
+
+    def random_weights_init(self, data):
+        """Initializes the weights of the SOM
+        picking random samples from data."""
+        self._check_input_len(data)
+        it = nditer(self._activation_map, flags=['multi_index'])
+        while not it.finished:
+            rand_i = self._random_generator.randint(len(data))
+            self._weights[it.multi_index] = data[rand_i]
+            it.iternext()
+
+    def pca_weights_init(self, data):
+        """Initializes the weights to span the first two principal components.
+
+        This initialization doesn't depend on random processes and
+        makes the training process converge faster.
+
+        It is strongly reccomended to normalize the data before initializing
+        the weights and use the same normalization for the training data.
+        """
+        if self._input_len == 1:
+            msg = 'The data needs at least 2 features for pca initialization'
+            raise ValueError(msg)
+        self._check_input_len(data)
+        if len(self._neigx) == 1 or len(self._neigy) == 1:
+            msg = 'PCA initialization inappropriate:' + \
+                  'One of the dimensions of the map is 1.'
+            warn(msg)
+        pc_length, pc = linalg.eig(cov(transpose(data)))
+        pc_order = argsort(-pc_length)
+        for i, c1 in enumerate(linspace(-1, 1, len(self._neigx))):
+            for j, c2 in enumerate(linspace(-1, 1, len(self._neigy))):
+                self._weights[i, j] = c1*pc[pc_order[0]] + c2*pc[pc_order[1]]
+
+    def train(self, data, num_iteration, random_order=False, verbose=False):
+        """Trains the SOM.
+
+        Parameters
+        ----------
+        data : np.array or list
+            Data matrix.
+
+        num_iteration : int
+            Maximum number of iterations (one iteration per sample).
+        random_order : bool (default=False)
+            If True, samples are picked in random order.
+            Otherwise the samples are picked sequentially.
+
+        verbose : bool (default=False)
+            If True the status of the training
+            will be printed at each iteration.
+        """
+        self._check_iteration_number(num_iteration)
+        self._check_input_len(data)
+        random_generator = None
+        if random_order:
+            random_generator = self._random_generator
+        iterations = _build_iteration_indexes(len(data), num_iteration,
+                                              verbose, random_generator)
+        for t, iteration in enumerate(iterations):
+            self.update(data[iteration], self.winner(data[iteration]),
+                        t, num_iteration)
+        if verbose:
+            print('\n quantization error:', self.quantization_error(data))
+
+    def train_random(self, data, num_iteration, verbose=False):
+        """Trains the SOM picking samples at random from data.
+
+        Parameters
+        ----------
+        data : np.array or list
+            Data matrix.
+
+        num_iteration : int
+            Maximum number of iterations (one iteration per sample).
+
+        verbose : bool (default=False)
+            If True the status of the training
+            will be printed at each iteration.
+        """
+        self.train(data, num_iteration, random_order=True, verbose=verbose)
+
+    def train_batch(self, data, num_iteration, verbose=False):
+        """Trains the SOM using all the vectors in data sequentially.
+
+        Parameters
+        ----------
+        data : np.array or list
+            Data matrix.
+
+        num_iteration : int
+            Maximum number of iterations (one iteration per sample).
+
+        verbose : bool (default=False)
+            If True the status of the training
+            will be printed at each iteration.
+        """
+        self.train(data, num_iteration, random_order=False, verbose=verbose)
+
+    def distance_map(self):
+        """Returns the distance map of the weights.
+        Each cell is the normalised sum of the distances between
+        a neuron and its neighbours. Note that this method uses
+        the euclidean distance."""
+        um = zeros((self._weights.shape[0],
+                    self._weights.shape[1],
+                    8))  # 2 spots more for hexagonal topology
+
+        ii = [[0, -1, -1, -1, 0, 1, 1, 1]]*2
+        jj = [[-1, -1, 0, 1, 1, 1, 0, -1]]*2
+
+        if self.topology == 'hexagonal':
+            ii = [[1, 1, 1, 0, -1, 0], [0, 1, 0, -1, -1, -1]]
+            jj = [[1, 0, -1, -1, 0, 1], [1, 0, -1, -1, 0, 1]]
+
+        for x in range(self._weights.shape[0]):
+            for y in range(self._weights.shape[1]):
+                w_2 = self._weights[x, y]
+                e = y % 2 == 0   # only used on hexagonal topology
+                for k, (i, j) in enumerate(zip(ii[e], jj[e])):
+                    if (x+i >= 0 and x+i < self._weights.shape[0] and
+                            y+j >= 0 and y+j < self._weights.shape[1]):
+                        w_1 = self._weights[x+i, y+j]
+                        um[x, y, k] = fast_norm(w_2-w_1)
+
+        um = um.sum(axis=2)
+        return um/um.max()
+
+    def activation_response(self, data):
+        """
+            Returns a matrix where the element i,j is the number of times
+            that the neuron i,j have been winner.
+        """
+        self._check_input_len(data)
+        a = zeros((self._weights.shape[0], self._weights.shape[1]))
+        for x in data:
+            a[self.winner(x)] += 1
+        return a
+
+    def _distance_from_weights(self, data):
+        """Returns a matrix d where d[i,j] is the euclidean distance between
+        data[i] and the j-th weight.
+        """
+        input_data = array(data)
+        weights_flat = self._weights.reshape(-1, self._weights.shape[2])
+        input_data_sq = power(input_data, 2).sum(axis=1, keepdims=True)
+        weights_flat_sq = power(weights_flat, 2).sum(axis=1, keepdims=True)
+        cross_term = dot(input_data, weights_flat.T)
+        return sqrt(-2 * cross_term + input_data_sq + weights_flat_sq.T)
+
+    def quantization_error(self, data):
+        """Returns the quantization error computed as the average
+        distance between each input sample and its best matching unit."""
+        self._check_input_len(data)
+        return norm(data-self.quantization(data), axis=1).mean()
+
+    def topographic_error(self, data):
+        """Returns the topographic error computed by finding
+        the best-matching and second-best-matching neuron in the map
+        for each input and then evaluating the positions.
+
+        A sample for which these two nodes are not adjacent counts as
+        an error. The topographic error is given by the
+        the total number of errors divided by the total of samples.
+
+        If the topographic error is 0, no error occurred.
+        If 1, the topology was not preserved for any of the samples."""
+        self._check_input_len(data)
+        if self.topology == 'hexagonal':
+            msg = 'Topographic error not implemented for hexagonal topology.'
+            raise NotImplementedError(msg)
+        total_neurons = prod(self._activation_map.shape)
+        if total_neurons == 1:
+            warn('The topographic error is not defined for a 1-by-1 map.')
+            return nan
+
+        t = 1.42
+        # b2mu: best 2 matching units
+        b2mu_inds = argsort(self._distance_from_weights(data), axis=1)[:, :2]
+        b2my_xy = unravel_index(b2mu_inds, self._weights.shape[:2])
+        b2mu_x, b2mu_y = b2my_xy[0], b2my_xy[1]
+        dxdy = hstack([diff(b2mu_x), diff(b2mu_y)])
+        distance = norm(dxdy, axis=1)
+        return (distance > t).mean()
+
+    def win_map(self, data, return_indices=False):
+        """Returns a dictionary wm where wm[(i,j)] is a list with:
+        - all the patterns that have been mapped to the position (i,j),
+          if return_indices=False (default)
+        - all indices of the elements that have been mapped to the
+          position (i,j) if return_indices=True"""
+        self._check_input_len(data)
+        winmap = defaultdict(list)
+        for i, x in enumerate(data):
+            winmap[self.winner(x)].append(i if return_indices else x)
+        return winmap
+
+    def labels_map(self, data, labels):
+        """Returns a dictionary wm where wm[(i,j)] is a dictionary
+        that contains the number of samples from a given label
+        that have been mapped in position i,j.
+
+        Parameters
+        ----------
+        data : np.array or list
+            Data matrix.
+
+        label : np.array or list
+            Labels for each sample in data.
+        """
+        self._check_input_len(data)
+        if not len(data) == len(labels):
+            raise ValueError('data and labels must have the same length.')
+        winmap = defaultdict(list)
+        for x, l in zip(data, labels):
+            winmap[self.winner(x)].append(l)
+        for position in winmap:
+            winmap[position] = Counter(winmap[position])
+        return winmap
+
+    def partitioned_quant_error(self,data,chunks):
+        n=len(data)
+        n_chunks = list(range(0,n,chunks))
+        n_chunks.append(n)
+        errorq=0
+        for j in range(len(n_chunks)-1):
+            interval = n_chunks[j+1]-n_chunks[j]
+            errorq = errorq+self.quantization_error(data[n_chunks[j]:n_chunks[j+1]])*interval/n
+        return errorq
+
+    def calculate_hexa_topographical_error(self, data):
+        bmus_1st_and_2nd = argsort(self._distance_from_weights(data), axis=1)[:, :2]
+        n_rows = self._weights.shape[0]
+        n_columns = self._weights.shape[1]
+        neighbors=list(map(lambda t:get_neighbors_from_index(t, n_rows, n_columns), bmus_1st_and_2nd[:,0]))
+
+        e_t=1-mean([second in neighs for (second,neighs) in zip(bmus_1st_and_2nd[:,1], neighbors)])
+        return(e_t)
+
+    def partitioned_topo_error(self,data,chunks):
+        n=len(data)
+        n_chunks = list(range(0,n,chunks))
+        n_chunks.append(n)
+        errort=0
+        for j in range(len(n_chunks)-1):
+            interval = n_chunks[j+1]-n_chunks[j]
+            errort = errort+self.calculate_hexa_topographical_error(data[n_chunks[j]:n_chunks[j+1]])*interval/n
+        return errort
+
+
+
+
+class TestMinisom(unittest.TestCase):
+    def setUp(self):
+        self.som = MiniSom(5, 5, 1)
+        for i in range(5):
+            for j in range(5):
+                # checking weights normalization
+                assert_almost_equal(1.0, linalg.norm(self.som._weights[i, j]))
+        self.som._weights = zeros((5, 5, 1))  # fake weights
+        self.som._weights[2, 3] = 5.0
+        self.som._weights[1, 1] = 2.0
+
+    def test_decay_function(self):
+        assert self.som._decay_function(1., 2., 3.) == 1./(1.+2./(3./2))
+
+    def test_fast_norm(self):
+        assert fast_norm(array([1, 3])) == sqrt(1+9)
+
+    def test_euclidean_distance(self):
+        x = zeros((1, 2))
+        w = ones((2, 2, 2))
+        d = self.som._euclidean_distance(x, w)
+        assert_array_almost_equal(d, [[1.41421356, 1.41421356],
+                                      [1.41421356, 1.41421356]])
+
+    def test_cosine_distance(self):
+        x = zeros((1, 2))
+        w = ones((2, 2, 2))
+        d = self.som._cosine_distance(x, w)
+        assert_array_almost_equal(d, [[1., 1.],
+                                      [1., 1.]])
+
+    def test_manhattan_distance(self):
+        x = zeros((1, 2))
+        w = ones((2, 2, 2))
+        d = self.som._manhattan_distance(x, w)
+        assert_array_almost_equal(d, [[2., 2.],
+                                      [2., 2.]])
+
+    def test_chebyshev_distance(self):
+        x = array([1, 3])
+        w = ones((2, 2, 2))
+        d = self.som._chebyshev_distance(x, w)
+        assert_array_almost_equal(d, [[2., 2.],
+                                      [2., 2.]])
+
+    def test_check_input_len(self):
+        with self.assertRaises(ValueError):
+            self.som.train_batch([[1, 2]], 1)
+
+        with self.assertRaises(ValueError):
+            self.som.random_weights_init(array([[1, 2]]))
+
+        with self.assertRaises(ValueError):
+            self.som._check_input_len(array([[1, 2]]))
+
+        self.som._check_input_len(array([[1]]))
+        self.som._check_input_len([[1]])
+
+    def test_unavailable_neigh_function(self):
+        with self.assertRaises(ValueError):
+            MiniSom(5, 5, 1, neighborhood_function='boooom')
+
+    def test_unavailable_distance_function(self):
+        with self.assertRaises(ValueError):
+            MiniSom(5, 5, 1, activation_distance='ridethewave')
+
+    def test_gaussian(self):
+        bell = self.som._gaussian((2, 2), 1)
+        assert bell.max() == 1.0
+        assert bell.argmax() == 12  # unravel(12) = (2,2)
+
+    def test_mexican_hat(self):
+        bell = self.som._mexican_hat((2, 2), 1)
+        assert bell.max() == 1.0
+        assert bell.argmax() == 12  # unravel(12) = (2,2)
+
+    def test_bubble(self):
+        bubble = self.som._bubble((2, 2), 1)
+        assert bubble[2, 2] == 1
+        assert sum(sum(bubble)) == 1
+
+    def test_triangle(self):
+        bubble = self.som._triangle((2, 2), 1)
+        assert bubble[2, 2] == 1
+        assert sum(sum(bubble)) == 1
+
+    def test_win_map(self):
+        winners = self.som.win_map([[5.0], [2.0]])
+        assert winners[(2, 3)][0] == [5.0]
+        assert winners[(1, 1)][0] == [2.0]
+
+    def test_win_map_indices(self):
+        winners = self.som.win_map([[5.0], [2.0]], return_indices=True)
+        assert winners[(2, 3)] == [0]
+        assert winners[(1, 1)] == [1]
+
+    def test_labels_map(self):
+        labels_map = self.som.labels_map([[5.0], [2.0]], ['a', 'b'])
+        assert labels_map[(2, 3)]['a'] == 1
+        assert labels_map[(1, 1)]['b'] == 1
+        with self.assertRaises(ValueError):
+            self.som.labels_map([[5.0]], ['a', 'b'])
+
+    def test_activation_reponse(self):
+        response = self.som.activation_response([[5.0], [2.0]])
+        assert response[2, 3] == 1
+        assert response[1, 1] == 1
+
+    def test_activate(self):
+        assert self.som.activate(5.0).argmin() == 13.0  # unravel(13) = (2,3)
+
+    def test_distance_from_weights(self):
+        data = arange(-5, 5).reshape(-1, 1)
+        weights = self.som._weights.reshape(-1, self.som._weights.shape[2])
+        distances = self.som._distance_from_weights(data)
+        for i in range(len(data)):
+            for j in range(len(weights)):
+                assert(distances[i][j] == norm(data[i] - weights[j]))
+
+    def test_quantization_error(self):
+        assert self.som.quantization_error([[5], [2]]) == 0.0
+        assert self.som.quantization_error([[4], [1]]) == 1.0
+
+    def test_topographic_error(self):
+        # 5 will have bmu_1 in (2,3) and bmu_2 in (2, 4)
+        # which are in the same neighborhood
+        self.som._weights[2, 4] = 6.0
+        # 15 will have bmu_1 in (4, 4) and bmu_2 in (0, 0)
+        # which are not in the same neighborhood
+        self.som._weights[4, 4] = 15.0
+        self.som._weights[0, 0] = 14.
+        assert self.som.topographic_error([[5]]) == 0.0
+        assert self.som.topographic_error([[15]]) == 1.0
+
+        self.som.topology = 'hexagonal'
+        with self.assertRaises(NotImplementedError):
+            assert self.som.topographic_error([[5]]) == 0.0
+        self.som.topology = 'rectangular'
+
+    def test_quantization(self):
+        q = self.som.quantization(array([[4], [2]]))
+        assert q[0] == 5.0
+        assert q[1] == 2.0
+
+    def test_random_seed(self):
+        som1 = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
+        som2 = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
+        # same initialization
+        assert_array_almost_equal(som1._weights, som2._weights)
+        data = random.rand(100, 2)
+        som1 = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
+        som1.train_random(data, 10)
+        som2 = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
+        som2.train_random(data, 10)
+        # same state after training
+        assert_array_almost_equal(som1._weights, som2._weights)
+
+    def test_train_batch(self):
+        som = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
+        data = array([[4, 2], [3, 1]])
+        q1 = som.quantization_error(data)
+        som.train(data, 10)
+        assert q1 > som.quantization_error(data)
+
+        data = array([[1, 5], [6, 7]])
+        q1 = som.quantization_error(data)
+        som.train_batch(data, 10, verbose=True)
+        assert q1 > som.quantization_error(data)
+
+    def test_train_random(self):
+        som = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
+        data = array([[4, 2], [3, 1]])
+        q1 = som.quantization_error(data)
+        som.train(data, 10, random_order=True)
+        assert q1 > som.quantization_error(data)
+
+        data = array([[1, 5], [6, 7]])
+        q1 = som.quantization_error(data)
+        som.train_random(data, 10, verbose=True)
+        assert q1 > som.quantization_error(data)
+
+    def test_random_weights_init(self):
+        som = MiniSom(2, 2, 2, random_seed=1)
+        som.random_weights_init(array([[1.0, .0]]))
+        for w in som._weights:
+            assert_array_equal(w[0], array([1.0, .0]))
+
+    def test_pca_weights_init(self):
+        som = MiniSom(2, 2, 2)
+        som.pca_weights_init(array([[1.,  0.], [0., 1.], [1., 0.], [0., 1.]]))
+        expected = array([[[0., -1.41421356], [-1.41421356, 0.]],
+                          [[1.41421356, 0.], [0., 1.41421356]]])
+        assert_array_almost_equal(som._weights, expected)
+
+    def test_distance_map(self):
+        som = MiniSom(2, 2, 2, random_seed=1)
+        som._weights = array([[[1.,  0.], [0., 1.]], [[1., 0.], [0., 1.]]])
+        assert_array_equal(som.distance_map(), array([[1., 1.], [1., 1.]]))
+
+        som = MiniSom(2, 2, 2, topology='hexagonal', random_seed=1)
+        som._weights = array([[[1.,  0.], [0., 1.]], [[1., 0.], [0., 1.]]])
+        assert_array_equal(som.distance_map(), array([[.5, 1.], [1., .5]]))
+
+    def test_pickling(self):
+        with open('som.p', 'wb') as outfile:
+            pickle.dump(self.som, outfile)
+        with open('som.p', 'rb') as infile:
+            pickle.load(infile)
+        os.remove('som.p')
+    
\ No newline at end of file
diff --git a/paper/paper.bib b/paper/paper.bib
new file mode 100644
index 0000000..cdda8df
--- /dev/null
+++ b/paper/paper.bib
@@ -0,0 +1,85 @@
+
+@Article{matplotlib,
+  Author    = {Hunter, J. D.},
+  Title     = {Matplotlib: A 2D graphics environment},
+  Journal   = {Computing in Science \& Engineering},
+  Volume    = {9},
+  Number    = {3},
+  Pages     = {90--95},
+  abstract  = {Matplotlib is a 2D graphics package used for Python for
+  application development, interactive scripting, and publication-quality
+  image generation across user interfaces and operating systems.},
+  publisher = {IEEE COMPUTER SOC},
+  doi       = {10.1109/MCSE.2007.55},
+  year      = 2007
+}
+
+@article{pedregosa2011scikit,
+  title={Scikit-learn: Machine learning in Python},
+  author={Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and others},
+  journal={the Journal of machine Learning research},
+  volume={12},
+  pages={2825--2830},
+  year={2011},
+  publisher={JMLR. org}
+}
+
+@article{kohonen1990self,
+  title={The self-organizing map},
+  author={Kohonen, Teuvo},
+  journal={Proceedings of the IEEE},
+  volume={78},
+  number={9},
+  pages={1464--1480},
+  year={1990},
+  publisher={IEEE}
+}
+
+@inproceedings{vettigli2017fuzzy,
+  title={Fuzzy clustering of structured data: Some preliminary results},
+  author={Vettigli, Giuseppe and Ciaramella, Angelo},
+  booktitle={2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
+  pages={1--6},
+  year={2017},
+  organization={IEEE}
+}
+
+@article{thompson2020synoptic,
+  title={A synoptic climatology of potential seiche-inducing winds in a large intermontane lake: Quesnel Lake, British Columbia, Canada},
+  author={Thompson, Hadleigh D and D{\'e}ry, Stephen J and Jackson, Peter L and Laval, Bernard E},
+  journal={International Journal of Climatology},
+  year={2020},
+  publisher={Wiley Online Library}
+}
+
+
+@article{lessin2020modeling,
+  title={Modeling the seasonality and controls of nitrous oxide emissions on the northwest European continental shelf},
+  author={Lessin, Gennadi and Polimene, Luca and Artioli, Yuri and Butensch{\"o}n, Momme and Clark, Darren R and Brown, Ian and Rees, Andrew P},
+  journal={Journal of Geophysical Research: Biogeosciences},
+  pages={e2019JG005613},
+  year={2020},
+  publisher={Wiley Online Library}
+}
+
+@article{fortuin2018som,
+  title={Som-vae: Interpretable discrete representation learning on time series},
+  author={Fortuin, Vincent and H{\"u}ser, Matthias and Locatello, Francesco and Strathmann, Heiko and R{\"a}tsch, Gunnar},
+  journal={arXiv preprint arXiv:1806.02199},
+  year={2018}
+}
+
+@inproceedings{makiyama2015text,
+  title={Text Mining Applied to SQL Queries: A Case Study for the SDSS SkyServer.},
+  author={Makiyama, Vitor Hirota and Raddick, Jordan and Santos, Rafael DC},
+  booktitle={SIMBig},
+  pages={66--72},
+  year={2015}
+}
+
+@misc{lisbonuni,
+  author={Ludwig Krippahl, Francisco Azevedo},
+  title = {Lecure notes for the course of automatic learning at the University of Lisbon},
+  howpublished = {\url{http://aa.ssdi.di.fct.unl.pt/files/AA-16_notes.pdf}},
+  note = {Accessed: 2020-07-23}
+}
diff --git a/paper/paper.md b/paper/paper.md
new file mode 100644
index 0000000..4037dbd
--- /dev/null
+++ b/paper/paper.md
@@ -0,0 +1,37 @@
+---
+title: 'MiniSom, a minimalistic and Numpy based implementation of the Self Organizing Maps'
+tags:
+  - Python
+  - Self Organizing Maps
+  - Machine Learning
+  - Neural Networks
+authors:
+  - name: Giuseppe Vettigli
+    orcid: 0000-0002-3939-2801
+    affiliation: 1
+affiliations:
+ - name: Centrica plc
+   index: 1
+date: 23 July 2020
+bibliography: paper.bib
+---
+
+# Summary
+
+`MiniSom` is a minimalistic and Numpy based implementation of the Self Organizing Maps (SOM). SOM is a type of Artificial Neural Network able to convert complex nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display.
+
+
+# Statement of need 
+
+SOM is a well known type of Artificial Neural Network [@kohonen1990self] that is able to organize itself so that each specific areas respond to similarly to similar outputs. This model is suitable for different Machine Learning tasks, specifically clustering, dimensionality reduction and vector quantization. Since the first formulation, it became a tool in a plethora of applications in many scientific fields.  The Machine Learning community has not only found numerous applications for it but has developed a staggering amount of variants of the original model.
+
+`MiniSom` is a Python library that implements SOM and it is designed to be easy to modify and adapt. The goal is to give researchers the ability to easily create variants of the main SOM model and give students an implementation of SOM which is easy to understand.
+
+At the time I am writing, `Minisom` has been cited in more than 50 scientific publications. It has been used in many typical Machine Learning applications, as time series modeling [@fortuin2018som] and text mining [@makiyama2015text]. And it has also been been used as a tool in a variety of fields as Geophysics [@lessin2020modeling] and Climatology [@thompson2020synoptic]. It's also worth mentioning that `MiniSom` has been used for the creation of teaching materials on Machine Learning, see [@lisbonuni] for an example.
+
+The interface of `MiniSom` has evolved to blend easily with the general Machine Learning framework `scikit-learn` [@pedregosa2011scikit] and the visualization library `matplotlib` [@matplotlib]. The documentation of the library is proposed through examples and makes heavily use of the cited libraries.
+
+The library was originally developed while developing a Machine Learning methodology to embed structured data (graphs and trees) into vectorial spaces [@vettigli2017fuzzy].
+
+
+# References
\ No newline at end of file
diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 0000000..0bc5bd0
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,2 @@
+[metadata]
+description-file = Readme.md
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..513c02d
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,18 @@
+#!/usr/bin/env python
+from distutils.core import setup
+
+description = 'Minimalistic implementation of the Self Organizing Maps (SOM)'
+keywords = ['machine learning', 'neural networks', 'clustering', 'dimentionality reduction']
+
+setup(name='MiniSom',
+      version='2.2.7',
+      description=description,
+      author='Giuseppe Vettigli',
+      package_data={'': ['Readme.md']},
+      include_package_data=True,
+      license="CC BY 3.0",
+      py_modules=['minisom'],
+      requires=['numpy'],
+      url='https://github.com/JustGlowing/minisom',
+      download_url='https://github.com/JustGlowing/minisom/archive/master.zip',
+      keywords=keywords)