{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "42ee7ada",
   "metadata": {},
   "source": [
    "# <ins>Fuel Efficiency of Canadian Vehicles<ins>\n",
    "\n",
    "A Project By: Kai Groden-Gilchrist and Alexander Proskiw"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18aec430",
   "metadata": {},
   "source": [
    "# Table Of Contents\n",
    "\n",
    "1. Introduction\n",
    "\n",
    "\n",
    "2. Importing and Cleaning the Data\n",
    "\n",
    "    2.1 Required Import Statements\n",
    "\n",
    "    2.2 Read and Concatenate the Data\n",
    "\n",
    "    2.3 Cleaning the Data\n",
    "\n",
    "    2.4 Dataset Summary\n",
    "    \n",
    "\n",
    "3. Visualizations and Key Findings\n",
    "\n",
    "    3.1 Effect of the Explanatory Variables on Fuel Efficiency\n",
    "\n",
    "    3.2 Interactions Between Year and Other Explanatory Variables\n",
    "\n",
    "    3.3 Most Efficient Vehicles and Makes\n",
    "    \n",
    "\n",
    "4. Modelling and Predicting Fuel Efficiency\n",
    "\n",
    "    4.1 Models With No Interactions\n",
    "\n",
    "    4.2 Models With Interactions With Year\n",
    "\n",
    "    4.3 Predicting Fuel Efficiency of 2024 Vehicles\n",
    "    \n",
    "\n",
    "5. R&D Spending\n",
    "\n",
    "\n",
    "6. Discussion\n",
    "\n",
    "\n",
    "7. Conclusion and Future Opportunities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba606711",
   "metadata": {},
   "source": [
    "# 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "565792b5",
   "metadata": {},
   "source": [
    "With gasoline prices reaching record highs in 2022 and remaining elevated since, fuel efficiency is a key concern for consumers looking to buy a vehicle. There is lots of generic advice thrown around regarding purchasing a vehicle, such as \"new vehicles are so much more fuel efficient\" or \"SUVs are all just gas guzzlers\". But what is the reality of the fuel efficiency of vehicles sold in the Canadian market? The goal of this research project is to explore the fuel efficiency of Canadian vehicles by answering the following questions:\n",
    "> -----\n",
    "> <ins>**Key Research Questions**</ins>\n",
    ">\n",
    "> 1. Has vehicle fuel efficiency improved from 2000 to 2023?\n",
    ">\n",
    "> 2. Which manufacturer produces, on average, the most fuel efficient vehicles?\n",
    ">\n",
    "> 3. Can existing fuel efficiency information be used to predict the fuel efficiency of future vehicles?\n",
    ">\n",
    "> 4. Can we use a company's R&D spending to predict fuel efficiency improvements?\n",
    "> -----\n",
    "\n",
    "The dataset chosen for this project is the [Natural Resources Canada Fuel Consumption Ratings Dataset](https://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64?fbclid=IwAR1rVpyNw3KJSeAcT6ryyCAADAnmB7uDtujvwlswFkOzaiLlnjIt29EekiY) published by the Government of Canada. This dataset includes fuel consumption information for vehicles sold in Canada from 2000 to 2023, along with supporting information about each vehicle such as make, model, vehicle type, engine size, and transmission type."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e012545",
   "metadata": {},
   "source": [
    "# 2. Importing and Cleaning the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba1a1c43",
   "metadata": {},
   "source": [
    "Prior to performing any visualizations or modelling, all the separate fuel consumption datasets must be imported and combined. Due to the unique abbreviations used for certain columns of data, as well as differences in how the data is presented between years, data cleaning is also required. The following sections detail the import and cleaning of the raw datasets:\n",
    "- Section 2.1 imports all the libraries and packages used in this investigation.\n",
    "\n",
    "- Section 2.2 reads in the datasets from csv files, and combines them into one pandas DataFrame.\n",
    "\n",
    "- Section 2.3 cleans the DataFrame for future manipulation. \n",
    "\n",
    "- Section 2.4 gives an initial overview of the data and how it will be used."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "562923c2",
   "metadata": {},
   "source": [
    "## 2.1 Required Import Statements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29daad51",
   "metadata": {},
   "source": [
    "First all python packages that will be utilized in this project are imported. Additionally, custom figure display and text output options are specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3148815f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import seaborn as sns\n",
    "import patsy\n",
    "import tabula\n",
    "from plotly import express as px, graph_objs as go\n",
    "from sklearn import (linear_model, metrics, tree, model_selection, neural_network, preprocessing, pipeline)\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from itertools import cycle\n",
    "from datetime import datetime\n",
    "\n",
    "pd.set_option('display.max_rows', 100)\n",
    "sns.set_palette(\"muted\")\n",
    "sns.set_style(\"darkgrid\")\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ed0f1d1",
   "metadata": {},
   "source": [
    "## 2.2 Read and Concatenate the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd485de",
   "metadata": {},
   "source": [
    "Next the csv files from the Natural Resources Canada Database are read, specifically the aggregated files for 2000-2004, 2005-2009, and 2010-2014, as well as the yearly files from 2015 to 2023."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "502aabb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "fuel_cons_2023 = pd.read_csv(\"https://www.nrcan.gc.ca/sites/nrcan/files/oee/files/csv/MY2023%20Fuel%20Consumption\"\n",
    "                             + \"%20Ratings.csv\", encoding=\"Windows-1252\", low_memory=False)\n",
    "fuel_cons_2022 = pd.read_csv(\"https://www.nrcan.gc.ca/sites/nrcan/files/oee/files/csv/MY2022%20Fuel%20Consumption\"\n",
    "                             + \"%20Ratings.csv\", encoding=\"Windows-1252\", low_memory=False)\n",
    "fuel_cons_2021 = pd.read_csv(\"https://www.nrcan.gc.ca/sites/nrcan/files/oee/files/csv/MY2021%20Fuel%20Consumption\"\n",
    "                             + \"%20Ratings.csv\", encoding=\"Windows-1252\", low_memory=False)\n",
    "fuel_cons_2020 = pd.read_csv(\"https://www.nrcan.gc.ca/sites/nrcan/files/oee/files/csv/MY2020%20Fuel%20Consumption\"\n",
    "                             + \"%20Ratings.csv\", encoding=\"Windows-1252\", low_memory=False)\n",
    "fuel_cons_2019 = pd.read_csv(\"https://www.nrcan.gc.ca/sites/nrcan/files/oee/files/csv/MY2019%20Fuel%20Consumption\"\n",
    "                             + \"%20Ratings.csv\", encoding=\"Windows-1252\", low_memory=False)\n",
    "fuel_cons_2018 = pd.read_csv(\"https://www.nrcan.gc.ca/sites/nrcan/files/oee/files/csv/MY2018%20Fuel%20Consumption\"\n",
    "                             + \"%20Ratings.csv\", encoding=\"Windows-1252\", low_memory=False)\n",
    "fuel_cons_2017 = pd.read_csv(\"https://www.nrcan.gc.ca/sites/nrcan/files/oee/files/csv/MY2017%20Fuel%20Consumption\"\n",
    "                             + \"%20Ratings.csv\", encoding=\"Windows-1252\", low_memory=False)\n",
    "fuel_cons_2016 = pd.read_csv(\"https://www.nrcan.gc.ca/sites/nrcan/files/oee/files/csv/MY2016%20Fuel%20Consumption\"\n",
    "                             + \"%20Ratings.csv\", encoding=\"Windows-1252\", low_memory=False)\n",
    "fuel_cons_2015 = pd.read_csv(\"https://www.nrcan.gc.ca/sites/nrcan/files/oee/files/csv/MY2015%20Fuel%20Consumption\"\n",
    "                             + \"%20Ratings.csv\", encoding=\"Windows-1252\", low_memory=False)\n",
    "fuel_cons_2010_2014 = pd.read_csv(\"https://www.nrcan.gc.ca/sites/www.rncan.gc.ca/files/oee/files/csv/MY2010-2014\"\n",
    "                             + \"%20Fuel%20Consumption%20Ratings%205-cycle.csv\", encoding=\"Windows-1252\", \n",
    "                                  low_memory=False)\n",
    "fuel_cons_2005_2009 = pd.read_csv(\"https://www.nrcan.gc.ca/sites/www.rncan.gc.ca/files/oee/files/csv/MY2005-2009\"\n",
    "                             + \"%20Fuel%20Consumption%20Ratings%205-cycle.csv\", encoding=\"Windows-1252\", \n",
    "                                  low_memory=False)\n",
    "fuel_cons_2000_2004 = pd.read_csv(\"https://www.nrcan.gc.ca/sites/www.rncan.gc.ca/files/oee/files/csv/MY2000-2004\"\n",
    "                             + \"%20Fuel%20Consumption%20Ratings%205-cycle.csv\", encoding=\"Windows-1252\", \n",
    "                                  low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "459fd913",
   "metadata": {},
   "source": [
    "Next the individual datasets are combined into a single dataset. This includes removing rows with NA values, and dropping certain non-data rows at the bottom of each spreadsheet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d209b2bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop nans and combine datasets from each year\n",
    "list_of_fuel_data = [fuel_cons_2023, fuel_cons_2022, fuel_cons_2021, fuel_cons_2020, fuel_cons_2019,\n",
    "                    fuel_cons_2018, fuel_cons_2017, fuel_cons_2016, fuel_cons_2015, fuel_cons_2010_2014,\n",
    "                    fuel_cons_2005_2009, fuel_cons_2000_2004]\n",
    "\n",
    "col_names = [\"model_year\", \"make\", \"model\", \"vehicle_class\", \"engine_size_L\", \"cylinders\", \"transmission\", \n",
    "             \"fuel_type\", \"city_fuel_cons\", \"hwy_fuel_cons\", \"comb_lpkm\", \"comb_mpg\", \"co2_emissions\", \n",
    "             \"co2_rating\",\"smog_rating\"]\n",
    "\n",
    "for i, dataset in enumerate(list_of_fuel_data):\n",
    "    # drop rows of entirely NaN, then cols of entirely NaN, then rows with some NaN\n",
    "    # there was around 50 rows at the bottom with text explaining the data, hence the need for the last drop\n",
    "    dataset = dataset.dropna(axis=0, how='all').dropna(axis=1, how='all').dropna(axis=0)\n",
    "    try: \n",
    "        dataset.columns = col_names\n",
    "    # older datasets are missing the last one or two cols. Not needed, as we'll drop those anyways\n",
    "    except ValueError:\n",
    "        try: \n",
    "            dataset.columns = col_names[:-1]\n",
    "        except ValueError:\n",
    "            dataset.columns = col_names[:-2]\n",
    "    list_of_fuel_data[i] = dataset\n",
    "            \n",
    "full_dataset = pd.DataFrame()\n",
    "\n",
    "for dataset in list_of_fuel_data:\n",
    "    full_dataset = pd.concat([full_dataset, dataset], axis=0)\n",
    "    \n",
    "full_dataset.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baccf13c",
   "metadata": {},
   "source": [
    "The original csv files contain data on the city, highway, and combined (55% city, 45% highway) fuel economy for each vehicle. For the purposes of this research project, **<ins>the combined fuel economy (in litres per 100 km, abbreviated as L/100km) is selected as the response variable</ins>** so as to get a broad representation of each vehicle's efficiency. \n",
    "\n",
    "Hence, after concatenating the individual datasets, the following unnecessary columns are dropped:\n",
    "- City fuel economy and highway fuel economy, as the combined fuel economy represents their weighted average.\n",
    "\n",
    "- Combined fuel economy in miles per gallon, as this is just a unit conversion from L/100km.\n",
    "\n",
    "- CO2 emissions, CO2 rating, and Smog rating, as this research project is focusing on fuel efficiency and not emissions or smog."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "52424f4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\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>model_year</th>\n",
       "      <th>make</th>\n",
       "      <th>model</th>\n",
       "      <th>vehicle_class</th>\n",
       "      <th>engine_size_L</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>transmission</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>comb_lpkm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023</td>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra</td>\n",
       "      <td>Full-size</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AV7</td>\n",
       "      <td>Z</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023</td>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra A-SPEC</td>\n",
       "      <td>Full-size</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AV7</td>\n",
       "      <td>Z</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023</td>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra A-SPEC</td>\n",
       "      <td>Full-size</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023</td>\n",
       "      <td>Acura</td>\n",
       "      <td>MDX SH-AWD</td>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS10</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023</td>\n",
       "      <td>Acura</td>\n",
       "      <td>MDX SH-AWD Type S</td>\n",
       "      <td>SUV: Standard</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS10</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23499</th>\n",
       "      <td>2004</td>\n",
       "      <td>VOLVO</td>\n",
       "      <td>V70 T5 TURBO</td>\n",
       "      <td>STATION WAGON - MID-SIZE</td>\n",
       "      <td>2.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23500</th>\n",
       "      <td>2004</td>\n",
       "      <td>VOLVO</td>\n",
       "      <td>V70 T5 TURBO</td>\n",
       "      <td>STATION WAGON - MID-SIZE</td>\n",
       "      <td>2.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>M5</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23501</th>\n",
       "      <td>2004</td>\n",
       "      <td>VOLVO</td>\n",
       "      <td>XC70 AWD TURBO</td>\n",
       "      <td>SUV</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23502</th>\n",
       "      <td>2004</td>\n",
       "      <td>VOLVO</td>\n",
       "      <td>XC90 2.5T AWD TURBO</td>\n",
       "      <td>SUV</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23503</th>\n",
       "      <td>2004</td>\n",
       "      <td>VOLVO</td>\n",
       "      <td>XC90 T6 AWD TURBO</td>\n",
       "      <td>SUV</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23504 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      model_year   make                model             vehicle_class  \\\n",
       "0           2023  Acura              Integra                 Full-size   \n",
       "1           2023  Acura       Integra A-SPEC                 Full-size   \n",
       "2           2023  Acura       Integra A-SPEC                 Full-size   \n",
       "3           2023  Acura           MDX SH-AWD                SUV: Small   \n",
       "4           2023  Acura    MDX SH-AWD Type S             SUV: Standard   \n",
       "...          ...    ...                  ...                       ...   \n",
       "23499       2004  VOLVO         V70 T5 TURBO  STATION WAGON - MID-SIZE   \n",
       "23500       2004  VOLVO         V70 T5 TURBO  STATION WAGON - MID-SIZE   \n",
       "23501       2004  VOLVO       XC70 AWD TURBO                       SUV   \n",
       "23502       2004  VOLVO  XC90 2.5T AWD TURBO                       SUV   \n",
       "23503       2004  VOLVO    XC90 T6 AWD TURBO                       SUV   \n",
       "\n",
       "      engine_size_L  cylinders transmission fuel_type comb_lpkm  \n",
       "0               1.5        4.0          AV7         Z       7.2  \n",
       "1               1.5        4.0          AV7         Z       7.4  \n",
       "2               1.5        4.0           M6         Z       7.8  \n",
       "3               3.5        6.0         AS10         Z      11.2  \n",
       "4               3.0        6.0         AS10         Z      12.4  \n",
       "...             ...        ...          ...       ...       ...  \n",
       "23499           2.3        5.0          AS5         Z      12.5  \n",
       "23500           2.3        5.0           M5         Z      11.8  \n",
       "23501           2.5        5.0          AS5         Z      12.5  \n",
       "23502           2.5        5.0          AS5         Z      13.0  \n",
       "23503           2.9        6.0          AS5         Z      15.1  \n",
       "\n",
       "[23504 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop unnecessary columns\n",
    "full_dataset.drop([\"city_fuel_cons\", \"hwy_fuel_cons\", \"comb_mpg\", \"co2_emissions\", \"co2_rating\", \"smog_rating\"], \n",
    "                  axis=1, inplace=True)\n",
    "# preview the dataset\n",
    "full_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd4cc212",
   "metadata": {},
   "source": [
    "The output above provides the first look at the dataset for this project.\n",
    "\n",
    "It is immediately noted that several of the rows have somewhat confusing information. For example, fuel_type is represented as a letter, transmission is an abbreviation, and vehicle class appears inconsistent with three different labelings for SUVs present in just the first and last five rows of the dataset alone.\n",
    "\n",
    "All of these issues need to be addressed through the cleaning of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37458512",
   "metadata": {},
   "source": [
    "## 2.3 Cleaning the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68a6e90f",
   "metadata": {},
   "source": [
    "First the datatypes for each column are determined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fc6b5d6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model_year        object\n",
       "make              object\n",
       "model             object\n",
       "vehicle_class     object\n",
       "engine_size_L     object\n",
       "cylinders        float64\n",
       "transmission      object\n",
       "fuel_type         object\n",
       "comb_lpkm         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_dataset.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f66e1140",
   "metadata": {},
   "source": [
    "Aside from cylinders, all datatypes are currently of type \"object\". Certain columns need to be converted to numeric data, namely integers for the year and floats for the engine size and fuel efficiency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ec2c0856",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model_year         int64\n",
       "make              object\n",
       "model             object\n",
       "vehicle_class     object\n",
       "engine_size_L    float64\n",
       "cylinders        float64\n",
       "transmission      object\n",
       "fuel_type         object\n",
       "comb_lpkm        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert appropriate object datatypes to numerical\n",
    "full_dataset[\"model_year\"] = full_dataset[\"model_year\"].astype(int)\n",
    "cols = [\"engine_size_L\", \"comb_lpkm\"]\n",
    "full_dataset[cols] = full_dataset[cols].astype(float)\n",
    "full_dataset.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1b711a3",
   "metadata": {},
   "source": [
    "Next, the 'make' column needs to be cleaned. This column represents the manufacturer of a particular vehicle.\n",
    "\n",
    "Minor improvements include fixing inconsistent capitalization and combining \"Ram\" and \"Dodge\" into a single brand.\n",
    "\n",
    "More importantly, rows are removed for any manufacturer that didn't produce at least one vehicle in every year from 2000 to 2023. This step is implemented because the project aims to assess fuel efficiency changes over the entire 2000-2023 time period. A manufacturer risks skewing the results if they went bankrupt in the early 2000s, or didn't start producing cars until the late 2010s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d12847e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Acura' 'Audi' 'Bmw' 'Buick' 'Cadillac' 'Chevrolet' 'Chrysler' 'Dodge'\n",
      " 'Ford' 'Gmc' 'Honda' 'Hyundai' 'Infiniti' 'Jaguar' 'Jeep' 'Kia' 'Lexus'\n",
      " 'Lincoln' 'Mazda' 'Mercedes-Benz' 'Nissan' 'Porsche' 'Subaru' 'Toyota'\n",
      " 'Volkswagen' 'Volvo']\n"
     ]
    }
   ],
   "source": [
    "# clean the 'make' series\n",
    "\n",
    "# different capitalizations of makes were used in different years. Update for consistency.\n",
    "full_dataset[\"make\"] = full_dataset[\"make\"].str.title()\n",
    "\n",
    "# also adjust to account for Dodge rebranding their pickup trucks under the Ram name\n",
    "full_dataset[\"make\"].replace({\"Ram\": \"Dodge\"}, inplace = True)\n",
    "\n",
    "# remove data from the datset for manufacturers that didn't make cars from 2000 to 2023\n",
    "makes = full_dataset.groupby(\"make\")\n",
    "for make in makes.groups:\n",
    "    matches_make = (full_dataset[\"make\"] == make)\n",
    "    only_current_make_dataset = full_dataset[matches_make]\n",
    "    num_years = only_current_make_dataset[\"model_year\"].nunique()\n",
    "    not_full_range_of_years = (num_years != 24)\n",
    "    if not_full_range_of_years:\n",
    "        full_dataset.drop(only_current_make_dataset.index, inplace=True)\n",
    "full_dataset.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# verify changes\n",
    "print(full_dataset[\"make\"].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b9f5b6b",
   "metadata": {},
   "source": [
    "Upon cleaning the vehicle 'make' column, it is verified that the dataset now contains 26 unique vehicle manufacturers from around the globe. These companies all produced vehicles every year from 2000 to 2023."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14242669",
   "metadata": {},
   "source": [
    "Next is to clean the 'vehicle_class' column. This column represents the broad category that a vehicle belongs to, such as whether a vehicle is a car or an SUV. The dataset initially contains many different categories, some of which are duplicates with inconsistent capitalization. For simplicity, the various sub-categories are reduced into 4 broad categories:\n",
    "- Car\n",
    "\n",
    "- SUV\n",
    "\n",
    "- Truck\n",
    "\n",
    "- Van\n",
    "\n",
    "Passenger/cargo vans were removed from the dataset at this stage, as they are generally used as business vehicles (e.g., as an Amazon delivery van) rather than by typical consumers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "818c63bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Car' 'SUV' 'Truck' 'Van']\n"
     ]
    }
   ],
   "source": [
    "# clean the 'vehicle_class' series\n",
    "\n",
    "# simplify into 5 categories: Car, SUV, Van, Truck, and Passenger/Cargo Van\n",
    "full_dataset[\"vehicle_class\"].replace({\"Full-size\": \"Car\",\n",
    "                                       \"SUV: Small\": \"SUV\",\n",
    "                                       \"SUV: Standard\": \"SUV\",\n",
    "                                       \"Compact\": \"Car\",\n",
    "                                       \"Subcompact\": \"Car\",\n",
    "                                       \"Station wagon: Small\": \"Car\",\n",
    "                                       \"Mid-size\": \"Car\",\n",
    "                                       \"Station wagon: Mid-size\": \"Car\",\n",
    "                                       \"Two-seater\": \"Car\",\n",
    "                                       \"Pickup truck: Standard\": \"Truck\",\n",
    "                                       \"Minivan\": \"Van\",\n",
    "                                       \"Pickup truck: Small\": \"Truck\",\n",
    "                                       \"Special purpose vehicle\": \"Passenger/Cargo Van\",\n",
    "                                       \"Minicompact\": \"Car\",\n",
    "                                       \"Van: Passenger\": \"Passenger/Cargo Van\",\n",
    "                                       \"COMPACT\": \"Car\",\n",
    "                                       \"SUV - SMALL\": \"SUV\",\n",
    "                                       \"TWO-SEATER\": \"Car\",\n",
    "                                       \"MID-SIZE\": \"Car\",\n",
    "                                       \"SUBCOMPACT\": \"Car\",\n",
    "                                       \"STATION WAGON - SMALL\": \"Car\",\n",
    "                                       \"FULL-SIZE\": \"Car\",\n",
    "                                       \"SUV - STANDARD\": \"SUV\",\n",
    "                                       \"SPECIAL PURPOSE VEHICLE\": \"Van\",\n",
    "                                       \"PICKUP TRUCK - SMALL\": \"Truck\",\n",
    "                                       \"VAN - PASSENGER\": \"Passenger/Cargo Van\",\n",
    "                                       \"PICKUP TRUCK - STANDARD\": \"Truck\",\n",
    "                                       \"MINIVAN\": \"Van\",\n",
    "                                       \"STATION WAGON - MID-SIZE\": \"Car\",\n",
    "                                       \"MINICOMPACT\": \"Car\",\n",
    "                                       \"SUV\": \"SUV\",\n",
    "                                       \"VAN - CARGO\": \"Passenger/Cargo Van\",\n",
    "                                       \"UL\": \"SUV\"}, inplace = True)\n",
    "\n",
    "# remove the passenger/cargo van category as these are typically business vehicles\n",
    "is_passenger_cargo_van = full_dataset[\"vehicle_class\"] == \"Passenger/Cargo Van\"\n",
    "only_passenger_cargo_van_dataset = full_dataset[is_passenger_cargo_van]\n",
    "full_dataset.drop(only_passenger_cargo_van_dataset.index, inplace=True)\n",
    "full_dataset.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# verify changes\n",
    "print(full_dataset[\"vehicle_class\"].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "885508a7",
   "metadata": {},
   "source": [
    "Next is to clean the 'fuel_type' column. Using the information from [Natural Resource Canada](https://natural-resources.canada.ca/energy-efficiency/transportation-alternative-fuels/personal-vehicles/choosing-right-vehicle/buying-electric-vehicle/understanding-the-tables/21383), the single letter codes are mapped to their corresponding fuel types:\n",
    "- Regular Gasoline\n",
    "\n",
    "- Premium Gasoline\n",
    "\n",
    "- Diesel\n",
    "\n",
    "- Ethanol\n",
    "\n",
    "- Natural Gas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "06a96053",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Premium Gasoline' 'Regular Gasoline' 'Diesel' 'Ethanol' 'Natural Gas']\n"
     ]
    }
   ],
   "source": [
    "# clean the 'fuel_type' series\n",
    "\n",
    "# update the fuel types from letters to their actual meanings\n",
    "full_dataset[\"fuel_type\"].replace({\"X\": \"Regular Gasoline\",\n",
    "                                   \"Z\": \"Premium Gasoline\",\n",
    "                                   \"D\": \"Diesel\",\n",
    "                                   \"E\": \"Ethanol\",\n",
    "                                   \"N\": \"Natural Gas\"}, inplace = True)\n",
    "\n",
    "# verify changes\n",
    "print(full_dataset[\"fuel_type\"].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c3f27a3",
   "metadata": {},
   "source": [
    "Next is to clean the 'transmission' column. This is achieved by separating the alphanumeric abbreviation into two separate columns: one for the transmission type and one for the number of gears. Transmission types include:\n",
    "- Automatic\n",
    "\n",
    "- Automated Manual\n",
    "\n",
    "- Automatic Select Shift\n",
    "\n",
    "- Continuously Variable\n",
    "\n",
    "- Manual\n",
    "\n",
    "Note that for the number of gears, continuously variable transmissions have been assigned as having 1 gear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fdcaeb23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>model_year</th>\n",
       "      <th>make</th>\n",
       "      <th>model</th>\n",
       "      <th>vehicle_class</th>\n",
       "      <th>engine_size_L</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>comb_lpkm</th>\n",
       "      <th>transmission_type</th>\n",
       "      <th>transmission_gears</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023</td>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra</td>\n",
       "      <td>Car</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>7.2</td>\n",
       "      <td>Continuously Variable</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023</td>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra A-SPEC</td>\n",
       "      <td>Car</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>7.4</td>\n",
       "      <td>Continuously Variable</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023</td>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra A-SPEC</td>\n",
       "      <td>Car</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Manual</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023</td>\n",
       "      <td>Acura</td>\n",
       "      <td>MDX SH-AWD</td>\n",
       "      <td>SUV</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>11.2</td>\n",
       "      <td>Automatic Select Shift</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023</td>\n",
       "      <td>Acura</td>\n",
       "      <td>MDX SH-AWD Type S</td>\n",
       "      <td>SUV</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Automatic Select Shift</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19953</th>\n",
       "      <td>2004</td>\n",
       "      <td>Volvo</td>\n",
       "      <td>V70 T5 TURBO</td>\n",
       "      <td>Car</td>\n",
       "      <td>2.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>12.5</td>\n",
       "      <td>Automatic Select Shift</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19954</th>\n",
       "      <td>2004</td>\n",
       "      <td>Volvo</td>\n",
       "      <td>V70 T5 TURBO</td>\n",
       "      <td>Car</td>\n",
       "      <td>2.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>11.8</td>\n",
       "      <td>Manual</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19955</th>\n",
       "      <td>2004</td>\n",
       "      <td>Volvo</td>\n",
       "      <td>XC70 AWD TURBO</td>\n",
       "      <td>SUV</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>12.5</td>\n",
       "      <td>Automatic Select Shift</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19956</th>\n",
       "      <td>2004</td>\n",
       "      <td>Volvo</td>\n",
       "      <td>XC90 2.5T AWD TURBO</td>\n",
       "      <td>SUV</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Automatic Select Shift</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19957</th>\n",
       "      <td>2004</td>\n",
       "      <td>Volvo</td>\n",
       "      <td>XC90 T6 AWD TURBO</td>\n",
       "      <td>SUV</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>15.1</td>\n",
       "      <td>Automatic Select Shift</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19958 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       model_year   make                model vehicle_class  engine_size_L  \\\n",
       "0            2023  Acura              Integra           Car            1.5   \n",
       "1            2023  Acura       Integra A-SPEC           Car            1.5   \n",
       "2            2023  Acura       Integra A-SPEC           Car            1.5   \n",
       "3            2023  Acura           MDX SH-AWD           SUV            3.5   \n",
       "4            2023  Acura    MDX SH-AWD Type S           SUV            3.0   \n",
       "...           ...    ...                  ...           ...            ...   \n",
       "19953        2004  Volvo         V70 T5 TURBO           Car            2.3   \n",
       "19954        2004  Volvo         V70 T5 TURBO           Car            2.3   \n",
       "19955        2004  Volvo       XC70 AWD TURBO           SUV            2.5   \n",
       "19956        2004  Volvo  XC90 2.5T AWD TURBO           SUV            2.5   \n",
       "19957        2004  Volvo    XC90 T6 AWD TURBO           SUV            2.9   \n",
       "\n",
       "       cylinders         fuel_type  comb_lpkm       transmission_type  \\\n",
       "0            4.0  Premium Gasoline        7.2   Continuously Variable   \n",
       "1            4.0  Premium Gasoline        7.4   Continuously Variable   \n",
       "2            4.0  Premium Gasoline        7.8                  Manual   \n",
       "3            6.0  Premium Gasoline       11.2  Automatic Select Shift   \n",
       "4            6.0  Premium Gasoline       12.4  Automatic Select Shift   \n",
       "...          ...               ...        ...                     ...   \n",
       "19953        5.0  Premium Gasoline       12.5  Automatic Select Shift   \n",
       "19954        5.0  Premium Gasoline       11.8                  Manual   \n",
       "19955        5.0  Premium Gasoline       12.5  Automatic Select Shift   \n",
       "19956        5.0  Premium Gasoline       13.0  Automatic Select Shift   \n",
       "19957        6.0  Premium Gasoline       15.1  Automatic Select Shift   \n",
       "\n",
       "       transmission_gears  \n",
       "0                       7  \n",
       "1                       7  \n",
       "2                       6  \n",
       "3                      10  \n",
       "4                      10  \n",
       "...                   ...  \n",
       "19953                   5  \n",
       "19954                   5  \n",
       "19955                   5  \n",
       "19956                   5  \n",
       "19957                   5  \n",
       "\n",
       "[19958 rows x 10 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clean the 'transmission' series\n",
    "\n",
    "# separate each into the transmission type and gears\n",
    "def convert_transmission_to_type(s):\n",
    "    transmission_type = re.findall('([A-Za-z]+)', s)\n",
    "    return transmission_type[0]\n",
    "\n",
    "def convert_transmission_to_gears(s):\n",
    "    transmission_gears = re.findall('\\d+', s)\n",
    "    if (transmission_gears == []):\n",
    "        transmission_gears = [1]  \n",
    "    return transmission_gears[0]\n",
    "\n",
    "# separate the transmission column into separate columns for transmission type and gears, remove original column\n",
    "full_dataset[\"transmission_type\"] = full_dataset[\"transmission\"].apply(convert_transmission_to_type)\n",
    "full_dataset[\"transmission_gears\"] = full_dataset[\"transmission\"].apply(convert_transmission_to_gears)\n",
    "full_dataset[\"transmission_gears\"] = full_dataset[\"transmission_gears\"].astype(int)\n",
    "full_dataset.drop([\"transmission\"], axis=1, inplace=True)\n",
    "\n",
    "# update the transmission types from abbreviations to their actual meanings\n",
    "full_dataset[\"transmission_type\"].replace({\"A\": \"Automatic\",\n",
    "                                           \"AM\": \"Automated Manual\",\n",
    "                                           \"AS\": \"Automatic Select Shift\",\n",
    "                                           \"AV\": \"Continuously Variable\",\n",
    "                                           \"M\": \"Manual\",}, inplace = True)\n",
    "\n",
    "# verify changes\n",
    "full_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ef98bff",
   "metadata": {},
   "source": [
    "Having successfully cleaned all the data columns, they are reordered intuitively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2b6d0aeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_year</th>\n",
       "      <th>make</th>\n",
       "      <th>model</th>\n",
       "      <th>vehicle_class</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>engine_size_L</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>transmission_type</th>\n",
       "      <th>transmission_gears</th>\n",
       "      <th>comb_lpkm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023</td>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra</td>\n",
       "      <td>Car</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Continuously Variable</td>\n",
       "      <td>7</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023</td>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra A-SPEC</td>\n",
       "      <td>Car</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Continuously Variable</td>\n",
       "      <td>7</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023</td>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra A-SPEC</td>\n",
       "      <td>Car</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>6</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023</td>\n",
       "      <td>Acura</td>\n",
       "      <td>MDX SH-AWD</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Automatic Select Shift</td>\n",
       "      <td>10</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023</td>\n",
       "      <td>Acura</td>\n",
       "      <td>MDX SH-AWD Type S</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Automatic Select Shift</td>\n",
       "      <td>10</td>\n",
       "      <td>12.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19953</th>\n",
       "      <td>2004</td>\n",
       "      <td>Volvo</td>\n",
       "      <td>V70 T5 TURBO</td>\n",
       "      <td>Car</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>2.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Automatic Select Shift</td>\n",
       "      <td>5</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19954</th>\n",
       "      <td>2004</td>\n",
       "      <td>Volvo</td>\n",
       "      <td>V70 T5 TURBO</td>\n",
       "      <td>Car</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>2.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>5</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19955</th>\n",
       "      <td>2004</td>\n",
       "      <td>Volvo</td>\n",
       "      <td>XC70 AWD TURBO</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Automatic Select Shift</td>\n",
       "      <td>5</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19956</th>\n",
       "      <td>2004</td>\n",
       "      <td>Volvo</td>\n",
       "      <td>XC90 2.5T AWD TURBO</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Automatic Select Shift</td>\n",
       "      <td>5</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19957</th>\n",
       "      <td>2004</td>\n",
       "      <td>Volvo</td>\n",
       "      <td>XC90 T6 AWD TURBO</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Automatic Select Shift</td>\n",
       "      <td>5</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19958 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       model_year   make                model vehicle_class         fuel_type  \\\n",
       "0            2023  Acura              Integra           Car  Premium Gasoline   \n",
       "1            2023  Acura       Integra A-SPEC           Car  Premium Gasoline   \n",
       "2            2023  Acura       Integra A-SPEC           Car  Premium Gasoline   \n",
       "3            2023  Acura           MDX SH-AWD           SUV  Premium Gasoline   \n",
       "4            2023  Acura    MDX SH-AWD Type S           SUV  Premium Gasoline   \n",
       "...           ...    ...                  ...           ...               ...   \n",
       "19953        2004  Volvo         V70 T5 TURBO           Car  Premium Gasoline   \n",
       "19954        2004  Volvo         V70 T5 TURBO           Car  Premium Gasoline   \n",
       "19955        2004  Volvo       XC70 AWD TURBO           SUV  Premium Gasoline   \n",
       "19956        2004  Volvo  XC90 2.5T AWD TURBO           SUV  Premium Gasoline   \n",
       "19957        2004  Volvo    XC90 T6 AWD TURBO           SUV  Premium Gasoline   \n",
       "\n",
       "       engine_size_L  cylinders       transmission_type  transmission_gears  \\\n",
       "0                1.5        4.0   Continuously Variable                   7   \n",
       "1                1.5        4.0   Continuously Variable                   7   \n",
       "2                1.5        4.0                  Manual                   6   \n",
       "3                3.5        6.0  Automatic Select Shift                  10   \n",
       "4                3.0        6.0  Automatic Select Shift                  10   \n",
       "...              ...        ...                     ...                 ...   \n",
       "19953            2.3        5.0  Automatic Select Shift                   5   \n",
       "19954            2.3        5.0                  Manual                   5   \n",
       "19955            2.5        5.0  Automatic Select Shift                   5   \n",
       "19956            2.5        5.0  Automatic Select Shift                   5   \n",
       "19957            2.9        6.0  Automatic Select Shift                   5   \n",
       "\n",
       "       comb_lpkm  \n",
       "0            7.2  \n",
       "1            7.4  \n",
       "2            7.8  \n",
       "3           11.2  \n",
       "4           12.4  \n",
       "...          ...  \n",
       "19953       12.5  \n",
       "19954       11.8  \n",
       "19955       12.5  \n",
       "19956       13.0  \n",
       "19957       15.1  \n",
       "\n",
       "[19958 rows x 10 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reorder the columns\n",
    "full_dataset = full_dataset[[\"model_year\", \"make\", \"model\", \"vehicle_class\", \"fuel_type\",\n",
    "                             \"engine_size_L\", \"cylinders\", \"transmission_type\", \n",
    "                             \"transmission_gears\", \"comb_lpkm\"]]\n",
    "\n",
    "# verify changes\n",
    "full_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbcd4122",
   "metadata": {},
   "source": [
    "## 2.4 Dataset Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b955d8d",
   "metadata": {},
   "source": [
    "Each row in the cleaned dataset represents a specific vehicle sold in a specific year. The columns represent different variables, broken down as follows:\n",
    "\n",
    "<ins>Response Variable:</ins>\n",
    "- The \"comb_lpkm\" column represents the combined fuel efficiency of each vehicle in litres per 100 kilometres (L/100km). This is a continuous numerical variable.\n",
    "\n",
    "> -----\n",
    "> <ins>**Important Note**</ins>\n",
    ">\n",
    "> When using L/100km, a smaller number represents a more efficient vehicle. \n",
    ">\n",
    "> This is the opposite of miles per gallon, where higher numbers are more efficient.\n",
    ">\n",
    "> -----\n",
    "\n",
    "<ins>Explanatory Variables:</ins>\n",
    "- The \"model_year\" column represents the year a vehicle was produced in. This is a continuous numerical variable.\n",
    "\n",
    "- The \"make\" column represents the manufacturer of a vehicle. A total of 26 different manufacturers are included in the dataset. This is a categorical variable.\n",
    "\n",
    "- The \"vehicle_class\" column represents whether a vehicle is a car, truck, SUV, or van. This is a categorical variable.\n",
    "\n",
    "- The \"fuel_type\" column represents what type of fuel a vehicle uses. This is a categorical variable.\n",
    "\n",
    "- The \"engine_size_L\" column represents the size of the engine of a vehicle, in litres. This is a continuous numerical variable.\n",
    "\n",
    "- The \"cylinders\" column represents the number of cylinders in the engine of a vehicle. This is a continuous numerical variable.\n",
    "\n",
    "- The \"transmission_type\" column represents the type of transmission of a vehicle. This is a categorical variable.\n",
    "\n",
    "- The \"transmission_gears\" column represents the number of gears in the transmission of a vehicle. This is a continuous numerical variable.\n",
    "\n",
    "<ins>Additional Variable:</ins>\n",
    "- The \"model\" column includes the specific name of each vehicle. This column will not be used in any visualization or modelling, but is helpful when determining the most/least efficient vehicles."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "921a4dbc",
   "metadata": {},
   "source": [
    "# 3. Visualizations and Key Findings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a97e5ffd",
   "metadata": {},
   "source": [
    "This section presents a set of visualizations aimed at gaining a deeper understanding of vehicle fuel efficiency.\n",
    "\n",
    "Section 3.1 examines the relationship between fuel economy and each of the explanatory variables in isolation (year, make, vehicle class, fuel type, engine size, cylinders, transmission type, and transmission gears).\n",
    "\n",
    "Section 3.2 explores how the year of a vehicle interacts with some of the other explanatory variables.\n",
    "\n",
    "Section 3.3 takes a closer look at the most efficient vehicles and makes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98163ba1",
   "metadata": {},
   "source": [
    "## 3.1 Effect of the Explanatory Variables on Fuel Efficiency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e3850d8",
   "metadata": {},
   "source": [
    "### 3.1.1 Model Year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8572395d",
   "metadata": {},
   "source": [
    "To examine how fuel efficiency varies by year, data is aggregated across all the vehicles for each year. Three metrics for each year are plotted: the average fuel efficiency along with the 5th and 95th percentiles (representing a 90% confidence interval)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d359658c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5th Percentile\n",
    "def q05(x):\n",
    "    return x.quantile(0.05)\n",
    "\n",
    "# 95th Percentile\n",
    "def q95(x):\n",
    "    return x.quantile(0.95)\n",
    "\n",
    "\n",
    "fuel_economy_by_year = full_dataset.pivot_table(index=\"model_year\", \n",
    "                                                values=\"comb_lpkm\", \n",
    "                                                aggfunc=[\"mean\", q05, q95])\n",
    "ax = fuel_economy_by_year.plot()\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Combined Fuel Economy (L/100km)\")\n",
    "ax.set_title(\"Figure 1. Vehicle Fuel Efficiency by Year\")\n",
    "ax.set_ylim(0, 20)\n",
    "plt.gca().get_lines()[0].set_linewidth(3)\n",
    "plt.gca().get_lines()[1].set_color(\"black\")\n",
    "plt.gca().get_lines()[1].set_linestyle(\"--\")\n",
    "plt.gca().get_lines()[1].set_alpha(0.8)\n",
    "plt.gca().get_lines()[2].set_color(\"black\")\n",
    "plt.gca().get_lines()[2].set_linestyle(\"--\")\n",
    "plt.gca().get_lines()[2].set_alpha(0.8)\n",
    "ax.legend(['Average', '5th and 95th Percentiles'])\n",
    "plt.gcf().set_size_inches(9, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9294cba",
   "metadata": {},
   "source": [
    "The figure above clearly demonstrates a downward trend in the combined fuel economy of vehicles from 2000 to 2023. \n",
    "\n",
    "The average fuel economy across all makes and models decreased from 12.8 L/100km in 2000 to 10.8 L/100km in 2023 (approximately 16%). Decreases were also observed for the 5th and 95th percentiles of vehicles. \n",
    "\n",
    "However, the improvements in fuel economy do not appear to be uniform. Little change is observed from 2000 to 2010, followed by a large improvement in fuel economy from 2010 to 2015. Since 2015, there has been little change.\n",
    "\n",
    "While newer cars generally appear to be more fuel-efficient than older models, it will likely be beneficial to analyze each manufacturer individually."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1be91ca",
   "metadata": {},
   "source": [
    "### 3.1.2 Make"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4733a7",
   "metadata": {},
   "source": [
    "To examine how fuel efficiency varies by make, data is aggregated across all the vehicles and years for each make. \n",
    "\n",
    "This equates to an average fuel efficiency value for all the vehicles each manufacturer has produced from 2000 to 2023.\n",
    "\n",
    "A figure showing this average fuel economy for each manufacturer is provided below, ordered from most to least efficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b31c5ed1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Figure 2. Fuel Economy by Different Manufacturers, Averaged Across all Years')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fuel_economy_by_year_and_make = full_dataset.pivot_table(index=\"model_year\", \n",
    "                                                         columns=\"make\", \n",
    "                                                         values=\"comb_lpkm\", \n",
    "                                                         aggfunc=\"mean\")\n",
    "\n",
    "ax = fuel_economy_by_year_and_make.mean().sort_values(ascending=False).plot(kind=\"barh\", figsize=(9, 9))\n",
    "ax.set_ylabel(\"Vehicle Make\")\n",
    "ax.set_xlabel(\"Combined Fuel Economy (L/100km)\")\n",
    "ax.set_title(\"Figure 2. Fuel Economy by Different Manufacturers, Averaged Across all Years\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a82c955",
   "metadata": {},
   "source": [
    "The figure above shows the average combined fuel economy for each manufacturer included in this investigation. On average, Honda vehicles are the most efficient (average of 9.2 L/100km), while GMC vehicles are the least efficient (average of 14.8 L/100km). These results will be unsurprising to most since Honda primarily manufactures cars while GMC specializes in trucks and SUVs. \n",
    "\n",
    "It's evident, then, that the vehicle type may play an important role in determining fuel economy. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6bd0a6a",
   "metadata": {},
   "source": [
    "### 3.1.3 Vehicle Type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7117a821",
   "metadata": {},
   "source": [
    "To assess the effect of vehicle type a box plot visualization is utilized. Each box represents all the vehicles of that type in the dataset (across all years, makes, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cf2bb11a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fuel efficiency by vehicle type\n",
    "ax = sns.boxplot(data=full_dataset, x=\"vehicle_class\", y=\"comb_lpkm\")\n",
    "ax.set(xlabel=\"Vehicle Type\", ylabel=\"Combined Fuel Economy (L/100km)\", title=\"Figure 3. Fuel Efficiency by Vehicle Type\")\n",
    "ax.set_ylim(0, 30)\n",
    "plt.gcf().set_size_inches(9, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e81169",
   "metadata": {},
   "source": [
    "The figure above presents the average combined fuel economy for each vehicle class that was examined. It is evident that cars have the lowest average fuel economy. SUVs and vans have similar means (although SUVs exhibit a greater degree of variation around it), and trucks have the highest average fuel economy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60db9a83",
   "metadata": {},
   "source": [
    "### 3.1.4 Fuel Type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b11fb0c",
   "metadata": {},
   "source": [
    "Similar to assessing vehicle types, a box plot is utilized to view the differences between the distributions of each fuel type. Each box represents all the vehicles of that fuel type in the dataset (across all years, makes, etc.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "20cde7a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fuel efficiency by fuel type\n",
    "ax = sns.boxplot(data=full_dataset, x=\"fuel_type\", y=\"comb_lpkm\")\n",
    "ax.set(xlabel=\"Fuel Type\", ylabel=\"Combined Fuel Economy (L/100km)\", title=\"Figure 4. Fuel Efficiency by Fuel Type\")\n",
    "ax.set_ylim(0, 30)\n",
    "plt.gcf().set_size_inches(9, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c55ef90b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Regular Gasoline    10623\n",
       "Premium Gasoline     8027\n",
       "Ethanol               909\n",
       "Diesel                381\n",
       "Natural Gas            18\n",
       "Name: fuel_type, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many of each number of fuel types?\n",
    "full_dataset[\"fuel_type\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5c51d49",
   "metadata": {},
   "source": [
    "The figure above illustrates the combined fuel efficiency of different engine types. While the average is lowest for diesel vehicles, the lower range of both premium and regular gasoline vehicles appear more efficient than any observed diesel vehicle. That said, the upper range for both types of gasoline vehicles are far less efficient than any observed diesel vehicle. Ethanol and natural gas-powered vehicles are evidently the least efficient in this dataset. However, natural gas has a relatively small sample size of only 18 observations, so it may be subject to sampling bias."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "928a7bd0",
   "metadata": {},
   "source": [
    "### 3.1.5 Engine Size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "012c612b",
   "metadata": {},
   "source": [
    "The scatter plot below is used to compare the engine size and fuel economy of each vehicle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1025f4c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fuel efficiency by engine size\n",
    "ax = sns.lmplot(data=full_dataset, x=\"engine_size_L\", y=\"comb_lpkm\", fit_reg=False)\n",
    "ax.set(xlabel=\"Engine Size (Liters)\", ylabel=\"Combined Fuel Economy (L/100km)\", \n",
    "       title=\"Figure 5. Fuel Efficiency by Engine Size\")\n",
    "ax.set(ylim=(0, 30))\n",
    "plt.gcf().set_size_inches(9, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7e34a51",
   "metadata": {},
   "source": [
    "The figure above shows a positive correlation between engine size and combined fuel economy. In simpler terms, it indicates that vehicles equipped with larger engines tend to be less fuel-efficient. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1d8cd37",
   "metadata": {},
   "source": [
    "### 3.1.6 Effect of Engine Cylinders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c49b293b",
   "metadata": {},
   "source": [
    "The scatter plot below is used to compare the number of engine cylinders and fuel economy of each vehicle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0f8f8310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fuel efficiency by engine cylinders\n",
    "ax = sns.lmplot(data=full_dataset, x=\"cylinders\", y=\"comb_lpkm\", fit_reg=False)\n",
    "ax.set(xlabel=\"Number of Engine Cylinders\", ylabel=\"Combined Fuel Economy (L/100km)\", \n",
    "       title=\"Figure 6. Fuel Efficiency by Number of Engine Cylinders\")\n",
    "ax.set(ylim=(0, 30))\n",
    "ax.fig.set_size_inches(9, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "88f36441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0     7608\n",
       "4.0     7027\n",
       "8.0     4514\n",
       "5.0      483\n",
       "12.0     149\n",
       "10.0     102\n",
       "3.0       59\n",
       "2.0       16\n",
       "Name: cylinders, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many of each number of engine cylinders?\n",
    "full_dataset[\"cylinders\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4de8ad9",
   "metadata": {},
   "source": [
    "The figure above indicates a positive correlation between the number of engine cylinders and combined fuel economy for vehicles with more than 3 cylinders. Vehicles with 2 cylinders exhibit a higher than expected combined fuel economy; however, with a relatively small sample size of only 16 observations, it may be subject to sampling bias."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78a03199",
   "metadata": {},
   "source": [
    "### 3.1.7 Transmission Type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aabbcaa",
   "metadata": {},
   "source": [
    "A box plot is utilized to view the differences between the distributions of each transmission type. Each box represents all the vehicles of that transmission type, aggregated across all years, makes, and other variables in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eb0a6c8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fuel efficiency by transmission type\n",
    "ax = sns.boxplot(data=full_dataset, x=\"transmission_type\", y=\"comb_lpkm\")\n",
    "ax.set(xlabel=\"Tranmission Type\", ylabel=\"Combined Fuel Economy (L/100km)\", \n",
    "       title=\"Figure 7. Fuel Efficiency by Transmission Type\")\n",
    "ax.set(ylim=(0, 25))\n",
    "plt.gcf().set_size_inches(9, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a87a2e7",
   "metadata": {},
   "source": [
    "Evident in this figure, continuously variable transmissions appear to be the most fuel-efficient, while automatic transmissions appear to be the least efficient. Continuously variable transmissions also appear to have the smallest variability in fuel economy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b8de6d",
   "metadata": {},
   "source": [
    "### 3.1.8 Number of Transmission Gears"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0419b94b",
   "metadata": {},
   "source": [
    "The final variable is the number of transmission gears, and it's relationship with fuel economy is represented as a scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ed128fdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fuel efficiency by transmission gears\n",
    "ax = sns.lmplot(data=full_dataset, x=\"transmission_gears\", y=\"comb_lpkm\", fit_reg=False)\n",
    "ax.set(xlabel=\"Number of Gears in Transmision\", ylabel=\"Combined Fuel Economy (L/100km)\", \n",
    "       title=\"Figure 8. Fuel Efficiency by Number of Gears in Transmission\")\n",
    "ax.set(ylim=(0, 30))\n",
    "plt.gcf().set_size_inches(9, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "265582a3",
   "metadata": {},
   "source": [
    "The figure above shows that the number of gears in the transmission of a vehicle appears generally uncorrelated with the combined fuel economy, with the exception of single gear transmissions (i.e., continuously variable transmissions) which appear to be generally more efficient."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7932c02e",
   "metadata": {},
   "source": [
    "## 3.2 Interactions Between Year and Other Explanatory Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf17d982",
   "metadata": {},
   "source": [
    "The previous section focused on exploring how each individual explanatory variable influences fuel efficiency. However, it was observed that the average fuel efficiency has improved from 2000 to 2023, which begs the question:  is there an interaction between the year and some of the other factors?\n",
    "\n",
    "This section will examine interactions between:\n",
    "- Year and Make\n",
    "\n",
    "- Year and Vehicle Type\n",
    "\n",
    "- Year and Fuel Type\n",
    "\n",
    "- Year and Transmission Type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b11ee1e6",
   "metadata": {},
   "source": [
    "### 3.2.1 Interaction between Year and Make"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfb7e87d",
   "metadata": {},
   "source": [
    "One rationale for exploring the relationship between year and make is that certain manufacturers may have improved their average fuel efficiency more than others over the 2000-2023 time period.\n",
    "\n",
    "To assess this, fuel efficiency data is averaged for vehicles by year and make. The resulting figure is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0b15964a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x3000 with 26 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# exploring interaction between year and make\n",
    "fuel_economy_by_year_and_make = full_dataset.pivot_table(index=\"model_year\", \n",
    "                                                         columns=\"make\", \n",
    "                                                         values=\"comb_lpkm\", \n",
    "                                                         aggfunc=\"mean\")\n",
    "\n",
    "axs = fuel_economy_by_year_and_make.plot.line(\n",
    "    figsize=(9, 30), subplots=True, legend=False, sharex=False,\n",
    "    sharey=True, layout=(13, 2), xlabel=\"Year\"\n",
    ")\n",
    "\n",
    "axs[0,0].get_figure().tight_layout(rect=[0, 0.03, 1, 0.97])\n",
    "axs[0,0].get_figure().suptitle('Figure 9. Average Fuel Efficiency (L/100km) by Year and Make')\n",
    "axs = axs.ravel()\n",
    "titles = fuel_economy_by_year_and_make.columns\n",
    "i = 0\n",
    "for ax in axs[:]:\n",
    "    title = titles[i]\n",
    "    ax.set_title(title, y=1.0, pad=-14)\n",
    "    ax.set_ylim(0, 20)\n",
    "    ax.set_ylabel(\"Combined Fuel Economy \\n (L/100km)\")\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85dbeb5b",
   "metadata": {},
   "source": [
    "From the figure above, there are a few interesting observations.\n",
    "\n",
    "First, fuel efficiency did not improve every single year for all manufacturers. For example, Chevrolet, Hyundai, and Infiniti saw their average fuel efficiency get worse from 2000 to 2005.\n",
    "\n",
    "Second, every manufacturer improved their average fuel efficiency from 2000 to 2023. However, it is apparent that there is variation in how much each manufacturer has improved. For example, it appears that Acura and Subaru have made minimal improvements in average fuel efficiency from 2000 to 2023, although they both started with relatively low averages. In contrast, Mazda and Toyota have made much larger improvements in average fuel efficiency from 2000 to 2023.\n",
    "\n",
    "To refine this analysis, companies are grouped based on whether their fuel efficiency between 2000 and 2023 improved by <15%, between 15% and 20%, or >20%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "faf5d74d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Year')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x2000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fuel_economy_by_year_and_make = full_dataset.pivot_table(index=\"model_year\", \n",
    "                                                         columns=\"make\", \n",
    "                                                         values=\"comb_lpkm\", \n",
    "                                                         aggfunc=\"mean\")\n",
    "\n",
    "# separate dataset into makes which have decreased combined fuel economy by >= 15%, 20% and those who haven't\n",
    "ratio = (fuel_economy_by_year_and_make.loc[2023] / fuel_economy_by_year_and_make.loc[2000])\n",
    "flats = fuel_economy_by_year_and_make.loc[:, ratio >= 0.85]\n",
    "mids = fuel_economy_by_year_and_make.loc[:, (ratio >= 0.8) & (ratio < 0.85)]\n",
    "declining = fuel_economy_by_year_and_make.loc[:, ratio < 0.8]\n",
    "\n",
    "fig, ax = plt.subplots(3, figsize=(12, 20))\n",
    "fig.suptitle(\"Figure 10. Improvements in combined fuel economy over time, averaged by vehicle make\", \n",
    "            fontsize=16, fontweight='bold')\n",
    "fig.subplots_adjust(top=0.95)\n",
    "ax[0].plot(flats)\n",
    "ax[0].set_ylim(7,20)\n",
    "ax[0].legend(labels=flats.columns, loc='upper right', ncol=3)\n",
    "ax[0].set_title('Makes whose average combined fuel economy has improved by < 15%')\n",
    "ax[0].set_ylabel(\"Combined Fuel Economy \\n (L/100km)\")\n",
    "ax[1].plot(mids)\n",
    "ax[1].set_ylim(7,20)\n",
    "ax[1].legend(labels=mids.columns, loc='upper right', ncol=2)\n",
    "ax[1].set_title('Makes whose average combined fuel economy has improved by 15-20%')\n",
    "ax[1].set_ylabel(\"Combined Fuel Economy \\n (L/100km)\")\n",
    "ax[2].plot(declining)\n",
    "ax[2].set_ylim(7,20)\n",
    "ax[2].legend(labels=declining.columns, loc='upper right', ncol=2)\n",
    "ax[2].set_title('Makes whose average combined fuel economy has improved by > 20%')\n",
    "ax[2].set_ylabel(\"Combined Fuel Economy \\n (L/100km)\")\n",
    "ax[2].set_xlabel(\"Year\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b5dbbc4",
   "metadata": {},
   "source": [
    "This figure displays the changes in average combined fuel economy over time for each manufacturer from 2000-2023. \n",
    "\n",
    "8 of the 26 brands (Buick, Jeep, Kia, Lexus, Maxda, Nissan, Toyota, and Volvo) were found to have improved their average vehicle's combined fuel economy by the most, each boasting over 20% improvement since 2000. \n",
    "\n",
    "In contrast, 11 of the 26 brands (Acura, Audi, BMW, Chevrolet, Chrysler, Ford, Infiniti, Mercedes-Benz, Porsche, Subaru, and Volkswagen) were each found to have improved their average vehicle's combined fuel economy by less than 15%.\n",
    "\n",
    "Once again, it is observed that no individual manufacturer, even among the least improved group, ended up with worse fuel economy in 2023 than they started with in 2000."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abcf5828",
   "metadata": {},
   "source": [
    "### 3.2.2 Interaction Between Year and Vehicle Class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc8b39eb",
   "metadata": {},
   "source": [
    "One rationale for exploring the relationship between year and vehicle class is that certain categories of vehicles may have improved more than others over time. For example, SUVs are commonly thought of as \"gas guzzlers\". But have they improved more than cars from 2000 to 2023?\n",
    "\n",
    "To assess this, fuel efficiency data is averaged for vehicles by year and vehicle type. The resulting figure is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "de85d143",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fuel_economy_by_year_and_vehicle_type = full_dataset.pivot_table(index=\"model_year\", \n",
    "                                                                 columns=\"vehicle_class\", \n",
    "                                                                 values=\"comb_lpkm\", \n",
    "                                                                 aggfunc=\"mean\")\n",
    "\n",
    "ax = fuel_economy_by_year_and_vehicle_type.plot.line()\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Combined Fuel Economy (L/100km)\")\n",
    "ax.set_ylim(0, 25)\n",
    "ax.set_title(\"Figure 11. Average Fuel Efficiency by Year and Vehicle Class\")\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels, title=\"Vehicle Class\", loc='upper right')\n",
    "plt.gcf().set_size_inches(9, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a12d28b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vehicle_class\n",
       "Car      12.466224\n",
       "SUV      25.815724\n",
       "Truck    13.894628\n",
       "Van      29.362664\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_average = fuel_economy_by_year_and_vehicle_type.T[2000]\n",
    "new_average = fuel_economy_by_year_and_vehicle_type.T[2023]\n",
    "percent_diff = abs(new_average - original_average)/original_average*100\n",
    "percent_diff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4f0641e",
   "metadata": {},
   "source": [
    "From the figure above, it is observed that:\n",
    "- All vehicle types have experienced an improvement in fuel efficiency from 2000 to 2023.\n",
    "\n",
    "- Cars experienced the smallest improvement, at 12.5%. However, cars consistently had the best average fuel efficiency, except from 2021 to 2023.\n",
    "\n",
    "- Trucks experienced a 13.9% improvement. They also had the worst average fuel efficiency across all the years examined.\n",
    "\n",
    "- SUVs experienced a 25.8% improvement. They initially started with an average fuel efficiency close to trucks in 2000, but ended up with an average fuel efficiency closer to cars and vans in 2023.\n",
    "\n",
    "- Vans experienced the largest improvement, at 29.4%. In 2021, they overtook cars as having the most efficient average fuel economy.\n",
    "\n",
    "From these observations, it can be concluded that if a consumer is in the market for a fuel-efficient vehicle, the impact of the model year on their decision differs depending on the type of vehicle they want. For instance, if they are interested in a fuel-efficient car or truck, the model year may not be as crucial as it would be if they were searching for a fuel-efficient SUV or van. In those cases, it would be advisable to consider newer vehicles if possible."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "495979fe",
   "metadata": {},
   "source": [
    "### 3.2.3 Interaction between Year and Fuel Type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e4e6f95",
   "metadata": {},
   "source": [
    "For the average consumer, the primary fuel types to consider are premium or regular gasoline since diesel, ethanol, and natural gas vehicles are usually specialized. Some vehicle engines are manufactured to perform optimally when given higher octane fuel (premium gasoline), whereas for others regular gasoline is suffucicient. \n",
    "\n",
    "The figure below is used to compare the improvements in efficiency for each fuel type over time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "06c91cd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fuel_economy_by_year_and_fuel_type = full_dataset.pivot_table(index=\"model_year\", \n",
    "                                                              columns=\"fuel_type\", \n",
    "                                                              values=\"comb_lpkm\", \n",
    "                                                              aggfunc=\"mean\")\n",
    "ax = fuel_economy_by_year_and_fuel_type.plot.line()\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Combined Fuel Economy (L/100km)\")\n",
    "ax.set_ylim(0, 25)\n",
    "ax.set_title(\"Figure 12. Average Fuel Efficiency by Year and Fuel Type\")\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels, title=\"Fuel Type\", loc='upper right')\n",
    "plt.gcf().set_size_inches(9, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d85294c",
   "metadata": {},
   "source": [
    "Between regular and premium gasoline vehicles, regular gasoline vehicles have improved in efficiency the most since 2000. While premium gasoline vehicles started the century as the slightly more efficient fuel type, by 2005 they were overtaken by regular gasoline vehicles. Regular gasoline vehicles decreased their average combined fuel efficiency from 13.0 L/100km in 2000 down to 10.1 L/100km in 2023, while premium gasoline vehicles only saw a decrease from 12.4 L/100km to 11.5 L/100km over that same time period. \n",
    "\n",
    "With regard to the other fuel types:\n",
    "- Ethanol has the worst fuel economy out of all the fuel types. It also saw almost no improvement, starting at 17.7 L/100km in 2000 and ending at 17.5 L/100km in 2023.\n",
    "\n",
    "- Natural gas was only used as a fuel source until 2004 and was similar in efficiency to ethanol. \n",
    "\n",
    "- Interestingly, diesel is the only fuel source whose efficiency has worsened from 2000 to 2023, increasing from 6.6 L/100km to 10.1 L/100km. Despite this worsening in average fuel economy, it remained the most efficient fuel source for the entire 2000-2023 period, although from 2020 onward, it is practically tied with regular gasoline."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0d4accd",
   "metadata": {},
   "source": [
    "### 3.2.4 Interaction between Year and Transmission Type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c4f45a8",
   "metadata": {},
   "source": [
    "Transmission type, beyond the distinction between manual and automatic, may be an overlooked factor when purchasing a vehicle. While it was observed in Figure 7 that continuously variable transmission vehicles tend to be more efficient overall, it begs the question of how vehicle efficiency for each transmission type has changed over time. We explore these changes through a visualization and compare the percent improvements. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b1c23603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fuel_economy_by_year_and_transmission_type = full_dataset.pivot_table(index=\"model_year\", \n",
    "                                                                      columns=\"transmission_type\", \n",
    "                                                                      values=\"comb_lpkm\", \n",
    "                                                                      aggfunc=\"mean\")\n",
    "\n",
    "ax = fuel_economy_by_year_and_transmission_type.plot.line()\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Combined Fuel Economy (L/100km)\")\n",
    "ax.set_ylim(0, 25)\n",
    "ax.set_title(\"Figure 13. Average Fuel Efficiency by Year and Transmission Type\")\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels, title=\"Transmission Type\", loc='upper right')\n",
    "plt.gcf().set_size_inches(9, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1afa5c9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transmission_type\n",
       "Automated Manual          29.134566\n",
       "Automatic                 10.991164\n",
       "Automatic Select Shift     9.521882\n",
       "Continuously Variable     24.950433\n",
       "Manual                     8.214050\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_average = fuel_economy_by_year_and_transmission_type.T[2003]\n",
    "new_average = fuel_economy_by_year_and_transmission_type.T[2023]\n",
    "percent_diff = abs(new_average - original_average)/original_average*100\n",
    "percent_diff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c4f5065",
   "metadata": {},
   "source": [
    "For this comparison, 2003 was chosen as the baseline since continuously variable transmission (CVT) and automated manual tranmission are newer technologies and were not prominent before then (hence the oddity in the CVT measurement from 2001 in the visualization above). \n",
    "\n",
    "Improvements in fuel efficiency by transmission type from 2003 to 2023, ranked from best to worst, are as follows:\n",
    "- Automated Manual: 29.1%\n",
    "- Continously Variable: 25.0%\n",
    "- Automatic: 11.0%\n",
    "- Automatic Select Shift: 9.5%\n",
    "- Manual: 8.2%\n",
    "\n",
    "Automatics have consistently been the least efficient for every year aside from 2006-2011, where automated manuals were worse. In contrast, CVT has consistently been the most efficient transmission type for every year since its inception and also boasts the second most improved fuel economy over the period from 2003 to 2023 (25%). It's clear then that newer CVT vehicles are the optimal choice if available. For other transmission types, we see a much smaller improvement in average fuel economy over time. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8daaf1e",
   "metadata": {},
   "source": [
    "## 3.3 Most Efficient Vehicles and Makes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed519649",
   "metadata": {},
   "source": [
    "If a consumer is simply looking for the most efficient vehicle on the market, that can be easily determine as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c070580c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Hyundai IONIQ Blue from 2022 was the most efficient vehicle at 4.0 L/100km\n",
      "The Dodge RAM 1500 4X4 FFV from 2005 was the least efficient vehicle at 26.0 L/100km\n"
     ]
    }
   ],
   "source": [
    "# finding the most efficient vehicle\n",
    "min_ID = full_dataset[\"comb_lpkm\"].idxmin()\n",
    "most_efficient_comb_lpkm = full_dataset[\"comb_lpkm\"][min_ID]\n",
    "most_efficient_year = full_dataset[\"model_year\"][min_ID]\n",
    "most_efficient_make = full_dataset[\"make\"][min_ID]\n",
    "most_efficient_model = full_dataset[\"model\"][min_ID]\n",
    "print(f\"The {most_efficient_make} {most_efficient_model} from {most_efficient_year} \" + \n",
    "      f\"was the most efficient vehicle at {most_efficient_comb_lpkm} L/100km\")\n",
    "\n",
    "# finding the least efficient vehicle\n",
    "max_ID = full_dataset[\"comb_lpkm\"].idxmax()\n",
    "least_efficient_comb_lpkm = full_dataset[\"comb_lpkm\"][max_ID]\n",
    "least_efficient_year = full_dataset[\"model_year\"][max_ID]\n",
    "least_efficient_make = full_dataset[\"make\"][max_ID]\n",
    "least_efficient_model = full_dataset[\"model\"][max_ID]\n",
    "print(f\"The {least_efficient_make} {least_efficient_model} from {least_efficient_year} \" + \n",
    "      f\"was the least efficient vehicle at {least_efficient_comb_lpkm} L/100km\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52d6fa21",
   "metadata": {},
   "source": [
    "It is noted that the 2022 Hyundai IONIQ Blue is the most efficient vehicle in our dataset, boasting a combined fuel economy of 4 L/100km. In contrast, the least efficient vehicle was the 2005 Dodge RAM 1500 4X4 FFV, with a combined fuel economy of 26 L/100km. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb0783dc",
   "metadata": {},
   "source": [
    "However, price and functionality are also generally considered when purchasing a vehicle. For example:\n",
    "- Sometimes a consumer needs a van due to a large family.\n",
    "\n",
    "- Sometimes older vehicles are the only feasible option due to budget constraints.\n",
    "\n",
    "By separating each make by vehicle class and obtaining an average fuel economy for each year, we can point consumers in the right direction based on their desired vehicle type and budget. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8d034c2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>vehicle_class</th>\n",
       "      <th>Car</th>\n",
       "      <th>SUV</th>\n",
       "      <th>Truck</th>\n",
       "      <th>Van</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Chevrolet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Chevrolet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Mazda</td>\n",
       "      <td>Chevrolet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Chevrolet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Mazda</td>\n",
       "      <td>Mazda</td>\n",
       "      <td>Dodge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Mazda</td>\n",
       "      <td>Honda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Mazda</td>\n",
       "      <td>Honda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Mazda</td>\n",
       "      <td>Toyota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Mazda</td>\n",
       "      <td>Mazda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Mazda</td>\n",
       "      <td>Mazda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Hyundai</td>\n",
       "      <td>Mazda</td>\n",
       "      <td>Mazda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Hyundai</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Ford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Kia</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Mazda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Mazda</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Mazda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Mazda</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Mazda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Dodge</td>\n",
       "      <td>Chevrolet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Mazda</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Chevrolet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Chevrolet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Honda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Honda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>Kia</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Honda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>Hyundai</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Toyota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>Kia</td>\n",
       "      <td>Hyundai</td>\n",
       "      <td>Hyundai</td>\n",
       "      <td>Toyota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Hyundai</td>\n",
       "      <td>Toyota</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "vehicle_class      Car      SUV    Truck        Van\n",
       "model_year                                         \n",
       "2000             Honda   Subaru   Nissan  Chevrolet\n",
       "2001             Honda   Subaru   Toyota  Chevrolet\n",
       "2002             Honda   Subaru    Mazda  Chevrolet\n",
       "2003             Honda   Subaru   Toyota  Chevrolet\n",
       "2004             Honda    Mazda    Mazda      Dodge\n",
       "2005             Honda   Subaru    Mazda      Honda\n",
       "2006             Honda   Subaru    Mazda      Honda\n",
       "2007            Toyota   Subaru    Mazda     Toyota\n",
       "2008            Toyota   Subaru    Mazda      Mazda\n",
       "2009            Toyota   Subaru    Mazda      Mazda\n",
       "2010             Honda  Hyundai    Mazda      Mazda\n",
       "2011             Honda  Hyundai   Toyota       Ford\n",
       "2012            Toyota      Kia   Toyota      Mazda\n",
       "2013            Toyota    Mazda   Toyota      Mazda\n",
       "2014            Toyota    Mazda   Toyota      Mazda\n",
       "2015            Toyota   Subaru    Dodge  Chevrolet\n",
       "2016            Toyota    Mazda   Nissan  Chevrolet\n",
       "2017            Toyota   Subaru    Honda  Chevrolet\n",
       "2018            Toyota   Subaru    Honda      Honda\n",
       "2019            Toyota   Subaru    Honda      Honda\n",
       "2020               Kia   Subaru    Honda      Honda\n",
       "2021           Hyundai   Subaru     Jeep     Toyota\n",
       "2022               Kia  Hyundai  Hyundai     Toyota\n",
       "2023            Toyota   Toyota  Hyundai     Toyota"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exploring which companies made the most efficient vehicles in each year, based on vehicle class\n",
    "fuel_economy_by_year_make_and_vehicle_type = full_dataset.pivot_table(index=[\"model_year\", \"make\"], \n",
    "                                                                      columns=\"vehicle_class\", \n",
    "                                                                      values=\"comb_lpkm\", \n",
    "                                                                      aggfunc=\"mean\")\n",
    "\n",
    "fuel_economy_by_year_make_and_vehicle_type.reset_index([\"model_year\"], inplace=True)\n",
    "fuel_economy_by_year_make_and_vehicle_type\n",
    "\n",
    "by_year = fuel_economy_by_year_make_and_vehicle_type.groupby(\"model_year\")\n",
    "by_year.idxmin()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "438bfb56",
   "metadata": {},
   "source": [
    "As can be seen in the table above, the most efficient make per year for each vehicle type changes year by year. In general:\n",
    "- For cars, Honda appears to be a good choice for older vehicles, while Toyota is a good choice from 2007 to 2019.\n",
    "\n",
    "- For SUVs, Subaru appears to be the most consistent manufacturer of efficient vehicles. Other manufacturers that are, on average, more efficient in certain years include Mazda, Hyundai, and Kia.\n",
    "\n",
    "- For trucks and vans, various manufacturers were all the most efficient in different years, often for periods of several years in a row.\n",
    "\n",
    "- For the newest cars, SUVs, and trucks (2020 onwards), both Kia and Hyundai make very efficient vehicles."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c34827",
   "metadata": {},
   "source": [
    "## 4. Modelling and Predicting Fuel Efficiency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "682d0bcb",
   "metadata": {},
   "source": [
    "Predicting a vehicle's fuel economy could have wide ranging applications, such as determining whether a company should offer incentives on existing models because they will be less desirable in the future, or whether a consumer should purchase a vehicle this year versus waiting for next year's model. \n",
    "\n",
    "In Section 4.1, various models are fitted to the dataset (a linear regression model, a lasso model, a regression tree, and a neural network), assuming no interactions between predictors. \n",
    "\n",
    "In Section 4.2, various models are fitted to the dataset (a linear regression model, a lasso model, a regression tree, and a neural network), considering the interaction between each predictor and the model year.\n",
    "\n",
    "In Section 4.3, the best model is tested against new 2024 vehicles that have yet to be included in our data to further assess the predictive power of the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d9bf568",
   "metadata": {},
   "source": [
    "### 4.1 Models With No Interactions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b92aa361",
   "metadata": {},
   "source": [
    "Prior to fitting any models, the data is split into training (75%) and testing (25%) sets. Categorical variables are defined where appropriate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "29d03a0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create X and y training and testing datasets\n",
    "y, X = patsy.dmatrices(\"comb_lpkm ~ model_year + C(make) + C(vehicle_class) + \" +\n",
    "                       \"C(fuel_type) + engine_size_L + cylinders + C(transmission_type) + \" +\n",
    "                        \"transmission_gears\", full_dataset, return_type='matrix')\n",
    "train = full_dataset.sample(frac = 0.75,random_state = 42) \n",
    "test = full_dataset.drop(train.index)\n",
    "y_train = y[train.index]\n",
    "X_train = X[train.index]\n",
    "y_test = y[test.index]\n",
    "X_test = X[test.index]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f3b4ea6",
   "metadata": {},
   "source": [
    "The first model fitted is a linear regression model to predict combined fuel economy considering all parameters. For comparison with other models, the mean squared error is obtained on each of the training and testing data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6b7a101e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MSE on the training data is: 1.1620400121426588 and the MSE on the testing data is 1.1506700935637841\n"
     ]
    }
   ],
   "source": [
    "# fit a linear regression model\n",
    "fuel_efficiency_lr_model = linear_model.LinearRegression()\n",
    "fuel_efficiency_lr_model.fit(X_train, y_train)\n",
    "\n",
    "mse_train = metrics.mean_squared_error(y_train, fuel_efficiency_lr_model.predict(X_train))\n",
    "mse_test = metrics.mean_squared_error(y_test, fuel_efficiency_lr_model.predict(X_test))\n",
    "\n",
    "print(f\"The MSE on the training data is: {mse_train} and the MSE on the testing data is {mse_test}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "377f2cd0",
   "metadata": {},
   "source": [
    "Next, a lasso model is fit and the mean squared error is obtained on each of the training and testing data in order to see if there's any benefit to excluding some predictors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a23007ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MSE on the training data is: 3.5272806880460683 and the MSE on the testing data is 3.3775429050443733\n"
     ]
    }
   ],
   "source": [
    "# fit a lasso regression model\n",
    "fuel_efficiency_lasso_model = linear_model.Lasso()\n",
    "fuel_efficiency_lasso_model.fit(X_train, y_train)\n",
    "\n",
    "mse_train = metrics.mean_squared_error(y_train, fuel_efficiency_lasso_model.predict(X_train))\n",
    "mse_test = metrics.mean_squared_error(y_test, fuel_efficiency_lasso_model.predict(X_test))\n",
    "\n",
    "print(f\"The MSE on the training data is: {mse_train} and the MSE on the testing data is {mse_test}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12faa518",
   "metadata": {},
   "source": [
    "A comparison can then be performed of the different terms included in each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "37177624",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lasso</th>\n",
       "      <th>linreg</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.748812e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Audi]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>8.881889e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Bmw]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>4.264449e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Buick]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>4.633695e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Cadillac]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.900215e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Chevrolet]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.233515e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Chrysler]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>5.251900e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Dodge]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.044475e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Ford]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.243481e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Gmc]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.155382e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Honda]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>1.304258e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Hyundai]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>1.794431e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Infiniti]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>3.497407e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Jaguar]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>3.783994e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Jeep]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.675053e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Kia]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>4.071534e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Lexus]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-1.369504e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Lincoln]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.136485e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Mazda]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>2.600062e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Mercedes-Benz]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>6.439870e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Nissan]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>8.403923e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Porsche]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>6.451316e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Subaru]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>1.134667e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Toyota]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>1.918752e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Volkswagen]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>5.233185e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Volvo]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>8.276537e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(vehicle_class)[T.SUV]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.581034e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(vehicle_class)[T.Truck]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.161321e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(vehicle_class)[T.Van]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.122972e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(fuel_type)[T.Ethanol]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.942817e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(fuel_type)[T.Natural Gas]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.566965e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(fuel_type)[T.Premium Gasoline]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>2.313938e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(fuel_type)[T.Regular Gasoline]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>1.616321e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(transmission_type)[T.Automatic]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.594742e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(transmission_type)[T.Automatic Select Shift]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-1.482608e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(transmission_type)[T.Continuously Variable]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-1.911166e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(transmission_type)[T.Manual]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-3.722735e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year</th>\n",
       "      <td>-0.082818</td>\n",
       "      <td>-8.897613e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_size_L</th>\n",
       "      <td>0.317024</td>\n",
       "      <td>8.009450e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinders</th>\n",
       "      <td>0.701447</td>\n",
       "      <td>4.625473e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transmission_gears</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6.618770e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   lasso        linreg\n",
       "Intercept                                       0.000000  3.748812e-17\n",
       "C(make)[T.Audi]                                -0.000000  8.881889e-01\n",
       "C(make)[T.Bmw]                                 -0.000000  4.264449e-01\n",
       "C(make)[T.Buick]                               -0.000000  4.633695e-01\n",
       "C(make)[T.Cadillac]                             0.000000  8.900215e-01\n",
       "C(make)[T.Chevrolet]                            0.000000  1.233515e-01\n",
       "C(make)[T.Chrysler]                            -0.000000  5.251900e-01\n",
       "C(make)[T.Dodge]                                0.000000  5.044475e-01\n",
       "C(make)[T.Ford]                                 0.000000  6.243481e-01\n",
       "C(make)[T.Gmc]                                  0.000000  3.155382e-01\n",
       "C(make)[T.Honda]                               -0.000000  1.304258e-01\n",
       "C(make)[T.Hyundai]                             -0.000000  1.794431e-01\n",
       "C(make)[T.Infiniti]                            -0.000000  3.497407e-01\n",
       "C(make)[T.Jaguar]                              -0.000000  3.783994e-01\n",
       "C(make)[T.Jeep]                                 0.000000  6.675053e-01\n",
       "C(make)[T.Kia]                                 -0.000000  4.071534e-01\n",
       "C(make)[T.Lexus]                               -0.000000 -1.369504e-01\n",
       "C(make)[T.Lincoln]                              0.000000  1.136485e+00\n",
       "C(make)[T.Mazda]                               -0.000000  2.600062e-01\n",
       "C(make)[T.Mercedes-Benz]                       -0.000000  6.439870e-01\n",
       "C(make)[T.Nissan]                              -0.000000  8.403923e-01\n",
       "C(make)[T.Porsche]                             -0.000000  6.451316e-01\n",
       "C(make)[T.Subaru]                              -0.000000  1.134667e+00\n",
       "C(make)[T.Toyota]                              -0.000000  1.918752e-02\n",
       "C(make)[T.Volkswagen]                          -0.000000  5.233185e-01\n",
       "C(make)[T.Volvo]                               -0.000000  8.276537e-01\n",
       "C(vehicle_class)[T.SUV]                         0.000000  1.581034e+00\n",
       "C(vehicle_class)[T.Truck]                       0.000000  2.161321e+00\n",
       "C(vehicle_class)[T.Van]                         0.000000  1.122972e+00\n",
       "C(fuel_type)[T.Ethanol]                         0.000000  5.942817e+00\n",
       "C(fuel_type)[T.Natural Gas]                     0.000000  4.566965e+00\n",
       "C(fuel_type)[T.Premium Gasoline]               -0.000000  2.313938e+00\n",
       "C(fuel_type)[T.Regular Gasoline]               -0.000000  1.616321e+00\n",
       "C(transmission_type)[T.Automatic]               0.000000 -7.594742e-02\n",
       "C(transmission_type)[T.Automatic Select Shift] -0.000000 -1.482608e-01\n",
       "C(transmission_type)[T.Continuously Variable]  -0.000000 -1.911166e+00\n",
       "C(transmission_type)[T.Manual]                 -0.000000 -3.722735e-02\n",
       "model_year                                     -0.082818 -8.897613e-02\n",
       "engine_size_L                                   0.317024  8.009450e-01\n",
       "cylinders                                       0.701447  4.625473e-01\n",
       "transmission_gears                              0.000000 -6.618770e-02"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_names = X.design_info.column_names\n",
    "lasso_coefs = pd.Series(dict(zip(param_names, fuel_efficiency_lasso_model.coef_)))\n",
    "lr_coefs = pd.Series(dict(zip(param_names, fuel_efficiency_lr_model.coef_[0])))\n",
    "coefs = pd.DataFrame(dict(lasso=lasso_coefs, linreg=lr_coefs))\n",
    "coefs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d89069",
   "metadata": {},
   "source": [
    "A higher MSE for both the training and testing data is obtained for the lasso model (training MSE = 3.53, testing MSE = 3.38), suggesting that the original linear regression model (training MSE = 1.16, testing MSE = 1.15) is a more effective predictor. \n",
    "\n",
    "It is noted that in the lasso model, the three most influential predictors are cylinders, engine size, and model year. However, perhaps a better lasso model can be obtained by performing 5-fold cross-validation to optimize alpha. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1e6ba3ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(-0.8334944602183618, 8.510786990727286, '5 fold cross-validation')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# number of cross-validation folds\n",
    "k = 5\n",
    "\n",
    "# create a function for calculating mses\n",
    "def fit_and_report_mses(mod, X_train, X_test, y_train, y_test):\n",
    "    mod.fit(X_train, y_train)\n",
    "    return dict(\n",
    "        mse_train=metrics.mean_squared_error(y_train, mod.predict(X_train)),\n",
    "        mse_test=metrics.mean_squared_error(y_test, mod.predict(X_test))\n",
    "    )\n",
    "\n",
    "# create the alphas and mse values for different alphas\n",
    "alphas = np.exp(np.linspace(10, -10, 100))\n",
    "mse = pd.DataFrame([fit_and_report_mses(linear_model.Lasso(alpha=alpha, max_iter=50000),\n",
    "                           X_train, X_test, y_train, y_test)\n",
    "                    for alpha in alphas])\n",
    "mse[\"log_alpha\"] = -np.log10(alphas)\n",
    "mse[\"cv\"] = [-np.mean(cross_val_score(linear_model.Lasso(alpha=alpha, max_iter=50000),\n",
    "                                  X_train, y_train, cv=k, scoring='neg_mean_squared_error'))\n",
    "          for alpha in alphas]\n",
    "\n",
    "# create the plot\n",
    "colors = ['#165aa7', '#cb495c', '#fec630']\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "mse.plot(x=\"log_alpha\", y=\"mse_test\", c=colors[0], ax=ax)\n",
    "mse.plot(x=\"log_alpha\", y=\"mse_train\", c=colors[1], ax=ax)\n",
    "mse.plot(x=\"log_alpha\", y=\"cv\", c=colors[2], ax=ax)\n",
    "ax.set_xlabel(r\"$-\\log(\\alpha)$\")\n",
    "ax.set_ylabel(\"MSE\")\n",
    "ax.set_title(\"Figure 14. 5-Fold Cross Validation to Optimize Alpha for Lasso Modelling\")\n",
    "ax.get_legend().remove()\n",
    "ax.annotate(\"test\",(mse.log_alpha[15], mse.mse_test[15]),color=colors[0])\n",
    "ax.annotate(\"train\",(mse.log_alpha[30], mse.mse_train[30]),color=colors[1])\n",
    "ax.annotate(f\"{k} fold cross-validation\", (mse.log_alpha[40], mse.cv[40]), color=colors[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "36e9cfa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The optimal alpha is 0.005524118007066434\n"
     ]
    }
   ],
   "source": [
    "#retrieve optimal alpha\n",
    "lasso = linear_model.LassoCV(cv=5).fit(X_train,y_train.flatten())\n",
    "neg_log_alpha = -np.log10(lasso.alpha_)\n",
    "optimal_alpha = 10**(-1 * neg_log_alpha)\n",
    "print(f\"The optimal alpha is {optimal_alpha}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb44715",
   "metadata": {},
   "source": [
    "Performing the 5-fold cross validation, the optimal alpha value is 0.0055. The lasso model can be updated to utilize this alpha value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "57234cf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MSE on the training data is: 1.2188896098802495 and the MSE on the testing data is 1.1978752095507952\n"
     ]
    }
   ],
   "source": [
    "# fit an updated lasso regression model with an alpha to optimize MSE\n",
    "fuel_efficiency_lasso_model = linear_model.Lasso(alpha=optimal_alpha)\n",
    "fuel_efficiency_lasso_model.fit(X_train, y_train)\n",
    "\n",
    "mse_train = metrics.mean_squared_error(y_train, fuel_efficiency_lasso_model.predict(X_train))\n",
    "mse_test = metrics.mean_squared_error(y_test, fuel_efficiency_lasso_model.predict(X_test))\n",
    "\n",
    "print(f\"The MSE on the training data is: {mse_train} and the MSE on the testing data is {mse_test}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "23231634",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>lasso</th>\n",
       "      <th>linreg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.748812e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Audi]</th>\n",
       "      <td>0.246032</td>\n",
       "      <td>8.881889e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Bmw]</th>\n",
       "      <td>-0.012373</td>\n",
       "      <td>4.264449e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Buick]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.633695e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Cadillac]</th>\n",
       "      <td>0.132657</td>\n",
       "      <td>8.900215e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Chevrolet]</th>\n",
       "      <td>-0.260826</td>\n",
       "      <td>1.233515e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Chrysler]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.251900e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Dodge]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.044475e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Ford]</th>\n",
       "      <td>0.149807</td>\n",
       "      <td>6.243481e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Gmc]</th>\n",
       "      <td>-0.022470</td>\n",
       "      <td>3.155382e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Honda]</th>\n",
       "      <td>-0.139828</td>\n",
       "      <td>1.304258e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Hyundai]</th>\n",
       "      <td>-0.065504</td>\n",
       "      <td>1.794431e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Infiniti]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>3.497407e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Jaguar]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>3.783994e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Jeep]</th>\n",
       "      <td>0.018643</td>\n",
       "      <td>6.675053e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Kia]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>4.071534e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Lexus]</th>\n",
       "      <td>-0.444753</td>\n",
       "      <td>-1.369504e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Lincoln]</th>\n",
       "      <td>0.196164</td>\n",
       "      <td>1.136485e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Mazda]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>2.600062e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Mercedes-Benz]</th>\n",
       "      <td>0.005031</td>\n",
       "      <td>6.439870e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Nissan]</th>\n",
       "      <td>0.227912</td>\n",
       "      <td>8.403923e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Porsche]</th>\n",
       "      <td>0.059375</td>\n",
       "      <td>6.451316e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Subaru]</th>\n",
       "      <td>0.445329</td>\n",
       "      <td>1.134667e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Toyota]</th>\n",
       "      <td>-0.316024</td>\n",
       "      <td>1.918752e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Volkswagen]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>5.233185e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Volvo]</th>\n",
       "      <td>0.130123</td>\n",
       "      <td>8.276537e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(vehicle_class)[T.SUV]</th>\n",
       "      <td>1.486728</td>\n",
       "      <td>1.581034e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(vehicle_class)[T.Truck]</th>\n",
       "      <td>1.982196</td>\n",
       "      <td>2.161321e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(vehicle_class)[T.Van]</th>\n",
       "      <td>0.808147</td>\n",
       "      <td>1.122972e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(fuel_type)[T.Ethanol]</th>\n",
       "      <td>4.756598</td>\n",
       "      <td>5.942817e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(fuel_type)[T.Natural Gas]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.566965e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(fuel_type)[T.Premium Gasoline]</th>\n",
       "      <td>1.234840</td>\n",
       "      <td>2.313938e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(fuel_type)[T.Regular Gasoline]</th>\n",
       "      <td>0.536760</td>\n",
       "      <td>1.616321e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(transmission_type)[T.Automatic]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.594742e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(transmission_type)[T.Automatic Select Shift]</th>\n",
       "      <td>-0.056226</td>\n",
       "      <td>-1.482608e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(transmission_type)[T.Continuously Variable]</th>\n",
       "      <td>-1.644398</td>\n",
       "      <td>-1.911166e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(transmission_type)[T.Manual]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.722735e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year</th>\n",
       "      <td>-0.093537</td>\n",
       "      <td>-8.897613e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_size_L</th>\n",
       "      <td>0.841024</td>\n",
       "      <td>8.009450e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinders</th>\n",
       "      <td>0.454137</td>\n",
       "      <td>4.625473e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transmission_gears</th>\n",
       "      <td>-0.050349</td>\n",
       "      <td>-6.618770e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   lasso        linreg\n",
       "Intercept                                       0.000000  3.748812e-17\n",
       "C(make)[T.Audi]                                 0.246032  8.881889e-01\n",
       "C(make)[T.Bmw]                                 -0.012373  4.264449e-01\n",
       "C(make)[T.Buick]                                0.000000  4.633695e-01\n",
       "C(make)[T.Cadillac]                             0.132657  8.900215e-01\n",
       "C(make)[T.Chevrolet]                           -0.260826  1.233515e-01\n",
       "C(make)[T.Chrysler]                             0.000000  5.251900e-01\n",
       "C(make)[T.Dodge]                                0.000000  5.044475e-01\n",
       "C(make)[T.Ford]                                 0.149807  6.243481e-01\n",
       "C(make)[T.Gmc]                                 -0.022470  3.155382e-01\n",
       "C(make)[T.Honda]                               -0.139828  1.304258e-01\n",
       "C(make)[T.Hyundai]                             -0.065504  1.794431e-01\n",
       "C(make)[T.Infiniti]                            -0.000000  3.497407e-01\n",
       "C(make)[T.Jaguar]                              -0.000000  3.783994e-01\n",
       "C(make)[T.Jeep]                                 0.018643  6.675053e-01\n",
       "C(make)[T.Kia]                                 -0.000000  4.071534e-01\n",
       "C(make)[T.Lexus]                               -0.444753 -1.369504e-01\n",
       "C(make)[T.Lincoln]                              0.196164  1.136485e+00\n",
       "C(make)[T.Mazda]                               -0.000000  2.600062e-01\n",
       "C(make)[T.Mercedes-Benz]                        0.005031  6.439870e-01\n",
       "C(make)[T.Nissan]                               0.227912  8.403923e-01\n",
       "C(make)[T.Porsche]                              0.059375  6.451316e-01\n",
       "C(make)[T.Subaru]                               0.445329  1.134667e+00\n",
       "C(make)[T.Toyota]                              -0.316024  1.918752e-02\n",
       "C(make)[T.Volkswagen]                          -0.000000  5.233185e-01\n",
       "C(make)[T.Volvo]                                0.130123  8.276537e-01\n",
       "C(vehicle_class)[T.SUV]                         1.486728  1.581034e+00\n",
       "C(vehicle_class)[T.Truck]                       1.982196  2.161321e+00\n",
       "C(vehicle_class)[T.Van]                         0.808147  1.122972e+00\n",
       "C(fuel_type)[T.Ethanol]                         4.756598  5.942817e+00\n",
       "C(fuel_type)[T.Natural Gas]                     0.000000  4.566965e+00\n",
       "C(fuel_type)[T.Premium Gasoline]                1.234840  2.313938e+00\n",
       "C(fuel_type)[T.Regular Gasoline]                0.536760  1.616321e+00\n",
       "C(transmission_type)[T.Automatic]               0.000000 -7.594742e-02\n",
       "C(transmission_type)[T.Automatic Select Shift] -0.056226 -1.482608e-01\n",
       "C(transmission_type)[T.Continuously Variable]  -1.644398 -1.911166e+00\n",
       "C(transmission_type)[T.Manual]                  0.000000 -3.722735e-02\n",
       "model_year                                     -0.093537 -8.897613e-02\n",
       "engine_size_L                                   0.841024  8.009450e-01\n",
       "cylinders                                       0.454137  4.625473e-01\n",
       "transmission_gears                             -0.050349 -6.618770e-02"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_names = X.design_info.column_names\n",
    "lasso_coefs = pd.Series(dict(zip(param_names, fuel_efficiency_lasso_model.coef_)))\n",
    "lr_coefs = pd.Series(dict(zip(param_names, fuel_efficiency_lr_model.coef_[0])))\n",
    "coefs = pd.DataFrame(dict(lasso=lasso_coefs, linreg=lr_coefs))\n",
    "coefs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af20d27f",
   "metadata": {},
   "source": [
    "In this optimized lasso model, the inclusion of more predictors has reduced the MSE considerably (training MSE = 1.22, testing MSE = 1.20) compared to the old lasso model (training MSE = 3.53, testing MSE = 3.38). However, it is still higher than the MSE of the original linear regression model (training MSE = 1.16, testing MSE = 1.15)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb21c164",
   "metadata": {},
   "source": [
    "A regression tree can be modelled to assess if it is able to improve the MSE of the testing data. Various depths of the tree are tested in order to optimize the MSE on the testing set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3fd3f73a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With depth 5, the MSE on the training data is: 1.5828691171324911 and the MSE on the testing data is 1.6169732678579032\n",
      "With depth 10, the MSE on the training data is: 0.62340567834194 and the MSE on the testing data is 0.7316791321658219\n",
      "With depth 15, the MSE on the training data is: 0.28982983056411554 and the MSE on the testing data is 0.5320354691044021\n",
      "With depth 20, the MSE on the training data is: 0.20541046713341882 and the MSE on the testing data is 0.5019106185234794\n",
      "With depth 25, the MSE on the training data is: 0.18578963638767215 and the MSE on the testing data is 0.5251161191270354\n",
      "With depth 30, the MSE on the training data is: 0.1816324868258142 and the MSE on the testing data is 0.5112718579110468\n"
     ]
    }
   ],
   "source": [
    "# fitting a regression tree\n",
    "depths = [5, 10, 15, 20, 25, 30]\n",
    "for depth in depths:\n",
    "    fuel_efficiency_fitted_tree = tree.DecisionTreeRegressor(max_depth=depth, random_state=123).fit(X_train,y_train)\n",
    "    mse_train = metrics.mean_squared_error(y_train, fuel_efficiency_fitted_tree.predict(X_train))\n",
    "    mse_test = metrics.mean_squared_error(y_test, fuel_efficiency_fitted_tree.predict(X_test))\n",
    "    print(f\"With depth {depth}, the MSE on the training data is: {mse_train} and the MSE on the testing data is {mse_test}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db796724",
   "metadata": {},
   "source": [
    "For the regression tree, the optimal depth is 20, where the testing MSE of 0.502 is an improvement over any of the previous models. The model might be overfitting, however, as the training MSE is lower at 0.205."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7efc476e",
   "metadata": {},
   "source": [
    "Finally, a neural network is considered. Data is scaled, and various combinations of hidden layers are tested in order to optimize the MSE on the testing set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "650fa273",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With a hidden layer of (10, 10) the MSE on the training data is: 0.7222217032185008 and the MSE on the testing data is 0.7518720943799218\n",
      "With a hidden layer of (50, 50) the MSE on the training data is: 0.40802121438129685 and the MSE on the testing data is 0.46783186391517984\n",
      "With a hidden layer of (100, 100) the MSE on the training data is: 0.3410442246628378 and the MSE on the testing data is 0.4331101963093887\n",
      "With a hidden layer of (200, 200) the MSE on the training data is: 0.3707679211064601 and the MSE on the testing data is 0.453264348695245\n",
      "With a hidden layer of (10, 10, 10) the MSE on the training data is: 0.7077366562554993 and the MSE on the testing data is 0.7417754239157209\n",
      "With a hidden layer of (100, 100, 100) the MSE on the training data is: 0.400447401887473 and the MSE on the testing data is 0.47367770820701993\n"
     ]
    }
   ],
   "source": [
    "# fitting a neural network\n",
    "\n",
    "hidden_layers = [(10,10), (50, 50), (100, 100), (200, 200), (10, 10, 10), (100, 100, 100)]\n",
    "for hidden_layer in hidden_layers:\n",
    "    fuel_efficiency_nn_scaled_model = pipeline.make_pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        neural_network.MLPRegressor(hidden_layer, max_iter=500, random_state=123)\n",
    "    )\n",
    " \n",
    "    fuel_efficiency_nn_scaled_model.fit(X_train, y_train.flatten())\n",
    "    mse_nn_train = metrics.mean_squared_error(y_train, fuel_efficiency_nn_scaled_model.predict(X_train))\n",
    "    mse_nn_test = metrics.mean_squared_error(y_test, fuel_efficiency_nn_scaled_model.predict(X_test))\n",
    "    print(f\"With a hidden layer of {hidden_layer} the MSE on the training data is: {mse_nn_train} and the MSE on the testing data is {mse_nn_test}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81a7b496",
   "metadata": {},
   "source": [
    "The two-layered neural network with 100 neurons each (100, 100) boasts the lowest testing MSE at 0.433 (and a training MSE of 0.341, so the model is not dramatically overfitting). This model is yet another improvement over any of the previous ones."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "837f5d23",
   "metadata": {},
   "source": [
    "### 4.2 Models With Interactions With Year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85ef3f62",
   "metadata": {},
   "source": [
    "As observed in Section 3.2, several predictors appeared to have a relationship with the model year.\n",
    "\n",
    "Accordingly, the model fitting process from Section 4.1 will be repeated, but this time accounting for the possible interaction between the model year and each of our predictors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "258e56c3",
   "metadata": {},
   "source": [
    "Prior to fitting any models, the data is split into training (75%) and testing (25%) sets. Categorical variables are defined where appropriate, and the interaction with model year is accounted for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b2d12e13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create X and y training and testing datasets\n",
    "y, X = patsy.dmatrices(\"comb_lpkm ~ model_year*(C(make) + C(vehicle_class) + \" +\n",
    "                       \"C(fuel_type) + engine_size_L + cylinders + C(transmission_type) + \" +\n",
    "                        \"transmission_gears)\", full_dataset, return_type='matrix')\n",
    "train = full_dataset.sample(frac = 0.75,random_state = 42) \n",
    "test = full_dataset.drop(train.index)\n",
    "y_train = y[train.index]\n",
    "X_train = X[train.index]\n",
    "y_test = y[test.index]\n",
    "X_test = X[test.index]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a85b4db5",
   "metadata": {},
   "source": [
    "The first model fitted is a linear regression model to predict combined fuel economy considering all parameters with interactions with model year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "54812757",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MSE on the training data is: 1.033495568979455 and the MSE on the testing data is 1.0190613925599326\n"
     ]
    }
   ],
   "source": [
    "# fit a linear regression model\n",
    "fuel_efficiency_interaction_lr_model = linear_model.LinearRegression()\n",
    "fuel_efficiency_interaction_lr_model.fit(X_train, y_train)\n",
    "\n",
    "mse_train = metrics.mean_squared_error(y_train, fuel_efficiency_interaction_lr_model.predict(X_train))\n",
    "mse_test = metrics.mean_squared_error(y_test, fuel_efficiency_interaction_lr_model.predict(X_test))\n",
    "\n",
    "print(f\"The MSE on the training data is: {mse_train} and the MSE on the testing data is {mse_test}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "356f79ef",
   "metadata": {},
   "source": [
    "Next, a lasso model is fit, and the mean squared error is obtained on each of the training and testing data in order to see if there's any benefit to excluding some predictors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1cbe3586",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MSE on the training data is: 1.2215918023683736 and the MSE on the testing data is 1.2176466744773364\n"
     ]
    }
   ],
   "source": [
    "# fit a lasso regression model\n",
    "fuel_efficiency_interaction_lasso_model = linear_model.Lasso()\n",
    "fuel_efficiency_interaction_lasso_model.fit(X_train, y_train)\n",
    "\n",
    "mse_train = metrics.mean_squared_error(y_train, fuel_efficiency_interaction_lasso_model.predict(X_train))\n",
    "mse_test = metrics.mean_squared_error(y_test, fuel_efficiency_interaction_lasso_model.predict(X_test))\n",
    "\n",
    "print(f\"The MSE on the training data is: {mse_train} and the MSE on the testing data is {mse_test}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c726db",
   "metadata": {},
   "source": [
    "A comparison can then be performed of the different terms included in each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "14e201e4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lasso</th>\n",
       "      <th>linreg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.178512e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Audi]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.587387e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Bmw]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.749726e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Buick]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.177062e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Cadillac]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.292725e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Chevrolet]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-1.656334e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Chrysler]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6.217718e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Dodge]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.167091e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Ford]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.994071e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Gmc]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-1.201274e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Honda]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-1.447087e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Hyundai]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-4.313210e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Infiniti]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.614014e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Jaguar]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-7.952594e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Jeep]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.359767e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Kia]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-1.715393e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Lexus]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-4.290396e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Lincoln]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.935359e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Mazda]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>1.231651e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Mercedes-Benz]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.197359e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Nissan]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.184111e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Porsche]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6.851416e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Subaru]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.438170e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Toyota]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-9.372539e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Volkswagen]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.644060e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(make)[T.Volvo]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.701068e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(vehicle_class)[T.SUV]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.144799e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(vehicle_class)[T.Truck]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.204274e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(vehicle_class)[T.Van]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.300903e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(fuel_type)[T.Ethanol]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.305189e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(fuel_type)[T.Natural Gas]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.519135e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(fuel_type)[T.Premium Gasoline]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.307189e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(fuel_type)[T.Regular Gasoline]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.695243e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(transmission_type)[T.Automatic]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.987191e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(transmission_type)[T.Automatic Select Shift]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-4.087571e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(transmission_type)[T.Continuously Variable]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-3.165157e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(transmission_type)[T.Manual]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.957801e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year</th>\n",
       "      <td>-0.037317</td>\n",
       "      <td>-1.869768e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Audi]</th>\n",
       "      <td>0.000270</td>\n",
       "      <td>-2.224979e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Bmw]</th>\n",
       "      <td>0.000022</td>\n",
       "      <td>1.397162e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Buick]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.083991e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Cadillac]</th>\n",
       "      <td>0.000247</td>\n",
       "      <td>3.664463e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Chevrolet]</th>\n",
       "      <td>-0.000186</td>\n",
       "      <td>8.232236e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Chrysler]</th>\n",
       "      <td>0.000083</td>\n",
       "      <td>3.111830e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Dodge]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.584300e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Ford]</th>\n",
       "      <td>0.000112</td>\n",
       "      <td>9.941491e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Gmc]</th>\n",
       "      <td>-0.000100</td>\n",
       "      <td>5.978026e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Honda]</th>\n",
       "      <td>-0.000149</td>\n",
       "      <td>7.200256e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Hyundai]</th>\n",
       "      <td>-0.000123</td>\n",
       "      <td>2.156547e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Infiniti]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.817274e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Jaguar]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>3.979765e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Jeep]</th>\n",
       "      <td>0.000082</td>\n",
       "      <td>2.700854e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Kia]</th>\n",
       "      <td>-0.000023</td>\n",
       "      <td>8.772897e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Lexus]</th>\n",
       "      <td>-0.000231</td>\n",
       "      <td>2.130557e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Lincoln]</th>\n",
       "      <td>0.000336</td>\n",
       "      <td>9.679597e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Mazda]</th>\n",
       "      <td>-0.000077</td>\n",
       "      <td>-5.982238e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Mercedes-Benz]</th>\n",
       "      <td>0.000120</td>\n",
       "      <td>-1.696062e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Nissan]</th>\n",
       "      <td>0.000196</td>\n",
       "      <td>5.928494e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Porsche]</th>\n",
       "      <td>0.000122</td>\n",
       "      <td>3.441345e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Subaru]</th>\n",
       "      <td>0.000398</td>\n",
       "      <td>7.202364e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Toyota]</th>\n",
       "      <td>-0.000209</td>\n",
       "      <td>4.660603e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Volkswagen]</th>\n",
       "      <td>0.000078</td>\n",
       "      <td>2.345211e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(make)[T.Volvo]</th>\n",
       "      <td>0.000264</td>\n",
       "      <td>1.810240e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(vehicle_class)[T.SUV]</th>\n",
       "      <td>0.000771</td>\n",
       "      <td>-5.612809e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(vehicle_class)[T.Truck]</th>\n",
       "      <td>0.001072</td>\n",
       "      <td>-5.884992e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(vehicle_class)[T.Van]</th>\n",
       "      <td>0.000559</td>\n",
       "      <td>1.197314e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(fuel_type)[T.Ethanol]</th>\n",
       "      <td>0.002827</td>\n",
       "      <td>-1.611607e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(fuel_type)[T.Natural Gas]</th>\n",
       "      <td>0.001941</td>\n",
       "      <td>-7.329601e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(fuel_type)[T.Premium Gasoline]</th>\n",
       "      <td>0.001074</td>\n",
       "      <td>-6.374094e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(fuel_type)[T.Regular Gasoline]</th>\n",
       "      <td>0.000707</td>\n",
       "      <td>-8.338179e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(transmission_type)[T.Automatic]</th>\n",
       "      <td>0.000107</td>\n",
       "      <td>2.968854e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(transmission_type)[T.Automatic Select Shift]</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>2.023572e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(transmission_type)[T.Continuously Variable]</th>\n",
       "      <td>-0.001138</td>\n",
       "      <td>1.559562e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:C(transmission_type)[T.Manual]</th>\n",
       "      <td>0.000115</td>\n",
       "      <td>2.464015e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_size_L</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.471022e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinders</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.222601e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transmission_gears</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.882262e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:engine_size_L</th>\n",
       "      <td>0.000395</td>\n",
       "      <td>-2.674973e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:cylinders</th>\n",
       "      <td>0.000245</td>\n",
       "      <td>1.125374e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year:transmission_gears</th>\n",
       "      <td>-0.000106</td>\n",
       "      <td>2.912754e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                       lasso        linreg\n",
       "Intercept                                           0.000000 -1.178512e-10\n",
       "C(make)[T.Audi]                                     0.000000  4.587387e+01\n",
       "C(make)[T.Bmw]                                      0.000000 -2.749726e+01\n",
       "C(make)[T.Buick]                                    0.000000 -2.177062e+02\n",
       "C(make)[T.Cadillac]                                 0.000000 -7.292725e+01\n",
       "C(make)[T.Chevrolet]                               -0.000000 -1.656334e+02\n",
       "C(make)[T.Chrysler]                                 0.000000 -6.217718e+01\n",
       "C(make)[T.Dodge]                                    0.000000 -7.167091e+01\n",
       "C(make)[T.Ford]                                     0.000000 -1.994071e+02\n",
       "C(make)[T.Gmc]                                     -0.000000 -1.201274e+02\n",
       "C(make)[T.Honda]                                   -0.000000 -1.447087e+02\n",
       "C(make)[T.Hyundai]                                 -0.000000 -4.313210e+01\n",
       "C(make)[T.Infiniti]                                 0.000000 -3.614014e+01\n",
       "C(make)[T.Jaguar]                                  -0.000000 -7.952594e+01\n",
       "C(make)[T.Jeep]                                     0.000000 -5.359767e+01\n",
       "C(make)[T.Kia]                                     -0.000000 -1.715393e+01\n",
       "C(make)[T.Lexus]                                   -0.000000 -4.290396e+01\n",
       "C(make)[T.Lincoln]                                  0.000000 -1.935359e+02\n",
       "C(make)[T.Mazda]                                   -0.000000  1.231651e+01\n",
       "C(make)[T.Mercedes-Benz]                            0.000000  4.197359e+00\n",
       "C(make)[T.Nissan]                                   0.000000 -1.184111e+02\n",
       "C(make)[T.Porsche]                                  0.000000 -6.851416e+01\n",
       "C(make)[T.Subaru]                                   0.000000 -1.438170e+02\n",
       "C(make)[T.Toyota]                                  -0.000000 -9.372539e+01\n",
       "C(make)[T.Volkswagen]                               0.000000 -4.644060e+01\n",
       "C(make)[T.Volvo]                                    0.000000 -2.701068e+00\n",
       "C(vehicle_class)[T.SUV]                             0.000000  1.144799e+02\n",
       "C(vehicle_class)[T.Truck]                           0.000000  1.204274e+02\n",
       "C(vehicle_class)[T.Van]                             0.000000 -2.300903e+01\n",
       "C(fuel_type)[T.Ethanol]                             0.000000  3.305189e+02\n",
       "C(fuel_type)[T.Natural Gas]                         0.000000  1.519135e+02\n",
       "C(fuel_type)[T.Premium Gasoline]                    0.000000  1.307189e+02\n",
       "C(fuel_type)[T.Regular Gasoline]                    0.000000  1.695243e+02\n",
       "C(transmission_type)[T.Automatic]                   0.000000 -5.987191e+01\n",
       "C(transmission_type)[T.Automatic Select Shift]     -0.000000 -4.087571e+01\n",
       "C(transmission_type)[T.Continuously Variable]      -0.000000 -3.165157e+02\n",
       "C(transmission_type)[T.Manual]                      0.000000 -4.957801e+01\n",
       "model_year                                         -0.037317 -1.869768e-01\n",
       "model_year:C(make)[T.Audi]                          0.000270 -2.224979e-02\n",
       "model_year:C(make)[T.Bmw]                           0.000022  1.397162e-02\n",
       "model_year:C(make)[T.Buick]                         0.000000  1.083991e-01\n",
       "model_year:C(make)[T.Cadillac]                      0.000247  3.664463e-02\n",
       "model_year:C(make)[T.Chevrolet]                    -0.000186  8.232236e-02\n",
       "model_year:C(make)[T.Chrysler]                      0.000083  3.111830e-02\n",
       "model_year:C(make)[T.Dodge]                         0.000000  3.584300e-02\n",
       "model_year:C(make)[T.Ford]                          0.000112  9.941491e-02\n",
       "model_year:C(make)[T.Gmc]                          -0.000100  5.978026e-02\n",
       "model_year:C(make)[T.Honda]                        -0.000149  7.200256e-02\n",
       "model_year:C(make)[T.Hyundai]                      -0.000123  2.156547e-02\n",
       "model_year:C(make)[T.Infiniti]                      0.000000  1.817274e-02\n",
       "model_year:C(make)[T.Jaguar]                       -0.000000  3.979765e-02\n",
       "model_year:C(make)[T.Jeep]                          0.000082  2.700854e-02\n",
       "model_year:C(make)[T.Kia]                          -0.000023  8.772897e-03\n",
       "model_year:C(make)[T.Lexus]                        -0.000231  2.130557e-02\n",
       "model_year:C(make)[T.Lincoln]                       0.000336  9.679597e-02\n",
       "model_year:C(make)[T.Mazda]                        -0.000077 -5.982238e-03\n",
       "model_year:C(make)[T.Mercedes-Benz]                 0.000120 -1.696062e-03\n",
       "model_year:C(make)[T.Nissan]                        0.000196  5.928494e-02\n",
       "model_year:C(make)[T.Porsche]                       0.000122  3.441345e-02\n",
       "model_year:C(make)[T.Subaru]                        0.000398  7.202364e-02\n",
       "model_year:C(make)[T.Toyota]                       -0.000209  4.660603e-02\n",
       "model_year:C(make)[T.Volkswagen]                    0.000078  2.345211e-02\n",
       "model_year:C(make)[T.Volvo]                         0.000264  1.810240e-03\n",
       "model_year:C(vehicle_class)[T.SUV]                  0.000771 -5.612809e-02\n",
       "model_year:C(vehicle_class)[T.Truck]                0.001072 -5.884992e-02\n",
       "model_year:C(vehicle_class)[T.Van]                  0.000559  1.197314e-02\n",
       "model_year:C(fuel_type)[T.Ethanol]                  0.002827 -1.611607e-01\n",
       "model_year:C(fuel_type)[T.Natural Gas]              0.001941 -7.329601e-02\n",
       "model_year:C(fuel_type)[T.Premium Gasoline]         0.001074 -6.374094e-02\n",
       "model_year:C(fuel_type)[T.Regular Gasoline]         0.000707 -8.338179e-02\n",
       "model_year:C(transmission_type)[T.Automatic]        0.000107  2.968854e-02\n",
       "model_year:C(transmission_type)[T.Automatic Sel... -0.000000  2.023572e-02\n",
       "model_year:C(transmission_type)[T.Continuously ... -0.001138  1.559562e-01\n",
       "model_year:C(transmission_type)[T.Manual]           0.000115  2.464015e-02\n",
       "engine_size_L                                       0.000000  5.471022e+01\n",
       "cylinders                                           0.000000 -2.222601e+01\n",
       "transmission_gears                                  0.000000 -5.882262e+01\n",
       "model_year:engine_size_L                            0.000395 -2.674973e-02\n",
       "model_year:cylinders                                0.000245  1.125374e-02\n",
       "model_year:transmission_gears                      -0.000106  2.912754e-02"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_names = X.design_info.column_names\n",
    "lasso_coefs = pd.Series(dict(zip(param_names, fuel_efficiency_interaction_lasso_model.coef_)))\n",
    "lr_coefs = pd.Series(dict(zip(param_names, fuel_efficiency_interaction_lr_model.coef_[0])))\n",
    "coefs = pd.DataFrame(dict(lasso=lasso_coefs, linreg=lr_coefs))\n",
    "coefs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e2b751e",
   "metadata": {},
   "source": [
    "Despite not specifying an optimal alpha value, the lasso model is much less discriminating this time. Various parameters are included, although there is a preference fo parameters that have the interaction with year. Despite this, the lasso model still has a higher MSE for both the training and testing data (training MSE = 1.22, testing MSE = 1.22) compared to the linear regression model (training MSE = 1.03, testing MSE = 1.02)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a02db2f",
   "metadata": {},
   "source": [
    "As in the previous section, a regression tree will be fitted to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f5dae85f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With depth 5, the MSE on the training data is: 1.5772852703183724 and the MSE on the testing data is 1.624342273505169\n",
      "With depth 10, the MSE on the training data is: 0.6083717914124509 and the MSE on the testing data is 0.737280503553302\n",
      "With depth 15, the MSE on the training data is: 0.2903357931601419 and the MSE on the testing data is 0.5579054208766558\n",
      "With depth 20, the MSE on the training data is: 0.21142201667661645 and the MSE on the testing data is 0.5512144420966664\n",
      "With depth 25, the MSE on the training data is: 0.18969549570350072 and the MSE on the testing data is 0.5492599131458888\n",
      "With depth 30, the MSE on the training data is: 0.1820438071392778 and the MSE on the testing data is 0.5549822240458253\n"
     ]
    }
   ],
   "source": [
    "# fitting a regression tree\n",
    "depths = [5, 10, 15, 20, 25, 30]\n",
    "for depth in depths:\n",
    "    fuel_efficiency_fitted_tree = tree.DecisionTreeRegressor(max_depth=depth, random_state=123).fit(X_train,y_train)\n",
    "    mse_train = metrics.mean_squared_error(y_train, fuel_efficiency_fitted_tree.predict(X_train))\n",
    "    mse_test = metrics.mean_squared_error(y_test, fuel_efficiency_fitted_tree.predict(X_test))\n",
    "    print(f\"With depth {depth}, the MSE on the training data is: {mse_train} and the MSE on the testing data is {mse_test}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1827ea3c",
   "metadata": {},
   "source": [
    "For the regression tree, the optimal depth is 25, where the testing MSE of 0.549 is an improvement over any of the previous models. The model might be overfitting, however, as the training MSE is much lower at 0.190.\n",
    "\n",
    "Interestingly, despite this model including interactions with the model year, the corresponding regression tree model from Section 4.1 without the interaction has a slightly lower testing MSE of 0.502."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f07041dd",
   "metadata": {},
   "source": [
    "Finally, a neural network is considered. Data is scaled, and various combinations of hidden layers are tested in order to optimize the MSE on the testing set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "dd9d74e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With a hidden layer of (10, 10) the MSE on the training data is: 0.7604979285573674 and the MSE on the testing data is 0.7902787139566677\n",
      "With a hidden layer of (50, 50) the MSE on the training data is: 0.389339855216927 and the MSE on the testing data is 0.4690148263363044\n",
      "With a hidden layer of (100, 100) the MSE on the training data is: 0.3347671056916174 and the MSE on the testing data is 0.43310551369218514\n",
      "With a hidden layer of (200, 200) the MSE on the training data is: 0.3221416072933792 and the MSE on the testing data is 0.4273035673283773\n",
      "With a hidden layer of (10, 10, 10) the MSE on the training data is: 0.7076168959816598 and the MSE on the testing data is 0.7240637776583712\n",
      "With a hidden layer of (100, 100, 100) the MSE on the training data is: 0.3185138845803399 and the MSE on the testing data is 0.4203995166824714\n"
     ]
    }
   ],
   "source": [
    "# fitting a neural network\n",
    "hidden_layers = [(10,10), (50, 50), (100, 100), (200, 200), (10, 10, 10), (100, 100, 100)]\n",
    "for hidden_layer in hidden_layers:\n",
    "\n",
    "    fuel_efficiency_interaction_nn_scaled_model = pipeline.make_pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        neural_network.MLPRegressor(hidden_layer, max_iter=500, random_state=123)\n",
    "    )\n",
    " \n",
    "    fuel_efficiency_interaction_nn_scaled_model.fit(X_train, y_train.flatten())\n",
    "    mse_nn_train = metrics.mean_squared_error(y_train, fuel_efficiency_interaction_nn_scaled_model.predict(X_train))\n",
    "    mse_nn_test = metrics.mean_squared_error(y_test, fuel_efficiency_interaction_nn_scaled_model.predict(X_test))\n",
    "    print(f\"With a hidden layer of {hidden_layer} the MSE on the training data is: {mse_nn_train} and the MSE on the testing data is {mse_nn_test}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dff2c101",
   "metadata": {},
   "source": [
    "As with the non-interactive predictors, the neural network is the model that yields the lowest MSE (0.420). \n",
    "\n",
    "The neural network with 3 hidden layers of 100 neurons boasts the lowest testing MSE on both the training and testing sets (and a training MSE of 0.319, so the model is not dramatically overfitting). This model is yet another improvement over any of the previous ones with interaction terms.\n",
    "\n",
    "Furthermore, the corresponding neural network from Section 4.1 with no interaction terms had a higher testing MSE at 0.433. As such, the neural network with interactions with year will be used to predict future fuel efficiency."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4ede5ac",
   "metadata": {},
   "source": [
    "This most efficient model is defined for future prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e0a96399",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('mlpregressor',\n",
       "                 MLPRegressor(hidden_layer_sizes=(100, 100, 100), max_iter=500,\n",
       "                              random_state=123))])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# defining the best model with no interaction\n",
    "fuel_efficiency_interaction_best_nn_scaled_model = pipeline.make_pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        neural_network.MLPRegressor((100,100,100), max_iter=500, random_state=123)\n",
    "    )\n",
    " \n",
    "fuel_efficiency_interaction_best_nn_scaled_model.fit(X_train, y_train.flatten())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01bf9cdd",
   "metadata": {},
   "source": [
    "### 4.3 Predicting Fuel Efficiency of 2024 Vehicles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fedcc6be",
   "metadata": {},
   "source": [
    "This project will use the best model fitted to the dataset (i.e., the neural network with no interactions with year) to attempt to predict the fuel efficiency of future vehicles. Specifically, the model was trained on vehicle data from 2000 to 2023, and it will be utilized to predict the fuel efficiency of several announced 2024 vehicles.\n",
    "\n",
    "Several vehicles announced to be released in 2024 include:\n",
    "- 2024 Ford F-150 (a truck) with a 3.5L V6 gasoline engine and a 10 speed automatic transmission, that gets an estimated 11.7 L/100km\n",
    "\n",
    "- 2024 Honda Civic (a car) with a 2.0L 4-Cylinder gasoline engine and continuously variable transmission that gets an estimated 6.5 L/100km\n",
    "\n",
    "- 2024 Mazda CX-90 (a SUV) with a 3.3L V6 premium gasoline engine and an 8 speed automatic transmission that gets an estimated 9.4 L/100km\n",
    "\n",
    "- 2024 Kia Carnival (a van) with a 3.5L V6 gasoline engine and an 8 speed automatic transmission that gets an estimated 10.7 L/100km\n",
    "\n",
    "- 2024 Chevrolet Trax (a SUV) with a 1.2L 3-Cylinder gasoline engine and a 6 speed automatic transmission that gets an estimated 7.8 L/100 km\n",
    "\n",
    "- 2024 Hyundai Elantra (a car) with a 2.0L 4-Cylinder gasoline engine and continuously variable transmission that gets an estimated 6.2 L/100km\n",
    "\n",
    "- 2024 Chevrolet Silverado 1500 (a truck) with a 3.0L 6-Cylinder diesel engine and a 10 speed automatic transmission that gets an estimated 9.0 L/100km\n",
    "\n",
    "Note that most 2024 models aren't officially published on manufacturer's websites, so this data was obtained from the following sources:\n",
    "- https://www.edmunds.com/ford/f-150/2024/#:~:text=EPA%2Destimated%20fuel%20economy%20is,V8%2Dequipped%20Silverado%20or%20Ram\n",
    "- https://www.caranddriver.com/honda/civic\n",
    "- https://www.caranddriver.com/news/a42789780/2024-mazda-cx-90-power-mpg-specs/\n",
    "- https://driving.ca/kia/carnival/\n",
    "- https://www.caranddriver.com/chevrolet/trax\n",
    "- https://www.caranddriver.com/hyundai/elantra\n",
    "- https://www.motortrend.com/cars/chevrolet/silverado-1500/2024/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab624989",
   "metadata": {},
   "source": [
    "Based on the gathered data, a new dataset is created with the relevant information for the selected 2024 vehicles. It is then concatenated to the existing dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "27fdb87e",
   "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>model_year</th>\n",
       "      <th>make</th>\n",
       "      <th>model</th>\n",
       "      <th>vehicle_class</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>engine_size_L</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>transmission_type</th>\n",
       "      <th>transmission_gears</th>\n",
       "      <th>comb_lpkm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19958</th>\n",
       "      <td>2024</td>\n",
       "      <td>Ford</td>\n",
       "      <td>F-150</td>\n",
       "      <td>Truck</td>\n",
       "      <td>Regular Gasoline</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>10</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19959</th>\n",
       "      <td>2024</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Civic</td>\n",
       "      <td>Car</td>\n",
       "      <td>Regular Gasoline</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Continuously Variable</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19960</th>\n",
       "      <td>2024</td>\n",
       "      <td>Mazda</td>\n",
       "      <td>CX-90</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>8</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19961</th>\n",
       "      <td>2024</td>\n",
       "      <td>Kia</td>\n",
       "      <td>Carnival</td>\n",
       "      <td>Van</td>\n",
       "      <td>Regular Gasoline</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>8</td>\n",
       "      <td>10.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19962</th>\n",
       "      <td>2024</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Trax</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Regular Gasoline</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>6</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19963</th>\n",
       "      <td>2024</td>\n",
       "      <td>Hyundai</td>\n",
       "      <td>Elantra</td>\n",
       "      <td>Car</td>\n",
       "      <td>Regular Gasoline</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Continuously Variable</td>\n",
       "      <td>1</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19964</th>\n",
       "      <td>2024</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Silverado 1500</td>\n",
       "      <td>Truck</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>10</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model_year       make           model vehicle_class         fuel_type  \\\n",
       "19958        2024       Ford           F-150         Truck  Regular Gasoline   \n",
       "19959        2024      Honda           Civic           Car  Regular Gasoline   \n",
       "19960        2024      Mazda           CX-90           SUV  Premium Gasoline   \n",
       "19961        2024        Kia        Carnival           Van  Regular Gasoline   \n",
       "19962        2024  Chevrolet            Trax           SUV  Regular Gasoline   \n",
       "19963        2024    Hyundai         Elantra           Car  Regular Gasoline   \n",
       "19964        2024  Chevrolet  Silverado 1500         Truck            Diesel   \n",
       "\n",
       "       engine_size_L  cylinders      transmission_type  transmission_gears  \\\n",
       "19958            3.5        6.0              Automatic                  10   \n",
       "19959            2.0        4.0  Continuously Variable                   1   \n",
       "19960            3.3        6.0              Automatic                   8   \n",
       "19961            3.5        6.0              Automatic                   8   \n",
       "19962            1.2        3.0              Automatic                   6   \n",
       "19963            2.0        4.0  Continuously Variable                   1   \n",
       "19964            3.0        6.0              Automatic                  10   \n",
       "\n",
       "       comb_lpkm  \n",
       "19958       11.7  \n",
       "19959        6.5  \n",
       "19960        9.4  \n",
       "19961       10.7  \n",
       "19962        7.8  \n",
       "19963        6.2  \n",
       "19964        9.0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating the dataset\n",
    "data = {\"model_year\": [2024, 2024, 2024, 2024, 2024, 2024, 2024],\n",
    "                 \"make\": [\"Ford\", \"Honda\", \"Mazda\", \"Kia\", \"Chevrolet\", \"Hyundai\", \"Chevrolet\"],\n",
    "                 \"model\": [\"F-150\", \"Civic\", \"CX-90\", \"Carnival\", \"Trax\", \"Elantra\", \"Silverado 1500\"],\n",
    "                 \"vehicle_class\": [\"Truck\", \"Car\", \"SUV\", \"Van\", \"SUV\", \"Car\", \"Truck\"],\n",
    "                 \"fuel_type\": [\"Regular Gasoline\", \"Regular Gasoline\", \"Premium Gasoline\", \"Regular Gasoline\", \n",
    "                               \"Regular Gasoline\", \"Regular Gasoline\", \"Diesel\"],\n",
    "                 \"engine_size_L\": [3.5, 2, 3.3, 3.5, 1.2, 2.0, 3.0],\n",
    "                 \"cylinders\": [6.0, 4.0, 6.0, 6.0, 3.0, 4.0, 6.0],\n",
    "                 \"transmission_type\": [\"Automatic\", \"Continuously Variable\", \"Automatic\", \"Automatic\", \n",
    "                                       \"Automatic\", \"Continuously Variable\", \"Automatic\"],\n",
    "                 \"transmission_gears\": [10, 1, 8, 8, 6, 1, 10],\n",
    "                 \"comb_lpkm\": [11.7, 6.5, 9.4, 10.7, 7.8, 6.2, 9.0]}\n",
    "\n",
    "vehicles_to_predict = pd.DataFrame(data)\n",
    "full_dataset_with_predictions = pd.concat([full_dataset, vehicles_to_predict])\n",
    "full_dataset_with_predictions.reset_index(drop=True,inplace=True)\n",
    "full_dataset_with_predictions.loc[full_dataset_with_predictions.index[-7]:full_dataset_with_predictions.index[-1],:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1afa14d3",
   "metadata": {},
   "source": [
    "As mentioned in Sections 4.1 and 4.2, the neural network (with hidden layers of (100, 100)) with no interaction with the model year yielded the lowest MSE on the testing data. Accordingly, this model is used to predict the efficiency of the 2024 vehicles. The code below performs this prediction, and displays a table showing the predicted efficiency, actual efficiency, and percent difference for each of the 2024 vehicles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "10a65a31",
   "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>predicted_efficiency</th>\n",
       "      <th>actual_efficiency</th>\n",
       "      <th>percent_difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024 Ford F150</th>\n",
       "      <td>12.056278</td>\n",
       "      <td>11.7</td>\n",
       "      <td>3.045108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024 Honda Civic</th>\n",
       "      <td>6.774213</td>\n",
       "      <td>6.5</td>\n",
       "      <td>4.218659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024 Mazda CX-90</th>\n",
       "      <td>11.608603</td>\n",
       "      <td>9.4</td>\n",
       "      <td>23.495780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024 Kia Carnival</th>\n",
       "      <td>10.210859</td>\n",
       "      <td>10.7</td>\n",
       "      <td>4.571407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024 Chevrolet Trax</th>\n",
       "      <td>8.643956</td>\n",
       "      <td>7.8</td>\n",
       "      <td>10.819948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024 Hyundai Elantra</th>\n",
       "      <td>6.503524</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.895548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024 Chevrolet Silverado 1500</th>\n",
       "      <td>10.673919</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.599096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               predicted_efficiency  actual_efficiency  \\\n",
       "2024 Ford F150                            12.056278               11.7   \n",
       "2024 Honda Civic                           6.774213                6.5   \n",
       "2024 Mazda CX-90                          11.608603                9.4   \n",
       "2024 Kia Carnival                         10.210859               10.7   \n",
       "2024 Chevrolet Trax                        8.643956                7.8   \n",
       "2024 Hyundai Elantra                       6.503524                6.2   \n",
       "2024 Chevrolet Silverado 1500             10.673919                9.0   \n",
       "\n",
       "                               percent_difference  \n",
       "2024 Ford F150                           3.045108  \n",
       "2024 Honda Civic                         4.218659  \n",
       "2024 Mazda CX-90                        23.495780  \n",
       "2024 Kia Carnival                        4.571407  \n",
       "2024 Chevrolet Trax                     10.819948  \n",
       "2024 Hyundai Elantra                     4.895548  \n",
       "2024 Chevrolet Silverado 1500           18.599096  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using the best neural network model with no interaction\n",
    "y, X = patsy.dmatrices(\"comb_lpkm ~ model_year*(C(make) + C(vehicle_class) + \" +\n",
    "                       \"C(fuel_type) + engine_size_L + cylinders + C(transmission_type) + \" +\n",
    "                        \"transmission_gears)\", full_dataset_with_predictions, return_type='matrix')\n",
    "test_vehicles = X[-7:,:]\n",
    "predicted_efficiency = fuel_efficiency_interaction_best_nn_scaled_model.predict(test_vehicles)\n",
    "predicted_efficiency = predicted_efficiency.flatten()\n",
    "actual_efficiency = y[-7:]\n",
    "actual_efficiency = actual_efficiency.flatten()\n",
    "percent_error = abs(predicted_efficiency-actual_efficiency)/actual_efficiency*100\n",
    "vehicle_names = (\"2024 Ford F150\", \"2024 Honda Civic\", \"2024 Mazda CX-90\", \"2024 Kia Carnival\", \n",
    "                 \"2024 Chevrolet Trax\", \"2024 Hyundai Elantra\", \"2024 Chevrolet Silverado 1500\")\n",
    "predicted = pd.Series(dict(zip(vehicle_names, predicted_efficiency)))\n",
    "actual = pd.Series(dict(zip(vehicle_names, actual_efficiency)))\n",
    "difference = pd.Series(dict(zip(vehicle_names, percent_error)))\n",
    "result = pd.DataFrame(dict(predicted_efficiency=predicted, actual_efficiency=actual, percent_difference=difference))\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f15df532",
   "metadata": {},
   "source": [
    "Overall, the model predicts the fuel economy of these unreleased vehicles within 5% of their actual value in 4 out of 7 cases, and within 20% in 6 out of 7 cases. However, it overpredicts the 2024 Mazda CX-90 by almost 23.5%. \n",
    "\n",
    "The model may have a bias towards overpredicting the fuel economy of future models, as the predicted efficiency was above the actual efficiency in 6 of the 7 cases, although the sample size is too small to draw meaningful conclusions. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3cfe39e",
   "metadata": {},
   "source": [
    "# 5. R&D Spending"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bc22bbc",
   "metadata": {},
   "source": [
    "While the model above was able to accurately predict vehicle efficiency, it also required a large amount of information about the vehicle itself in order to make that prediction. It may, however, be useful to be able to predict the efficiency of vehicles based on historical data alone. \n",
    "\n",
    "One potential solution involves leveraging a company's research and development expenses to predict future fuel efficiency. Since efficiency improvements require significant investment in R&D, it follows that incorporating data on previous efficiency levels and R&D expenses could predict future efficiency levels. If past year's R&D expenses are a good predictor of future efficiency, the company's forecasted spending alongside present day efficiency could be used to predict the improvements in efficiency of next year's model. This will allow consumers to determine the efficiency increase they can expect by waiting a year to purchase next year's model without knowing anything specific about the model itself (e.g., engine size, transmission type, etc.).\n",
    "\n",
    "The first step is to obtain R&D expense data from annual financial reports. To do so, the tabula library can be used to parse tables from the PDF files. The R&D expenses can then be located and extracted. \n",
    "\n",
    "Toyota was selected since they are a prominent manufacturer across all vehicle classes, providing a versatile source of data for this analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2bb85668",
   "metadata": {},
   "outputs": [],
   "source": [
    "def retrieve_toyota_rnd_exp(url):\n",
    "    \"\"\"\n",
    "    Parse the RnD Expense for each year from the given url (pdf file of Toyota FY financial results)\n",
    "    Returns a dict with years and RnD expense (billions of yen).\n",
    "    \n",
    "    The parser was designed to handle variations in structure and language that exist among the different \n",
    "    formats of the PDF files.\n",
    "    \"\"\"\n",
    "    \n",
    "    rnd_regex = re.compile(r\"(?:R&D|Research & Development)\", flags=re.IGNORECASE)\n",
    "\n",
    "    # extract all tables from the pdf\n",
    "    all_dfs = tabula.read_pdf(url, pages='all', multiple_tables=True)\n",
    "    \n",
    "    def locate_rnd_table(all_dfs):\n",
    "        \"\"\"Locate the DataFrame containing the R&D expense data from the list of parsed dfs\"\"\"\n",
    "        for df in all_dfs:\n",
    "            string_cols = df.select_dtypes(include=['object']).columns\n",
    "            if df[string_cols].apply(lambda col: col.str.contains(rnd_regex, na=False)).any().any():\n",
    "                RnD_df = df\n",
    "                RnD_df.reset_index(drop=True, inplace=True)\n",
    "                break\n",
    "        RnD_df = RnD_df.set_index(RnD_df.columns[0])\n",
    "        return RnD_df\n",
    "    \n",
    "    RnD_df = locate_rnd_table(all_dfs)\n",
    "    \n",
    "    def locate_annual_rnd_columns(RnD_df):\n",
    "        \"\"\"Locate the columns containing the R&D expense data from the given DataFrame\"\"\"\n",
    "        # remove forecast/prospects column\n",
    "        RnD_df = RnD_df.drop(columns=[col for col in RnD_df.columns if 'Forecast' in col or 'Prospects' in col])\n",
    "        RnD_df = RnD_df.drop(columns=[col for col in RnD_df.columns if \n",
    "                                      RnD_df.applymap(lambda x: 'Forecast' in str(x) or \n",
    "                                                      'Prospects' in str(x))[col].any()])\n",
    "\n",
    "        # since the df has unlabeled quarterly and annual data, by assumption - keeping only the two columns \n",
    "        #   with the largest R&D expense values will give us the annual columns\n",
    "        rnd_row = RnD_df.loc[RnD_df.index.str.contains(rnd_regex, na=False)].dropna(axis=1)\n",
    "        has_spaces = rnd_row.applymap(lambda x: ' ' in str(x))\n",
    "        drop_cols = has_spaces.any(axis=0)\n",
    "        rnd_row = rnd_row.loc[:, ~drop_cols]\n",
    "        rnd_row = rnd_row.replace('[^0-9\\.]+', '', regex=True).astype(float)\n",
    "        largest_val_cols = rnd_row.apply(lambda row: row.nlargest(2).index.tolist(), axis=1)\n",
    "        selected_cols = [col for col in RnD_df.columns if col in largest_val_cols[0]]\n",
    "        filtered_RnD_df = RnD_df.loc[:, selected_cols]\n",
    "        \n",
    "        return filtered_RnD_df\n",
    "\n",
    "    filtered_RnD_df = locate_annual_rnd_columns(RnD_df)\n",
    "    \n",
    "    def find_rnd_years(filtered_RnD_df, url):\n",
    "        \"\"\"Get the years corresponding to the R&D data from the url, then set them as the column names in the df\"\"\"\n",
    "        year2 = int(re.findall(r'20\\d{2}', url)[0])\n",
    "        year1 = year2-1\n",
    "        filtered_RnD_df.columns = [year1, year2]\n",
    "\n",
    "        return filtered_RnD_df\n",
    "\n",
    "    find_rnd_years(filtered_RnD_df, url)\n",
    "\n",
    "    def create_dict(filtered_RnD_df):\n",
    "        \"\"\"Create a dict with the years as keys, and corresponding R&D expense as values\"\"\"\n",
    "        toyota_rnd_expenses = filtered_RnD_df.loc[filtered_RnD_df.index.str.contains(rnd_regex).fillna(False)]\n",
    "        toyota_rnd_dict = toyota_rnd_expenses.iloc[0].to_dict()\n",
    "        for key, value in toyota_rnd_dict.items():\n",
    "            toyota_rnd_dict[key] = float(value.replace(\",\",\"\"))\n",
    "        \n",
    "        return toyota_rnd_dict\n",
    "    \n",
    "    toyota_rnd_dict = create_dict(filtered_RnD_df)\n",
    "    \n",
    "    return toyota_rnd_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "aec23855",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Got stderr: Apr. 24, 2023 7:49:24 A.M. org.apache.fontbox.ttf.CmapSubtable processSubtype14\n",
      "WARNING: Format 14 cmap table is not supported and will be ignored\n",
      "Apr. 24, 2023 7:49:25 A.M. org.apache.fontbox.ttf.CmapSubtable processSubtype14\n",
      "WARNING: Format 14 cmap table is not supported and will be ignored\n",
      "Apr. 24, 2023 7:49:25 A.M. org.apache.fontbox.ttf.CmapSubtable processSubtype14\n",
      "WARNING: Format 14 cmap table is not supported and will be ignored\n",
      "Apr. 24, 2023 7:49:26 A.M. org.apache.fontbox.ttf.CmapSubtable processSubtype14\n",
      "WARNING: Format 14 cmap table is not supported and will be ignored\n",
      "Apr. 24, 2023 7:49:26 A.M. org.apache.fontbox.ttf.CmapSubtable processSubtype14\n",
      "WARNING: Format 14 cmap table is not supported and will be ignored\n",
      "\n",
      "Got stderr: Apr. 24, 2023 7:49:35 A.M. org.apache.fontbox.ttf.CmapSubtable processSubtype14\n",
      "WARNING: Format 14 cmap table is not supported and will be ignored\n",
      "Apr. 24, 2023 7:49:36 A.M. org.apache.fontbox.ttf.CmapSubtable processSubtype14\n",
      "WARNING: Format 14 cmap table is not supported and will be ignored\n",
      "Apr. 24, 2023 7:49:37 A.M. org.apache.fontbox.ttf.CmapSubtable processSubtype14\n",
      "WARNING: Format 14 cmap table is not supported and will be ignored\n",
      "Apr. 24, 2023 7:49:37 A.M. org.apache.fontbox.ttf.CmapSubtable processSubtype14\n",
      "WARNING: Format 14 cmap table is not supported and will be ignored\n",
      "Apr. 24, 2023 7:49:38 A.M. org.apache.fontbox.ttf.CmapSubtable processSubtype14\n",
      "WARNING: Format 14 cmap table is not supported and will be ignored\n",
      "\n",
      "Got stderr: Apr. 24, 2023 7:50:31 A.M. org.apache.pdfbox.pdmodel.font.PDTrueTypeFont <init>\n",
      "WARNING: Using fallback font 'Times-Roman' for 'Century'\n",
      "Apr. 24, 2023 7:50:32 A.M. org.apache.pdfbox.pdmodel.font.PDTrueTypeFont <init>\n",
      "WARNING: Using fallback font 'Times-Roman' for 'Century'\n",
      "Apr. 24, 2023 7:50:32 A.M. org.apache.pdfbox.pdmodel.font.PDTrueTypeFont <init>\n",
      "WARNING: Using fallback font 'Times-Roman' for 'Century'\n",
      "\n"
     ]
    }
   ],
   "source": [
    "toyota_rnd_21_22 = retrieve_toyota_rnd_exp(\"https://global.toyota/pages/global_toyota/ir/financial-results/2022_4q_summary_en.pdf\")\n",
    "toyota_rnd_19_20 = retrieve_toyota_rnd_exp(\"https://global.toyota/pages/global_toyota/ir/financial-results/2020_4q_summary_en.pdf\")\n",
    "toyota_rnd_17_18 = retrieve_toyota_rnd_exp(\"https://global.toyota/pages/global_toyota/ir/financial-results/archives/en/May_9_2018_summary.pdf\")\n",
    "toyota_rnd_15_16 = retrieve_toyota_rnd_exp(\"https://global.toyota/pages/global_toyota/ir/financial-results/archives/en/May_11_2016_consolidated.pdf\")\n",
    "toyota_rnd_13_14 = retrieve_toyota_rnd_exp(\"https://global.toyota/pages/global_toyota/ir/financial-results/archives/en/May_8_2014_consolidated.pdf\")\n",
    "toyota_rnd_11_12 = retrieve_toyota_rnd_exp(\"https://global.toyota/pages/global_toyota/ir/financial-results/archives/en/May_9_2012_consolidated.pdf\")\n",
    "toyota_rnd_09_10 = retrieve_toyota_rnd_exp(\"https://global.toyota/pages/global_toyota/ir/financial-results/archives/en/May_11_2010_consolidated.pdf\")\n",
    "toyota_rnd_07_08 = retrieve_toyota_rnd_exp(\"https://global.toyota/pages/global_toyota/ir/financial-results/archives/en/May_8_2008_consolidated.pdf\")\n",
    "toyota_rnd_05_06 = retrieve_toyota_rnd_exp('https://global.toyota/pages/global_toyota/ir/financial-results/archives/en/May_10_2006_consolidated.pdf')\n",
    "toyota_rnd_03_04 = retrieve_toyota_rnd_exp('https://global.toyota/pages/global_toyota/ir/financial-results/archives/en/May_11_2004_consolidated.pdf')\n",
    "toyota_rnd_01_02 = retrieve_toyota_rnd_exp('https://global.toyota/pages/global_toyota/ir/financial-results/archives/en/May_13_2002_Highlights_Consolidated.pdf')\n",
    "toyota_rnd_00_01 = retrieve_toyota_rnd_exp('https://global.toyota/pages/global_toyota/ir/financial-results/archives/en/May_16_2001_Highlights_Unconsolidated.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6da67d22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{2000: 402.8, 2001: 479.9, 2002: 592.5, 2003: 668.4, 2004: 682.2, 2005: 755.1, 2006: 812.6, 2007: 890.7, 2008: 958.8, 2009: 904.0, 2010: 725.3, 2011: 730.3, 2012: 779.8, 2013: 807.4, 2014: 910.5, 2015: 1004.5, 2016: 1055.6, 2017: 1037.5, 2018: 1064.2, 2019: 1048.8, 2020: 1110.3, 2021: 1090.4, 2022: 1124.2}\n"
     ]
    }
   ],
   "source": [
    "rnd_dicts = [toyota_rnd_00_01, toyota_rnd_01_02, toyota_rnd_03_04, toyota_rnd_05_06, toyota_rnd_07_08,\n",
    "            toyota_rnd_09_10, toyota_rnd_11_12, toyota_rnd_13_14, toyota_rnd_15_16, toyota_rnd_17_18,\n",
    "            toyota_rnd_19_20, toyota_rnd_21_22]\n",
    "toyota_rnd = {}\n",
    "for dict in rnd_dicts:\n",
    "    toyota_rnd.update(dict)\n",
    "print(toyota_rnd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b2f4de2",
   "metadata": {},
   "source": [
    "Since the goal is to use the previous year's R&D expense data to predict the subsequent year's efficiency, the dictionary keys are shifted by one year to align the columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "70be88a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{2001: 402.8, 2002: 479.9, 2003: 592.5, 2004: 668.4, 2005: 682.2, 2006: 755.1, 2007: 812.6, 2008: 890.7, 2009: 958.8, 2010: 904.0, 2011: 725.3, 2012: 730.3, 2013: 779.8, 2014: 807.4, 2015: 910.5, 2016: 1004.5, 2017: 1055.6, 2018: 1037.5, 2019: 1064.2, 2020: 1048.8, 2021: 1110.3, 2022: 1090.4, 2023: 1124.2}\n"
     ]
    }
   ],
   "source": [
    "aligned_toyota_rnd = {key+1: value for key, value in toyota_rnd.items()}\n",
    "print(aligned_toyota_rnd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8f020bd",
   "metadata": {},
   "source": [
    "Next, the relationship between the vehicle information and R&D expense data is examined. The goal is to use the previous year's vehicle information and Toyota's R&D expenses to try and predict the current year's combined fuel economy. \n",
    "\n",
    "The parsed R&D expense data is added to the vehicle information, then visualized. The vehicles are separated by vehicle class, since oftentimes consumers will enter the market with the need for a specific vehicle class (ie. a construction worker may need a truck for their job, or a family with children may need an SUV...), and use the average fuel economy of each year for each class to build the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b6aa786e",
   "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>model_year</th>\n",
       "      <th>vehicle_class</th>\n",
       "      <th>comb_lpkm</th>\n",
       "      <th>rnd_expense</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17343</th>\n",
       "      <td>2000</td>\n",
       "      <td>Truck</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17325</th>\n",
       "      <td>2000</td>\n",
       "      <td>SUV</td>\n",
       "      <td>11.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17326</th>\n",
       "      <td>2000</td>\n",
       "      <td>SUV</td>\n",
       "      <td>11.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17327</th>\n",
       "      <td>2000</td>\n",
       "      <td>SUV</td>\n",
       "      <td>10.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17328</th>\n",
       "      <td>2000</td>\n",
       "      <td>SUV</td>\n",
       "      <td>10.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>2023</td>\n",
       "      <td>SUV</td>\n",
       "      <td>6.7</td>\n",
       "      <td>1124.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>2023</td>\n",
       "      <td>SUV</td>\n",
       "      <td>6.7</td>\n",
       "      <td>1124.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>2023</td>\n",
       "      <td>SUV</td>\n",
       "      <td>9.9</td>\n",
       "      <td>1124.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>2023</td>\n",
       "      <td>SUV</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1124.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>2023</td>\n",
       "      <td>SUV</td>\n",
       "      <td>13.8</td>\n",
       "      <td>1124.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1047 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       model_year vehicle_class  comb_lpkm  rnd_expense\n",
       "17343        2000         Truck       16.0          NaN\n",
       "17325        2000           SUV       11.2          NaN\n",
       "17326        2000           SUV       11.2          NaN\n",
       "17327        2000           SUV       10.2          NaN\n",
       "17328        2000           SUV       10.1          NaN\n",
       "...           ...           ...        ...          ...\n",
       "647          2023           SUV        6.7       1124.2\n",
       "646          2023           SUV        6.7       1124.2\n",
       "645          2023           SUV        9.9       1124.2\n",
       "654          2023           SUV       11.7       1124.2\n",
       "620          2023           SUV       13.8       1124.2\n",
       "\n",
       "[1047 rows x 4 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toyota_dataset = full_dataset[full_dataset['make'] == 'Toyota'][['model_year', 'vehicle_class', 'comb_lpkm']].sort_values('model_year')\n",
    "toyota_dataset['rnd_expense'] = toyota_dataset['model_year'].map(aligned_toyota_rnd)\n",
    "toyota_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f83b9adb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "/*!\n",
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       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
       " * @license  MIT\n",
       " */\n",
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t[0]=s*(h*v-p*g)-f*(l*v-c*g)+m*(l*p-c*h),t[1]=-(n*(h*v-p*g)-f*(i*v-a*g)+m*(i*p-a*h)),t[2]=n*(l*v-c*g)-s*(i*v-a*g)+m*(i*c-a*l),t[3]=-(n*(l*p-c*h)-s*(i*p-a*h)+f*(i*c-a*l)),t[4]=-(o*(h*v-p*g)-u*(l*v-c*g)+d*(l*p-c*h)),t[5]=r*(h*v-p*g)-u*(i*v-a*g)+d*(i*p-a*h),t[6]=-(r*(l*v-c*g)-o*(i*v-a*g)+d*(i*c-a*l)),t[7]=r*(l*p-c*h)-o*(i*p-a*h)+u*(i*c-a*l),t[8]=o*(f*v-p*m)-u*(s*v-c*m)+d*(s*p-c*f),t[9]=-(r*(f*v-p*m)-u*(n*v-a*m)+d*(n*p-a*f)),t[10]=r*(s*v-c*m)-o*(n*v-a*m)+d*(n*c-a*s),t[11]=-(r*(s*p-c*f)-o*(n*p-a*f)+u*(n*c-a*s)),t[12]=-(o*(f*g-h*m)-u*(s*g-l*m)+d*(s*h-l*f)),t[13]=r*(f*g-h*m)-u*(n*g-i*m)+d*(n*h-i*f),t[14]=-(r*(s*g-l*m)-o*(n*g-i*m)+d*(n*l-i*s)),t[15]=r*(s*h-l*f)-o*(n*h-i*f)+u*(n*l-i*s),t}},{}],196:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return 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t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],203:[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}},{}],204:[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}},{}],205:[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}},{}],206:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return 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n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{\"./identity\":209}],213:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],214:[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}},{}],215:[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}},{}],216:[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}},{}],217:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],218:[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}},{}],219:[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}},{}],220:[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}},{}],221:[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}},{}],222:[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]+\")\"}},{}],223:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],224:[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}},{}],225:[function(t,e,r){\"use strict\";var n=t(\"css-font\"),i=t(\"pick-by-alias\"),a=t(\"regl\"),o=t(\"gl-util/context\"),s=t(\"es6-weak-map\"),l=t(\"color-normalize\"),c=t(\"font-atlas\"),u=t(\"typedarray-pool\"),f=t(\"parse-rect\"),h=t(\"is-plain-obj\"),p=t(\"parse-unit\"),d=t(\"to-px\"),m=t(\"detect-kerning\"),g=t(\"object-assign\"),v=t(\"font-measure\"),y=t(\"flatten-vertex-data\"),x=t(\"bit-twiddle\").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement(\"div\"));w.style.font=\"italic small-caps bold condensed 16px/2 cursive\",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return\"function\"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop(\"count\"),offset:t.prop(\"offset\"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this(\"sizeBuffer\")},width:{offset:0,stride:8,buffer:t.this(\"sizeBuffer\")},char:t.this(\"charBuffer\"),position:t.this(\"position\")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop(\"color\"),opacity:t.prop(\"opacity\"),viewport:t.this(\"viewportArray\"),scale:t.this(\"scale\"),align:t.prop(\"align\"),baseline:t.prop(\"baseline\"),translate:t.this(\"translate\"),positionOffset:t.prop(\"positionOffset\")},primitive:\"points\",viewport:t.this(\"viewport\"),vert:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tattribute float width, charOffset, char;\\n\\t\\t\\tattribute vec2 position;\\n\\t\\t\\tuniform float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ vec2(positionOffset.x, -positionOffset.y)))\\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\\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 float fontSize, charStep, opacity;\\n\\t\\t\\tuniform vec2 atlasSize;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform sampler2D atlas;\\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),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&(\"number\"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+\"px \"+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(\", \"),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:\"top\",fontSize:T.baseFontSize,fontStyle:u.join(\" \")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);g(t.kerning,m(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),A=0,M=0;A<this.counts.length;A++){var S=this.counts[A];if(w)for(var E=0;E<S;E++)k[M++]=t.position[2*A],k[M++]=t.position[2*A+1];else for(var L=0;L<S;L++)k[M++]=t.position[A][0],k[M++]=t.position[A][1]}this.position.call?this.position({type:\"float\",data:k}):this.position=this.regl.buffer({type:\"float\",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=u.mallocUint8(this.count),P=u.mallocFloat(2*this.count);this.textWidth=[];for(var I=0,O=0;I<this.counts.length;I++){for(var z=this.counts[I],D=this.font[I]||this.font[0],R=this.fontAtlas[I]||this.fontAtlas[0],F=0;F<z;F++){var B=this.text.charAt(O),N=this.text.charAt(O-1);if(C[O]=R.ids[B],P[2*O]=D.width[B],F){var j=P[2*O-2],U=P[2*O],V=P[2*O-1]+.5*j+.5*U;if(this.kerning){var H=D.kerning[N+B];H&&(V+=.001*H)}P[2*O+1]=V}else P[2*O+1]=.5*P[2*O];O++}this.textWidth.push(P.length?.5*P[2*O-2]+P[2*O-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:C,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:P,type:\"float\",usage:\"stream\"}),u.freeUint8(C),u.freeFloat(P),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),u=x(s*i);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if(\"number\"==typeof n)return n;switch(n){case\"right\":case\"end\":return-t;case\"center\":case\"centre\":case\"middle\":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+=\"number\"==typeof t?t-n.baseline:-n[t],i*=-1}))),null!=t.color)if(t.color||(t.color=\"transparent\"),\"string\"!=typeof t.color&&isNaN(t.color)){var q;if(\"number\"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;q=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)q.set(l(Y(W,W+4),\"uint8\"),W)}else{var X=t.color.length;q=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)q.set(l(t.color[Z]||0,\"uint8\"),4*Z)}this.color=q}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 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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 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r=e.parse(t[1],1,Bt);if(!r)return null;var n=[],i=null;e.expectedType&&\"value\"!==e.expectedType.kind&&(i=e.expectedType);for(var a=1;a<t.length;a+=2){var o=1===a?-1/0:t[a],s=t[a+1],l=a,c=a+1;if(\"number\"!=typeof o)return e.error('Input/output pairs for \"step\" expressions must be defined using literal numeric values (not computed expressions) for the input values.',l);if(n.length&&n[n.length-1][0]>=o)return e.error('Input/output pairs for \"step\" expressions must be arranged with input values in strictly ascending order.',l);var u=e.parse(s,c,i);if(!u)return null;i=i||u.type,n.push([o,u])}return new He(i,r,n)},He.prototype.evaluate=function(t){var e=this.labels,r=this.outputs;if(1===e.length)return r[0].evaluate(t);var n=this.input.evaluate(t);if(n<=e[0])return r[0].evaluate(t);var i=e.length;return n>=e[i-1]?r[i-1].evaluate(t):r[Ve(e,n)].evaluate(t)},He.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1){t(r[e])}},He.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},He.prototype.serialize=function(){for(var t=[\"step\",this.input.serialize()],e=0;e<this.labels.length;e++)e>0&&t.push(this.labels[e]),t.push(this.outputs[e].serialize());return t};var Ge=Object.freeze({__proto__:null,number:qe,color:function(t,e,r){return new te(qe(t.r,e.r,r),qe(t.g,e.g,r),qe(t.b,e.b,r),qe(t.a,e.a,r))},array:function(t,e,r){return t.map((function(t,n){return qe(t,e[n],r)}))}}),Ye=6/29,We=3*Ye*Ye,Xe=Math.PI/180,Ze=180/Math.PI;function Je(t){return t>.008856451679035631?Math.pow(t,1/3):t/We+4/29}function Ke(t){return t>Ye?t*t*t:We*(t-4/29)}function Qe(t){return 255*(t<=.0031308?12.92*t:1.055*Math.pow(t,1/2.4)-.055)}function $e(t){return(t/=255)<=.04045?t/12.92:Math.pow((t+.055)/1.055,2.4)}function tr(t){var e=$e(t.r),r=$e(t.g),n=$e(t.b),i=Je((.4124564*e+.3575761*r+.1804375*n)/.95047),a=Je((.2126729*e+.7151522*r+.072175*n)/1);return{l:116*a-16,a:500*(i-a),b:200*(a-Je((.0193339*e+.119192*r+.9503041*n)/1.08883)),alpha:t.a}}function er(t){var e=(t.l+16)/116,r=isNaN(t.a)?e:e+t.a/500,n=isNaN(t.b)?e:e-t.b/200;return e=1*Ke(e),r=.95047*Ke(r),n=1.08883*Ke(n),new te(Qe(3.2404542*r-1.5371385*e-.4985314*n),Qe(-.969266*r+1.8760108*e+.041556*n),Qe(.0556434*r-.2040259*e+1.0572252*n),t.alpha)}function rr(t,e,r){var n=e-t;return t+r*(n>180||n<-180?n-360*Math.round(n/360):n)}var nr={forward:tr,reverse:er,interpolate:function(t,e,r){return{l:qe(t.l,e.l,r),a:qe(t.a,e.a,r),b:qe(t.b,e.b,r),alpha:qe(t.alpha,e.alpha,r)}}},ir={forward:function(t){var e=tr(t),r=e.l,n=e.a,i=e.b,a=Math.atan2(i,n)*Ze;return{h:a<0?a+360:a,c:Math.sqrt(n*n+i*i),l:r,alpha:t.a}},reverse:function(t){var e=t.h*Xe,r=t.c;return er({l:t.l,a:Math.cos(e)*r,b:Math.sin(e)*r,alpha:t.alpha})},interpolate:function(t,e,r){return{h:rr(t.h,e.h,r),c:qe(t.c,e.c,r),l:qe(t.l,e.l,r),alpha:qe(t.alpha,e.alpha,r)}}},ar=Object.freeze({__proto__:null,lab:nr,hcl:ir}),or=function(t,e,r,n,i){this.type=t,this.operator=e,this.interpolation=r,this.input=n,this.labels=[],this.outputs=[];for(var a=0,o=i;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1];this.labels.push(l),this.outputs.push(c)}};function sr(t,e,r,n){var i=n-r,a=t-r;return 0===i?0:1===e?a/i:(Math.pow(e,a)-1)/(Math.pow(e,i)-1)}or.interpolationFactor=function(t,e,n,i){var a=0;if(\"exponential\"===t.name)a=sr(e,t.base,n,i);else if(\"linear\"===t.name)a=sr(e,1,n,i);else if(\"cubic-bezier\"===t.name){var o=t.controlPoints;a=new r(o[0],o[1],o[2],o[3]).solve(sr(e,1,n,i))}return a},or.parse=function(t,e){var r=t[0],n=t[1],i=t[2],a=t.slice(3);if(!Array.isArray(n)||0===n.length)return e.error(\"Expected an interpolation type expression.\",1);if(\"linear\"===n[0])n={name:\"linear\"};else if(\"exponential\"===n[0]){var o=n[1];if(\"number\"!=typeof o)return e.error(\"Exponential interpolation requires a numeric base.\",1,1);n={name:\"exponential\",base:o}}else{if(\"cubic-bezier\"!==n[0])return e.error(\"Unknown interpolation type \"+String(n[0]),1,0);var s=n.slice(1);if(4!==s.length||s.some((function(t){return\"number\"!=typeof t||t<0||t>1})))return e.error(\"Cubic bezier interpolation requires four numeric arguments with values between 0 and 1.\",1);n={name:\"cubic-bezier\",controlPoints:s}}if(t.length-1<4)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if((t.length-1)%2!=0)return e.error(\"Expected an even number of arguments.\");if(!(i=e.parse(i,2,Bt)))return null;var l=[],c=null;\"interpolate-hcl\"===r||\"interpolate-lab\"===r?c=Ut:e.expectedType&&\"value\"!==e.expectedType.kind&&(c=e.expectedType);for(var u=0;u<a.length;u+=2){var f=a[u],h=a[u+1],p=u+3,d=u+4;if(\"number\"!=typeof f)return e.error('Input/output pairs for 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a=Ve(e,n),o=e[a],s=e[a+1],l=or.interpolationFactor(this.interpolation,n,o,s),c=r[a].evaluate(t),u=r[a+1].evaluate(t);return\"interpolate\"===this.operator?Ge[this.type.kind.toLowerCase()](c,u,l):\"interpolate-hcl\"===this.operator?ir.reverse(ir.interpolate(ir.forward(c),ir.forward(u),l)):nr.reverse(nr.interpolate(nr.forward(c),nr.forward(u),l))},or.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1){t(r[e])}},or.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},or.prototype.serialize=function(){var t;t=\"linear\"===this.interpolation.name?[\"linear\"]:\"exponential\"===this.interpolation.name?1===this.interpolation.base?[\"linear\"]:[\"exponential\",this.interpolation.base]:[\"cubic-bezier\"].concat(this.interpolation.controlPoints);for(var e=[this.operator,t,this.input.serialize()],r=0;r<this.labels.length;r++)e.push(this.labels[r],this.outputs[r].serialize());return e};var lr=function(t,e){this.type=t,this.args=e};lr.parse=function(t,e){if(t.length<2)return e.error(\"Expectected at least one argument.\");var r=null,n=e.expectedType;n&&\"value\"!==n.kind&&(r=n);for(var i=[],a=0,o=t.slice(1);a<o.length;a+=1){var s=o[a],l=e.parse(s,1+i.length,r,void 0,{typeAnnotation:\"omit\"});if(!l)return null;r=r||l.type,i.push(l)}var c=n&&i.some((function(t){return Jt(n,t.type)}));return new lr(c?Ht:r,i)},lr.prototype.evaluate=function(t){for(var e,r=null,n=0,i=0,a=this.args;i<a.length;i+=1){if(n++,(r=a[i].evaluate(t))&&r instanceof ie&&!r.available&&(e||(e=r.name),r=null,n===this.args.length&&(r=e)),null!==r)break}return r},lr.prototype.eachChild=function(t){this.args.forEach(t)},lr.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},lr.prototype.serialize=function(){var t=[\"coalesce\"];return this.eachChild((function(e){t.push(e.serialize())})),t};var cr=function(t,e){this.type=e.type,this.bindings=[].concat(t),this.result=e};cr.prototype.evaluate=function(t){return this.result.evaluate(t)},cr.prototype.eachChild=function(t){for(var e=0,r=this.bindings;e<r.length;e+=1){t(r[e][1])}t(this.result)},cr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found \"+(t.length-1)+\" instead.\");for(var r=[],n=1;n<t.length-1;n+=2){var i=t[n];if(\"string\"!=typeof i)return e.error(\"Expected string, but found \"+typeof i+\" instead.\",n);if(/[^a-zA-Z0-9_]/.test(i))return e.error(\"Variable names must contain only alphanumeric characters or '_'.\",n);var a=e.parse(t[n+1],n+1);if(!a)return null;r.push([i,a])}var o=e.parse(t[t.length-1],t.length-1,e.expectedType,r);return o?new cr(r,o):null},cr.prototype.outputDefined=function(){return this.result.outputDefined()},cr.prototype.serialize=function(){for(var t=[\"let\"],e=0,r=this.bindings;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t.push(i,a.serialize())}return t.push(this.result.serialize()),t};var ur=function(t,e,r){this.type=t,this.index=e,this.input=r};ur.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Bt),n=e.parse(t[2],2,Wt(e.expectedType||Ht));if(!r||!n)return null;var i=n.type;return new ur(i.itemType,r,n)},ur.prototype.evaluate=function(t){var e=this.index.evaluate(t),r=this.input.evaluate(t);if(e<0)throw new ue(\"Array index out of bounds: \"+e+\" < 0.\");if(e>=r.length)throw new ue(\"Array index out of bounds: \"+e+\" > \"+(r.length-1)+\".\");if(e!==Math.floor(e))throw new ue(\"Array index must be an integer, but found \"+e+\" instead.\");return r[e]},ur.prototype.eachChild=function(t){t(this.index),t(this.input)},ur.prototype.outputDefined=function(){return!1},ur.prototype.serialize=function(){return[\"at\",this.index.serialize(),this.input.serialize()]};var fr=function(t,e){this.type=jt,this.needle=t,this.haystack=e};fr.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Ht),n=e.parse(t[2],2,Ht);return r&&n?Kt(r.type,[jt,Nt,Bt,Ft,Ht])?new fr(r,n):e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(r.type)+\" instead\"):null},fr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!r)return!1;if(!Qt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ue(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(se(e))+\" instead.\");if(!Qt(r,[\"string\",\"array\"]))throw new ue(\"Expected second argument to be of type array or string, but found \"+Xt(se(r))+\" instead.\");return r.indexOf(e)>=0},fr.prototype.eachChild=function(t){t(this.needle),t(this.haystack)},fr.prototype.outputDefined=function(){return!0},fr.prototype.serialize=function(){return[\"in\",this.needle.serialize(),this.haystack.serialize()]};var hr=function(t,e,r){this.type=Bt,this.needle=t,this.haystack=e,this.fromIndex=r};hr.parse=function(t,e){if(t.length<=2||t.length>=5)return e.error(\"Expected 3 or 4 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Ht),n=e.parse(t[2],2,Ht);if(!r||!n)return null;if(!Kt(r.type,[jt,Nt,Bt,Ft,Ht]))return e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(r.type)+\" instead\");if(4===t.length){var i=e.parse(t[3],3,Bt);return i?new hr(r,n,i):null}return new hr(r,n)},hr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!Qt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ue(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(se(e))+\" instead.\");if(!Qt(r,[\"string\",\"array\"]))throw new ue(\"Expected second argument to be of type array or string, but found \"+Xt(se(r))+\" instead.\");if(this.fromIndex){var n=this.fromIndex.evaluate(t);return r.indexOf(e,n)}return r.indexOf(e)},hr.prototype.eachChild=function(t){t(this.needle),t(this.haystack),this.fromIndex&&t(this.fromIndex)},hr.prototype.outputDefined=function(){return!1},hr.prototype.serialize=function(){if(null!=this.fromIndex&&void 0!==this.fromIndex){var t=this.fromIndex.serialize();return[\"index-of\",this.needle.serialize(),this.haystack.serialize(),t]}return[\"index-of\",this.needle.serialize(),this.haystack.serialize()]};var pr=function(t,e,r,n,i,a){this.inputType=t,this.type=e,this.input=r,this.cases=n,this.outputs=i,this.otherwise=a};pr.parse=function(t,e){if(t.length<5)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=1)return e.error(\"Expected an even number of 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m?\"value\"!==d.type.kind&&e.concat(1).checkSubtype(r,d.type)?null:new pr(r,n,d,i,a,m):null},pr.prototype.evaluate=function(t){var e=this.input.evaluate(t);return(se(e)===this.inputType&&this.outputs[this.cases[e]]||this.otherwise).evaluate(t)},pr.prototype.eachChild=function(t){t(this.input),this.outputs.forEach(t),t(this.otherwise)},pr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))&&this.otherwise.outputDefined()},pr.prototype.serialize=function(){for(var t=this,e=[\"match\",this.input.serialize()],r=[],n={},i=0,a=Object.keys(this.cases).sort();i<a.length;i+=1){var o=a[i];void 0===(f=n[this.cases[o]])?(n[this.cases[o]]=r.length,r.push([this.cases[o],[o]])):r[f][1].push(o)}for(var s=function(e){return\"number\"===t.inputType.kind?Number(e):e},l=0,c=r;l<c.length;l+=1){var u=c[l],f=u[0],h=u[1];1===h.length?e.push(s(h[0])):e.push(h.map(s)),e.push(this.outputs[outputIndex$1].serialize())}return e.push(this.otherwise.serialize()),e};var dr=function(t,e,r){this.type=t,this.branches=e,this.otherwise=r};dr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=0)return e.error(\"Expected an odd number of arguments.\");var r;e.expectedType&&\"value\"!==e.expectedType.kind&&(r=e.expectedType);for(var n=[],i=1;i<t.length-1;i+=2){var a=e.parse(t[i],i,jt);if(!a)return null;var o=e.parse(t[i+1],i+1,r);if(!o)return null;n.push([a,o]),r=r||o.type}var s=e.parse(t[t.length-1],t.length-1,r);return s?new dr(r,n,s):null},dr.prototype.evaluate=function(t){for(var e=0,r=this.branches;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];if(i.evaluate(t))return a.evaluate(t)}return this.otherwise.evaluate(t)},dr.prototype.eachChild=function(t){for(var e=0,r=this.branches;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t(i),t(a)}t(this.otherwise)},dr.prototype.outputDefined=function(){return this.branches.every((function(t){t[0];return 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or?a.interpolation:void 0;return Ir(new Zr(n?\"camera\":\"composite\",t.value,a.labels,o))}Zr.prototype.evaluateWithoutErrorHandling=function(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)},Zr.prototype.evaluate=function(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)},Zr.prototype.interpolationFactor=function(t,e,r){return this.interpolationType?or.interpolationFactor(this.interpolationType,t,e,r):0};var Kr=function(t,e){this._parameters=t,this._specification=e,It(this,function t(e,r){var n,i,a,o=\"color\"===r.type,s=e.stops&&\"object\"==typeof e.stops[0][0],l=s||void 0!==e.property,c=s||!l,u=e.type||(Rr(r)?\"exponential\":\"interval\");if(o&&((e=It({},e)).stops&&(e.stops=e.stops.map((function(t){return[t[0],te.parse(t[1])]}))),e.default?e.default=te.parse(e.default):e.default=te.parse(r.default)),e.colorSpace&&\"rgb\"!==e.colorSpace&&!ar[e.colorSpace])throw new Error(\"Unknown color space: 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n&&r<n.minimum?[new Ct(e,r,r+\" is less than the minimum value \"+n.minimum)]:\"maximum\"in n&&r>n.maximum?[new Ct(e,r,r+\" is greater than the maximum value \"+n.maximum)]:[]}function en(t){var e,r,n,i=t.valueSpec,a=Ot(t.value.type),o={},s=\"categorical\"!==a&&void 0===t.value.property,l=!s,c=\"array\"===Fr(t.value.stops)&&\"array\"===Fr(t.value.stops[0])&&\"object\"===Fr(t.value.stops[0][0]),u=Qr({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if(\"identity\"===a)return[new Ct(t.key,t.value,'identity function may not have a \"stops\" property')];var e=[],r=t.value;e=e.concat($r({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:f})),\"array\"===Fr(r)&&0===r.length&&e.push(new Ct(t.key,r,\"array must have at least one stop\"));return e},default:function(t){return kn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return\"identity\"===a&&s&&u.push(new Ct(t.key,t.value,'missing required property \"property\"')),\"identity\"===a||t.value.stops||u.push(new Ct(t.key,t.value,'missing required property \"stops\"')),\"exponential\"===a&&t.valueSpec.expression&&!Rr(t.valueSpec)&&u.push(new Ct(t.key,t.value,\"exponential functions not supported\")),t.styleSpec.$version>=8&&(l&&!zr(t.valueSpec)?u.push(new Ct(t.key,t.value,\"property functions not supported\")):s&&!Dr(t.valueSpec)&&u.push(new Ct(t.key,t.value,\"zoom functions not supported\"))),\"categorical\"!==a&&!c||void 0!==t.value.property||u.push(new Ct(t.key,t.value,'\"property\" property is required')),u;function f(t){var e=[],a=t.value,s=t.key;if(\"array\"!==Fr(a))return[new Ct(s,a,\"array expected, \"+Fr(a)+\" found\")];if(2!==a.length)return[new Ct(s,a,\"array length 2 expected, length \"+a.length+\" found\")];if(c){if(\"object\"!==Fr(a[0]))return[new Ct(s,a,\"object expected, \"+Fr(a[0])+\" found\")];if(void 0===a[0].zoom)return[new Ct(s,a,\"object stop key must have zoom\")];if(void 0===a[0].value)return[new Ct(s,a,\"object stop key must have value\")];if(n&&n>Ot(a[0].zoom))return[new Ct(s,a[0].zoom,\"stop zoom values must appear in ascending order\")];Ot(a[0].zoom)!==n&&(n=Ot(a[0].zoom),r=void 0,o={}),e=e.concat(Qr({key:s+\"[0]\",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:tn,value:h}}))}else e=e.concat(h({key:s+\"[0]\",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Yr(zt(a[1]))?e.concat([new Ct(s+\"[1]\",a[1],\"expressions are not allowed in function stops.\")]):e.concat(kn({key:s+\"[1]\",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=Fr(t.value),l=Ot(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new Ct(t.key,c,s+\" stop domain type must match previous stop domain type \"+e)]}else e=s;if(\"number\"!==s&&\"string\"!==s&&\"boolean\"!==s)return[new Ct(t.key,c,\"stop domain value must be a number, string, or boolean\")];if(\"number\"!==s&&\"categorical\"!==a){var u=\"number expected, \"+s+\" found\";return zr(i)&&void 0===a&&(u+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new Ct(t.key,c,u)]}return\"categorical\"!==a||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==a&&\"number\"===s&&void 0!==r&&l<r?[new Ct(t.key,c,\"stop domain values must appear in ascending order\")]:(r=l,\"categorical\"===a&&l in o?[new Ct(t.key,c,\"stop domain values must be unique\")]:(o[l]=!0,[])):[new Ct(t.key,c,\"integer expected, found \"+l)]}}function rn(t){var e=(\"property\"===t.expressionContext?Jr:Wr)(zt(t.value),t.valueSpec);if(\"error\"===e.result)return e.value.map((function(e){return new Ct(\"\"+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if(\"property\"===t.expressionContext&&\"text-font\"===t.propertyKey&&!r.outputDefined())return[new Ct(t.key,t.value,'Invalid data expression for \"'+t.propertyKey+'\". Output values must be contained as literals within the expression.')];if(\"property\"===t.expressionContext&&\"layout\"===t.propertyType&&!Be(r))return[new Ct(t.key,t.value,'\"feature-state\" data expressions are not supported with layout properties.')];if(\"filter\"===t.expressionContext&&!Be(r))return[new Ct(t.key,t.value,'\"feature-state\" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf(\"cluster\")){if(!Ne(r,[\"zoom\",\"feature-state\"]))return[new Ct(t.key,t.value,'\"zoom\" and \"feature-state\" expressions are not supported with cluster properties.')];if(\"cluster-initial\"===t.expressionContext&&!Fe(r))return[new Ct(t.key,t.value,\"Feature data expressions are not supported with initial expression part of cluster properties.\")]}return[]}function nn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ot(r))&&i.push(new Ct(e,r,\"expected one of [\"+n.values.join(\", \")+\"], 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on={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function sn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};an(t)||(t=cn(t));var e=Wr(t,on);if(\"error\"===e.result)throw new Error(e.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if(\"within\"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function ln(t,e){return t<e?-1:t>e?1:0}function cn(t){if(!t)return!0;var e,r=t[0];return t.length<=1?\"any\"!==r:\"==\"===r?un(t[1],t[2],\"==\"):\"!=\"===r?pn(un(t[1],t[2],\"==\")):\"<\"===r||\">\"===r||\"<=\"===r||\">=\"===r?un(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(cn))):\"all\"===r?[\"all\"].concat(t.slice(1).map(cn)):\"none\"===r?[\"all\"].concat(t.slice(1).map(cn).map(pn)):\"in\"===r?fn(t[1],t.slice(2)):\"!in\"===r?pn(fn(t[1],t.slice(2))):\"has\"===r?hn(t[1]):\"!has\"===r?pn(hn(t[1])):\"within\"!==r||t}function un(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function fn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(ln)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function hn(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function pn(t){return[\"!\",t]}function dn(t){return an(zt(t.value))?rn(It({},t,{expressionContext:\"filter\",valueSpec:{value:\"boolean\"}})):function t(e){var r=e.value,n=e.key;if(\"array\"!==Fr(r))return[new Ct(n,r,\"array expected, \"+Fr(r)+\" found\")];var i,a=e.styleSpec,o=[];if(r.length<1)return[new Ct(n,r,\"filter array must have at least 1 element\")];switch(o=o.concat(nn({key:n+\"[0]\",value:r[0],valueSpec:a.filter_operator,style:e.style,styleSpec:e.styleSpec})),Ot(r[0])){case\"<\":case\"<=\":case\">\":case\">=\":r.length>=2&&\"$type\"===Ot(r[1])&&o.push(new Ct(n,r,'\"$type\" cannot be use with operator \"'+r[0]+'\"'));case\"==\":case\"!=\":3!==r.length&&o.push(new Ct(n,r,'filter array for operator \"'+r[0]+'\" must have 3 elements'));case\"in\":case\"!in\":r.length>=2&&\"string\"!==(i=Fr(r[1]))&&o.push(new Ct(n+\"[1]\",r[1],\"string expected, \"+i+\" 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r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+\"_\"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if(\"paint\"===e&&l&&s[l[1]]&&s[l[1]].transition)return kn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new Ct(r,a,'unknown property \"'+o+'\"')];if(\"string\"===Fr(a)&&zr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new Ct(r,a,'\"'+o+'\" does not support interpolation syntax\\nUse an identity property function instead: `{ \"type\": \"identity\", \"property\": '+JSON.stringify(c[1])+\" }`.\")];var f=[];return\"symbol\"===t.layerType&&(\"text-field\"===o&&n&&!n.glyphs&&f.push(new Ct(r,a,'use of \"text-field\" requires a style \"glyphs\" property')),\"text-font\"===o&&Br(zt(a))&&\"identity\"===Ot(a.type)&&f.push(new Ct(r,a,'\"text-font\" does not support identity 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null!=hi.applyArabicShaping&&null!=hi.processBidirectionalText&&null!=hi.processStyledBidirectionalText},getPluginURL:function(){return oi}},pi=function(t,e){this.zoom=t,e?(this.now=e.now,this.fadeDuration=e.fadeDuration,this.zoomHistory=e.zoomHistory,this.transition=e.transition):(this.now=0,this.fadeDuration=0,this.zoomHistory=new Gn,this.transition={})};pi.prototype.isSupportedScript=function(t){return function(t,e){for(var r=0,n=t;r<n.length;r+=1){if(!Qn(n[r].charCodeAt(0),e))return!1}return!0}(t,hi.isLoaded())},pi.prototype.crossFadingFactor=function(){return 0===this.fadeDuration?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)},pi.prototype.getCrossfadeParameters=function(){var t=this.zoom,e=t-Math.floor(t),r=this.crossFadingFactor();return t>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:e+(1-e)*r}:{fromScale:.5,toScale:1,t:1-(1-r)*e}};var di=function(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(Br(t))return new Kr(t,e);if(Yr(t)){var r=Jr(t,e);if(\"error\"===r.result)throw new Error(r.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return r.value}var n=t;return\"string\"==typeof t&&\"color\"===e.type&&(n=te.parse(t)),{kind:\"constant\",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};di.prototype.isDataDriven=function(){return\"source\"===this.expression.kind||\"composite\"===this.expression.kind},di.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var mi=function(t){this.property=t,this.value=new di(t,void 0)};mi.prototype.transitioned=function(t,e){return new vi(this.property,this.value,e,u({},t.transition,this.transition),t.now)},mi.prototype.untransitioned=function(){return new vi(this.property,this.value,null,{},0)};var gi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};gi.prototype.getValue=function(t){return x(this._values[t].value.value)},gi.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new mi(this._values[t].property)),this._values[t].value=new di(this._values[t].property,null===e?void 0:x(e))},gi.prototype.getTransition=function(t){return x(this._values[t].transition)},gi.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new mi(this._values[t].property)),this._values[t].transition=x(e)||void 0},gi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i);var a=this.getTransition(n);void 0!==a&&(t[n+\"-transition\"]=a)}return t},gi.prototype.transitioned=function(t,e){for(var r=new yi(this._properties),n=0,i=Object.keys(this._values);n<i.length;n+=1){var a=i[n];r._values[a]=this._values[a].transitioned(t,e._values[a])}return r},gi.prototype.untransitioned=function(){for(var t=new yi(this._properties),e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e];t._values[n]=this._values[n].untransitioned()}return t};var vi=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};vi.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var yi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};yi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},yi.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var xi=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};xi.prototype.getValue=function(t){return x(this._values[t].value)},xi.prototype.setValue=function(t,e){this._values[t]=new di(this._values[t].property,null===e?void 0:x(e))},xi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},xi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var bi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};bi.prototype.isConstant=function(){return\"constant\"===this.value.kind},bi.prototype.constantOr=function(t){return\"constant\"===this.value.kind?this.value.value:t},bi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var _i=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};_i.prototype.get=function(t){return this._values[t]};var wi=function(t){this.specification=t};wi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},wi.prototype.interpolate=function(t,e,r){var n=Ge[this.specification.type];return n?n(t,e,r):t};var Ti=function(t,e){this.specification=t,this.overrides=e};Ti.prototype.possiblyEvaluate=function(t,e,r,n){return\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?new bi(this,{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)},e):new bi(this,t.expression,e)},Ti.prototype.interpolate=function(t,e,r){if(\"constant\"!==t.value.kind||\"constant\"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new bi(this,{kind:\"constant\",value:void 0},t.parameters);var n=Ge[this.specification.type];return n?new bi(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.parameters):t},Ti.prototype.evaluate=function(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new bi(this,{kind:\"constant\",value:void 0},e);if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a=\"resolvedImage\"===t.property.specification.type&&\"string\"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new bi(this,{kind:\"constant\",value:o},e)}if(\"camera\"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new bi(this,{kind:\"constant\",value:s},e)}return new bi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if(\"source\"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return\"composite\"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Ti),Ai=function(t){this.specification=t};Ai.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new pi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new pi(Math.floor(e.zoom),e)),t.expression.evaluate(new pi(Math.floor(e.zoom+1),e)),e)}},Ai.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Ai.prototype.interpolate=function(t){return t};var Mi=function(t){this.specification=t};Mi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},Mi.prototype.interpolate=function(){return!1};var Si=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new di(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new mi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Nn(\"DataDrivenProperty\",Ti),Nn(\"DataConstantProperty\",wi),Nn(\"CrossFadedDataDrivenProperty\",ki),Nn(\"CrossFadedProperty\",Ai),Nn(\"ColorRampProperty\",Mi);var Ei=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},\"custom\"!==e.type&&(e=e,this.metadata=e.metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,\"background\"!==e.type&&(this.source=e.source,this.sourceLayer=e[\"source-layer\"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new xi(r.layout)),r.paint)){for(var n in this._transitionablePaint=new gi(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new _i(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".layout.\"+t;if(this._validate(In,n,t,e,r))return}\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return g(t,\"-transition\")?this._transitionablePaint.getTransition(t.slice(0,-\"-transition\".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".paint.\"+t;if(this._validate(Pn,n,t,e,r))return!1}if(g(t,\"-transition\"))return this._transitionablePaint.setTransition(t.slice(0,-\"-transition\".length),e||void 0),!1;var i=this._transitionablePaint._values[t],a=\"cross-faded-data-driven\"===i.property.specification[\"property-type\"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||(!!(this.maxzoom&&t>=this.maxzoom)||\"none\"===this.visibility)},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&On(this,t.call(Ln,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Lt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof bi&&zr(e.property.specification)&&((\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent))return!0}return!1},e}(Et),Li={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Ci=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Pi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ii(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Li[i].BYTES_PER_ELEMENT),o=r=Oi(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:Oi(r,Math.max(n,e)),alignment:e}}function Oi(t,e){return Math.ceil(t/e)*e}Pi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Pi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Pi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Pi.prototype.clear=function(){this.length=0},Pi.prototype.resize=function(t){this.reserve(t),this.length=t},Pi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Pi.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Pi);zi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout2i4\",zi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(Pi);Di.prototype.bytesPerElement=8,Nn(\"StructArrayLayout4i8\",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Ri.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2i4i12\",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Pi);Fi.prototype.bytesPerElement=8,Nn(\"StructArrayLayout2i4ub8\",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(Pi);Bi.prototype.bytesPerElement=18,Nn(\"StructArrayLayout8ui2ub18\",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Pi);Ni.prototype.bytesPerElement=24,Nn(\"StructArrayLayout4i4ui4i24\",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Pi);ji.prototype.bytesPerElement=12,Nn(\"StructArrayLayout3f12\",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Pi);Ui.prototype.bytesPerElement=4,Nn(\"StructArrayLayout1ul4\",Ui);var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(Pi);Vi.prototype.bytesPerElement=20,Nn(\"StructArrayLayout6i1ul2ui20\",Vi);var Hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Hi.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2i2i2i12\",Hi);var qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Pi);qi.prototype.bytesPerElement=16,Nn(\"StructArrayLayout2f1f2i16\",qi);var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Pi);Gi.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2ub2f12\",Gi);var Yi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(Pi);Yi.prototype.bytesPerElement=6,Nn(\"StructArrayLayout3ui6\",Yi);var Wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=m,this.uint32[x+10]=g,this.int16[y+22]=v,t},e}(Pi);Wi.prototype.bytesPerElement=48,Nn(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",Wi);var Xi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=c,this.uint16[L+9]=u,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=m,this.uint16[L+15]=g,this.uint16[L+16]=v,this.uint16[L+17]=y,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=A,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Pi);Xi.prototype.bytesPerElement=68,Nn(\"StructArrayLayout8i15ui1ul4f68\",Xi);var Zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.float32[r+0]=e,t},e}(Pi);Zi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout1f4\",Zi);var Ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(Pi);Ji.prototype.bytesPerElement=6,Nn(\"StructArrayLayout3i6\",Ji);var Ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(Pi);Ki.prototype.bytesPerElement=8,Nn(\"StructArrayLayout1ul2ui8\",Ki);var Qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(Pi);Qi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout2ui4\",Qi);var $i=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(Pi);$i.prototype.bytesPerElement=2,Nn(\"StructArrayLayout1ui2\",$i);var ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Pi);ta.prototype.bytesPerElement=8,Nn(\"StructArrayLayout2f8\",ta);var ea=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Pi);ea.prototype.bytesPerElement=16,Nn(\"StructArrayLayout4f16\",ea);var ra=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new i(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Ci);ra.prototype.size=20;var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ra(this,t)},e}(Vi);Nn(\"CollisionBoxArray\",na);var ia=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},placedOrientation:{configurable:!0},hidden:{configurable:!0},crossTileID:{configurable:!0},associatedIconIndex:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},r.placedOrientation.set=function(t){this._structArray.uint8[this._pos1+37]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Ci);ia.prototype.size=48;var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ia(this,t)},e}(Wi);Nn(\"PlacedSymbolArray\",aa);var oa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return 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c=t.readVarint();n=7&c,a=c>>3}if(a--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new i(o,s));else{if(7!==n)throw new Error(\"unknown command \"+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},ws.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos<e;){if(n<=0){var u=t.readVarint();r=7&u,n=u>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>c&&(c=a);else if(7!==r)throw new Error(\"unknown command \"+r)}return[o,l,s,c]},ws.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=ws.types[this.type];function u(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];u(l=f);break;case 2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=ks(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}r&&i.push(r);return i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c=\"Multi\"+c;var h={type:\"Feature\",geometry:{type:c,coordinates:l},properties:this.properties};return\"id\"in this&&(h.id=this.id),h};var As=Ms;function Ms(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(Ss,this,e),this.length=this._features.length}function Ss(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){var e=null,r=t.readVarint()+t.pos;for(;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Es(t,e,r){if(3===t){var n=new As(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ms.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new _s(this._pbf,e,this.extent,this._keys,this._values)};var Ls={VectorTile:function(t,e){this.layers=t.readFields(Es,{},e)},VectorTileFeature:_s,VectorTileLayer:As},Cs=Ls.VectorTileFeature.types,Ps=Math.pow(2,13);function Is(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Ps)+o,i*Ps*2,a*Ps*2,Math.round(s))}var Os=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Ri,this.indexArray=new Yi,this.programConfigurations=new Ua(bs,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function zs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}function Ds(t){return t.every((function(t){return t.x<0}))||t.every((function(t){return t.x>8192}))||t.every((function(t){return t.y<0}))||t.every((function(t){return t.y>8192}))}Os.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ds(\"fill-extrusion\",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Ya(o):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Ya(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(ms(\"fill-extrusion\",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},Os.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},Os.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Os.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Os.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Os.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,bs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Os.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Os.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!Ds(d))for(var m=0,g=0;g<d.length;g++){var v=d[g];if(g>=1){var y=d[g-1];if(!zs(v,y)){f.vertexLength+4>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);m+b>32768&&(m=0),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,m),m+=b,Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,m);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),\"Polygon\"===Cs[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Is(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=Do(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(k+C[P],k+C[P+2],k+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Nn(\"FillExtrusionBucket\",Os,{omit:[\"layers\",\"features\"]});var Rs={paint:new Si({\"fill-extrusion-opacity\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new ki(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})},Fs=function(t){function e(e){t.call(this,e,Rs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new Os(t)},e.prototype.queryRadius=function(){return so(this.paint.get(\"fill-extrusion-translate\"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s,l){var c=lo(t,this.paint.get(\"fill-extrusion-translate\"),this.paint.get(\"fill-extrusion-translate-anchor\"),o.angle,s),u=this.paint.get(\"fill-extrusion-height\").evaluate(e,r),f=this.paint.get(\"fill-extrusion-base\").evaluate(e,r),h=function(t,e,r,n){for(var a=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,n,1];xo(c,c,e),a.push(new i(c[0]/c[3],c[1]/c[3]))}return a}(c,l,0,0),p=function(t,e,r,n){for(var a=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,m=0,g=t;m<g.length;m+=1){for(var v=g[m],y=[],x=[],b=0,_=v;b<_.length;b+=1){var w=_[b],T=w.x,k=w.y,A=n[0]*T+n[4]*k+n[12],M=n[1]*T+n[5]*k+n[13],S=n[2]*T+n[6]*k+n[14],E=n[3]*T+n[7]*k+n[15],L=S+c,C=E+u,P=A+f,I=M+h,O=S+p,z=E+d,D=new i((A+s)/C,(M+l)/C);D.z=L/C,y.push(D);var R=new i(P/z,I/z);R.z=O/z,x.push(R)}a.push(y),o.push(x)}return[a,o]}(n,f,u,l);return function(t,e,r){var n=1/0;Ka(r,e)&&(n=Ns(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],c=a[s+1],u=o[s],f=o[s+1],h=[l,c,f,u,l];Za(r,h)&&(n=Math.min(n,Ns(r,h)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ei);function Bs(t,e){return t.x*e.x+t.y*e.y}function Ns(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Bs(s,s),f=Bs(s,l),h=Bs(l,l),p=Bs(c,s),d=Bs(c,l),m=u*h-f*f,g=(h*p-f*d)/m,v=(u*d-f*p)/m,y=1-g-v,x=i.z*y+r.z*g+a.z*v;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var js=Ii([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4).members,Us=Ls.VectorTileFeature.types,Vs=Math.cos(Math.PI/180*37.5),Hs=Math.pow(2,14)/.5,qs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Fi,this.indexArray=new Yi,this.programConfigurations=new Ua(js,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};qs.prototype.populate=function(t,e,r){this.hasPattern=ds(\"line\",this.layers,e);for(var n=this.layers[0].layout.get(\"line-sort-key\"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,c=s.id,u=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:c,properties:l.properties,geometry:h?Ya(l):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),p,r)){h||(p.geometry=Ya(l));var d=n?n.evaluate(p,{},r):void 0,m={id:c,properties:l.properties,type:l.type,sourceLayerIndex:f,index:u,geometry:p.geometry,patterns:{},sortKey:d};i.push(m)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,v=i;g<v.length;g+=1){var y=v[g],x=y,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=ms(\"line\",this.layers,y,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(y,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},qs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},qs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},qs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},qs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},qs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,js),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},qs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},qs.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get(\"line-join\").evaluate(t,{}),s=a.get(\"line-cap\"),l=a.get(\"line-miter-limit\"),c=a.get(\"line-round-limit\"),u=0,f=e;u<f.length;u+=1){var h=f[u];this.addLine(h,t,o,s,l,c)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},qs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty(\"mapbox_clip_start\")&&e.properties.hasOwnProperty(\"mapbox_clip_end\")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s=\"Polygon\"===Us[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){\"bevel\"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,m=void 0,g=void 0;this.e1=this.e2=-1,s&&(u=t[l-2],g=t[c].sub(u)._unit()._perp());for(var v=c;v<l;v++)if(!(d=v===l-1?s?t[c+1]:void 0:t[v+1])||!t[v].equals(d)){g&&(m=g),u&&(p=u),u=t[v],g=d?d.sub(u)._unit()._perp():m;var y=(m=m||g).add(g);0===y.x&&0===y.y||y._unit();var x=m.x*g.x+m.y*g.y,b=y.x*g.x+y.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<Vs&&p&&d,k=m.x*g.y-m.y*g.x>0;if(T&&v>c){var A=u.dist(p);if(A>2*f){var M=u.sub(u.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,m,0,0,h),p=M}}var S=p&&d,E=S?r:s?\"butt\":n;if(S&&\"round\"===E&&(_<a?E=\"miter\":_<=2&&(E=\"fakeround\")),\"miter\"===E&&_>i&&(E=\"bevel\"),\"bevel\"===E&&(_>2&&(E=\"flipbevel\"),_<i&&(E=\"miter\")),p&&this.updateDistance(p,u),\"miter\"===E)y._mult(_),this.addCurrentVertex(u,y,0,0,h);else if(\"flipbevel\"===E){if(_>100)y=g.mult(-1);else{var L=_*m.add(g).mag()/m.sub(g).mag();y._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(u,y,0,0,h),this.addCurrentVertex(u,y.mult(-1),0,0,h)}else if(\"bevel\"===E||\"fakeround\"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,I=k?0:C;if(p&&this.addCurrentVertex(u,m,P,I,h),\"fakeround\"===E)for(var O=Math.round(180*w/Math.PI/20),z=1;z<O;z++){var D=z/O;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(m)._mult(D)._add(m)._unit()._mult(k?-1:1);this.addHalfVertex(u,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(u,g,-P,-I,h)}else if(\"butt\"===E)this.addCurrentVertex(u,y,0,0,h);else if(\"square\"===E){var B=p?1:-1;this.addCurrentVertex(u,y,B,B,h)}else\"round\"===E&&(p&&(this.addCurrentVertex(u,m,0,0,h),this.addCurrentVertex(u,m,1,1,h,!0)),d&&(this.addCurrentVertex(u,g,-1,-1,h,!0),this.addCurrentVertex(u,g,0,0,h)));if(T&&v<l-1){var N=u.dist(d);if(N>2*f){var j=u.add(d.sub(u)._mult(f/N)._round());this.updateDistance(u,j),this.addCurrentVertex(j,g,0,0,h),u=j}}}}},qs.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>Hs/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},qs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,c=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&c)<<2,c>>6);var u=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,u),o.primitiveLength++),i?this.e2=u:this.e1=u},qs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Hs-1):this.distance},qs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Nn(\"LineBucket\",qs,{omit:[\"layers\",\"patternFeatures\"]});var Gs=new Si({\"line-cap\":new 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e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new pi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=u({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Ti))(Ys.paint.properties[\"line-width\"].specification);Ws.useIntegerZoom=!0;var Xs=function(t){function e(e){t.call(this,e,Ys)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){\"line-gradient\"===t&&this._updateGradient()},e.prototype._updateGradient=function(){var 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o=t[a],s=[],l=0;l<o.length;l++){var c=o[l-1],u=o[l],f=o[l+1],h=0===l?n:u.sub(c)._unit()._perp(),p=l===o.length-1?n:f.sub(u)._unit()._perp(),d=h._add(p)._unit(),m=d.x*p.x+d.y*p.y;d._mult(1/m),s.push(d._mult(e)._add(u))}r.push(s)}return r}(n,u*s)),function(t,e,r){for(var n=0;n<e.length;n++){var i=e[n];if(t.length>=3)for(var a=0;a<i.length;a++)if(io(t,i[a]))return!0;if(Qa(t,i,r))return!0}return!1}(l,n,c)},e.prototype.isTileClipped=function(){return!0},e}(Ei);function Zs(t,e){return e>0?e+2*t:t}var 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nl=function(t,e,r,n,i){var a,o,s=8*i-n-1,l=(1<<s)-1,c=l>>1,u=-7,f=r?i-1:0,h=r?-1:1,p=t[e+f];for(f+=h,a=p&(1<<-u)-1,p>>=-u,u+=s;u>0;a=256*a+t[e+f],f+=h,u-=8);for(o=a&(1<<-u)-1,a>>=-u,u+=n;u>0;o=256*o+t[e+f],f+=h,u-=8);if(0===a)a=1-c;else{if(a===l)return o?NaN:1/0*(p?-1:1);o+=Math.pow(2,n),a-=c}return(p?-1:1)*o*Math.pow(2,a-n)},il=function(t,e,r,n,i,a){var o,s,l,c=8*a-i-1,u=(1<<c)-1,f=u>>1,h=23===i?Math.pow(2,-24)-Math.pow(2,-77):0,p=n?0:a-1,d=n?1:-1,m=e<0||0===e&&1/e<0?1:0;for(e=Math.abs(e),isNaN(e)||e===1/0?(s=isNaN(e)?1:0,o=u):(o=Math.floor(Math.log(e)/Math.LN2),e*(l=Math.pow(2,-o))<1&&(o--,l*=2),(e+=o+f>=1?h/l:h*Math.pow(2,1-f))*l>=2&&(o++,l/=2),o+f>=u?(s=0,o=u):o+f>=1?(s=(e*l-1)*Math.pow(2,i),o+=f):(s=e*Math.pow(2,f-1)*Math.pow(2,i),o=0));i>=8;t[r+p]=255&s,p+=d,s/=256,i-=8);for(o=o<<i|s,c+=i;c>0;t[r+p]=255&o,p+=d,o/=256,c-=8);t[r+p-d]|=128*m},al=ol;function ol(t){this.buf=ArrayBuffer.isView&&ArrayBuffer.isView(t)?t:new 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e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed32());return t},readPackedFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readFixed64());return t},readPackedSFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readSFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed64());return t},skip:function(t){var e=7&t;if(e===ol.Varint)for(;this.buf[this.pos++]>127;);else if(e===ol.Bytes)this.pos=this.readVarint()+this.pos;else if(e===ol.Fixed32)this.pos+=4;else{if(e!==ol.Fixed64)throw new Error(\"Unimplemented type: \"+e);this.pos+=8}},writeTag:function(t,e){this.writeVarint(t<<3|e)},realloc:function(t){for(var e=this.length||16;e<this.pos+t;)e*=2;if(e!==this.length){var r=new Uint8Array(e);r.set(this.buf),this.buf=r,this.length=e}},finish:function(){return 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r=(7&t)<<4;if(e.buf[e.pos++]|=r|((t>>>=3)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;e.buf[e.pos++]=127&t}(n,e)}(t,this):(this.realloc(4),this.buf[this.pos++]=127&t|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=t>>>7&127))))},writeSVarint:function(t){this.writeVarint(t<0?2*-t-1:2*t)},writeBoolean:function(t){this.writeVarint(Boolean(t))},writeString:function(t){t=String(t),this.realloc(4*t.length),this.pos++;var e=this.pos;this.pos=function(t,e,r){for(var n,i,a=0;a<e.length;a++){if((n=e.charCodeAt(a))>55295&&n<57344){if(!i){n>56319||a+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):i=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,i=n;continue}n=i-55296<<10|n-56320|65536,i=null}else i&&(t[r++]=239,t[r++]=191,t[r++]=189,i=null);n<128?t[r++]=n:(n<2048?t[r++]=n>>6|192:(n<65536?t[r++]=n>>12|224:(t[r++]=n>>18|240,t[r++]=n>>12&63|128),t[r++]=n>>6&63|128),t[r++]=63&n|128)}return r}(this.buf,t,this.pos);var r=this.pos-e;r>=128&&ul(e,r,this),this.pos=e-1,this.writeVarint(r),this.pos+=r},writeFloat:function(t){this.realloc(4),il(this.buf,t,this.pos,!0,23,4),this.pos+=4},writeDouble:function(t){this.realloc(8),il(this.buf,t,this.pos,!0,52,8),this.pos+=8},writeBytes:function(t){var e=t.length;this.writeVarint(e),this.realloc(e);for(var r=0;r<e;r++)this.buf[this.pos++]=t[r]},writeRawMessage:function(t,e){this.pos++;var r=this.pos;t(e,this);var n=this.pos-r;n>=128&&ul(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,ol.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,fl,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,hl,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,ml,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,pl,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,dl,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,gl,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,vl,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,yl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,xl,e)},writeBytesField:function(t,e){this.writeTag(t,ol.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,ol.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,ol.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};function Tl(t,e,r){1===t&&r.readMessage(kl,e)}function kl(t,e,r){if(3===t){var n=r.readMessage(Al,{}),i=n.id,a=n.bitmap,o=n.width,s=n.height,l=n.left,c=n.top,u=n.advance;e.push({id:i,bitmap:new So({width:o+6,height:s+6},a),metrics:{width:o,height:s,left:l,top:c,advance:u}})}}function Al(t,e,r){1===t?e.id=r.readVarint():2===t?e.bitmap=r.readBytes():3===t?e.width=r.readVarint():4===t?e.height=r.readVarint():5===t?e.left=r.readSVarint():6===t?e.top=r.readSVarint():7===t&&(e.advance=r.readVarint())}function Ml(t){for(var e=0,r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];e+=a.w*a.h,r=Math.max(r,a.w)}t.sort((function(t,e){return e.h-t.h}));for(var o=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),r),h:1/0}],s=0,l=0,c=0,u=t;c<u.length;c+=1)for(var f=u[c],h=o.length-1;h>=0;h--){var p=o[h];if(!(f.w>p.w||f.h>p.h)){if(f.x=p.x,f.y=p.y,l=Math.max(l,f.y+f.h),s=Math.max(s,f.x+f.w),f.w===p.w&&f.h===p.h){var d=o.pop();h<o.length&&(o[h]=d)}else 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Eo({width:o||1,height:s||1});for(var c in t){var u=t[c],f=r[c].paddedRect;Eo.copy(u.data,l,{x:0,y:0},{x:f.x+1,y:f.y+1},u.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,m=d.x+1,g=d.y+1,v=p.data.width,y=p.data.height;Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g},p.data),Eo.copy(p.data,l,{x:0,y:y-1},{x:m,y:g-1},{width:v,height:1}),Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g+y},{width:v,height:1}),Eo.copy(p.data,l,{x:v-1,y:0},{x:m-1,y:g},{width:1,height:y}),Eo.copy(p.data,l,{x:0,y:0},{x:m+v,y:g},{width:1,height:y})}this.image=l,this.iconPositions=r,this.patternPositions=n};Ll.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new Sl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},Ll.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},Ll.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl,i=n[0],a=n[1];r.update(e.data,void 0,{x:i,y:a})}},Nn(\"ImagePosition\",Sl),Nn(\"ImageAtlas\",Ll);var Cl={horizontal:1,vertical:2,horizontalOnly:3};var Pl=function(){this.scale=1,this.fontStack=\"\",this.imageName=null};Pl.forText=function(t,e){var r=new Pl;return r.scale=t||1,r.fontStack=e,r},Pl.forImage=function(t){var e=new Pl;return e.imageName=t,e};var Il=function(){this.text=\"\",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function Ol(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=Il.fromFeature(t,i);f===Cl.vertical&&v.verticalizePunctuation();var y=hi.processBidirectionalText,x=hi.processStyledBidirectionalText;if(y&&1===v.sections.length){g=[];for(var b=0,_=y(v.toString(),jl(v,c,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new Il;T.text=w,T.sections=v.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);g.push(T)}}else if(x){g=[];for(var A=0,M=x(v.text,v.sectionIndex,jl(v,c,a,e,n,p,d));A<M.length;A+=1){var S=M[A],E=new Il;E.text=S[0],E.sectionIndex=S[1],E.sections=v.sections,g.push(E)}}else g=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(v,jl(v,c,a,e,n,p,d));var L=[],C={positionedLines:L,text:v.toString(),top:u[1],bottom:u[1],left:u[0],right:u[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=0,p=-17,d=0,m=0,g=\"right\"===s?1:\"left\"===s?0:.5,v=0,y=0,x=i;y<x.length;y+=1){var b=x[y];b.trim();var _=b.getMaxScale(),w=24*(_-1),T={positionedGlyphs:[],lineOffset:0};t.positionedLines[v]=T;var k=T.positionedGlyphs,A=0;if(b.length()){for(var M=0;M<b.length();M++){var S=b.getSection(M),E=b.getSectionIndex(M),L=b.getCharCode(M),C=0,P=null,I=null,O=null,z=24,D=!(l===Cl.horizontal||!u&&!Zn(L)||u&&(zl[L]||(Y=L,Yn.Arabic(Y)||Yn[\"Arabic Supplement\"](Y)||Yn[\"Arabic Extended-A\"](Y)||Yn[\"Arabic Presentation Forms-A\"](Y)||Yn[\"Arabic Presentation Forms-B\"](Y))));if(S.imageName){var R=n[S.imageName];if(!R)continue;O=S.imageName,t.iconsInText=t.iconsInText||!0,I=R.paddedRect;var F=R.displaySize;S.scale=24*S.scale/f,P={width:F[0],height:F[1],left:1,top:-3,advance:D?F[1]:F[0]};var B=24-F[1]*S.scale;C=w+B,z=P.advance;var N=D?F[0]*S.scale-24*_:F[1]*S.scale-24*_;N>0&&N>A&&(A=N)}else{var j=r[S.fontStack],U=j&&j[L];if(U&&U.rect)I=U.rect,P=U.metrics;else{var V=e[S.fontStack],H=V&&V[L];if(!H)continue;P=H.metrics}C=24*(_-S.scale)}D?(t.verticalizable=!0,k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=z*S.scale+c):(k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=P.advance*S.scale+c)}if(0!==k.length){var q=h-c;d=Math.max(q,d),Vl(k,0,k.length-1,g,A)}h=0;var G=a*_+A;T.lineOffset=Math.max(A,w),p+=G,m=Math.max(G,m),++v}else p+=a,++v}var Y;var W=p- -17,X=Ul(o),Z=X.horizontalAlign,J=X.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var c=(e-r)*i,u=0;u=a!==o?-s*n- -17:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];g.x+=c,g.y+=u}})(t.positionedLines,g,Z,J,d,m,a,W,i.length),t.top+=-J*W,t.bottom=t.top+W,t.left+=-Z*d,t.right=t.left+d}(C,e,r,n,g,o,s,l,f,c,h,m),!function(t){for(var e=0,r=t;e<r.length;e+=1){if(0!==r[e].positionedGlyphs.length)return!1}return!0}(L)&&C}Il.fromFeature=function(t,e){for(var r=new Il,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},Il.prototype.length=function(){return this.text.length},Il.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},Il.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},Il.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},Il.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e=\"\",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;(!n||!Jn(n)||rl[t[r+1]])&&(!i||!Jn(i)||rl[t[r-1]])&&rl[t[r]]?e+=rl[t[r]]:e+=t[r]}return e}(this.text)},Il.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&zl[this.text.charCodeAt(e)];e++)t++;for(var 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e=this.data,r=this.compare,n=this.length>>1,i=e[t];t<n;){var a=1+(t<<1),o=e[a],s=a+1;if(s<this.length&&r(e[s],o)<0&&(a=s,o=e[s]),r(o,i)>=0)break;e[t]=o,t=a}e[t]=i};var pc=Number.POSITIVE_INFINITY;function dc(t,e){return e[1]!==pc?function(t,e,r){var n=0,i=0;switch(e=Math.abs(e),r=Math.abs(r),t){case\"top-right\":case\"top-left\":case\"top\":i=r-7;break;case\"bottom-right\":case\"bottom-left\":case\"bottom\":i=7-r}switch(t){case\"top-right\":case\"bottom-right\":case\"right\":n=-e;break;case\"top-left\":case\"bottom-left\":case\"left\":n=e}return[n,i]}(t,e[0],e[1]):function(t,e){var r=0,n=0;e<0&&(e=0);var i=e/Math.sqrt(2);switch(t){case\"top-right\":case\"top-left\":n=i-7;break;case\"bottom-right\":case\"bottom-left\":n=7-i;break;case\"bottom\":n=7-e;break;case\"top\":n=e-7}switch(t){case\"top-right\":case\"bottom-right\":r=-i;break;case\"top-left\":case\"bottom-left\":r=i;break;case\"left\":r=e;break;case\"right\":r=-e}return[r,n]}(t,e[0])}function mc(t){switch(t){case\"right\":case\"top-right\":case\"bottom-right\":return\"right\";case\"left\":case\"top-left\":case\"bottom-left\":return\"left\"}return\"center\"}function gc(t,e,r,n,a,o,s,l,c,u,f,h,p,d,m){var g=function(t,e,r,n,a,o,s,l){for(var c=n.layout.get(\"text-rotate\").evaluate(o,{})*Math.PI/180,u=[],f=0,h=e.positionedLines;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];if(g.rect){var v=g.rect||{},y=4,x=!0,b=1,_=0,w=(a||l)&&g.vertical,T=g.metrics.advance*g.scale/2;if(l&&e.verticalizable){var k=24*(g.scale-1),A=(24-g.metrics.width*g.scale)/2;_=p.lineOffset/2-(g.imageName?-A:k)}if(g.imageName){var M=s[g.imageName];x=M.sdf,y=1/(b=M.pixelRatio)}var S=a?[g.x+T,g.y]:[0,0],E=a?[0,0]:[g.x+T+r[0],g.y+r[1]-_],L=[0,0];w&&(L=E,E=[0,0]);var C=(g.metrics.left-y)*g.scale-T+E[0],P=(-g.metrics.top-y)*g.scale+E[1],I=C+v.w*g.scale/b,O=P+v.h*g.scale/b,z=new i(C,P),D=new i(I,P),R=new i(C,O),F=new i(I,O);if(w){var B=new i(-T,T- -17),N=-Math.PI/2,j=12-T,U=g.imageName?j:0,V=new i(22-j,-U),H=new(Function.prototype.bind.apply(i,[null].concat(L)));z._rotateAround(N,B)._add(V)._add(H),D._rotateAround(N,B)._add(V)._add(H),R._rotateAround(N,B)._add(V)._add(H),F._rotateAround(N,B)._add(V)._add(H)}if(c){var q=Math.sin(c),G=Math.cos(c),Y=[G,-q,q,G];z._matMult(Y),D._matMult(Y),R._matMult(Y),F._matMult(Y)}var W=new i(0,0),X=new i(0,0);u.push({tl:z,tr:D,bl:R,br:F,tex:v,writingMode:e.writingMode,glyphOffset:S,sectionIndex:g.sectionIndex,isSDF:x,pixelOffsetTL:W,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;\"source\"===v.kind?(y=[128*a.layout.get(\"text-size\").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. Reduce your \"text-size\".'):\"composite\"===v.kind&&((y=[128*d.compositeTextSizes[0].evaluate(s,{},m),128*d.compositeTextSizes[1].evaluate(s,{},m)])[0]>32640||y[1]>32640)&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. Reduce your \"text-size\".'),t.addSymbols(t.text,g,y,l,o,s,u,e,c.lineStartIndex,c.lineLength,p,m);for(var x=0,b=f;x<b.length;x+=1){h[b[x]]=t.text.placedSymbolArray.length-1}return 4*g.length}function vc(t){for(var e in t)return t[e];return null}function yc(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var xc=Ls.VectorTileFeature.types,bc=[{name:\"a_fade_opacity\",components:1,type:\"Uint8\",offset:0}];function _c(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=s?Math.min(32640,Math.round(s[0])):0,d=s?Math.min(32640,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*c,16*u,256*f,256*h)}function wc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function Tc(t){for(var e=0,r=t.sections;e<r.length;e+=1){if($n(r[e].text))return!0}return!1}var kc=function(t){this.layoutVertexArray=new Ni,this.indexArray=new Yi,this.programConfigurations=t,this.segments=new pa,this.dynamicLayoutVertexArray=new ji,this.opacityVertexArray=new Ui,this.placedSymbolArray=new aa};kc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},kc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Js.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Ks.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,bc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},kc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Nn(\"SymbolBuffers\",kc);var Ac=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new pa,this.collisionVertexArray=new Gi};Ac.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Qs.members,!0)},Ac.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Nn(\"CollisionBuffers\",Ac);var Mc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ho([]),this.placementViewportMatrix=ho([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Gl(this.zoom,e[\"text-size\"]),this.iconSizeData=Gl(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout,n=r.get(\"symbol-sort-key\"),i=r.get(\"symbol-z-order\");this.sortFeaturesByKey=\"viewport-y\"!==i&&void 0!==n.constantOr(1);var a=\"viewport-y\"===i||\"auto\"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get(\"text-allow-overlap\")||r.get(\"icon-allow-overlap\")||r.get(\"text-ignore-placement\")||r.get(\"icon-ignore-placement\")),\"point\"===r.get(\"symbol-placement\")&&(this.writingModes=r.get(\"text-writing-mode\").map((function(t){return Cl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};Mc.prototype.createArrays=function(){this.text=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new la,this.lineVertexArray=new ca,this.symbolInstances=new sa},Mc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=rl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},Mc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get(\"text-font\"),o=i.get(\"text-field\"),s=i.get(\"icon-image\"),l=(\"constant\"!==o.value.kind||o.value.value instanceof ne&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&(\"constant\"!==a.value.kind||a.value.value.length>0),c=\"constant\"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get(\"symbol-sort-key\");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new pi(this.zoom),m=0,g=t;m<g.length;m+=1){var v=g[m],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Ya(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Ya(y));var k=void 0;if(l){var A=n.getValueAndResolveTokens(\"text-field\",T,r,p),M=ne.factory(A);Tc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||\"unavailable\"===ui()||this.hasRTLText&&hi.isParsed())&&(k=el(M,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens(\"icon-image\",T,r,p);S=E instanceof ie?E:ie.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Ya(y),properties:y.properties,type:xc[y.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(\",\"),I=\"map\"===i.get(\"text-rotation-alignment\")&&\"point\"!==i.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(Cl.vertical)>=0;for(var O=0,z=k.sections;O<z.length;O+=1){var D=z[O];if(D.image)f[D.image.name]=!0;else{var R=Wn(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,I,this.allowVerticalPlacement,R)}}}}}}\"line\"===i.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var m=s(p,d,f),g=o(p,d,n[m].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[m].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},Mc.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))},Mc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},Mc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Mc.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},Mc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Mc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Mc.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}},Mc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),m=this.glyphOffsetArray.length,g=d.vertexLength,v=this.allowVerticalPlacement&&o===Cl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,I=b.sectionIndex,O=d.vertexLength,z=C[1];_c(p,s.x,s.y,_.x,z+_.y,A.x,A.y,r,P,M.x,M.y,E,L),_c(p,s.x,s.y,w.x,z+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),_c(p,s.x,s.y,T.x,z+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),_c(p,s.x,s.y,k.x,z+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),wc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(O,O+1,O+2),h.emplaceBack(O+1,O+2,O+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&I===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[I])}t.placedSymbolArray.emplaceBack(s.x,s.y,m,this.glyphOffsetArray.length-m,g,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},Mc.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))},Mc.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},Mc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,c=a.y2;this.addCollisionDebugVertices(o,s,l,c,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},Mc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new Ac(Hi,$s.members,Qi),this.iconCollisionBox=new Ac(Hi,$s.members,Qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},Mc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var m=t.get(d);c.iconBox={x1:m.x1,y1:m.y1,x2:m.x2,y2:m.y2,anchorPointX:m.anchorPointX,anchorPointY:m.anchorPointY},c.iconFeatureIndex=m.featureIndex;break}for(var g=s;g<l;g++){var v=t.get(g);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},Mc.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))}},Mc.prototype.hasTextData=function(){return this.text.segments.get().length>0},Mc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Mc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Mc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Mc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Mc.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)},Mc.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},Mc.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})},Mc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Nn(\"SymbolBucket\",Mc,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),Mc.MAX_GLYPHS=65535,Mc.addDynamicAttributes=wc;var Sc=new Si({\"symbol-placement\":new wi(Lt.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new wi(Lt.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new wi(Lt.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new Ti(Lt.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new wi(Lt.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new wi(Lt.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new wi(Lt.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new wi(Lt.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new wi(Lt.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new Ti(Lt.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new wi(Lt.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new wi(Lt.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new Ti(Lt.layout_symbol[\"icon-image\"]),\"icon-rotate\":new Ti(Lt.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new wi(Lt.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new wi(Lt.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new Ti(Lt.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new Ti(Lt.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new wi(Lt.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new wi(Lt.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new wi(Lt.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new Ti(Lt.layout_symbol[\"text-field\"]),\"text-font\":new Ti(Lt.layout_symbol[\"text-font\"]),\"text-size\":new Ti(Lt.layout_symbol[\"text-size\"]),\"text-max-width\":new Ti(Lt.layout_symbol[\"text-max-width\"]),\"text-line-height\":new wi(Lt.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new Ti(Lt.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new Ti(Lt.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new Ti(Lt.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new wi(Lt.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new Ti(Lt.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new wi(Lt.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new wi(Lt.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new Ti(Lt.layout_symbol[\"text-rotate\"]),\"text-padding\":new wi(Lt.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new wi(Lt.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new Ti(Lt.layout_symbol[\"text-transform\"]),\"text-offset\":new Ti(Lt.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new wi(Lt.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new wi(Lt.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new wi(Lt.layout_symbol[\"text-optional\"])}),Ec={paint:new Si({\"icon-opacity\":new Ti(Lt.paint_symbol[\"icon-opacity\"]),\"icon-color\":new Ti(Lt.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new Ti(Lt.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new Ti(Lt.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new Ti(Lt.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new wi(Lt.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new wi(Lt.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new Ti(Lt.paint_symbol[\"text-opacity\"]),\"text-color\":new Ti(Lt.paint_symbol[\"text-color\"],{runtimeType:Ut,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new Ti(Lt.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new Ti(Lt.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new Ti(Lt.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new wi(Lt.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new wi(Lt.paint_symbol[\"text-translate-anchor\"])}),layout:Sc},Lc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ft,this.defaultValue=t};Lc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},Lc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},Lc.prototype.outputDefined=function(){return!1},Lc.prototype.serialize=function(){return null},Nn(\"FormatSectionOverride\",Lc,{omit:[\"defaultValue\"]});var Cc=function(t){function e(e){t.call(this,e,Ec)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values[\"text-writing-mode\"]=i}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Yr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):\"\"}))}(e.properties,i)},e.prototype.createBucket=function(t){return new Mc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=Ec.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i=this.paint.get(n),a=new Lc(i),o=new Gr(a,i.property.specification),s=null;s=\"constant\"===i.value.kind||\"source\"===i.value.kind?new Xr(\"source\",o):new Zr(\"composite\",o,i.value.zoomStops,i.value._interpolationType),this.paint._values[n]=new bi(i.property,s,i.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get(\"text-field\"),n=Ec.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if(\"constant\"===r.value.kind&&r.value.value instanceof ne)a(r.value.value.sections);else if(\"source\"===r.value.kind){var o=function(t){if(!i)if(t instanceof ce&&se(t.value)===Gt){var e=t.value;a(e.sections)}else t instanceof pe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ei),Pc={paint:new Si({\"background-color\":new wi(Lt.paint_background[\"background-color\"]),\"background-pattern\":new Ai(Lt.paint_background[\"background-pattern\"]),\"background-opacity\":new wi(Lt.paint_background[\"background-opacity\"])})},Ic=function(t){function e(e){t.call(this,e,Pc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei),Oc={paint:new Si({\"raster-opacity\":new wi(Lt.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new wi(Lt.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new wi(Lt.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new wi(Lt.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new wi(Lt.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new wi(Lt.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new wi(Lt.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new wi(Lt.paint_raster[\"raster-fade-duration\"])})},zc=function(t){function e(e){t.call(this,e,Oc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei);var Dc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return\"3d\"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ei),Rc={circle:_o,heatmap:Po,hillshade:Oo,fill:xs,\"fill-extrusion\":Fs,line:Xs,symbol:Cc,background:Ic,raster:zc};var Fc=self.HTMLImageElement,Bc=self.HTMLCanvasElement,Nc=self.HTMLVideoElement,jc=self.ImageData,Uc=self.ImageBitmap,Vc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Vc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Vc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Vc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Vc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Hc=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()})};Hc.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))},Hc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var qc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},m([\"receive\",\"process\"],this),this.invoker=new Hc(this.process),this.target.addEventListener(\"message\",this.receive,!1),this.globalScope=k()?t:self};function Gc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}qc.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:Hn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:\"<cancel>\",targetMapId:n,sourceMapId:a.mapId})}}},qc.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)},qc.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)}},qc.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(qn(e.error)):n(null,qn(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?Hn(e):null,data:Hn(n,a)},a)}:function(t){i=!0},s=null,l=qn(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)}},qc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Yc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Yc.prototype.setNorthEast=function(t){return this._ne=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.setSouthWest=function(t){return this._sw=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Wc)e=t,r=t;else{if(!(t instanceof Yc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Yc.convert(a))}var o=t;return this.extend(Wc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Wc(e.lng,e.lat),this._ne=new Wc(r.lng,r.lat)),this},Yc.prototype.getCenter=function(){return new Wc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Yc.prototype.getSouthWest=function(){return this._sw},Yc.prototype.getNorthEast=function(){return this._ne},Yc.prototype.getNorthWest=function(){return new Wc(this.getWest(),this.getNorth())},Yc.prototype.getSouthEast=function(){return new Wc(this.getEast(),this.getSouth())},Yc.prototype.getWest=function(){return this._sw.lng},Yc.prototype.getSouth=function(){return this._sw.lat},Yc.prototype.getEast=function(){return this._ne.lng},Yc.prototype.getNorth=function(){return this._ne.lat},Yc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Yc.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Yc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Yc.prototype.contains=function(t){var e=Wc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Yc.convert=function(t){return!t||t instanceof Yc?t:new Yc(t)};var Wc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};Wc.prototype.wrap=function(){return new Wc(c(this.lng,-180,180),this.lat)},Wc.prototype.toArray=function(){return[this.lng,this.lat]},Wc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Wc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Wc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Yc(new Wc(this.lng-r,this.lat-e),new Wc(this.lng+r,this.lat+e))},Wc.convert=function(t){if(t instanceof Wc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Wc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Wc(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")};var Xc=2*Math.PI*6371008.8;function Zc(t){return Xc*Math.cos(t*Math.PI/180)}function Jc(t){return(180+t)/360}function Kc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Qc(t,e){return t/Zc(e)}function $c(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var tu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};tu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Wc.convert(t);return new tu(Jc(r.lng),Kc(r.lat),Qc(e,r.lat))},tu.prototype.toLngLat=function(){return new Wc(360*this.x-180,$c(this.y))},tu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Zc($c(e));var t,e},tu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Xc*(t=$c(this.y),1/Math.cos(t*Math.PI/180));var t};var eu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=iu(0,t,t,e,r)};eu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},eu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Gc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Gc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",String(this.z)).replace(\"{x}\",String(this.x)).replace(\"{y}\",String(\"tms\"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace(\"{quadkey}\",l).replace(\"{bbox-epsg-3857}\",s)},eu.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},eu.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y};var ru=function(t,e){this.wrap=t,this.canonical=e,this.key=iu(t,e.z,e.z,e.x,e.y)},nu=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new eu(r,+n,+i),this.key=iu(e,t,r,n,i)};function iu(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}nu.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},nu.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new nu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new nu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},nu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?iu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):iu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},nu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},nu.prototype.children=function(t){if(this.overscaledZ>=t)return[new nu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new nu(e,this.wrap,e,r,n),new nu(e,this.wrap,e,r+1,n),new nu(e,this.wrap,e,r,n+1),new nu(e,this.wrap,e,r+1,n+1)]},nu.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},nu.prototype.wrapped=function(){return new nu(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.unwrapTo=function(t){return new nu(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},nu.prototype.toUnwrapped=function(){return new ru(this.wrap,this.canonical)},nu.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},nu.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new tu(t.x-this.wrap,t.y))},Nn(\"CanonicalTileID\",eu),Nn(\"OverscaledTileID\",nu,{omit:[\"posMatrix\"]});var au=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError(\"DEM tiles must be square\");if(r&&\"mapbox\"!==r&&\"terrarium\"!==r)return _('\"'+r+'\" is not a valid encoding type. Valid types include \"mapbox\" and \"terrarium\".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||\"mapbox\";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};au.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return(\"terrarium\"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},au.prototype.getUnpackVector=function(){return\"terrarium\"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},au.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},au.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},au.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},au.prototype.getPixels=function(){return new Eo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},au.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Nn(\"DEMData\",au);var ou=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};ou.prototype.encode=function(t){return this._stringToNumber[t]},ou.prototype.decode=function(t){return this._numberToString[t]};var su=function(t,e,r,n,i){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},lu={geometry:{configurable:!0}};lu.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},lu.geometry.set=function(t){this._geometry=t},su.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)\"_geometry\"!==e&&\"_vectorTileFeature\"!==e&&(t[e]=this[e]);return t},Object.defineProperties(su.prototype,lu);var cu=function(){this.state={},this.stateChanges={},this.deletedStates={}};cu.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r){this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]}},cu.prototype.removeFeatureState=function(t,e,r){if(!(null===this.deletedStates[t])){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e){if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null}else this.deletedStates[t]=null}},cu.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=u({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},cu.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},cu.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1){var p=h[f];delete this.state[o][c][p]}s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t){t[d].setFeatureState(r,e)}};var uu=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new zn(8192,16,0),this.grid3D=new zn(8192,16,0),this.featureIndexArray=new fa,this.promoteId=e};function fu(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof _i?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function hu(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function pu(t,e){return e-t}uu.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},uu.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Ls.VectorTile(new al(this.rawTileData)).layers,this.sourceLayerCoder=new ou(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},uu.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=sn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=hu(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=hu(t.cameraQueryGeometry),d=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1){if(io(t,f[u]))return!0}for(var h=0;h<t.length-1;h++){if(ao(t[h],t[h+1],c))return!0}return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)})),m=0,g=d;m<g.length;m+=1){var v=g[m];h.push(v)}h.sort(pu);for(var y,x={},b=function(i){var u=h[i];if(u!==y){y=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(x,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Ya(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},_=0;_<h.length;_++)b(_);return x},uu.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),m=0;m<f.length;m++){var g=f[m];if(!(a&&a.indexOf(g)<0)){var v=s[g];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||\"_geojsonTileLayer\",d));var x=l[g];x.paint=fu(x.paint,v.paint,p,y,o),x.layout=fu(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new su(p,this.z,this.x,this.y,d);_.layer=x;var w=t[g];void 0===w&&(w=t[g]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},uu.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=sn(i),u=0,f=t;u<f.length;u+=1){var h=f[u];this.loadMatchingFeature(l,r,n,h,c,a,o,s,e)}return l},uu.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1){if(t===i[n])return!0}return!1},uu.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n=\"string\"==typeof this.promoteId?this.promoteId:this.promoteId[e];\"boolean\"==typeof(r=t.properties[n])&&(r=Number(r))}return r},Nn(\"FeatureIndex\",uu,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});var du=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};du.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},du.prototype.wasRequested=function(){return\"errored\"===this.state||\"loaded\"===this.state||\"reloading\"===this.state},du.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof Mc){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 Mc&&o.hasRTLText){this.hasRTLText=!0,hi.isLoading()||hi.isLoaded()||\"deferred\"!==ui()||fi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new na},du.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},du.prototype.getBucket=function(t){return this.buckets[t.id]},du.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Vc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Vc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},du.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},du.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},du.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:\"\",a=n._geojsonTileLayer||n[i];if(a)for(var o=sn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),m=new su(p,l,c,u,d);m.tile=f,t.push(m)}}}},du.prototype.hasData=function(){return\"loaded\"===this.state||\"reloading\"===this.state||\"expired\"===this.state},du.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},du.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=A(t.cacheControl);r[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*r[\"max-age\"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},du.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},du.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||\"_geojsonTileLayer\",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},du.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},du.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},du.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},du.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},du.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){r[i[n]]=!0}this.dependencies[t]=r},du.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1){if(a[s[o]])return!0}}return!1};var 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r},t.makeRequest=yt,t.mapObject=v,t.mercatorXfromLng=Jc,t.mercatorYfromLat=Kc,t.mercatorZfromAltitude=Qc,t.mul=mo,t.multiply=po,t.mvt=Ls,t.normalize=function(t,e){var r=e[0],n=e[1],i=e[2],a=r*r+n*n+i*i;return a>0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t},t.number=qe,t.offscreenCanvasSupported=pt,t.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t},t.parseGlyphPBF=function(t){return new al(t).readFields(Tl,[])},t.pbf=al,t.performSymbolLayout=function(t,e,r,n,i,a,o){t.createArrays();var s=512*t.overscaling;t.tilePixelRatio=8192/s,t.compareText={},t.iconsNeedLinear=!1;var l=t.layers[0].layout,c=t.layers[0]._unevaluatedLayout._values,u={};if(\"composite\"===t.textSizeData.kind){var f=t.textSizeData,h=f.minZoom,p=f.maxZoom;u.compositeTextSizes=[c[\"text-size\"].possiblyEvaluate(new 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g=(c+1)%2;(0===c?r-i<=d:n-i<=m)&&(o.push(f),o.push(p-1),o.push(g)),(0===c?r+i>=d:n+i>=m)&&(o.push(p+1),o.push(u),o.push(g))}}return s}(this.ids,this.coords,t,e,r,this.nodeSize)};var j={minZoom:0,maxZoom:16,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:function(t){return t}},U=function(t){this.options=X(Object.create(j),t),this.trees=new Array(this.options.maxZoom+1)};function V(t,e,r,n,i){return{x:t,y:e,zoom:1/0,id:r,parentId:-1,numPoints:n,properties:i}}function H(t,e){var r=t.geometry.coordinates,n=r[0],i=r[1];return{x:Y(n),y:W(i),zoom:1/0,index:e,parentId:-1}}function q(t){return{type:\"Feature\",id:t.id,properties:G(t),geometry:{type:\"Point\",coordinates:[(n=t.x,360*(n-.5)),(e=t.y,r=(180-360*e)*Math.PI/180,360*Math.atan(Math.exp(r))/Math.PI-90)]}};var e,r,n}function G(t){var e=t.numPoints,r=e>=1e4?Math.round(e/1e3)+\"k\":e>=1e3?Math.round(e/100)/10+\"k\":e;return 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r=0;r<e.length;r+=3)t.minX=Math.min(t.minX,e[r]),t.minY=Math.min(t.minY,e[r+1]),t.maxX=Math.max(t.maxX,e[r]),t.maxY=Math.max(t.maxY,e[r+1])}function tt(t,e,r,n){if(e.geometry){var i=e.geometry.coordinates,a=e.geometry.type,o=Math.pow(r.tolerance/((1<<r.maxZoom)*r.extent),2),s=[],l=e.id;if(r.promoteId?l=e.properties[r.promoteId]:r.generateId&&(l=n||0),\"Point\"===a)et(i,s);else if(\"MultiPoint\"===a)for(var c=0;c<i.length;c++)et(i[c],s);else if(\"LineString\"===a)rt(i,s,o,!1);else if(\"MultiLineString\"===a){if(r.lineMetrics){for(c=0;c<i.length;c++)s=[],rt(i[c],s,o,!1),t.push(Q(l,\"LineString\",s,e.properties));return}nt(i,s,o,!1)}else if(\"Polygon\"===a)nt(i,s,o,!0);else{if(\"MultiPolygon\"!==a){if(\"GeometryCollection\"===a){for(c=0;c<e.geometry.geometries.length;c++)tt(t,{id:l,geometry:e.geometry.geometries[c],properties:e.properties},r,n);return}throw new Error(\"Input data is not a valid GeoJSON object.\")}for(c=0;c<i.length;c++){var 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u=t[c],f=u.geometry,h=u.type,p=0===i?u.minX:u.minY,d=0===i?u.maxX:u.maxY;if(p>=r&&d<n)l.push(u);else if(!(d<r||p>=n)){var m=[];if(\"Point\"===h||\"MultiPoint\"===h)st(f,m,r,n,i);else if(\"LineString\"===h)lt(f,m,r,n,i,!1,s.lineMetrics);else if(\"MultiLineString\"===h)ut(f,m,r,n,i,!1);else if(\"Polygon\"===h)ut(f,m,r,n,i,!0);else if(\"MultiPolygon\"===h)for(var g=0;g<f.length;g++){var v=[];ut(f[g],v,r,n,i,!0),v.length&&m.push(v)}if(m.length){if(s.lineMetrics&&\"LineString\"===h){for(g=0;g<m.length;g++)l.push(Q(u.id,h,m[g],u.tags));continue}\"LineString\"!==h&&\"MultiLineString\"!==h||(1===m.length?(h=\"LineString\",m=m[0]):h=\"MultiLineString\"),\"Point\"!==h&&\"MultiPoint\"!==h||(h=3===m.length?\"Point\":\"MultiPoint\"),l.push(Q(u.id,h,m,u.tags))}}}return l.length?l:null}function st(t,e,r,n,i){for(var a=0;a<t.length;a+=3){var o=t[a+i];o>=r&&o<=n&&(e.push(t[a]),e.push(t[a+1]),e.push(t[a+2]))}}function lt(t,e,r,n,i,a,o){for(var 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r=[],n=0;n<t.length;n++){var i,a=t[n],o=a.type;if(\"Point\"===o||\"MultiPoint\"===o||\"LineString\"===o)i=mt(a.geometry,e);else if(\"MultiLineString\"===o||\"Polygon\"===o){i=[];for(var s=0;s<a.geometry.length;s++)i.push(mt(a.geometry[s],e))}else if(\"MultiPolygon\"===o)for(i=[],s=0;s<a.geometry.length;s++){for(var l=[],c=0;c<a.geometry[s].length;c++)l.push(mt(a.geometry[s][c],e));i.push(l)}r.push(Q(a.id,o,i,a.tags))}return r}function mt(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function gt(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],c=l.geometry,u=l.type;if(l.geometry=[],1===u)for(n=0;n<c.length;n+=2)l.geometry.push(vt(c[n],c[n+1],e,a,o,s));else for(n=0;n<c.length;n++){var f=[];for(i=0;i<c[n].length;i+=2)f.push(vt(c[n][i],c[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function 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u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:\"Polygon\"===a||\"MultiPolygon\"===a?3:\"LineString\"===a||\"MultiLineString\"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function bt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function _t(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time(\"preprocess data\"),e.maxZoom<0||e.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");if(e.promoteId&&e.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n<t.features.length;n++)tt(r,t.features[n],e,n);else\"Feature\"===t.type?tt(r,t,e):tt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",e.indexMaxZoom,e.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=ot(t,1,-1-r,r,0,-1,2,e),a=ot(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=ot(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=dt(i,1).concat(n)),a&&(n=n.concat(dt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}function wt(t,e,r){return 32*((1<<t)*r+e)+t}function Tt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}U.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time(\"total time\");var o=\"prepare \"+t.length+\" points\";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new N(s,Z,J,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new N(s,Z,J,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",c,s.length,+Date.now()-u)}return r&&console.timeEnd(\"total time\"),this},U.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(Y(r),W(a),Y(i),W(n));u<f.length;u+=1){var h=f[u],p=l.points[h];c.push(p.numPoints?q(p):this.points[p.index])}return c},U.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n=\"No cluster with the specified id.\",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=c[l],f=i.points[u];f.parentId===t&&s.push(f.numPoints?q(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},U.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},U.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.points,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.points,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.points,-1,r,i,u),u.features.length?u:null},U.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},U.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},U.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?G(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},U.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},U.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var c=t[l];if(!(c.zoom<=e)){c.zoom=e;for(var u=this.trees[e+1],f=u.within(c.x,c.y,s),h=c.numPoints||1,p=c.x*h,d=c.y*h,m=o&&h>1?this._map(c,!0):null,g=(l<<5)+(e+1)+this.points.length,v=0,y=f;v<y.length;v+=1){var x=y[v],b=u.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=g,o&&(m||(m=this._map(c,!0)),o(m,this._map(b)))}}1===h?r.push(c):(c.parentId=g,r.push(V(p/h,d/h,g,h,m)))}}return r},U.prototype._getOriginId=function(t){return t-this.points.length>>5},U.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},U.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},_t.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},_t.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=wt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time(\"creation\"),h=this.tiles[f]=yt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time(\"clipping\");var m,g,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;m=g=v=y=null,x=ot(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=ot(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(m=ot(x,u,n-_,n+T,1,h.minY,h.maxY,l),g=ot(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=ot(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=ot(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd(\"clipping\"),s.push(m||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},_t.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=wt(t,e=(e%o+o)%o,r);if(this.tiles[s])return gt(this.tiles[s],i);a>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[wt(c,u,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",c,u,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?gt(this.tiles[s],i):null):null};var kt=function(e){function r(t,r,n,i){e.call(this,t,r,n,Tt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));f(o,!0);try{e._geoJSONIndex=n.cluster?new U(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=h[1],m=t.createExpression(d),g=t.createExpression(\"string\"==typeof p?[p,[\"accumulated\"],[\"get\",f]]:p);i[f]=m.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new _t(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){\"Coalescing\"===this._state?this._state=\"Idle\":\"NeedsLoadData\"===this._state&&(this._state=\"Coalescing\",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON 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t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var n=e.getBoundingClientRect(),i=[],a=0;a<r.length;a++)i.push(new t.Point(r[a].clientX-n.left-e.clientLeft,r[a].clientY-n.top-e.clientTop));return i},r.mouseButton=function(e){return void 0!==t.window.InstallTrigger&&2===e.button&&e.ctrlKey&&t.window.navigator.platform.toUpperCase().indexOf(\"MAC\")>=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var h=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e<r.length;e+=1){var n=r[e],i=n.ids,a=n.callback;this._notify(i,a)}this.requestors=[]}},r.prototype.getImage=function(t){return this.images[t]},r.prototype.addImage=function(t,e){this._validate(t,e)&&(this.images[t]=e)},r.prototype._validate=function(e,r){var n=!0;return this._validateStretch(r.stretchX,r.data&&r.data.width)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchX\" value'))),n=!1),this._validateStretch(r.stretchY,r.data&&r.data.height)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchY\" value'))),n=!1),this._validateContent(r.content,r)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"content\" value'))),n=!1),n},r.prototype._validateStretch=function(t,e){if(!t)return!0;for(var r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];if(a[0]<r||a[1]<a[0]||e<a[1])return!1;r=a[1]}return!0},r.prototype._validateContent=function(t,e){return!t||4===t.length&&(!(t[0]<0||e.data.width<t[0])&&(!(t[1]<0||e.data.height<t[1])&&(!(t[2]<0||e.data.width<t[2])&&(!(t[3]<0||e.data.height<t[3])&&(!(t[2]<t[0])&&!(t[3]<t[1]))))))},r.prototype.updateImage=function(t,e){var r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event(\"styleimagemissing\",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image \"'+o+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=g,d=g,m=1e20;function g(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||\"sans-serif\",this.fontWeight=a||\"normal\",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-m,n[1]=+m;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+m}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}g.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?m:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?m:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(v(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),v(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1){(0,o[i])(t,e)}delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar[\"CJK Unified Ideographs\"](e)||t.isChar[\"Hangul Syllables\"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,\"-transition\")?this._transitionable.setTransition(n.slice(0,-\"-transition\".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var c=t[l];i=s*r,a=(s+=c)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=void 0,d=i/r*(n+1);if(l.isDash){var m=n-Math.abs(d);p=Math.sqrt(h*h+m*m)}else p=n-Math.sqrt(h*h+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=l.isDash?h:-h;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name=\"Worker \"+a,this.actors.push(s)}};function A(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var M=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};M.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]},M.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=t.extend({type:\"vector\"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=A(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 M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&\"expired\"!==e.state?\"loading\"===e.state?e.reloadCallback=r:e.request=e.actor.send(\"reloadTile\",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send(\"loadTile\",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send(\"abortTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:\"raster\"},n),t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=A(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 M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(i)e.state=\"errored\",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state=\"loaded\",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type=\"raster-dem\",this.maxzoom=22,this._options=t.extend({type:\"raster-dem\"},n),this.encoding=n.encoding||\"mapbox\"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(n)e.state=\"errored\",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&\"expired\"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send(\"loadDEMTile\",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state=\"unloaded\",t.actor&&t.actor.send(\"removeDEMTile\",{uid:t.uid,source:this.id})},r}(E),C=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:\"source\",sourceDataType:\"metadata\"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=i-r,u=a-n,f=Math.max(c,u),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state=\"errored\",e(null))},r.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var O=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type=\"video\",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener(\"playing\",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var 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e=this.gl;e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}},e}(_t),Tt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(_t),kt=function(t,e,r,n){this.context=t,this.width=e,this.height=r;var i=t.gl,a=this.framebuffer=i.createFramebuffer();this.colorAttachment=new wt(t,a),n&&(this.depthAttachment=new Tt(t,a))};kt.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r)}t.deleteFramebuffer(this.framebuffer)};var At=function(t,e,r){this.func=t,this.mask=e,this.range=r};At.ReadOnly=!1,At.ReadWrite=!0,At.disabled=new At(519,At.ReadOnly,[0,1]);var Mt=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};Mt.disabled=new Mt({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Lt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.clearColor=new q(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new mt(this),this.bindElementBuffer=new gt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension(\"EXT_texture_filter_anisotropic\")||t.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||t.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&(t.getExtension(\"OES_texture_half_float_linear\"),this.extRenderToTextureHalfFloat=t.getExtension(\"EXT_color_buffer_half_float\")),this.extTimerQuery=t.getExtension(\"EXT_disjoint_timer_query\")};Lt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Lt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Lt.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Lt.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Lt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Lt.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Lt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Lt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Lt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Lt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Lt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Lt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ct=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on(\"data\",(function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on(\"error\",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error(\"Expected Source id to be \"+e+\" instead of \"+a.id);return t.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)\"errored\"!==this._tiles[t].state&&this._reloadTile(t,\"reloading\")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&(\"loading\"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state=\"errored\",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),\"expired\"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),\"raster-dem\"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event(\"data\",{dataType:\"source\",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(It(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],m=this._tiles[p];if(m&&!(m.fadeEndTime&&m.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),c[g.tileID.key]=g.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],m=this._tiles[d.key];if(!m.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],v=this.getTile(g);if(v&&v.hasData()){n[g.key]=g;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=m.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(m=this.getTile(_))&&x&&(m=this._addTile(_)),m&&(n[_.key]=_,x=m.wasRequested(),m.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,\"expired\"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,m=0,g=c;m<g.length;m+=1){var v=g[m];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,m=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/m,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-g<t.EXTENT&&v[0].y-g<t.EXTENT&&v[1].x+g>=0&&v[1].y+g>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:m})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(It(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles){this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\")}this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function Pt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function It(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function Ot(){return new t.window.Worker(Zi.workerUrl)}Ct.maxOverzooming=10,Ct.maxUnderzooming=3;var zt=\"mapboxgl_preloaded_worker_pool\",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new Ot);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[zt]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)\"ref\"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)\"ref\"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function Ht(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function qt(t,e,r,n){Ht(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}function Zt(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Ht(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]}):qt(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),m=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],m[m.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.lastIndexOf(o,m.length-a),1)):a++,c=m[m.length-i],n.push({command:Ut.addLayer,args:[d[o],c]}),m.splice(m.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s[\"source-layer\"],l[\"source-layer\"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=m[m.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:Ut.setStyle,args:[r]}]}return n}var Jt=function(t,e){this.reset(t,e)};Jt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Jt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Kt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Qt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function $t(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function te(e,r){var n=[e.x,e.y,0,1];fe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ee(t,e){return.5+t/e*.5}function re(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ne(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,m=n.transform.width/n.transform.height,g=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!g)ue(y.numGlyphs,h);else{g=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),re(x,f)){var b=x[3],_=ee(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(c,u,y),T=s?w/_:w*_,k=new t.Point(y.anchorX,y.anchorY),A=te(k,a).point,M={},S=oe(y,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&oe(y,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m).notEnoughRoom)&&ue(y.numGlyphs,h)}else ue(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ie(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),m=e.getoffsetX(f-1),g=le(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!g)return null;var v=le(t*m,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:g,last:v}:null}function ae(e,r,n,i){if(e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i)return{useVertical:!0};return(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function oe(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=r/24,v=e.lineOffsetX*g,y=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ie(g,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=te(w.first.point,s).point,k=te(w.last.point,s).point;if(i&&!n){var A=ae(e.writingMode,T,k,d);if(A)return A}m=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)m.push(le(g*l.getoffsetX(M),v,y,n,f,h,e.segment,b,_,c,o,p));m.push(w.last)}else{if(i&&!n){var S=te(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(c.getx(E),c.gety(E)),C=te(L,a),P=C.signedDistanceFromCamera>0?C.point:se(h,L,S,1,a),I=ae(e.writingMode,S,P,d);if(I)return I}var O=le(g*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!O)return{notEnoughRoom:!0};m=[O]}for(var z=0,D=m;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function se(t,e,r,n,i){var a=te(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function le(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,m=0;i&&(d*=-1,m=Math.PI),d<0&&(m+=Math.PI);for(var g=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=c)return null;if(y=v,w.push(v),void 0===(v=h[g])){var T=new t.Point(u.getx(g),u.gety(g)),k=te(T,f);if(k.signedDistanceFromCamera>0)v=h[g]=k.point;else{var A=g-d;v=se(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var M=(_-x)/b,S=v.sub(y),E=S.mult(M)._add(y);E._add(S._unit()._perp()._mult(n*d));var L=m+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:L,path:w}}Kt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Kt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Kt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Kt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Kt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Kt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Kt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Kt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Kt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Kt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Kt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var m=this.circleCells[i];if(null!==m)for(var g=this.circles,v=0,y=m;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Kt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var m=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[m+0],f[m+1],f[m+2],f[m+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var v=this.circles,y=0,x=g;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Kt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Kt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Kt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Kt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Kt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var ce=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ue(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(ce,3*n)}}function fe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var he=function(t,e,r){void 0===e&&(e=new Kt(t.width+200,t.height+200,25)),void 0===r&&(r=new Kt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function pe(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}he.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},he.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],m=new t.Point(r.anchorX,r.anchorY),g=te(m,o),v=ee(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=te(m,s).point,b=ie(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,m,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Jt,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var I=1;I<L.path.length;I++)C.push(L.path[I]);var O=2.5*k;if(l){var z=C.map((function(t){return te(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V=0;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/O)+1;for(var H=0;H<V;H++){var q=H/Math.max(V-1,1),G=S.lerp(q),Y=G.x+100,W=G.y+100;d.push(Y,W,k,0);var X=Y-k,Z=W-k,J=Y+k,K=W+k;if(T=T&&this.isOffscreen(X,Z,J,K),w=w||this.isInsideGrid(X,Z,J,K),!e&&this.grid.hitTestCircle(Y,W,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},he.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var m=d[p],g=m.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var v=[new t.Point(m.x1,m.y1),new t.Point(m.x2,m.y1),new t.Point(m.x2,m.y2),new t.Point(m.x1,m.y2)];t.polygonIntersectsPolygon(r,v)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},he.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},he.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},he.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return fe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},he.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},he.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},he.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var de=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};de.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new de(t?t.text:null,e,r,i),this.icon=new de(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var ge=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ve=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ye=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},xe=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function be(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function _e(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}xe.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var we=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new he(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new xe(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Te(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}we.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h=\"map\"===l.get(\"text-pitch-alignment\"),p=\"map\"===l.get(\"text-rotation-alignment\"),d=pe(n,1,this.transform.zoom),m=Qt(f,h,p,this.transform,d),g=null;if(h){var v=$t(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ye(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:m,labelToScreenMatrix:g,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},we.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=[f.textOffset0,f.textOffset1],v=be(t,r,n,g,i),y=this.collisionIndex.placeCollisionBox(_e(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(d&&0===this.collisionIndex.placeCollisionBox(_e(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return;if(y.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(m=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:m},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}},we.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,m=a.collisionGroup,g=s.get(\"text-optional\"),v=s.get(\"icon-optional\"),y=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=y&&(x||!o.hasIconData()||v),A=x&&(y||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new ge(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,I=null,O=0,z=0,D=0;a.textFeatureIndex?O=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(O=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length;i+=1){if(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),L&&L.box&&L.box.length)break}else L=r()};if(s.get(\"text-variable-anchor\")){var N=s.get(\"text-variable-anchor\");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],v=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,c,b,_,f,l,m,v,e,o,n,u);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,m.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get(\"text-padding\"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,m.predicate,X,W),T=y||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?_e(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,m.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(I=Z(a.verticalIconBox)).box.length>0:(I=Z(a.iconBox)).box.length>0,S=S&&I.offscreen}var J=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=v||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,m.ID):i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,m.ID)),M&&I&&i.collisionIndex.insertCollisionBox(I.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,D,m.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,m.ID),n)){var Q=o.bucketInstanceId,$=i.collisionCircleArrays[Q];void 0===$&&($=i.collisionCircleArrays[Q]=new ve);for(var tt=0;tt<P.circles.length;tt+=4)$.circles.push(P.circles[tt+0]),$.circles.push(P.circles[tt+1]),$.circles.push(P.circles[tt+2]),$.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new ge(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},we.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var c=l[s];c>=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},we.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},we.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new me(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},we.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},we.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get(\"text-allow-overlap\"),l=a.get(\"icon-allow-overlap\"),c=a.get(\"text-variable-anchor\"),u=\"map\"===a.get(\"text-rotation-alignment\"),f=\"map\"===a.get(\"text-pitch-alignment\"),h=\"none\"!==a.get(\"icon-text-fit\"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get(\"icon-optional\")),l&&(s||!e.hasTextData()||a.get(\"text-optional\")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},m=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,m=a.crossTileID,g=r[m],v=i.opacities[m];g?v=o:v||(v=p,i.opacities[m]=v),r[m]=!0;var y=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(y){var T=Pe(v.text),k=_?Ie:T;d(e.text,s,k);var A=w?Ie:T;d(e.text,l,A);var M=v.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,\"left\",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=Pe(v.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:Ie;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=v.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var I=C?Ie:L;d(e.icon,a.numVerticalIconVertices,I),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=v.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var O=e.collisionArrays[n];if(O){var z=new t.Point(0,0);if(O.textBox||O.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[m];R?(z=be(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}O.textBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||_,z.x,z.y),O.verticalTextBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&O.verticalIconBox);O.iconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,F,h?z.x:0,h?z.y:0),O.verticalIconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,!F,h?z.x:0,h?z.y:0)}}},g=0;g<e.symbolInstances.length;g++)m(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},we.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},we.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},we.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},we.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},we.prototype.setStale=function(){this.stale=!0};var ke=Math.pow(2,25),Ae=Math.pow(2,24),Me=Math.pow(2,17),Se=Math.pow(2,16),Ee=Math.pow(2,9),Le=Math.pow(2,8),Ce=Math.pow(2,1);function Pe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*ke+e*Ae+r*Me+e*Se+r*Ee+e*Le+r*Ce+e}var Ie=0,Oe=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Oe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new we(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Oe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var De=512/t.EXTENT/2,Re=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Re.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=De/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Re.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Fe=function(){this.maxCrossTileID=0};Fe.prototype.generate=function(){return++this.maxCrossTileID};var Be=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Be.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Be.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++){e.symbolInstances.get(n).crossTileID=0}this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Re(t,e.symbolInstances,e.bucketInstanceId),!0},Be.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Be.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ne=function(){this.layerIndexes={},this.crossTileIDs=new Fe,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ne.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Be);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ne.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var je=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return\"source.canvas\"!==t.identifier})))},Ue=t.pick(Ut,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Ve=t.pick(Ut,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),He=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a=null;null!=(a=\"version\"===n?r:\"array\"===i.type?[]:{})&&(e[n]=a)}}return e}(),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 Ne,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast(\"syncRTLPluginState\",r,(function(e,r){if((t.triggerPluginCompletionEvent(e),r)&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on(\"data\",(function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"}));var i=\"boolean\"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._load(He,!1)},r.prototype._load=function(e,r){if(!r||!je(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event(\"data\",{dataType:\"style\"})),this.fire(new t.Event(\"style.load\"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,m=l.stretchX,g=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:m,stretchY:g,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];\"custom\"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];\"reload\"===o?this._reloadSource(a):\"clear\"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event(\"data\",{dataType:\"style\"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies([\"icons\",\"patterns\"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),je(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=Zt(this.serialize(),e).filter((function(t){return!(t.command in Ve)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in Ue)}));if(i.length>0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0)||!this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n)){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ct(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(je(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is requred to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1){var h=f[u];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var m=this._order[d];if(r(m))for(var g=i.length-1;g>=0;g--){var v=i[g].feature;if(n[v.layer.id]<d)break;p.push(v),i.pop()}else for(var y=0,x=t;y<x.length;y+=1){var b=x[y][m];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,\"queryRenderedFeatures.filter\",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error(\"The layer '\"+s+\"' does not exist in the map's style and cannot be queried for features.\"))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1){var h=f[u];c.push(o[h])}c.sort(B);for(var p=function(){var r=m[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1){var h=f[u];o.push(h)}}},d=0,m=c;d<m.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer[\"source-layer\"],i.id);i.source=i.layer.source,i.layer[\"source-layer\"]&&(i.sourceLayer=i.layer[\"source-layer\"]),i.state=o}))};for(var v in s)g(v);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,\"querySourceFeatures.filter\",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called \"'+t+'\" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&je(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off(\"pluginStateChange\",this._rtlTextPluginCallback),this._layers){this._layers[e].setEventedParent(null)}for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];if(\"symbol\"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var m=0,g=this._order;m<g.length;m+=1){var v=g[m],y=this._layers[v];\"symbol\"===y.type&&this.placement.updateLayerOpacities(y,l[y.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var Ge=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Ye=yr(\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\",\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\"),We=yr(\"uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Xe=yr(\"uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),Ze=yr(\"varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main(void) {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}\"),Je=yr(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Ke=yr(\"uniform highp float u_intensity;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main() {\\n#pragma mapbox: initialize highp float weight\\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\nconst highp float ZERO=1.0/255.0/16.0;\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main(void) {\\n#pragma mapbox: initialize highp float weight\\n#pragma mapbox: initialize mediump float radius\\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}\"),Qe=yr(\"uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(0.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),$e=yr(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),tr=yr(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),er=yr(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),rr=yr(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),nr=yr(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ir=yr(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ar=yr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),or=yr(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),sr=yr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),lr=yr(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),cr=yr(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),ur=yr(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),fr=yr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define MAX_LINE_DISTANCE 32767.0\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),hr=yr(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),pr=yr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),dr=yr(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),mr=yr(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),gr=yr(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),vr=yr(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function yr(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o=\"float\"===i?\"vec2\":\"vec4\",s=a.match(/color/)?\"color\":o;return n[a]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"}))}}var xr=Object.freeze({__proto__:null,prelude:Ye,background:We,backgroundPattern:Xe,circle:Ze,clippingMask:Je,heatmap:Ke,heatmapTexture:Qe,collisionBox:$e,collisionCircle:tr,debug:er,fill:rr,fillOutline:nr,fillOutlinePattern:ir,fillPattern:ar,fillExtrusion:or,fillExtrusionPattern:sr,hillshadePrepare:lr,hillshade:cr,line:ur,lineGradient:fr,linePattern:hr,lineSDF:pr,raster:dr,symbolIcon:mr,symbolSDF:gr,symbolTextAndIcon:vr}),br=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};br.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},br.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1){p[h].enableAttributes(u,t)}a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,m=r;d<m.length;d+=1){var g=m[d];g.bind(),g.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},br.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var _r=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push(\"#define OVERDRAW_INSPECTOR;\");var s=o.concat(Ye.fragmentSource,e.fragmentSource).join(\"\\n\"),l=o.concat(Ye.vertexSource,e.vertexSource).join(\"\\n\"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var m=a.getActiveAttrib(this.program,d);m&&(this.attributes[m.name]=a.getAttribLocation(this.program,m.name))}for(var g=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<g;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function wr(t,e,r){var n=1/pe(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}_r.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[v.LINES]=2,g[v.TRIANGLES]=3,g[v.LINE_STRIP]=1,g)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new br)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,m),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Tr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},kr=function(e,r,n,i,a,o,s){return t.extend(Tr(e,r,n,i),wr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Ar=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(Ar(e),wr(n,r,i))},Sr=function(t,e){return{u_matrix:t,u_world:e}},Er=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Lr=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=pe(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Cr=function(t,e,r){var n=pe(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Pr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Ir=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Or=function(t){return{u_matrix:t}},zr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:pe(e,1,r),u_intensity:n}};function Dr(e,r){var n=Math.pow(2,r.canonical.z),i=r.canonical.y;return[new t.MercatorCoordinate(0,i/n).toLngLat().lat,new t.MercatorCoordinate(0,(i+1)/n).toLngLat().lat]}var Rr=function(e,r,n){var i=e.transform;return{u_matrix:Ur(e,r,n),u_ratio:1/pe(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Fr=function(e,r,n){return t.extend(Rr(e,r,n),{u_image:0})},Br=function(e,r,n,i){var a=e.transform,o=jr(r,a);return{u_matrix:Ur(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/pe(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Nr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=jr(r,o),c=\"round\"===n.layout.get(\"line-cap\"),u=s.getDash(i.from,c),f=s.getDash(i.to,c),h=u.width*a.fromScale,p=f.width*a.toScale;return t.extend(Rr(e,r,n),{u_patternscale_a:[l/h,-u.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:f.y,u_mix:a.t})};function jr(t,e){return 1/pe(t,1,e.tileZoom)}function Ur(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var Vr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Hr(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Hr(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 qr,Gr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Yr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(Gr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Wr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Yr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Xr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Zr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/pe(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Jr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Kr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram(\"collisionBox\"),f=[],h=0,p=0,d=0;d<i.length;d++){var m=i[d],g=r.getTile(m),v=g.getBucket(n);if(v){var y=m.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(m.posMatrix,g,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],I=E.circleArray[C+1],O=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(A++,P,I,O,z,0),k.emplace(A++,P,I,O,z,1),k.emplace(A++,P,I,O,z,2),k.emplace(A++,P,I,O,z,3)}(!qr||qr.length<2*h)&&(qr=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(qr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Pr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Qr=t.identity(new Float32Array(16));function $r(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function tn(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,m={};p.clear();for(var g=0;g<h.length;g++){var v=h.get(g),y=e.allowVerticalPlacement&&!v.placedOrientation,x=v.hidden||!v.crossTileID||y?null:i[v.crossTileID];if(x){var b=new t.Point(v.anchorX,v.anchorY),_=te(b,n?l:s),w=ee(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,u,v)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/c);for(var k=x.width,A=x.height,M=$r(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?te(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<v.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&v.associatedIconIndex>=0&&(m[v.associatedIconIndex]={shiftedAnchor:S,angle:E})}else ue(v.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var I=C.get(P);if(I.hidden)ue(I.numGlyphs,d);else{var O=m[P];if(O)for(var z=0;z<I.numGlyphs;z++)t.addDynamicAttributes(d,O.shiftedAnchor,O.angle);else ue(I.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function en(t,e,r){return r.iconsInText&&e?\"symbolTextAndIcon\":t?\"symbolSDF\":\"symbolIcon\"}function rn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,m=e.transform,g=\"map\"===l,v=\"map\"===c,y=g&&\"point\"!==n.layout.get(\"symbol-placement\"),x=g&&!v&&!y,b=void 0!==n.layout.get(\"symbol-sort-key\").constantOr(1),_=e.depthModeForSublayer(0,At.ReadOnly),w=n.layout.get(\"text-variable-anchor\"),T=[],k=0,A=i;k<A.length;k+=1){var M=A[k],S=r.getTile(M),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,I=a?E.textSizeData:E.iconSizeData,O=v||0!==m.pitch,z=e.useProgram(en(P,a,E),C),D=t.evaluateSizeForZoom(I,m.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var V=\"composite\"===I.kind||\"camera\"===I.kind;U=O||e.options.rotating||e.options.zooming||V?d.LINEAR:d.NEAREST}}else{var H=1!==n.layout.get(\"icon-size\").constantOr(0)||E.iconsNeedLinear;B=S.imageAtlasTexture,N=P||e.options.rotating||e.options.zooming||H||O?d.LINEAR:d.NEAREST,R=S.imageAtlasTexture.size}var q=pe(S,1,e.transform.zoom),G=Qt(M.posMatrix,v,g,e.transform,q),Y=$t(M.posMatrix,v,g,e.transform,q),W=w&&E.hasTextData(),X=\"none\"!==n.layout.get(\"icon-text-fit\")&&W&&E.hasIconData();y&&ne(E,M.posMatrix,e,a,G,Y,v,u);var Z=e.translatePosMatrix(M.posMatrix,S,o,s),J=y||a&&w||X?Qr:G,K=e.translatePosMatrix(Y,S,o,s,!0),Q=P&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1),$={program:z,buffers:L,uniformValues:P?E.iconsInText?Wr(I.kind,D,x,v,e,Z,J,K,R,F):Yr(I.kind,D,x,v,e,Z,J,K,a,R,!0):Gr(I.kind,D,x,v,e,Z,J,K,a,R),atlasTexture:B,atlasTextureIcon:j,atlasInterpolation:N,atlasInterpolationIcon:U,isSDF:P,hasHalo:Q};if(b)for(var tt=0,et=L.segments.get();tt<et.length;tt+=1){var rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:$})}else T.push({segments:L.segments,sortKey:0,state:$})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function nn(t,e,r,n,i,a,o,s,l){var c=n.context,u=c.gl;i.draw(c,u.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function an(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get(\"fill-pattern\"),d=p&&p.constantOr(1),m=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",s=h.LINES):(l=d?\"fillPattern\":\"fill\",s=h.TRIANGLES);for(var g=0,v=n;g<v.length;g+=1){var y=v[g],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(m));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get(\"fill-translate\"),r.paint.get(\"fill-translate-anchor\"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c=\"fillOutlinePattern\"===l&&d?Er(S,t,m,x,E):Sr(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,m,x):Ar(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function on(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get(\"fill-extrusion-pattern\"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get(\"fill-extrusion-opacity\"),p=0,d=n;p<d.length;p+=1){var m=d[p],g=e.getTile(m),v=g.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?\"fillExtrusionPattern\":\"fillExtrusion\",y);u&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(m.posMatrix,g,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\")),A=r.paint.get(\"fill-extrusion-vertical-gradient\"),M=u?kr(k,t,A,h,m,f,g):Tr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,M,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function sn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var c=t.useProgram(\"hillshade\");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var u=function(t,e,r){var n=r.paint.get(\"hillshade-shadow-color\"),i=r.paint.get(\"hillshade-highlight-color\"),a=r.paint.get(\"hillshade-accent-color\"),o=r.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===r.paint.get(\"hillshade-illumination-anchor\")&&(o-=t.transform.angle);var s=!t.options.moving;return{u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),s),u_image:0,u_latrange:Dr(t,e.tileID),u_light:[r.paint.get(\"hillshade-exaggeration\"),o],u_shadow:n,u_highlight:i,u_accent:a}}(t,e,r);c.draw(o,s.TRIANGLES,n,i,a,Et.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function ln(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var m=r.fbo;if(!m){var g=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);g.bind(c.LINEAR,c.CLAMP_TO_EDGE),(m=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(g.texture)}l.bindFramebuffer.set(m.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram(\"hillshadePrepare\").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function cn(e,r,n,i,a){var o=i.paint.get(\"raster-fade-duration\");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var un=new t.Color(1,0,0,1),fn=new t.Color(0,1,0,1),hn=new t.Color(0,0,1,1),pn=new t.Color(1,0,1,1),dn=new t.Color(0,1,1,1);function mn(t){var e=t.transform.padding;gn(t,t.transform.height-(e.top||0),3,un),gn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,hn),vn(t,t.transform.width-(e.right||0),3,pn);var r=t.transform.centerPoint;!function(t,e,r,n){yn(t,e-1,r-10,2,20,n),yn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,dn)}function gn(t,e,r,n){yn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){yn(t,e-r/2,0,r,t.transform.height,n)}function yn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function xn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=At.disabled,c=Mt.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),\"$debug\",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=f&&f.byteLength||0,p=Math.floor(h/1024),d=r.getTile(n).tileSize,m=512/Math.min(d,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+\" \"+p+\"kb\"),s.draw(i,a.TRIANGLES,l,c,St.alphaBlended,Et.disabled,Ir(o,t.Color.transparent,m),\"$debug\",e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var bn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=Mt.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,c=\"map\"===a,u=\"map\"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),m=d.getBucket(n);if(m&&m.text&&m.text.segments.get().length){var g=m.textSizeData,v=t.evaluateSizeForZoom(g,l.zoom),y=pe(d,1,r.transform.zoom),x=Qt(p.posMatrix,u,c,r.transform,y),b=\"none\"!==n.layout.get(\"icon-text-fit\")&&m.hasIconData();if(v){var _=Math.pow(2,l.zoom-d.tileID.overscaledZ);tn(m,c,u,s,t.symbolSize,l,x,p.posMatrix,_,v,b)}}}}(i,e,n,r,n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),a),0!==n.paint.get(\"icon-opacity\").constantOr(1)&&rn(e,r,n,i,!1,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),n.layout.get(\"icon-rotation-alignment\"),n.layout.get(\"icon-pitch-alignment\"),n.layout.get(\"icon-keep-upright\"),o,s),0!==n.paint.get(\"text-opacity\").constantOr(1)&&rn(e,r,n,i,!0,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.layout.get(\"text-keep-upright\"),o,s),r.map.showCollisionBoxes&&(Kr(e,r,n,i,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),!0),Kr(e,r,n,i,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),!1))}},circle:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"circle-opacity\"),o=n.paint.get(\"circle-stroke-width\"),s=n.paint.get(\"circle-stroke-opacity\"),l=void 0!==n.layout.get(\"circle-sort-key\").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,At.ReadOnly),h=Mt.disabled,p=e.colorModeForRenderPass(),d=[],m=0;m<i.length;m++){var g=i[m],v=r.getTile(g),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram(\"circle\",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Lr(e,g,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,I=S.uniformValues,O=M.segments;L.draw(c,u.TRIANGLES,f,h,p,Et.disabled,I,n.id,C,P,O,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===e.renderPass){var a=e.context,o=a.gl,s=Mt.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram(\"heatmap\",p),m=e.transform.zoom;d.draw(a,o.TRIANGLES,At.disabled,s,l,Et.disabled,zr(u.posMatrix,f,m,n.paint.get(\"heatmap-intensity\")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(!a)return;n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA));o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram(\"heatmapTexture\").draw(n,i.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get(\"heatmap-opacity\")}}(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}(e,n))},line:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,At.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get(\"line-dasharray\"),u=n.paint.get(\"line-pattern\"),f=u.constantOr(1),h=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=f?\"linePattern\":c?\"lineSDF\":h?\"lineGradient\":\"line\",m=e.context,g=m.gl,v=!0;if(h){m.activeTexture.set(g.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(m,n.gradient,g.RGBA)),y.bind(g.LINEAR,g.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,k),S=v||M.program!==A,E=u.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var I=f?Br(e,w,n,p):c?Nr(e,w,n,c,p):h?Fr(e,w,n):Rr(e,w,n);f?(m.activeTexture.set(g.TEXTURE0),w.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(m.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(m)),M.draw(m,g.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,I,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get(\"fill-color\"),o=n.paint.get(\"fill-opacity\");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get(\"fill-pattern\"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?\"opaque\":\"translucent\";if(e.renderPass===c){var u=e.depthModeForSublayer(1,\"opaque\"===e.renderPass?At.ReadWrite:At.ReadOnly);an(e,r,n,i,u,s,!1)}if(\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")){var f=e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,At.ReadOnly);an(e,r,n,i,f,s,!0)}}},\"fill-extrusion\":function(t,e,r,n){var i=r.paint.get(\"fill-extrusion-opacity\");if(0!==i&&\"translucent\"===t.renderPass){var a=new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get(\"fill-extrusion-pattern\").constantOr(1))on(t,e,r,n,a,Mt.disabled,St.disabled),on(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();on(t,e,r,n,a,Mt.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,At.ReadOnly),s=t.colorModeForRenderPass(),l=\"translucent\"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&\"offscreen\"===t.renderPass?ln(t,p,r,a,o,Mt.disabled,s):\"translucent\"===t.renderPass&&sn(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram(\"raster\"),l=t.colorModeForRenderPass(),c=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,m=f;d<m.length;d+=1){var g=m[d],v=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get(\"raster-opacity\")?At.ReadWrite:At.ReadOnly,a.LESS),y=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);y.registerFadeDuration(r.paint.get(\"raster-fade-duration\"));var b=e.findLoadedParent(g,0),_=cn(y,b,e,r,t.transform),w=void 0,T=void 0,k=\"nearest\"===r.paint.get(\"raster-resampling\")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Vr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,v,Mt.disabled,l,Et.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[g.overscaledZ],l,Et.disabled,A,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=Mt.disabled,h=t.depthModeForSublayer(0,\"opaque\"===u?At.ReadWrite:At.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?\"backgroundPattern\":\"background\"),m=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),v=0,y=m;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Zr(b,i,t,c,{tileID:x,tileSize:l},g):Xr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)xn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if(\"offscreen\"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if(\"translucent\"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Mt.disabled);var o=\"3d\"===i.renderingMode?new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,At.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},_n=function(t,e){this.context=new Lt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ct.maxUnderzooming+Ct.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ne,this.gpuTimers={}};_n.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},_n.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Ge.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Ge.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ge.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Mt({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},_n.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(e,r.TRIANGLES,At.disabled,this.stencilClearMode,St.disabled,Et.disabled,Or(n),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},_n.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(At.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,At.disabled,new Mt({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Or(s.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},_n.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Mt({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Mt({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Mt({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]=Mt.disabled,e),n]},_n.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){return new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0])}return\"opaque\"===this.renderPass?St.unblended:St.alphaBlended},_n.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return At.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new At(r||this.context.gl.LEQUAL,e,[n,n])},_n.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},_n.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var m=0;m<i.length;m++){var g=i[m];if(this.style._layers[g].is3D()){this.opaquePassCutoff=m;break}}this.renderPass=\"offscreen\";for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];(\"custom\"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass=\"opaque\",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=(\"symbol\"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,u[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&bn.debug(this,l,l.getVisibleCoordinates()));this.options.showPadding&&mn(this),this.context.setDefault()},_n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||\"custom\"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),bn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},_n.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},_n.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},_n.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},_n.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},_n.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:pe(r,n[0],this.transform.zoom),a?n[1]:pe(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},_n.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},_n.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},_n.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},_n.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new _r(this.context,xr[t],e,Jr[t],this._showOverdrawInspector)),this.cache[r]},_n.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},_n.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},_n.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},_n.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var wn=function(t,e){this.points=t,this.planes=e};wn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new wn(a,o)};var Tn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Tn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Tn(n,i)},Tn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Tn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Tn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var kn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};kn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},kn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},kn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},kn.prototype.clone=function(){return new kn(this.top,this.bottom,this.left,this.right)},kn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var An=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new kn,this._posMatrixCache={},this._alignedPosMatrixCache={}},Mn={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}};An.prototype.clone=function(){var t=new An(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},Mn.minZoom.get=function(){return this._minZoom},Mn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Mn.maxZoom.get=function(){return this._maxZoom},Mn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Mn.minPitch.get=function(){return this._minPitch},Mn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Mn.maxPitch.get=function(){return this._maxPitch},Mn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Mn.renderWorldCopies.get=function(){return this._renderWorldCopies},Mn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Mn.worldSize.get=function(){return this.tileSize*this.scale},Mn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Mn.size.get=function(){return new t.Point(this.width,this.height)},Mn.bearing.get=function(){return-this.angle/Math.PI*180},Mn.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))},Mn.pitch.get=function(){return this._pitch/Math.PI*180},Mn.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())},Mn.fov.get=function(){return this._fov/Math.PI*180},Mn.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())},Mn.zoom.get=function(){return this._zoom},Mn.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())},Mn.center.get=function(){return this._center},Mn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Mn.padding.get=function(){return this._edgeInsets.toJSON()},Mn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Mn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},An.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},An.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},An.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)},An.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},An.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=wn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Tn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var m=u.pop(),g=m.x,v=m.y,y=m.fullyVisible;if(!y){var x=m.aabb.intersects(s);if(0===x)continue;y=2===x}var b=m.aabb.distanceX(o),_=m.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-m.zoom)-2;if(m.zoom===h||w>T&&m.zoom>=l)f.push({tileID:new t.OverscaledTileID(m.zoom===h?p:m.zoom,m.wrap,m.zoom,g,v),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-v])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(v<<1)+(k>>1);u.push({aabb:m.aabb.quadrant(k),zoom:m.zoom+1,x:A,y:M,wrap:m.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},An.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Mn.unmodified.get=function(){return this._unmodified},An.prototype.zoomScale=function(t){return Math.pow(2,t)},An.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},An.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)},An.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Mn.point.get=function(){return this.project(this.center)},An.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())},An.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},An.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},An.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},An.prototype.coordinateLocation=function(t){return t.toLngLat()},An.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,f=n[2]/a,h=u===f?0:(0-u)/(f-u);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,c,h)/this.worldSize)},An.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])},An.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)))},An.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},An.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])},An.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]},An.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},An.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var m=p.y,g=c.y/2;m-g<a&&(i=a+g),m+g>o&&(i=o-g)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},An.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,u,c),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),m=Math.sin(this.angle),g=s-Math.round(s)+d*h+m*p,v=l-Math.round(l)+d*p+m*h,y=new Float64Array(f);if(t.translate(y,y,[g>.5?g-1:g,v>.5?v-1:v,0]),this.alignedProjMatrix=y,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},An.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},An.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},An.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(An.prototype,Mn);var Sn=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};Sn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},Sn.prototype.remove=function(){return t.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Sn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u=\"\";if(u+=e?\"/\"+o+\"/\"+s+\"/\"+n:n+\"/\"+s+\"/\"+o,(l||c)&&(u+=\"/\"+Math.round(10*l)/10),c&&(u+=\"/\"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split(\"&\").map((function(t){var e=t.split(\"=\")[0];return e===f?(h=!0,e+\"=\"+u):t})).filter((function(t){return t}));return h||p.push(f+\"=\"+u),\"#\"+p.join(\"&\")}return\"#\"+u},Sn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace(\"#\",\"\");return this._hashName?(n.split(\"&\").map((function(t){return t.split(\"=\")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||\"\").split(\"/\")):n.split(\"/\")},Sn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},Sn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,\"\",e)}catch(t){}};var En={linearity:.3,easing:t.bezier(0,0,.3,1)},Ln=t.extend({deceleration:2500,maxSpeed:1400},En),Cn=t.extend({deceleration:20,maxSpeed:1400},En),Pn=t.extend({deceleration:1e3,maxSpeed:360},En),In=t.extend({deceleration:1e3,maxSpeed:90},En),On=function(t){this._map=t,this.clear()};function zn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Dn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}On.prototype.clear=function(){this._inertiaBuffer=[]},On.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},On.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},On.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Dn(r.pan.mag(),o,t.extend({},Ln,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,zn(s,l)}if(r.zoom){var c=Dn(r.zoom,o,Cn);s.zoom=this._map.transform.zoom+c.amount,zn(s,c)}if(r.bearing){var u=Dn(r.bearing,o,Pn);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),zn(s,u)}if(r.pitch){var f=Dn(r.pitch,o,In);s.pitch=this._map.transform.pitch+f.amount,zn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Rn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Fn=function(e){function n(n,i,a){var o=\"touchend\"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Bn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Nn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Nn.prototype.reset=function(){delete this._mousedownPos},Nn.prototype.wheel=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Nn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseup=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.dblclick=function(t){return this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseover=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.mouseout=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.touchstart=function(t){return this._firePreventable(new Fn(t.type,this._map,t))},Nn.prototype.touchmove=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchend=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchcancel=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Nn.prototype.isEnabled=function(){return!0},Nn.prototype.isActive=function(){return!1},Nn.prototype.enable=function(){},Nn.prototype.disable=function(){};var jn=function(t){this._map=t};jn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},jn.prototype.mousemove=function(t){this._map.fire(new Rn(t.type,this._map,t))},jn.prototype.mousedown=function(){this._delayContextMenu=!0},jn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Rn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},jn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Rn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},jn.prototype.isEnabled=function(){return!0},jn.prototype.isActive=function(){return!1},jn.prototype.enable=function(){},jn.prototype.disable=function(){};var Un=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Vn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Un.prototype.isEnabled=function(){return!!this._enabled},Un.prototype.isActive=function(){return!!this._active},Un.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Un.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Un.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Un.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,\"translate(\"+a+\"px,\"+s+\"px)\"),this._box.style.width=o-a+\"px\",this._box.style.height=l-s+\"px\"}}},Un.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event(\"boxzoomend\",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent(\"boxzoomcancel\",e)}},Un.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))},Un.prototype.reset=function(){this._active=!1,this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Un.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Hn=function(t){this.reset(),this.numTouches=t.numTouches};Hn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Hn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Vn(n,r)))},Hn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Vn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Hn.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 qn=function(t){this.singleTap=new Hn(t),this.numTaps=t.numTaps,this.reset()};qn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},qn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},qn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},qn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Gn=function(){this._zoomIn=new qn({numTouches:1,numTaps:2}),this._zoomOut=new qn({numTouches:2,numTaps:1}),this.reset()};Gn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Gn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Gn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Gn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Gn.prototype.touchcancel=function(){this.reset()},Gn.prototype.enable=function(){this._enabled=!0},Gn.prototype.disable=function(){this._enabled=!1,this.reset()},Gn.prototype.isEnabled=function(){return this._enabled},Gn.prototype.isActive=function(){return this._active};var Yn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Yn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Yn.prototype._correctButton=function(t,e){return!1},Yn.prototype._move=function(t,e){return{}},Yn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Yn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Yn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Yn.prototype.enable=function(){this._enabled=!0},Yn.prototype.disable=function(){this._enabled=!1,this.reset()},Yn.prototype.isEnabled=function(){return this._enabled},Yn.prototype.isActive=function(){return this._active};var Wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Yn),Xn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Zn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Jn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Jn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Jn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Jn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Jn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Vn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(){this.reset()};function Qn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}Kn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Kn.prototype._start=function(t){},Kn.prototype._move=function(t,e,r){return{}},Kn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Kn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=Qn(r,e,i),s=Qn(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},Kn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=Qn(n,e,a),l=Qn(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},Kn.prototype.touchcancel=function(){this.reset()},Kn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};function $n(t,e){return Math.log(t/e)/Math.LN2}var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs($n(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:$n(this._distance,r),pinchAround:e}},e}(Kn);function ei(t,e){return 180*t.angleWith(e)/Math.PI}var ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:ei(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=ei(t,this._startVector);return Math.abs(r)<e},e}(Kn);function ni(t){return Math.abs(t.y)>Math.abs(t.x)}var ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ni(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid){this._lastPoints=t,this._active=!0;return{pitchDelta:-.5*((n.y+i.y)/2)}}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ni(t)&&ni(e)&&a}},e}(Kn),ai={panStep:100,bearingStep:15,pitchStep:10},oi=function(){var t=ai;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function si(t){return t*(2-t)}oi.prototype.reset=function(){this._active=!1},oi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:si,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},oi.prototype.enable=function(){this._enabled=!0},oi.prototype.disable=function(){this._enabled=!1,this.reset()},oi.prototype.isEnabled=function(){return this._enabled},oi.prototype.isActive=function(){return this._active};var li=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};li.prototype.setZoomRate=function(t){this._defaultZoomRate=t},li.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},li.prototype.isEnabled=function(){return!!this._enabled},li.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},li.prototype.isZooming=function(){return!!this._zooming},li.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},li.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},li.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},li.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},li.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},li.prototype.renderFrame=function(){return this._onScrollFrame()},li.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if(\"wheel\"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},li.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},li.prototype.reset=function(){this._active=!1};var ci=function(t,e){this._clickZoom=t,this._tapZoom=e};ci.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},ci.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},ci.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},ci.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var ui=function(){this.reset()};ui.prototype.reset=function(){this._active=!1},ui.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},ui.prototype.enable=function(){this._enabled=!0},ui.prototype.disable=function(){this._enabled=!1,this.reset()},ui.prototype.isEnabled=function(){return this._enabled},ui.prototype.isActive=function(){return this._active};var fi=function(){this._tap=new qn({numTouches:1,numTaps:1}),this.reset()};fi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},fi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},fi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},fi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},fi.prototype.touchcancel=function(){this.reset()},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var hi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};hi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},hi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},hi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},hi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var pi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};pi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},pi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},pi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},pi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var di=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};di.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},di.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},di.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},di.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},di.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},di.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var mi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function vi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var yi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new On(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!1}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1],u=s[2];r.addEventListener(l,c,l===t.window.document?this.handleWindowEvent:this.handleEvent,u)}};yi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},yi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new Nn(e,t));var n=e.boxZoom=new Un(e,t);this._add(\"boxZoom\",n);var i=new Gn,a=new ui;e.doubleClickZoom=new ci(a,i),this._add(\"tapZoom\",i),this._add(\"clickZoom\",a);var o=new fi;this._add(\"tapDragZoom\",o);var s=e.touchPitch=new ii;this._add(\"touchPitch\",s);var l=new Xn(t),c=new Zn(t);e.dragRotate=new pi(t,l,c),this._add(\"mouseRotate\",l,[\"mousePitch\"]),this._add(\"mousePitch\",c,[\"mouseRotate\"]);var u=new Wn(t),f=new Jn(t);e.dragPan=new hi(r,u,f),this._add(\"mousePan\",u),this._add(\"touchPan\",f,[\"touchZoom\",\"touchRotate\"]);var h=new ri,p=new ti;e.touchZoomRotate=new di(r,p,h,o),this._add(\"touchRotate\",h,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",p,[\"touchPan\",\"touchRotate\"]);var d=e.scrollZoom=new li(e,this);this._add(\"scrollZoom\",d,[\"mousePan\"]);var m=e.keyboard=new oi;this._add(\"keyboard\",m),this._add(\"blockableMapEvent\",new jn(e));for(var g=0,v=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];g<v.length;g+=1){var y=v[g];t.interactive&&t[y]&&e[y].enable(t[y])}},yi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},yi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1){e[t].handler.reset()}this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},yi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1){if(e[t].handler.isActive())return!0}return!1},yi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},yi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},yi.prototype.isMoving=function(){return Boolean(mi(this._eventsInProgress))||this.isZooming()},yi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},yi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+\"Window\")},yi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},yi.prototype.handleEvent=function(t,e){if(\"blur\"!==t.type){this._updatingCamera=!0;for(var n=\"renderFrame\"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var m=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(m=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,m,h,n),m&&m.needsRenderFrame&&this._triggerRenderFrame()),(m||p.isActive())&&(o[h]=p)}}var g={};for(var v in this._previousActiveHandlers)o[v]||(g[v]=n);this._previousActiveHandlers=o,(Object.keys(g).length||vi(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||vi(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},yi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},yi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},yi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!vi(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},yi.prototype._fireEvents=function(e,r){var n=this,i=mi(this._eventsInProgress),a=mi(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+\"start\"]=l),this._eventsInProgress[s]=e[s]}for(var c in!i&&a&&this._fireEvent(\"movestart\",a.originalEvent),o)this._fireEvent(c,o[c]);for(var u in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent(\"move\",a.originalEvent),e){var f=e[u].originalEvent;this._fireEvent(u,f)}var h,p={};for(var d in this._eventsInProgress){var m=this._eventsInProgress[d],g=m.handlerName,v=m.originalEvent;this._handlersById[g].isActive()||(delete this._eventsInProgress[d],h=r[g]||v,p[d+\"end\"]=h)}for(var y in p)this._fireEvent(y,p[y]);var x=mi(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event(\"moveend\",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},yi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},yi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new gi(\"renderFrame\",{timeStamp:e})),t._applyChanges()})))};var xi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll([\"_renderFrameCallback\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if(\"number\"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),m=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/m.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/m.y;if(!(v<0||g<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(g,v)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(w)),zoom:y,bearing:n}}t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),i&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),a&&this.fire(new t.Event(\"rotatestart\",r)).fire(new t.Event(\"rotate\",r)).fire(new t.Event(\"rotateend\",r)),o&&this.fire(new t.Event(\"pitchstart\",r)).fire(new t.Event(\"pitch\",r)).fire(new t.Event(\"pitchend\",r)),this.fire(new t.Event(\"moveend\",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c=\"zoom\"in e?+e.zoom:a,u=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,f=\"pitch\"in e?+e.pitch:s,h=\"padding\"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),m=i.pointLocation(d),g=t.LngLat.convert(e.center||m);this._normalizeCenter(g);var v,y,x=i.project(m),b=i.project(g).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var m=i.zoomScale(i.zoom-a),g=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(m));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(u-o),m=t.Point.convert(e.offset),g=a.centerPoint.add(m),v=a.pointLocation(g),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),I=function(t){return C(P)/C(P+_*t)},O=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,O=function(){return 0},I=function(t){return Math.exp(D*_*t)}}if(\"duration\"in e)e.duration=+e.duration;else{var R=\"screenSpeed\"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/I(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),g=a.centerPoint.add(m));var v=1===e?y:a.unproject(x.add(b.mult(O(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),bi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};bi.prototype.getDefaultPosition=function(){return\"bottom-right\"},bi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},bi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},bi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n<r.length-1?\"&\":\"\")),t}),\"?\");e.href=t.config.FEEDBACK_URL+\"/\"+n+(this._map._hash?this._map._hash.getHashString(!0):\"\"),e.rel=\"noopener nofollow\"}},bi.prototype._updateData=function(t){!t||\"metadata\"!==t.sourceDataType&&\"style\"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},bi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return\"string\"!=typeof t?\"\":t}))):\"string\"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},bi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var _i=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};_i.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},_i.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},_i.prototype.getDefaultPosition=function(){return\"bottom-left\"},_i.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},_i.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t){if(t[e].getSource().mapbox_logo)return!0}return!1}},_i.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var wi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};wi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},wi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},wi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},wi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ti={\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\"},ki=t.window.HTMLImageElement,Ai=t.window.HTMLElement,Mi=t.window.ImageBitmap,Si={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:\"sans-serif\",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Ei=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Si,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new An(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new wi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ti,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof Ai))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1)),this.handlers=new yi(this,e);var a=\"string\"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Sn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new bi({customAttribution:e.customAttribution})),this.addControl(new _i,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r=\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Rn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Rn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Rn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||\"number\"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error(\"Missing UI string '\"+t+\"'\");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new 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;this._lazyInitEmptyStyle();if(r instanceof ki||Mi&&r instanceof Mi){var c=t.browser.getImageData(r),u=c.width,f=c.height,h=c.data;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var p=r.width,d=r.height,m=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(m)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));var i=r instanceof ki||Mi&&r instanceof Mi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));var l=!(r instanceof ki||Mi&&r instanceof Mi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){\"rgb(250, 128, 114)\"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue(\"background-color\")&&t.warnOnce(\"This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.\")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add(\"mapboxgl-map\"),(this._missingCSSCanary=r.create(\"div\",\"mapboxgl-canary\",t)).style.visibility=\"hidden\",this._detectMissingCSS();var e=this._canvasContainer=r.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=r.create(\"canvas\",\"mapboxgl-canvas\",e),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create(\"div\",\"mapboxgl-control-container\",t),a=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((function(t){a[t]=r.create(\"div\",\"mapboxgl-ctrl-\"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext(\"webgl\",r)||this._canvas.getContext(\"experimental-webgl\",r);n?(this.painter=new _n(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens(\"gpu-timing-frame\")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens(\"gpu-timing-layer\")}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens(\"gpu-timing-frame\")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event(\"gpu-timing-frame\",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens(\"gpu-timing-layer\")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event(\"gpu-timing-layer\",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event(\"idle\"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1){r[e].onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener(\"resize\",this._onWindowResize,!1),t.window.removeEventListener(\"online\",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");n&&n.loseContext(),Li(this._canvasContainer),Li(this._controlContainer),Li(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this._removed=!0,this.fire(new t.Event(\"remove\"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(xi);function Li(t){t.parentNode&&t.parentNode.removeChild(t)}var Ci={showCompass:!0,showZoom:!0,visualizePitch:!1},Pi=function(e){var n=this;this.options=t.extend({},Ci,e),this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll([\"_setButtonTitle\",\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-zoom-in\",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",!0),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-zoom-out\",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",!0)),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-compass\",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create(\"span\",\"mapboxgl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",!0))};Pi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Pi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?\"scale(\"+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+\") rotateX(\"+this._map.transform.pitch+\"deg) rotateZ(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\":\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassIcon.style.transform=t},Pi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ii(this._map,this._compass,this.options.visualizePitch)),this._container},Pi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Pi.prototype._createButton=function(t,e){var n=r.create(\"button\",t,this._container);return n.type=\"button\",n.addEventListener(\"click\",e),n},Pi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString(\"NavigationControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)};var Ii=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Xn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Zn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll([\"mousedown\",\"mousemove\",\"mouseup\",\"touchstart\",\"touchmove\",\"touchend\",\"reset\"],this),r.addEventListener(n,\"mousedown\",this.mousedown),r.addEventListener(n,\"touchstart\",this.touchstart,{passive:!1}),r.addEventListener(n,\"touchmove\",this.touchmove),r.addEventListener(n,\"touchend\",this.touchend),r.addEventListener(n,\"touchcancel\",this.reset)};function Oi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ii.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ii.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ii.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ii.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ii.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ii.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ii.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ii.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ii.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ii.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ii.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var zi={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Di(t,e,r){var n=t.classList;for(var i in zi)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var Ri,Fi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\",\"_onKeyPress\"],this),this._anchor=n&&n.anchor||\"center\",this._color=n&&n.color||\"#3FB1CE\",this._draggable=n&&n.draggable||!1,this._state=\"inactive\",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||\"auto\",this._pitchAlignment=n&&n.pitchAlignment&&\"auto\"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");var o=r.createNS(\"http://www.w3.org/2000/svg\",\"svg\");o.setAttributeNS(null,\"display\",\"block\"),o.setAttributeNS(null,\"height\",\"41px\"),o.setAttributeNS(null,\"width\",\"27px\"),o.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var s=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"stroke\",\"none\"),s.setAttributeNS(null,\"stroke-width\",\"1\"),s.setAttributeNS(null,\"fill\",\"none\"),s.setAttributeNS(null,\"fill-rule\",\"evenodd\");var l=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"fill-rule\",\"nonzero\");var c=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");c.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),c.setAttributeNS(null,\"fill\",\"#000000\");for(var u=0,f=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];u<f.length;u+=1){var h=f[u],p=r.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");p.setAttributeNS(null,\"opacity\",\"0.04\"),p.setAttributeNS(null,\"cx\",\"10.5\"),p.setAttributeNS(null,\"cy\",\"5.80029008\"),p.setAttributeNS(null,\"rx\",h.rx),p.setAttributeNS(null,\"ry\",h.ry),c.appendChild(p)}var d=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");d.setAttributeNS(null,\"fill\",this._color);var m=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");m.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),d.appendChild(m);var g=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");g.setAttributeNS(null,\"opacity\",\"0.25\"),g.setAttributeNS(null,\"fill\",\"#000000\");var v=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");v.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),g.appendChild(v);var y=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");y.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),y.setAttributeNS(null,\"fill\",\"#FFFFFF\");var x=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");x.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var b=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");b.setAttributeNS(null,\"fill\",\"#000000\"),b.setAttributeNS(null,\"opacity\",\"0.25\"),b.setAttributeNS(null,\"cx\",\"5.5\"),b.setAttributeNS(null,\"cy\",\"5.5\"),b.setAttributeNS(null,\"r\",\"5.4999962\");var _=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");_.setAttributeNS(null,\"fill\",\"#FFFFFF\"),_.setAttributeNS(null,\"cx\",\"5.5\"),_.setAttributeNS(null,\"cy\",\"5.5\"),_.setAttributeNS(null,\"r\",\"5.4999962\"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(g),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",(function(t){t.preventDefault()})),this._element.addEventListener(\"mousedown\",(function(t){t.preventDefault()})),this._element.addEventListener(\"focus\",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Di(this._element,this._anchor,\"marker\"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),t){if(!(\"offset\"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[e,-1*(24.6+e)],\"bottom-right\":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;\"Space\"!==e&&\"Enter\"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e=\"\";\"viewport\"===this._rotationAlignment||\"auto\"===this._rotationAlignment?e=\"rotateZ(\"+this._rotation+\"deg)\":\"map\"===this._rotationAlignment&&(e=\"rotateZ(\"+(this._rotation-this._map.getBearing())+\"deg)\");var n=\"\";\"viewport\"===this._pitchAlignment||\"auto\"===this._pitchAlignment?n=\"rotateX(0deg)\":\"map\"===this._pitchAlignment&&(n=\"rotateX(\"+this._map.getPitch()+\"deg)\"),t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,zi[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px) \"+n+\" \"+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\"))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Bi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Ni=0,ji=!1,Ui=function(e){function n(r){e.call(this),this.options=t.extend({},Bi,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==Ri?n(Ri):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){Ri=\"denied\"!==t.state,n(Ri)})):(Ri=!!t.window.navigator.geolocation,n(Ri)),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Ni=0,ji=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&ji)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Fi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Fi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){var r=e.originalEvent&&\"resize\"===e.originalEvent.type;e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||r||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Ni--,ji=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Ni>1?(e={maximumAge:6e5,timeout:0},ji=!0):(e=this.options.positionOptions,ji=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Vi={maxWidth:100,unit:\"metric\"},Hi=function(e){this.options=t.extend({},Vi,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;if(l>5280)Gi(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\"));else Gi(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else if(r&&\"nautical\"===r.unit){Gi(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\"))}else s>=1e3?Gi(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):Gi(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function Gi(t,e,r,n){var i,a,o,s=(i=r,a=Math.pow(10,(\"\"+Math.floor(i)).length-1),o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o),a*o),l=s/r;t.style.width=e*l+\"px\",t.innerHTML=s+\"&nbsp;\"+n}Hi.prototype.getDefaultPosition=function(){return\"bottom-left\"},Hi.prototype._onMove=function(){qi(this._map,this._container,this.options)},Hi.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},Hi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},Hi.prototype.setUnit=function(t){this.options.unit=t,qi(this._map,this._container,this.options)};var Yi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};Yi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},Yi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Yi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},Yi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},Yi.prototype._isFullscreen=function(){return this._fullscreen},Yi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},Yi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Wi={closeButton:!0,closeOnClick:!0,className:\"\",maxWidth:\"240px\"},Xi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Wi),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this,i=this._lngLat||this._trackPointer;if(this._map&&i&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var a=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),o=this.options.anchor,s=function e(r){if(r){if(\"number\"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),\"top-left\":new t.Point(n,n),\"top-right\":new t.Point(-n,n),bottom:new t.Point(0,-r),\"bottom-left\":new t.Point(n,-n),\"bottom-right\":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,\"top-left\":i,\"top-right\":i,bottom:i,\"bottom-left\":i,\"bottom-right\":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),\"top-left\":t.Point.convert(r[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(r[\"top-right\"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),\"bottom-left\":t.Point.convert(r[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(r[\"bottom-right\"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!o){var l,c=this._container.offsetWidth,u=this._container.offsetHeight;l=a.y+s.bottom.y<u?[\"top\"]:a.y>this._map.transform.height-u?[\"bottom\"]:[],a.x<c/2?l.push(\"left\"):a.x>this._map.transform.width-c/2&&l.push(\"right\"),o=0===l.length?\"bottom\":l.join(\"-\")}var f=a.add(s[o]).round();r.setTransform(this._container,zi[o]+\" translate(\"+f.x+\"px,\"+f.y+\"px)\"),Di(this._container,o,\"popup\")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);var Zi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Ei,NavigationControl:Pi,GeolocateControl:Ui,AttributionControl:bi,ScaleControl:Hi,FullscreenControl:Yi,Popup:Xi,Marker:Fi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(zt)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(zt),Rt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:\"\"};return Zi})),r}))},{}],240:[function(t,e,r){\"use strict\";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],241:[function(t,e,r){\"use strict\";e.exports=function(t,e){e||(e=t,t=window);var r=0,i=0,a=0,o={shift:!1,alt:!1,control:!1,meta:!1},s=!1;function l(t){var e=!1;return\"altKey\"in t&&(e=e||t.altKey!==o.alt,o.alt=!!t.altKey),\"shiftKey\"in t&&(e=e||t.shiftKey!==o.shift,o.shift=!!t.shiftKey),\"ctrlKey\"in t&&(e=e||t.ctrlKey!==o.control,o.control=!!t.ctrlKey),\"metaKey\"in t&&(e=e||t.metaKey!==o.meta,o.meta=!!t.metaKey),e}function c(t,s){var c=n.x(s),u=n.y(s);\"buttons\"in s&&(t=0|s.buttons),(t!==r||c!==i||u!==a||l(s))&&(r=0|t,i=c||0,a=u||0,e&&e(r,i,a,o))}function u(t){c(0,t)}function f(){(r||i||a||o.shift||o.alt||o.meta||o.control)&&(i=a=0,r=0,o.shift=o.alt=o.control=o.meta=!1,e&&e(0,0,0,o))}function h(t){l(t)&&e&&e(r,i,a,o)}function p(t){0===n.buttons(t)?c(0,t):c(r,t)}function d(t){c(r|n.buttons(t),t)}function m(t){c(r&~n.buttons(t),t)}function g(){s||(s=!0,t.addEventListener(\"mousemove\",p),t.addEventListener(\"mousedown\",d),t.addEventListener(\"mouseup\",m),t.addEventListener(\"mouseleave\",u),t.addEventListener(\"mouseenter\",u),t.addEventListener(\"mouseout\",u),t.addEventListener(\"mouseover\",u),t.addEventListener(\"blur\",f),t.addEventListener(\"keyup\",h),t.addEventListener(\"keydown\",h),t.addEventListener(\"keypress\",h),t!==window&&(window.addEventListener(\"blur\",f),window.addEventListener(\"keyup\",h),window.addEventListener(\"keydown\",h),window.addEventListener(\"keypress\",h)))}g();var v={element:t};return Object.defineProperties(v,{enabled:{get:function(){return s},set:function(e){e?g():function(){if(!s)return;s=!1,t.removeEventListener(\"mousemove\",p),t.removeEventListener(\"mousedown\",d),t.removeEventListener(\"mouseup\",m),t.removeEventListener(\"mouseleave\",u),t.removeEventListener(\"mouseenter\",u),t.removeEventListener(\"mouseout\",u),t.removeEventListener(\"mouseover\",u),t.removeEventListener(\"blur\",f),t.removeEventListener(\"keyup\",h),t.removeEventListener(\"keydown\",h),t.removeEventListener(\"keypress\",h),t!==window&&(window.removeEventListener(\"blur\",f),window.removeEventListener(\"keyup\",h),window.removeEventListener(\"keydown\",h),window.removeEventListener(\"keypress\",h))}()},enumerable:!0},buttons:{get:function(){return r},enumerable:!0},x:{get:function(){return i},enumerable:!0},y:{get:function(){return a},enumerable:!0},mods:{get:function(){return o},enumerable:!0}}),v};var n=t(\"mouse-event\")},{\"mouse-event\":243}],242:[function(t,e,r){var n={left:0,top:0};e.exports=function(t,e,r){e=e||t.currentTarget||t.srcElement,Array.isArray(r)||(r=[0,0]);var i=t.clientX||0,a=t.clientY||0,o=(s=e,s===window||s===document||s===document.body?n:s.getBoundingClientRect());var s;return r[0]=i-o.left,r[1]=a-o.top,r}},{}],243:[function(t,e,r){\"use strict\";function n(t){return t.target||t.srcElement||window}r.buttons=function(t){if(\"object\"==typeof t){if(\"buttons\"in t)return t.buttons;if(\"which\"in t){if(2===(e=t.which))return 4;if(3===e)return 2;if(e>0)return 1<<e-1}else if(\"button\"in t){var e;if(1===(e=t.button))return 4;if(2===e)return 2;if(e>=0)return 1<<e}}return 0},r.element=n,r.x=function(t){if(\"object\"==typeof t){if(\"offsetX\"in t)return t.offsetX;var e=n(t).getBoundingClientRect();return t.clientX-e.left}return 0},r.y=function(t){if(\"object\"==typeof t){if(\"offsetY\"in t)return t.offsetY;var e=n(t).getBoundingClientRect();return t.clientY-e.top}return 0}},{}],244:[function(t,e,r){\"use strict\";var n=t(\"to-px\");e.exports=function(t,e,r){\"function\"==typeof t&&(r=!!e,e=t,t=window);var i=n(\"ex\",t),a=function(t){r&&t.preventDefault();var n=t.deltaX||0,a=t.deltaY||0,o=t.deltaZ||0,s=1;switch(t.deltaMode){case 1:s=i;break;case 2:s=window.innerHeight}if(a*=s,o*=s,(n*=s)||a||o)return e(n,a,o,t)};return t.addEventListener(\"wheel\",a),a}},{\"to-px\":314}],245:[function(t,e,r){(function(t,r){(function(){\n",
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       "/*\n",
       " * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc\n",
       " * @license MIT\n",
       " * @preserve Project Home: https://github.com/voidqk/polybooljs\n",
       " */\n",
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r=0,n=t[t.length-1][0],i=t[t.length-1][1],a=[],o=0;o<t.length;o++){var s=t[o][0],l=t[o][1];a.push([s,l]),r+=l*n-s*i,n=s,i=l}return r<0!==e&&a.reverse(),a.push([a[0][0],a[0][1]]),a}var u=[];function f(t){var e=[c(t.region,!1)];u.push(e);for(var r=0;r<t.children.length;r++)e.push(h(t.children[r]))}function h(t){for(var e=0;e<t.children.length;e++)f(t.children[e]);return c(t.region,!0)}for(s=0;s<a.children.length;s++)f(a.children[s]);return u.length<=0?{type:\"Polygon\",coordinates:[]}:1==u.length?{type:\"Polygon\",coordinates:u[0]}:{type:\"MultiPolygon\",coordinates:u}}};e.exports=n},{}],258:[function(t,e,r){var n=t(\"./linked-list\");e.exports=function(t,e,r){function i(t,e,n){return{id:r?r.segmentId():-1,start:t,end:e,myFill:{above:n.myFill.above,below:n.myFill.below},otherFill:null}}var a=n.create();function o(t,r){a.insertBefore(t,(function(n){return function(t,r,n,i,a,o){var s=e.pointsCompare(r,a);return 0!==s?s:e.pointsSame(n,o)?0:t!==i?t?1:-1:e.pointAboveOrOnLine(n,i?a:o,i?o:a)?1:-1}(t.isStart,t.pt,r,n.isStart,n.pt,n.other.pt)<0}))}function s(t,e){var r=function(t,e){var r=n.node({isStart:!0,pt:t.start,seg:t,primary:e,other:null,status:null});return o(r,t.end),r}(t,e);return function(t,e,r){var i=n.node({isStart:!1,pt:e.end,seg:e,primary:r,other:t,status:null});t.other=i,o(i,t.pt)}(r,t,e),r}function l(t,e){var n=i(e,t.seg.end,t.seg);return function(t,e){r&&r.segmentChop(t.seg,e),t.other.remove(),t.seg.end=e,t.other.pt=e,o(t.other,t.pt)}(t,e),s(n,t.primary)}function c(i,o){var s=n.create();function c(t){return s.findTransition((function(r){var n,i,a,o,s,l;return(n=t,i=r.ev,a=n.seg.start,o=n.seg.end,s=i.seg.start,l=i.seg.end,e.pointsCollinear(a,s,l)?e.pointsCollinear(o,s,l)||e.pointAboveOrOnLine(o,s,l)?1:-1:e.pointAboveOrOnLine(a,s,l)?1:-1)>0}))}function u(t,n){var i=t.seg,a=n.seg,o=i.start,s=i.end,c=a.start,u=a.end;r&&r.checkIntersection(i,a);var f=e.linesIntersect(o,s,c,u);if(!1===f){if(!e.pointsCollinear(o,s,c))return!1;if(e.pointsSame(o,u)||e.pointsSame(s,c))return!1;var h=e.pointsSame(o,c),p=e.pointsSame(s,u);if(h&&p)return n;var d=!h&&e.pointBetween(o,c,u),m=!p&&e.pointBetween(s,c,u);if(h)return m?l(n,s):l(t,u),n;d&&(p||(m?l(n,s):l(t,u)),l(n,o))}else 0===f.alongA&&(-1===f.alongB?l(t,c):0===f.alongB?l(t,f.pt):1===f.alongB&&l(t,u)),0===f.alongB&&(-1===f.alongA?l(n,o):0===f.alongA?l(n,f.pt):1===f.alongA&&l(n,s));return!1}for(var f=[];!a.isEmpty();){var h=a.getHead();if(r&&r.vert(h.pt[0]),h.isStart){r&&r.segmentNew(h.seg,h.primary);var p=c(h),d=p.before?p.before.ev:null,m=p.after?p.after.ev:null;function g(){if(d){var t=u(h,d);if(t)return t}return!!m&&u(h,m)}r&&r.tempStatus(h.seg,!!d&&d.seg,!!m&&m.seg);var v,y=g();if(y){var x;if(t)(x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below)&&(y.seg.myFill.above=!y.seg.myFill.above);else y.seg.otherFill=h.seg.myFill;r&&r.segmentUpdate(y.seg),h.other.remove(),h.remove()}if(a.getHead()!==h){r&&r.rewind(h.seg);continue}if(t)x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below,h.seg.myFill.below=m?m.seg.myFill.above:i,h.seg.myFill.above=x?!h.seg.myFill.below:h.seg.myFill.below;else if(null===h.seg.otherFill)v=m?h.primary===m.primary?m.seg.otherFill.above:m.seg.myFill.above:h.primary?o:i,h.seg.otherFill={above:v,below:v};r&&r.status(h.seg,!!d&&d.seg,!!m&&m.seg),h.other.status=p.insert(n.node({ev:h}))}else{var b=h.status;if(null===b)throw new Error(\"PolyBool: Zero-length segment detected; your epsilon is probably too small or too large\");if(s.exists(b.prev)&&s.exists(b.next)&&u(b.prev.ev,b.next.ev),r&&r.statusRemove(b.ev.seg),b.remove(),!h.primary){var _=h.seg.myFill;h.seg.myFill=h.seg.otherFill,h.seg.otherFill=_}f.push(h.seg)}a.getHead().remove()}return r&&r.done(),f}return t?{addRegion:function(t){for(var n,i,a,o=t[t.length-1],l=0;l<t.length;l++){n=o,o=t[l];var c=e.pointsCompare(n,o);0!==c&&s((i=c<0?n:o,a=c<0?o:n,{id:r?r.segmentId():-1,start:i,end:a,myFill:{above:null,below:null},otherFill:null}),!0)}},calculate:function(t){return c(t,!1)}}:{calculate:function(t,e,r,n){return t.forEach((function(t){s(i(t.start,t.end,t),!0)})),r.forEach((function(t){s(i(t.start,t.end,t),!1)})),c(e,n)}}}},{\"./linked-list\":259}],259:[function(t,e,r){e.exports={create:function(){var t={root:{root:!0,next:null},exists:function(e){return null!==e&&e!==t.root},isEmpty:function(){return null===t.root.next},getHead:function(){return t.root.next},insertBefore:function(e,r){for(var n=t.root,i=t.root.next;null!==i;){if(r(i))return e.prev=i.prev,e.next=i,i.prev.next=e,void(i.prev=e);n=i,i=i.next}n.next=e,e.prev=n,e.next=null},findTransition:function(e){for(var r=t.root,n=t.root.next;null!==n&&!e(n);)r=n,n=n.next;return{before:r===t.root?null:r,after:n,insert:function(t){return t.prev=r,t.next=n,r.next=t,null!==n&&(n.prev=t),t}}}};return t},node:function(t){return t.prev=null,t.next=null,t.remove=function(){t.prev.next=t.next,t.next&&(t.next.prev=t.prev),t.prev=null,t.next=null},t}}},{}],260:[function(t,e,r){e.exports=function(t,e,r){var n=[],i=[];return t.forEach((function(t){var a=t.start,o=t.end;if(e.pointsSame(a,o))console.warn(\"PolyBool: Warning: Zero-length segment detected; your epsilon is probably too small or too large\");else{r&&r.chainStart(t);for(var s={index:0,matches_head:!1,matches_pt1:!1},l={index:0,matches_head:!1,matches_pt1:!1},c=s,u=0;u<n.length;u++){var f=(g=n[u])[0],h=(g[1],g[g.length-1]);g[g.length-2];if(e.pointsSame(f,a)){if(k(u,!0,!0))break}else if(e.pointsSame(f,o)){if(k(u,!0,!1))break}else if(e.pointsSame(h,a)){if(k(u,!1,!0))break}else if(e.pointsSame(h,o)&&k(u,!1,!1))break}if(c===s)return n.push([a,o]),void(r&&r.chainNew(a,o));if(c===l){r&&r.chainMatch(s.index);var p=s.index,d=s.matches_pt1?o:a,m=s.matches_head,g=n[p],v=m?g[0]:g[g.length-1],y=m?g[1]:g[g.length-2],x=m?g[g.length-1]:g[0],b=m?g[g.length-2]:g[1];return e.pointsCollinear(y,v,d)&&(m?(r&&r.chainRemoveHead(s.index,d),g.shift()):(r&&r.chainRemoveTail(s.index,d),g.pop()),v=y),e.pointsSame(x,d)?(n.splice(p,1),e.pointsCollinear(b,x,v)&&(m?(r&&r.chainRemoveTail(s.index,v),g.pop()):(r&&r.chainRemoveHead(s.index,v),g.shift())),r&&r.chainClose(s.index),void i.push(g)):void(m?(r&&r.chainAddHead(s.index,d),g.unshift(d)):(r&&r.chainAddTail(s.index,d),g.push(d)))}var _=s.index,w=l.index;r&&r.chainConnect(_,w);var T=n[_].length<n[w].length;s.matches_head?l.matches_head?T?(A(_),M(_,w)):(A(w),M(w,_)):M(w,_):l.matches_head?M(_,w):T?(A(_),M(w,_)):(A(w),M(_,w))}function k(t,e,r){return c.index=t,c.matches_head=e,c.matches_pt1=r,c===s?(c=l,!1):(c=null,!0)}function A(t){r&&r.chainReverse(t),n[t].reverse()}function M(t,i){var a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],c=o[0],u=o[1];e.pointsCollinear(l,s,c)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,c,u)&&(r&&r.chainRemoveHead(i,c),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},{}],261:[function(t,e,r){function n(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var i={union:function(t,e){return n(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],e)},intersect:function(t,e){return n(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],e)},difference:function(t,e){return n(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],e)},differenceRev:function(t,e){return n(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],e)},xor:function(t,e){return n(t,[0,2,1,0,2,0,0,1,1,0,0,2,0,1,2,0],e)}};e.exports=i},{}],262:[function(t,e,r){\"use strict\";var n=t(\"stream\").Transform,i=t(\"stream-parser\");function a(){n.call(this,{readableObjectMode:!0})}function o(t,e,r){Error.call(this),Error.captureStackTrace?Error.captureStackTrace(this,this.constructor):this.stack=(new Error).stack||\"\",this.name=this.constructor.name,this.message=t,e&&(this.code=e),r&&(this.statusCode=r)}a.prototype=Object.create(n.prototype),a.prototype.constructor=a,i(a.prototype),r.ParserStream=a,r.sliceEq=function(t,e,r){for(var n=e,i=0;i<r.length;)if(t[n++]!==r[i++])return!1;return!0},r.str2arr=function(t,e){var r=[],n=0;if(e&&\"hex\"===e)for(;n<t.length;)r.push(parseInt(t.slice(n,n+2),16)),n+=2;else for(;n<t.length;n++)r.push(255&t.charCodeAt(n));return r},r.readUInt16LE=function(t,e){return t[e]|t[e+1]<<8},r.readUInt16BE=function(t,e){return t[e+1]|t[e]<<8},r.readUInt32LE=function(t,e){return t[e]|t[e+1]<<8|t[e+2]<<16|16777216*t[e+3]},r.readUInt32BE=function(t,e){return t[e+3]|t[e+2]<<8|t[e+1]<<16|16777216*t[e]},o.prototype=Object.create(Error.prototype),o.prototype.constructor=o,r.ProbeError=o},{stream:285,\"stream-parser\":301}],263:[function(t,e,r){\"use strict\";function n(t,e){var r=new Error(t);return r.code=e,r}function i(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function a(t,e,r){this.input=t.subarray(e,r),this.start=e;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if(\"II*\\0\"!==i&&\"MM\\0*\"!==i)throw n(\"invalid TIFF signature\",\"EBADDATA\");this.big_endian=\"M\"===i[0]}a.prototype.each=function(t){this.aborted=!1;var e=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:e}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,t)}},a.prototype.read_uint16=function(t){var e=this.input;if(t+2>e.length)throw n(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?256*e[t]+e[t+1]:e[t]+256*e[t+1]},a.prototype.read_uint32=function(t){var e=this.input;if(t+4>e.length)throw n(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?16777216*e[t]+65536*e[t+1]+256*e[t+2]+e[t+3]:e[t]+256*e[t+1]+65536*e[t+2]+16777216*e[t+3]},a.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},a.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},a.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return r=this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return r=this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return r=this.read_uint32(e);case 9:return 0|(r=this.read_uint32(e));case 5:case 10:case 11:case 12:case 7:default:return null}},a.prototype.scan_ifd=function(t,e,r){var a=this.read_uint16(e);e+=2;for(var o=0;o<a;o++){var s=this.read_uint16(e),l=this.read_uint16(e+2),c=this.read_uint32(e+4),u=this.exif_format_length(l),f=c*u,h=f<=4?e+8:this.read_uint32(e+8),p=!1;if(h+f>this.input.length)throw n(\"unexpected EOF\",\"EBADDATA\");for(var d=[],m=h,g=0;g<c;g++,m+=u){var v=this.exif_format_read(l,m);if(null===v){d=null;break}d.push(v)}if(Array.isArray(d)&&2===l&&(d=i(String.fromCharCode.apply(null,d)))&&\"\\0\"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===r({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:c,entry_offset:e+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);e+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(e)})},e.exports.ExifParser=a,e.exports.get_orientation=function(t){var e=0;try{return new a(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},{}],264:[function(t,e,r){\"use strict\";var n=t(\"./common\").readUInt16BE,i=t(\"./common\").readUInt32BE;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return t.length<r+e||r<8?null:{boxtype:String.fromCharCode.apply(null,t.slice(e+4,e+8)),data:t.slice(e+8,e+r),end:e+r}}function o(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;switch(n.boxtype){case\"ispe\":e.sizes.push({width:i(n.data,4),height:i(n.data,8)});break;case\"irot\":e.transforms.push({type:\"irot\",value:3&n.data[0]});break;case\"imir\":e.transforms.push({type:\"imir\",value:1&n.data[0]})}r=n.end}}function s(t,e,r){for(var n=0,i=0;i<r;i++)n=256*n+(t[e+i]||0);return n}function l(t,e){for(var r=t[4]>>4&15,i=15&t[4],a=t[5]>>4&15,o=n(t,6),l=8,c=0;c<o;c++){var u=n(t,l),f=n(t,l+=2),h=s(t,l+=2,a),p=n(t,l+=a);if(l+=2,0===f&&1===p){var d=s(t,l,r),m=s(t,l+r,i);e.item_loc[u]={length:m,offset:d+h}}l+=p*(r+i)}}function c(t,e){for(var r=n(t,4),i=6,o=0;o<r;o++){var s=a(t,i);if(!s)break;if(\"infe\"===s.boxtype){for(var l=n(s.data,4),c=\"\",u=8;u<s.data.length&&s.data[u];u++)c+=String.fromCharCode(s.data[u]);e.item_inf[c]=l}i=s.end}}function u(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;\"ipco\"===n.boxtype&&o(n.data,e),r=n.end}}e.exports.unbox=a,e.exports.readSizeFromMeta=function(t){var e={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(function(t,e){for(var r=4;;){var n=a(t,r);if(!n)break;\"iprp\"===n.boxtype&&u(n.data,e),\"iloc\"===n.boxtype&&l(n.data,e),\"iinf\"===n.boxtype&&c(n.data,e),r=n.end}}(t,e),e.sizes.length){var r,n,i,o=(r=e.sizes,n=r.reduce((function(t,e){return t.width>e.width||t.width===e.width&&t.height>e.height?t:e})),i=r.reduce((function(t,e){return t.height>e.height||t.height===e.height&&t.width>e.width?t:e})),n.width>i.height||n.width===i.height&&n.height>i.width?n:i),s=1;e.transforms.forEach((function(t){var e={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},r={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if(\"imir\"===t.type&&(s=0===t.value?r[s]:e[s=e[s=r[s]]]),\"irot\"===t.type)for(var n=0;n<t.value;n++)s=e[s]}));var f=null;return e.item_inf.Exif&&(f=e.item_loc[e.item_inf.Exif]),{width:o.width,height:o.height,orientation:e.transforms.length?s:null,variants:e.sizes,exif_location:f}}},e.exports.getMimeType=function(t){var e=String.fromCharCode.apply(null,t.slice(0,4)),r={};r[e]=!0;for(var 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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\":71,\"color-normalize\":89,\"flatten-vertex-data\":191,\"object-assign\":247,\"pick-by-alias\":253,\"to-float32\":313,\"update-diff\":329}],280:[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\"),m=t(\"parse-rect\"),g=t(\"array-find-index\");function v(t,e){if(!(this instanceof v))return new v(t,e);if(\"function\"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=v.shaders.has(t)?v.shaders.get(t):v.shaders.set(t,v.createShaders(t)).get(t),this.update(e)}e.exports=v,v.dashMult=2,v.maxPatternLength=256,v.precisionThreshold=3e6,v.maxPoints=1e4,v.maxLines=2048,v.shaders=new d,v.createShaders=function(t){var e,r=t.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),n={primitive:\"triangle strip\",instances:t.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:function(t,e){return\"round\"===e.join?2:1},miterLimit:t.prop(\"miterLimit\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),thickness:t.prop(\"thickness\"),dashTexture:t.prop(\"dashTexture\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),dashLength: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 float dashLength, pixelRatio, thickness, opacity, id;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvoid main() {\\n\\tfloat alpha = 1.;\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, 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 float dashLength, pixelRatio, thickness, opacity, id, miterMode;\\nuniform sampler2D dashTexture;\\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) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * 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t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>v.precisionThreshold||e.scale[1]*e.viewport.height>v.precisionThreshold||\"rect\"===e.join||!e.join&&(e.thickness<=2||e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if(\"number\"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin 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Uint8Array})},t=a({},v.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<v.maxLines&&(d.depth=2*(v.maxLines-1-f%v.maxLines)/v.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=m(t.viewport)),d.viewport||(d.viewport=m([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var y,x;if(t.positions.x&&t.positions.y){var b=t.positions.x,_=t.positions.y;x=d.count=Math.max(b.length,_.length),y=new Float64Array(2*x);for(var w=0;w<x;w++)y[2*w]=b[w],y[2*w+1]=_[w]}else y=l(t.positions,\"float64\"),x=d.count=Math.floor(y.length/2);var T=d.bounds=i(y,2);if(d.fill){for(var k=[],A={},M=0,S=0,E=0,L=d.count;S<L;S++){var C=y[2*S],P=y[2*S+1];isNaN(C)||isNaN(P)||null==C||null==P?(C=y[2*M],P=y[2*M+1],A[S]=M):M=S,k[E++]=C,k[E++]=P}if(t.splitNull){d.count-1 in A||(A[d.count]=d.count-1);var I=Object.keys(A).map(Number).sort((function(t,e){return t-e})),O=[],z=0,D=null!=d.hole?d.hole[0]:null;if(null!=D){var R=g(I,(function(t){return t>=D}));(I=I.slice(0,R)).push(D)}for(var F=function(t){var e=k.slice(2*z,2*I[t]).concat(D?k.slice(2*D):[]),r=(d.hole||[]).map((function(e){return e-D+(I[t]-z)})),n=c(e,r);n=n.map((function(e){return e+z+(e+z<I[t]?0:D-I[t])})),O.push.apply(O,n),z=I[t]+1},B=0;B<I.length;B++)F(B);for(var N=0,j=O.length;N<j;N++)null!=A[O[N]]&&(O[N]=A[O[N]]);d.triangles=O}else{for(var U=c(k,d.hole||[]),V=0,H=U.length;V<H;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);d.triangles=U}}var q=new Float64Array(y);u(q,2,T);var G=new 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at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*v.dashMult*.5;ot<st;++ot)$[rt++]=nt;nt^=255}}d.dashLength=tt,d.dashTexture({channels:1,data:$,width:$.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(t.color){var lt=d.count,ct=t.color;ct||(ct=\"transparent\");var ut=new Uint8Array(4*lt+4);if(Array.isArray(ct)&&\"number\"!=typeof ct[0]){for(var ft=0;ft<lt;ft++){var ht=n(ct[ft],\"uint8\");ut.set(ht,4*ft)}ut.set(n(ct[0],\"uint8\"),4*lt)}else for(var pt=n(ct,\"uint8\"),dt=0;dt<lt+1;dt++)ut.set(pt,4*dt);d.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:ut})}}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 y=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&y.push(this.passes[x]);return this.passes=y,this}},v.prototype.destroy=function(){return 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o={uniforms:{constPointSize:!!e.constPointSize,opacity:t.prop(\"opacity\"),paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},pixelRatio:t.context(\"pixelRatio\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translate:t.prop(\"translate\"),translateFract:t.prop(\"translateFract\"),markerTexture:t.prop(\"markerTexture\"),paletteTexture:n},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 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mix(color, 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 bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\\nuniform sampler2D paletteTexture;\\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(paletteTexture,\\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;\\nvarying float fragBorderRadius, fragWidth;\\n\\nuniform float opacity;\\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 bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 paletteSize, scale, scaleFract, translate, translateFract;\\nuniform sampler2D paletteTexture;\\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(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = (size + borderSize) * pointSizeScale;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\\n  fragColor = color;\\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n}\\n\"]),g&&(l.frag=l.frag.replace(\"smoothstep\",\"smoothStep\"),s.frag=s.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(l)}b.defaults={color:\"black\",borderColor:\"transparent\",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if(\"number\"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),m=0;m<r.length;m++){var g=r[m];d[g]=p?p[g]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:\"function\"==typeof e?e={ondraw:e}:\"number\"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:\"positions data 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 Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"})},e=f({},b.defaults,e)),e.positions&&!(\"marker\"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!(\"positions\"in e)&&(e.positions=n.positions,delete n.positions);var g=0,x=0;if(d(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=b.defaults.size),g+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=b.defaults.borderSize),g+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=b.defaults.color),e=t.updateColor(e),x++,e},borderColor:function(e,r){return null==e&&(e=b.defaults.borderColor),e=t.updateColor(e),x++,e},bounds:function(t,e,r){return\"range\"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,s=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=m(t,\"float64\");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:\"points\",usage:\"static\",data:e.tree,type:\"uint32\"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=v.float32(t);return i({data:p,usage:\"dynamic\"}),a({data:v.fract32(t,p),usage:\"dynamic\"}),s({data:new Uint8Array(c),type:\"uint8\",usage:\"stream\"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&\"number\"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:\"uint8\",usage:\"static\"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=v.fract(e.scale),e.translateFract=v.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),g){var _=n,w=_.count,T=_.size,k=_.borderSize,A=_.sizeBuffer,M=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)M[2*S]=Math.round(255*(null==T[S]?T:T[S])/s),M[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/s);A({data:M,usage:\"dynamic\"})}if(x){var E,L=n,C=L.count,P=L.color,I=L.borderColor,O=L.colorBuffer;if(t.tooManyColors){if(P.length||I.length){E=new Uint8Array(8*C);for(var z=0;z<C;z++){var D=P[z];E[8*z]=h[4*D],E[8*z+1]=h[4*D+1],E[8*z+2]=h[4*D+2],E[8*z+3]=h[4*D+3];var R=I[z];E[8*z+4]=h[4*R],E[8*z+5]=h[4*R+1],E[8*z+6]=h[4*R+2],E[8*z+7]=h[4*R+3]}}}else if(P.length||I.length){E=new Uint8Array(4*C+2);for(var F=0;F<C;F++)null!=P[F]&&(E[4*F]=P[F]%c,E[4*F+1]=Math.floor(P[F]/c)),null!=I[F]&&(E[4*F+2]=I[F]%c,E[4*F+3]=Math.floor(I[F]/c))}O({data:E||new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return n}))}},b.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:\"linear\",min:\"linear\"})),a},b.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if(\"number\"==typeof t[0]){var a=[];if(Array.isArray(t))for(var o=0;o<t.length;o+=4)a.push(t.slice(o,o+4));else for(var l=0;l<t.length;l+=4)a.push(t.subarray(l,l+4));t=a}for(var u=0;u<t.length;u++){var f=t[u];f=s(f,\"uint8\");var h=c(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[u]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},b.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},b.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var _=t(\"object-assign\");e.exports=function(t,e){var r=new x(t,e),n=r.render.bind(r);return _(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},{\"@plotly/point-cluster\":59,\"array-bounds\":71,\"color-id\":87,\"color-normalize\":89,\"flatten-vertex-data\":191,glslify:227,\"is-iexplorer\":233,\"object-assign\":247,\"parse-rect\":249,\"pick-by-alias\":253,\"to-float32\":313,\"update-diff\":329}],282:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"pick-by-alias\"),a=t(\"array-bounds\"),o=t(\"raf\"),s=t(\"array-range\"),l=t(\"parse-rect\"),c=t(\"flatten-vertex-data\");function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x+s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y+l.height),[a,n,o,i]}function p(t){if(\"number\"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}e.exports=u,u.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},u.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,c=0;c<l.length;c++)i.push(this.passes[l[c]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},u.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:\"data items columns rows values dimensions samples x\",snap:\"snap cluster\",size:\"sizes size radius\",color:\"colors color fill fill-color fillColor\",opacity:\"opacity alpha transparency opaque\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",borderColor:\"borderColors borderColor bordercolor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range ranges databox dataBox\",viewport:\"viewport viewBox viewbox\",domain:\"domain domains area areas\",padding:\"pad padding paddings pads margin margins\",transpose:\"transpose transposed\",diagonal:\"diagonal diag showDiagonal\",upper:\"upper up top upperhalf upperHalf showupperhalf showUpper showUpperHalf\",lower:\"lower low bottom lowerhalf lowerHalf showlowerhalf showLowerHalf showLower\"}),s=this.traces[t]||(this.traces[t]={id:t,buffer:r.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array}),color:\"black\",marker:null,size:12,borderColor:\"transparent\",borderSize:1,viewport:l([r._gl.drawingBufferWidth,r._gl.drawingBufferHeight]),padding:[0,0,0,0],opacity:1,diagonal:!0,upper:!0,lower:!0});if(null!=o.color&&(s.color=o.color),null!=o.size&&(s.size=o.size),null!=o.marker&&(s.marker=o.marker),null!=o.borderColor&&(s.borderColor=o.borderColor),null!=o.borderSize&&(s.borderSize=o.borderSize),null!=o.opacity&&(s.opacity=o.opacity),o.viewport&&(s.viewport=l(o.viewport)),null!=o.diagonal&&(s.diagonal=o.diagonal),null!=o.upper&&(s.upper=o.upper),null!=o.lower&&(s.lower=o.lower),o.data){s.buffer(c(o.data)),s.columns=o.data.length,s.count=o.data[0].length,s.bounds=[];for(var 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 m=s.columns,g=s.count,v=s.viewport.width,y=s.viewport.height,x=s.viewport.x,b=s.viewport.y,_=v/m,w=y/m;s.passes=[];for(var T=0;T<m;T++)for(var k=0;k<m;k++)if((s.diagonal||k!==T)&&(s.upper||!(T>k))&&(s.lower||!(T<k))){var A=f(s.id,T,k),M=this.passes[A]||(this.passes[A]={});if(o.data&&(o.transpose?M.positions={x:{buffer:s.buffer,offset:k,count:g,stride:m},y:{buffer:s.buffer,offset:T,count:g,stride:m}}:M.positions={x:{buffer:s.buffer,offset:k*g,count:g},y:{buffer:s.buffer,offset:T*g,count:g}},M.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),L=E[0],C=E[1],P=E[2],I=E[3];M.viewport=[x+L*v+S[0],b+C*y+S[1],x+P*v-S[2],b+I*y-S[3]]}else M.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&&(M.color=s.color),o.size&&(M.size=s.size),o.marker&&(M.marker=s.marker),o.borderSize&&(M.borderSize=s.borderSize),o.borderColor&&(M.borderColor=s.borderColor),o.opacity&&(M.opacity=s.opacity),o.range&&(M.range=n?h(s.range,T,k):s.range||M.bounds),s.passes.push(A)}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 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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 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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\":346,\"object-assign\":247}],335:[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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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\":346,\"object-assign\":247}],337:[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\":346,\"object-assign\":247}],338:[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\":346,\"object-assign\":247}],339:[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\":346,\"object-assign\":247}],340:[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\":346,\"object-assign\":247}],341:[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 this.daysInYear(t)!==this.daysPerYear},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){if(t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==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=n.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var 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++)void 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 682*((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var 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i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(i.year());return a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(i.year());return a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),n.calendars.taiwan=o},{\"../main\":346,\"object-assign\":247}],344:[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\":346,\"object-assign\":247}],345:[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\":503,\"../../plot_api/plot_template\":543,\"../../registry\":638}],352:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../color\");e.exports=function(t,e,r,a){a(\"opacity\");var o=a(\"bgcolor\"),s=a(\"bordercolor\"),l=i.opacity(s);a(\"borderpad\");var c=a(\"borderwidth\"),u=a(\"showarrow\");if(a(\"text\",u?\" \":r._dfltTitle.annotation),a(\"textangle\"),n.coerceFont(a,\"font\",r.font),a(\"width\"),a(\"align\"),a(\"height\")&&a(\"valign\"),u){var f,h,p=a(\"arrowside\");-1!==p.indexOf(\"end\")&&(f=a(\"arrowhead\"),h=a(\"arrowsize\")),-1!==p.indexOf(\"start\")&&(a(\"startarrowhead\",f),a(\"startarrowsize\",h)),a(\"arrowcolor\",l?e.bordercolor:i.defaultLine),a(\"arrowwidth\",2*(l&&c||1)),a(\"standoff\"),a(\"startstandoff\")}var d=a(\"hovertext\"),m=r.hoverlabel||{};if(d){var g=a(\"hoverlabel.bgcolor\",m.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a(\"hoverlabel.bordercolor\",m.bordercolor||i.contrast(g));n.coerceFont(a,\"hoverlabel.font\",{family:m.font.family,size:m.font.size,color:m.font.color||v})}a(\"captureevents\",!!d)}},{\"../../lib\":503,\"../color\":366}],353:[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\":531,\"fast-isnumeric\":190}],354:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../plots/array_container_defaults\"),o=t(\"./common_defaults\"),s=t(\"./attributes\");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a(\"visible\"),c=a(\"clicktoshow\");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=[\"x\",\"y\"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var m=f[d],g=i.coerceRef(t,e,p,m,\"\",\"paper\");if(\"paper\"!==g)i.getFromId(p,g)._annIndices.push(e._index);if(i.coercePosition(e,p,a,g,m,.5),u){var v=\"a\"+m,y=i.coerceRef(t,e,p,v,\"pixel\",[\"pixel\",\"paper\"]);\"pixel\"!==y&&y!==g&&(y=e[v]=\"pixel\");var 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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 A=m(t.layout,_,e),M=A.modifyBase,S=A.modifyItem,E=A.getUpdateObj;w._infolayer.selectAll(\".\"+b+'[data-index=\"'+r+'\"]').remove();var L=\"clip\"+w._uid+\"_ann\"+r;if(e._input&&!1!==e.visible){var C={x:{},y:{}},P=+e.textangle||0,I=w._infolayer.append(\"g\").classed(b,!0).attr(\"data-index\",String(r)).style(\"opacity\",e.opacity),O=I.append(\"g\").classed(\"annotation-text-g\",!0),z=k[e.showarrow?\"annotationTail\":\"annotationPosition\"],D=e.captureevents||k.annotationText||z,R=O.append(\"g\").style(\"pointer-events\",D?\"all\":null).call(p,\"pointer\").on(\"click\",(function(){t._dragging=!1,t.emit(\"plotly_clickannotation\",Y(n.event))}));e.hovertext&&R.on(\"mouseover\",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on(\"mouseout\",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append(\"rect\").attr(\"class\",\"bg\").style(\"stroke-width\",F+\"px\").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll(\"#\"+L).data(U?[0]:[]);V.enter().append(\"clipPath\").classed(\"annclip\",!0).attr(\"id\",L).append(\"rect\"),V.exit().remove();var H=e.font,q=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append(\"text\").classed(\"annotation-text\",!0).text(q);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&&M(v._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0),i.call(\"_guiRelayout\",t,E())})):G.call(W)}else n.selectAll(\"#\"+L).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,H).attr({\"text-anchor\":{left:\"start\",right:\"end\"}[e.align]||\"middle\"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll(\"a\");1===r.size()&&r.text()===G.text()&&R.insert(\"a\",\":first-child\").attr({\"xlink:xlink:href\":r.attr(\"xlink:href\"),\"xlink:xlink:show\":r.attr(\"xlink:show\")}).style({cursor:\"pointer\"}).node().appendChild(j.node());var n=R.select(\".annotation-text-math-group\"),f=!n.empty(),m=u.bBox((f?n:G).node()),b=m.width,_=m.height,A=e.width||b,D=e.height||_,B=Math.round(A+2*N),H=Math.round(D+2*N);function q(t,e){return\"auto\"===e&&(e=t<1/3?\"left\":t>2/3?\"right\":\"center\"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=[\"x\",\"y\"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+\"ref\"]||et,nt=e[\"a\"+et+\"ref\"],it={x:v,y:x}[et],at=(P+(\"x\"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=H*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+\"anchor\"],ut=e[et+\"shift\"]*(\"x\"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&\"domain\"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e[\"a\"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt=\"domain\"===ht;\"x\"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var mt=e[\"a\"+et];if(tt=ot*q(.5,e.xanchor)-st*q(.5,e.yanchor),nt===rt){var gt=l.getRefType(nt);\"domain\"===gt?(\"y\"===et&&(mt=1-mt),ft.tail=it._offset+it._length*mt):\"paper\"===gt?\"y\"===et?(mt=1-mt,ft.tail=T.t+T.h*mt):ft.tail=T.l+T.w*mt:ft.tail=it._offset+it.r2p(mt),K=tt}else ft.tail=J+mt,K=tt+mt;ft.text=ft.tail+tt;var vt=w[\"x\"===et?\"width\":\"height\"];if(\"paper\"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),\"pixel\"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*q($,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=(A-b)*(\"center\"===e.align?.5:1)),\"top\"!==e.valign&&(_t=(D-_)*(\"middle\"===e.valign?.5:1)),f)n.select(\"svg\").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?L:null,t);else{var wt=N+_t-m.top,Tt=N+bt-m.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?L:null,t)}V.select(\"rect\").call(u.setRect,N,N,A,D),j.call(u.setRect,F/2,F/2,B-F,H-F),R.call(u.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-H/2)),O.attr({transform:\"rotate(\"+P+\",\"+C.x.text+\",\"+C.y.text+\")\"});var kt,At=function(r,n){I.selectAll(\".annotation-arrow-g\").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,m=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,m,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr(\"width\"),z=+j.attr(\"height\"),D=m-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);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,H=e.arrowcolor,q=e.arrowside,G=I.append(\"g\").style({opacity:c.opacity(H)}).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(H));if(g(Y,q,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 J,K,Q=G.append(\"path\").classed(\"annotation-arrow\",!0).classed(\"anndrag\",!0).classed(\"cursor-move\",!0).attr({d:\"M3,3H-3V-3H3ZM0,0L\"+(h-W)+\",\"+(p-X),transform:s(W,X)}).style(\"stroke-width\",V+6+\"px\").call(c.stroke,\"rgba(0,0,0,0)\").call(c.fill,\"rgba(0,0,0,0)\");d.init({element:Q.node(),gd:t,prepFn:function(){var t=u.getTranslate(R);J=t.x,K=t.y,v&&v.autorange&&M(v._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0)},moveFn:function(t,r){var n=w(J,K),i=n[0]+t,a=n[1]+r;R.call(u.setTranslate,i,a),S(\"x\",y(v,t,\"x\",T,e)),S(\"y\",y(x,r,\"y\",T,e)),e.axref===e.xref&&S(\"ax\",y(v,t,\"ax\",T,e)),e.ayref===e.yref&&S(\"ay\",y(x,r,\"ay\",T,e)),G.attr(\"transform\",s(t,r)),O.attr({transform:\"rotate(\"+P+\",\"+i+\",\"+a+\")\"})},doneFn:function(){i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}};if(e.showarrow&&At(0,0),z)d.init({element:R.node(),gd:t,prepFn:function(){kt=O.attr(\"transform\")},moveFn:function(t,r){var n=\"pointer\";if(e.showarrow)e.axref===e.xref?S(\"ax\",y(v,t,\"ax\",T,e)):S(\"ax\",e.ax+t),e.ayref===e.yref?S(\"ay\",y(x,r,\"ay\",T.w,e)):S(\"ay\",e.ay+r),At(t,r);else{if(a)return;var i,o;if(v)i=y(v,t,\"x\",T,e);else{var l=e._xsize/T.w,c=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(c+t/T.w,l,0,1,e.xanchor)}if(x)o=y(x,r,\"y\",T,e);else{var u=e._ysize/T.h,f=e.y-(e._yshift+e.yshift)/T.h-u/2;o=d.align(f-r/T.h,u,0,1,e.yanchor)}S(\"x\",i),S(\"y\",o),v&&x||(n=d.getCursor(v?.5:i,x?.5:o,e.xanchor,e.yanchor))}O.attr({transform:s(t,r)+kt}),p(R,n)},clickFn:function(r,n){e.captureevents&&t.emit(\"plotly_clickannotation\",Y(n))},doneFn:function(){p(R),i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}}e.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(\".annotation\").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&v(t,r);return a.previousPromises(t)},drawOne:v,drawRaw:x}},{\"../../lib\":503,\"../../lib/setcursor\":524,\"../../lib/svg_text_utils\":529,\"../../plot_api/plot_template\":543,\"../../plots/cartesian/axes\":554,\"../../plots/plots\":619,\"../../registry\":638,\"../color\":366,\"../dragelement\":385,\"../drawing\":388,\"../fx\":406,\"./draw_arrow_head\":356,\"@plotly/d3\":58}],356:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../color\"),a=t(\"./arrow_paths\"),o=t(\"../../lib\"),s=o.strScale,l=o.strRotate,c=o.strTranslate;e.exports=function(t,e,r){var o,u,f,h,p=t.node(),d=a[r.arrowhead||0],m=a[r.startarrowhead||0],g=(r.arrowwidth||1)*(r.arrowsize||1),v=(r.arrowwidth||1)*(r.startarrowsize||1),y=e.indexOf(\"start\")>=0,x=e.indexOf(\"end\")>=0,b=d.backoff*g+r.standoff,_=m.backoff*v+r.startstandoff;if(\"line\"===p.nodeName){o={x:+t.attr(\"x1\"),y:+t.attr(\"y1\")},u={x:+t.attr(\"x2\"),y:+t.attr(\"y2\")};var w=o.x-u.x,T=o.y-u.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void z();if(b){if(b*b>w*w+T*T)return void z();var k=b*Math.cos(f),A=b*Math.sin(f);u.x+=k,u.y+=A,t.attr({x2:u.x,y2:u.y})}if(_){if(_*_>w*w+T*T)return void z();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if(\"path\"===p.nodeName){var E=p.getTotalLength(),L=\"\";if(E<b+_)return void z();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L=\"0px,\"+_+\"px,\";var 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n=t(\"@plotly/d3\"),i=t(\"tinycolor2\"),a=t(\"../../plots/plots\"),o=t(\"../../registry\"),s=t(\"../../plots/cartesian/axes\"),l=t(\"../dragelement\"),c=t(\"../../lib\"),u=c.strTranslate,f=t(\"../../lib/extend\").extendFlat,h=t(\"../../lib/setcursor\"),p=t(\"../drawing\"),d=t(\"../color\"),m=t(\"../titles\"),g=t(\"../../lib/svg_text_utils\"),v=t(\"../colorscale/helpers\").flipScale,y=t(\"../../plots/cartesian/axis_defaults\"),x=t(\"../../plots/cartesian/position_defaults\"),b=t(\"../../plots/cartesian/layout_attributes\"),_=t(\"../../constants/alignment\"),w=_.LINE_SPACING,T=_.FROM_TL,k=_.FROM_BR,A=t(\"./constants\").cn;e.exports={draw:function(t){var e=t._fullLayout._infolayer.selectAll(\"g.\"+A.colorbar).data(function(t){var e,r,n,i,a=t._fullLayout,o=t.calcdata,s=[];function l(t){return f(t,{_fillcolor:null,_line:{color:null,width:null,dash:null},_levels:{start:null,end:null,size:null},_filllevels:null,_fillgradient:null,_zrange:null})}function c(){\"function\"==typeof 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r=n.select(this);c.ensureSingle(r,\"rect\",A.cbbg),c.ensureSingle(r,\"g\",A.cbfills),c.ensureSingle(r,\"g\",A.cblines),c.ensureSingle(r,\"g\",A.cbaxis,(function(t){t.classed(A.crisp,!0)})),c.ensureSingle(r,\"g\",A.cbtitleunshift,(function(t){t.append(\"g\").classed(A.cbtitle,!0)})),c.ensureSingle(r,\"rect\",A.cboutline);var v=function(t,e,r){var o=\"v\"===e.orientation,l=e.len,h=e.lenmode,v=e.thickness,_=e.thicknessmode,M=e.outlinewidth,S=e.borderwidth,E=e.bgcolor,L=e.xanchor,C=e.yanchor,P=e.xpad,I=e.ypad,O=e.x,z=o?e.y:1-e.y,D=r._fullLayout,R=D._size,F=e._fillcolor,B=e._line,N=e.title,j=N.side,U=e._zrange||n.extent((\"function\"==typeof F?F:B.color).domain()),V=\"function\"==typeof B.color?B.color:function(){return B.color},H=\"function\"==typeof F?F:function(){return F},q=e._levels,G=function(t,e,r){var n,i,a=e._levels,o=[],s=[],l=a.end+a.size/100,c=a.size,u=1.001*r[0]-.001*r[1],f=1.001*r[1]-.001*r[0];for(i=0;i<1e5&&(n=a.start+i*c,!(c>0?n>=l:n<=l));i++)n>u&&n<f&&o.push(n);if(e._fillgradient)s=[0];else if(\"function\"==typeof e._fillcolor){var h=e._filllevels;if(h)for(l=h.end+h.size/100,c=h.size,i=0;i<1e5&&(n=h.start+i*c,!(c>0?n>=l:n<=l));i++)n>r[0]&&n<r[1]&&s.push(n);else(s=o.map((function(t){return t-a.size/2}))).push(s[s.length-1]+a.size)}else e._fillcolor&&\"string\"==typeof e._fillcolor&&(s=[0]);a.size<0&&(o.reverse(),s.reverse());return{line:o,fill:s}}(0,e,U),Y=G.fill,W=G.line,X=Math.round(v*(\"fraction\"===_?o?R.w:R.h:1)),Z=X/(o?R.w:R.h),J=Math.round(l*(\"fraction\"===h?o?R.h:R.w:1)),K=J/(o?R.h:R.w),Q=Math.round(o?O*R.w+P:z*R.h+I),$={center:.5,right:1}[L]||0,tt={top:1,middle:.5}[C]||0,et=o?O-$*Z:z-tt*Z,rt=o?z-tt*K:O-$*K,nt=Math.round(o?R.h*(1-rt):R.w*rt);e._lenFrac=K,e._thickFrac=Z,e._uFrac=et,e._vFrac=rt;var it=e._axis=function(t,e,r){var n=t._fullLayout,i=\"v\"===e.orientation,a={type:\"linear\",range:r,tickmode:e.tickmode,nticks:e.nticks,tick0:e.tick0,dtick:e.dtick,tickvals:e.tickvals,ticktext:e.ticktext,ticks:e.ticks,ticklen:e.ticklen,tickwidth:e.tickwidth,tickcolor:e.tickcolor,showticklabels:e.showticklabels,ticklabelposition:e.ticklabelposition,ticklabeloverflow:e.ticklabeloverflow,ticklabelstep:e.ticklabelstep,tickfont:e.tickfont,tickangle:e.tickangle,tickformat:e.tickformat,exponentformat:e.exponentformat,minexponent:e.minexponent,separatethousands:e.separatethousands,showexponent:e.showexponent,showtickprefix:e.showtickprefix,tickprefix:e.tickprefix,showticksuffix:e.showticksuffix,ticksuffix:e.ticksuffix,title:e.title,showline:!0,anchor:\"free\",side:i?\"right\":\"bottom\",position:1},o=i?\"y\":\"x\",s={type:\"linear\",_id:o+e._id},l={letter:o,font:n.font,noHover:!0,noTickson:!0,noTicklabelmode:!0,calendar:n.calendar};function u(t,e){return c.coerce(a,s,b,t,e)}return y(a,s,u,l,n),x(a,s,u,l),s}(r,e,U);it.position=Z+(o?O+P/R.w:z+I/R.h);var at=-1!==[\"top\",\"bottom\"].indexOf(j);o&&at&&(it.title.side=j,it.titlex=O+P/R.w,it.titley=rt+(\"top\"===N.side?K-I/R.h:I/R.h));o||at||(it.title.side=j,it.titley=z+I/R.h,it.titlex=rt+P/R.w);if(B.color&&\"auto\"===e.tickmode){it.tickmode=\"linear\",it.tick0=q.start;var ot=q.size,st=c.constrain(J/50,4,15)+1,lt=(U[1]-U[0])/((e.nticks||st)*ot);if(lt>1){var ct=Math.pow(10,Math.floor(Math.log(lt)/Math.LN10));ot*=ct*c.roundUp(lt/ct,[2,5,10]),(Math.abs(q.start)/q.size+1e-6)%1<2e-6&&(it.tick0=0)}it.dtick=ot}it.domain=o?[rt+I/R.h,rt+K-I/R.h]:[rt+P/R.w,rt+K-P/R.w],it.setScale(),t.attr(\"transform\",u(Math.round(R.l),Math.round(R.t)));var ut,ft=t.select(\".\"+A.cbtitleunshift).attr(\"transform\",u(-Math.round(R.l),-Math.round(R.t))),ht=it.ticklabelposition,pt=it.title.font.size,dt=t.select(\".\"+A.cbaxis),mt=0,gt=0;function vt(n,i){var a={propContainer:it,propName:e._propPrefix+\"title\",traceIndex:e._traceIndex,_meta:e._meta,placeholder:D._dfltTitle.colorbar,containerGroup:t.select(\".\"+A.cbtitle)},o=\"h\"===n.charAt(0)?n.substr(1):\"h\"+n;t.selectAll(\".\"+o+\",.\"+o+\"-math-group\").remove(),m.draw(r,n,f(a,i||{}))}return c.syncOrAsync([a.previousPromises,function(){var t,e;(o&&at||!o&&!at)&&(\"top\"===j&&(t=P+R.l+R.w*O,e=I+R.t+R.h*(1-rt-K)+3+.75*pt),\"bottom\"===j&&(t=P+R.l+R.w*O,e=I+R.t+R.h*(1-rt)-3-.25*pt),\"right\"===j&&(e=I+R.t+R.h*z+3+.75*pt,t=P+R.l+R.w*rt),vt(it._id+\"title\",{attributes:{x:t,y:e,\"text-anchor\":o?\"start\":\"middle\"}}))},function(){if(!o&&!at||o&&at){var a,l=t.select(\".\"+A.cbtitle),f=l.select(\"text\"),h=[-M/2,M/2],d=l.select(\".h\"+it._id+\"title-math-group\").node(),m=15.6;if(f.node()&&(m=parseInt(f.node().style.fontSize,10)*w),d?(a=p.bBox(d),gt=a.width,(mt=a.height)>m&&(h[1]-=(mt-m)/2)):f.node()&&!f.classed(A.jsPlaceholder)&&(a=p.bBox(f.node()),gt=a.width,mt=a.height),o){if(mt){if(mt+=5,\"top\"===j)it.domain[1]-=mt/R.h,h[1]*=-1;else{it.domain[0]+=mt/R.h;var v=g.lineCount(f);h[1]+=(1-v)*m}l.attr(\"transform\",u(h[0],h[1])),it.setScale()}}else gt&&(\"right\"===j&&(it.domain[0]+=(gt+pt/2)/R.w),l.attr(\"transform\",u(h[0],h[1])),it.setScale())}t.selectAll(\".\"+A.cbfills+\",.\"+A.cblines).attr(\"transform\",o?u(0,Math.round(R.h*(1-it.domain[1]))):u(Math.round(R.w*it.domain[0]),0)),dt.attr(\"transform\",o?u(0,Math.round(-R.t)):u(Math.round(-R.l),0));var y=t.select(\".\"+A.cbfills).selectAll(\"rect.\"+A.cbfill).attr(\"style\",\"\").data(Y);y.enter().append(\"rect\").classed(A.cbfill,!0).style(\"stroke\",\"none\"),y.exit().remove();var x=U.map(it.c2p).map(Math.round).sort((function(t,e){return t-e}));y.each((function(t,a){var s=[0===a?U[0]:(Y[a]+Y[a-1])/2,a===Y.length-1?U[1]:(Y[a]+Y[a+1])/2].map(it.c2p).map(Math.round);o&&(s[1]=c.constrain(s[1]+(s[1]>s[0])?1:-1,x[0],x[1]));var l=n.select(this).attr(o?\"x\":\"y\",Q).attr(o?\"y\":\"x\",n.min(s)).attr(o?\"width\":\"height\",Math.max(X,2)).attr(o?\"height\":\"width\",Math.max(n.max(s)-n.min(s),2));if(e._fillgradient)p.gradient(l,r,e._id,o?\"vertical\":\"horizontalreversed\",e._fillgradient,\"fill\");else{var u=H(t).replace(\"e-\",\"\");l.attr(\"fill\",i(u).toHexString())}}));var b=t.select(\".\"+A.cblines).selectAll(\"path.\"+A.cbline).data(B.color&&B.width?W:[]);b.enter().append(\"path\").classed(A.cbline,!0),b.exit().remove(),b.each((function(t){var e=Q,r=Math.round(it.c2p(t))+B.width/2%1;n.select(this).attr(\"d\",\"M\"+(o?e+\",\"+r:r+\",\"+e)+(o?\"h\":\"v\")+X).call(p.lineGroupStyle,B.width,V(t),B.dash)})),dt.selectAll(\"g.\"+it._id+\"tick,path\").remove();var _=Q+X+(M||0)/2-(\"outside\"===e.ticks?1:0),T=s.calcTicks(it),k=s.getTickSigns(it)[2];return s.drawTicks(r,it,{vals:\"inside\"===it.ticks?s.clipEnds(it,T):T,layer:dt,path:s.makeTickPath(it,_,k),transFn:s.makeTransTickFn(it)}),s.drawLabels(r,it,{vals:T,layer:dt,transFn:s.makeTransTickLabelFn(it),labelFns:s.makeLabelFns(it,_)})},function(){if(o&&!at||!o&&at){var t,i,a=it.position||0,s=it._offset+it._length/2;if(\"right\"===j)i=s,t=R.l+R.w*a+10+pt*(it.showticklabels?1:.5);else if(t=s,\"bottom\"===j&&(i=R.t+R.h*a+10+(-1===ht.indexOf(\"inside\")?it.tickfont.size:0)+(\"intside\"!==it.ticks&&e.ticklen||0)),\"top\"===j){var l=N.text.split(\"<br>\").length;i=R.t+R.h*a+10-X-w*pt*l}vt((o?\"h\":\"v\")+it._id+\"title\",{avoid:{selection:n.select(r).selectAll(\"g.\"+it._id+\"tick\"),side:j,offsetTop:o?0:R.t,offsetLeft:o?R.l:0,maxShift:o?D.width:D.height},attributes:{x:t,y:i,\"text-anchor\":\"middle\"},transform:{rotate:o?-90:0,offset:0}})}},a.previousPromises,function(){var n,s=X+M/2;-1===ht.indexOf(\"inside\")&&(n=p.bBox(dt.node()),s+=o?n.width:n.height),ut=ft.select(\"text\");var c=0,f=o&&\"top\"===j,m=!o&&\"right\"===j,g=0;if(ut.node()&&!ut.classed(A.jsPlaceholder)){var y,x=ft.select(\".h\"+it._id+\"title-math-group\").node();x&&(o&&at||!o&&!at)?(c=(n=p.bBox(x)).width,y=n.height):(c=(n=p.bBox(ft.node())).right-R.l-(o?Q:nt),y=n.bottom-R.t-(o?nt:Q),o||\"top\"!==j||(s+=n.height,g=n.height)),m&&(ut.attr(\"transform\",u(c/2+pt/2,0)),c*=2),s=Math.max(s,o?c:y)}var b=2*(o?P:I)+s+S+M/2,w=0;!o&&N.text&&\"bottom\"===C&&z<=0&&(b+=w=b/2,g+=w),D._hColorbarMoveTitle=w,D._hColorbarMoveCBTitle=g;var F=S+M;t.select(\".\"+A.cbbg).attr(\"x\",(o?Q:nt)-F/2-(o?P:0)).attr(\"y\",(o?nt:Q)-(o?J:I+g-w)).attr(o?\"width\":\"height\",Math.max(b-w,2)).attr(o?\"height\":\"width\",Math.max(J+F,2)).call(d.fill,E).call(d.stroke,e.bordercolor).style(\"stroke-width\",S);var B=m?Math.max(c-10,0):0;if(t.selectAll(\".\"+A.cboutline).attr(\"x\",(o?Q:nt+P)+B).attr(\"y\",(o?nt+I-J:Q)+(f?mt:0)).attr(o?\"width\":\"height\",Math.max(X,2)).attr(o?\"height\":\"width\",Math.max(J-(o?2*I+mt:2*P+B),2)).call(d.stroke,e.outlinecolor).style({fill:\"none\",\"stroke-width\":M}),t.attr(\"transform\",u(R.l-(o?$*b:0),R.t-(o?0:(1-tt)*b-g))),!o&&(S||i(E).getAlpha()&&!i.equals(D.paper_bgcolor,E))){var U=dt.selectAll(\"text\"),V=U[0].length,H=t.select(\".\"+A.cbbg).node(),q=p.bBox(H),G=p.getTranslate(t);U.each((function(t,e){var r=V-1;if(0===e||e===r){var n,i=p.bBox(this),a=p.getTranslate(this);if(e===r){var o=i.right+a.x;(n=q.right+G.x+nt-S-2+O-o)>0&&(n=0)}else if(0===e){var s=i.left+a.x;(n=q.left+G.x+nt+S+2-s)<0&&(n=0)}n&&(V<3?this.setAttribute(\"transform\",\"translate(\"+n+\",0) \"+this.getAttribute(\"transform\")):this.setAttribute(\"visibility\",\"hidden\"))}}))}var Y={},W=T[L],Z=k[L],K=T[C],et=k[C],rt=b-X;o?(\"pixels\"===h?(Y.y=z,Y.t=J*K,Y.b=J*et):(Y.t=Y.b=0,Y.yt=z+l*K,Y.yb=z-l*et),\"pixels\"===_?(Y.x=O,Y.l=b*W,Y.r=b*Z):(Y.l=rt*W,Y.r=rt*Z,Y.xl=O-v*W,Y.xr=O+v*Z)):(\"pixels\"===h?(Y.x=O,Y.l=J*W,Y.r=J*Z):(Y.l=Y.r=0,Y.xl=O+l*W,Y.xr=O-l*Z),\"pixels\"===_?(Y.y=1-z,Y.t=b*K,Y.b=b*et):(Y.t=rt*K,Y.b=rt*et,Y.yt=z-v*K,Y.yb=z+v*et)),a.autoMargin(r,e._id,Y)}],r)}(r,e,t);v&&v.then&&(t._promises||[]).push(v),t._context.edits.colorbarPosition&&function(t,e,r){var n,i,a,s=\"v\"===e.orientation,c=r._fullLayout._size;l.init({element:t.node(),gd:r,prepFn:function(){n=t.attr(\"transform\"),h(t)},moveFn:function(r,o){t.attr(\"transform\",n+u(r,o)),i=l.align((s?e._uFrac:e._vFrac)+r/c.w,s?e._thickFrac:e._lenFrac,0,1,e.xanchor),a=l.align((s?e._vFrac:1-e._uFrac)-o/c.h,s?e._lenFrac:e._thickFrac,0,1,e.yanchor);var f=l.getCursor(i,a,e.xanchor,e.yanchor);h(t,f)},doneFn:function(){if(h(t),void 0!==i&&void 0!==a){var n={};n[e._propPrefix+\"x\"]=i,n[e._propPrefix+\"y\"]=a,void 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o.warn(\"Fx.hover failed\",e,t),d.unhoverRaw(t,e)}var ct=1/0;function ut(t,r){for(G=0;G<rt.length;G++)if((Y=rt[G])&&Y[0]&&Y[0].trace&&!0===(W=Y[0].trace).visible&&0!==W._length&&-1===[\"carpet\",\"contourcarpet\"].indexOf(W._module.name)){if(\"splom\"===W.type?X=l[Z=0]:(X=v.getSubplot(W),Z=l.indexOf(X)),J=P,v.isUnifiedHover(J)&&(J=J.charAt(0)),$={cd:Y,trace:W,xa:_[Z],ya:w[Z],maxHoverDistance:O,maxSpikeDistance:B,index:!1,distance:Math.min(ct,O),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:p.defaultLine,name:W.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},u[X]&&($.subplot=u[X]._subplot),u._splomScenes&&u._splomScenes[W.uid]&&($.scene=u._splomScenes[W.uid]),tt=et.length,\"array\"===J){var n=e[G];\"pointNumber\"in n?($.index=n.pointNumber,J=\"closest\"):(J=\"\",\"xval\"in n&&(K=n.xval,J=\"x\"),\"yval\"in n&&(Q=n.yval,J=J?\"closest\":\"y\"))}else void 0!==t&&void 0!==r?(K=t,Q=r):(K=V[Z],Q=H[Z]);if(0!==O)if(W._module&&W._module.hoverPoints){var a=W._module.hoverPoints($,K,Q,J,{finiteRange:!0,hoverLayer:u._hoverlayer});if(a)for(var s,c=0;c<a.length;c++)s=a[c],i(s.x0)&&i(s.y0)&&et.push(D(s,P))}else o.log(\"Unrecognized trace type in hover:\",W);if(\"closest\"===P&&et.length>tt&&(et.splice(0,tt),ct=et[0].distance),y&&0!==B&&0===et.length){$.distance=B,$.index=!1;var f=W._module.hoverPoints($,K,Q,\"closest\",{hoverLayer:u._hoverlayer});if(f&&(f=f.filter((function(t){return t.spikeDistance<=B}))),f&&f.length){var h,d=f.filter((function(t){return t.xa.showspikes&&\"hovered data\"!==t.xa.spikesnap}));if(d.length){var m=d[0];i(m.x0)&&i(m.y0)&&(h=ht(m),(!nt.vLinePoint||nt.vLinePoint.spikeDistance>h.spikeDistance)&&(nt.vLinePoint=h))}var g=f.filter((function(t){return t.ya.showspikes&&\"hovered data\"!==t.ya.spikesnap}));if(g.length){var x=g[0];i(x.x0)&&i(x.y0)&&(h=ht(x),(!nt.hLinePoint||nt.hLinePoint.spikeDistance>h.spikeDistance)&&(nt.hLinePoint=h))}}}}}function ft(t,e,r){for(var n,i=null,a=1/0,o=0;o<t.length;o++)n=t[o].spikeDistance,r&&0===o&&(n=-1/0),n<=a&&n<=e&&(i=t[o],a=n);return i}function ht(t){return t?{xa:t.xa,ya:t.ya,x:void 0!==t.xSpike?t.xSpike:(t.x0+t.x1)/2,y:void 0!==t.ySpike?t.ySpike:(t.y0+t.y1)/2,distance:t.distance,spikeDistance:t.spikeDistance,curveNumber:t.trace.index,color:t.color,pointNumber:t.index}:null}ut();var pt={fullLayout:u,container:u._hoverlayer,event:e},dt=t._spikepoints,mt={vLinePoint:nt.vLinePoint,hLinePoint:nt.hLinePoint};t._spikepoints=mt;var gt=function(){et.sort((function(t,e){return t.distance-e.distance})),et=function(t,e){for(var r=e.charAt(0),n=[],i=[],a=[],o=0;o<t.length;o++){var s=t[o];g.traceIs(s.trace,\"bar-like\")||g.traceIs(s.trace,\"box-violin\")?a.push(s):s.trace[r+\"period\"]?i.push(s):n.push(s)}return n.concat(i).concat(a)}(et,P)};gt();var vt=P.charAt(0),yt=(\"x\"===vt||\"y\"===vt)&&et[0]&&L[et[0].trace.type];if(y&&0!==B&&0!==et.length){var xt=ft(et.filter((function(t){return t.ya.showspikes})),B,yt);nt.hLinePoint=ht(xt);var bt=ft(et.filter((function(t){return t.xa.showspikes})),B,yt);nt.vLinePoint=ht(bt)}if(0===et.length){var _t=d.unhoverRaw(t,e);return!y||null===nt.hLinePoint&&null===nt.vLinePoint||F(dt)&&R(t,nt,pt),_t}y&&F(dt)&&R(t,nt,pt);if(v.isXYhover(J)&&0!==et[0].length&&\"splom\"!==et[0].trace.type){var wt=et[0],Tt=(et=E[wt.trace.type]?et.filter((function(t){return t.trace.index===wt.trace.index})):[wt]).length,kt=N(\"x\",wt,u),At=N(\"y\",wt,u);ut(kt,At);var Mt,St=[],Et={},Lt=0,Ct=function(t){var e=E[t.trace.type]?C(t):t.trace.index;if(Et[e]){var r=Et[e]-1,n=St[r];r>0&&Math.abs(t.distance)<Math.abs(n.distance)&&(St[r]=t)}else Lt++,Et[e]=Lt,St.push(t)};for(Mt=0;Mt<Tt;Mt++)Ct(et[Mt]);for(Mt=et.length-1;Mt>Tt-1;Mt--)Ct(et[Mt]);et=St,gt()}var Pt=t._hoverdata,It=[],Ot=j(t),zt=U(t);for(q=0;q<et.length;q++){var Dt=et[q],Rt=v.makeEventData(Dt,Dt.trace,Dt.cd);if(!1!==Dt.hovertemplate){var Ft=!1;Dt.cd[Dt.index]&&Dt.cd[Dt.index].ht&&(Ft=Dt.cd[Dt.index].ht),Dt.hovertemplate=Ft||Dt.trace.hovertemplate||!1}if(Dt.xa&&Dt.ya){var Bt=Dt.x0+Dt.xa._offset,Nt=Dt.x1+Dt.xa._offset,jt=Dt.y0+Dt.ya._offset,Ut=Dt.y1+Dt.ya._offset,Vt=Math.min(Bt,Nt),Ht=Math.max(Bt,Nt),qt=Math.min(jt,Ut),Gt=Math.max(jt,Ut);Rt.bbox={x0:Vt+zt,x1:Ht+zt,y0:qt+Ot,y1:Gt+Ot}}Dt.eventData=[Rt],It.push(Rt)}t._hoverdata=It;var Yt=\"y\"===P&&(rt.length>1||et.length>1)||\"closest\"===P&&it&&et.length>1,Wt=p.combine(u.plot_bgcolor||p.background,u.paper_bgcolor),Xt=I(et,{gd:t,hovermode:P,rotateLabels:Yt,bgColor:Wt,container:u._hoverlayer,outerContainer:u._paper.node(),commonLabelOpts:u.hoverlabel,hoverdistance:u.hoverdistance});v.isUnifiedHover(P)||(!function(t,e,r){var n,i,a,o,s,l,c,u=0,f=1,h=t.size(),p=new Array(h),d=0;function m(t){var e=t[0],r=t[t.length-1];if(i=e.pmin-e.pos-e.dp+e.size,a=r.pos+r.dp+r.size-e.pmax,i>.01){for(s=t.length-1;s>=0;s--)t[s].dp+=i;n=!1}if(!(a<.01)){if(i<-.01){for(s=t.length-1;s>=0;s--)t[s].dp-=a;n=!1}if(n){var c=0;for(o=0;o<t.length;o++)(l=t[o]).pos+l.dp+l.size>e.pmax&&c++;for(o=t.length-1;o>=0&&!(c<=0);o--)(l=t[o]).pos>e.pmax-1&&(l.del=!0,c--);for(o=0;o<t.length&&!(c<=0);o++)if((l=t[o]).pos<e.pmin+1)for(l.del=!0,c--,a=2*l.size,s=t.length-1;s>=0;s--)t[s].dp-=a;for(o=t.length-1;o>=0&&!(c<=0);o--)(l=t[o]).pos+l.dp+l.size>e.pmax&&(l.del=!0,c--)}}}t.each((function(t){var n=t[e],i=\"x\"===n._id.charAt(0),a=n.range;0===d&&a&&a[0]>a[1]!==i&&(f=-1),p[d++]=[{datum:t,traceIndex:t.trace.index,dp:0,pos:t.pos,posref:t.posref,size:t.by*(i?T:1)/2,pmin:0,pmax:i?r.width:r.height}]})),p.sort((function(t,e){return t[0].posref-e[0].posref||f*(e[0].traceIndex-t[0].traceIndex)}));for(;!n&&u<=h;){for(u++,n=!0,o=0;o<p.length-1;){var g=p[o],v=p[o+1],y=g[g.length-1],x=v[0];if((i=y.pos+y.dp+y.size-x.pos-x.dp+x.size)>.01&&y.pmin===x.pmin&&y.pmax===x.pmax){for(s=v.length-1;s>=0;s--)v[s].dp+=i;for(g.push.apply(g,v),p.splice(o+1,1),c=0,s=g.length-1;s>=0;s--)c+=g[s].dp;for(a=c/g.length,s=g.length-1;s>=0;s--)g[s].dp-=a;n=!1}else o++}p.forEach(m)}for(o=p.length-1;o>=0;o--){var b=p[o];for(s=b.length-1;s>=0;s--){var _=b[s],w=_.datum;w.offset=_.dp,w.del=_.del}}}(Xt,Yt?\"xa\":\"ya\",u),z(Xt,Yt,u._invScaleX,u._invScaleY));if(s&&s.tagName){var Zt=g.getComponentMethod(\"annotations\",\"hasClickToShow\")(t,It);f(n.select(s),Zt?\"pointer\":\"\")}if(!s||a||!function(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var i=r[n],a=t._hoverdata[n];if(i.curveNumber!==a.curveNumber||String(i.pointNumber)!==String(a.pointNumber)||String(i.pointNumbers)!==String(a.pointNumbers))return!0}return!1}(t,0,Pt))return;Pt&&t.emit(\"plotly_unhover\",{event:e,points:Pt});t.emit(\"plotly_hover\",{event:e,points:t._hoverdata,xaxes:_,yaxes:w,xvals:V,yvals:H})}(t,e,r,a,s)}))},r.loneHover=function(t,e){var r=!0;Array.isArray(t)||(r=!1,t=[t]);var i=e.gd,a=j(i),o=U(i),s=I(t.map((function(t){var r=t._x0||t.x0||t.x||0,n=t._x1||t.x1||t.x||0,s=t._y0||t.y0||t.y||0,l=t._y1||t.y1||t.y||0,c=t.eventData;if(c){var u=Math.min(r,n),f=Math.max(r,n),h=Math.min(s,l),d=Math.max(s,l),m=t.trace;if(g.traceIs(m,\"gl3d\")){var v=i._fullLayout[m.scene]._scene.container,y=v.offsetLeft,x=v.offsetTop;u+=y,f+=y,h+=x,d+=x}c.bbox={x0:u+o,x1:f+o,y0:h+a,y1:d+a},e.inOut_bbox&&e.inOut_bbox.push(c.bbox)}else c=!1;return{color:t.color||p.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,nameLength:t.nameLength,textAlign:t.textAlign,trace:t.trace||{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:t.hovertemplate||!1,hovertemplateLabels:t.hovertemplateLabels||!1,eventData:c}})),{gd:i,hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor||p.background,container:n.select(e.container),outerContainer:e.outerContainer||e.container}),l=0,c=0;return s.sort((function(t,e){return t.y0-e.y0})).each((function(t,r){var n=t.y0-t.by/2;t.offset=n-5<l?l-n+5:0,l=n+t.by+t.offset,r===e.anchorIndex&&(c=t.offset)})).each((function(t){t.offset-=c})),z(s,!1,i._fullLayout._invScaleX,i._fullLayout._invScaleY),r?s:s.node()};var P=/<extra>([\\s\\S]*)<\\/extra>/;function I(t,e){var r=e.gd,i=r._fullLayout,a=e.hovermode,c=e.rotateLabels,f=e.bgColor,d=e.container,m=e.outerContainer,w=e.commonLabelOpts||{};if(0===t.length)return[[]];var T=e.fontFamily||y.HOVERFONT,k=e.fontSize||y.HOVERFONTSIZE,A=t[0],E=A.xa,L=A.ya,P=a.charAt(0),I=A[P+\"Label\"],z=V(r,m),D=z.top,R=z.width,F=z.height,B=void 0!==I&&A.distance<=e.hoverdistance&&(\"x\"===a||\"y\"===a);if(B){var N,j,U=!0;for(N=0;N<t.length;N++)if(U&&void 0===t[N].zLabel&&(U=!1),j=t[N].hoverinfo||t[N].trace.hoverinfo){var H=Array.isArray(j)?j:j.split(\"+\");if(-1===H.indexOf(\"all\")&&-1===H.indexOf(a)){B=!1;break}}U&&(B=!1)}var q=d.selectAll(\"g.axistext\").data(B?[0]:[]);if(q.enter().append(\"g\").classed(\"axistext\",!0),q.exit().remove(),q.each((function(){var t=n.select(this),e=o.ensureSingle(t,\"path\",\"\",(function(t){t.style({\"stroke-width\":\"1px\"})})),l=o.ensureSingle(t,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),c=w.bgcolor||p.defaultLine,f=w.bordercolor||p.contrast(c),d=p.contrast(c),m={family:w.font.family||T,size:w.font.size||k,color:w.font.color||d};e.style({fill:c,stroke:f}),l.text(I).call(h.font,m).call(u.positionText,0,0).call(u.convertToTspans,r),t.attr(\"transform\",\"\");var g,v,y=V(r,l.node());if(\"x\"===a){var x=\"top\"===E.side?\"-\":\"\";l.attr(\"text-anchor\",\"middle\").call(u.positionText,0,\"top\"===E.side?D-y.bottom-M-S:D-y.top+M+S),g=E._offset+(A.x0+A.x1)/2,v=L._offset+(\"top\"===E.side?0:L._length);var b=y.width/2+S;g<b?(g=b,e.attr(\"d\",\"M-\"+(b-M)+\",0L-\"+(b-2*M)+\",\"+x+M+\"H\"+(S+y.width/2)+\"v\"+x+(2*S+y.height)+\"H-\"+b+\"V\"+x+M+\"Z\")):g>i.width-b?(g=i.width-b,e.attr(\"d\",\"M\"+(b-M)+\",0L\"+b+\",\"+x+M+\"v\"+x+(2*S+y.height)+\"H-\"+b+\"V\"+x+M+\"H\"+(b-2*M)+\"Z\")):e.attr(\"d\",\"M0,0L\"+M+\",\"+x+M+\"H\"+(S+y.width/2)+\"v\"+x+(2*S+y.height)+\"H-\"+(S+y.width/2)+\"V\"+x+M+\"H-\"+M+\"Z\")}else{var _,C,P;\"right\"===L.side?(_=\"start\",C=1,P=\"\",g=E._offset+E._length):(_=\"end\",C=-1,P=\"-\",g=E._offset),v=L._offset+(A.y0+A.y1)/2,l.attr(\"text-anchor\",_),e.attr(\"d\",\"M0,0L\"+P+M+\",\"+M+\"V\"+(S+y.height/2)+\"h\"+P+(2*S+y.width)+\"V-\"+(S+y.height/2)+\"H\"+P+M+\"V-\"+M+\"Z\");var O,z=y.height/2,R=D-y.top-z,F=\"clip\"+i._uid+\"commonlabel\"+L._id;if(g<y.width+2*S+M){O=\"M-\"+(M+S)+\"-\"+z+\"h-\"+(y.width-S)+\"V\"+z+\"h\"+(y.width-S)+\"Z\";var B=y.width-g+S;u.positionText(l,B,R),\"end\"===_&&l.selectAll(\"tspan\").each((function(){var t=n.select(this),e=h.tester.append(\"text\").text(t.text()).call(h.font,m),i=V(r,e.node());Math.round(i.width)<Math.round(y.width)&&t.attr(\"x\",B-i.width),e.remove()}))}else u.positionText(l,C*(S+M),R),O=null;var N=i._topclips.selectAll(\"#\"+F).data(O?[0]:[]);N.enter().append(\"clipPath\").attr(\"id\",F).append(\"path\"),N.exit().remove(),N.select(\"path\").attr(\"d\",O),h.setClipUrl(l,O?F:null,r)}t.attr(\"transform\",s(g,v))})),v.isUnifiedHover(a)){d.selectAll(\"g.hovertext\").remove();var G=t.filter((function(t){return\"none\"!==t.hoverinfo}));if(0===G.length)return;var Y=i.hoverlabel,W=Y.font,X={showlegend:!0,legend:{title:{text:I,font:W},font:W,bgcolor:Y.bgcolor,bordercolor:Y.bordercolor,borderwidth:1,tracegroupgap:7,traceorder:i.legend?i.legend.traceorder:void 0,orientation:\"v\"}},Z={font:W};x(X,Z,r._fullData);var J=Z.legend;J.entries=[];for(var K=0;K<G.length;K++){var Q=G[K];if(\"none\"!==Q.hoverinfo){var $=O(Q,!0,a,i,I),tt=$[0],et=$[1];Q.name=et,Q.text=\"\"!==et?et+\" : 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n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=t(\"../../plots/plots\"),o=t(\"../../registry\"),s=t(\"../../lib/events\"),l=t(\"../dragelement\"),c=t(\"../drawing\"),u=t(\"../color\"),f=t(\"../../lib/svg_text_utils\"),h=t(\"./handle_click\"),p=t(\"./constants\"),d=t(\"../../constants/alignment\"),m=d.LINE_SPACING,g=d.FROM_TL,v=d.FROM_BR,y=t(\"./get_legend_data\"),x=t(\"./style\"),b=t(\"./helpers\");function _(t,e,r,n,i){var a=r.data()[0][0].trace,l={event:i,node:r.node(),curveNumber:a.index,expandedIndex:a._expandedIndex,data:t.data,layout:t.layout,frames:t._transitionData._frames,config:t._context,fullData:t._fullData,fullLayout:t._fullLayout};if(a._group&&(l.group=a._group),o.traceIs(a,\"pie-like\")&&(l.label=r.datum()[0].label),!1!==s.triggerHandler(t,\"plotly_legendclick\",l))if(1===n)e._clickTimeout=setTimeout((function(){t._fullLayout&&h(r,t,n)}),t._context.doubleClickDelay);else 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rectangle\")},attr:\"dragmode\",val:\"drawrect\",icon:o.drawrect,click:f},u.drawcircle={name:\"drawcircle\",title:function(t){return c(t,\"Draw circle\")},attr:\"dragmode\",val:\"drawcircle\",icon:o.drawcircle,click:f},u.eraseshape={name:\"eraseshape\",title:function(t){return c(t,\"Erase active shape\")},icon:o.eraseshape,click:s},u.zoomIn2d={name:\"zoomIn2d\",_cat:\"zoomin\",title:function(t){return c(t,\"Zoom in\")},attr:\"zoom\",val:\"in\",icon:o.zoom_plus,click:f},u.zoomOut2d={name:\"zoomOut2d\",_cat:\"zoomout\",title:function(t){return c(t,\"Zoom out\")},attr:\"zoom\",val:\"out\",icon:o.zoom_minus,click:f},u.autoScale2d={name:\"autoScale2d\",_cat:\"autoscale\",title:function(t){return c(t,\"Autoscale\")},attr:\"zoom\",val:\"auto\",icon:o.autoscale,click:f},u.resetScale2d={name:\"resetScale2d\",_cat:\"resetscale\",title:function(t){return c(t,\"Reset axes\")},attr:\"zoom\",val:\"reset\",icon:o.home,click:f},u.hoverClosestCartesian={name:\"hoverClosestCartesian\",_cat:\"hoverclosest\",title:function(t){return c(t,\"Show closest data on hover\")},attr:\"hovermode\",val:\"closest\",icon:o.tooltip_basic,gravity:\"ne\",click:f},u.hoverCompareCartesian={name:\"hoverCompareCartesian\",_cat:\"hoverCompare\",title:function(t){return c(t,\"Compare data on hover\")},attr:\"hovermode\",val:function(t){return t._fullLayout._isHoriz?\"y\":\"x\"},icon:o.tooltip_compare,gravity:\"ne\",click:f},u.zoom3d={name:\"zoom3d\",_cat:\"zoom\",title:function(t){return c(t,\"Zoom\")},attr:\"scene.dragmode\",val:\"zoom\",icon:o.zoombox,click:h},u.pan3d={name:\"pan3d\",_cat:\"pan\",title:function(t){return c(t,\"Pan\")},attr:\"scene.dragmode\",val:\"pan\",icon:o.pan,click:h},u.orbitRotation={name:\"orbitRotation\",title:function(t){return c(t,\"Orbital rotation\")},attr:\"scene.dragmode\",val:\"orbit\",icon:o[\"3d_rotate\"],click:h},u.tableRotation={name:\"tableRotation\",title:function(t){return c(t,\"Turntable rotation\")},attr:\"scene.dragmode\",val:\"turntable\",icon:o[\"z-axis\"],click:h},u.resetCameraDefault3d={name:\"resetCameraDefault3d\",_cat:\"resetCameraDefault\",title:function(t){return c(t,\"Reset camera to default\")},attr:\"resetDefault\",icon:o.home,click:p},u.resetCameraLastSave3d={name:\"resetCameraLastSave3d\",_cat:\"resetCameraLastSave\",title:function(t){return c(t,\"Reset camera to last save\")},attr:\"resetLastSave\",icon:o.movie,click:p},u.hoverClosest3d={name:\"hoverClosest3d\",_cat:\"hoverclosest\",title:function(t){return c(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:o.tooltip_basic,gravity:\"ne\",click:function(t,e){var r=d(t,e);n.call(\"_guiRelayout\",t,r)}},u.zoomInGeo={name:\"zoomInGeo\",_cat:\"zoomin\",title:function(t){return c(t,\"Zoom in\")},attr:\"zoom\",val:\"in\",icon:o.zoom_plus,click:m},u.zoomOutGeo={name:\"zoomOutGeo\",_cat:\"zoomout\",title:function(t){return c(t,\"Zoom out\")},attr:\"zoom\",val:\"out\",icon:o.zoom_minus,click:m},u.resetGeo={name:\"resetGeo\",_cat:\"reset\",title:function(t){return c(t,\"Reset\")},attr:\"reset\",val:null,icon:o.autoscale,click:m},u.hoverClosestGeo={name:\"hoverClosestGeo\",_cat:\"hoverclosest\",title:function(t){return c(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:o.tooltip_basic,gravity:\"ne\",click:v},u.hoverClosestGl2d={name:\"hoverClosestGl2d\",_cat:\"hoverclosest\",title:function(t){return c(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:o.tooltip_basic,gravity:\"ne\",click:v},u.hoverClosestPie={name:\"hoverClosestPie\",_cat:\"hoverclosest\",title:function(t){return c(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:\"closest\",icon:o.tooltip_basic,gravity:\"ne\",click:v},u.resetViewSankey={name:\"resetSankeyGroup\",title:function(t){return c(t,\"Reset view\")},icon:o.home,click:function(t){for(var e={\"node.groups\":[],\"node.x\":[],\"node.y\":[]},r=0;r<t._fullData.length;r++){var i=t._fullData[r]._viewInitial;e[\"node.groups\"].push(i.node.groups.slice()),e[\"node.x\"].push(i.node.x.slice()),e[\"node.y\"].push(i.node.y.slice())}n.call(\"restyle\",t,e)}},u.toggleHover={name:\"toggleHover\",title:function(t){return c(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:o.tooltip_basic,gravity:\"ne\",click:function(t,e){var r=d(t,e);r.hovermode=g(t),n.call(\"_guiRelayout\",t,r)}},u.resetViews={name:\"resetViews\",title:function(t){return c(t,\"Reset views\")},icon:o.home,click:function(t,e){var r=e.currentTarget;r.setAttribute(\"data-attr\",\"zoom\"),r.setAttribute(\"data-val\",\"reset\"),f(t,e),r.setAttribute(\"data-attr\",\"resetLastSave\"),p(t,e),x(t,\"geo\"),x(t,\"mapbox\")}},u.toggleSpikelines={name:\"toggleSpikelines\",title:function(t){return c(t,\"Toggle Spike Lines\")},icon:o.spikeline,attr:\"_cartesianSpikesEnabled\",val:\"on\",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled=\"on\"===r?\"off\":\"on\",n.call(\"_guiRelayout\",t,function(t){for(var e=\"on\"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+\".showspikes\"]=!!e||o._showSpikeInitial}return n}(t))}},u.resetViewMapbox={name:\"resetViewMapbox\",_cat:\"resetView\",title:function(t){return c(t,\"Reset view\")},attr:\"reset\",icon:o.home,click:function(t){x(t,\"mapbox\")}},u.zoomInMapbox={name:\"zoomInMapbox\",_cat:\"zoomin\",title:function(t){return c(t,\"Zoom in\")},attr:\"zoom\",val:\"in\",icon:o.zoom_plus,click:y},u.zoomOutMapbox={name:\"zoomOutMapbox\",_cat:\"zoomout\",title:function(t){return c(t,\"Zoom out\")},attr:\"zoom\",val:\"out\",icon:o.zoom_minus,click:y}},{\"../../fonts/ploticon\":482,\"../../lib\":503,\"../../plots/cartesian/axis_ids\":558,\"../../plots/plots\":619,\"../../registry\":638,\"../shapes/draw\":450}],427:[function(t,e,r){\"use strict\";var n=t(\"./buttons\"),i=Object.keys(n),a=[\"drawline\",\"drawopenpath\",\"drawclosedpath\",\"drawcircle\",\"drawrect\",\"eraseshape\"],o=[\"v1hovermode\",\"hoverclosest\",\"hovercompare\",\"togglehover\",\"togglespikelines\"].concat(a),s=[];i.forEach((function(t){!function(t){if(-1===o.indexOf(t._cat||t.name)){var e=t.name,r=(t._cat||t.name).toLowerCase();-1===s.indexOf(e)&&s.push(e),-1===s.indexOf(r)&&s.push(r)}}(n[t])})),s.sort(),e.exports={DRAW_MODES:a,backButtons:o,foreButtons:s}},{\"./buttons\":426}],428:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../color\"),a=t(\"../../plot_api/plot_template\"),o=t(\"./attributes\");e.exports=function(t,e){var r=t.modebar||{},s=a.newContainer(e,\"modebar\");function l(t,e){return n.coerce(r,s,o,t,e)}l(\"orientation\"),l(\"bgcolor\",i.addOpacity(e.paper_bgcolor,.5));var c=i.contrast(i.rgb(e.modebar.bgcolor));l(\"color\",i.addOpacity(c,.3)),l(\"activecolor\",i.addOpacity(c,.7)),l(\"uirevision\",e.uirevision),l(\"add\"),l(\"remove\")}},{\"../../lib\":503,\"../../plot_api/plot_template\":543,\"../color\":366,\"./attributes\":425}],429:[function(t,e,r){\"use strict\";e.exports={moduleType:\"component\",name:\"modebar\",layoutAttributes:t(\"./attributes\"),supplyLayoutDefaults:t(\"./defaults\"),manage:t(\"./manage\")}},{\"./attributes\":425,\"./defaults\":428,\"./manage\":430}],430:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axis_ids\"),i=t(\"../../traces/scatter/subtypes\"),a=t(\"../../registry\"),o=t(\"../fx/helpers\").isUnifiedHover,s=t(\"./modebar\"),l=t(\"./buttons\"),c=t(\"./constants\").DRAW_MODES,u=t(\"../../lib\").extendDeep;e.exports=function(t){var e=t._fullLayout,r=t._context,f=e._modeBar;if(r.displayModeBar||r.watermark){if(!Array.isArray(r.modeBarButtonsToRemove))throw new Error([\"*modeBarButtonsToRemove* configuration options\",\"must be an array.\"].join(\" \"));if(!Array.isArray(r.modeBarButtonsToAdd))throw new Error([\"*modeBarButtonsToAdd* configuration options\",\"must be an array.\"].join(\" \"));var h,p=r.modeBarButtons;h=Array.isArray(p)&&p.length?function(t){for(var e=u([],t),r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++){var a=n[i];if(\"string\"==typeof a){if(void 0===l[a])throw new Error([\"*modeBarButtons* configuration options\",\"invalid button name\"].join(\" \"));e[r][i]=l[a]}}return e}(p):!r.displayModeBar&&r.watermark?[]:function(t){var e=t._fullLayout,r=t._fullData,s=t._context;function u(t,e){if(\"string\"==typeof e){if(e.toLowerCase()===t.toLowerCase())return!0}else{var r=e.name,n=e._cat||e.name;if(r===t||n===t.toLowerCase())return!0}return!1}var f=e.modebar.add;\"string\"==typeof f&&(f=[f]);var h=e.modebar.remove;\"string\"==typeof h&&(h=[h]);var p=s.modeBarButtonsToAdd.concat(f.filter((function(t){for(var e=0;e<s.modeBarButtonsToRemove.length;e++)if(u(t,s.modeBarButtonsToRemove[e]))return!1;return!0}))),d=s.modeBarButtonsToRemove.concat(h.filter((function(t){for(var e=0;e<s.modeBarButtonsToAdd.length;e++)if(u(t,s.modeBarButtonsToAdd[e]))return!1;return!0}))),m=e._has(\"cartesian\"),g=e._has(\"gl3d\"),v=e._has(\"geo\"),y=e._has(\"pie\"),x=e._has(\"funnelarea\"),b=e._has(\"gl2d\"),_=e._has(\"ternary\"),w=e._has(\"mapbox\"),T=e._has(\"polar\"),k=e._has(\"smith\"),A=e._has(\"sankey\"),M=function(t){for(var e=n.list({_fullLayout:t},null,!0),r=0;r<e.length;r++)if(!e[r].fixedrange)return!1;return!0}(e),S=o(e.hovermode),E=[];function L(t){if(t.length){for(var e=[],r=0;r<t.length;r++){for(var n=t[r],i=l[n],a=i.name.toLowerCase(),o=(i._cat||i.name).toLowerCase(),s=!1,c=0;c<d.length;c++){var u=d[c].toLowerCase();if(u===a||u===o){s=!0;break}}s||e.push(l[n])}E.push(e)}}var C=[\"toImage\"];s.showEditInChartStudio?C.push(\"editInChartStudio\"):s.showSendToCloud&&C.push(\"sendDataToCloud\");L(C);var P=[],I=[],O=[],z=[];(m||b||y||x||_)+v+g+w+T+k>1?(I=[\"toggleHover\"],O=[\"resetViews\"]):v?(P=[\"zoomInGeo\",\"zoomOutGeo\"],I=[\"hoverClosestGeo\"],O=[\"resetGeo\"]):g?(I=[\"hoverClosest3d\"],O=[\"resetCameraDefault3d\",\"resetCameraLastSave3d\"]):w?(P=[\"zoomInMapbox\",\"zoomOutMapbox\"],I=[\"toggleHover\"],O=[\"resetViewMapbox\"]):b?I=[\"hoverClosestGl2d\"]:y?I=[\"hoverClosestPie\"]:A?(I=[\"hoverClosestCartesian\",\"hoverCompareCartesian\"],O=[\"resetViewSankey\"]):I=[\"toggleHover\"];m&&(I=[\"toggleSpikelines\",\"hoverClosestCartesian\",\"hoverCompareCartesian\"]);(function(t){for(var e=0;e<t.length;e++)if(!a.traceIs(t[e],\"noHover\"))return!1;return!0}(r)||S)&&(I=[]);!m&&!b||M||(P=[\"zoomIn2d\",\"zoomOut2d\",\"autoScale2d\"],\"resetViews\"!==O[0]&&(O=[\"resetScale2d\"]));g?z=[\"zoom3d\",\"pan3d\",\"orbitRotation\",\"tableRotation\"]:(m||b)&&!M||_?z=[\"zoom2d\",\"pan2d\"]:w||v?z=[\"pan2d\"]:T&&(z=[\"zoom2d\"]);(function(t){for(var e=!1,r=0;r<t.length&&!e;r++){var n=t[r];n._module&&n._module.selectPoints&&(a.traceIs(n,\"scatter-like\")?(i.hasMarkers(n)||i.hasText(n))&&(e=!0):a.traceIs(n,\"box-violin\")&&\"all\"!==n.boxpoints&&\"all\"!==n.points||(e=!0))}return e})(r)&&z.push(\"select2d\",\"lasso2d\");var D=[],R=function(t){-1===D.indexOf(t)&&-1!==I.indexOf(t)&&D.push(t)};if(Array.isArray(p)){for(var F=[],B=0;B<p.length;B++){var N=p[B];\"string\"==typeof N?(N=N.toLowerCase(),-1!==c.indexOf(N)?(e._has(\"mapbox\")||e._has(\"cartesian\"))&&z.push(N):\"togglespikelines\"===N?R(\"toggleSpikelines\"):\"togglehover\"===N?R(\"toggleHover\"):\"hovercompare\"===N?R(\"hoverCompareCartesian\"):\"hoverclosest\"===N?(R(\"hoverClosestCartesian\"),R(\"hoverClosestGeo\"),R(\"hoverClosest3d\"),R(\"hoverClosestGl2d\"),R(\"hoverClosestPie\")):\"v1hovermode\"===N&&(R(\"toggleHover\"),R(\"hoverClosestCartesian\"),R(\"hoverCompareCartesian\"),R(\"hoverClosestGeo\"),R(\"hoverClosest3d\"),R(\"hoverClosestGl2d\"),R(\"hoverClosestPie\"))):F.push(N)}p=F}return L(z),L(P.concat(O)),L(D),function(t,e){if(e.length)if(Array.isArray(e[0]))for(var r=0;r<e.length;r++)t.push(e[r]);else t.push(e);return t}(E,p)}(t),f?f.update(t,h):e._modeBar=s(t,h)}else f&&(f.destroy(),delete e._modeBar)}},{\"../../lib\":503,\"../../plots/cartesian/axis_ids\":558,\"../../registry\":638,\"../../traces/scatter/subtypes\":952,\"../fx/helpers\":402,\"./buttons\":426,\"./constants\":427,\"./modebar\":431}],431:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"fast-isnumeric\"),a=t(\"../../lib\"),o=t(\"../../fonts/ploticon\"),s=t(\"../../version\").version,l=new DOMParser;function c(t){this.container=t.container,this.element=document.createElement(\"div\"),this.update(t.graphInfo,t.buttons),this.container.appendChild(this.element)}var u=c.prototype;u.update=function(t,e){this.graphInfo=t;var r=this.graphInfo._context,n=this.graphInfo._fullLayout,i=\"modebar-\"+n._uid;this.element.setAttribute(\"id\",i),this._uid=i,this.element.className=\"modebar\",\"hover\"===r.displayModeBar&&(this.element.className+=\" modebar--hover ease-bg\"),\"v\"===n.modebar.orientation&&(this.element.className+=\" vertical\",e=e.reverse());var o=n.modebar,s=\"hover\"===r.displayModeBar?\".js-plotly-plot .plotly:hover \":\"\";a.deleteRelatedStyleRule(i),a.addRelatedStyleRule(i,s+\"#\"+i+\" .modebar-group\",\"background-color: \"+o.bgcolor),a.addRelatedStyleRule(i,\"#\"+i+\" .modebar-btn .icon path\",\"fill: \"+o.color),a.addRelatedStyleRule(i,\"#\"+i+\" .modebar-btn:hover .icon path\",\"fill: \"+o.activecolor),a.addRelatedStyleRule(i,\"#\"+i+\" .modebar-btn.active .icon path\",\"fill: \"+o.activecolor);var l=!this.hasButtons(e),c=this.hasLogo!==r.displaylogo,u=this.locale!==r.locale;if(this.locale=r.locale,(l||c||u)&&(this.removeAllButtons(),this.updateButtons(e),r.watermark||r.displaylogo)){var f=this.getLogo();r.watermark&&(f.className=f.className+\" watermark\"),\"v\"===n.modebar.orientation?this.element.insertBefore(f,this.element.childNodes[0]):this.element.appendChild(f),this.hasLogo=!0}this.updateActiveButton()},u.updateButtons=function(t){var e=this;this.buttons=t,this.buttonElements=[],this.buttonsNames=[],this.buttons.forEach((function(t){var r=e.createGroup();t.forEach((function(t){var n=t.name;if(!n)throw new Error(\"must provide button 'name' in button config\");if(-1!==e.buttonsNames.indexOf(n))throw new Error(\"button name '\"+n+\"' is taken\");e.buttonsNames.push(n);var i=e.createButton(t);e.buttonElements.push(i),r.appendChild(i)})),e.element.appendChild(r)}))},u.createGroup=function(){var t=document.createElement(\"div\");return t.className=\"modebar-group\",t},u.createButton=function(t){var e=this,r=document.createElement(\"a\");r.setAttribute(\"rel\",\"tooltip\"),r.className=\"modebar-btn\";var i=t.title;void 0===i?i=t.name:\"function\"==typeof i&&(i=i(this.graphInfo)),(i||0===i)&&r.setAttribute(\"data-title\",i),void 0!==t.attr&&r.setAttribute(\"data-attr\",t.attr);var a=t.val;if(void 0!==a&&(\"function\"==typeof a&&(a=a(this.graphInfo)),r.setAttribute(\"data-val\",a)),\"function\"!=typeof t.click)throw new Error(\"must provide button 'click' function in button config\");r.addEventListener(\"click\",(function(r){t.click(e.graphInfo,r),e.updateActiveButton(r.currentTarget)})),r.setAttribute(\"data-toggle\",t.toggle||!1),t.toggle&&n.select(r).classed(\"active\",!0);var s=t.icon;return\"function\"==typeof s?r.appendChild(s()):r.appendChild(this.createIcon(s||o.question)),r.setAttribute(\"data-gravity\",t.gravity||\"n\"),r},u.createIcon=function(t){var e,r=i(t.height)?Number(t.height):t.ascent-t.descent,n=\"http://www.w3.org/2000/svg\";if(t.path){(e=document.createElementNS(n,\"svg\")).setAttribute(\"viewBox\",[0,0,t.width,r].join(\" \")),e.setAttribute(\"class\",\"icon\");var a=document.createElementNS(n,\"path\");a.setAttribute(\"d\",t.path),t.transform?a.setAttribute(\"transform\",t.transform):void 0!==t.ascent&&a.setAttribute(\"transform\",\"matrix(1 0 0 -1 0 \"+t.ascent+\")\"),e.appendChild(a)}t.svg&&(e=l.parseFromString(t.svg,\"application/xml\").childNodes[0]);return e.setAttribute(\"height\",\"1em\"),e.setAttribute(\"width\",\"1em\"),e},u.updateActiveButton=function(t){var e=this.graphInfo._fullLayout,r=void 0!==t?t.getAttribute(\"data-attr\"):null;this.buttonElements.forEach((function(t){var i=t.getAttribute(\"data-val\")||!0,o=t.getAttribute(\"data-attr\"),s=\"true\"===t.getAttribute(\"data-toggle\"),l=n.select(t);if(s)o===r&&l.classed(\"active\",!l.classed(\"active\"));else{var c=null===o?o:a.nestedProperty(e,o).get();l.classed(\"active\",c===i)}}))},u.hasButtons=function(t){var e=this.buttons;if(!e)return!1;if(t.length!==e.length)return!1;for(var r=0;r<t.length;++r){if(t[r].length!==e[r].length)return!1;for(var 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t.match(n.segmentRE).forEach((function(t){var a=e[t.charAt(0)].drawn;if(void 0!==a){var o=t.substr(1).match(n.paramRE);!o||o.length<a||r.push(i.cleanNumber(o[a]))}})),r},r.getDataToPixel=function(t,e,n,i){var a,o=t._fullLayout._size;if(e)if(\"domain\"===i)a=function(t){return e._length*(n?1-t:t)+e._offset};else{var s=r.shapePositionToRange(e);a=function(t){return e._offset+e.r2p(s(t,!0))},\"date\"===e.type&&(a=r.decodeDate(a))}else a=n?function(t){return o.t+o.h*(1-t)}:function(t){return o.l+o.w*t};return a},r.getPixelToData=function(t,e,n,i){var a,o=t._fullLayout._size;if(e)if(\"domain\"===i)a=function(t){var r=(t-e._offset)/e._length;return n?1-r:r};else{var s=r.rangeToShapePosition(e);a=function(t){return s(e.p2r(t-e._offset))}}else a=n?function(t){return 1-(t-o.t)/o.h}:function(t){return(t-o.l)/o.w};return a},r.roundPositionForSharpStrokeRendering=function(t,e){var r=1===Math.round(e%2),n=Math.round(t);return r?n+.5:n},r.makeOptionsAndPlotinfo=function(t,e){var 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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\":493,\"../../plot_api/edit_types\":536,\"../../plot_api/plot_template\":543,\"../../plots/animation_attributes\":548,\"../../plots/font_attributes\":585,\"../../plots/pad_attributes\":618,\"./constants\":460}],460:[function(t,e,r){\"use 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T={y:e.y,b:u.height*m[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*m[b]):(T.x=e.x,T.l=u.outerLength*d[b],T.r=u.outerLength*m[b]),i.autoMargin(t,g(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 m=c.lineCount(l),g=(a.currentValueMaxLines+1-m)*e.currentvalue.font.size*p;return 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t=s(),a=L(t,n.mouse(i)[0]);T(e,r,t,a,!1)}function h(){var t=s();t._dragging=!1,l.call(a.fill,t.bgcolor),o.on(\"mouseup\",null),o.on(\"mousemove\",null),o.on(\"touchend\",null),o.on(\"touchmove\",null),e.emit(\"plotly_sliderend\",{slider:t,step:t.steps[t.active]})}T(e,r,t,c,!0),t._dragging=!0,o.on(\"mousemove\",u),o.on(\"touchmove\",u),o.on(\"mouseup\",h),o.on(\"touchend\",h)}t.on(\"mousedown\",l),t.on(\"touchstart\",l)}function M(t,e){var r=t.selectAll(\"rect.\"+f.tickRectClass).data(e._visibleSteps),i=e._dims;r.enter().append(\"rect\").classed(f.tickRectClass,!0),r.exit().remove(),r.attr({width:e.tickwidth+\"px\",\"shape-rendering\":\"crispEdges\"}),r.each((function(t,r){var s=r%i.labelStride==0,l=n.select(this);l.attr({height:s?e.ticklen:e.minorticklen}).call(a.fill,e.tickcolor),o.setTranslate(l,E(e,r/(e._stepCount-1))-.5*e.tickwidth,(s?f.tickOffset:f.minorTickOffset)+i.currentValueTotalHeight)}))}function S(t,e,r){for(var 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n=t(\"@plotly/d3\"),i=t(\"d3-time-format\").utcFormat,a=t(\"d3-format\").format,o=t(\"fast-isnumeric\"),s=t(\"../constants/numerical\"),l=s.FP_SAFE,c=-l,u=s.BADNUM,f=e.exports={};f.adjustFormat=function(t){return!t||/^\\d[.]\\df/.test(t)||/[.]\\d%/.test(t)?t:\"0.f\"===t?\"~f\":/^\\d%/.test(t)?\"~%\":/^\\ds/.test(t)?\"~s\":!/^[~,.0$]/.test(t)&&/[&fps]/.test(t)?\"~\"+t:t};var h={};f.warnBadFormat=function(t){var e=String(t);h[e]||(h[e]=1,f.warn('encountered bad format: \"'+e+'\"'))},f.noFormat=function(t){return String(t)},f.numberFormat=function(t){var e;try{e=a(f.adjustFormat(t))}catch(e){return f.warnBadFormat(t),f.noFormat}return e},f.nestedProperty=t(\"./nested_property\"),f.keyedContainer=t(\"./keyed_container\"),f.relativeAttr=t(\"./relative_attr\"),f.isPlainObject=t(\"./is_plain_object\"),f.toLogRange=t(\"./to_log_range\"),f.relinkPrivateKeys=t(\"./relink_private\");var 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b=0;b<t.calcdata.length;b++)t.calcdata[b][0].trace=t._fullData[b];t._context.responsive?t._responsiveChartHandler||(t._responsiveChartHandler=function(){o.isHidden(t)||h.resize(t)},window.addEventListener(\"resize\",t._responsiveChartHandler)):o.clearResponsive(t);var T=o.extendFlat({},m._size),k=0;function A(){if(h.clearAutoMarginIds(t),w.drawMarginPushers(t),p.allowAutoMargin(t),m._has(\"pie\"))for(var e=t._fullData,r=0;r<e.length;r++){var n=e[r];\"pie\"===n.type&&n.automargin&&h.allowAutoMargin(t,\"pie.\"+n.uid+\".automargin\")}return h.doAutoMargin(t),h.previousPromises(t)}function S(){t._transitioning||(w.doAutoRangeAndConstraints(t),f&&p.saveRangeInitial(t),u.getComponentMethod(\"rangeslider\",\"calcAutorange\")(t))}var E=[h.previousPromises,function(){if(s)return r.addFrames(t,s)},function e(){for(var 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u=e._clips.selectAll(\".axesclip\").data(c,(function(t){return t.x._id+t.y._id}));u.enter().append(\"clipPath\").classed(\"axesclip\",!0).attr(\"id\",(function(t){return\"clip\"+e._uid+t.x._id+t.y._id})).append(\"rect\"),u.exit().remove(),u.each((function(t){n.select(this).select(\"rect\").attr({x:t.x._offset||0,y:t.y._offset||0,width:t.x._length||1,height:t.y._length||1})}))}},H.draw=function(t,e,r){var n=t._fullLayout;\"redraw\"===e&&n._paper.selectAll(\"g.subplot\").each((function(t){var e=t[0],r=n._plots[e];if(r){var i=r.xaxis,a=r.yaxis;r.xaxislayer.selectAll(\".\"+i._id+\"tick\").remove(),r.yaxislayer.selectAll(\".\"+a._id+\"tick\").remove(),r.xaxislayer.selectAll(\".\"+i._id+\"tick2\").remove(),r.yaxislayer.selectAll(\".\"+a._id+\"tick2\").remove(),r.xaxislayer.selectAll(\".\"+i._id+\"divider\").remove(),r.yaxislayer.selectAll(\".\"+a._id+\"divider\").remove(),r.minorGridlayer&&r.minorGridlayer.selectAll(\"path\").remove(),r.gridlayer&&r.gridlayer.selectAll(\"path\").remove(),r.zerolinelayer&&r.zerolinelayer.selectAll(\"path\").remove(),n._infolayer.select(\".g-\"+i._id+\"title\").remove(),n._infolayer.select(\".g-\"+a._id+\"title\").remove()}}));var i=e&&\"redraw\"!==e?e:H.listIds(t);return s.syncOrAsync(i.map((function(e){return function(){if(e){var n=H.getFromId(t,e),i=H.drawOne(t,n,r);return n._r=n.range.slice(),n._rl=s.simpleMap(n._r,n.r2l),i}}})))},H.drawOne=function(t,e,r){var n,i,l;r=r||{},e.setScale();var c=t._fullLayout,p=e._id,d=p.charAt(0),m=H.counterLetter(p),g=c._plots[e._mainSubplot];if(g){var v=g[d+\"axislayer\"],y=e._mainLinePosition,x=e._mainMirrorPosition,b=e._vals=H.calcTicks(e),_=[e.mirror,y,x].join(\"_\");for(n=0;n<b.length;n++)b[n].axInfo=_;e._selections={},e._tickAngles&&(e._prevTickAngles=e._tickAngles),e._tickAngles={},e._depth=null;var w={};if(e.visible){var T,k,A=H.makeTransTickFn(e),M=H.makeTransTickLabelFn(e),S=\"inside\"===e.ticks,E=\"outside\"===e.ticks;if(\"boundaries\"===e.tickson){var L=function(t,e){var r,n=[],i=function(t,e){var r=t.xbnd[e];null!==r&&n.push(s.extendFlat({},t,{x:r}))};if(e.length){for(r=0;r<e.length;r++)i(e[r],0);i(e[r-1],1)}return n}(0,b);k=H.clipEnds(e,L),T=S?k:L}else k=H.clipEnds(e,b),T=S&&\"period\"!==e.ticklabelmode?k:b;var C,P=e._gridVals=k,I=function(t,e){var r,n,i=[],a=e.length&&e[e.length-1].x<e[0].x,o=function(t,e){var r=t.xbnd[e];null!==r&&i.push(s.extendFlat({},t,{x:r}))};if(t.showdividers&&e.length){for(r=0;r<e.length;r++){var l=e[r];l.text2!==n&&o(l,a?1:0),n=l.text2}o(e[r-1],a?0:1)}return i}(e,b);if(!c._hasOnlyLargeSploms){var O=e._subplotsWith,z={};for(n=0;n<O.length;n++){i=O[n];var D=(l=c._plots[i])[m+\"axis\"],R=D._mainAxis._id;if(!z[R]){z[R]=1;var F=\"x\"===d?\"M0,\"+D._offset+\"v\"+D._length:\"M\"+D._offset+\",0h\"+D._length;H.drawGrid(t,e,{vals:P,counterAxis:D,layer:l.gridlayer.select(\".\"+p),minorLayer:l.minorGridlayer.select(\".\"+p),path:F,transFn:A}),H.drawZeroLine(t,e,{counterAxis:D,layer:l.zerolinelayer,path:F,transFn:A})}}}var B=H.getTickSigns(e),N=H.getTickSigns(e,\"minor\");if(e.ticks||e.minor&&e.minor.ticks){var j,q,G,Y,W=H.makeTickPath(e,y,B[2]),X=H.makeTickPath(e,y,N[2],{minor:!0});if(e._anchorAxis&&e.mirror&&!0!==e.mirror?(j=H.makeTickPath(e,x,B[3]),q=H.makeTickPath(e,x,N[3],{minor:!0}),G=W+j,Y=X+q):(j=\"\",q=\"\",G=W,Y=X),e.showdividers&&E&&\"boundaries\"===e.tickson){var Z={};for(n=0;n<I.length;n++)Z[I[n].x]=1;C=function(t){return Z[t.x]?j:G}}else C=function(t){return t.minor?Y:G}}if(H.drawTicks(t,e,{vals:T,layer:v,path:C,transFn:A}),\"allticks\"===e.mirror){var J=Object.keys(e._linepositions||{});for(n=0;n<J.length;n++){i=J[n],l=c._plots[i];var K=e._linepositions[i]||[],Q=K[0],$=K[1],tt=K[2],et=H.makeTickPath(e,Q,tt?B[0]:N[0],{minor:tt})+H.makeTickPath(e,$,tt?B[1]:N[1],{minor:tt});H.drawTicks(t,e,{vals:T,layer:l[d+\"axislayer\"],path:et,transFn:A})}}var rt=[];if(rt.push((function(){return H.drawLabels(t,e,{vals:b,layer:v,plotinfo:l,transFn:M,labelFns:H.makeLabelFns(e,y)})})),\"multicategory\"===e.type){var nt={x:2,y:10}[d];rt.push((function(){var r={x:\"height\",y:\"width\"}[d],n=at()[r]+nt+(e._tickAngles[p+\"tick\"]?e.tickfont.size*U:0);return H.drawLabels(t,e,{vals:vt(e,b),layer:v,cls:p+\"tick2\",repositionOnUpdate:!0,secondary:!0,transFn:A,labelFns:H.makeLabelFns(e,y+n*B[4])})})),rt.push((function(){return e._depth=B[4]*(at(\"tick2\")[e.side]-y),function(t,e,r){var n=e._id+\"divider\",i=r.vals,a=r.layer.selectAll(\"path.\"+n).data(i,xt);a.exit().remove(),a.enter().insert(\"path\",\":first-child\").classed(n,1).classed(\"crisp\",1).call(f.stroke,e.dividercolor).style(\"stroke-width\",h.crispRound(t,e.dividerwidth,1)+\"px\"),a.attr(\"transform\",r.transFn).attr(\"d\",r.path)}(t,e,{vals:I,layer:v,path:H.makeTickPath(e,y,B[4],{len:e._depth}),transFn:A})}))}else e.title.hasOwnProperty(\"standoff\")&&rt.push((function(){e._depth=B[4]*(at()[e.side]-y)}));var it=o.getComponentMethod(\"rangeslider\",\"isVisible\")(e);return rt.push((function(){var r,n,i,s,l=e.side.charAt(0),u=V[e.side].charAt(0),f=H.getPxPosition(t,e),h=E?e.ticklen:0;if((e.automargin||it)&&(\"multicategory\"===e.type?r=at(\"tick2\"):(r=at(),\"x\"===d&&\"b\"===l&&(e._depth=Math.max(r.width>0?r.bottom-f:0,h)))),e.automargin){n={x:0,y:0,r:0,l:0,t:0,b:0};var p=[0,1];if(\"x\"===d){if(\"b\"===l?n[l]=e._depth:(n[l]=e._depth=Math.max(r.width>0?f-r.top:0,h),p.reverse()),r.width>0){var g=r.right-(e._offset+e._length);g>0&&(n.xr=1,n.r=g);var v=e._offset-r.left;v>0&&(n.xl=0,n.l=v)}}else if(\"l\"===l?n[l]=e._depth=Math.max(r.height>0?f-r.left:0,h):(n[l]=e._depth=Math.max(r.height>0?r.right-f:0,h),p.reverse()),r.height>0){var y=r.bottom-(e._offset+e._length);y>0&&(n.yb=0,n.b=y);var x=e._offset-r.top;x>0&&(n.yt=1,n.t=x)}n[m]=\"free\"===e.anchor?e.position:e._anchorAxis.domain[p[0]],e.title.text!==c._dfltTitle[d]&&(n[l]+=bt(e)+(e.title.standoff||0)),e.mirror&&\"free\"!==e.anchor&&((i={x:0,y:0,r:0,l:0,t:0,b:0})[u]=e.linewidth,e.mirror&&!0!==e.mirror&&(i[u]+=h),!0===e.mirror||\"ticks\"===e.mirror?i[m]=e._anchorAxis.domain[p[1]]:\"all\"!==e.mirror&&\"allticks\"!==e.mirror||(i[m]=[e._counterDomainMin,e._counterDomainMax][p[1]]))}it&&(s=o.getComponentMethod(\"rangeslider\",\"autoMarginOpts\")(t,e)),a.autoMargin(t,Tt(e),n),a.autoMargin(t,kt(e),i),a.autoMargin(t,At(e),s)})),r.skipTitle||it&&\"bottom\"===e.side||rt.push((function(){return function(t,e){var r,n=t._fullLayout,i=e._id,a=i.charAt(0),o=e.title.font.size;if(e.title.hasOwnProperty(\"standoff\"))r=e._depth+e.title.standoff+bt(e);else{var s=Pt(e);if(\"multicategory\"===e.type)r=e._depth;else{var l=1.5*o;s&&(l=.5*o,\"outside\"===e.ticks&&(l+=e.ticklen)),r=10+l+(e.linewidth?e.linewidth-1:0)}s||(r+=\"x\"===a?\"top\"===e.side?o*(e.showticklabels?1:0):o*(e.showticklabels?1.5:.5):\"right\"===e.side?o*(e.showticklabels?1:.5):o*(e.showticklabels?.5:0))}var c,f,p,d,m=H.getPxPosition(t,e);\"x\"===a?(f=e._offset+e._length/2,p=\"top\"===e.side?m-r:m+r):(p=e._offset+e._length/2,f=\"right\"===e.side?m+r:m-r,c={rotate:\"-90\",offset:0});if(\"multicategory\"!==e.type){var g=e._selections[e._id+\"tick\"];if(d={selection:g,side:e.side},g&&g.node()&&g.node().parentNode){var v=h.getTranslate(g.node().parentNode);d.offsetLeft=v.x,d.offsetTop=v.y}e.title.hasOwnProperty(\"standoff\")&&(d.pad=0)}return u.draw(t,i+\"title\",{propContainer:e,propName:e._name+\".title.text\",placeholder:n._dfltTitle[a],avoid:d,transform:c,attributes:{x:f,y:p,\"text-anchor\":\"middle\"}})}(t,e)})),s.syncOrAsync(rt)}}function at(t){var r=p+(t||\"tick\");return w[r]||(w[r]=function(t,e){var r,n,i,a;t._selections[e].size()?(r=1/0,n=-1/0,i=1/0,a=-1/0,t._selections[e].each((function(){var t=wt(this),e=h.bBox(t.node().parentNode);r=Math.min(r,e.top),n=Math.max(n,e.bottom),i=Math.min(i,e.left),a=Math.max(a,e.right)}))):(r=0,n=0,i=0,a=0);return{top:r,bottom:n,left:i,right:a,height:n-r,width:a-i}}(e,r)),w[r]}},H.getTickSigns=function(t,e){var r=t._id.charAt(0),n={x:\"top\",y:\"right\"}[r],i=t.side===n?1:-1,a=[-1,1,i,-i];return\"inside\"!==(e?(t.minor||{}).ticks:t.ticks)==(\"x\"===r)&&(a=a.map((function(t){return-t}))),t.side&&a.push({l:-1,t:-1,r:1,b:1}[t.side.charAt(0)]),a},H.makeTransTickFn=function(t){return\"x\"===t._id.charAt(0)?function(e){return l(t._offset+t.l2p(e.x),0)}:function(e){return l(0,t._offset+t.l2p(e.x))}},H.makeTransTickLabelFn=function(t){var e=function(t){var e=t.ticklabelposition||\"\",r=function(t){return-1!==e.indexOf(t)},n=r(\"top\"),i=r(\"left\"),a=r(\"right\"),o=r(\"bottom\"),s=r(\"inside\"),l=o||i||n||a;if(!l&&!s)return[0,0];var c=t.side,u=l?(t.tickwidth||0)/2:0,f=3,h=t.tickfont?t.tickfont.size:12;(o||n)&&(u+=h*j,f+=(t.linewidth||0)/2);(i||a)&&(u+=(t.linewidth||0)/2,f+=3);s&&\"top\"===c&&(f-=h*(1-j));(i||n)&&(u=-u);\"bottom\"!==c&&\"right\"!==c||(f=-f);return[l?u:0,s?f:0]}(t),r=e[0],n=e[1];return\"x\"===t._id.charAt(0)?function(e){return l(r+t._offset+t.l2p(yt(e)),n)}:function(e){return l(n,r+t._offset+t.l2p(yt(e)))}},H.makeTickPath=function(t,e,r,n){n||(n={});var i=n.minor;if(i&&!t.minor)return\"\";var a=void 0!==n.len?n.len:i?t.minor.ticklen:t.ticklen,o=t._id.charAt(0),s=(t.linewidth||1)/2;return\"x\"===o?\"M0,\"+(e+s*r)+\"v\"+a*r:\"M\"+(e+s*r)+\",0h\"+a*r},H.makeLabelFns=function(t,e,r){var n=t.ticklabelposition||\"\",a=function(t){return-1!==n.indexOf(t)},o=a(\"top\"),l=a(\"left\"),c=a(\"right\"),u=a(\"bottom\")||l||o||c,f=a(\"inside\"),h=\"inside\"===n&&\"inside\"===t.ticks||!f&&\"outside\"===t.ticks&&\"boundaries\"!==t.tickson,p=0,d=0,m=h?t.ticklen:0;if(f?m*=-1:u&&(m=0),h&&(p+=m,r)){var g=s.deg2rad(r);p=m*Math.cos(g)+1,d=m*Math.sin(g)}t.showticklabels&&(h||t.showline)&&(p+=.2*t.tickfont.size);var v,y,x,b,_,w={labelStandoff:p+=(t.linewidth||1)/2*(f?-1:1),labelShift:d},T=0,k=t.side,A=t._id.charAt(0),M=t.tickangle;if(\"x\"===A)b=(_=!f&&\"bottom\"===k||f&&\"top\"===k)?1:-1,f&&(b*=-1),v=d*b,y=e+p*b,x=_?1:-.2,90===Math.abs(M)&&(f?x+=N:x=-90===M&&\"bottom\"===k?j:90===M&&\"top\"===k?N:.5,T=N/2*(M/90)),w.xFn=function(t){return t.dx+v+T*t.fontSize},w.yFn=function(t){return t.dy+y+t.fontSize*x},w.anchorFn=function(t,e){if(u){if(l)return\"end\";if(c)return\"start\"}return i(e)&&0!==e&&180!==e?e*b<0!==f?\"end\":\"start\":\"middle\"},w.heightFn=function(e,r,n){return r<-60||r>60?-.5*n:\"top\"===t.side!==f?-n:0};else if(\"y\"===A){if(b=(_=!f&&\"left\"===k||f&&\"right\"===k)?1:-1,f&&(b*=-1),v=p,y=d*b,x=0,f||90!==Math.abs(M)||(x=-90===M&&\"left\"===k||90===M&&\"right\"===k?j:.5),f){var S=i(M)?+M:0;if(0!==S){var E=s.deg2rad(S);T=Math.abs(Math.sin(E))*j*b,x=0}}w.xFn=function(t){return t.dx+e-(v+t.fontSize*x)*b+T*t.fontSize},w.yFn=function(t){return t.dy+y+t.fontSize*N},w.anchorFn=function(t,e){return i(e)&&90===Math.abs(e)?\"middle\":_?\"end\":\"start\"},w.heightFn=function(e,r,n){return\"right\"===t.side&&(r*=-1),r<-30?-n:r<30?-.5*n:0}}return w},H.drawTicks=function(t,e,r){r=r||{};var i=e._id+\"tick\",a=[].concat(e.minor&&e.minor.ticks?r.vals.filter((function(t){return t.minor&&!t.noTick})):[]).concat(e.ticks?r.vals.filter((function(t){return!t.minor&&!t.noTick})):[]),o=r.layer.selectAll(\"path.\"+i).data(a,xt);o.exit().remove(),o.enter().append(\"path\").classed(i,1).classed(\"ticks\",1).classed(\"crisp\",!1!==r.crisp).each((function(t){return f.stroke(n.select(this),t.minor?e.minor.tickcolor:e.tickcolor)})).style(\"stroke-width\",(function(r){return h.crispRound(t,r.minor?e.minor.tickwidth:e.tickwidth,1)+\"px\"})).attr(\"d\",r.path).style(\"display\",null),It(e,[R]),o.attr(\"transform\",r.transFn)},H.drawGrid=function(t,e,r){r=r||{};var i=e._id+\"grid\",a=e.minor&&e.minor.showgrid,o=a?r.vals.filter((function(t){return t.minor})):[],s=e.showgrid?r.vals.filter((function(t){return!t.minor})):[],l=r.counterAxis;if(l&&H.shouldShowZeroLine(t,e,l))for(var c=\"array\"===e.tickmode,u=0;u<s.length;u++){var p=s[u].x;if(c?!p:Math.abs(p)<e.dtick/100){if(s=s.slice(0,u).concat(s.slice(u+1)),!c)break;u--}}e._gw=h.crispRound(t,e.gridwidth,1);for(var d=a?h.crispRound(t,e.minor.gridwidth,1):0,m=r.layer,g=r.minorLayer,v=1;v>=0;v--){var y=v?m:g;if(y){var x=y.selectAll(\"path.\"+i).data(v?s:o,xt);x.exit().remove(),x.enter().append(\"path\").classed(i,1).classed(\"crisp\",!1!==r.crisp),x.attr(\"transform\",r.transFn).attr(\"d\",r.path).each((function(t){return f.stroke(n.select(this),t.minor?e.minor.gridcolor:e.gridcolor||\"#ddd\")})).style(\"stroke-dasharray\",(function(t){return h.dashStyle(t.minor?e.minor.griddash:e.griddash,t.minor?e.minor.gridwidth:e.gridwidth)})).style(\"stroke-width\",(function(t){return(t.minor?d:e._gw)+\"px\"})).style(\"display\",null),\"function\"==typeof r.path&&x.attr(\"d\",r.path)}}It(e,[z,D])},H.drawZeroLine=function(t,e,r){r=r||r;var n=e._id+\"zl\",i=H.shouldShowZeroLine(t,e,r.counterAxis),a=r.layer.selectAll(\"path.\"+n).data(i?[{x:0,id:e._id}]:[]);a.exit().remove(),a.enter().append(\"path\").classed(n,1).classed(\"zl\",1).classed(\"crisp\",!1!==r.crisp).each((function(){r.layer.selectAll(\"path\").sort((function(t,e){return Y(t.id,e.id)}))})),a.attr(\"transform\",r.transFn).attr(\"d\",r.path).call(f.stroke,e.zerolinecolor||f.defaultLine).style(\"stroke-width\",h.crispRound(t,e.zerolinewidth,e._gw||1)+\"px\").style(\"display\",null),It(e,[O])},H.drawLabels=function(t,e,r){r=r||{};var a=t._fullLayout,o=e._id,u=o.charAt(0),f=r.cls||o+\"tick\",p=r.vals.filter((function(t){return t.text})),d=r.labelFns,m=r.secondary?0:e.tickangle,g=(e._prevTickAngles||{})[f],v=r.layer.selectAll(\"g.\"+f).data(e.showticklabels?p:[],xt),y=[];function x(t,a){t.each((function(t){var o=n.select(this),s=o.select(\".text-math-group\"),u=d.anchorFn(t,a),f=r.transFn.call(o.node(),t)+(i(a)&&0!=+a?\" rotate(\"+a+\",\"+d.xFn(t)+\",\"+(d.yFn(t)-t.fontSize/2)+\")\":\"\"),p=c.lineCount(o),m=U*t.fontSize,g=d.heightFn(t,i(a)?+a:0,(p-1)*m);if(g&&(f+=l(0,g)),s.empty()){var v=o.select(\"text\");v.attr({transform:f,\"text-anchor\":u}),v.style(\"opacity\",1),e._adjustTickLabelsOverflow&&e._adjustTickLabelsOverflow()}else{var y=h.bBox(s.node()).width*{end:-.5,start:.5}[u];s.attr(\"transform\",f+l(y,0))}}))}v.enter().append(\"g\").classed(f,1).append(\"text\").attr(\"text-anchor\",\"middle\").each((function(e){var r=n.select(this),i=t._promises.length;r.call(c.positionText,d.xFn(e),d.yFn(e)).call(h.font,e.font,e.fontSize,e.fontColor).text(e.text).call(c.convertToTspans,t),t._promises[i]?y.push(t._promises.pop().then((function(){x(r,m)}))):x(r,m)})),It(e,[F]),v.exit().remove(),r.repositionOnUpdate&&v.each((function(t){n.select(this).select(\"text\").call(c.positionText,d.xFn(t),d.yFn(t))})),e._adjustTickLabelsOverflow=function(){var r=e.ticklabeloverflow;if(r&&\"allow\"!==r){var i=-1!==r.indexOf(\"hide\"),o=\"x\"===e._id.charAt(0),l=0,c=o?t._fullLayout.width:t._fullLayout.height;if(-1!==r.indexOf(\"domain\")){var u=s.simpleMap(e.range,e.r2l);l=e.l2p(u[0])+e._offset,c=e.l2p(u[1])+e._offset}var f=Math.min(l,c),p=Math.max(l,c),d=e.side,m=1/0,g=-1/0;for(var y in v.each((function(t){var 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o=\"tick\"===t.K&&\"text\"===t.L&&\"period\"===e.ticklabelmode,s=a._plots[e._mainSubplot];(t.K===O.K?s.zerolinelayer.selectAll(\".\"+e._id+\"zl\"):t.K===D.K?s.minorGridlayer.selectAll(\".\"+e._id):t.K===z.K?s.gridlayer.selectAll(\".\"+e._id):s[e._id.charAt(0)+\"axislayer\"]).each((function(){var a=n.select(this);t.L&&(a=a.selectAll(t.L)),a.each((function(a){var s=e.l2p(o?yt(a):a.x)+e._offset,l=n.select(this);s<e[\"_visibleLabelMax_\"+r._id]&&s>e[\"_visibleLabelMin_\"+r._id]?l.style(\"display\",\"none\"):\"tick\"!==t.K||i||l.style(\"display\",null)}))}))}))}))},x(v,g+1?g:m);var b=null;e._selections&&(e._selections[f]=v);var _=[function(){return y.length&&Promise.all(y)}];e.automargin&&a._redrawFromAutoMarginCount&&90===g?(b=90,_.push((function(){x(v,g)}))):_.push((function(){if(x(v,m),p.length&&\"x\"===u&&!i(m)&&(\"log\"!==e.type||\"D\"!==String(e.dtick).charAt(0))){b=0;var t,n=0,a=[];if(v.each((function(t){n=Math.max(n,t.fontSize);var 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n=t(\"fast-isnumeric\"),i=t(\"../../registry\"),a=t(\"../../lib\"),o=t(\"../../plot_api/plot_template\"),s=t(\"../array_container_defaults\"),l=t(\"./layout_attributes\"),c=t(\"./tick_value_defaults\"),u=t(\"./tick_mark_defaults\"),f=t(\"./tick_label_defaults\"),h=t(\"./prefix_suffix_defaults\"),p=t(\"./category_order_defaults\"),d=t(\"./line_grid_defaults\"),m=t(\"./set_convert\"),g=t(\"./constants\").WEEKDAY_PATTERN,v=t(\"./constants\").HOUR_PATTERN;function y(t,e,r){function i(r,n){return a.coerce(t,e,l.rangebreaks,r,n)}if(i(\"enabled\")){var o=i(\"bounds\");if(o&&o.length>=2){var s,c,u=\"\";if(2===o.length)for(s=0;s<2;s++)if(c=b(o[s])){u=g;break}var f=i(\"pattern\",u);if(f===g)for(s=0;s<2;s++)(c=b(o[s]))&&(e.bounds[s]=o[s]=c-1);if(f)for(s=0;s<2;s++)switch(c=o[s],f){case g:if(!n(c))return void(e.enabled=!1);if((c=+c)!==Math.floor(c)||c<0||c>=7)return void(e.enabled=!1);e.bounds[s]=o[s]=c;break;case v:if(!n(c))return void(e.enabled=!1);if((c=+c)<0||c>24)return 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a=Pt.dragmode,s=t._fullLayout.dragmode;s!==a&&(Pt.dragmode=s),yt(),pt=t._fullLayout._invScaleX,dt=t._fullLayout._invScaleY,ot||(gt?e.shiftKey?\"pan\"===s?s=\"zoom\":y(s)||(s=\"pan\"):e.ctrlKey&&(s=\"pan\"):s=\"pan\"),x(s)?Pt.minDrag=1:Pt.minDrag=void 0,y(s)?(Pt.xaxes=J,Pt.yaxes=K,A(e,r,n,Pt,s)):(Pt.clickFn=Ot,y(a)&&It(),ot||(\"zoom\"===s?(Pt.moveFn=zt,Pt.doneFn=Rt,Pt.minDrag=1,function(e,r,n){var a=bt.getBoundingClientRect();_t=r-a.left,wt=n-a.top,t._fullLayout._calcInverseTransform(t);var s=i.apply3DTransform(t._fullLayout._invTransform)(_t,wt);_t=s[0],wt=s[1],Tt={l:_t,r:_t,w:0,t:wt,b:wt,h:0},kt=t._hmpixcount?t._hmlumcount/t._hmpixcount:o(t._fullLayout.plot_bgcolor).getLuminance(),Mt=!1,St=\"xy\",Ct=!1,Et=N(mt,kt,Q,$,At=\"M0,0H\"+tt+\"V\"+et+\"H0V0\"),Lt=j(mt,Q,$)}(0,r,n)):\"pan\"===s&&(Pt.moveFn=Ut,Pt.doneFn=Gt))),t._fullLayout._redrag=function(){var e=t._dragdata;if(e&&e.element===bt){var r=t._fullLayout.dragmode;y(r)||(yt(),Yt([0,0,tt,et]),Pt.moveFn(e.dx,e.dy))}}},g.init(Pt);var 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r,n,a,o,s=t._fullLayout,c=s._plots,u=s._subplots.cartesian;if(ut&&l.subplotsRegistry.splom.drag(t),ct)for(r=0;r<u.length;r++)if(a=(n=c[u[r]]).xaxis,o=n.yaxis,n._scene){var f=i.simpleMap(a.range,a.r2l),p=i.simpleMap(o.range,o.r2l);n._scene.update({range:[f[0],p[0],f[1],p[1]]})}if((ut||ct)&&(_(t),w(t)),ft){var d=e[2]/I._length,g=e[3]/O._length;for(r=0;r<u.length;r++){a=(n=c[u[r]]).xaxis,o=n.yaxis;var y,x,b,T,k=(st||nt.isSubplotConstrained)&&!a.fixedrange&&V[a._id],A=(lt||nt.isSubplotConstrained)&&!o.fixedrange&&Z[o._id];if(k?(y=d,b=v||nt.isSubplotConstrained?e[0]:Zt(a,y)):nt.xaHash[a._id]?(y=d,b=e[0]*a._length/I._length):nt.yaHash[a._id]?(y=g,b=\"ns\"===at?-e[1]*a._length/O._length:Zt(a,y,{n:\"top\",s:\"bottom\"}[at])):b=Xt(a,y=Wt(a,d,g)),A?(x=g,T=m||nt.isSubplotConstrained?e[1]:Zt(o,x)):nt.yaHash[o._id]?(x=g,T=e[1]*o._length/O._length):nt.xaHash[o._id]?(x=d,T=\"ew\"===it?-e[0]*o._length/I._length:Zt(o,x,{e:\"right\",w:\"left\"}[it])):T=Xt(o,x=Wt(o,d,g)),y||x){y||(y=1),x||(x=1);var M=a._offset-b/y,S=o._offset-T/x;n.clipRect.call(h.setTranslate,b,T).call(h.setScale,y,x),n.plot.call(h.setTranslate,M,S).call(h.setScale,1/y,1/x),y===n.xScaleFactor&&x===n.yScaleFactor||(h.setPointGroupScale(n.zoomScalePts,y,x),h.setTextPointsScale(n.zoomScaleTxt,y,x)),h.hideOutsideRangePoints(n.clipOnAxisFalseTraces,n),n.xScaleFactor=y,n.yScaleFactor=x}}}}function Wt(t,e,r){return t.fixedrange?0:st&&rt.xaHash[t._id]?e:lt&&(rt.isSubplotConstrained?rt.xaHash:rt.yaHash)[t._id]?r:0}function Xt(t,e){return e?(t.range=t._r.slice(),E(t,e),Zt(t,e)):0}function Zt(t,e,r){return t._length*(1-e)*b[r||t.constraintoward||\"middle\"]}return m.length*v.length!=1&&W(bt,(function(e){if(t._context._scrollZoom.cartesian||t._fullLayout._enablescrollzoom){if(It(),t._transitioningWithDuration)return e.preventDefault(),void e.stopPropagation();yt(),clearTimeout(Bt);var r=-e.deltaY;if(isFinite(r)||(r=e.wheelDelta/10),isFinite(r)){var n,a=Math.exp(-Math.min(Math.max(r,-20),20)/200),o=jt.draglayer.select(\".nsewdrag\").node().getBoundingClientRect(),s=(e.clientX-o.left)/o.width,l=(o.bottom-e.clientY)/o.height;if(st){for(v||(s=.5),n=0;n<J.length;n++)c(J[n],s,a);Vt(\"x\"),Ft[2]*=a,Ft[0]+=Ft[2]*s*(1/a-1)}if(lt){for(m||(l=.5),n=0;n<K.length;n++)c(K[n],l,a);Vt(\"y\"),Ft[3]*=a,Ft[1]+=Ft[3]*(1-l)*(1/a-1)}Yt(Ft),Ht(),t.emit(\"plotly_relayouting\",ht),Bt=setTimeout((function(){t._fullLayout&&(Ft=[0,0,tt,et],Gt())}),Nt),e.preventDefault()}else i.log(\"Did not find wheel motion attributes: \",e)}function c(t,e,r){if(!t.fixedrange){var n=i.simpleMap(t.range,t.r2l),a=n[0]+(n[1]-n[0])*e;t.range=n.map((function(e){return t.l2r(a+(e-a)*r)}))}}})),bt},makeDragger:O,makeRectDragger:z,makeZoombox:N,makeCorners:j,updateZoombox:U,xyCorners:G,transitionZoombox:V,removeZoombox:H,showDoubleClickNotifier:q,attachWheelEventHandler:W}},{\"../../components/color\":366,\"../../components/dragelement\":385,\"../../components/dragelement/helpers\":384,\"../../components/drawing\":388,\"../../components/fx\":406,\"../../constants/alignment\":471,\"../../lib\":503,\"../../lib/clear_gl_canvases\":487,\"../../lib/setcursor\":524,\"../../lib/svg_text_utils\":529,\"../../plot_api/subroutines\":544,\"../../registry\":638,\"../plots\":619,\"./axes\":554,\"./axis_ids\":558,\"./constants\":561,\"./scale_zoom\":574,\"./select\":575,\"@plotly/d3\":58,\"has-passive-events\":229,tinycolor2:312}],564:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../components/fx\"),a=t(\"../../components/dragelement\"),o=t(\"../../lib/setcursor\"),s=t(\"./dragbox\").makeDragBox,l=t(\"./constants\").DRAGGERSIZE;r.initInteractions=function(t){var e=t._fullLayout;if(t._context.staticPlot)n.select(t).selectAll(\".drag\").remove();else if(e._has(\"cartesian\")||e._has(\"splom\")){Object.keys(e._plots||{}).sort((function(t,r){if((e._plots[t].mainplot&&!0)===(e._plots[r].mainplot&&!0)){var n=t.split(\"y\"),i=r.split(\"y\");return n[0]===i[0]?Number(n[1]||1)-Number(i[1]||1):Number(n[0]||1)-Number(i[0]||1)}return e._plots[t].mainplot?1:-1})).forEach((function(r){var n=e._plots[r],o=n.xaxis,c=n.yaxis;if(!n.mainplot){var u=s(t,n,o._offset,c._offset,o._length,c._length,\"ns\",\"ew\");u.onmousemove=function(e){t._fullLayout._rehover=function(){t._fullLayout._hoversubplot===r&&t._fullLayout._plots[r]&&i.hover(t,e,r)},i.hover(t,e,r),t._fullLayout._lasthover=u,t._fullLayout._hoversubplot=r},u.onmouseout=function(e){t._dragging||(t._fullLayout._hoversubplot=null,a.unhover(t,e))},t._context.showAxisDragHandles&&(s(t,n,o._offset-l,c._offset-l,l,l,\"n\",\"w\"),s(t,n,o._offset+o._length,c._offset-l,l,l,\"n\",\"e\"),s(t,n,o._offset-l,c._offset+c._length,l,l,\"s\",\"w\"),s(t,n,o._offset+o._length,c._offset+c._length,l,l,\"s\",\"e\"))}if(t._context.showAxisDragHandles){if(r===o._mainSubplot){var f=o._mainLinePosition;\"top\"===o.side&&(f-=l),s(t,n,o._offset+.1*o._length,f,.8*o._length,l,\"\",\"ew\"),s(t,n,o._offset,f,.1*o._length,l,\"\",\"w\"),s(t,n,o._offset+.9*o._length,f,.1*o._length,l,\"\",\"e\")}if(r===c._mainSubplot){var h=c._mainLinePosition;\"right\"!==c.side&&(h-=l),s(t,n,h,c._offset+.1*c._length,l,.8*c._length,\"ns\",\"\"),s(t,n,h,c._offset+.9*c._length,l,.1*c._length,\"s\",\"\"),s(t,n,h,c._offset,l,.1*c._length,\"n\",\"\")}}}));var o=e._hoverlayer.node();o.onmousemove=function(r){r.target=t._fullLayout._lasthover,i.hover(t,r,e._hoversubplot)},o.onclick=function(e){e.target=t._fullLayout._lasthover,i.click(t,e)},o.onmousedown=function(e){t._fullLayout._lasthover.onmousedown(e)},r.updateFx(t)}},r.updateFx=function(t){var e=t._fullLayout,r=\"pan\"===e.dragmode?\"move\":\"crosshair\";o(e._draggers,r)}},{\"../../components/dragelement\":385,\"../../components/fx\":406,\"../../lib/setcursor\":524,\"./constants\":561,\"./dragbox\":563,\"@plotly/d3\":58}],565:[function(t,e,r){\"use strict\";e.exports={clearOutlineControllers:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(\".outline-controllers\").remove()},clearSelect:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(\".select-outline\").remove(),t._fullLayout._drawing=!1}}},{}],566:[function(t,e,r){\"use strict\";var n=t(\"../../lib\").strTranslate;function i(t,e){switch(t.type){case\"log\":return t.p2d(e);case\"date\":return t.p2r(e,0,t.calendar);default:return t.p2r(e)}}e.exports={p2r:i,r2p:function(t,e){switch(t.type){case\"log\":return t.d2p(e);case\"date\":return t.r2p(e,0,t.calendar);default:return t.r2p(e)}},axValue:function(t){var e=\"y\"===t._id.charAt(0)?1:0;return function(r){return i(t,r[e])}},getTransform:function(t){return n(t.xaxis._offset,t.yaxis._offset)}}},{\"../../lib\":503}],567:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"./axis_ids\");e.exports=function(t){return function(e,r){var o=e[t];if(Array.isArray(o))for(var s=n.subplotsRegistry.cartesian,l=s.idRegex,c=r._subplots,u=c.xaxis,f=c.yaxis,h=c.cartesian,p=r._has(\"cartesian\")||r._has(\"gl2d\"),d=0;d<o.length;d++){var m=o[d];if(i.isPlainObject(m)){var g=a.cleanId(m.xref,\"x\",!1),v=a.cleanId(m.yref,\"y\",!1),y=l.x.test(g),x=l.y.test(v);if(y||x){p||i.pushUnique(r._basePlotModules,s);var b=!1;y&&-1===u.indexOf(g)&&(u.push(g),b=!0),x&&-1===f.indexOf(v)&&(f.push(v),b=!0),b&&y&&x&&h.push(g+v)}}}}}},{\"../../lib\":503,\"../../registry\":638,\"./axis_ids\":558}],568:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../registry\"),a=t(\"../../lib\"),o=t(\"../plots\"),s=t(\"../../components/drawing\"),l=t(\"../get_data\").getModuleCalcData,c=t(\"./axis_ids\"),u=t(\"./constants\"),f=t(\"../../constants/xmlns_namespaces\"),h=a.ensureSingle;function p(t,e,r){return a.ensureSingle(t,e,r,(function(t){t.datum(r)}))}function d(t,e,r,a,o){for(var c,f,h,p=u.traceLayerClasses,d=t._fullLayout,m=d._modules,g=[],v=[],y=0;y<m.length;y++){var x=(c=m[y]).name,b=i.modules[x].categories;if(b.svg){var _=c.layerName||x+\"layer\",w=c.plot;h=(f=l(r,w))[0],r=f[1],h.length&&g.push({i:p.indexOf(_),className:_,plotMethod:w,cdModule:h}),b.zoomScale&&v.push(\".\"+_)}}g.sort((function(t,e){return t.i-e.i}));var T=e.plot.selectAll(\"g.mlayer\").data(g,(function(t){return t.className}));if(T.enter().append(\"g\").attr(\"class\",(function(t){return t.className})).classed(\"mlayer\",!0).classed(\"rangeplot\",e.isRangePlot),T.exit().remove(),T.order(),T.each((function(r){var i=n.select(this),l=r.className;r.plotMethod(t,e,r.cdModule,i,a,o),-1===u.clipOnAxisFalseQuery.indexOf(\".\"+l)&&s.setClipUrl(i,e.layerClipId,t)})),d._has(\"scattergl\")&&(c=i.getModule(\"scattergl\"),h=l(r,c)[0],c.plot(t,e,h)),!t._context.staticPlot&&(e._hasClipOnAxisFalse&&(e.clipOnAxisFalseTraces=e.plot.selectAll(u.clipOnAxisFalseQuery.join(\",\")).selectAll(\".trace\")),v.length)){var k=e.plot.selectAll(v.join(\",\")).selectAll(\".trace\");e.zoomScalePts=k.selectAll(\"path.point\"),e.zoomScaleTxt=k.selectAll(\".textpoint\")}}function m(t,e){var r=e.plotgroup,n=e.id,i=u.layerValue2layerClass[e.xaxis.layer],a=u.layerValue2layerClass[e.yaxis.layer],o=t._fullLayout._hasOnlyLargeSploms;if(e.mainplot){var s=e.mainplotinfo,l=s.plotgroup,f=n+\"-x\",d=n+\"-y\";e.minorGridlayer=s.minorGridlayer,e.gridlayer=s.gridlayer,e.zerolinelayer=s.zerolinelayer,h(s.overlinesBelow,\"path\",f),h(s.overlinesBelow,\"path\",d),h(s.overaxesBelow,\"g\",f),h(s.overaxesBelow,\"g\",d),e.plot=h(s.overplot,\"g\",n),h(s.overlinesAbove,\"path\",f),h(s.overlinesAbove,\"path\",d),h(s.overaxesAbove,\"g\",f),h(s.overaxesAbove,\"g\",d),e.xlines=l.select(\".overlines-\"+i).select(\".\"+f),e.ylines=l.select(\".overlines-\"+a).select(\".\"+d),e.xaxislayer=l.select(\".overaxes-\"+i).select(\".\"+f),e.yaxislayer=l.select(\".overaxes-\"+a).select(\".\"+d)}else 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yt=b(\"rangeslider\",\"handleDefaults\"),xt=b(\"rangeselector\",\"handleDefaults\");for(y=0;y<j.length;y++)q=j[y],W=t[q],X=e[q],yt(t,e,q),\"date\"===X.type&&xt(W,X,e,U,X.calendar),K(\"fixedrange\");for(y=0;y<U.length;y++){q=U[y],W=t[q],X=e[q];var bt=e[m(X.anchor)];K(\"fixedrange\",b(\"rangeslider\",\"isVisible\")(bt))}h.handleDefaults(t,e,{axIds:rt.concat(it).sort(d.idSort),axHasImage:P})}},{\"../../components/color\":366,\"../../components/fx/helpers\":402,\"../../components/fx/hovermode_defaults\":405,\"../../lib\":503,\"../../plot_api/plot_template\":543,\"../../registry\":638,\"../layout_attributes\":610,\"./axis_defaults\":556,\"./axis_ids\":558,\"./constants\":561,\"./constraints\":562,\"./layout_attributes\":569,\"./position_defaults\":572,\"./type_defaults\":582}],571:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../components/color/attributes\"),a=t(\"../../lib\");e.exports=function(t,e,r,o){var s=(o=o||{}).dfltColor;function l(r,n){return a.coerce2(t,e,o.attributes,r,n)}var c=l(\"linecolor\",s),u=l(\"linewidth\");r(\"showline\",o.showLine||!!c||!!u)||(delete e.linecolor,delete e.linewidth);var f=l(\"gridcolor\",n(s,o.bgColor,o.blend||i.lightFraction).toRgbString()),h=l(\"gridwidth\"),p=l(\"griddash\");if(r(\"showgrid\",o.showGrid||!!f||!!h||!!p)||(delete e.gridcolor,delete e.gridwidth,delete e.griddash),o.hasMinor){var d=l(\"minor.gridcolor\",n(e.gridcolor,o.bgColor,67).toRgbString()),m=l(\"minor.gridwidth\",e.gridwidth||1),g=l(\"minor.griddash\",e.griddash||\"solid\");r(\"minor.showgrid\",!!d||!!m||!!g)||(delete e.minor.gridcolor,delete e.minor.gridwidth,delete e.minor.griddash)}if(!o.noZeroLine){var v=l(\"zerolinecolor\",s),y=l(\"zerolinewidth\");r(\"zeroline\",o.showGrid||!!v||!!y)||(delete e.zerolinecolor,delete e.zerolinewidth)}}},{\"../../components/color/attributes\":365,\"../../lib\":503,tinycolor2:312}],572:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\");e.exports=function(t,e,r,a){var o,s,l,c,u=a.counterAxes||[],f=a.overlayableAxes||[],h=a.letter,p=a.grid;p&&(s=p._domains[h][p._axisMap[e._id]],o=p._anchors[e._id],s&&(l=p[h+\"side\"].split(\" \")[0],c=p.domain[h][\"right\"===l||\"top\"===l?1:0])),s=s||[0,1],o=o||(n(t.position)?\"free\":u[0]||\"free\"),l=l||(\"x\"===h?\"bottom\":\"left\"),c=c||0,\"free\"===i.coerce(t,e,{anchor:{valType:\"enumerated\",values:[\"free\"].concat(u),dflt:o}},\"anchor\")&&r(\"position\",c),i.coerce(t,e,{side:{valType:\"enumerated\",values:\"x\"===h?[\"bottom\",\"top\"]:[\"left\",\"right\"],dflt:l}},\"side\");var d=!1;if(f.length&&(d=i.coerce(t,e,{overlaying:{valType:\"enumerated\",values:[!1].concat(f),dflt:!1}},\"overlaying\")),!d){var m=r(\"domain\",s);m[0]>m[1]-1/4096&&(e.domain=s),i.noneOrAll(t.domain,e.domain,s)}return r(\"layer\"),e}},{\"../../lib\":503,\"fast-isnumeric\":190}],573:[function(t,e,r){\"use strict\";var n=t(\"./show_dflt\");e.exports=function(t,e,r,i,a){a||(a={});var o=a.tickSuffixDflt,s=n(t);r(\"tickprefix\")&&r(\"showtickprefix\",s),r(\"ticksuffix\",o)&&r(\"showticksuffix\",s)}},{\"./show_dflt\":577}],574:[function(t,e,r){\"use strict\";var n=t(\"../../constants/alignment\").FROM_BL;e.exports=function(t,e,r){void 0===r&&(r=n[t.constraintoward||\"center\"]);var i=[t.r2l(t.range[0]),t.r2l(t.range[1])],a=i[0]+(i[1]-i[0])*r;t.range=t._input.range=[t.l2r(a+(i[0]-a)*e),t.l2r(a+(i[1]-a)*e)],t.setScale()}},{\"../../constants/alignment\":471}],575:[function(t,e,r){\"use strict\";var n=t(\"polybooljs\"),i=t(\"../../registry\"),a=t(\"../../components/drawing\").dashStyle,o=t(\"../../components/color\"),s=t(\"../../components/fx\"),l=t(\"../../components/fx/helpers\").makeEventData,c=t(\"../../components/dragelement/helpers\"),u=c.freeMode,f=c.rectMode,h=c.drawMode,p=c.openMode,d=c.selectMode,m=t(\"../../components/shapes/draw_newshape/display_outlines\"),g=t(\"../../components/shapes/draw_newshape/helpers\").handleEllipse,v=t(\"../../components/shapes/draw_newshape/newshapes\"),y=t(\"../../lib\"),x=t(\"../../lib/polygon\"),b=t(\"../../lib/throttle\"),_=t(\"./axis_ids\").getFromId,w=t(\"../../lib/clear_gl_canvases\"),T=t(\"../../plot_api/subroutines\").redrawReglTraces,k=t(\"./constants\"),A=k.MINSELECT,M=x.filter,S=x.tester,E=t(\"./handle_outline\").clearSelect,L=t(\"./helpers\"),C=L.p2r,P=L.axValue,I=L.getTransform;function O(t,e,r,n,i,a,o){var s,l,c,u,f,h,d,g,v,y=e._hoverdata,x=e._fullLayout.clickmode.indexOf(\"event\")>-1,b=[];if(function(t){return 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e=[],r=z(t[0])?0:t[0][0][0],n=r,i=z(t[0])?0:t[0][0][1],a=i,o=0;o<t.length;o++)if(z(t[o]))e.push(D(t[o]));else{var s=x.tester(t[o]);s.subtract=t[o].subtract,e.push(s),r=Math.min(r,s.xmin),n=Math.max(n,s.xmax),i=Math.min(i,s.ymin),a=Math.max(a,s.ymax)}return{xmin:r,xmax:n,ymin:i,ymax:a,pts:[],contains:function(t,r,n,i){for(var a=!1,o=0;o<e.length;o++)e[o].contains(t,r,n,i)&&(a=!1===e[o].subtract);return a},isRect:!1,degenerate:!1}}function F(t,e,r){e._fullLayout._drawing=!1;var n=e._fullLayout,i=r.plotinfo,a=r.dragmode,o=n._lastSelectedSubplot&&n._lastSelectedSubplot===i.id,s=(t.shiftKey||t.altKey)&&!(h(a)&&p(a));o&&s&&i.selection&&i.selection.selectionDefs&&!r.selectionDefs?(r.selectionDefs=i.selection.selectionDefs,r.mergedPolygons=i.selection.mergedPolygons):s&&i.selection||B(r),o||(E(e),n._lastSelectedSubplot=i.id)}function B(t){var e=t.dragmode,r=t.plotinfo,n=t.gd;if(n._fullLayout._activeShapeIndex>=0&&n._fullLayout._deactivateShape(n),h(e)){var 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r=this.bounds,n=(e.framewidth||0)/2,i=r[0][0]-n,a=r[0][1]-n,o=r[1][0]-i+n,s=r[1][1]-a+n;h.setRect(this.clipRect,i,a,o,s),this.bgRect.call(h.setRect,i,a,o,s).call(f.fill,e.bgcolor),this.xaxis._offset=i,this.xaxis._length=o,this.yaxis._offset=a,this.yaxis._length=s},S.updateFx=function(t,e){var r=this,i=r.graphDiv,a=r.bgRect,o=t.dragmode,s=t.clickmode;if(!r.isStatic){var u;\"select\"===o?u=function(t,e){(t.range={})[r.id]=[h([e.xmin,e.ymin]),h([e.xmax,e.ymax])]}:\"lasso\"===o&&(u=function(t,e,n){(t.lassoPoints={})[r.id]=n.filtered.map(h)});var f={element:r.bgRect.node(),gd:i,plotinfo:{id:r.id,xaxis:r.xaxis,yaxis:r.yaxis,fillRangeItems:u},xaxes:[r.xaxis],yaxes:[r.yaxis],subplot:r.id,clickFn:function(t){2===t&&x(i)}};\"pan\"===o?(a.node().onmousedown=null,a.call(_(r,e)),a.on(\"dblclick.zoom\",(function(){var t=r.viewInitial,e={};for(var n in 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t=this,e=t.graphDiv,r=e._fullLayout,i=\"clip\"+r._uid+t.id;t.clipDef=r._clips.append(\"clipPath\").attr(\"id\",i),t.clipRect=t.clipDef.append(\"rect\"),t.framework=n.select(t.container).append(\"g\").attr(\"class\",\"geo \"+t.id).call(h.setClipUrl,i,e),t.project=function(e){var r=t.projection(e);return r?[r[0]-t.xaxis._offset,r[1]-t.yaxis._offset]:[null,null]},t.xaxis={_id:\"x\",c2p:function(e){return t.project(e)[0]}},t.yaxis={_id:\"y\",c2p:function(e){return t.project(e)[1]}},t.mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},m.setConvert(t.mockAxis,r)},S.saveViewInitial=function(t){var 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e=t._fullLayout,r=t.calcdata,i=e._subplots.geo,s=0;s<i.length;s++){var l=i[s],c=n(r,o,l),u=e[l]._subplot;u||(u=a({id:l,graphDiv:t,container:e._geolayer.node(),topojsonURL:t._context.topojsonURL,staticPlot:t._context.staticPlot}),e[l]._subplot=u),u.plot(c,e,t._promises)}},updateFx:function(t){for(var e=t._fullLayout,r=e._subplots.geo,n=0;n<r.length;n++){var i=e[r[n]];i._subplot.updateFx(e,i)}},clean:function(t,e,r,n){for(var i=n._subplots.geo||[],a=0;a<i.length;a++){var o=i[a],s=n[o]._subplot;!e[o]&&s&&(s.framework.remove(),s.clipDef.remove())}}}},{\"../../lib\":503,\"../../plots/get_data\":593,\"./geo\":588,\"./layout_attributes\":590,\"./layout_defaults\":591}],590:[function(t,e,r){\"use strict\";var n=t(\"../../components/color/attributes\"),i=t(\"../domain\").attributes,a=t(\"../../components/drawing/attributes\").dash,o=t(\"./constants\"),s=t(\"../../plot_api/edit_types\").overrideAll,l=t(\"../../lib/sort_object_keys\"),c={range:{valType:\"info_array\",items:[{valType:\"number\"},{valType:\"number\"}]},showgrid:{valType:\"boolean\",dflt:!1},tick0:{valType:\"number\",dflt:0},dtick:{valType:\"number\"},gridcolor:{valType:\"color\",dflt:n.lightLine},gridwidth:{valType:\"number\",min:0,dflt:1},griddash:a};(e.exports=s({domain:i({name:\"geo\"},{}),fitbounds:{valType:\"enumerated\",values:[!1,\"locations\",\"geojson\"],dflt:!1,editType:\"plot\"},resolution:{valType:\"enumerated\",values:[110,50],dflt:110,coerceNumber:!0},scope:{valType:\"enumerated\",values:l(o.scopeDefaults),dflt:\"world\"},projection:{type:{valType:\"enumerated\",values:l(o.projNames)},rotation:{lon:{valType:\"number\"},lat:{valType:\"number\"},roll:{valType:\"number\"}},tilt:{valType:\"number\",dflt:0},distance:{valType:\"number\",min:1.001,dflt:2},parallels:{valType:\"info_array\",items:[{valType:\"number\"},{valType:\"number\"}]},scale:{valType:\"number\",min:0,dflt:1}},center:{lon:{valType:\"number\"},lat:{valType:\"number\"}},visible:{valType:\"boolean\",dflt:!0},showcoastlines:{valType:\"boolean\"},coastlinecolor:{valType:\"color\",dflt:n.defaultLine},coastlinewidth:{valType:\"number\",min:0,dflt:1},showland:{valType:\"boolean\",dflt:!1},landcolor:{valType:\"color\",dflt:o.landColor},showocean:{valType:\"boolean\",dflt:!1},oceancolor:{valType:\"color\",dflt:o.waterColor},showlakes:{valType:\"boolean\",dflt:!1},lakecolor:{valType:\"color\",dflt:o.waterColor},showrivers:{valType:\"boolean\",dflt:!1},rivercolor:{valType:\"color\",dflt:o.waterColor},riverwidth:{valType:\"number\",min:0,dflt:1},showcountries:{valType:\"boolean\"},countrycolor:{valType:\"color\",dflt:n.defaultLine},countrywidth:{valType:\"number\",min:0,dflt:1},showsubunits:{valType:\"boolean\"},subunitcolor:{valType:\"color\",dflt:n.defaultLine},subunitwidth:{valType:\"number\",min:0,dflt:1},showframe:{valType:\"boolean\"},framecolor:{valType:\"color\",dflt:n.defaultLine},framewidth:{valType:\"number\",min:0,dflt:1},bgcolor:{valType:\"color\",dflt:n.background},lonaxis:c,lataxis:c},\"plot\",\"from-root\")).uirevision={valType:\"any\",editType:\"none\"}},{\"../../components/color/attributes\":365,\"../../components/drawing/attributes\":387,\"../../lib/sort_object_keys\":526,\"../../plot_api/edit_types\":536,\"../domain\":584,\"./constants\":587}],591:[function(t,e,r){\"use 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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 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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\":407,\"../../constants/xmlns_namespaces\":480,\"../../plot_api/edit_types\":536,\"../cartesian\":568,\"../cartesian/attributes\":552,\"../cartesian/constants\":561,\"../get_data\":593,\"../layout_attributes\":610,\"./scene2d\":597}],597:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../registry\"),o=t(\"../../plots/cartesian/axes\"),s=t(\"../../components/fx\"),l=t(\"../../../stackgl_modules\").gl_plot2d,c=t(\"../../../stackgl_modules\").gl_spikes2d,u=t(\"../../../stackgl_modules\").gl_select_box,f=t(\"webgl-context\"),h=t(\"./convert\"),p=t(\"./camera\"),d=t(\"../../lib/show_no_webgl_msg\"),m=t(\"../cartesian/constraints\"),g=m.enforce,v=m.clean,y=t(\"../cartesian/autorange\").doAutoRange,x=t(\"../../components/dragelement/helpers\"),b=x.drawMode,_=x.selectMode,w=[\"xaxis\",\"yaxis\"],T=t(\"../cartesian/constants\").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var A=k.prototype;A.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}))},A.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\",{willReadFrequently:!0}),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},A.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},A.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},A.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]},A.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)},A.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())},A.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)}},A.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}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+\"px\",this.mouseContainer.style.height=u.h*(h[1]-h[0])+\"px\",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+\"px\",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+\"px\",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.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])]},A.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])]},A.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}))},A.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},A.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]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var m=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var g=this.fullData[m.trace.index]||{},v=m.pointIndex,y=s.castHoverinfo(g,i,v);if(y&&\"all\"!==y){var x=y.split(\"+\");-1===x.indexOf(\"x\")&&(m.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(m.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(m.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(m.textLabel=void 0),-1===x.indexOf(\"name\")&&(m.name=void 0)}s.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",m.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:s.castHoverOption(g,v,\"bgcolor\")||m.color,borderColor:s.castHoverOption(g,v,\"bordercolor\"),fontFamily:s.castHoverOption(g,v,\"font.family\"),fontSize:s.castHoverOption(g,v,\"font.size\"),fontColor:s.castHoverOption(g,v,\"font.color\"),nameLength:s.castHoverOption(g,v,\"namelength\"),textAlign:s.castHoverOption(g,v,\"align\")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../../stackgl_modules\":1124,\"../../components/dragelement/helpers\":384,\"../../components/fx\":406,\"../../lib/show_no_webgl_msg\":525,\"../../plots/cartesian/axes\":554,\"../../registry\":638,\"../cartesian/autorange\":553,\"../cartesian/constants\":561,\"../cartesian/constraints\":562,\"./camera\":594,\"./convert\":595,\"webgl-context\":331}],598:[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\":407,\"../../constants/xmlns_namespaces\":480,\"../../lib\":503,\"../../plot_api/edit_types\":536,\"../get_data\":593,\"./layout/attributes\":599,\"./layout/defaults\":603,\"./layout/layout_attributes\":604,\"./scene\":608}],599:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],600:[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\":366,\"../../../lib/extend\":493,\"../../../plot_api/edit_types\":536,\"../../cartesian/layout_attributes\":569}],601:[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,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h(\"gridcolor\",n(f.color,r.bgColor,13600/187).toRgbString()),h(\"title.text\",d[0]),f.setScale=i.noop,h(\"showspikes\")&&(h(\"spikesides\"),h(\"spikethickness\"),h(\"spikecolor\",f.color)),h(\"showaxeslabels\"),h(\"showbackground\")&&h(\"backgroundcolor\")}}},{\"../../../lib\":503,\"../../../plot_api/plot_template\":543,\"../../cartesian/axis_defaults\":556,\"../../cartesian/type_defaults\":582,\"./axis_attributes\":600,tinycolor2:312}],602:[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\":503,\"../../../lib/str2rgbarray\":528}],603:[function(t,e,r){\"use strict\";var n=t(\"../../../lib\"),i=t(\"../../../components/color\"),a=t(\"../../../registry\"),o=t(\"../../subplot_defaults\"),s=t(\"./axis_defaults\"),l=t(\"./layout_attributes\"),c=t(\"../../get_data\").getSubplotData;function u(t,e,r,n){for(var o=r(\"bgcolor\"),l=i.combine(o,n.paper_bgcolor),u=[\"up\",\"center\",\"eye\"],f=0;f<u.length;f++)r(\"camera.\"+u[f]+\".x\"),r(\"camera.\"+u[f]+\".y\"),r(\"camera.\"+u[f]+\".z\");r(\"camera.projection.type\");var h=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),p=r(\"aspectmode\",h?\"manual\":\"auto\");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===p&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode);var d=c(n.fullData,\"gl3d\",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n);var m=n.getDfltFromLayout(\"dragmode\");if(!1!==m&&!m)if(m=\"orbit\",t.camera&&t.camera.up){var g=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(g&&v&&y?y/Math.sqrt(g*g+v*v+y*y)>.999&&(m=\"turntable\"):m=\"turntable\")}else m=\"turntable\";r(\"dragmode\",m),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:\"gl3d\",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":366,\"../../../lib\":503,\"../../../registry\":638,\"../../get_data\":593,\"../../subplot_defaults\":632,\"./axis_defaults\":601,\"./layout_attributes\":604}],604:[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\":503,\"../../../lib/extend\":493,\"../../domain\":584,\"./axis_attributes\":600}],605:[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\":528}],606:[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\":503,\"../../cartesian/axes\":554}],607:[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])))}},{}],608:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../../stackgl_modules\").gl_plot3d,o=a.createCamera,s=a.createScene,l=t(\"webgl-context\"),c=t(\"has-passive-events\"),u=t(\"../../registry\"),f=t(\"../../lib\"),h=f.preserveDrawingBuffer(),p=t(\"../../plots/cartesian/axes\"),d=t(\"../../components/fx\"),m=t(\"../../lib/str2rgbarray\"),g=t(\"../../lib/show_no_webgl_msg\"),v=t(\"./project\"),y=t(\"./layout/convert\"),x=t(\"./layout/spikes\"),b=t(\"./layout/tick_marks\");function _(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=u.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement(\"canvas\"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e=\"orthographic\"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:\"orbit\"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+\".camera\"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+\".aspectratio\"]=t.glplot.getAspectratio(),\"manual\"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+\".aspectmode\"]=\"manual\")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit(\"plotly_relayout\",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener(\"mouseup\",(function(){i(t)})),t.glplot.canvas.addEventListener(\"wheel\",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener(\"mousemove\",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit(\"plotly_relayouting\",e)}})),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",(function(r){e&&e.emit&&e.emit(\"plotly_webglcontextlost\",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c=Object.keys(e.traces),u=null,h=e.glplot.selection,m=0;m<c.length;++m)\"skip\"!==(t=e.traces[c[m]]).data.hoverinfo&&t.handlePick(h)&&(u=t),t.setContourLevels&&t.setContourLevels();function g(t,r,n){var i=e.fullSceneLayout[t+\"axis\"];return\"log\"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==u){var y=v(e.glplot.cameraParams,h.dataCoordinate);t=u.data;var x,_=r._fullData[t.index],w=h.index,T={xLabel:g(\"x\",h.traceCoordinate[0],t.xhoverformat),yLabel:g(\"y\",h.traceCoordinate[1],t.yhoverformat),zLabel:g(\"z\",h.traceCoordinate[2],t.zhoverformat)},k=d.castHoverinfo(_,e.fullLayout,w),A=(k||\"\").split(\"+\"),M=k&&\"all\"===k;_.hovertemplate||M||(-1===A.indexOf(\"x\")&&(T.xLabel=void 0),-1===A.indexOf(\"y\")&&(T.yLabel=void 0),-1===A.indexOf(\"z\")&&(T.zLabel=void 0),-1===A.indexOf(\"text\")&&(h.textLabel=void 0),-1===A.indexOf(\"name\")&&(u.name=void 0));var S=[];\"cone\"===t.type||\"streamtube\"===t.type?(T.uLabel=g(\"x\",h.traceCoordinate[3],t.uhoverformat),(M||-1!==A.indexOf(\"u\"))&&S.push(\"u: \"+T.uLabel),T.vLabel=g(\"y\",h.traceCoordinate[4],t.vhoverformat),(M||-1!==A.indexOf(\"v\"))&&S.push(\"v: \"+T.vLabel),T.wLabel=g(\"z\",h.traceCoordinate[5],t.whoverformat),(M||-1!==A.indexOf(\"w\"))&&S.push(\"w: \"+T.wLabel),T.normLabel=h.traceCoordinate[6].toPrecision(3),(M||-1!==A.indexOf(\"norm\"))&&S.push(\"norm: \"+T.normLabel),\"streamtube\"===t.type&&(T.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(M||-1!==A.indexOf(\"divergence\"))&&S.push(\"divergence: \"+T.divergenceLabel)),h.textLabel&&S.push(h.textLabel),x=S.join(\"<br>\")):\"isosurface\"===t.type||\"volume\"===t.type?(T.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),S.push(\"value: \"+T.valueLabel),h.textLabel&&S.push(h.textLabel),x=S.join(\"<br>\")):x=h.textLabel;var E={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:w};d.appendArrayPointValue(E,_,w),t._module.eventData&&(E=_._module.eventData(E,h,_,{},w));var L={points:[E]};if(e.fullSceneLayout.hovermode){var C=[];d.loneHover({trace:_,x:(.5+.5*y[0]/y[3])*s,y:(.5-.5*y[1]/y[3])*l,xLabel:T.xLabel,yLabel:T.yLabel,zLabel:T.zLabel,text:x,name:u.name,color:d.castHoverOption(_,w,\"bgcolor\")||u.color,borderColor:d.castHoverOption(_,w,\"bordercolor\"),fontFamily:d.castHoverOption(_,w,\"font.family\"),fontSize:d.castHoverOption(_,w,\"font.size\"),fontColor:d.castHoverOption(_,w,\"font.color\"),nameLength:d.castHoverOption(_,w,\"namelength\"),textAlign:d.castHoverOption(_,w,\"align\"),hovertemplate:f.castOption(_,w,\"hovertemplate\"),hovertemplateLabels:f.extendFlat({},E,T),eventData:[E]},{container:n,gd:r,inOut_bbox:C}),E.bbox=C[0]}h.buttons&&h.distance<5?r.emit(\"plotly_click\",L):r.emit(\"plotly_hover\",L),this.oldEventData=L}else d.loneUnhover(n),this.oldEventData&&r.emit(\"plotly_unhover\",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")};requestAnimationFrame(e)};var k=[\"xaxis\",\"yaxis\",\"zaxis\"];function A(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+\"calendar\"],u=e[\"_\"+o+\"length\"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var 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(m(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&&A(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 d,g=[[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){g[0][a]=1/0,g[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,M=w._trace.data._pad||0;\"ErrorBars\"===w.constructor.name&&s._lowerLogErrorBound?g[0][a]=Math.min(g[0][a],s._lowerLogErrorBound):g[0][a]=Math.min(g[0][a],T[0][a]/h[a]-M),g[1][a]=Math.max(g[1][a],T[1][a]/h[a]+M)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);g[0][a]=Math.min(g[0][a],E),g[1][a]=Math.max(g[1][a],E)}}if(\"rangemode\"in s&&\"tozero\"===s.rangemode&&(g[0][a]=Math.min(g[0][a],0),g[1][a]=Math.max(g[1][a],0)),g[0][a]>g[1][a])g[0][a]=-1,g[1][a]=1;else{var L=g[1][a]-g[0][a];g[0][a]-=L/32,g[1][a]+=L/32}if(\"reversed\"===s.autorange){var C=g[0][a];g[0][a]=g[1][a],g[1][a]=C}}else{var P=s.range;g[0][a]=s.r2l(P[0]),g[1][a]=s.r2l(P[1])}g[0][a]===g[1][a]&&(g[0][a]-=1,g[1][a]+=1),v[a]=g[1][a]-g[0][a],this.glplot.setBounds(a,{min:g[0][a]*h[a],max:g[1][a]*h[a]})}var I=c.aspectmode;if(\"cube\"===I)d=[1,1,1];else if(\"manual\"===I){var O=c.aspectratio;d=[O.x,O.y,O.z]}else{if(\"auto\"!==I&&\"data\"!==I)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var z=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[k[a]]).type];z[a]=Math.pow(D.acc,1/D.count)/h[a]}d=\"data\"===I||Math.max.apply(null,z)/Math.min.apply(null,z)<=4?z:[1,1,1]}c.aspectratio.x=u.aspectratio.x=d[0],c.aspectratio.y=u.aspectratio.y=d[1],c.aspectratio.z=u.aspectratio.z=d[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position=\"absolute\",B.left=F.l+R.x[0]*F.w+\"px\",B.top=F.t+(1-R.y[1])*F.h+\"px\",B.width=F.w*(R.x[1]-R.x[0])+\"px\",B.height=F.h*(R.y[1]-R.y[0])+\"px\"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?\"orthographic\":\"perspective\"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),\"orthographic\"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+\".camera\").get();function n(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+\".aspectratio\").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+\".camera\")).get(),p[this.id+\".camera\"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+\".aspectratio\")).get(),p[this.id+\".aspectratio\"]=o),u.call(\"_storeDirectGUIEdit\",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+\".camera\").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+\".aspectratio\").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if(\"orbit\"===t)r.mode=\"orbit\",r.keyBindingMode=\"rotate\";else if(\"turntable\"===t){r.up=[0,0,1],r.mode=\"turntable\",r.keyBindingMode=\"rotate\";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+\".camera.up\",h={x:0,y:0,z:1},p={};p[c]=h;var d=n.layout;u.call(\"_storeDirectGUIEdit\",d,i._preGUI,p),a.up=h,f.nestedProperty(d,c).set(h)}}else r.keyBindingMode=t;this.fullSceneLayout.hovermode=e},w.toImage=function(t){t||(t=\"png\"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a),function(t,e,r){for(var n=0,i=r-1;n<i;++n,--i)for(var a=0;a<e;++a)for(var o=0;o<4;++o){var s=4*(e*n+a)+o,l=4*(e*i+a)+o,c=t[s];t[s]=t[l],t[l]=c}}(a,r,i),function(t,e,r){for(var n=0;n<r;++n)for(var i=0;i<e;++i){var a=4*(e*n+i),o=t[a+3];if(o>0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(a,r,i);var o=document.createElement(\"canvas\");o.width=r,o.height=i;var s,l=o.getContext(\"2d\",{willReadFrequently:!0}),c=l.createImageData(r,i);switch(c.data.set(a),l.putImageData(c,0,0),t){case\"jpeg\":s=o.toDataURL(\"image/jpeg\");break;case\"webp\":s=o.toDataURL(\"image/webp\");break;default:s=o.toDataURL(\"image/png\")}return this.staticMode&&this.container.removeChild(n),s},w.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[k[t]];p.setConvert(e,this.fullLayout),e.setScale=f.noop}},w.make4thDimension=function(){var t=this.graphDiv._fullLayout;this._mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},p.setConvert(this._mockAxis,t)},e.exports=_},{\"../../../stackgl_modules\":1124,\"../../components/fx\":406,\"../../lib\":503,\"../../lib/show_no_webgl_msg\":525,\"../../lib/str2rgbarray\":528,\"../../plots/cartesian/axes\":554,\"../../registry\":638,\"./layout/convert\":602,\"./layout/spikes\":605,\"./layout/tick_marks\":606,\"./project\":607,\"has-passive-events\":229,\"webgl-context\":331}],609:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;a<n;a++)i[a]=[t[a],e[a],r[a]];return i}},{}],610:[function(t,e,r){\"use strict\";var n=t(\"./font_attributes\"),i=t(\"./animation_attributes\"),a=t(\"../components/color/attributes\"),o=t(\"../components/shapes/draw_newshape/attributes\"),s=t(\"./pad_attributes\"),l=t(\"../lib/extend\").extendFlat,c=n({editType:\"calc\"});c.family.dflt='\"Open Sans\", verdana, arial, sans-serif',c.size.dflt=12,c.color.dflt=a.defaultLine,e.exports={font:c,title:{text:{valType:\"string\",editType:\"layoutstyle\"},font:n({editType:\"layoutstyle\"}),xref:{valType:\"enumerated\",dflt:\"container\",values:[\"container\",\"paper\"],editType:\"layoutstyle\"},yref:{valType:\"enumerated\",dflt:\"container\",values:[\"container\",\"paper\"],editType:\"layoutstyle\"},x:{valType:\"number\",min:0,max:1,dflt:.5,editType:\"layoutstyle\"},y:{valType:\"number\",min:0,max:1,dflt:\"auto\",editType:\"layoutstyle\"},xanchor:{valType:\"enumerated\",dflt:\"auto\",values:[\"auto\",\"left\",\"center\",\"right\"],editType:\"layoutstyle\"},yanchor:{valType:\"enumerated\",dflt:\"auto\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],editType:\"layoutstyle\"},pad:l(s({editType:\"layoutstyle\"}),{}),editType:\"layoutstyle\"},uniformtext:{mode:{valType:\"enumerated\",values:[!1,\"hide\",\"show\"],dflt:!1,editType:\"plot\"},minsize:{valType:\"number\",min:0,dflt:0,editType:\"plot\"},editType:\"plot\"},autosize:{valType:\"boolean\",dflt:!1,editType:\"none\"},width:{valType:\"number\",min:10,dflt:700,editType:\"plot\"},height:{valType:\"number\",min:10,dflt:450,editType:\"plot\"},margin:{l:{valType:\"number\",min:0,dflt:80,editType:\"plot\"},r:{valType:\"number\",min:0,dflt:80,editType:\"plot\"},t:{valType:\"number\",min:0,dflt:100,editType:\"plot\"},b:{valType:\"number\",min:0,dflt:80,editType:\"plot\"},pad:{valType:\"number\",min:0,dflt:0,editType:\"plot\"},autoexpand:{valType:\"boolean\",dflt:!0,editType:\"plot\"},editType:\"plot\"},computed:{valType:\"any\",editType:\"none\"},paper_bgcolor:{valType:\"color\",dflt:a.background,editType:\"plot\"},plot_bgcolor:{valType:\"color\",dflt:a.background,editType:\"layoutstyle\"},autotypenumbers:{valType:\"enumerated\",values:[\"convert types\",\"strict\"],dflt:\"convert types\",editType:\"calc\"},separators:{valType:\"string\",editType:\"plot\"},hidesources:{valType:\"boolean\",dflt:!1,editType:\"plot\"},showlegend:{valType:\"boolean\",editType:\"legend\"},colorway:{valType:\"colorlist\",dflt:a.defaults,editType:\"calc\"},datarevision:{valType:\"any\",editType:\"calc\"},uirevision:{valType:\"any\",editType:\"none\"},editrevision:{valType:\"any\",editType:\"none\"},selectionrevision:{valType:\"any\",editType:\"none\"},template:{valType:\"any\",editType:\"calc\"},newshape:o.newshape,activeshape:o.activeshape,meta:{valType:\"any\",arrayOk:!0,editType:\"plot\"},transition:l({},i.transition,{editType:\"none\"}),_deprecated:{title:{valType:\"string\",editType:\"layoutstyle\"},titlefont:n({editType:\"layoutstyle\"})}}},{\"../components/color/attributes\":365,\"../components/shapes/draw_newshape/attributes\":451,\"../lib/extend\":493,\"./animation_attributes\":548,\"./font_attributes\":585,\"./pad_attributes\":618}],611:[function(t,e,r){\"use strict\";var n=t(\"../../lib/sort_object_keys\"),i='\\xa9 <a target=\"_blank\" href=\"https://www.openstreetmap.org/copyright\">OpenStreetMap</a> contributors',a=['\\xa9 <a target=\"_blank\" href=\"https://carto.com/\">Carto</a>',i].join(\" \"),o=['Map tiles by <a target=\"_blank\" href=\"https://stamen.com\">Stamen Design</a>','under <a target=\"_blank\" href=\"https://creativecommons.org/licenses/by/3.0\">CC BY 3.0</a>',\"|\",'Data by <a target=\"_blank\" href=\"https://openstreetmap.org\">OpenStreetMap</a> contributors','under <a target=\"_blank\" href=\"https://www.openstreetmap.org/copyright\">ODbL</a>'].join(\" \"),s={\"open-street-map\":{id:\"osm\",version:8,sources:{\"plotly-osm-tiles\":{type:\"raster\",attribution:i,tiles:[\"https://a.tile.openstreetmap.org/{z}/{x}/{y}.png\",\"https://b.tile.openstreetmap.org/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-osm-tiles\",type:\"raster\",source:\"plotly-osm-tiles\",minzoom:0,maxzoom:22}]},\"white-bg\":{id:\"white-bg\",version:8,sources:{},layers:[{id:\"white-bg\",type:\"background\",paint:{\"background-color\":\"#FFFFFF\"},minzoom:0,maxzoom:22}]},\"carto-positron\":{id:\"carto-positron\",version:8,sources:{\"plotly-carto-positron\":{type:\"raster\",attribution:a,tiles:[\"https://cartodb-basemaps-c.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-carto-positron\",type:\"raster\",source:\"plotly-carto-positron\",minzoom:0,maxzoom:22}]},\"carto-darkmatter\":{id:\"carto-darkmatter\",version:8,sources:{\"plotly-carto-darkmatter\":{type:\"raster\",attribution:a,tiles:[\"https://cartodb-basemaps-c.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-carto-darkmatter\",type:\"raster\",source:\"plotly-carto-darkmatter\",minzoom:0,maxzoom:22}]},\"stamen-terrain\":{id:\"stamen-terrain\",version:8,sources:{\"plotly-stamen-terrain\":{type:\"raster\",attribution:o,tiles:[\"https://stamen-tiles.a.ssl.fastly.net/terrain/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-stamen-terrain\",type:\"raster\",source:\"plotly-stamen-terrain\",minzoom:0,maxzoom:22}]},\"stamen-toner\":{id:\"stamen-toner\",version:8,sources:{\"plotly-stamen-toner\":{type:\"raster\",attribution:o,tiles:[\"https://stamen-tiles.a.ssl.fastly.net/toner/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-stamen-toner\",type:\"raster\",source:\"plotly-stamen-toner\",minzoom:0,maxzoom:22}]},\"stamen-watercolor\":{id:\"stamen-watercolor\",version:8,sources:{\"plotly-stamen-watercolor\":{type:\"raster\",attribution:['Map 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t&&(-1!==p.styleValuesMapbox.indexOf(t)||0===t.indexOf(\"mapbox://\"))}r.name=\"mapbox\",r.attr=\"subplot\",r.idRoot=\"mapbox\",r.idRegex=r.attrRegex=i.counterRegex(\"mapbox\"),r.attributes={subplot:{valType:\"subplotid\",dflt:\"mapbox\",editType:\"calc\"}},r.layoutAttributes=t(\"./layout_attributes\"),r.supplyLayoutDefaults=t(\"./layout_defaults\"),r.plot=function(t){var e=t._fullLayout,r=t.calcdata,a=e._subplots.mapbox;if(n.version!==p.requiredVersion)throw new Error(p.wrongVersionErrorMsg);var o=function(t,e){var r=t._fullLayout;if(\"\"===t._context.mapboxAccessToken)return\"\";for(var n=[],a=[],o=!1,s=!1,l=0;l<e.length;l++){var c=r[e[l]],u=c.accesstoken;d(c.style)&&(u?i.pushUnique(n,u):(d(c._input.style)&&(i.error(\"Uses Mapbox map style, but did not set an access token.\"),o=!0),s=!0)),u&&i.pushUnique(a,u)}if(s){var f=o?p.noAccessTokenErrorMsg:p.missingStyleErrorMsg;throw i.error(f),new Error(f)}return 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m=c.select(s._subplot.div);if(!(null===m.select(\".mapboxgl-ctrl-logo\").node().offsetParent)){var g=e._glimages.append(\"g\");g.attr(\"transform\",a(n.l+n.w*h.x[0]+10,n.t+n.h*(1-h.y[0])-31)),g.append(\"path\").attr(\"d\",p.mapboxLogo.path0).style({opacity:.9,fill:\"#ffffff\",\"enable-background\":\"new\"}),g.append(\"path\").attr(\"d\",p.mapboxLogo.path1).style(\"opacity\",.35).style(\"enable-background\",\"new\"),g.append(\"path\").attr(\"d\",p.mapboxLogo.path2).style(\"opacity\",.35).style(\"enable-background\",\"new\"),g.append(\"polygon\").attr(\"points\",p.mapboxLogo.polygon).style({opacity:.9,fill:\"#ffffff\",\"enable-background\":\"new\"})}var v=m.select(\".mapboxgl-ctrl-attrib\").text().replace(\"Improve this map\",\"\"),y=e._glimages.append(\"g\"),x=y.append(\"text\");x.text(v).classed(\"static-attribution\",!0).attr({\"font-size\":12,\"font-family\":\"Arial\",color:\"rgba(0, 0, 0, 0.75)\",\"text-anchor\":\"end\",\"data-unformatted\":v});var b=u.bBox(x.node()),_=n.w*(h.x[1]-h.x[0]);if(b.width>_/2){var w=v.split(\"|\").join(\"<br>\");x.text(w).attr(\"data-unformatted\",w).call(f.convertToTspans,t),b=u.bBox(x.node())}x.attr(\"transform\",a(-3,8-b.height)),y.insert(\"rect\",\".static-attribution\").attr({x:-b.width-6,y:-b.height-3,width:b.width+6,height:b.height+3,fill:\"rgba(255, 255, 255, 0.75)\"});var T=1;b.width+6>_&&(T=_/(b.width+6));var k=[n.l+n.w*h.x[1],n.t+n.h*(1-h.y[0])];y.attr(\"transform\",a(k[0],k[1])+o(T))}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.mapbox,n=0;n<r.length;n++){e[r[n]]._subplot.updateFx(e)}}},{\"../../components/drawing\":388,\"../../constants/xmlns_namespaces\":480,\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"../../plots/get_data\":593,\"./constants\":611,\"./layout_attributes\":615,\"./layout_defaults\":616,\"./mapbox\":617,\"@plotly/d3\":58,\"mapbox-gl/dist/mapbox-gl-unminified\":239}],614:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../lib/svg_text_utils\").sanitizeHTML,a=t(\"./convert_text_opts\"),o=t(\"./constants\");function s(t,e){this.subplot=t,this.uid=t.uid+\"-\"+e,this.index=e,this.idSource=\"source-\"+this.uid,this.idLayer=o.layoutLayerPrefix+this.uid,this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}var l=s.prototype;function c(t){if(!t.visible)return!1;var e=t.source;if(Array.isArray(e)&&e.length>0){for(var r=0;r<e.length;r++)if(\"string\"!=typeof e[r]||0===e[r].length)return!1;return!0}return n.isPlainObject(e)||\"string\"==typeof e&&e.length>0}function u(t){var e={},r={};switch(t.type){case\"circle\":n.extendFlat(r,{\"circle-radius\":t.circle.radius,\"circle-color\":t.color,\"circle-opacity\":t.opacity});break;case\"line\":n.extendFlat(r,{\"line-width\":t.line.width,\"line-color\":t.color,\"line-opacity\":t.opacity,\"line-dasharray\":t.line.dash});break;case\"fill\":n.extendFlat(r,{\"fill-color\":t.color,\"fill-outline-color\":t.fill.outlinecolor,\"fill-opacity\":t.opacity});break;case\"symbol\":var i=t.symbol,o=a(i.textposition,i.iconsize);n.extendFlat(e,{\"icon-image\":i.icon+\"-15\",\"icon-size\":i.iconsize/10,\"text-field\":i.text,\"text-size\":i.textfont.size,\"text-anchor\":o.anchor,\"text-offset\":o.offset,\"symbol-placement\":i.placement}),n.extendFlat(r,{\"icon-color\":t.color,\"text-color\":i.textfont.color,\"text-opacity\":t.opacity});break;case\"raster\":n.extendFlat(r,{\"raster-fade-duration\":0,\"raster-opacity\":t.opacity})}return{layout:e,paint:r}}l.update=function(t){this.visible?this.needsNewImage(t)?this.updateImage(t):this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=c(t)},l.needsNewImage=function(t){return this.subplot.map.getSource(this.idSource)&&\"image\"===this.sourceType&&\"image\"===t.sourcetype&&(this.source!==t.source||JSON.stringify(this.coordinates)!==JSON.stringify(t.coordinates))},l.needsNewSource=function(t){return this.sourceType!==t.sourcetype||JSON.stringify(this.source)!==JSON.stringify(t.source)||this.layerType!==t.type},l.needsNewLayer=function(t){return this.layerType!==t.type||this.below!==this.subplot.belowLookup[\"layout-\"+this.index]},l.lookupBelow=function(){return this.subplot.belowLookup[\"layout-\"+this.index]},l.updateImage=function(t){this.subplot.map.getSource(this.idSource).updateImage({url:t.source,coordinates:t.coordinates});var e=this.findFollowingMapboxLayerId(this.lookupBelow());null!==e&&this.subplot.map.moveLayer(this.idLayer,e)},l.updateSource=function(t){var e=this.subplot.map;if(e.getSource(this.idSource)&&e.removeSource(this.idSource),this.sourceType=t.sourcetype,this.source=t.source,c(t)){var r=function(t){var e,r=t.sourcetype,n=t.source,a={type:r};\"geojson\"===r?e=\"data\":\"vector\"===r?e=\"string\"==typeof n?\"url\":\"tiles\":\"raster\"===r?(e=\"tiles\",a.tileSize=256):\"image\"===r&&(e=\"url\",a.coordinates=t.coordinates);a[e]=n,t.sourceattribution&&(a.attribution=i(t.sourceattribution));return a}(t);e.addSource(this.idSource,r)}},l.findFollowingMapboxLayerId=function(t){if(\"traces\"===t)for(var e=this.subplot.getMapLayers(),r=0;r<e.length;r++){var n=e[r].id;if(\"string\"==typeof n&&0===n.indexOf(o.traceLayerPrefix)){t=n;break}}return t},l.updateLayer=function(t){var e=this.subplot,r=u(t),n=this.lookupBelow(),i=this.findFollowingMapboxLayerId(n);this.removeLayer(),c(t)&&e.addLayer({id:this.idLayer,source:this.idSource,\"source-layer\":t.sourcelayer||\"\",type:t.type,minzoom:t.minzoom,maxzoom:t.maxzoom,layout:r.layout,paint:r.paint},i),this.layerType=t.type,this.below=n},l.updateStyle=function(t){if(c(t)){var e=u(t);this.subplot.setOptions(this.idLayer,\"setLayoutProperty\",e.layout),this.subplot.setOptions(this.idLayer,\"setPaintProperty\",e.paint)}},l.removeLayer=function(){var t=this.subplot.map;t.getLayer(this.idLayer)&&t.removeLayer(this.idLayer)},l.dispose=function(){var t=this.subplot.map;t.getLayer(this.idLayer)&&t.removeLayer(this.idLayer),t.getSource(this.idSource)&&t.removeSource(this.idSource)},e.exports=function(t,e,r){var n=new s(t,e);return n.update(r),n}},{\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"./constants\":611,\"./convert_text_opts\":612}],615:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\").defaultLine,a=t(\"../domain\").attributes,o=t(\"../font_attributes\"),s=t(\"../../traces/scatter/attributes\").textposition,l=t(\"../../plot_api/edit_types\").overrideAll,c=t(\"../../plot_api/plot_template\").templatedArray,u=t(\"./constants\"),f=o({});f.family.dflt=\"Open Sans Regular, Arial Unicode MS 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e,r,s=this,c=s.isSmith,u=s.gd,f=s.layers,h=t._zoomlayer,p=S.MINZOOM,d=S.OFFEDGE,m=s.radius,x=s.innerRadius,T=s.cx,k=s.cy,A=s.cxx,M=s.cyy,L=s.sectorInRad,C=s.vangles,P=s.radialAxis,I=E.clampTiny,O=E.findXYatLength,z=E.findEnclosingVertexAngles,D=S.cornerHalfWidth,R=S.cornerLen/2,F=g.makeDragger(f,\"path\",\"maindrag\",!1===t.dragmode?\"none\":\"crosshair\");n.select(F).attr(\"d\",s.pathSubplot()).attr(\"transform\",l(T,k)),F.onmousemove=function(t){y.hover(u,t,s.id),u._fullLayout._lasthover=F,u._fullLayout._hoversubplot=s.id},F.onmouseout=function(t){u._dragging||v.unhover(u,t)};var B,N,j,U,V,H,q,G,Y,W={element:F,gd:u,subplot:s.id,plotinfo:{id:s.id,xaxis:s.xaxis,yaxis:s.yaxis},xaxes:[s.xaxis],yaxes:[s.yaxis]};function X(t,e){return Math.sqrt(t*t+e*e)}function Z(t,e){return X(t-A,e-M)}function J(t,e){return Math.atan2(M-e,t-A)}function K(t,e){return[t*Math.cos(e),t*Math.sin(-e)]}function Q(t,e){if(0===t)return s.pathSector(2*D);var 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O,z,D,B=g.makeRectDragger(u,T,\"crosshair\",-x,-x,y,y),N={element:B,gd:c};!1===t.dragmode&&(N.dragmode=!1),V(n.select(B),m.visible&&h<f,{transform:l(b,_)}),N.prepFn=function(){O=null,z=null,D=null,N.moveFn=j,N.doneFn=H,w(c)},N.clampFn=function(t,e){return Math.sqrt(t*t+e*e)<S.MINDRAG&&(t=0,e=0),[t,e]},v.init(N)}function j(t,e){if(O)O(t,e);else{var n=[t,-e],a=[Math.cos(M),Math.sin(M)],s=Math.abs(o.dot(n,a)/Math.sqrt(o.dot(n,n)));isNaN(s)||(O=s<.5?q:G)}var l={};!function(t){null!==z?t[i.id+\".radialaxis.angle\"]=z:null!==D&&(t[i.id+\".radialaxis.range[\"+r+\"]\"]=D)}(l),c.emit(\"plotly_relayouting\",l)}function H(){null!==z?a.call(\"_guiRelayout\",c,i.id+\".radialaxis.angle\",z):null!==D&&a.call(\"_guiRelayout\",c,i.id+\".radialaxis.range[\"+r+\"]\",D)}function q(t,e){if(0!==r){var n=b+t,a=_+e;z=Math.atan2(d-a,n-p),i.vangles&&(z=U(z,i.vangles)),z=F(z);var o=l(p,d)+s(-z);u[\"radial-axis\"].attr(\"transform\",o),u[\"radial-line\"].select(\"line\").attr(\"transform\",o);var 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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 traces\"===t.aaxis.layer&&r.push(\"aaxis\",\"aline\"),\"below traces\"===t.baxis.layer&&r.push(\"baxis\",\"bline\"),\"below traces\"===t.caxis.layer&&r.push(\"caxis\",\"cline\"),r.push(\"frontplot\"),\"above traces\"===t.aaxis.layer&&r.push(\"aaxis\",\"aline\"),\"above traces\"===t.baxis.layer&&r.push(\"baxis\",\"bline\"),\"above 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r,n,i,a,o,l,p=this,d=t.domain,m=(d.x[0]+d.x[1])/2,g=(d.y[0]+d.y[1])/2,v=d.x[1]-d.x[0],y=d.y[1]-d.y[0],x=v*e.w,b=y*e.h,_=t.sum,w=t.aaxis.min,T=t.baxis.min,k=t.caxis.min;x>E*b?i=(a=b)*E:a=(i=x)/E,o=v*i/x,l=y*a/b,r=e.l+e.w*m-i/2,n=e.t+e.h*(1-g)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=_,p.xaxis={type:\"linear\",range:[w+2*k-_,_-w-2*T],domain:[m-o/2,m+o/2],_id:\"x\"},f(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:\"linear\",range:[w,_-T-k],domain:[g-l/2,g+l/2],_id:\"y\"},f(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var A=p.yaxis.domain[0],M=p.aaxis=h({},t.aaxis,{range:[w,_-T-k],side:\"left\",tickangle:(+t.aaxis.tickangle||0)-30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(M,p.graphDiv._fullLayout),M.setScale();var 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D=s(r+i/2,n)+\"rotate(-30)\"+s(0,-L._offset);p.layers.caxis.attr(\"transform\",D),p.layers.cgrid.attr(\"transform\",D),p.drawAxes(!0),p.layers.aline.select(\"path\").attr(\"d\",M.showline?\"M\"+r+\",\"+(n+a)+\"l\"+i/2+\",-\"+a:\"M0,0\").call(c.stroke,M.linecolor||\"#000\").style(\"stroke-width\",(M.linewidth||0)+\"px\"),p.layers.bline.select(\"path\").attr(\"d\",S.showline?\"M\"+r+\",\"+(n+a)+\"h\"+i:\"M0,0\").call(c.stroke,S.linecolor||\"#000\").style(\"stroke-width\",(S.linewidth||0)+\"px\"),p.layers.cline.select(\"path\").attr(\"d\",L.showline?\"M\"+(r+i/2)+\",\"+n+\"l\"+i/2+\",\"+a:\"M0,0\").call(c.stroke,L.linecolor||\"#000\").style(\"stroke-width\",(L.linewidth||0)+\"px\"),p.graphDiv._context.staticPlot||p.initInteractions(),u.setClipUrl(p.layers.frontplot,p._hasClipOnAxisFalse?null:p.clipId,p.graphDiv)},S.drawAxes=function(t){var 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L=A.MINZOOM/2+.87,C=\"m-0.87,.5h\"+L+\"v3h-\"+(L+5.2)+\"l\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l2.6,1.5l-\"+L/2+\",\"+.87*L+\"Z\",P=\"m0.87,.5h-\"+L+\"v3h\"+(L+5.2)+\"l-\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l-2.6,1.5l\"+L/2+\",\"+.87*L+\"Z\",I=\"m0,1l\"+L/2+\",\"+.87*L+\"l2.6,-1.5l-\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l-\"+(L/2+2.6)+\",\"+(.87*L+4.5)+\"l2.6,1.5l\"+L/2+\",-\"+.87*L+\"Z\",O=!0;function z(t){n.select(t).selectAll(\".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners\").remove()}S.clearSelect=function(){k(this.dragOptions),T(this.dragOptions.gd)},S.initInteractions=function(){var t,e,r,n,f,h,p,d,v,b,T,k,M=this,S=M.layers.plotbg.select(\"path\").node(),L=M.graphDiv,D=L._fullLayout._zoomlayer;function R(t){var e={};return e[M.id+\".aaxis.min\"]=t.a,e[M.id+\".baxis.min\"]=t.b,e[M.id+\".caxis.min\"]=t.c,e}function F(t,e){var 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r=t/M.xaxis._m,n=e/M.yaxis._m,i=[(p={a:f.a-n,b:f.b+(r+n)/2,c:f.c-(r-n)/2}).a,p.b,p.c].sort(o.sorterAsc),a=i.indexOf(p.a),l=i.indexOf(p.b),c=i.indexOf(p.c);i[0]<0&&(i[1]+i[0]/2<0?(i[2]+=i[0]+i[1],i[0]=i[1]=0):(i[2]+=i[0]/2,i[1]+=i[0]/2,i[0]=0),p={a:i[a],b:i[l],c:i[c]},e=(f.a-p.a)*M.yaxis._m,t=(f.c-p.c-f.b+p.b)*M.xaxis._m);var h=s(M.x0+t,M.y0+e);M.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",h);var d=s(-t,-e);M.clipDefRelative.select(\"path\").attr(\"transform\",d),M.aaxis.range=[p.a,M.sum-p.b-p.c],M.baxis.range=[M.sum-p.a-p.c,p.b],M.caxis.range=[M.sum-p.a-p.b,p.c],M.drawAxes(!1),M._hasClipOnAxisFalse&&M.plotContainer.select(\".scatterlayer\").selectAll(\".trace\").call(u.hideOutsideRangePoints,M),L.emit(\"plotly_relayouting\",R(p))}function 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n=t(\"../../components/colorscale/defaults\"),i=t(\"./label_defaults\");e.exports=function(t,e,r,a,o){var s,l=r(\"contours.coloring\"),c=\"\";\"fill\"===l&&(s=r(\"contours.showlines\")),!1!==s&&(\"lines\"!==l&&(c=r(\"line.color\",\"#000\")),r(\"line.width\",.5),r(\"line.dash\")),\"none\"!==l&&(!0!==t.showlegend&&(e.showlegend=!1),e._dfltShowLegend=!1,n(t,e,a,r,{prefix:\"\",cLetter:\"z\"})),r(\"line.smoothing\"),i(r,a,c,o)}},{\"../../components/colorscale/defaults\":376,\"./label_defaults\":750}],757:[function(t,e,r){\"use strict\";var n=t(\"../heatmap/attributes\"),i=t(\"../contour/attributes\"),a=t(\"../../components/colorscale/attributes\"),o=t(\"../../lib/extend\").extendFlat,s=i.contours;e.exports=o({carpet:{valType:\"string\",editType:\"calc\"},z:n.z,a:n.x,a0:n.x0,da:n.dx,b:n.y,b0:n.y0,db:n.dy,text:n.text,hovertext:n.hovertext,transpose:n.transpose,atype:n.xtype,btype:n.ytype,fillcolor:i.fillcolor,autocontour:i.autocontour,ncontours:i.ncontours,contours:{type:s.type,start:s.start,end:s.end,size:s.size,coloring:{valType:\"enumerated\",values:[\"fill\",\"lines\",\"none\"],dflt:\"fill\",editType:\"calc\"},showlines:s.showlines,showlabels:s.showlabels,labelfont:s.labelfont,labelformat:s.labelformat,operation:s.operation,value:s.value,editType:\"calc\",impliedEdits:{autocontour:!1}},line:{color:i.line.color,width:i.line.width,dash:i.line.dash,smoothing:i.line.smoothing,editType:\"plot\"},transforms:void 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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\":908}],789:[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\":503,\"./layout_attributes\":788}],790:[function(t,e,r){\"use strict\";var 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n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../plots/font_attributes\"),o=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,s=t(\"../../plots/template_attributes\").hovertemplateAttrs,l=t(\"../../plots/template_attributes\").texttemplateAttrs,c=t(\"../../components/colorscale/attributes\"),u=t(\"../../lib/extend\").extendFlat;e.exports=u({z:{valType:\"data_array\",editType:\"calc\"},x:u({},n.x,{impliedEdits:{xtype:\"array\"}}),x0:u({},n.x0,{impliedEdits:{xtype:\"scaled\"}}),dx:u({},n.dx,{impliedEdits:{xtype:\"scaled\"}}),y:u({},n.y,{impliedEdits:{ytype:\"array\"}}),y0:u({},n.y0,{impliedEdits:{ytype:\"scaled\"}}),dy:u({},n.dy,{impliedEdits:{ytype:\"scaled\"}}),xperiod:u({},n.xperiod,{impliedEdits:{xtype:\"scaled\"}}),yperiod:u({},n.yperiod,{impliedEdits:{ytype:\"scaled\"}}),xperiod0:u({},n.xperiod0,{impliedEdits:{xtype:\"scaled\"}}),yperiod0:u({},n.yperiod0,{impliedEdits:{ytype:\"scaled\"}}),xperiodalignment:u({},n.xperiodalignment,{impliedEdits:{xtype:\"scaled\"}}),yperiodalignment:u({},n.yperiodalignment,{impliedEdits:{ytype:\"scaled\"}}),text:{valType:\"data_array\",editType:\"calc\"},hovertext:{valType:\"data_array\",editType:\"calc\"},transpose:{valType:\"boolean\",dflt:!1,editType:\"calc\"},xtype:{valType:\"enumerated\",values:[\"array\",\"scaled\"],editType:\"calc+clearAxisTypes\"},ytype:{valType:\"enumerated\",values:[\"array\",\"scaled\"],editType:\"calc+clearAxisTypes\"},zsmooth:{valType:\"enumerated\",values:[\"fast\",\"best\",!1],dflt:!1,editType:\"calc\"},hoverongaps:{valType:\"boolean\",dflt:!0,editType:\"none\"},connectgaps:{valType:\"boolean\",editType:\"calc\"},xgap:{valType:\"number\",dflt:0,min:0,editType:\"plot\"},ygap:{valType:\"number\",dflt:0,min:0,editType:\"plot\"},xhoverformat:o(\"x\"),yhoverformat:o(\"y\"),zhoverformat:o(\"z\",1),hovertemplate:s(),texttemplate:l({arrayOk:!1,editType:\"plot\"},{keys:[\"x\",\"y\",\"z\",\"text\"]}),textfont:a({editType:\"plot\",autoSize:!0,autoColor:!0,colorEditType:\"style\"}),showlegend:u({},i.showlegend,{dflt:!1})},{transforms:void 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W=_.selectAll(\"g.gauge-outline\").data([x]);W.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"path\"),W.select(\"path\").call(P).call(T),W.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:Y,layer:W,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var X=O.selectAll(\"g.bullet\").data(R?e:[]);X.exit().remove();var Z=O.selectAll(\"g.bulletaxis\").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,c,u=r[0].trace,f=n.gauge,p=n.layer,m=n.gaugeBg,g=n.gaugeOutline,v=n.size,x=u.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append(\"g\").classed(\"bullet\",!0),f.attr(\"transform\",l(v.l,v.t)),p.enter().append(\"g\").classed(\"bulletaxis\",!0).classed(\"crisp\",!0),p.selectAll(\"g.xbulletaxistick,path,text\").remove();var A=v.h,M=u.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(u._hasNumber||u._hasDelta?1-h.bulletNumberDomainSize:1);(i=k(t,u.gauge.axis))._id=\"xbulletaxis\",i.domain=[S,E],i.setScale(),a=d.calcTicks(i),o=d.makeTransTickFn(i),s=d.getTickSigns(i)[2],c=v.t+v.h,i.visible&&(d.drawTicks(t,i,{vals:\"inside\"===i.ticks?d.clipEnds(i,a):a,layer:p,path:d.makeTickPath(i,c,s),transFn:o}),d.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:d.makeLabelFns(i,c)}));function L(t){t.attr(\"width\",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr(\"x\",(function(t){return i.c2p(t.range[0])})).attr(\"y\",(function(t){return.5*(1-t.thickness)*A})).attr(\"height\",(function(t){return t.thickness*A}))}var C=[m].concat(u.gauge.steps),P=f.selectAll(\"g.bg-bullet\").data(C);P.enter().append(\"g\").classed(\"bg-bullet\",!0).append(\"rect\"),P.select(\"rect\").call(L).call(T),P.exit().remove();var 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n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../surface/attributes\"),s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat;e.exports=l({x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},i:{valType:\"data_array\",editType:\"calc\"},j:{valType:\"data_array\",editType:\"calc\"},k:{valType:\"data_array\",editType:\"calc\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertemplate:a({editType:\"calc\"}),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),delaunayaxis:{valType:\"enumerated\",values:[\"x\",\"y\",\"z\"],dflt:\"z\",editType:\"calc\"},alphahull:{valType:\"number\",dflt:-1,editType:\"calc\"},intensity:{valType:\"data_array\",editType:\"calc\"},intensitymode:{valType:\"enumerated\",values:[\"vertex\",\"cell\"],dflt:\"vertex\",editType:\"calc\"},color:{valType:\"color\",editType:\"calc\"},vertexcolor:{valType:\"data_array\",editType:\"calc\"},facecolor:{valType:\"data_array\",editType:\"calc\"},transforms:void 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n=t(\"../../components/colorscale/calc\");e.exports=function(t,e){e.intensity&&n(t,e,{vals:e.intensity,containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":374}],869:[function(t,e,r){\"use strict\";var n=t(\"../../../stackgl_modules\").gl_mesh3d,i=t(\"../../../stackgl_modules\").delaunay_triangulate,a=t(\"../../../stackgl_modules\").alpha_shape,o=t(\"../../../stackgl_modules\").convex_hull,s=t(\"../../lib/gl_format_color\").parseColorScale,l=t(\"../../lib/str2rgbarray\"),c=t(\"../../components/colorscale\").extractOpts,u=t(\"../../plots/gl3d/zip3\");function f(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name=\"\",this.color=\"#fff\",this.data=null,this.showContour=!1}var h=f.prototype;function p(t){for(var e=[],r=t.length,n=0;n<r;n++)e[n]=l(t[n]);return e}function d(t,e,r,n){for(var i=[],a=e.length,o=0;o<a;o++)i[o]=t.d2l(e[o],0,n)*r;return i}function m(t){for(var e=[],r=t.length,n=0;n<r;n++)e[n]=Math.round(t[n]);return e}function g(t,e){for(var r=t.length,n=0;n<r;n++)if(t[n]<=-.5||t[n]>=e-.5)return!1;return!0}h.handlePick=function(t){if(t.object===this.mesh){var e=t.index=t.data.index;t.data._cellCenter?t.traceCoordinate=t.data.dataCoordinate:t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]];var r=this.data.hovertext||this.data.text;return Array.isArray(r)&&void 0!==r[e]?t.textLabel=r[e]:r&&(t.textLabel=r),!0}},h.update=function(t){var e=this.scene,r=e.fullSceneLayout;this.data=t;var n,f=t.x.length,h=u(d(r.xaxis,t.x,e.dataScale[0],t.xcalendar),d(r.yaxis,t.y,e.dataScale[1],t.ycalendar),d(r.zaxis,t.z,e.dataScale[2],t.zcalendar));if(t.i&&t.j&&t.k){if(t.i.length!==t.j.length||t.j.length!==t.k.length||!g(t.i,f)||!g(t.j,f)||!g(t.k,f))return;n=u(m(t.i),m(t.j),m(t.k))}else n=0===t.alphahull?o(h):t.alphahull>0?a(t.alphahull,h):function(t,e){for(var r=[\"x\",\"y\",\"z\"].indexOf(t),n=[],a=e.length,o=0;o<a;o++)n[o]=[e[o][(r+1)%3],e[o][(r+2)%3]];return i(n)}(t.delaunayaxis,h);var v={positions:h,cells:n,lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:l(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading};if(t.intensity){var y=c(t);this.color=\"#fff\";var x=t.intensitymode;v[x+\"Intensity\"]=t.intensity,v[x+\"IntensityBounds\"]=[y.min,y.max],v.colormap=s(t)}else t.vertexcolor?(this.color=t.vertexcolor[0],v.vertexColors=p(t.vertexcolor)):t.facecolor?(this.color=t.facecolor[0],v.cellColors=p(t.facecolor)):(this.color=t.color,v.meshColor=l(t.color));this.mesh.update(v)},h.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new 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n=t(\"../../lib\").extendFlat,i=t(\"../scatter/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,o=t(\"../../components/drawing/attributes\").dash,s=t(\"../../components/fx/attributes\"),l=t(\"../../constants/delta.js\"),c=l.INCREASING.COLOR,u=l.DECREASING.COLOR,f=i.line;function 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E=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\"})),k(E),S.exit().remove(),M.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var z=e._fullLayout.paper_bgcolor;A.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",f.makeTextShadow(z)).style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return d(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),M.append(\"text\").attr(\"class\",\"dimlabel\"),A.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){c.font(n.select(this),t.parcatsViewModel.labelfont)})),A.selectAll(\"rect.bandrect\").on(\"mouseover\",L).on(\"mouseout\",C),A.exit().remove(),w.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",P).on(\"drag\",I).on(\"dragend\",O)),u.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\")})),u.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function m(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\")){s.raiseToTop(this),w(n.select(this));var e=y(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit(\"plotly_hover\",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\")){var i,a,l,c=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),m=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=c&&c<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],v=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+v.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=u.mostReadable(t.model.color,[\"black\",\"white\"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&S.push([\"Count:\",M.countLabel].join(\" \")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&S.push([\"P:\",M.probabilityLabel].join(\" \"));var E=S.join(\"<br>\"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+m.left,y:_-d.top+m.top,text:E,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:T,idealAlign:L<b?\"right\":\"left\",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function v(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(m),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event,constraints:r})}}function y(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 x(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 b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:e,event:n.event,constraints:r})}}function _(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 w(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",(function(t){return u.mostReadable(t.model.color,[\"black\",\"white\"])})).attr(\"stroke-width\",.3)}function T(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function k(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function A(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){A(t).each((function(t){Array.prototype.push.apply(l,y(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,y(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 E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select(\"rect.catrect\"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a=\"left\"):(i=c.left+c.width,a=\"right\");var m=u.model.count,g=u.model.categoryLabel,v=m/u.parcatsViewModel.model.count,y={countLabel:m,categoryLabel:g,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&x.push([\"Count:\",y.countLabel].join(\" \")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&x.push([\"P(\"+y.categoryLabel+\"):\",y.probabilityLabel].join(\" \"));var b=x.join(\"<br>\");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:12,fontColor:\"black\",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:y,eventData:[{data:p._input,fullData:p,count:m,category:g,probability:v}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron;if(\"color\"===l?(!function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll(\"rect.bandrect\").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr(\"stroke\",\"black\").attr(\"stroke-width\",1.5)}))}(this),S(this,\"plotly_hover\",n.event)):(!function(t){n.select(t.parentNode).selectAll(\"rect.bandrect\").each((function(t){var e=A(t);w(e),e.each((function(){s.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=E(r,a,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(),c=n.select(r).datum(),f=c.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,m=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a=\"left\"):(i=l.left+l.width,a=\"right\");var g=f.model.categoryLabel,v=c.parcatsViewModel.model.count,y=0;c.categoryViewModel.bands.forEach((function(t){t.color===c.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===c.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:g,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&A.push([\"Count:\",k.countLabel].join(\" \")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&(A.push(\"P(color \\u2229 \"+g+\"): \"+k.probabilityLabel),A.push(\"P(\"+g+\" | color): \"+w.toFixed(3)),A.push(\"P(color | \"+g+\"): \"+T.toFixed(3)));var M=A.join(\"<br>\"),S=u.mostReadable(c.color,[\"black\",\"white\"]);return{trace:d,x:o*(i-e.left),y:s*(m-e.top),text:M,color:c.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,a,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(E(t,e,this))})),i}(r,a,this)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(_(e.pathSelection),T(e.dimensionSelection.selectAll(\"g.category\")),k(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(m),-1===e.hoverinfoItems.indexOf(\"skip\"))){\"color\"===t.parcatsViewModel.hoveron?S(this,\"plotly_unhover\",n.event):M(this,\"plotly_unhover\",n.event)}}function P(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,s.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,o.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}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function O(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=z(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.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?S(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,j(t.parcatsViewModel),N(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each((function(){R(t.parcatsViewModel,!0),D(t.parcatsViewModel,!0)})).each(\"end\",(function(){(o||s)&&a.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 D(t,e){var r;void 0===e&&(e=!1),t.pathSelection.data((function(t){return t.paths}),p),(r=t.pathSelection,e?r.transition():r).attr(\"d\",(function(t){return t.svgD}))}function R(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data((function(t){return t.dimensions}),p);var i=t.dimensionSelection.selectAll(\"g.category\").data((function(t){return t.categories}),p);r(t.dimensionSelection).attr(\"transform\",(function(t){return l(t.x,0)})),r(i).attr(\"transform\",(function(t){return l(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 d(t)?\"start\":\"end\"})).attr(\"x\",(function(t){return d(t)?t.width+5:-5})).each((function(t){var e,r;d(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}),p),o=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})),k(o),a.each((function(){s.raiseToTop(this)})),a.exit().remove()}function F(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 m={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&&(j(m),N(m)),m}function B(t,e,r,n,a){var o,s,l=[],c=[];for(s=0;s<r.length-1;s++)o=i(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,\"+n+\" \",u+=\"l -\"+r[r.length-1]+\",0 \",s=r.length-2;s>=0;s--)u+=\"C\"+c[s]+\",\"+(e[s+1]+n)+\" \"+l[s]+\",\"+(e[s]+n)+\" \"+(t[s]+r[s])+\",\"+(e[s]+n),u+=\"l-\"+r[s]+\",0 \";return u+=\"Z\"}function N(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})),m=0;m<c.length;m++){var g,v=c[m];g=p>0?d*(v.count/p):0;for(var y,x=new Array(n.length),b=0;b<v.categoryInds.length;b++){var 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n=t(\"../../lib\").isTypedArray;r.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},r.isOrdinal=function(t){return!!t.tickvals},r.isVisible=function(t){return t.visible||!(\"visible\"in t)}},{\"../../lib\":503}],896:[function(t,e,r){\"use strict\";var n=t(\"./base_index\");n.plot=t(\"./plot\"),e.exports=n},{\"./base_index\":890,\"./plot\":900}],897:[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 float maskHeight;\\nuniform float drwLayer; // 0: context, 1: focus, 2: pick\\nuniform vec4 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);\\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(maskTexture,\\n                        vec2(\\n                            (float(i * 2 + j / 2) + 0.5) / 8.0,\\n                            (pnts[i][j][k] * (maskHeight - 1.0) + 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I(t){r=t.model,n=t.viewModel,l=n.dimensions.slice(),g=l[0]?l[0].values.length:0;var e=r.lines,i=w?e.color.map((function(t,r){return r/e.color.length})):e.color,a=function(t,e,r){for(var n,i=new Array(t*(o+4)),a=0,s=0;s<t;s++){for(var l=0;l<o;l++)i[a++]=l<e.length?e[l].paddedUnitValues[s]:.5;i[a++]=d(s,2),i[a++]=d(s,1),i[a++]=d(s,0),i[a++]=(n=r[s],Math.max(1e-6,Math.min(.999999,n)))}return i}(g,l,i);!function(t,e,r){for(var n=0;n<=o;n+=4)t[v(n)](m(n/4,e,r))}(E,g,a),_||w||(L=T.texture(s.extendFlat({data:p(r.unitToColor,255)},u)))}return{render:function(t,e,n){var i,a,o,s=t.length,c=1/0,u=-1/0;for(i=0;i<s;i++)t[i].dim0.canvasX<c&&(c=t[i].dim0.canvasX,a=i),t[i].dim1.canvasX>u&&(u=t[i].dim1.canvasX,o=i);0===s&&f(T,0,0,r.canvasWidth,r.canvasHeight);var p=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&n<l.length?l[n].brush.filter.getBounds():[-1/0,1/0];i[0][n]=a[0],i[1][n]=a[1]}var o=new Array(16384);for(e=0;e<16384;e++)o[e]=255;if(!t)for(e=0;e<l.length;e++){var 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i=new Uint8Array(4*r*n);return T.read({x:t,y:e,width:r,height:n,data:i}),i},destroy:function(){for(var e in t.style[\"pointer-events\"]=\"none\",L.destroy(),b&&b.destroy(),E)E[e].destroy()},update:I}}},{\"../../lib\":503,\"./constants\":893,glslify:227}],898:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n){var i,a;for(n||(n=1/0),i=0;i<e.length;i++)(a=e[i]).visible&&(n=Math.min(n,a[r].length));for(n===1/0&&(n=0),t._length=n,i=0;i<e.length;i++)(a=e[i]).visible&&(a._length=n);return n}},{}],899:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=i.numberFormat,o=t(\"color-rgba\"),s=t(\"../../plots/cartesian/axes\"),l=i.strRotate,c=i.strTranslate,u=t(\"../../lib/svg_text_utils\"),f=t(\"../../components/drawing\"),h=t(\"../../components/colorscale\"),p=t(\"../../lib/gup\"),d=p.keyFun,m=p.repeat,g=p.unwrap,v=t(\"./helpers\"),y=t(\"./constants\"),x=t(\"./axisbrush\"),b=t(\"./lines\");function _(t,e,r){return i.aggNums(t,null,e,r)}function w(t,e){return k(_(Math.min,t,e),_(Math.max,t,e))}function T(t){var e=t.range;return e?k(e[0],e[1]):w(t.values,t._length)}function k(t,e){return!isNaN(t)&&isFinite(t)||(t=0),!isNaN(e)&&isFinite(e)||(e=0),t===e&&(0===t?(t-=1,e+=1):(t*=.9,e*=1.1)),[t,e]}function A(t,e,r,i,o){var s,l,c=T(r);return i?n.scale.ordinal().domain(i.map((s=a(r.tickformat),l=o,l?function(t,e){var r=l[e];return null==r?s(t):r}:s))).range(i.map((function(r){var n=(r-c[0])/(c[1]-c[0]);return t-e+n*(2*e-t)}))):n.scale.linear().domain(c).range([t-e,e])}function M(t){if(t.tickvals){var e=T(t);return n.scale.ordinal().domain(t.tickvals).range(t.tickvals.map((function(t){return(t-e[0])/(e[1]-e[0])})))}}function S(t){var e=t.map((function(t){return t[0]})),r=t.map((function(t){var e=o(t[1]);return n.rgb(\"rgb(\"+e[0]+\",\"+e[1]+\",\"+e[2]+\")\")})),i=\"rgb\".split(\"\").map((function(t){return n.scale.linear().clamp(!0).domain(e).range(r.map((i=t,function(t){return t[i]})));var i}));return function(t){return i.map((function(e){return 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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 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r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],c=e.labels,u=e.marker.colors||[],f=e.values,h=e._length,p=e._hasValues&&h;if(e.dlabel)for(c=new Array(h),r=0;r<h;r++)c[r]=String(e.label0+r*e.dlabel);var d={},m=s(o[\"_\"+e.type+\"colormap\"]),g=0,v=!1;for(r=0;r<h;r++){var y,x,b;if(p){if(y=f[r],!n(y))continue;y=+y}else y=1;void 0!==(x=c[r])&&\"\"!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(g+=y),a.push({v:y,label:x,color:m(u[r],x),i:r,pts:[r],hidden:b})):(v=!0,(i=a[_]).v+=y,i.pts.push(r),i.hidden||(g+=y),!1===i.color&&u[r]&&(i.color=m(u[r],x)))}return a=a.filter((function(t){return t.v>=0})),(\"funnelarea\"===e.type?v:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var c=0,u=0;u<i.length;u++){var f=i[u];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[c%a.length],c++))}}},makePullColorFn:s,generateExtendedColors:l}},{\"../../components/color\":366,\"fast-isnumeric\":190,tinycolor2:312}],904:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\"),a=t(\"./attributes\"),o=t(\"../../plots/domain\").defaults,s=t(\"../bar/defaults\").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c(\"labels\"),c(\"values\")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),f){e._length=f,c(\"marker.line.width\")&&c(\"marker.line.color\"),c(\"marker.colors\"),c(\"scalegroup\");var h,p=c(\"text\"),d=c(\"texttemplate\");if(d||(h=c(\"textinfo\",Array.isArray(p)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),d||h&&\"none\"!==h){var m=c(\"textposition\");s(t,e,n,c,m,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(m)||\"auto\"===m||\"outside\"===m)&&c(\"automargin\"),(\"inside\"===m||\"auto\"===m||Array.isArray(m))&&c(\"insidetextorientation\")}o(e,n,c);var g=c(\"hole\");if(c(\"title.text\")){var v=c(\"title.position\",g?\"middle center\":\"top center\");g||\"middle center\"!==v||(e.title.position=\"top center\"),i.coerceFont(c,\"title.font\",n.font)}c(\"sort\"),c(\"direction\"),c(\"rotation\"),c(\"pull\")}else e.visible=!1}}},{\"../../lib\":503,\"../../plots/domain\":584,\"../bar/defaults\":652,\"./attributes\":901,\"fast-isnumeric\":190}],905:[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,bbox:t.bbox,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\":402}],906:[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\":503}],907:[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\":901,\"./base_plot\":902,\"./calc\":903,\"./defaults\":904,\"./layout_attributes\":908,\"./layout_defaults\":909,\"./plot\":910,\"./style\":911,\"./style_one\":912}],908:[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\"}}},{}],909:[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\":503,\"./layout_attributes\":908}],910:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../plots/plots\"),a=t(\"../../components/fx\"),o=t(\"../../components/color\"),s=t(\"../../components/drawing\"),l=t(\"../../lib\"),c=l.strScale,u=l.strTranslate,f=t(\"../../lib/svg_text_utils\"),h=t(\"../bar/uniform_text\"),p=h.recordMinTextSize,d=h.clearMinTextSize,m=t(\"../bar/constants\").TEXTPAD,g=t(\"./helpers\"),v=t(\"./event_data\"),y=t(\"../../lib\").isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,c=i.trace,u=\"funnelarea\"===c.type;\"_hasHoverLabel\"in c||(c._hasHoverLabel=!1),\"_hasHoverEvent\"in c||(c._hasHoverEvent=!1),t.on(\"mouseover\",(function(t){var r=e._fullLayout,f=e._fullData[c.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:c._module},r,0)),\"all\"===h&&(h=\"label+text+value+percent+name\"),f.hovertemplate||\"none\"!==h&&\"skip\"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),m=s+t.pxmid[1]*(1-p),y=r.separators,x=[];if(h&&-1!==h.indexOf(\"label\")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf(\"text\")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,y),h&&-1!==h.indexOf(\"value\")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,y),h&&-1!==h.indexOf(\"percent\")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:c,x0:d-p*i.r,x1:d+p*i.r,y:m,_x0:u?o+t.TL[0]:d-p*i.r,_x1:u?o+t.TR[0]:d+p*i.r,_y0:u?s+t.TL[1]:m-p*i.r,_y1:u?s+t.BL[1]:m+p*i.r,text:x.join(\"<br>\"),name:f.hovertemplate||-1!==h.indexOf(\"name\")?f.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],c._hasHoverLabel=!0}c._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[v(t,f)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[c.index],o=n.select(this).datum();c._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[v(o,i)],event:n.event}),c._hasHoverEvent=!1),c._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),c._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[c.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append(\"text\").attr(\"data-notex\",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f=\"horizontal\"===u,h=\"tangential\"===u,p=\"radial\"===u,d=\"auto\"===u,m=[];if(!d){var g,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a=\"tan\"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,m.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)v(Math.PI*g,\"tan\");for(g=4;g>=-4;g-=2)v(Math.PI*(g+1),\"tan\")}if(f||p){for(g=4;g>=-4;g-=2)v(Math.PI*(g+1.5),\"rad\");for(g=4;g>=-4;g-=2)v(Math.PI*(g+.5),\"rad\")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;m.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a));for(var x=0,b=0,_=0;_<m.length;_++){var w=m[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return m[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=I(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?f*=2:-1!==a.title.position.indexOf(\"right\")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function I(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function O(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&\"middle center\"!==a.title.position&&(l-=P(i,e));var c=s/2,u=l/2;\"funnelarea\"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+I(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf(\"bottom\")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if(\"pie\"===i.type)l=r.r*r.r;else if(\"funnelarea\"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;\"funnelarea\"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&\"none\"!==o){var s,c=o.split(\"+\"),u=function(t){return-1!==c.indexOf(t)},f=u(\"label\"),h=u(\"text\"),p=u(\"value\"),d=u(\"percent\"),m=n.separators;if(s=f?[e.label]:[],h){var v=g.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(g.formatPieValue(e.v,m)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,m)),e.text=s.join(\"<br>\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=g.getFirstFilled(i.text,e.pts);(y(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d(\"pie\",r),_(e,t),O(e,a);var h=l.makeTraceGroups(r._pielayer,e,\"trace\").each((function(e){var h=n.select(this),d=e[0],m=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c=\"px0\",u=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c=\"px1\",u=\"px0\"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,s+=l*r.v/2,n=z(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr(\"stroke-linejoin\",\"round\"),h.each((function(){var v=n.select(this).selectAll(\"g.slice\").data(e);v.enter().append(\"g\").classed(\"slice\",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=m.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll(\"path.surface\").data([i]);if(h.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),u.call(x,t,e),m.pull){var v=+g.castOption(m.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=m.hole;if(i.v===d.vTotal){var k=\"M\"+(o+i.px0[0])+\",\"+(c+i.px0[1])+C(i.px0,i.pxmid,!0,1)+C(i.pxmid,i.px0,!0,1)+\"Z\";T?h.attr(\"d\",\"M\"+(o+T*i.px0[0])+\",\"+(c+T*i.px0[1])+C(i.px0,i.pxmid,!1,T)+C(i.pxmid,i.px0,!1,T)+\"Z\"+k):h.attr(\"d\",k)}else{var A=C(i.px0,i.px1,!0,1);if(T){var M=1-T;h.attr(\"d\",\"M\"+(o+T*i.px1[0])+\",\"+(c+T*i.px1[1])+C(i.px1,i.px0,!1,T)+\"l\"+M*i.px0[0]+\",\"+M*i.px0[1]+A+\"Z\")}else h.attr(\"d\",\"M\"+o+\",\"+c+\"l\"+i.px0[0]+\",\"+i.px0[1]+A+\"Z\")}D(t,i,d);var S=g.castOption(m.textposition,i.pts),E=u.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==S?[0]:[]);E.enter().append(\"g\").classed(\"slicetext\",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),h=l.ensureUniformFontSize(t,\"outside\"===S?function(t,e,r){var n=g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,i=g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(m,i,r.font):b(m,i,r.font));u.text(i.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if(\"outside\"===S)v=L(y,i);else if(v=w(y,i,d),\"auto\"===S&&v.scale<1){var x=l.ensureUniformFontSize(t,m.outsidetextfont);u.call(s.font,x),v=L(y=s.bBox(u.node()),i)}var T=v.textPosAngle,k=void 0===T?i.pxmid:z(d.r,T);if(v.targetX=o+k[0]*v.rCenter+(v.x||0),v.targetY=c+k[1]*v.rCenter+(v.y||0),R(v,y),v.outside){var A=v.targetY;i.yLabelMin=A-y.height/2,i.yLabelMid=A,i.yLabelMax=A+y.height/2,i.labelExtraX=0,i.labelExtraY=0,_=!0}v.fontSize=h.size,p(m.type,v,r),e[a].transform=v,u.attr(\"transform\",l.getTextTransform(v))}))}function C(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return\"a\"+n*d.r+\",\"+n*d.r+\" 0 \"+i.largeArc+(r?\" 1 \":\" 0 \")+a+\",\"+o}}));var T=n.select(this).selectAll(\"g.titletext\").data(m.title.text?[0]:[]);if(T.enter().append(\"g\").classed(\"titletext\",!0),T.exit().remove(),T.each((function(){var e,r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),i=m.title.text;m._meta&&(i=l.templateString(i,m._meta)),r.text(i).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,m.title.font).call(f.convertToTspans,t),e=\"middle center\"===m.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(d):C(d,a),r.attr(\"transform\",u(e.x,e.y)+c(Math.min(1,e.scale))+u(e.tx,e.ty))})),_&&function(t,e){var r,n,i,a,o,s,l,c,u,f,h,p,d;function m(t,e){return t.pxmid[1]-e.pxmid[1]}function v(t,e){return e.pxmid[1]-t.pxmid[1]}function y(t,r){r||(r={});var 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n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=i.numberFormat,o=t(\"./render\"),s=t(\"../../components/fx\"),l=t(\"../../components/color\"),c=t(\"./constants\").cn,u=i._;function f(t){return\"\"!==t}function h(t,e){return t.filter((function(t){return t.key===e.traceId}))}function p(t,e){n.select(t).select(\"path\").style(\"fill-opacity\",e),n.select(t).select(\"rect\").style(\"fill-opacity\",e)}function d(t){n.select(t).select(\"text.name\").style(\"fill\",\"black\")}function m(t){return function(e){return-1!==t.node.sourceLinks.indexOf(e.link)||-1!==t.node.targetLinks.indexOf(e.link)}}function g(t){return function(e){return-1!==e.node.sourceLinks.indexOf(t.link)||-1!==e.node.targetLinks.indexOf(t.link)}}function v(t,e,r){e&&r&&h(r,e).selectAll(\".\"+c.sankeyLink).filter(m(e)).call(x.bind(0,e,r,!1))}function y(t,e,r){e&&r&&h(r,e).selectAll(\".\"+c.sankeyLink).filter(m(e)).call(b.bind(0,e,r,!1))}function x(t,e,r,n){var i=n.datum().link.label;n.style(\"fill-opacity\",(function(t){if(!t.link.concentrationscale)return.4})),i&&h(e,t).selectAll(\".\"+c.sankeyLink).filter((function(t){return t.link.label===i})).style(\"fill-opacity\",(function(t){if(!t.link.concentrationscale)return.4})),r&&h(e,t).selectAll(\".\"+c.sankeyNode).filter(g(t)).call(v)}function b(t,e,r,n){var i=n.datum().link.label;n.style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})),i&&h(e,t).selectAll(\".\"+c.sankeyLink).filter((function(t){return t.link.label===i})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})),r&&h(e,t).selectAll(c.sankeyNode).filter(g(t)).call(y)}function _(t,e){var r=t.hoverlabel||{},n=i.nestedProperty(r,e).get();return!Array.isArray(n)&&n}e.exports=function(t,e){for(var r=t._fullLayout,i=r._paper,h=r._size,m=0;m<t._fullData.length;m++)if(t._fullData[m].visible&&t._fullData[m].type===c.sankey&&!t._fullData[m]._viewInitial){var g=t._fullData[m].node;t._fullData[m]._viewInitial={node:{groups:g.groups.slice(),x:g.x.slice(),y:g.y.slice()}}}var w=u(t,\"source:\")+\" \",T=u(t,\"target:\")+\" \",k=u(t,\"concentration:\")+\" \",A=u(t,\"incoming flow count:\")+\" \",M=u(t,\"outgoing flow count:\")+\" \";o(t,i,e,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},{linkEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(x.bind(0,r,i,!0)),\"skip\"!==r.link.trace.link.hoverinfo&&(r.link.fullData=r.link.trace,t.emit(\"plotly_hover\",{event:n.event,points:[r.link]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.link.trace.link;if(\"none\"!==o.hoverinfo&&\"skip\"!==o.hoverinfo){for(var c=[],u=0,h=0;h<i.flow.links.length;h++){var m=i.flow.links[h];if(\"closest\"!==t._fullLayout.hovermode||i.link.pointNumber===m.pointNumber){i.link.pointNumber===m.pointNumber&&(u=h),m.fullData=m.trace,o=i.link.trace.link;var g=y(m),v={valueLabel:a(i.valueFormat)(m.value)+i.valueSuffix};c.push({x:g[0],y:g[1],name:v.valueLabel,text:[m.label||\"\",w+m.source.label,T+m.target.label,m.concentrationscale?k+a(\"%0.2f\")(m.flow.labelConcentration):\"\"].filter(f).join(\"<br>\"),color:_(o,\"bgcolor\")||l.addOpacity(m.color,1),borderColor:_(o,\"bordercolor\"),fontFamily:_(o,\"font.family\"),fontSize:_(o,\"font.size\"),fontColor:_(o,\"font.color\"),nameLength:_(o,\"namelength\"),textAlign:_(o,\"align\"),idealAlign:n.event.x<g[0]?\"right\":\"left\",hovertemplate:o.hovertemplate,hovertemplateLabels:v,eventData:[m]})}}s.loneHover(c,{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t,anchorIndex:u}).each((function(){i.link.concentrationscale||p(this,.65),d(this)}))}}function y(t){var e,r;t.circular?(e=(t.circularPathData.leftInnerExtent+t.circularPathData.rightInnerExtent)/2,r=t.circularPathData.verticalFullExtent):(e=(t.source.x1+t.target.x0)/2,r=(t.y0+t.y1)/2);var n=[e,r];return\"v\"===t.trace.orientation&&n.reverse(),n[0]+=i.parent.translateX,n[1]+=i.parent.translateY,n}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(b.bind(0,i,a,!0)),\"skip\"!==i.link.trace.link.hoverinfo&&(i.link.fullData=i.link.trace,t.emit(\"plotly_unhover\",{event:n.event,points:[i.link]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r){var i=r.link;i.originalEvent=n.event,t._hoverdata=[i],s.click(t,{target:!0})}},nodeEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(v,r,i),\"skip\"!==r.node.trace.node.hoverinfo&&(r.node.fullData=r.node.trace,t.emit(\"plotly_hover\",{event:n.event,points:[r.node]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.node.trace.node;if(\"none\"!==o.hoverinfo&&\"skip\"!==o.hoverinfo){var l=n.select(e).select(\".\"+c.nodeRect),u=t._fullLayout._paperdiv.node().getBoundingClientRect(),h=l.node().getBoundingClientRect(),m=h.left-2-u.left,g=h.right+2-u.left,v=h.top+h.height/4-u.top,y={valueLabel:a(i.valueFormat)(i.node.value)+i.valueSuffix};i.node.fullData=i.node.trace,t._fullLayout._calcInverseTransform(t);var x=t._fullLayout._invScaleX,b=t._fullLayout._invScaleY,w=s.loneHover({x0:x*m,x1:x*g,y:b*v,name:a(i.valueFormat)(i.node.value)+i.valueSuffix,text:[i.node.label,A+i.node.targetLinks.length,M+i.node.sourceLinks.length].filter(f).join(\"<br>\"),color:_(o,\"bgcolor\")||i.tinyColorHue,borderColor:_(o,\"bordercolor\"),fontFamily:_(o,\"font.family\"),fontSize:_(o,\"font.size\"),fontColor:_(o,\"font.color\"),nameLength:_(o,\"namelength\"),textAlign:_(o,\"align\"),idealAlign:\"left\",hovertemplate:o.hovertemplate,hovertemplateLabels:y,eventData:[i.node]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t});p(w,.85),d(w)}}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(y,i,a),\"skip\"!==i.node.trace.node.hoverinfo&&(i.node.fullData=i.node.trace,t.emit(\"plotly_unhover\",{event:n.event,points:[i.node]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r,i){var a=r.node;a.originalEvent=n.event,t._hoverdata=[a],n.select(e).call(y,r,i),s.click(t,{target:!0})}}})}},{\"../../components/color\":366,\"../../components/fx\":406,\"../../lib\":503,\"./constants\":920,\"./render\":924,\"@plotly/d3\":58}],924:[function(t,e,r){\"use strict\";var n=t(\"d3-force\"),i=t(\"d3-interpolate\").interpolateNumber,a=t(\"@plotly/d3\"),o=t(\"@plotly/d3-sankey\"),s=t(\"@plotly/d3-sankey-circular\"),l=t(\"./constants\"),c=t(\"tinycolor2\"),u=t(\"../../components/color\"),f=t(\"../../components/drawing\"),h=t(\"../../lib\"),p=h.strTranslate,d=h.strRotate,m=t(\"../../lib/gup\"),g=m.keyFun,v=m.repeat,y=m.unwrap,x=t(\"../../lib/svg_text_utils\"),b=t(\"../../registry\"),_=t(\"../../constants/alignment\"),w=_.CAP_SHIFT,T=_.LINE_SPACING;function k(t,e,r){var n,i=y(e),a=i.trace,u=a.domain,f=\"h\"===a.orientation,p=a.node.pad,d=a.node.thickness,m=t.width*(u.x[1]-u.x[0]),g=t.height*(u.y[1]-u.y[0]),v=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[m,g]:[g,m]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(v).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn(\"node.pad was reduced to \",n.nodePadding(),\" to fit within the figure.\"),i._groupLookup){var M,S=parseInt(i._groupLookup[A]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){M=k.nodes[_];break}if(M){var E={pointNumber:parseInt(A),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+\":\"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=c(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*m,a.node.y[_]*g];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}if(\"snap\"===a.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(v=k.nodes));n.update(k)}return{circular:b,key:r,trace:a,guid:h.randstr(),horizontal:f,width:m,height:g,nodePad:a.node.pad,nodeLineColor:a.node.line.color,nodeLineWidth:a.node.line.width,linkLineColor:a.link.line.color,linkLineWidth:a.link.line.width,valueFormat:a.valueformat,valueSuffix:a.valuesuffix,textFont:a.textfont,translateX:u.x[0]*t.width+t.margin.l,translateY:t.height-u.y[1]*t.height+t.margin.t,dragParallel:f?g:m,dragPerpendicular:f?m:g,arrangement:a.arrangement,sankey:n,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function A(t,e,r){var n=c(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:u.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){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,a=t.link.source.x1,o=t.link.target.x0,s=i(a,o),l=s(.5),c=s(.5),u=t.link.y0-t.link.width/2,f=t.link.y0+t.link.width/2,h=t.link.y1-t.link.width/2,p=t.link.y1+t.link.width/2;return\"M\"+a+\",\"+u+\"C\"+l+\",\"+u+\" \"+c+\",\"+h+\" 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e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,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(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,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:u.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,s].join(\"_\"),interactionState:t.interactionState,figure:t}}function E(t){t.attr(\"transform\",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function 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g=\"projection.\"+d[m];f(g+\".show\")&&(f(g+\".opacity\"),f(g+\".scale\"))}var v=n.getComponentMethod(\"errorbars\",\"supplyDefaults\");v(t,e,h||p||r,{axis:\"z\"}),v(t,e,h||p||r,{axis:\"y\",inherit:\"z\"}),v(t,e,h||p||r,{axis:\"x\",inherit:\"z\"})}else e.visible=!1}},{\"../../lib\":503,\"../../registry\":638,\"../scatter/line_defaults\":940,\"../scatter/marker_defaults\":946,\"../scatter/subtypes\":952,\"../scatter/text_defaults\":953,\"./attributes\":955}],960:[function(t,e,r){\"use 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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\":373,\"../../lib/extend\":493,\"../../plots/attributes\":550,\"../../plots/template_attributes\":633,\"../scatter/attributes\":927}],962:[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\"),m=p(\"b\"),g=Math.min(d.length,m.length);if(g){e._length=g,p(\"text\"),p(\"texttemplate\"),p(\"hovertext\"),p(\"mode\",g<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var v=[];(a.hasMarkers(e)||a.hasText(e))&&(p(\"marker.maxdisplayed\"),v.push(\"points\")),p(\"fill\"),\"none\"!==e.fill&&(u(t,e,r,p),a.hasLines(e)||l(t,e,p)),\"tonext\"!==e.fill&&\"toself\"!==e.fill||v.push(\"fills\"),\"fills\"!==p(\"hoveron\",v.join(\"+\")||\"points\")&&p(\"hovertemplate\"),n.coerceSelectionMarkerOpacity(e,p)}else e.visible=!1}},{\"../../lib\":503,\"../scatter/constants\":931,\"../scatter/fillcolor_defaults\":935,\"../scatter/line_defaults\":940,\"../scatter/line_shape_defaults\":942,\"../scatter/marker_defaults\":946,\"../scatter/subtypes\":952,\"../scatter/text_defaults\":953,\"./attributes\":961}],964:[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}},{}],965:[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}},{}],966:[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,m=p._module.formatLabels(h,p);s.yLabel=m.yLabel,delete s.text;var g=[];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),g.push(\"y: \"+s.yLabel),-1!==v.indexOf(\"text\")&&i(h,p,g),s.extraText=g.join(\"<br>\")}return o}function y(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,\"\"):t._hovertitle,g.push(r+\": \"+e.toFixed(3)+t.labelsuffix)}}},{\"../../lib\":503,\"../scatter/hover\":938}],967:[function(t,e,r){\"use 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n=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),a=t(\"../scatter/marker_defaults\"),o=t(\"../scatter/line_defaults\"),s=t(\"../scatter/text_defaults\"),l=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,c,r,i)}var h,p=f(\"locations\");if(p&&p.length){var d,m=f(\"geojson\");(\"string\"==typeof m&&\"\"!==m||n.isPlainObject(m))&&(d=\"geojson-id\"),\"geojson-id\"===f(\"locationmode\",d)&&f(\"featureidkey\"),h=p.length}else{var 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M=p.font[r]={};M.size=(s.isTypedArray(f)?f[r]:Array.isArray(f)?n(f[r])?f[r]:0:f)*d,M.family=Array.isArray(h)?h[r]:h}else p.font={size:f*d,family:h};return p}function x(t){var e,r,n=t._length,i=t.marker,o={},l=s.isArrayOrTypedArray(i.symbol),c=s.isArrayOrTypedArray(i.color),f=s.isArrayOrTypedArray(i.line.color),d=s.isArrayOrTypedArray(i.opacity),m=s.isArrayOrTypedArray(i.size),g=s.isArrayOrTypedArray(i.line.width);if(l||(r=p.isOpenSymbol(i.symbol)),l||c||f||d){o.colors=new Array(n),o.borderColors=new Array(n);var v=u(i,i.opacity,n),y=u(i.line,i.opacity,n);if(!Array.isArray(y[0])){var x=y;for(y=Array(n),e=0;e<n;e++)y[e]=x}if(!Array.isArray(v[0])){var b=v;for(v=Array(n),e=0;e<n;e++)v[e]=b}for(o.colors=v,o.borderColors=y,e=0;e<n;e++){if(l){var _=i.symbol[e];r=p.isOpenSymbol(_)}r&&(y[e]=v[e].slice(),v[e]=v[e].slice(),v[e][3]=0)}o.opacity=t.opacity}else 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i={opacity:1,text:e.text,texttemplate:e.texttemplate,textposition:e.textposition,textfont:s.extendFlat({},e.textfont)};r.textfont&&s.extendFlat(i.textfont,r.textfont),n=y(t,i)}return n}function w(t,e,r){var n={capSize:2*e.width*r,lineWidth:e.thickness*r,color:e.color};return e.copy_ystyle&&(n=t.error_y),n}var T=d.SYMBOL_SDF_SIZE,k=d.SYMBOL_SIZE,A=d.SYMBOL_STROKE,M={},S=l.symbolFuncs[0](.05*k);function E(t){if(\"circle\"===t)return null;var e,r,n=l.symbolNumber(t),a=l.symbolFuncs[n%100],o=!!l.symbolNoDot[n%100],s=!!l.symbolNoFill[n%100],c=p.isDotSymbol(t);return M[t]?M[t]:(e=c&&!o?a(1.1*k)+S:a(k),r=i(e,{w:T,h:T,viewBox:[-k,-k,k,k],stroke:s?A:-A}),M[t]=r,r||null)}e.exports={style:function(t,e){var r,n={marker:void 0,markerSel:void 0,markerUnsel:void 0,line:void 0,fill:void 0,errorX:void 0,errorY:void 0,text:void 0,textSel:void 0,textUnsel:void 0},i=t._context.plotGlPixelRatio;if(!0!==e.visible)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-_),m=Math.max(m,y+w)}}}h[a]={positions:r,errors:p,_bnds:[d,m]}}}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,m=g[p],v=g[d],y=u?u/.8+1:0,x=-v*y-.5*v;o.offset[i]=[m*y/h,x/h]}}return o}}},{\"../../components/drawing\":388,\"../../components/fx/helpers\":402,\"../../constants/interactions\":478,\"../../lib\":503,\"../../lib/gl_format_color\":499,\"../../plots/cartesian/axis_ids\":558,\"../../registry\":638,\"../scatter/make_bubble_size_func\":944,\"../scatter/subtypes\":952,\"./constants\":982,\"./helpers\":987,\"color-normalize\":89,\"fast-isnumeric\":190,\"svg-path-sdf\":310}],984:[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,m){function g(r,i){return n.coerce(t,e,o,r,i)}var v=!!t.marker&&a.isOpenSymbol(t.marker.symbol),y=l.isBubble(t),x=c(t,e,m,g);if(x){u(t,e,m,g),g(\"xhoverformat\"),g(\"yhoverformat\");var b=x<s.PTS_LINESONLY?\"lines+markers\":\"lines\";g(\"text\"),g(\"hovertext\"),g(\"hovertemplate\"),g(\"mode\",b),l.hasLines(e)&&(g(\"connectgaps\"),h(t,e,r,m,g),g(\"line.shape\")),l.hasMarkers(e)&&(f(t,e,r,m,g),g(\"marker.line.width\",v||y?1:0)),l.hasText(e)&&(g(\"texttemplate\"),d(t,e,m,g));var _=(e.line||{}).color,w=(e.marker||{}).color;g(\"fill\"),\"none\"!==e.fill&&p(t,e,r,g);var T=i.getComponentMethod(\"errorbars\",\"supplyDefaults\");T(t,e,_||w||r,{axis:\"y\"}),T(t,e,_||w||r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},{\"../../lib\":503,\"../../registry\":638,\"../scatter/constants\":931,\"../scatter/fillcolor_defaults\":935,\"../scatter/line_defaults\":940,\"../scatter/marker_defaults\":946,\"../scatter/period_defaults\":947,\"../scatter/subtypes\":952,\"../scatter/text_defaults\":953,\"../scatter/xy_defaults\":954,\"./attributes\":979,\"./helpers\":987}],985:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\"),a=t(\"../../constants/interactions\").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},m=c.textUnselectedOptions[u]||{},g=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=m.color,x=p.color,b=Array.isArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(g)}}},{\"../../components/color\":366,\"../../constants/interactions\":478,\"../../lib\":503}],986:[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\":936}],987:[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\":982}],988:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../scatter/get_trace_color\");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var m=d&&d.line;m&&(h.mlc=Array.isArray(m.color)?m.color[f]:m.color,h.mlw=i.isArrayOrTypedArray(m.width)?m.width[f]:m.width);var g=d&&d.gradient;g&&\"none\"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[f]:g.type,h.mgc=Array.isArray(g.color)?g.color[f]:g.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=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:A?A[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(h,o,M),M}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d,m=t.cd,g=m[0].t,v=m[0].trace,y=t.xa,x=t.ya,b=g.x,_=g.y,w=y.c2p(e),T=x.c2p(r),k=t.distance;if(g.tree){var A=y.p2c(w-k),M=y.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i=\"x\"===n?g.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):g.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=g.ids;var L=k;if(\"x\"===n){var C=!!v.xperiodalignment,P=!!v.yperiodalignment;for(u=0;u<i.length;u++){if(l=b[a=i[u]],f=Math.abs(y.c2p(l)-w),C){var I=y.c2p(v._xStarts[a]),O=y.c2p(v._xEnds[a]);f=w>=Math.min(I,O)&&w<=Math.max(I,O)?0:1/0}if(f<L){if(L=f,c=_[a],h=x.c2p(c)-T,P){var z=x.c2p(v._yStarts[a]),D=x.c2p(v._yEnds[a]);h=T>=Math.min(z,D)&&T<=Math.max(z,D)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[u]}}}else for(u=i.length-1;u>-1;u--)l=b[a=i[u]],c=_[a],f=y.c2p(l)-w,h=x.c2p(c)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 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_=y.count,w=v._glcanvas.data()[0].regl;if(f(t,e,r),y.dirty){if(!0===y.error2d&&(y.error2d=a(w)),!0===y.line2d&&(y.line2d=i(w)),!0===y.scatter2d&&(y.scatter2d=n(w)),!0===y.fill2d&&(y.fill2d=i(w)),!0===y.glText)for(y.glText=new Array(_),m=0;m<_;m++)y.glText[m]=new o(w);if(y.glText){if(_>y.glText.length){var T=_-y.glText.length;for(m=0;m<T;m++)y.glText.push(new o(w))}else if(_<y.glText.length){var k=y.glText.length-_;y.glText.splice(_,k).forEach((function(t){t.destroy()}))}for(m=0;m<_;m++)y.glText[m].update(y.textOptions[m])}if(y.line2d&&(y.line2d.update(y.lineOptions),y.lineOptions=y.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})),y.line2d.update(y.lineOptions)),y.error2d){var A=(y.errorXOptions||[]).concat(y.errorYOptions||[]);y.error2d.update(A)}y.scatter2d&&y.scatter2d.update(y.markerOptions),y.fillOrder=s.repeat(null,_),y.fill2d&&(y.fillOptions=y.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=y.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(y.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 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R=y.selectBatch[I]=s.selIndices2selPoints(C),F={};for(g=0;g<R.length;g++)F[R[g]]=1;var B=[];for(g=0;g<O;g++)F[g]||B.push(g);y.unselectBatch[I]=B}var N=P.xpx=new Array(O),j=P.ypx=new Array(O);for(g=0;g<O;g++)N[g]=x.c2p(z[g]),j[g]=b.c2p(D[g])}else P.xpx=P.ypx=null}if(S){if(y.select2d||(y.select2d=n(v._glcanvas.data()[1].regl)),y.scatter2d){var U=new Array(_);for(m=0;m<_;m++)U[m]=y.selectBatch[m].length||y.unselectBatch[m].length?y.markerUnselectedOptions[m]:{};y.scatter2d.update(U)}y.select2d&&(y.select2d.update(y.markerOptions),y.select2d.update(y.markerSelectedOptions)),y.glText&&r.forEach((function(t){var e=((t||[])[0]||{}).trace||{};u.hasText(e)&&h(t)}))}else y.scatter2d&&y.scatter2d.update(y.markerOptions);var V={viewport:d(v,x,b,t._context.plotGlPixelRatio),range:[(x._rl||x.range)[0],(b._rl||b.range)[0],(x._rl||x.range)[1],(b._rl||b.range)[1]]},H=s.repeat(V,y.count);y.fill2d&&y.fill2d.update(H),y.line2d&&y.line2d.update(H),y.error2d&&y.error2d.update(H.concat(H)),y.scatter2d&&y.scatter2d.update(H),y.select2d&&y.select2d.update(H),y.glText&&y.glText.forEach((function(t){t.update(V)}))}else y.init()}}).reglPrecompiled=p},{\"../../components/dragelement/helpers\":384,\"../../lib\":503,\"../../lib/prepare_regl\":516,\"../scatter/link_traces\":943,\"../scatter/subtypes\":952,\"./edit_style\":985,\"gl-text\":225,\"regl-error2d\":279,\"regl-line2d\":280,\"regl-scatter2d\":281}],991:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,c=r.selectBatch,u=r.unselectBatch,f=0;f<t;f++){if(e&&r.fillOrder[f]&&e.draw(r.fillOrder[f]),a&&r.lineOptions[f]&&a.draw(f),i&&(r.errorXOptions[f]&&i.draw(f),r.errorYOptions[f]&&i.draw(f+t)),o&&r.markerOptions[f])if(u[f].length){var h=n.repeat([],r.count);h[f]=u[f],o.draw(h)}else c[f].length||o.draw(f);s[f]&&r.textOptions[f]&&s[f].render()}l&&l.draw(c),r.dirty=!1},r.destroy=function(){r.fill2d&&r.fill2d.destroy&&r.fill2d.destroy(),r.scatter2d&&r.scatter2d.destroy&&r.scatter2d.destroy(),r.error2d&&r.error2d.destroy&&r.error2d.destroy(),r.line2d&&r.line2d.destroy&&r.line2d.destroy(),r.select2d&&r.select2d.destroy&&r.select2d.destroy(),r.glText&&r.glText.forEach((function(t){t.destroy&&t.destroy()})),r.lineOptions=null,r.fillOptions=null,r.markerOptions=null,r.markerSelectedOptions=null,r.markerUnselectedOptions=null,r.errorXOptions=null,r.errorYOptions=null,r.textOptions=null,r.textSelectedOptions=null,r.textUnselectedOptions=null,r.selectBatch=null,r.unselectBatch=null,e._scene=null}),r.dirty||n.extendFlat(r,i),r}},{\"../../lib\":503}],992:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\"),i=t(\"./edit_style\").styleTextSelection;e.exports=function(t,e){var r=t.cd,a=t.xaxis,o=t.yaxis,s=[],l=r[0].trace,c=r[0].t,u=l._length,f=c.x,h=c.y,p=c._scene,d=c.index;if(!p)return s;var m=n.hasText(l),g=n.hasMarkers(l),v=!g&&!m;if(!0!==l.visible||v)return s;var y=[],x=[];if(!1!==e&&!e.degenerate)for(var b=0;b<u;b++)e.contains([c.xpx[b],c.ypx[b]],!1,b,t)?(y.push(b),s.push({pointNumber:b,x:a.c2d(f[b]),y:o.c2d(h[b])})):x.push(b);if(g){var _=p.scatter2d;if(y.length||x.length){if(!p.selectBatch[d].length&&!p.unselectBatch[d].length){var w=new Array(p.count);w[d]=p.markerUnselectedOptions[d],_.update.apply(_,w)}}else{var T=new Array(p.count);T[d]=p.markerOptions[d],_.update.apply(_,T)}}return p.selectBatch[d]=y,p.unselectBatch[d]=x,m&&i(r),s}},{\"../scatter/subtypes\":952,\"./edit_style\":985}],993:[function(t,e,r){\"use strict\";var n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../scattergeo/attributes\"),o=t(\"../scatter/attributes\"),s=t(\"../../plots/mapbox/layout_attributes\"),l=t(\"../../plots/attributes\"),c=t(\"../../components/colorscale/attributes\"),u=t(\"../../lib/extend\").extendFlat,f=t(\"../../plot_api/edit_types\").overrideAll,h=a.line,p=a.marker;e.exports=f({lon:a.lon,lat:a.lat,mode:u({},o.mode,{dflt:\"markers\"}),text:u({},o.text,{}),texttemplate:i({editType:\"plot\"},{keys:[\"lat\",\"lon\",\"text\"]}),hovertext:u({},o.hovertext,{}),line:{color:h.color,width:h.width},connectgaps:o.connectgaps,marker:u({symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},angle:{valType:\"number\",dflt:\"auto\",arrayOk:!0},allowoverlap:{valType:\"boolean\",dflt:!1},opacity:p.opacity,size:p.size,sizeref:p.sizeref,sizemin:p.sizemin,sizemode:p.sizemode},c(\"marker\")),fill:a.fill,fillcolor:o.fillcolor,textfont:s.layers.symbol.textfont,textposition:s.layers.symbol.textposition,below:{valType:\"string\"},selected:{marker:o.selected.marker},unselected:{marker:o.unselected.marker},hoverinfo:u({},l.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]}),hovertemplate:n()},\"calc\",\"nested\")},{\"../../components/colorscale/attributes\":373,\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../../plots/attributes\":550,\"../../plots/mapbox/layout_attributes\":615,\"../../plots/template_attributes\":633,\"../scatter/attributes\":927,\"../scattergeo/attributes\":969}],994:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\"),a=t(\"../../constants/numerical\").BADNUM,o=t(\"../../lib/geojson_utils\"),s=t(\"../../components/colorscale\"),l=t(\"../../components/drawing\"),c=t(\"../scatter/make_bubble_size_func\"),u=t(\"../scatter/subtypes\"),f=t(\"../../plots/mapbox/convert_text_opts\"),h=t(\"../../components/fx/helpers\").appendArrayPointValue,p=t(\"../../lib/svg_text_utils\").NEWLINES,d=t(\"../../lib/svg_text_utils\").BR_TAG_ALL;function m(){return{geojson:o.makeBlank(),layout:{visibility:\"none\"},paint:{}}}function g(t,e){return i.isArrayOrTypedArray(t)?e?function(e){return n(t[e])?+t[e]:0}:function(e){return t[e]}:t?function(){return t}:v}function v(){return\"\"}function y(t){return t[0]===a}e.exports=function(t,e){var r,a=e[0].trace,x=!0===a.visible&&0!==a._length,b=\"none\"!==a.fill,_=u.hasLines(a),w=u.hasMarkers(a),T=u.hasText(a),k=w&&\"circle\"===a.marker.symbol,A=w&&\"circle\"!==a.marker.symbol,M=m(),S=m(),E=m(),L=m(),C={fill:M,line:S,circle:E,symbol:L};if(!x)return C;if((b||_)&&(r=o.calcTraceToLineCoords(e)),b&&(M.geojson=o.makePolygon(r),M.layout.visibility=\"visible\",i.extendFlat(M.paint,{\"fill-color\":a.fillcolor})),_&&(S.geojson=o.makeLine(r),S.layout.visibility=\"visible\",i.extendFlat(S.paint,{\"line-width\":a.line.width,\"line-color\":a.line.color,\"line-opacity\":a.opacity})),k){var P=function(t){var e,r,a,o,u=t[0].trace,f=u.marker,h=u.selectedpoints,p=i.isArrayOrTypedArray(f.color),d=i.isArrayOrTypedArray(f.size),m=i.isArrayOrTypedArray(f.opacity);function g(t){return u.opacity*t}p&&(r=s.hasColorscale(u,\"marker\")?s.makeColorScaleFuncFromTrace(f):i.identity);d&&(a=c(u));m&&(o=function(t){return g(n(t)?+i.constrain(t,0,1):0)});var v,x=[];for(e=0;e<t.length;e++){var 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n=t(\"../scatterpolar/attributes\"),i=t(\"../scattergl/attributes\"),a=t(\"../../plots/template_attributes\").texttemplateAttrs;e.exports={mode:n.mode,r:n.r,theta:n.theta,r0:n.r0,dr:n.dr,theta0:n.theta0,dtheta:n.dtheta,thetaunit:n.thetaunit,text:n.text,texttemplate:a({editType:\"plot\"},{keys:[\"r\",\"theta\",\"text\"]}),hovertext:n.hovertext,hovertemplate:n.hovertemplate,line:i.line,connectgaps:i.connectgaps,marker:i.marker,fill:i.fill,fillcolor:i.fillcolor,textposition:i.textposition,textfont:i.textfont,hoverinfo:n.hoverinfo,selected:n.selected,unselected:n.unselected}},{\"../../plots/template_attributes\":633,\"../scattergl/attributes\":979,\"../scatterpolar/attributes\":1002}],1010:[function(t,e,r){\"use 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A={};u.label&&(A.label=u.label),u.hasOwnProperty(\"v\")&&(A.value=u.v,A.valueLabel=b.formatValue(u.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),u.hasOwnProperty(\"color\")&&(A.color=u.color);var M=s.castOption(r,u.i,\"text\");return(s.isValidTextValue(M)||\"\"===M)&&(A.text=M),A.customdata=s.castOption(r,u.i,\"customdata\"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},{\"../../components/drawing\":388,\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"../bar/style\":662,\"../bar/uniform_text\":664,\"../pie/helpers\":906,\"../pie/plot\":910,\"./constants\":1052,\"./fx\":1054,\"./helpers\":1055,\"./style\":1060,\"@plotly/d3\":58,\"d3-hierarchy\":115,\"d3-interpolate\":116}],1060:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../components/color\"),a=t(\"../../lib\"),o=t(\"../bar/uniform_text\").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,\"marker.line.color\")||i.defaultLine,c=a.castOption(r,s,\"marker.line.width\")||0;t.style(\"stroke-width\",c).call(i.fill,n.color).call(i.stroke,l).style(\"opacity\",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(\".trace\");o(t,e,\"sunburst\"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style(\"opacity\",r.opacity),e.selectAll(\"path.surface\").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{\"../../components/color\":366,\"../../lib\":503,\"../bar/uniform_text\":664,\"@plotly/d3\":58}],1061:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"../../components/colorscale/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,o=t(\"../../plots/template_attributes\").hovertemplateAttrs,s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll;function u(t){return{show:{valType:\"boolean\",dflt:!1},start:{valType:\"number\",dflt:null,editType:\"plot\"},end:{valType:\"number\",dflt:null,editType:\"plot\"},size:{valType:\"number\",dflt:null,min:0,editType:\"plot\"},project:{x:{valType:\"boolean\",dflt:!1},y:{valType:\"boolean\",dflt:!1},z:{valType:\"boolean\",dflt:!1}},color:{valType:\"color\",dflt:n.defaultLine},usecolormap:{valType:\"boolean\",dflt:!1},width:{valType:\"number\",min:1,max:16,dflt:2},highlight:{valType:\"boolean\",dflt:!0},highlightcolor:{valType:\"color\",dflt:n.defaultLine},highlightwidth:{valType:\"number\",min:1,max:16,dflt:2}}}var f=e.exports=c(l({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:o(),xhoverformat:a(\"x\"),yhoverformat:a(\"y\"),zhoverformat:a(\"z\"),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or surfacecolor\",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:\"calc\"}),{contours:{x:u(),y:u(),z:u()},hidesurface:{valType:\"boolean\",dflt:!1},lightposition:{x:{valType:\"number\",min:-1e5,max:1e5,dflt:10},y:{valType:\"number\",min:-1e5,max:1e5,dflt:1e4},z:{valType:\"number\",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:\"number\",min:0,max:1,dflt:.8},diffuse:{valType:\"number\",min:0,max:1,dflt:.8},specular:{valType:\"number\",min:0,max:2,dflt:.05},roughness:{valType:\"number\",min:0,max:1,dflt:.5},fresnel:{valType:\"number\",min:0,max:5,dflt:.2}},opacity:{valType:\"number\",min:0,max:1,dflt:1},opacityscale:{valType:\"any\",editType:\"calc\"},_deprecated:{zauto:l({},i.zauto,{}),zmin:l({},i.zmin,{}),zmax:l({},i.zmax,{})},hoverinfo:l({},s.hoverinfo),showlegend:l({},s.showlegend,{dflt:!1})}),\"calc\",\"nested\");f.x.editType=f.y.editType=f.z.editType=\"calc+clearAxisTypes\",f.transforms=void 0},{\"../../components/color\":366,\"../../components/colorscale/attributes\":373,\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../../plots/attributes\":550,\"../../plots/cartesian/axis_format_attributes\":557,\"../../plots/template_attributes\":633}],1062:[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\":374}],1063:[function(t,e,r){\"use strict\";var n=t(\"../../../stackgl_modules\").gl_surface3d,i=t(\"../../../stackgl_modules\").ndarray,a=t(\"../../../stackgl_modules\").ndarray_linear_interpolate.d2,o=t(\"../heatmap/interp2d\"),s=t(\"../heatmap/find_empties\"),l=t(\"../../lib\").isArrayOrTypedArray,c=t(\"../../lib/gl_format_color\").parseColorScale,u=t(\"../../lib/str2rgbarray\"),f=t(\"../../components/colorscale\").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||\"\",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function m(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function g(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(m(t,n))}return e}function v(t){for(var e=g(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function y(t,e){if(!(t<1||e<1)){for(var r=g(t),n=g(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++)r(i,[l,c],n),t.set(l,c,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=y(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=v(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,c=1+a+1,u=i(new Float32Array(l*c),[l,c]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(u,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,u,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=c(t),m=l.dataScale,g=t.z[0].length,v=t._ylength,y=l.contourLevels;this.data=t;var x=[];for(e=0;e<3;e++)for(x[e]=[],r=0;r<g;r++)x[e][r]=[];for(r=0;r<g;r++)for(n=0;n<v;n++)x[0][r][n]=this.getXat(r,n,t.xcalendar,h.xaxis),x[1][r][n]=this.getYat(r,n,t.ycalendar,h.yaxis),x[2][r][n]=this.getZat(r,n,t.zcalendar,h.zaxis);if(t.connectgaps)for(t._emptypoints=s(x[2]),o(x[2],t._emptypoints),t._interpolatedZ=[],r=0;r<g;r++)for(t._interpolatedZ[r]=[],n=0;n<v;n++)t._interpolatedZ[r][n]=x[2][r][n];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null==(a=x[e][r][n])?x[e][r][n]=NaN:a=x[e][r][n]*=m[e];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(this.minValues[e]>a&&(this.minValues[e]=a),this.maxValues[e]<a&&(this.maxValues[e]=a));for(e=0;e<3;e++)this.objectOffset[e]=.5*(this.minValues[e]+this.maxValues[e]);for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(x[e][r][n]-=this.objectOffset[e]);var b=[i(new Float32Array(g*v),[g,v]),i(new Float32Array(g*v),[g,v]),i(new Float32Array(g*v),[g,v])];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)b[e].set(r,n,x[e][r][n]);x=[];var w={colormap:d,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!t.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:t.opacityscale,opacity:t.opacity},T=f(t);if(w.intensityBounds=[T.min,T.max],t.surfacecolor){var k=i(new Float32Array(g*v),[g,v]);for(r=0;r<g;r++)for(n=0;n<v;n++)k.set(r,n,t.surfacecolor[n][r]);b.push(k)}else w.intensityBounds[0]*=m[2],w.intensityBounds[1]*=m[2];(_<b[0].shape[0]||_<b[0].shape[1])&&(this.refineData=!1),!0===this.refineData&&(this.dataScaleX=this.estimateScale(b[0].shape[0],0),this.dataScaleY=this.estimateScale(b[0].shape[1],1),1===this.dataScaleX&&1===this.dataScaleY||this.refineCoords(b)),t.surfacecolor&&(w.intensity=b.pop());var A=[!0,!0,!0],M=[\"x\",\"y\",\"z\"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[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=A,p.enableHighlight=A,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}},{\"../../../stackgl_modules\":1124,\"../../components/colorscale\":378,\"../../lib\":503,\"../../lib/gl_format_color\":499,\"../../lib/str2rgbarray\":528,\"../heatmap/find_empties\":798,\"../heatmap/interp2d\":801}],1064:[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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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 d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,m=p>>>13,g=0|o[2],v=8191&g,y=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],I=8191&P,O=P>>>13,z=0|o[8],D=8191&z,R=z>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,mt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(m,U)|0,a=Math.imul(m,V);var vt=(c+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|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,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(m,q)|0,a=a+Math.imul(m,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,q)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(m,W)|0,a=a+Math.imul(m,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(m,$)|0,a=a+Math.imul(m,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(O,U)|0,a=Math.imul(O,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(I,q)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(O,q)|0,a=a+Math.imul(O,G)|0,n=n+Math.imul(L,W)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,q)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,X)|0)+Math.imul(O,W)|0,a=a+Math.imul(O,X)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var At=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,mt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,mt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(I,J)|0,i=(i=i+Math.imul(I,K)|0)+Math.imul(O,J)|0,a=a+Math.imul(O,K)|0,n=n+Math.imul(L,$)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,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 Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,mt)|0)+Math.imul(m,dt)|0))<<13)|0;c=((a=a+Math.imul(m,mt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(I,$)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(O,$)|0,a=a+Math.imul(O,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,mt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,mt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(O,rt)|0,a=a+Math.imul(O,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,mt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,mt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(O,at)|0,a=a+Math.imul(O,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ct)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,mt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,mt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ct)|0)+Math.imul(O,lt)|0,a=a+Math.imul(O,ct)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,mt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,mt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(I,ft)|0,i=(i=i+Math.imul(I,ht)|0)+Math.imul(O,ft)|0,a=a+Math.imul(O,ht)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,mt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((a=a+Math.imul(C,mt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var It=(c+(n=n+Math.imul(I,dt)|0)|0)+((8191&(i=(i=i+Math.imul(I,mt)|0)+Math.imul(O,dt)|0))<<13)|0;c=((a=a+Math.imul(O,mt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var Ot=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,mt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,mt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863;var zt=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,mt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,mt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=gt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=It,l[17]=Ot,l[18]=zt,0!==c&&(l[19]=c,r.length++),r};function m(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?d(this,t,e):r<63?p(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):m(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],m=n[u+p],g=r[u+p+o],v=n[u+p+o],y=f*g-h*v;v=f*v+h*g,g=y,r[u+p]=d+g,n[u+p]=m+v,r[u+p+o]=d-g,n[u+p+o]=m-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},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),m(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,m=1;0==(r.words[0]&m)&&d<26;++d,m<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return 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numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var v={k256:null,p224:null,p192:null,p25519:null};function y(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 x(){y.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function b(){y.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function _(){y.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function w(){y.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function T(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 k(t){T.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)}y.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},y.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):void 0!==r.strip?r.strip():r._strip(),r},y.prototype.split=function(t,e){t.iushrn(this.n,0,e)},y.prototype.imulK=function(t){return t.imul(this.k)},i(x,y),x.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},x.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(b,y),i(_,y),i(w,y),w.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(v[t])return v[t];var e;if(\"k256\"===t)e=new x;else if(\"p224\"===t)e=new b;else if(\"p192\"===t)e=new _;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new w}return v[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},T.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\")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.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)},T.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},T.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)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.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 m=p,g=0;0!==m.cmp(s);g++)m=m.redSqr();n(g<d);var v=this.pow(f,new a(1).iushln(d-g-1));h=h.redMul(v),f=v.redSqr(),p=p.redMul(f),d=g}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.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 f=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4===++s||0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),o=i;return i.cmp(this.m)>=0?o=i.isub(this.m):i.cmpn(0)<0&&(o=i.iadd(this.m)),o._forceRed(this)},k.prototype.invm=function(t){return this.imod(t._invmp(this.m).mul(this.r2))._forceRed(this)}}(void 0===e||e,this)},{buffer:2}],34:[function(t,e,r){\"use strict\";e.exports=function(t){var e,r,n,i=t.length,a=0;for(e=0;e<i;++e)a+=t[e].length;var o=new Array(a),s=0;for(e=0;e<i;++e){var l=t[e],c=l.length;for(r=0;r<c;++r){var u=o[s++]=new Array(c-1),f=0;for(n=0;n<c;++n)n!==r&&(u[f++]=l[n]);if(1&r){var h=u[1];u[1]=u[0],u[0]=h}}}return o}},{}],35:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){switch(arguments.length){case 1:return f(t);case 2:return\"function\"==typeof e?c(t,t,e,!0):h(t,e);case 3:return c(t,e,r,!1);default:throw new Error(\"box-intersect: Invalid arguments\")}};var n,i=t(\"typedarray-pool\"),a=t(\"./lib/sweep\"),o=t(\"./lib/intersect\");function s(t,e){for(var r=0;r<t;++r)if(!(e[r]<=e[r+t]))return!0;return!1}function l(t,e,r,n){for(var i=0,a=0,o=0,l=t.length;o<l;++o){var c=t[o];if(!s(e,c)){for(var u=0;u<2*e;++u)r[i++]=c[u];n[a++]=o}}return a}function c(t,e,r,n){var s=t.length,c=e.length;if(!(s<=0||c<=0)){var u=t[0].length>>>1;if(!(u<=0)){var f,h=i.mallocDouble(2*u*s),p=i.mallocInt32(s);if((s=l(t,u,h,p))>0){if(1===u&&n)a.init(s),f=a.sweepComplete(u,r,0,s,h,p,0,s,h,p);else{var d=i.mallocDouble(2*u*c),m=i.mallocInt32(c);(c=l(e,u,d,m))>0&&(a.init(s+c),f=1===u?a.sweepBipartite(u,r,0,s,h,p,0,c,d,m):o(u,r,n,s,h,p,c,d,m),i.free(d),i.free(m))}i.free(h),i.free(p)}return f}}}function u(t,e){n.push([t,e])}function f(t){return n=[],c(t,t,u,!0),n}function h(t,e){return n=[],c(t,e,u,!1),n}},{\"./lib/intersect\":37,\"./lib/sweep\":41,\"typedarray-pool\":308}],36:[function(t,e,r){\"use strict\";function n(t){return t?function(t,e,r,n,i,a,o,s,l,c,u){return i-n>l-s?function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=n,p=f*n;h<i;++h,p+=f){var d=a[e+p],m=a[e+p+t],g=o[h];t:for(var v=s,y=f*s;v<l;++v,y+=f){var x=c[e+y],b=c[e+y+t],_=u[v];if(!(b<d||m<x)){for(var w=e+1;w<t;++w){var T=a[w+p],k=a[w+t+p],A=c[w+y],M=c[w+t+y];if(k<A||M<T)continue t}var S=r(g,_);if(void 0!==S)return S}}}}(t,e,r,n,i,a,o,s,l,c,u):function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=s,p=f*s;h<l;++h,p+=f){var d=c[e+p],m=c[e+p+t],g=u[h];t:for(var v=n,y=f*n;v<i;++v,y+=f){var x=a[e+y],b=a[e+y+t],_=o[v];if(!(m<x||b<d)){for(var w=e+1;w<t;++w){var T=a[w+y],k=a[w+t+y],A=c[w+p],M=c[w+t+p];if(k<A||M<T)continue t}var S=r(_,g);if(void 0!==S)return S}}}}(t,e,r,n,i,a,o,s,l,c,u)}:function(t,e,r,n,i,a,o,s,l,c,u,f){return a-i>c-l?n?function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=n,p=f*n;h<i;++h,p+=f){var d=a[e+p],m=a[e+p+t],g=o[h];t:for(var v=s,y=f*s;v<l;++v,y+=f){var x=c[e+y],b=u[v];if(!(x<=d||m<x)){for(var _=e+1;_<t;++_){var w=a[_+p],T=a[_+t+p],k=c[_+y],A=c[_+t+y];if(T<k||A<w)continue t}var M=r(b,g);if(void 0!==M)return 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e=i.nextPow2(t);o.length<e&&(n.free(o),o=n.mallocInt32(e));s.length<e&&(n.free(s),s=n.mallocInt32(e));l.length<e&&(n.free(l),l=n.mallocInt32(e));c.length<e&&(n.free(c),c=n.mallocInt32(e));u.length<e&&(n.free(u),u=n.mallocInt32(e));f.length<e&&(n.free(f),f=n.mallocInt32(e));var r=8*e;h.length<r&&(n.free(h),h=n.mallocDouble(r))},sweepBipartite:function(t,e,r,n,i,u,f,m,g,v){for(var y=0,x=2*t,b=t-1,_=x-1,w=r;w<n;++w){var T=u[w],k=x*w;h[y++]=i[k+b],h[y++]=-(T+1),h[y++]=i[k+_],h[y++]=T}for(w=f;w<m;++w){T=v[w]+(1<<28);var A=x*w;h[y++]=g[A+b],h[y++]=-T,h[y++]=g[A+_],h[y++]=T}var M=y>>>1;a(h,M);var S=0,E=0;for(w=0;w<M;++w){var L=0|h[2*w+1];if(L>=1<<28)p(l,c,E--,L=L-(1<<28)|0);else if(L>=0)p(o,s,S--,L);else if(L<=-(1<<28)){L=-L-(1<<28)|0;for(var C=0;C<S;++C){if(void 0!==(P=e(o[C],L)))return P}d(l,c,E++,L)}else{L=-L-1|0;for(C=0;C<E;++C){var P;if(void 0!==(P=e(L,l[C])))return P}d(o,s,S++,L)}}},sweepComplete:function(t,e,r,n,i,m,g,v,y,x){for(var b=0,_=2*t,w=t-1,T=_-1,k=r;k<n;++k){var 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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.gt=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.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 m(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}})},{}],70:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t(\"./lib/text.js\"),i=t(\"./lib/lines.js\"),a=t(\"./lib/background.js\"),o=t(\"./lib/cube.js\"),s=t(\"./lib/ticks.js\"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=\"sans-serif\",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if(\"bounds\"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if(\"ticks\"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(u=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),c(\"tickColor\");var m=l(\"labels\");l(\"labelFont\")&&(m=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),c(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),c(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),c(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),c(\"gridColor\"),o(\"zeroEnable\"),c(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),c(\"backgroundColor\"),this._text?this._text&&(m||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var m=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E=m;for(A=0;A<3;++A)this.backgroundEnable[A]?E[A]=h[A]:E[A]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(A=0;A<3;++A){var L=[0,0,0];h[A]>0?L[A]=a[1][A]:L[A]=a[0][A];for(var C=0;C<2;++C){var P=(A+1+C)%3,I=(A+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,I,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(A+1+C)%3,I=(A+1+(1^C))%3;this.zeroEnable[I]&&Math.min(a[0][I],a[1][I])<=0&&Math.max(a[0][I],a[1][I])>=0&&this._lines.drawZero(P,I,this.bounds,L,this.zeroLineColor[I],this.zeroLineWidth[I]*this.pixelRatio)}}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var O=c(v,M[A].primalMinor),z=c(y,M[A].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=k/r[5*C];O[C]*=D[C]*R,z[C]*=D[C]*R}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,O,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*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(A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=c(x,M[A].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[A]&&(H[C]+=k*U[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]=\"auto\"):this.tickAlign[A]=-1,F=1,\"auto\"===(S=[this.tickAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),B=[0,0,0],j(A,U,V);for(C=0;C<3;++C)H[C]+=k*U[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,B,S)}if(this.labelEnable[A]){F=0,B=[0,0,0],this.labels[A].length>4&&(N(A),F=1),\"auto\"===(S=[this.labelAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]);for(C=0;C<3;++C)H[C]+=k*U[C]*this.labelPad[C]/r[5*C];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[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\":71,\"./lib/cube.js\":72,\"./lib/lines.js\":73,\"./lib/text.js\":75,\"./lib/ticks.js\":76}],71:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var m=-1;m<=1;m+=2)f[u]=m,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=c;c=u,u=g}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders\":74,\"gl-buffer\":78,\"gl-vao\":150}],72:[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 A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);o(c[O],c[P],c[I],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);var O,z=d([l[O],l[P],l[I],l[O+(1<<M)+(1<<S)]]);C?E=z:L=z}L>E&&(w|=1<<A)}}}var D=7^w,R=-1;for(x=0;x<8;++x)x!==w&&x!==D&&(R<0||c[R][1]>c[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x){if((N=R^1<<x)!==w&&N!==D)F<0&&(F=N),(S=c[N])[0]<c[F][0]&&(F=N)}var B=-1;for(x=0;x<3;++x){var N;if((N=R^1<<x)!==w&&N!==D&&N!==F)B<0&&(B=N),(S=c[N])[0]>c[B][0]&&(B=N)}var j=m;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===D?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=g,H=w;for(A=0;A<3;++A)V[A]=H&1<<A?-1:1;return v};var n=t(\"bit-twiddle\"),i=t(\"gl-mat4/multiply\"),a=t(\"split-polygon\"),o=t(\"robust-orientation\"),s=new Array(16),l=new Array(8),c=new Array(8),u=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],c[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var m=[1,1,1],g=[0,0,0],v={cubeEdges:m,axis:g}},{\"bit-twiddle\":32,\"gl-mat4/multiply\":100,\"robust-orientation\":284,\"split-polygon\":300}],73:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var m=+r[f][d].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){m=+r[f][v].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}g=o.length/3|0;c[f]=h,u[f]=g-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var m=f(l);m[t]=1,this.shader.uniforms.screenAxis=m,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{\"./shaders\":74,\"gl-buffer\":78,\"gl-vao\":150}],74:[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\":132,glslify:231}],75:[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 c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return a(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=u.positions,p=u.cells,d=0,m=p.length;d<m;++d)for(var g=p[d],v=2;v>=0;--v){var y=h[g[v]];o.push(f*y[0],-f*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(o.length/3|0)-f[d],c[d]=o.length/3|0;for(var m=0;m<n[d].length;++m)n[d][m].text&&s(n[d][m].x,n[d][m].text,n[d][m].font||i,n[d][m].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=f,this.labelCount=h},u.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this)}).call(this,t(\"_process\"))},{\"./shaders\":74,_process:5,\"gl-buffer\":78,\"gl-vao\":150,\"vectorize-text\":311}],76:[function(t,e,r){\"use strict\";function n(t,e){var r=t+\"\",n=r.indexOf(\".\"),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/a,c=o%a;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=\"\"+l;if(o<0&&(u=\"-\"+u),i){for(var f=\"\"+c;f.length<i;)f=\"0\"+f;return u+\".\"+f}return u}r.create=function(t,e){for(var r=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:n(e[i],o)});for(o=-1;o*e[i]>=t[0][i];--o)a.push({x:o*e[i],text:n(e[i],o)});r.push(a)}return r},r.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var i=t[r][n],a=e[r][n];if(i.x!==a.x||i.text!==a.text||i.font!==a.font||i.fontColor!==a.fontColor||i.fontSize!==a.fontSize||i.dx!==a.dx||i.dy!==a.dy)return!1}}return!0}},{}],77:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,l,f){var h=e.model||c,p=e.view||c,v=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,v,x,y)).axis;o(u,p,h),o(u,v,u);for(var _=m,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=y?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var I=d(g,u,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(I[C])))}}}}return _};var n=t(\"extract-frustum-planes\"),i=t(\"split-polygon\"),a=t(\"./lib/cube.js\"),o=t(\"gl-mat4/multiply\"),s=t(\"gl-mat4/transpose\"),l=t(\"gl-vec4/transformMat4\"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var m=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},{\"./lib/cube.js\":72,\"extract-frustum-planes\":67,\"gl-mat4/multiply\":100,\"gl-mat4/transpose\":109,\"gl-vec4/transformMat4\":221,\"split-polygon\":300}],78:[function(t,e,r){\"use strict\";var n=t(\"typedarray-pool\"),i=t(\"ndarray-ops\"),a=t(\"ndarray\"),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:259,\"ndarray-ops\":254,\"typedarray-pool\":308}],79:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,m=[],g=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),v=!1):v=!0}v||(p=x,d=b),m.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A;y=0;for(var M=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=m[y],P=n.length(C)*k,I=0;I<8;I++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var O=a.positions.length;a.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return a};var i=t(\"./lib/shaders\");e.exports.createMesh=t(\"./create_mesh\"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:\"cone\"})}},{\"./create_mesh\":80,\"./lib/shaders\":81,\"gl-vec3\":169}],80:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"gl-mat4/multiply\"),l=t(\"gl-mat4/invert\"),c=t(\"ndarray\"),u=t(\"colormap\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function m(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,m=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],m=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var v=p[g];d=Math.min(d,v),m=Math.max(m,v)}else for(g=0;g<n.length;++g){v=n[g][2];d=Math.min(d,v),m=Math.max(m,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(g=0;g<n.length;++g)for(var y=n[g],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(m-d),0]:[(y[2]-d)/(m-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var m=this.triShader;m.bind(),m.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=m(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,g,v,x,r.traceType||\"cone\");return b.update(e),b}},{colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-shader\":132,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259}],81:[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:231}],82:[function(t,e,r){e.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORM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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 m=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return m._shape[0]},set:function(t){return m.width=t}},1:{get:function(){return m._shape[1]},set:function(t){return m.height=t}}}),this._shapeVector=g,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,m=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension(\"WEBGL_depth_texture\");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):m&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var m=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(m,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),m.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},m.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":146}],87:[function(t,e,r){var n=t(\"sprintf-js\").sprintf,i=t(\"gl-constants/lookup\"),a=t(\"glsl-shader-name\"),o=t(\"add-line-numbers\");e.exports=function(t,e,r){\"use strict\";var s=a(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===i.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var c=n(\"Error compiling %s shader %s:\\n\",l,s),u=n(\"%s%s\",c,t),f=t.split(\"\\n\"),h={},p=0;p<f.length;p++){var d=f[p];if(\"\"!==d&&\"\\0\"!==d){var m=parseInt(d.split(\":\")[2]);if(isNaN(m))throw new Error(n(\"Could not parse error: %s\",d));h[m]=d}}var g=o(e).split(\"\\n\");for(p=0;p<g.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=g[p];if(c+=v+\"\\n\",h[p+1]){var y=h[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),c+=n(\"^^^ %s\\n\\n\",y)}}return{long:c.trim(),short:u.trim()}}},{\"add-line-numbers\":9,\"gl-constants/lookup\":83,\"glsl-shader-name\":223,\"sprintf-js\":301}],88:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t(\"binary-search-bounds\"),i=t(\"iota-array\"),a=t(\"typedarray-pool\"),o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"./lib/shaders\");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],m=t.colorValues||[0,0,0,1],g=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,I;l?(P=x*(o[C]-u),I=x*(o[C+1]-u)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-u):x*(o[C]-(o[C]-o[C-1])/2-u),I=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-u):x*(o[C]+(o[C]-o[C-1])/2-u));for(var O=0;O<E;++O){var z,D;l?(z=y*(r[O]-c),D=y*(r[O+1]-c)):(z=O<b-1?y*(r[O]-(r[O+1]-r[O])/2-c):y*(r[O]-(r[O]-r[O-1])/2-c),D=O<b-1?y*(r[O]+(r[O+1]-r[O])/2-c):y*(r[O]+(r[O]-r[O-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],H=s[l?(C+V)*b+(O+U):C*b+O],q=n.le(d,H);if(q<0)F=m[0],B=m[1],N=m[2],j=m[3];else if(q===g-1)F=m[4*g-4],B=m[4*g-3],N=m[4*g-2],j=m[4*g-1];else{var G=(H-d[q])/(d[q+1]-d[q]),Y=1-G,W=4*q,X=4*(q+1);F=Y*m[W]+G*m[X],B=Y*m[W+1]+G*m[X+1],N=Y*m[W+2]+G*m[X+2],j=Y*m[W+3]+G*m[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*I,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+O,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},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\":89,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132,\"iota-array\":235,\"typedarray-pool\":308}],89:[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:231}],90:[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\":132,glslify:231}],91:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var m=new v(e,r,o,s,l,d);return m.update(t),m};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"gl-texture2d\"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t(\"binary-search-bounds\"),c=t(\"ndarray\"),u=t(\"./lib/shaders\"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function m(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:m(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:m(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],m=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(m)?m.length>e-1?m[e-1]:m.length>0?m[m.length-1]:[0,0,0,1]:m;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3]);u+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,\"dashes\"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=c(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=n,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2],0),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),c=1-a,u=[0,0,0],f=0;f<3;++f)u[f]=c*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,u,h,this.points[h])}},{\"./lib/shaders\":90,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259}],92:[function(t,e,r){e.exports=function(t){var e=new Float32Array(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}},{}],93:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return 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t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],97:[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}},{}],98:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*m-f*d,A=u*g-h*d,M=u*v-p*d,S=f*g-h*m,E=f*v-p*m,L=h*v-p*g,C=y*L-x*E+b*S+_*M-w*A+T*k;if(!C)return null;return C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(m*T-g*w+v*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-c*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(g*b-d*T-v*x)*C,t[7]=(u*T-h*b+p*x)*C,t[8]=(o*E-s*M+c*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-m*b+v*y)*C,t[11]=(f*b-u*w-p*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(m*x-d*_-g*y)*C,t[15]=(u*_-f*x+h*y)*C,t}},{}],99:[function(t,e,r){var n=t(\"./identity\");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,m=e[0],g=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(m-_)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{\"./identity\":97}],100:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],101:[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}},{}],102:[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}},{}],103:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],104:[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}},{}],105:[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}},{}],106:[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}},{}],107:[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}},{}],108:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],109:[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}},{}],110:[function(t,e,r){\"use strict\";var n=t(\"barycentric\"),i=t(\"polytope-closest-point/lib/closest_point_2d.js\");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,m=0;m<2;++m)d+=Math.pow(u[f][m]-e[m],2);d<p&&(p=d,h=f)}var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(g[f]<-.001||g[f]>1.0001)return null;v+=g[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,g),g]}},{barycentric:14,\"polytope-closest-point/lib/closest_point_2d.js\":270}],111:[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:231}],112:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),f=t(\"colormap\"),h=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./lib/shaders\"),m=t(\"./lib/closest-point\"),g=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=v,this.edgeIds=m,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,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 A(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 M(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],m=n[f][1],g=i[f],v=1-g,y=this.positions[d],x=this.positions[m],b=0;b<3;++b)o[s++]=g*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:\"rgba\"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],m=[],g=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!I)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(O)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<O.length;++R){F=O[R];z=Math.min(z,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];z=Math.min(z,F),D=Math.max(D,F)}this.intensity=P||(O||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(O||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,H=0,q=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],m.push(Z[0],Z[1]),g.push(R)}H+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=m(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension(\"OES_standard_derivatives\")||t.getExtension(\"MOZ_OES_standard_derivatives\")||t.getExtension(\"WEBKIT_OES_standard_derivatives\");if(!r)throw new Error(\"derivatives not supported\");var n=M(t),s=S(t),l=E(t),c=L(t),f=C(t),h=P(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),m=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),A=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),I=i(t),O=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:O,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,m,g,v,x,b,k,_,w,A,I,R,O,z,D,F,B,N);return j.update(e),j}},{\"./lib/closest-point\":110,\"./lib/shaders\":111,colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-shader\":132,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259,normals:261,\"simplicial-complex-contour\":291,\"typedarray-pool\":308}],113:[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\":116,\"gl-buffer\":78,\"gl-shader\":132}],114:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"binary-search-bounds\"),o=t(\"./shaders\");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,m=0;m<2;++m){var g=a[m],v=a[m+2]-g,y=.5*(o[m+2]+o[m]),x=o[m+2]-o[m];u[m]=2*v/x,c[m]=2*(g-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(m=0;m<2;++m){f[0]=f[1]=0,f[m]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[m]/(s[m+2]-s[m])*d,r.uniforms.color=h[m];var _=6*n[m].length;p[m]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,m=s.viewBox,g=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=m[2]-m[0],_=m[3]-m[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,I=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),O=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*I,D=6*Math.max(0,O-I),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(m[0]-E[1])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[1]*g/y,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[1]-E[0])/x-1,o[0]=L[0]*g/y,o[1]=E[0]*g/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(m[2]+E[3])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[3]*g/y,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[3]+E[2])/x-1,o[0]=L[2]*g/y,o[1]=E[2]*g/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var m=0;m<6;++m)n[i++]=d,n[i++]=h[m],n[i++]=p[m]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":116,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132}],115:[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\":116,\"gl-buffer\":78,\"gl-shader\":132}],116:[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:231}],117:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"text-cache\"),o=t(\"binary-search-bounds\"),s=t(\"./shaders\");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,m=l.prototype;m.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,m=e.tickPad,g=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=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 M=2/h[2+(1^t)]-h[1^t];u[1^t]=M*s[1^t]-1,d[t]&&(u[1^t]-=M*p*m[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t]&&T&&(u[1^t]-=M*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]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*m[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),m.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),m.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],m=a[o+2]-f,g=i[o],v=i[o+2]-g;p[o]=2*l/u*m/v,h[o]=2*(s-c)/u*m/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),m.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,m=p.text,g=p.font||\"sans-serif\";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=m.split(\"\\n\"),b=0;b<x.length;b++)for(n=a(g,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:\"center\"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},m.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132,\"text-cache\":303}],118:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t(\"gl-select-static\"),i=t(\"./lib/grid\"),a=t(\"./lib/text\"),o=t(\"./lib/line\"),s=t(\"./lib/box\");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var m=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(m,e[1],m,e[3],p[d],h[d]):o.drawLine(e[0],m,e[2],m,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),g[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),g[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),g[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":113,\"./lib/grid\":114,\"./lib/line\":115,\"./lib/text\":117,\"gl-select-static\":131}],119:[function(t,e,r){\"use strict\";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];\"distanceLimits\"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);\"zoomMin\"in e&&(r[0]=e.zoomMin);\"zoomMax\"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&\"orthographic\"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),\"turntable\"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,u=\"zoom\"===o,h=!!a.control,p=!!a.alt,m=!!a.shift,g=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!m||g&&!h&&!p&&m)&&c.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!m||v||g&&h&&!p&&!m)&&c.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&g&&!h&&!p&&!m||y||g&&!h&&p&&!m)){var M=-d.zoomSpeed*_/window.innerHeight*(A-c.lastT())*100;c.pan(A,0,0,f*(Math.exp(M)-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\":7,\"has-passive-events\":232,\"mouse-change\":247,\"mouse-event-offset\":248,\"mouse-wheel\":250,\"right-now\":278}],120:[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\":132,glslify:231}],121:[function(t,e,r){\"use strict\";var n=t(\"./camera.js\"),i=t(\"gl-axes3d\"),a=t(\"gl-axes3d/properties\"),o=t(\"gl-spikes3d\"),s=t(\"gl-select-static\"),l=t(\"gl-fbo\"),c=t(\"a-big-triangle\"),u=t(\"mouse-change\"),f=t(\"gl-mat4/perspective\"),h=t(\"gl-mat4/ortho\"),p=t(\"./lib/shader\"),d=t(\"is-mobile\")({tablet:!0,featureDetect:!0});function m(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return\"boolean\"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement(\"canvas\"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error(\"webgl not supported\");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new m,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],I=!0,O=!0,z=new Array(16),D=new Array(16),R={view:null,projection:z,model:D,_ortho:!1},F=(O=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:A,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,O=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",I=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,F);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function H(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener(\"resize\",U),N.update=function(t){N._stopped||(t=t||{},I=!0,O=!0)},N.add=function(t){N._stopped||(t.axes=A,E.push(t),L.push(-1),I=!0,O=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),I=!0,O=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener(\"resize\",U),e.removeEventListener(\"webglcontextlost\",H),N.mouseListener.enabled=!1,!N.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(O=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(L[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),I=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),I=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener(\"webglcontextlost\",H);var q=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[q[0].slice(),q[1].slice()];function Y(){if(!H()){U();var t=N.camera.tick();R.view=N.camera.matrix,I=I||t,O=O||t,A.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=q[0],i=q[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=N.pixelRatio,C.axes=N.axes,I=I||!!C.dirty,O=O||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var m=.05*(i[p]-n[p]);n[p]=n[p]-m,i[p]=i[p]+m}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(O=O||v,I=I||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:y}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var m=0;for(d=0;d<3;++d)m=Math.max(m,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/m,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(C=E[o]).axesBounds=d,N.clipToBounds&&(C.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),O&&(O=!1,function(){if(!H()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;A.enable&&(M=M||A.isTransparent(),A.draw(R)),S.axes=A,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(C=E[o]).axes=A,C.pixelRatio=N.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(R),C.isTransparent&&C.isTransparent()&&(M=!0)}if(M){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}I=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(I=!0,Y())},N},createCamera:n}},{\"./camera.js\":119,\"./lib/shader\":120,\"a-big-triangle\":8,\"gl-axes3d\":70,\"gl-axes3d/properties\":77,\"gl-fbo\":86,\"gl-mat4/ortho\":101,\"gl-mat4/perspective\":102,\"gl-select-static\":131,\"gl-spikes3d\":141,\"is-mobile\":238,\"mouse-change\":247}],122:[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:231}],123:[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\":122,\"gl-buffer\":78,\"gl-shader\":132,\"typedarray-pool\":308}],124:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],m=r[2],g=r[3];(a=c*p+u*d+f*m+h*g)<0&&(a=-a,p=-p,d=-d,m=-m,g=-g);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*m,t[3]=s*h+l*g,t}},{}],125:[function(t,e,r){\"use strict\";e.exports=function(t){return t||0===t?t.toString():\"\"}},{}],126:[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\":311}],127:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n\\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (\\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n    interpColor.a * opacity == 0.\\n  ) discard;\\n  gl_FragColor = interpColor * opacity;\\n}\\n\"]),c=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},m={vertex:o,fragment:c,attributes:u},g={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,m)},r.createPickProject=function(t){return v(t,g)}},{\"gl-shader\":132,glslify:231}],128:[function(t,e,r){\"use strict\";var n=t(\"is-string-blank\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"typedarray-pool\"),s=t(\"gl-mat4/multiply\"),l=t(\"./lib/shaders\"),c=t(\"./lib/glyphs\"),u=t(\"./lib/get-simple-string\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function m(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),m=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new g(e,r,n,o,f,h,p,d,m,s,c,u);return v.update(t),v};var v=g.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function L(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,m=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=m,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var v=T,L=0;L<16;++L)v[L]=0;for(L=0;L<4;++L)v[5*L]=1;v[5*g]=0,i[g]<0?v[12+g]=d[0][g]:v[12+g]=d[1][g],s(v,c,v),l.model=v;var C=(g+1)%3,P=(g+2)%3,I=M(x),O=M(b);I[C]=1,O[P]=1;var z=p(0,0,0,S(_,I)),D=p(0,0,0,S(w,O));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=I,I=O,O=R;var F=C;C=P,P=F}z[0]<0&&(I[C]=-1),D[1]>0&&(O[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(c[4*C+L],2),N+=Math.pow(c[4*P+L],2);I[C]/=Math.sqrt(B),O[P]/=Math.sqrt(N),l.axes[0]=I,l.axes[1]=O,l.fragClipBounds[0]=E(k,m[0],g,-1e8),l.fragClipBounds[1]=E(k,m[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&L(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function I(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a=\"\\u25bc\",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=m(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=m(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||\"normal\",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var u=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,g=t.angle,v=t.lineColor,y=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;x+=3*k.cells.length,b+=2*A.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),P=o.mallocUint32(S);if(S>0){var O=0,z=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(v)&&Array.isArray(v[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T])}k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=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 H=0;Array.isArray(g)?H=n<g.length?+g[n]:0:g&&(H=+g);var q=Math.cos(H),G=Math.sin(H);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-M[0][0]:Y<0?1+M[1][0]:1,W*=W>0?1-M[0][1]:W<0?1+M[1][1]:1],Z=k.cells||[],J=k.positions||[];for(T=0;T<Z.length;++T)for(var K=Z[T],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*O+$]=w[$];for($=0;$<4;++$)L[4*O+$]=D[$];P[O]=y;var tt=J[K[Q]];C[2*O]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*O+1]=V*(G*tt[0]+q*tt[1]+X[1]),O+=1}for(Z=A.edges,J=A.positions,T=0;T<Z.length;++T)for(K=Z[T],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)L[4*z+$]=R[$];P[z]=y;tt=J[K[Q]];C[2*z]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*z+1]=V*(G*tt[0]+q*tt[1]+X[1]),z+=1}}}this.bounds=[u,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(P),o.free(E),o.free(L),o.free(C),o.free(P)},v.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{\"./lib/get-simple-string\":125,\"./lib/glyphs\":126,\"./lib/shaders\":127,\"gl-buffer\":78,\"gl-mat4/multiply\":100,\"gl-vao\":150,\"is-string-blank\":239,\"typedarray-pool\":308}],129:[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:231}],130:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"./lib/shaders\");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,f=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],h=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(f=Math.max(f,c[0]),h=Math.max(h,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<f||d<h)){o.bind();var m=s[2]-s[0],g=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,m,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,m,g,i),o.drawBox(p,h,m,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var v=r*u;o.drawBox(f-v,h-v,p+v,h+v,a),o.drawBox(f-v,d-v,p+v,d+v,a),o.drawBox(f-v,h-v,f+v,d+v,a),o.drawBox(p-v,h-v,p+v,d+v,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{\"./lib/shaders\":129,\"gl-buffer\":78,\"gl-shader\":132}],131:[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 l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}var c=l.prototype;Object.defineProperty(c,\"shape\",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){i.free(this.buffer);for(var n=this.buffer=i.mallocUint8(o(r*e*4)),a=0;a<r*e*4;++a)n[a]=255}return t}}}),c.begin=function(){var t=this.gl;this.shape;t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT))},c.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},c.query=function(t,e,r){if(!this.gl)return null;var n=this.fbo.shape.slice();t|=0,e|=0,\"number\"!=typeof r&&(r=1);var i=0|Math.min(Math.max(t-r,0),n[0]),o=0|Math.min(Math.max(t+r,0),n[0]),l=0|Math.min(Math.max(e-r,0),n[1]),c=0|Math.min(Math.max(e+r,0),n[1]);if(o<=i||c<=l)return null;var u=[o-i,c-l],f=a(this.buffer,[u[0],u[1],4],[4,4*n[0],1],4*(i+n[0]*l)),h=function(t,e,r){for(var n=1e8,i=-1,a=-1,o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++){var u=t.get(l,c,0),f=t.get(l,c,1),h=t.get(l,c,2),p=t.get(l,c,3);if(u<255||f<255||h<255||p<255){var d=e-l,m=r-c,g=d*d+m*m;g<n&&(n=g,i=l,a=c)}}return[i,a,n]}(f.hi(u[0],u[1],1),r,r),p=h[0],d=h[1];return p<0||Math.pow(this.radius,2)<h[2]?null:new s(p+i|0,d+l|0,f.get(p,d,0),[f.get(p,d,1),f.get(p,d,2),f.get(p,d,3)],Math.sqrt(h[2]))},c.dispose=function(){this.gl&&(this.fbo.dispose(),i.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},{\"bit-twiddle\":32,\"gl-fbo\":86,ndarray:259,\"typedarray-pool\":308}],132:[function(t,e,r){\"use strict\";var n=t(\"./lib/create-uniforms\"),i=t(\"./lib/create-attributes\"),a=t(\"./lib/reflect\"),o=t(\"./lib/shader-cache\"),s=t(\"./lib/runtime-reflect\"),l=t(\"./lib/GLError\");function c(t){this.gl=t,this.gl.lastAttribCount=0,this._vref=this._fref=this._relink=this.vertShader=this.fragShader=this.program=this.attributes=this.uniforms=this.types=null}var u=c.prototype;function f(t,e){return t.name<e.name?-1:1}u.bind=function(){var t;this.program||this._relink();var e=this.gl.getProgramParameter(this.program,this.gl.ACTIVE_ATTRIBUTES),r=this.gl.lastAttribCount;if(e>r)for(t=r;t<e;t++)this.gl.enableVertexAttribArray(t);else if(r>e)for(t=e;t<r;t++)this.gl.disableVertexAttribArray(t);this.gl.lastAttribCount=e,this.gl.useProgram(this.program)},u.dispose=function(){for(var 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w=c[y];if(w.type.indexOf(\"mat\")>=0){for(var T=0|w.type.charAt(w.type.length-1),k=new Array(T),A=0;A<T;++A)k[A]=_.length,b.push(w.name+\"[\"+A+\"]\"),\"number\"==typeof w.location?_.push(w.location+A):Array.isArray(w.location)&&w.location.length===T&&\"number\"==typeof w.location[A]?_.push(0|w.location[A]):_.push(-1);x.push({name:w.name,type:w.type,locations:k})}else x.push({name:w.name,type:w.type,locations:[_.length]}),b.push(w.name),\"number\"==typeof w.location?_.push(0|w.location):_.push(-1)}var M=0;for(y=0;y<_.length;++y)if(_[y]<0){for(;_.indexOf(M)>=0;)M+=1;_[y]=M}var S=new Array(r.length);function E(){h.program=o.program(p,h._vref,h._fref,b,_);for(var t=0;t<r.length;++t)S[t]=p.getUniformLocation(h.program,r[t].name)}E(),h._relink=E,h.types={uniforms:a(r),attributes:a(c)},h.attributes=i(p,h,x,_),Object.defineProperty(h,\"uniforms\",n(p,h,r,S))},e.exports=function(t,e,r,n,i){var a=new c(t);return 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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\":140,\"gl-buffer\":78,\"gl-vao\":150}],142:[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:231}],143:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\"),i=t(\"gl-vec4\"),a=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],m=0,g=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(m,g,g,g,m,m);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=y;y=v,v=S;var E=m;m=g,g=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var m=u[s],g=f.length;f=f.concat(m.positions),p=p.concat(m.vectors),d=d.concat(m.vertexIntensity);for(c=0;c<m.cells.length;c++){var v=m.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||\"+x+y+z\",p={};-1!==h.indexOf(\"-x\")&&(p.reversedX=!0),-1!==h.indexOf(\"-y\")&&(p.reversedY=!0),-1!==h.indexOf(\"-z\")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,\"\").replace(/\\+/g,\"\"));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),m=s(a[1],c),g=s(a[2],u),v=d+1,y=m+1,x=g+1;if(d=l(d,0,f-1),v=l(v,0,f-1),m=l(m,0,h-1),y=l(y,0,h-1),g=l(g,0,p-1),x=l(x,0,p-1),d<0||m<0||g<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][v],E=a[1][m],L=a[1][y],C=a[2][g],P=(o-M)/(S-M),I=(c-E)/(L-E),O=(u-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(I)||(I=.5),isFinite(O)||(O=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(m=h-1-m,y=h-1-y),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=m*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=g,A=x,b=d*p,_=v*p,w=m*p*f,T=y*p*f;break;case 3:w=m,T=y,k=g*h,A=x*h,b=d*h*p,_=v*h*p;break;case 2:w=m,T=y,b=d*h,_=v*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=v,k=g*f,A=x*f,w=m*f*p,T=y*f*p;break;default:b=d,_=v,w=m*f,T=y*f,k=g*f*h,A=x*f*h}var z=i[b+w+k],D=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),H=n.create(),q=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(H,D,N,P),n.lerp(q,R,j,P),n.lerp(G,F,U,P);var Y=n.create(),W=n.create();n.lerp(Y,V,q,I),n.lerp(W,H,G,I);var X=n.create();return n.lerp(X,Y,W,O),X}(e,t,p)},m=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),m=c(r),g=c(n),v=Math.min(d,m,g);return isFinite(v)?v:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],I=[],O=d(C),z=C;I.push(O);var D=[],R=m(C,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),g.push({points:P,velocities:I,divergences:D});for(var B=0;B<100*i&&P.length<i&&T(C);){B++;var N=n.clone(O),j=n.squaredLength(N);if(0===j)break;if(j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),O=d(N),n.squaredDistance(z,N)-A>-1e-4*A){P.push(N),z=N,I.push(O);R=m(N,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}C=N}}var U=o(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*M/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\":142,\"gl-cone3d\":79,\"gl-vec3\":169,\"gl-vec4\":205}],144:[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\":132,glslify:231}],145:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),m=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);m.minFilter=e.LINEAR,m.magFilter=e.LINEAR;var g=new M(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,m,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||\"jet\",g.update(v),g};var n=t(\"bit-twiddle\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"typedarray-pool\"),l=t(\"colormap\"),c=t(\"ndarray-ops\"),u=t(\"ndarray-pack\"),f=t(\"ndarray\"),h=t(\"surface-nets\"),p=t(\"gl-mat4/multiply\"),d=t(\"gl-mat4/invert\"),m=t(\"binary-search-bounds\"),g=t(\"ndarray-gradient\"),v=t(\"./lib/shaders\"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function A(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 M(t,e,r,n,i,a,o,l,c,u,h,p,d,m,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new A([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=m,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=M.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],L={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function C(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=L.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=L.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return L.showSurface=o,L.showContour=s,L}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=w.slice(),O=[1,0,0,0,1,0,0,0,1];function z(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=O,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=C(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var m=0;m<this.contourLevels[o].length;++m)m===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==m&&m-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][m]&&(f.uniforms.height=this.contourLevels[o][m],h.draw(r.LINES,this._contourCounts[o][m],this._contourOffsets[o][m]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return z.call(this,t,!1)},S.drawTransparent=function(t){return z.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=O;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=C(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,g=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*g;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=m.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=R(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=R(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=R(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=B(t.contourColor)),\"contourProject\"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=B(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var m=[0,0];m[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],m,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),y[o],\"mirror\");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),A=x.get(1,o,v,1),M=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-A*M,L=M*w-S*_,C=_*A-w*k,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,v,0,E*P),b.set(o,v,1,L*P),b.set(o,v,2,C*P)}s.free(x.data);var I=[1/0,1/0,1/0],O=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var H=0;H<2;++H)for(var q=0;q<3;++q){var G=this._field[q].get(1+o+V,1+v+H);if(isNaN(G)||!isFinite(G))continue t}for(q=0;q<6;++q){var Y=o+T[q][0],W=v+T[q][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),L=b.get(Y+1,W+1,1),C=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=L,N[j++]=C,I[0]=Math.min(I[0],X+this.objectOffset[0]),I[1]=Math.min(I[1],Z+this.objectOffset[1]),I[2]=Math.min(I[2],G+this.objectOffset[2]),z=Math.min(z,J),O[0]=Math.max(O[0],X+this.objectOffset[0]),O[1]=Math.max(O[1],Z+this.objectOffset[1]),O[2]=Math.max(O[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[I,O],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if(\"levels\"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(q=0;q<2;++q){var ot=it.positions[at[q]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var mt=($+dt+1)%3;for(V=0;V<2;++V){var gt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var vt=H?ht:1-ht;if(W=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[mt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=gt*vt;nt[dt]+=yt*G}}}if(pt){if(q>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),m=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<m.length;++e)for(var v=m[e],y=0;y<2;++y){var x=g[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,I=T*L,O=T*E,z=C*f.get(_,M)+P*f.get(_,S)+I*f.get(w,M)+O*f.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+I*p.get(w,M)+O*p.get(w,S);if(isNaN(z)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{\"./lib/shaders\":144,\"binary-search-bounds\":31,\"bit-twiddle\":32,colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259,\"ndarray-gradient\":252,\"ndarray-ops\":254,\"ndarray-pack\":255,\"surface-nets\":302,\"typedarray-pool\":308}],146:[function(t,e,r){\"use strict\";var n=t(\"ndarray\"),i=t(\"ndarray-ops\"),a=t(\"typedarray-pool\");e.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");o||c(t);if(\"number\"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function m(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function g(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function y(t,e,r,n,i,a){var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new p(t,o,r,n,i,a)}function x(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=m(o,e.stride.slice()),c=0;\"float32\"===r?c=t.FLOAT:\"float64\"===r?(c=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var u,h,d=0;if(2===o.length)d=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])d=t.ALPHA;else if(2===o[2])d=t.LUMINANCE_ALPHA;else if(3===o[2])d=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");d=t.RGBA}}c!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(c=t.UNSIGNED_BYTE,l=!1);var v=e.size;if(l)u=0===e.offset&&e.data.length===v?e.data:e.data.subarray(e.offset,e.offset+v);else{var y=[o[2],o[2]*o[0],1];h=a.malloc(v,r);var x=n(h,o,y,0);\"float32\"!==r&&\"float64\"!==r||c!==t.UNSIGNED_BYTE?i.assign(x,e):f(x,e),u=h.subarray(0,v)}var b=g(t);return t.texImage2D(t.TEXTURE_2D,0,d,o[0],o[1],0,d,c,u),l||a.free(h),new p(t,b,o[0],o[1],d,c)}Object.defineProperties(d,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,t),this._minFilter=t}},magFilter:{get:function(){return this._magFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,t),this._magFilter=t}},mipSamples:{get:function(){return this._anisoSamples},set:function(t){var e=this._anisoSamples;if(this._anisoSamples=0|Math.max(t,1),e!==this._anisoSamples){var r=this.gl.getExtension(\"EXT_texture_filter_anisotropic\");r&&this.gl.texParameterf(this.gl.TEXTURE_2D,r.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,t),this._wrapS=t}},wrapT:{get:function(){return this._wrapT},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,t),this._wrapT=t}},wrap:{get:function(){return this._wrapVector},set:function(t){if(Array.isArray(t)||(t=[t,t]),2!==t.length)throw new Error(\"gl-texture2d: Must specify wrap mode for rows and columns\");for(var 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       "/*!\n",
       " * Determine if an object is a Buffer\n",
       " *\n",
       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
       " * @license  MIT\n",
       " */\n",
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i=this.computedMatrix,a=i[0],o=i[4],s=i[8],u=i[1],f=i[5],h=i[9],p=i[2],d=i[6],m=i[10],g=e*a+r*u,v=e*o+r*f,y=e*s+r*h,x=-(d*y-m*v),b=-(m*g-p*y),_=-(p*v-d*g),w=Math.sqrt(Math.max(0,1-Math.pow(x,2)-Math.pow(b,2)-Math.pow(_,2))),T=c(x,b,_,w);T>1e-6?(x/=T,b/=T,_/=T,w/=T):(x=b=_=0,w=1);var k=this.computedRotation,A=k[0],M=k[1],S=k[2],E=k[3],L=A*w+E*x+M*_-S*b,C=M*w+E*b+S*x-A*_,P=S*w+E*_+A*b-M*x,I=E*w-A*x-M*b-S*_;if(n){x=p,b=d,_=m;var O=Math.sin(n)/l(x,b,_);x*=O,b*=O,_*=O,I=I*(w=Math.cos(e))-(L=L*w+I*x+C*_-P*b)*x-(C=C*w+I*b+P*x-L*_)*b-(P=P*w+I*_+L*b-C*x)*_}var z=c(L,C,P,I);z>1e-6?(L/=z,C/=z,P/=z,I/=z):(L=C=P=0,I=1),this.rotation.set(t,L,C,P,I)},h.lookAt=function(t,e,r,n){this.recalcMatrix(t),r=r||this.computedCenter,e=e||this.computedEye,n=n||this.computedUp;var a=this.computedMatrix;i(a,e,r,n);var o=this.computedRotation;s(o,a[0],a[1],a[2],a[4],a[5],a[6],a[8],a[9],a[10]),u(o,o),this.rotation.set(t,o[0],o[1],o[2],o[3]);for(var l=0,c=0;c<3;++c)l+=Math.pow(r[c]-e[c],2);this.radius.set(t,.5*Math.log(Math.max(l,1e-6))),this.center.set(t,r[0],r[1],r[2])},h.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},h.setMatrix=function(t,e){var r=this.computedRotation;s(r,e[0],e[1],e[2],e[4],e[5],e[6],e[8],e[9],e[10]),u(r,r),this.rotation.set(t,r[0],r[1],r[2],r[3]);var n=this.computedMatrix;o(n,e);var i=n[15];if(Math.abs(i)>1e-6){var a=n[12]/i,l=n[13]/i,c=n[14]/i;this.recalcMatrix(t);var f=Math.exp(this.computedRadius[0]);this.center.set(t,a-n[2]*f,l-n[6]*f,c-n[10]*f),this.radius.idle(t)}else this.center.idle(t),this.radius.idle(t)},h.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},h.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},h.getDistanceLimits=function(t){var e=this.radius.bounds;return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},h.toJSON=function(){return this.recalcMatrix(this.lastT()),{center:this.computedCenter.slice(),rotation:this.computedRotation.slice(),distance:Math.log(this.computedRadius[0]),zoomMin:this.radius.bounds[0][0],zoomMax:this.radius.bounds[1][0]}},h.fromJSON=function(t){var e=this.lastT(),r=t.center;r&&this.center.set(e,r[0],r[1],r[2]);var n=t.rotation;n&&this.rotation.set(e,n[0],n[1],n[2],n[3]);var i=t.distance;i&&i>0&&this.radius.set(e,Math.log(i)),this.setDistanceLimits(t.zoomMin,t.zoomMax)}},{\"./lib/quatFromFrame\":262,\"filtered-vector\":68,\"gl-mat4/fromQuat\":95,\"gl-mat4/invert\":98,\"gl-mat4/lookAt\":99}],264:[function(t,e,r){\n",
       "/*!\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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e=this.computedUp,r=this.computedRight,n=0,i=0,a=0;a<3;++a)i+=e[a]*r[a],n+=e[a]*e[a];var l=Math.sqrt(n),u=0;for(a=0;a<3;++a)r[a]-=e[a]*i/n,u+=r[a]*r[a],e[a]/=l;var f=Math.sqrt(u);for(a=0;a<3;++a)r[a]/=f;var h=this.computedToward;o(h,e,r),s(h,h);var p=Math.exp(this.computedRadius[0]),d=this.computedAngle[0],m=this.computedAngle[1],g=Math.cos(d),v=Math.sin(d),y=Math.cos(m),x=Math.sin(m),b=this.computedCenter,_=g*y,w=v*y,T=x,k=-g*x,A=-v*x,M=y,S=this.computedEye,E=this.computedMatrix;for(a=0;a<3;++a){var L=_*r[a]+w*h[a]+T*e[a];E[4*a+1]=k*r[a]+A*h[a]+M*e[a],E[4*a+2]=L,E[4*a+3]=0}var C=E[1],P=E[5],I=E[9],O=E[2],z=E[6],D=E[10],R=P*D-I*z,F=I*O-C*D,B=C*z-P*O,N=c(R,F,B);R/=N,F/=N,B/=N,E[0]=R,E[4]=F,E[8]=B;for(a=0;a<3;++a)S[a]=b[a]+E[2+4*a]*p;for(a=0;a<3;++a){u=0;for(var j=0;j<3;++j)u+=E[a+4*j]*S[j];E[12+a]=-u}E[15]=1},p.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r};var d=[0,0,0];p.rotate=function(t,e,r,n){if(this.angle.move(t,e,r),n){this.recalcMatrix(t);var i=this.computedMatrix;d[0]=i[2],d[1]=i[6],d[2]=i[10];for(var o=this.computedUp,s=this.computedRight,l=this.computedToward,c=0;c<3;++c)i[4*c]=o[c],i[4*c+1]=s[c],i[4*c+2]=l[c];a(i,i,n,d);for(c=0;c<3;++c)o[c]=i[4*c],s[c]=i[4*c+1];this.up.set(t,o[0],o[1],o[2]),this.right.set(t,s[0],s[1],s[2])}},p.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=(Math.exp(this.computedRadius[0]),i[1]),o=i[5],s=i[9],l=c(a,o,s);a/=l,o/=l,s/=l;var u=i[0],f=i[4],h=i[8],p=u*a+f*o+h*s,d=c(u-=a*p,f-=o*p,h-=s*p),m=(u/=d)*e+a*r,g=(f/=d)*e+o*r,v=(h/=d)*e+s*r;this.center.move(t,m,g,v);var y=Math.exp(this.computedRadius[0]);y=Math.max(1e-4,y+n),this.radius.set(t,Math.log(y))},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e,r,n){var a=1;\"number\"==typeof r&&(a=0|r),(a<0||a>3)&&(a=1);var o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],f=e[a+8];if(n){var h=Math.abs(s),p=Math.abs(l),d=Math.abs(f),m=Math.max(h,p,d);h===m?(s=s<0?-1:1,l=f=0):d===m?(f=f<0?-1:1,s=l=0):(l=l<0?-1:1,s=f=0)}else{var g=c(s,l,f);s/=g,l/=g,f/=g}var v,y,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*f,T=c(x-=s*w,b-=l*w,_-=f*w),k=l*(_/=T)-f*(b/=T),A=f*(x/=T)-s*_,M=s*b-l*x,S=c(k,A,M);if(k/=S,A/=S,M/=S,this.center.jump(t,q,G,Y),this.radius.idle(t),this.up.jump(t,s,l,f),this.right.jump(t,x,b,_),2===a){var E=e[1],L=e[5],C=e[9],P=E*x+L*b+C*_,I=E*k+L*A+C*M;v=R<0?-Math.PI/2:Math.PI/2,y=Math.atan2(I,P)}else{var O=e[2],z=e[6],D=e[10],R=O*s+z*l+D*f,F=O*x+z*b+D*_,B=O*k+z*A+D*M;v=Math.asin(u(R)),y=Math.atan2(B,F)}this.angle.jump(t,y,v),this.recalcMatrix(t);var N=e[2],j=e[6],U=e[10],V=this.computedMatrix;i(V,e);var H=V[15],q=V[12]/H,G=V[13]/H,Y=V[14]/H,W=Math.exp(this.computedRadius[0]);this.center.jump(t,q-N*W,G-j*W,Y-U*W)},p.lastT=function(){return 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The Relationship of R&D Expense Over Time on Average Toyota Fuel Economy\"},\"width\":1000,\"height\":800},                        {\"responsive\": true}                    ).then(function(){\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "averaged_toyota_dataset = toyota_dataset.groupby(['model_year', 'vehicle_class']).mean().reset_index()\n",
    "\n",
    "fig = px.scatter_3d(averaged_toyota_dataset, \n",
    "                    x='model_year', \n",
    "                    y='rnd_expense', \n",
    "                    z='comb_lpkm',\n",
    "                    color='vehicle_class')\n",
    "fig.update_layout(title='Figure 15. The Relationship of R&D Expense Over Time on Average Toyota Fuel Economy',\n",
    "                  scene={'xaxis_title':'Model Year',\n",
    "                         'yaxis_title':'R&D Expense (billions of yen)',\n",
    "                         'zaxis_title':'Combined Fuel Economy'},\n",
    "                  legend_title='Vehicle Class',\n",
    "                  width=1000, height=800)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7f9d573",
   "metadata": {},
   "source": [
    "One thing apparent in this plot is the precipitous drop in fuel economy of Toyota vans in 2021. In 2021, Toyota vans switched to continuously variable transmissions, which led to a significant increase in efficiency. The analysis continues using the whole range of van data (as using only the three years since the switch in transmission type is likely to be inaccurate), with the note that this regression is likely to overpredict fuel economy for upcoming models of vans. \n",
    "\n",
    "A regression plane can be created for each vehicle class, and added to the visualization.\n",
    "\n",
    "To determine whether the inclusion of R&D expense information enhances the accuracy of the model, a comparison is made between the MSE values obtained with and without incorporating R&D expense data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9eb56dbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "toyota_cars = toyota_dataset[toyota_dataset['vehicle_class'] == 'Car'].copy()\n",
    "toyota_SUVs = toyota_dataset[toyota_dataset['vehicle_class'] == 'SUV'].copy()\n",
    "toyota_trucks = toyota_dataset[toyota_dataset['vehicle_class'] == 'Truck'].copy()\n",
    "toyota_vans = toyota_dataset[toyota_dataset['vehicle_class'] == 'Van'].copy()\n",
    "\n",
    "toyota_subsets = [toyota_cars, toyota_SUVs, toyota_trucks, toyota_vans]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d5f73b17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model for Car with year alone: mse = 2.596473913589511\n",
      "Model for Car with R&D expense: mse = 2.5940816695477054\n",
      "Model for SUV with year alone: mse = 5.741542953572422\n",
      "Model for SUV with R&D expense: mse = 5.719066187748402\n",
      "Model for Truck with year alone: mse = 3.001175116284104\n",
      "Model for Truck with R&D expense: mse = 2.9982356709670737\n",
      "Model for Van with year alone: mse = 1.920415294633803\n",
      "Model for Van with R&D expense: mse = 1.8786752964374083\n"
     ]
    }
   ],
   "source": [
    "regression_dict = {}\n",
    "\n",
    "for subset in toyota_subsets:\n",
    "    make = subset['vehicle_class'].iloc[0]\n",
    "    length = len(subset)\n",
    "    subset.dropna(inplace=True)\n",
    "    X_year_alone = subset[['model_year']].copy()\n",
    "    y = subset['comb_lpkm']\n",
    "    lr_model_year_alone = linear_model.LinearRegression()\n",
    "    lr_model_year_alone.fit(X_year_alone, y)\n",
    "\n",
    "    mse_year_alone = metrics.mean_squared_error(y, lr_model_year_alone.predict(X_year_alone))\n",
    "    \n",
    "    print(f\"Model for {make} with year alone: mse = {mse_year_alone}\")\n",
    "    \n",
    "    lr_model_with_rnd = linear_model.LinearRegression()\n",
    "    X_with_rnd = subset[['model_year', 'rnd_expense']].copy()\n",
    "    lr_model_with_rnd.fit(X_with_rnd, y)\n",
    "    \n",
    "    beta_0 = lr_model_with_rnd.intercept_\n",
    "    beta_1 = lr_model_with_rnd.coef_[0]\n",
    "    beta_2 = lr_model_with_rnd.coef_[1]\n",
    "    \n",
    "    mse_with_rnd = metrics.mean_squared_error(y, lr_model_with_rnd.predict(X_with_rnd))\n",
    "    \n",
    "    print(f\"Model for {make} with R&D expense: mse = {mse_with_rnd}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dc2a92a",
   "metadata": {},
   "source": [
    "Unsurprisingly, for all vehicle classes the MSE is slightly lower for the regression including the R&D expense data. As such, the model will include the R&D expense data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "44e6d90b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model for Car: \n",
      " Combined Fuel Economy = 278.3918252318486 + -0.13458233853324725 * Model Year + 0.000610975008119144 * R&D Expense\n",
      "Model for SUV: \n",
      " Combined Fuel Economy = 545.67320633841 + -0.2662468195091635 * Model Year + 0.0017645761432270699 * R&D Expense\n",
      "Model for Truck: \n",
      " Combined Fuel Economy = 181.6332019630639 + -0.08382960933020638 * Model Year + 0.0006040939383946424 * R&D Expense\n",
      "Model for Van: \n",
      " Combined Fuel Economy = 588.9863851161135 + -0.28820117563043923 * Model Year + 0.0026092105678520295 * R&D Expense\n"
     ]
    }
   ],
   "source": [
    "regression_dict = {}\n",
    "\n",
    "for subset in toyota_subsets:\n",
    "    make = subset['vehicle_class'].iloc[0]\n",
    "    length = len(subset)\n",
    "    subset.dropna(inplace=True)\n",
    "    lr_model_with_rnd = linear_model.LinearRegression()\n",
    "    X_with_rnd = subset[['model_year', 'rnd_expense']].copy()\n",
    "    y = subset['comb_lpkm']\n",
    "\n",
    "    lr_model_with_rnd.fit(X_with_rnd, y)\n",
    "    \n",
    "    beta_0 = lr_model_with_rnd.intercept_\n",
    "    beta_1 = lr_model_with_rnd.coef_[0]\n",
    "    beta_2 = lr_model_with_rnd.coef_[1]\n",
    "    \n",
    "    regression_dict.update({subset['vehicle_class'].iloc[0]:{'beta_0':beta_0,\n",
    "                      'beta_1':beta_1,\n",
    "                      'beta_2':beta_2}})\n",
    "        \n",
    "    print(f\"Model for {make}: \\n Combined Fuel Economy = {beta_0} + {beta_1} * Model Year + {beta_2}\" + \n",
    "          f\" * R&D Expense\")"
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    "From each of the equations above, it's evident that all vehicle classes have a negative correlation with the model year and a positive correlation with R&D expense. \n",
    "\n",
    "Vans, followed closely by trucks, are the vehicle classes showing the most improvement in efficiency year over year. \n",
    "\n",
    "Surprisingly, larger R&D expenses are correlated with a higher fuel economy (and so, decreased efficiency). Toyota's R&D expenses dropped drastically following the 2008 financial crisis, and as noted in Figure 1, the period from 2010-2015 saw the largest improvements in vehicle fuel economy. These coinciding factors likely explain the positive correlation in these equations. \n",
    "\n",
    "Furthermore, improvements in fuel efficiency are only one area of research under the larger R&D umbrella. As such, in years when a smaller portion of the budget was allocated to improvements in efficiency, we may see little improvement in efficiency even with a large R&D expense.\n",
    "\n",
    "The analysis that follows includes the R&D expense data in spite of these unexpected results, given that their inclusion was found to improve the MSE. However, a deeper examination (more manufacturers and a larger time frame) of the effect of R&D expenses on vehicle efficiency is warranted, beyond what has been explored in this investigation. \n",
    "\n",
    "These equations are used to plot regression lines for each vehicle class."
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The Relationship of R&D Expense Over Time on Average Toyota Fuel Economy\"},\"width\":1000,\"height\":800},                        {\"responsive\": true}                    ).then(function(){\n",
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "averaged_toyota_dataset = toyota_dataset.groupby(['model_year', 'vehicle_class']).mean().reset_index()\n",
    "\n",
    "years = list(aligned_toyota_rnd.keys())\n",
    "years = np.array([int(year) for year in years])\n",
    "rnd_exp = list(aligned_toyota_rnd.values())\n",
    "rnd_exp = np.array([float(exp) for exp in rnd_exp])\n",
    "\n",
    "pred_cars = np.array([])\n",
    "pred_SUVs = np.array([])\n",
    "pred_trucks = np.array([])\n",
    "pred_vans = np.array([])\n",
    "\n",
    "\n",
    "for year, expense in zip(years, rnd_exp):\n",
    "        pred_cars = np.append(pred_cars, regression_dict['Car']['beta_0'] + regression_dict['Car']['beta_1']*year + regression_dict['Car']['beta_2']*expense)\n",
    "        pred_SUVs = np.append(pred_SUVs, regression_dict['SUV']['beta_0'] + regression_dict['SUV']['beta_1']*year + regression_dict['SUV']['beta_2']*expense)\n",
    "        pred_trucks = np.append(pred_trucks, regression_dict['Truck']['beta_0'] + regression_dict['Truck']['beta_1']*year + regression_dict['Truck']['beta_2']*expense)\n",
    "        pred_vans = np.append(pred_vans, regression_dict['Van']['beta_0'] + regression_dict['Van']['beta_1']*year + regression_dict['Van']['beta_2']*expense)\n",
    "\n",
    "fig.add_trace(go.Scatter3d(x=years, y=rnd_exp, z=pred_cars, mode='lines', name = \"Car regression\", \n",
    "                           line={'color':'blue'}))\n",
    "fig.add_trace(go.Scatter3d(x=years, y=rnd_exp, z=pred_SUVs, mode='lines', name = \"SUV regression\", \n",
    "                           line={'color':'red'}))\n",
    "fig.add_trace(go.Scatter3d(x=years, y=rnd_exp, z=pred_trucks, mode='lines', name = \"Truck regression\", \n",
    "                           line={'color':'green'}))\n",
    "fig.add_trace(go.Scatter3d(x=years, y=rnd_exp, z=pred_vans, mode='lines', name = \"Van regression\", \n",
    "                           line={'color':'purple'}))\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbf21328",
   "metadata": {},
   "source": [
    "Per Toyota's 2022 full-year financial summary, they forecast an R&D expense of 1130.0 billion yen in 2023. Incorporating this, the regression model calculates the 2024 expected average fuel economies for each vehicle class as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7a76483c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicted combined fuel economy of the average 2024 Toyota car is: 6.822156138264076 L/km\n",
      "The predicted combined fuel economy of the average 2024 Toyota SUV is: 9.049861513218797 L/km\n",
      "The predicted combined fuel economy of the average 2024 Toyota truck is: 12.728528438442343 L/km\n",
      "The predicted combined fuel economy of the average 2024 Toyota van is: 8.903814757407726 L/km\n"
     ]
    }
   ],
   "source": [
    "predicted_car_economy = regression_dict['Car']['beta_0'] + regression_dict['Car']['beta_1']*2023 + regression_dict['Car']['beta_2']*1130\n",
    "predicted_SUV_economy = regression_dict['SUV']['beta_0'] + regression_dict['SUV']['beta_1']*2023 + regression_dict['SUV']['beta_2']*1130\n",
    "predicted_truck_economy = regression_dict['Truck']['beta_0'] + regression_dict['Truck']['beta_1']*2023 + regression_dict['Truck']['beta_2']*1130\n",
    "predicted_van_economy = regression_dict['Van']['beta_0'] + regression_dict['Van']['beta_1']*2023 + regression_dict['Van']['beta_2']*1130\n",
    "\n",
    "\n",
    "print(f\"The predicted combined fuel economy of the average 2024 Toyota car is: {predicted_car_economy} L/km\")\n",
    "print(f\"The predicted combined fuel economy of the average 2024 Toyota SUV is: {predicted_SUV_economy} L/km\")\n",
    "print(f\"The predicted combined fuel economy of the average 2024 Toyota truck is: {predicted_truck_economy} L/km\")\n",
    "print(f\"The predicted combined fuel economy of the average 2024 Toyota van is: {predicted_van_economy} L/km\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82134193",
   "metadata": {},
   "source": [
    "This regression suggests a negative correlation between the model year and a vehicle's fuel economy and a positive correlation with Toyota's R&D expenses. \n",
    "\n",
    "The positive relationship between fuel economy and R&D expenses was unexpected and may be explained by the coincidental decrease in R&D expenses following the 2008 financial crisis and the massive improvements in fuel efficiency throughout the automotive industry from 2010-2015. Unexpected results, however, do not necessarily discredit the regression model. A further investigation involving more manufacturers and over a longer time period is warranted. \n",
    "\n",
    "That being said, the predicted efficiency for 2024 Toyota vehicles using a forecasted R&D expense budget of 1130 billion yen predicts an average combined fuel economy of:\n",
    "\n",
    "- Car: 6.82 L/km\n",
    "\n",
    "- SUV: 9.09 L/km\n",
    "\n",
    "- Truck: 12.7 L/km\n",
    "\n",
    "- Van: 8.90 L/km"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "860a7576",
   "metadata": {},
   "source": [
    "# 6. Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa251be5",
   "metadata": {},
   "source": [
    "At the start of this report, 4 key research questions were presented. Throughout the report, various visualizations, models, and interpretations were provided, addressing different aspects of these questions. A formal summary answering each is provided below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81e55a1b",
   "metadata": {},
   "source": [
    "<ins>**Question 1: Has vehicle fuel efficiency improved from 2000 to 2023**</ins>\n",
    "\n",
    "Yes, on average, vehicle fuel efficiency has improved from 2000 to 2023.\n",
    "\n",
    "Figure 1 shows that from 2000 to 2023, the fuel efficiency of the average vehicle in Canada has improved from 12.8 L/100km to 10.8 L/100km. \n",
    "\n",
    "Furthermore, Figure 9 and Figure 10 examine fuel efficiency changes by both make and year. It is observed that all manufacturers in the dataset have achieved improvements in fuel efficiency between 2000 and 2023, although some manufacturers saw more significant improvements than others."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82e367f0",
   "metadata": {},
   "source": [
    "<ins>**Question 2: Which manufacturer produces, on average, the most fuel efficient vehicles?**</ins>\n",
    "\n",
    "As shown in Figure 2, Honda makes the most efficient vehicles. This is in consideration of the average fuel economy of all the vehicles each manufacturer has created from 2000 to 2023.\n",
    "\n",
    "However, in Section 3.3, this analysis was refined by considering the best manufacturer for each year and vehicle type. Honda remained the most efficient manufacturer for older cars, but Toyota was more efficient for cars produced from 2007 to 2019. There was no clear most efficient manufacturer for SUVs, trucks, or vans, with variation observed between different years. It was noted, however, that for the newest vehicles (2020-2023), both Kia and Hyundai were very efficient manufacturers for cars, SUVs, and trucks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b644160",
   "metadata": {},
   "source": [
    "<ins>**Question 3: Can existing fuel efficiency information be used to predict the fuel efficiency of future vehicles?**</ins>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51170a1b",
   "metadata": {},
   "source": [
    "Yes, existing fuel efficiency can be used to predict the fuel efficiency of future vehicles. \n",
    "\n",
    "Section 4.1 and Section 4.2 fit various models to the dataset, including linear regression models, lasso models, regression trees, and neural networks. The neural network provided the lowest MSE, and in Section 4.3, it was used to predict the fuel efficiency of 7 different 2024 vehicles. For 4 of the 7 vehicles, the percent difference between the prediction and the actual fuel efficiency rating was less than 5%.\n",
    "\n",
    "The major limitation is that information about the future vehicle is required (such as the make, vehicle type, engine size, fuel type, transmission type, etc.) in order to make a prediction. By the time this information is publicly available, official published fuel efficiency ratings will already be available."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0b58aeb",
   "metadata": {},
   "source": [
    "<ins>**Question 4. Can we use a company's R&D spending to predict fuel efficiency improvements?**</ins>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a9e9d79",
   "metadata": {},
   "source": [
    "Yes, using Toyota as an example, utilizing their R&D expense data slightly improved our accuracy in predicting vehicles fuel economy across all vehicle classes. \n",
    "\n",
    "However, higher R&D expenses were unexpectedly correlated with reduced fuel efficiency. As discussed in Section 5, the dataset in this analysis was relatively small and may suffer from unfortunate sampling bias considering the inclusion of the 2008 financial crisis. That being said, it's also possible that the relationship between R&D expenses and a vehicle's fuel economy is more complex than initially assumed, and further research into this area should be considered. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c01e9ade",
   "metadata": {},
   "source": [
    "# 7. Conclusion and Future Opportunities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b72cf04f",
   "metadata": {},
   "source": [
    "Overall, it's clear that in general, the fuel efficiency of vehicles in Canada has been decreasing over time. There are various factors associated with a vehicle that influence its fuel efficiency, including the make, vehicle type, fuel type, engine size, number of cylinders, transmission type, and number of transmission gears. \n",
    "\n",
    "This report also explored how the influence of make, vehicle type, fuel type, and transmission type on fuel efficiency has changed over time. Some key findings include:\n",
    "- On average, Honda has produced the most efficient vehicles, while GMC has produced the least efficient vehicles.\n",
    "\n",
    "- Every manufacturer in this study has achieved an improvement in the average fuel efficiency of their vehicles from 2000 to 2023. The manufacturers that achieved the largest percentage improvements (>20%) in fuel efficiency between 2000 and 2023 are Buick, Jeep, Kia, Lexus, Maxda, Nissan, Toyota, and Volvo. \n",
    "\n",
    "- Cars have predominantly had the best fuel efficiency, while trucks have had the worst. However, SUVs and vans have experienced much larger percentage improvements in fuel efficiency from 2000 to 2023 compared to cars and trucks.\n",
    "\n",
    "- Regular gasoline vehicles have had a lower fuel economy than premium gasoline vehicles since 2005 and have improved their efficiency more from 2000 to 2023. Diesel vehicles are generally more efficient than gasoline vehicles.\n",
    "\n",
    "- Continuously variable transmissions are the most efficient transmission type and have improved their efficiency the most since their adoption. \n",
    "\n",
    "After visualizing and exploring the data, various models were fitted, including linear regression models, lasso models, regression trees, and neural networks. Each type of model was first fitted without considering any interaction between the variables, followed by considering the interaction between each variable and the vehicle's model year. Regardless of interaction or not, the neural network was found to be the most accurate predictor (i.e., the lowest mean squared error). The neural network was also able to predict the fuel efficiency of unreleased 2024 vehicle models with moderate success, with 4 of the 7 predictions falling within a 5% error of the actual value.\n",
    "\n",
    "Finally, Toyota's R&D expenses were imported from their full-year annual reports and utilized alongside the model year of a vehicle to make a regression model to predict the efficiency of future vehicles based on R&D spending."
   ]
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    "During the completion of this project, several opportunities for improvement have been identified. They include:\n",
    "- The dataset currently only includes vehicles with traditional combustion engines. However, hybrid and fully electric vehicles have dramatically increased in popularity in recent years. It would be interesting to incorporate those vehicles; however, a different response variable would be required as fully electric vehicles do not consume gasoline and hence have no combined fuel efficiency in L/100km.\n",
    "\n",
    "- Instead of just examining vehicle manufacturers, further exploration could be completed by clustering different manufacturers based on geographical patterns. For example, how does the fuel efficiency of European manufacturers compare with that of North American manufacturers? \n",
    "\n",
    "- The fitted models should be tested further as additional 2024 vehicles are released.\n",
    "\n",
    "- The relationship between R&D expenses and fuel economy should be explored with other manufacturers and over longer time periods. "
   ]
  }
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