{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "from requests import get\n",
    "import json \n",
    "import os \n",
    "from collections import defaultdict\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "#Plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "from IPython.display import HTML, IFrame\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "\n",
    "import folium #  conda install -c conda-forge folium\n",
    "from folium import plugins\n",
    "import geopandas as gpd\n",
    "\n",
    "# Our helpers\n",
    "from neural import prepare_future, predict_future # Wrapper to use RNNs\n",
    "from helpfunc import *\n",
    "from plots import *\n",
    "\n",
    "# Foor retrieving prices of food\n",
    "import nltk\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "import picos as pic # Library used to implement the convex optimization problem\n",
    "\n",
    "import plotly.graph_objects as go\n",
    "import plotly.offline as py\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_dict = {'food_balance_africa': 'data/raw/FoodBalanceSheets_E_Africa_1.csv',\n",
    "                'geoworld_json': 'data/raw/world-countries.json',\n",
    "                'africa_supply_rnn': 'data/processed/africa_cal.pkl',\n",
    "                'ages_calories_demand': 'data/raw/calories_demand.xlsx',\n",
    "                'african_countries_list': \"data/raw/african_countries.txt\",\n",
    "                'population_age_male': \"data/raw/POPULATION_BY_AGE_MALE.xlsx\",\n",
    "                'population_age_female': \"data/raw/POPULATION_BY_AGE_FEMALE.xlsx\",\n",
    "                'food_balance_europe': \"data/raw/FoodBalanceSheets_E_Europe_1.csv\",\n",
    "                'europe_supply_rnn': 'data/processed/europe_cal.pkl',\n",
    "                'european_countries_list': \"data/raw/european_countries.txt\", \n",
    "                'african_supply_map': 'visualization/africa_supply_map',\n",
    "                'african_demand_anim': 'visualization/african_cal_diff_animation.html',\n",
    "                'african_estimation_kcal': 'visualization/africa_est_kcal',\n",
    "                'african_kcal_need': \"visualization/african_kcal_need\",\n",
    "                 \"european_supply_map\":\"visualization/european_supply_map\",\n",
    "                 \"african_pop_growth\":\"visualization/african_pop_growth.html\", \n",
    "                 'european_pop_growth': \"visualization/european_pop_growth.html\",\n",
    "                 'european_estimation_kcal': 'visualization/europe_est_kcal',\n",
    "                 'european_demand_anim': 'visualization/european_cal_diff_animation.html',\n",
    "                 'european_kcal_surplus': \"visualization/european_kcal_surplus\",\n",
    "                 'world_kcal_surplus': \"visualization/world/world_kcal_surplus\",\n",
    "            }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (15,8) #set size of plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Determination of African countries with food deficit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) How much human food resources are available in African countries?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1) Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To answer this important question, we will need to import data from the **FAO Dataset**. More specifically, we will focus on the section **Food Balance Sheet** with respect to African countries only. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_balance_africa = pd.read_csv(path_dict['food_balance_africa'],encoding='latin-1');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, we will **remove** all the columns with title **\"Y----F\"** as they contain information about how the data was obtained (Calculated, Regression, Aggregate, FAO Estimation). In this context we will consider that FAO is a *highly renowned Agency* and hence we can assume these values are truthful without loss of generality. Furthermore we thought that it would be very handy to have numbers as columns representing years instead of **\"Y----\"**. We proceed on removing the letter **Y**. The helper functions `clean_Fs_and_years` does this cleaning on the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_balance_africa = clean_Fs_and_years(food_balance_africa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Secondly, we replace all the **NAN** values with **0** as Item was not available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_balance_africa = food_balance_africa.fillna(0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The third step to complete **the cleaning** of food_balance_africa consists of adapting names of countries in order to have consistency along our different dataframes. Since some countries changed their name over the years we will rename them. In particular, **Swaziland** to **Eswatini** and **South Africa** to **Southern Africa**. The function `replace_names_of_countries` takes the dataframe and the country names which should be replaced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_balance_africa = replace_names_of_countries(food_balance_africa, [('Swaziland', 'Eswatini'), ('South Africa', 'Southern Africa')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our Dataframe looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Area Code</th>\n",
       "      <th>Area</th>\n",
       "      <th>Item Code</th>\n",
       "      <th>Item</th>\n",
       "      <th>Element Code</th>\n",
       "      <th>Element</th>\n",
       "      <th>Unit</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2501</td>\n",
       "      <td>Population</td>\n",
       "      <td>511</td>\n",
       "      <td>Total Population - Both sexes</td>\n",
       "      <td>1000 persons</td>\n",
       "      <td>11561.0</td>\n",
       "      <td>11845.0</td>\n",
       "      <td>12136.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33461.0</td>\n",
       "      <td>33961.0</td>\n",
       "      <td>34507.0</td>\n",
       "      <td>35097.0</td>\n",
       "      <td>35725.0</td>\n",
       "      <td>36383.0</td>\n",
       "      <td>37063.0</td>\n",
       "      <td>37763.0</td>\n",
       "      <td>38482.0</td>\n",
       "      <td>39208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2511</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>5511</td>\n",
       "      <td>Production</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>686.0</td>\n",
       "      <td>1507.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2731.0</td>\n",
       "      <td>2415.0</td>\n",
       "      <td>2688.0</td>\n",
       "      <td>2319.0</td>\n",
       "      <td>1111.0</td>\n",
       "      <td>2953.0</td>\n",
       "      <td>2605.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>3432.0</td>\n",
       "      <td>3299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2511</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>5611</td>\n",
       "      <td>Import Quantity</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>469.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5123.0</td>\n",
       "      <td>5697.0</td>\n",
       "      <td>4987.0</td>\n",
       "      <td>4885.0</td>\n",
       "      <td>6508.0</td>\n",
       "      <td>5757.0</td>\n",
       "      <td>5109.0</td>\n",
       "      <td>7487.0</td>\n",
       "      <td>6385.0</td>\n",
       "      <td>6343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2511</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>5072</td>\n",
       "      <td>Stock Variation</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>353.0</td>\n",
       "      <td>-409.0</td>\n",
       "      <td>-408.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-456.0</td>\n",
       "      <td>-606.0</td>\n",
       "      <td>-459.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>-484.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>-1050.0</td>\n",
       "      <td>-350.0</td>\n",
       "      <td>-180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2511</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>5911</td>\n",
       "      <td>Export Quantity</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>46.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area Code     Area  Item Code                Item  Element Code  \\\n",
       "0          4  Algeria       2501          Population           511   \n",
       "1          4  Algeria       2511  Wheat and products          5511   \n",
       "2          4  Algeria       2511  Wheat and products          5611   \n",
       "3          4  Algeria       2511  Wheat and products          5072   \n",
       "4          4  Algeria       2511  Wheat and products          5911   \n",
       "\n",
       "                         Element          Unit     1961     1962     1963  \\\n",
       "0  Total Population - Both sexes  1000 persons  11561.0  11845.0  12136.0   \n",
       "1                     Production   1000 tonnes    686.0   1507.0   1590.0   \n",
       "2                Import Quantity   1000 tonnes    469.0    501.0    374.0   \n",
       "3                Stock Variation   1000 tonnes    353.0   -409.0   -408.0   \n",
       "4                Export Quantity   1000 tonnes     46.0     12.0     33.0   \n",
       "\n",
       "   ...     2004     2005     2006     2007     2008     2009     2010  \\\n",
       "0  ...  33461.0  33961.0  34507.0  35097.0  35725.0  36383.0  37063.0   \n",
       "1  ...   2731.0   2415.0   2688.0   2319.0   1111.0   2953.0   2605.0   \n",
       "2  ...   5123.0   5697.0   4987.0   4885.0   6508.0   5757.0   5109.0   \n",
       "3  ...   -456.0   -606.0   -459.0     -1.0    156.0   -484.0    515.0   \n",
       "4  ...      7.0     13.0      7.0     14.0     24.0     37.0     39.0   \n",
       "\n",
       "      2011     2012     2013  \n",
       "0  37763.0  38482.0  39208.0  \n",
       "1   2555.0   3432.0   3299.0  \n",
       "2   7487.0   6385.0   6343.0  \n",
       "3  -1050.0   -350.0   -180.0  \n",
       "4      5.0      5.0      2.0  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_balance_africa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysing our DataFrame *food_balance_africa* we can see that it's already well structured since it contains many key - value couples such as **Item Code - Item** and **Element Code - Element** . More specifically, we will take advantage of this structure to filter out only rows characterized by **Grand total** as an **Item** and **Food supply (kcal/capita/day)** as an **Element**. The corresponding key-values are **(Item Code, 2901) and (Element Code, 664)**.\n",
    "A reference to the documentation in the [FAO Website](http://www.fao.org/faostat/en/#data/FBS) explains the legend for Element Code and Element Item extensively. \n",
    "\n",
    "In order to keep our original Dataframe *food_balance_africa* as a reference we create a new Dataframe *food_supply_africa* in which we just keep **countries** and **food supplies** for every **year**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_supply_africa = obtain_supply(food_balance_africa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now group group by **Area** and see the supplies derived from each item available in countries for that particular year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>1966</th>\n",
       "      <th>1967</th>\n",
       "      <th>1968</th>\n",
       "      <th>1969</th>\n",
       "      <th>1970</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>1619.0</td>\n",
       "      <td>1569.0</td>\n",
       "      <td>1528.0</td>\n",
       "      <td>1540.0</td>\n",
       "      <td>1591.0</td>\n",
       "      <td>1571.0</td>\n",
       "      <td>1647.0</td>\n",
       "      <td>1706.0</td>\n",
       "      <td>1705.0</td>\n",
       "      <td>1675.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2987.0</td>\n",
       "      <td>2958.0</td>\n",
       "      <td>3047.0</td>\n",
       "      <td>3041.0</td>\n",
       "      <td>3048.0</td>\n",
       "      <td>3110.0</td>\n",
       "      <td>3142.0</td>\n",
       "      <td>3217.0</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>3296.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>1798.0</td>\n",
       "      <td>1819.0</td>\n",
       "      <td>1853.0</td>\n",
       "      <td>1862.0</td>\n",
       "      <td>1877.0</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>1921.0</td>\n",
       "      <td>1856.0</td>\n",
       "      <td>1946.0</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2030.0</td>\n",
       "      <td>2077.0</td>\n",
       "      <td>2119.0</td>\n",
       "      <td>2173.0</td>\n",
       "      <td>2245.0</td>\n",
       "      <td>2303.0</td>\n",
       "      <td>2345.0</td>\n",
       "      <td>2407.0</td>\n",
       "      <td>2384.0</td>\n",
       "      <td>2473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benin</th>\n",
       "      <td>1736.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>1703.0</td>\n",
       "      <td>1669.0</td>\n",
       "      <td>1812.0</td>\n",
       "      <td>1804.0</td>\n",
       "      <td>1833.0</td>\n",
       "      <td>1935.0</td>\n",
       "      <td>1871.0</td>\n",
       "      <td>1812.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2461.0</td>\n",
       "      <td>2435.0</td>\n",
       "      <td>2450.0</td>\n",
       "      <td>2564.0</td>\n",
       "      <td>2521.0</td>\n",
       "      <td>2565.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>2598.0</td>\n",
       "      <td>2610.0</td>\n",
       "      <td>2619.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>1976.0</td>\n",
       "      <td>1909.0</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>1947.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2052.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2035.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2191.0</td>\n",
       "      <td>2198.0</td>\n",
       "      <td>2150.0</td>\n",
       "      <td>2166.0</td>\n",
       "      <td>2184.0</td>\n",
       "      <td>2199.0</td>\n",
       "      <td>2234.0</td>\n",
       "      <td>2273.0</td>\n",
       "      <td>2342.0</td>\n",
       "      <td>2326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burkina Faso</th>\n",
       "      <td>1326.0</td>\n",
       "      <td>1308.0</td>\n",
       "      <td>1452.0</td>\n",
       "      <td>1555.0</td>\n",
       "      <td>1571.0</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>1587.0</td>\n",
       "      <td>1602.0</td>\n",
       "      <td>1604.0</td>\n",
       "      <td>1546.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2505.0</td>\n",
       "      <td>2463.0</td>\n",
       "      <td>2559.0</td>\n",
       "      <td>2546.0</td>\n",
       "      <td>2588.0</td>\n",
       "      <td>2639.0</td>\n",
       "      <td>2681.0</td>\n",
       "      <td>2664.0</td>\n",
       "      <td>2707.0</td>\n",
       "      <td>2720.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1961    1962    1963    1964    1965    1966    1967    1968  \\\n",
       "Area                                                                           \n",
       "Algeria       1619.0  1569.0  1528.0  1540.0  1591.0  1571.0  1647.0  1706.0   \n",
       "Angola        1798.0  1819.0  1853.0  1862.0  1877.0  1890.0  1921.0  1856.0   \n",
       "Benin         1736.0  1758.0  1703.0  1669.0  1812.0  1804.0  1833.0  1935.0   \n",
       "Botswana      1976.0  1909.0  1972.0  2001.0  2005.0  1947.0  2010.0  2052.0   \n",
       "Burkina Faso  1326.0  1308.0  1452.0  1555.0  1571.0  1560.0  1587.0  1602.0   \n",
       "\n",
       "                1969    1970  ...    2004    2005    2006    2007    2008  \\\n",
       "Area                          ...                                           \n",
       "Algeria       1705.0  1675.0  ...  2987.0  2958.0  3047.0  3041.0  3048.0   \n",
       "Angola        1946.0  1965.0  ...  2030.0  2077.0  2119.0  2173.0  2245.0   \n",
       "Benin         1871.0  1812.0  ...  2461.0  2435.0  2450.0  2564.0  2521.0   \n",
       "Botswana      2013.0  2035.0  ...  2191.0  2198.0  2150.0  2166.0  2184.0   \n",
       "Burkina Faso  1604.0  1546.0  ...  2505.0  2463.0  2559.0  2546.0  2588.0   \n",
       "\n",
       "                2009    2010    2011    2012    2013  \n",
       "Area                                                  \n",
       "Algeria       3110.0  3142.0  3217.0  3272.0  3296.0  \n",
       "Angola        2303.0  2345.0  2407.0  2384.0  2473.0  \n",
       "Benin         2565.0  2555.0  2598.0  2610.0  2619.0  \n",
       "Botswana      2199.0  2234.0  2273.0  2342.0  2326.0  \n",
       "Burkina Faso  2639.0  2681.0  2664.0  2707.0  2720.0  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_supply_africa = food_supply_africa.set_index(\"Area\")\n",
    "food_supply_africa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to check for anomalies in our data, we would like to analyze the **timeline**. We therefore transpose the dataframe and plot the timeline of how food supply in different countries evolved. Legend was suppressed as it is too large."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_supply_africa = food_supply_africa.transpose();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting the year from string to int\n",
    "food_supply_africa.index = food_supply_africa.index.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "timeline_supply(food_supply_africa, \"African\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This analysis shows that there are two inconsistencies. We therefore check for countries containing values equal to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Ethiopia', 'Ethiopia PDR', 'Sudan', 'Sudan (former)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_supply_africa.columns.values[(food_supply_africa == 0).any()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice that **Sudan** and **Ethiopia** appear twice as \"Sudan\" and \"Sudan (former)\" and \"Ethiopia\" and \"Ethiopia PDR\" respectively. This is due to the fact that South Sudan gained independence in 2011 (reference to https://en.wikipedia.org/wiki/South_Sudan), and the foundation of the Federal Democratic Republic of Ethiopia (reference to https://en.wikipedia.org/wiki/Ethiopia) in 1991. From then on, Ethiopia PDR was listed as Ethiopia. With food supply being consistently constant even after division, the newly introduced country \"Sudan\" is assumed to further on have accounted for both countries. For this reason, we will consider them to be one single country. \n",
    "Consequently, the two countries' data is merged into one continuous set each. The function `merge_countries` takes care of this, by substituting each key in dictionary (the second argument) with its value(s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_supply_africa = merge_countries(food_supply_africa, {'Sudan (former)': ['Sudan'], 'Ethiopia PDR': ['Ethiopia']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the newly generated data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "timeline_supply(food_supply_africa, \"African\", \" - Cleaned dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we want to add more columns representing future years until 2020 to prepare cells for extrapolation to make predictions about possible scenarios. `prepare_future` is used for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding columns for the new years\n",
    "food_supply_africa = prepare_future(food_supply_africa, 2014, 2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2) Extrapolation\n",
    "First of all, we want to simulate data until 2020 to match the population data. Furthermore, we also want to be able to make predicitions for individual countries to assess if they might run into food shortages in the near future.  \n",
    "The prediction for the new years are done by using a \"*Recurrent Neural Network (RNN)*\" and a window of size 10. Basically what we will do here is using all the past history of each country (windowed in block of 10 years each) to run a neural network and try to predict the future behaviour (up to 2020). During our test we found that the neural networks are able to predict good estimations.  \n",
    "As we don't want precise data, the **_estimations_** achieved by using ML are in this case more than acceptable for our purpose.  \n",
    "*Credits*: We don't know much about RNN, so the network used here is adapted from the *Time series forecasting tutorial* on **Tensorflow**, available [here](https://www.tensorflow.org/tutorials/structured_data/time_series)  \n",
    "*Note*: we already ran the networks and saved the results on Colab, running them each time requires more than an hour on Colab. For this reason, we just use the pickle here instead of running the network. This is achieved by the function `predict_future`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_supply_africa = predict_future(food_supply_africa, path_dict['africa_supply_rnn'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "timeline_supply(food_supply_africa, \"African\", \"- Predicted dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When looking at the predicted period (after 2013), a linear approximation can be observed with some countries being forecasted to oscillate. This can be explained by the fact that the neural network method recognised a recurring pattern in the data and extrapolates this behaviour. Among all the tested methods, this one is expected to reproduce the most realistic data, as no odd developments can be observed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3) Visualizing the data interactively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geopandas.geodataframe.GeoDataFrame"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Geographic coordinates for visualizing \n",
    "geojson_world = gpd.read_file(path_dict['geoworld_json'])\n",
    "type(geojson_world)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe `geojson_world` is a **GeoDataFrame**. Let's see how the data are sorted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>POLYGON ((61.21082 35.65007, 62.23065 35.27066...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>MULTIPOLYGON (((16.32653 -5.87747, 16.57318 -6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>POLYGON ((20.59025 41.85540, 20.46317 41.51509...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>POLYGON ((51.57952 24.24550, 51.75744 24.29407...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>MULTIPOLYGON (((-65.50000 -55.20000, -66.45000...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id                  name  \\\n",
       "0  AFG           Afghanistan   \n",
       "1  AGO                Angola   \n",
       "2  ALB               Albania   \n",
       "3  ARE  United Arab Emirates   \n",
       "4  ARG             Argentina   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((61.21082 35.65007, 62.23065 35.27066...  \n",
       "1  MULTIPOLYGON (((16.32653 -5.87747, 16.57318 -6...  \n",
       "2  POLYGON ((20.59025 41.85540, 20.46317 41.51509...  \n",
       "3  POLYGON ((51.57952 24.24550, 51.75744 24.29407...  \n",
       "4  MULTIPOLYGON (((-65.50000 -55.20000, -66.45000...  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geojson_world.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to make sure that `geojson_world` has data for every country under our analysis. In this case, we need to check for every country in Africa and more specifically every country taken into account in `food_supply_africa`. Since the **name** in `geojson_world` is not a good index to match and we don't have any comparable **id** in `food_supply_africa` we have to do this job by hand, which is feasible since the size is small enough. As a result, we filter `geojson_world` to `geojson_africa`. Let's display this new GeoDataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>MULTIPOLYGON (((16.32653 -5.87747, 16.57318 -6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BEN</td>\n",
       "      <td>Benin</td>\n",
       "      <td>POLYGON ((2.69170 6.25882, 1.86524 6.14216, 1....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BFA</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>POLYGON ((-2.82750 9.64246, -3.51190 9.90033, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>BWA</td>\n",
       "      <td>Botswana</td>\n",
       "      <td>POLYGON ((25.64916 -18.53603, 25.85039 -18.714...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CAF</td>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>POLYGON ((15.27946 7.42192, 16.10623 7.49709, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                      name  \\\n",
       "1   AGO                    Angola   \n",
       "13  BEN                     Benin   \n",
       "14  BFA              Burkina Faso   \n",
       "25  BWA                  Botswana   \n",
       "26  CAF  Central African Republic   \n",
       "\n",
       "                                             geometry  \n",
       "1   MULTIPOLYGON (((16.32653 -5.87747, 16.57318 -6...  \n",
       "13  POLYGON ((2.69170 6.25882, 1.86524 6.14216, 1....  \n",
       "14  POLYGON ((-2.82750 9.64246, -3.51190 9.90033, ...  \n",
       "25  POLYGON ((25.64916 -18.53603, 25.85039 -18.714...  \n",
       "26  POLYGON ((15.27946 7.42192, 16.10623 7.49709, ...  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "african_country_codes = [\"DZA\",\"AGO\",\"BEN\",\"BWA\",\"BFA\",\"CMR\",\"CAF\",\"TCD\",\"COD\",\"CIV\",\n",
    "                         \"DJI\",\"EGY\",\"SWZ\",\"ETH\",\"GAB\",\"GMB\",\"GHA\",\"GNQ\",\"GNB\",\"KEN\",\"LSO\",\"LBR\",\n",
    "                         \"MDG\",\"MWI\",\"MLI\",\"MRT\",\"MAR\",\"MOZ\",\"NAM\",\"NER\",\"NGA\",\"RWA\"\n",
    "                         ,\"SEN\",\"SLE\",\"ZAF\",\"SDN\",\"TGO\",\"TUN\",\"UGA\",\"TZA\",\"ZMB\",\"ZWE\"]\n",
    "\n",
    "african_country_names = ['Algeria', 'Angola', 'Benin', 'Botswana', 'Burkina Faso',\n",
    "                         'Cameroon', 'Central African Republic', 'Chad', 'Congo',\n",
    "                         \"Côte d'Ivoire\", 'Djibouti', 'Egypt', 'Eswatini','Ethiopia', 'Gabon',\n",
    "                         'Gambia', 'Ghana', 'Guinea', 'Guinea-Bissau', 'Kenya', 'Lesotho',\n",
    "                         'Liberia', 'Madagascar', 'Malawi', 'Mali', 'Mauritania', 'Morocco',\n",
    "                         'Mozambique', 'Namibia', 'Niger', 'Nigeria', 'Rwanda', 'Senegal',\n",
    "                         'Sierra Leone', 'Southern Africa', 'Sudan', 'Togo',\n",
    "                         'Tunisia', 'Uganda', 'United Republic of Tanzania', 'Zambia',\n",
    "                         'Zimbabwe']\n",
    "\n",
    "african_country_kv = pd.DataFrame({'codes': african_country_codes,\n",
    "                                    'names': african_country_names\n",
    "                                    })\n",
    "\n",
    "geojson_africa = geojson_world[geojson_world.id.isin(african_country_codes)]\n",
    "geojson_africa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ordered list **african_country_codes** contains all the countries available to be plotted as geometry is available. We found out manually that **Cabo Verde, Sao Tome and Principe and Mauritius** are not in this list. For the sake of simplicity, we will remove these three countries as they don't affect our analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_supply_africa = food_supply_africa.drop(columns=[\"Cabo Verde\",\"Mauritius\",\"Sao Tome and Principe\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can move to plot the Food supply for each country. All of this is done in the `plot_map` function. This function plots a world map centered in the zone of interest with interactive values while scrolling over the individual.  \n",
    "As we are particularly interested in the situation in *2020*, we'll plot our prediction of supply for this year in the notebook map. Could be interesting also to look at the evolving of the situation over the last 50 years, so we plot a map for each decade from 1970 to 2020.  \n",
    "**_Note_: the map visualized here is just for 2020, if maybe will not load. If this happens, [click here](https://manuleo.github.io/mADAm_files/africa_supply_map2020.html)**  \n",
    "\n",
    "The maps for the previous decade are available here:\n",
    "- [1970](https://manuleo.github.io/mADAm_files/africa_supply_map1970.html)\n",
    "- [1980](https://manuleo.github.io/mADAm_files/africa_supply_map1980.html)\n",
    "- [1990](https://manuleo.github.io/mADAm_files/africa_supply_map1990.html)\n",
    "- [2000](https://manuleo.github.io/mADAm_files/africa_supply_map2000.html)\n",
    "- [2010](https://manuleo.github.io/mADAm_files/africa_supply_map2010.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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    color_map_3003a3323ed24590874f539bbd9f74fc.x = d3.scale.linear()
              .domain([1899.2222742116044, 3629.348321336474])
              .range([0, 400]);

    color_map_3003a3323ed24590874f539bbd9f74fc.legend = L.control({position: 'topright'});
    color_map_3003a3323ed24590874f539bbd9f74fc.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
    color_map_3003a3323ed24590874f539bbd9f74fc.legend.addTo(map_1b3fae04ad7f48b18f4eecdfb729fbee);

    color_map_3003a3323ed24590874f539bbd9f74fc.xAxis = d3.svg.axis()
        .scale(color_map_3003a3323ed24590874f539bbd9f74fc.x)
        .orient("top")
        .tickSize(1)
        .tickValues([1899.2222742116044, 2187.5766153990826, 2475.930956586561, 2764.2852977740395, 3052.6396389615174, 3340.9939801489954, 3629.348321336474]);

    color_map_3003a3323ed24590874f539bbd9f74fc.svg = d3.select(".legend.leaflet-control").append("svg")
        .attr("id", 'legend')
        .attr("width", 450)
        .attr("height", 40);

    color_map_3003a3323ed24590874f539bbd9f74fc.g = color_map_3003a3323ed24590874f539bbd9f74fc.svg.append("g")
        .attr("class", "key")
        .attr("transform", "translate(25,16)");

    color_map_3003a3323ed24590874f539bbd9f74fc.g.selectAll("rect")
        .data(color_map_3003a3323ed24590874f539bbd9f74fc.color.range().map(function(d, i) {
          return {
            x0: i ? color_map_3003a3323ed24590874f539bbd9f74fc.x(color_map_3003a3323ed24590874f539bbd9f74fc.color.domain()[i - 1]) : color_map_3003a3323ed24590874f539bbd9f74fc.x.range()[0],
            x1: i < color_map_3003a3323ed24590874f539bbd9f74fc.color.domain().length ? color_map_3003a3323ed24590874f539bbd9f74fc.x(color_map_3003a3323ed24590874f539bbd9f74fc.color.domain()[i]) : color_map_3003a3323ed24590874f539bbd9f74fc.x.range()[1],
            z: d
          };
        }))
      .enter().append("rect")
        .attr("height", 10)
        .attr("x", function(d) { return d.x0; })
        .attr("width", function(d) { return d.x1 - d.x0; })
        .style("fill", function(d) { return d.z; });

    color_map_3003a3323ed24590874f539bbd9f74fc.g.call(color_map_3003a3323ed24590874f539bbd9f74fc.xAxis).append("text")
        .attr("class", "caption")
        .attr("y", 21)
        .text('Food supply (kcal/person/day)');
    
        function geo_json_c78d53241a724bb2b7dd4e3f1a809c4b_styler(feature) {
            switch(feature.id) {
                default:
                    return {"color": "#000000", "fillColor": "#ffffff", "fillOpacity": 0.1, "weight": 0.1};
            }
        }
        function geo_json_c78d53241a724bb2b7dd4e3f1a809c4b_highlighter(feature) {
            switch(feature.id) {
                default:
                    return {"color": "#000000", "fillColor": "#000000", "fillOpacity": 0.5, "weight": 0.1};
            }
        }
        function geo_json_c78d53241a724bb2b7dd4e3f1a809c4b_onEachFeature(feature, layer) {
            layer.on({
                mouseout: function(e) {
                    geo_json_c78d53241a724bb2b7dd4e3f1a809c4b.resetStyle(e.target);
                },
                mouseover: function(e) {
                    e.target.setStyle(geo_json_c78d53241a724bb2b7dd4e3f1a809c4b_highlighter(e.target.feature));
                },
                click: function(e) {
                    map_1b3fae04ad7f48b18f4eecdfb729fbee.fitBounds(e.target.getBounds());
                }
            });
        };
        var geo_json_c78d53241a724bb2b7dd4e3f1a809c4b = L.geoJson(null, {
                onEachFeature: geo_json_c78d53241a724bb2b7dd4e3f1a809c4b_onEachFeature,
            
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2271.645416679116}, "type": "Feature"}], "type": "FeatureCollection"});
        
    
        geo_json_c78d53241a724bb2b7dd4e3f1a809c4b.bindTooltip(
            function(layer){
            // Convert non-primitive to String.
            let handleObject = (feature)=>typeof(feature)=='object' ? JSON.stringify(feature) : feature;
            let fields = ["names", "val"];
            let aliases = ["Country: ", "Food supply (kcal/person/day)"];
            return '<table style="background-color: white; color: #333333; font-family: arial; font-size: 12px; padding: 10px;">' +
            String(
                fields.map(
                columnname=>
                    `<tr style="text-align: left;">
                    <th style="padding: 4px; padding-right: 10px;">
                        ${aliases[fields.indexOf(columnname)]
                        }
                    </th>
                    
                    <td style="padding: 4px;">${handleObject(layer.feature.properties[columnname])
                    }</td></tr>`
                ).join(''))
                +'</table>'
            }, {"sticky": true});
        
    
            var layer_control_8e16c78df4714c0bb0cadb6355c9a53b = {
                base_layers : {
                },
                overlays :  {
                    "Choropleth" : choropleth_84bd65751b4742318b9fa390799dc6c0,
                },
            };
            L.control.layers(
                layer_control_8e16c78df4714c0bb0cadb6355c9a53b.base_layers,
                layer_control_8e16c78df4714c0bb0cadb6355c9a53b.overlays,
                {"autoZIndex": true, "collapsed": true, "position": "topright"}
            ).addTo(map_1b3fae04ad7f48b18f4eecdfb729fbee);
        
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x126bae550>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Used to generate, don't run it\n",
    "legend_name = \"Food supply (kcal/person/day)\"\n",
    "for year in range(1970, 2030, 10):\n",
    "    africa_supply_map = plot_map(food_supply_africa.T, path_dict['geoworld_json'], \\\n",
    "    african_country_kv, year, \"Blues\", legend_name, legend_name, path_dict['african_supply_map'] + str(year) + \".html\")\n",
    "africa_supply_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://manuleo.github.io/mADAm_files/africa_supply_map2020.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x126cc5410>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing 2020 map\n",
    "IFrame(src='https://manuleo.github.io/mADAm_files/africa_supply_map2020.html', width = 800, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_map_data(geojson_africa, african_country_kv, food_supply_africa, \"docs/json/africa_supply/africa_supply_{}.geojson\", \"docs/json/africa_supply/africa_supply_ticks.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By analyzing the trend over the decades, we discover that the situation in the African region **partially improved** over the years, especially if we compare the 2020 with the 1970.  \n",
    "There are some countries, especially in North Africa, that improved their supplies by 1000 kcal. In contrast, the situation in the central-southern region is almost the same as 50 years ago. Another important point to see is that there is a bit of fluctuation of the values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) What is the ideal amount of kcal each African country needs?\n",
    "In this first part, we compute **kcal demand** for males and females for every age group. Secondly, we will conduct an extensive analysis on **African demographics**. Finally, we will be able to combine kcal demand with African population data into a unique dataframe that will be the answer of our inital question: **What is the kcal demand of a regular person in order to be healthy?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1) How many kilocalories does a regular person need daily?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, we load the calories demand datasets scraped from the webpage [Calories](https://health.gov/dietaryguidelines/2015/guidelines/appendix-2/). This information will be matched with the population datsets to receive the total caloric demand in each country, each year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_calory_demand = pd.read_excel(path_dict['ages_calories_demand'], header = None, sheet_name = 0, names = ['age', 'sedentary', 'moderate', 'active'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "female_calory_demand = pd.read_excel(path_dict['ages_calories_demand'], header = None, sheet_name = 1, names = ['age', 'sedentary', 'moderate', 'active'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to better work with the information we have collected, we will make some simplifications on the data. Mainly, we will:\n",
    "- take the **active lifestyle** column in the calories demands database. According to the [World Health Organization](https://www.afro.who.int/health-topics/physical-activity), regular physical activity helps to maintain a healthy body and reduces the risk of disease.\n",
    "- group the ages into ranges that match the ranges provided in the World Population Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_calories = male_calory_demand.drop(columns=['sedentary', 'moderate'])\n",
    "female_calories = female_calory_demand.drop(columns=['sedentary', 'moderate'])\n",
    "male_calories.rename(columns={'active':'input kcal'}, inplace=True)\n",
    "female_calories.rename(columns={'active':'input kcal'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have now obtained a caloric demand for simpler calculations in the future and stored in the two previous datasets.  \n",
    "Now, we need a  way to match the age groups in this dataframe to the ones in the population database we obtained. As such, let's analyse how ages are represented in our caloric demand dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       '19-20', '21-25', '26-30', '31-35', '36-40', '41-45', '46-50',\n",
       "       '51-55', '56-60', '61-65', '66-70', '71-75', '76 and up', nan],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_calories['age'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there are ranges of ages with different sizes (which makes sense, because different age groups have different caloric needs). The function `explode_age` returns the dataframe with one row per individual age."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We apply the function to our two dataframes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_calories = explode_age(male_calories)\n",
    "female_calories = explode_age(female_calories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,\n",
       "        15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,\n",
       "        41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,\n",
       "        54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,\n",
       "        67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  79,\n",
       "        80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,  92,\n",
       "        93,  94,  95,  96,  97,  98,  99, 100, 101])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_calories['age'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ages are now unique in each dataframe ( `male_calories` and `female_calories` ) and there's a caloric input value for each of them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last step to allow the match with the population database is to build the **same age groups** we have in that set. The `compress_ages` function takes care of the differences between datasets by grouping the **ages** into the same **ranges** as in the population dataset (and calculating the average needs)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can lastly apply the function to the dataframes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_calories = compress_ages(male_calories)\n",
    "female_calories = compress_ages(female_calories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also use the age group as new index and rename the columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_calories.index.name = 'age_group'\n",
    "male_calories = male_calories.rename(columns={0: 'input kcal'})\n",
    "female_calories.index.name = 'age_group'\n",
    "female_calories = female_calories.rename(columns={0: 'input kcal'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the result we have achieved and collected in our matchable dataframe `male_calories` and `female_calories`. The unit here is **kcal/person/day**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>input kcal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_group</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-4</th>\n",
       "      <td>1333.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-9</th>\n",
       "      <td>1840.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-14</th>\n",
       "      <td>2440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-19</th>\n",
       "      <td>3120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-24</th>\n",
       "      <td>3000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            input kcal\n",
       "age_group             \n",
       "0-4        1333.333333\n",
       "5-9        1840.000000\n",
       "10-14      2440.000000\n",
       "15-19      3120.000000\n",
       "20-24      3000.000000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_calories.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "axes[0].barh(male_calories.index, male_calories[\"input kcal\"])\n",
    "axes[1].barh(female_calories.index, female_calories[\"input kcal\"],color=\"#ffc0cb\")\n",
    "axes[0].set_xlabel(\"Amount of kcal needed for group age in males [kcal/persona/day]\")\n",
    "fig.suptitle(\"Estimation of energy needed to live an active lifestyle for males and females divided in group ages\")\n",
    "axes[1].set_xlabel(\"Amount of kcal needed for group age in females [kcal/persona/day]\")\n",
    "axes[1].set_xlim(0, 3300)\n",
    "axes[0].set_xlim(0, 3300)\n",
    "axes[0].set_ylabel(\"age groups\")\n",
    "axes[1].set_ylabel(\"age groups\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As as we can see, in general, males need more calories than females. For both genders, the maximum demand occurs at the age of 15-30."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our data story, we present a pyramid plot to show the same results. The code used is presented below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace(\n",
    "    go.Bar(\n",
    "        name=\"Male\",\n",
    "        orientation=\"h\",\n",
    "        marker_color=\"#AED6FF\",\n",
    "        y=male_calories.index,\n",
    "        x=male_calories['input kcal'],\n",
    "        hovertemplate=\"Caloric need: %{x:.2f} kcal/day<br>Age group: %{y}\"))\n",
    "\n",
    "fig.add_trace(\n",
    "    go.Bar(\n",
    "        name=\"Female\",\n",
    "        orientation=\"h\",\n",
    "        marker_color=\"#FFDCFD\",\n",
    "        y=female_calories.index,\n",
    "        x=-female_calories['input kcal'],\n",
    "        text=female_calories['input kcal'],\n",
    "        hovertemplate=\"Caloric need: %{text:.2f} kcal/day<br>Age group: %{y}\"))\n",
    "\n",
    "fig.update_layout(\n",
    "    barmode='overlay', \n",
    "    xaxis=dict(autorange=False, range=[-3500, 3500], ticks=\"\", \n",
    "               tickvals=[-3000,-2000,-1000,0,1000,2000,3000],\n",
    "               ticktext=[3000,2000,1000,0,1000,2000,3000],\n",
    "               title=\"Caloric need [kcal/day]\",\n",
    "               gridcolor=\"#eee\"),\n",
    "    yaxis=dict(title=\"Age group\"),\n",
    "    paper_bgcolor='rgba(0,0,0,0)',\n",
    "    plot_bgcolor='rgba(0,0,0,0)',\n",
    "    title=\"Caloric need per age and gender\"\n",
    "    )\n",
    "\n",
    "py.plot(fig, filename=\"docs/_includes/gender.html\", include_plotlyjs=False)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2) How many people live in Africa?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2.1) Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this second part of our analysis, we load the list of **African countries**. Secondly, we load the **World Population Database** (United Nation) and therefore we obtain two dataframes: one for males and the other one for females."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open (path_dict['african_countries_list'],'r') as af_c:\n",
    "    af_countries = [line.rstrip() for line in af_c] #loading list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to check if the FAO Database contains data regarding **every country** in Africa. We will check the intersection with the list **af_countries**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of countries for which no data is available: ['Mauritius', 'Mayotte', 'South Sudan', 'Réunion', 'Libya', 'Sao Tome and Principe', 'Eritrea', 'Burundi', 'Comoros', 'Equatorial Guinea', 'Cabo Verde', 'Somalia', 'Seychelles', 'Democratic Republic of the Congo', 'Western Sahara']\n"
     ]
    }
   ],
   "source": [
    "af_to_remove = list(set(af_countries) - set(food_supply_africa.columns.values))\n",
    "print(\"List of countries for which no data is available: \" + str(af_to_remove))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, many countries were not present in the FAO Database. The countries to remove are the following: **Eritrea, Burundi, Comoros, Democratic Republic of the Congo, Equatorial Guinea, Libya, Seychelles, Western Sahara, South Sudan, and Somalia**. Furthermore, **Mayotte** and **Réunion** are French territory islands so they will be removed as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of countries for which no data is available: []\n"
     ]
    }
   ],
   "source": [
    "af_countries = [i for i in af_countries if not i in af_to_remove]\n",
    "af_to_remove = list(set(af_countries)- set(food_supply_africa.columns.values))\n",
    "print(\"List of countries for which no data is available: \"+ str(af_to_remove))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can proceed to load the **population data** and clean it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading datasets\n",
    "pop_male = pd.read_excel(path_dict['population_age_male'], sheet_name=\"ESTIMATES\")\n",
    "pop_female = pd.read_excel(path_dict['population_age_female'], sheet_name=\"ESTIMATES\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `clean_pop_df` takes care of all the required cleaning. What it does is removing all the unnecessary columns and retaining just the rows of the needed countries. Then it multiplies the value by 1000 (Since the population data is per 1000 people, all entries are multiplied by the same factor (1000) to return to the real value)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_male_africa = clean_pop_df(pop_male, af_countries)\n",
    "pop_female_africa = clean_pop_df(pop_female, af_countries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**World Population Database** (United Nation) is now loaded and cleaned. This preprocessing is necessary in order to sort things out for next more complex steps.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the final version of male population data grouped by age:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>0-4</th>\n",
       "      <th>5-9</th>\n",
       "      <th>10-14</th>\n",
       "      <th>15-19</th>\n",
       "      <th>20-24</th>\n",
       "      <th>25-29</th>\n",
       "      <th>30-34</th>\n",
       "      <th>35-39</th>\n",
       "      <th>...</th>\n",
       "      <th>55-59</th>\n",
       "      <th>60-64</th>\n",
       "      <th>65-69</th>\n",
       "      <th>70-74</th>\n",
       "      <th>75-79</th>\n",
       "      <th>80-84</th>\n",
       "      <th>85-89</th>\n",
       "      <th>90-94</th>\n",
       "      <th>95-99</th>\n",
       "      <th>100+</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1950</td>\n",
       "      <td>6128</td>\n",
       "      <td>4593</td>\n",
       "      <td>3828</td>\n",
       "      <td>3247</td>\n",
       "      <td>2727</td>\n",
       "      <td>2266</td>\n",
       "      <td>1873</td>\n",
       "      <td>1538</td>\n",
       "      <td>...</td>\n",
       "      <td>589</td>\n",
       "      <td>422</td>\n",
       "      <td>277</td>\n",
       "      <td>160</td>\n",
       "      <td>74</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1955</td>\n",
       "      <td>5556</td>\n",
       "      <td>5527</td>\n",
       "      <td>4487</td>\n",
       "      <td>3773</td>\n",
       "      <td>3168</td>\n",
       "      <td>2639</td>\n",
       "      <td>2186</td>\n",
       "      <td>1800</td>\n",
       "      <td>...</td>\n",
       "      <td>696</td>\n",
       "      <td>506</td>\n",
       "      <td>337</td>\n",
       "      <td>198</td>\n",
       "      <td>94</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1960</td>\n",
       "      <td>7201</td>\n",
       "      <td>5360</td>\n",
       "      <td>5668</td>\n",
       "      <td>4640</td>\n",
       "      <td>3867</td>\n",
       "      <td>3218</td>\n",
       "      <td>2672</td>\n",
       "      <td>2203</td>\n",
       "      <td>...</td>\n",
       "      <td>868</td>\n",
       "      <td>631</td>\n",
       "      <td>428</td>\n",
       "      <td>256</td>\n",
       "      <td>126</td>\n",
       "      <td>46</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1965</td>\n",
       "      <td>10888</td>\n",
       "      <td>7814</td>\n",
       "      <td>6324</td>\n",
       "      <td>6545</td>\n",
       "      <td>5316</td>\n",
       "      <td>4389</td>\n",
       "      <td>3642</td>\n",
       "      <td>3006</td>\n",
       "      <td>...</td>\n",
       "      <td>1191</td>\n",
       "      <td>872</td>\n",
       "      <td>591</td>\n",
       "      <td>358</td>\n",
       "      <td>179</td>\n",
       "      <td>71</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1970</td>\n",
       "      <td>15553</td>\n",
       "      <td>11828</td>\n",
       "      <td>9094</td>\n",
       "      <td>7514</td>\n",
       "      <td>7519</td>\n",
       "      <td>6072</td>\n",
       "      <td>4994</td>\n",
       "      <td>4120</td>\n",
       "      <td>...</td>\n",
       "      <td>1646</td>\n",
       "      <td>1208</td>\n",
       "      <td>826</td>\n",
       "      <td>503</td>\n",
       "      <td>259</td>\n",
       "      <td>102</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      country  year    0-4    5-9 10-14 15-19 20-24 25-29 30-34 35-39  ...  \\\n",
       "420  Djibouti  1950   6128   4593  3828  3247  2727  2266  1873  1538  ...   \n",
       "421  Djibouti  1955   5556   5527  4487  3773  3168  2639  2186  1800  ...   \n",
       "422  Djibouti  1960   7201   5360  5668  4640  3867  3218  2672  2203  ...   \n",
       "423  Djibouti  1965  10888   7814  6324  6545  5316  4389  3642  3006  ...   \n",
       "424  Djibouti  1970  15553  11828  9094  7514  7519  6072  4994  4120  ...   \n",
       "\n",
       "    55-59 60-64 65-69 70-74 75-79 80-84 85-89 90-94 95-99 100+  \n",
       "420   589   422   277   160    74    24     7     0     0    0  \n",
       "421   696   506   337   198    94    33     7     0     0    0  \n",
       "422   868   631   428   256   126    46    11     0     0    0  \n",
       "423  1191   872   591   358   179    71    16     2     0    0  \n",
       "424  1646  1208   826   503   259   102    24     3     0    0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_male_africa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2.2) Interpolating the data on African population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this context, the population dataframe for males **pop_male** and for females **pop_female** contains measurements of population censi, for years from 1950 to 2020 with a frequency of **5 years**. Next, data is interpolated in order to obtain values for intermediate years. \n",
    "\n",
    "For our interpolation method to work, we need to know if the population evolution in these intervals of 5 years is linear. In order to do so, we need to visualize the growth of the population for a group of countries (plotting all of them would be confusing). So we select **3 countries** randomly and plot a simple animation of the growth over time.  \n",
    "*Note*: the code below is used to generate the HTML animation. If it doesn't load **click on [this link](https://manuleo.github.io/mADAm_files/african_pop_growth.html)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1200\"\n",
       "            height=\"600\"\n",
       "            src=\"https://manuleo.github.io/mADAm_files/african_pop_growth.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x14916e710>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(src='https://manuleo.github.io/mADAm_files/african_pop_growth.html', width = 1200, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Selecting countries\n",
    "\n",
    "# countryrand = []\n",
    "# n_countries = 3\n",
    "# for i in range(0, n_countries):\n",
    "#     countryrand.append(random.choice(pop_male_africa.country.drop_duplicates().values))\n",
    "\n",
    "# fig = plt.figure()\n",
    "# animator = animation.FuncAnimation(fig, timeline_country_gender, frames=(range(1950, 2025, 5)),\\\n",
    "#                                    fargs = (pop_male_africa, pop_female_africa, \"30-34\", countryrand), repeat=False)\n",
    "# #HTML(animator.to_jshtml())\n",
    "\n",
    "# with open(path_dict['african_pop_growth'], \"w\") as f:\n",
    "#     print(animator.to_html5_video(), file=f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The animation shows an almost linear growth for the 3 countries considered (with their respective scale), so we can continue with the interpolation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can apply our function `interpolate_years`, a simple linear interpolation, in order to obtain a frequency of **1 year**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_male_africa = interpolate_years(pop_male_africa, 1950, 2020)\n",
    "pop_female_africa = interpolate_years(pop_female_africa, 1950, 2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how the new dataframes for males and females population look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>0-4</th>\n",
       "      <th>5-9</th>\n",
       "      <th>10-14</th>\n",
       "      <th>15-19</th>\n",
       "      <th>20-24</th>\n",
       "      <th>25-29</th>\n",
       "      <th>30-34</th>\n",
       "      <th>35-39</th>\n",
       "      <th>...</th>\n",
       "      <th>55-59</th>\n",
       "      <th>60-64</th>\n",
       "      <th>65-69</th>\n",
       "      <th>70-74</th>\n",
       "      <th>75-79</th>\n",
       "      <th>80-84</th>\n",
       "      <th>85-89</th>\n",
       "      <th>90-94</th>\n",
       "      <th>95-99</th>\n",
       "      <th>100+</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1950</td>\n",
       "      <td>6128.0</td>\n",
       "      <td>4593.0</td>\n",
       "      <td>3828.0</td>\n",
       "      <td>3247.0</td>\n",
       "      <td>2727.0</td>\n",
       "      <td>2266.0</td>\n",
       "      <td>1873.0</td>\n",
       "      <td>1538.0</td>\n",
       "      <td>...</td>\n",
       "      <td>589.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1951</td>\n",
       "      <td>6013.6</td>\n",
       "      <td>4779.8</td>\n",
       "      <td>3959.8</td>\n",
       "      <td>3352.2</td>\n",
       "      <td>2815.2</td>\n",
       "      <td>2340.6</td>\n",
       "      <td>1935.6</td>\n",
       "      <td>1590.4</td>\n",
       "      <td>...</td>\n",
       "      <td>610.4</td>\n",
       "      <td>438.8</td>\n",
       "      <td>289.0</td>\n",
       "      <td>167.6</td>\n",
       "      <td>78.0</td>\n",
       "      <td>25.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1952</td>\n",
       "      <td>5899.2</td>\n",
       "      <td>4966.6</td>\n",
       "      <td>4091.6</td>\n",
       "      <td>3457.4</td>\n",
       "      <td>2903.4</td>\n",
       "      <td>2415.2</td>\n",
       "      <td>1998.2</td>\n",
       "      <td>1642.8</td>\n",
       "      <td>...</td>\n",
       "      <td>631.8</td>\n",
       "      <td>455.6</td>\n",
       "      <td>301.0</td>\n",
       "      <td>175.2</td>\n",
       "      <td>82.0</td>\n",
       "      <td>27.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1953</td>\n",
       "      <td>5784.8</td>\n",
       "      <td>5153.4</td>\n",
       "      <td>4223.4</td>\n",
       "      <td>3562.6</td>\n",
       "      <td>2991.6</td>\n",
       "      <td>2489.8</td>\n",
       "      <td>2060.8</td>\n",
       "      <td>1695.2</td>\n",
       "      <td>...</td>\n",
       "      <td>653.2</td>\n",
       "      <td>472.4</td>\n",
       "      <td>313.0</td>\n",
       "      <td>182.8</td>\n",
       "      <td>86.0</td>\n",
       "      <td>29.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1954</td>\n",
       "      <td>5670.4</td>\n",
       "      <td>5340.2</td>\n",
       "      <td>4355.2</td>\n",
       "      <td>3667.8</td>\n",
       "      <td>3079.8</td>\n",
       "      <td>2564.4</td>\n",
       "      <td>2123.4</td>\n",
       "      <td>1747.6</td>\n",
       "      <td>...</td>\n",
       "      <td>674.6</td>\n",
       "      <td>489.2</td>\n",
       "      <td>325.0</td>\n",
       "      <td>190.4</td>\n",
       "      <td>90.0</td>\n",
       "      <td>31.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  year     0-4     5-9   10-14   15-19   20-24   25-29   30-34  \\\n",
       "0  Djibouti  1950  6128.0  4593.0  3828.0  3247.0  2727.0  2266.0  1873.0   \n",
       "1  Djibouti  1951  6013.6  4779.8  3959.8  3352.2  2815.2  2340.6  1935.6   \n",
       "2  Djibouti  1952  5899.2  4966.6  4091.6  3457.4  2903.4  2415.2  1998.2   \n",
       "3  Djibouti  1953  5784.8  5153.4  4223.4  3562.6  2991.6  2489.8  2060.8   \n",
       "4  Djibouti  1954  5670.4  5340.2  4355.2  3667.8  3079.8  2564.4  2123.4   \n",
       "\n",
       "    35-39  ...  55-59  60-64  65-69  70-74  75-79  80-84  85-89  90-94  95-99  \\\n",
       "0  1538.0  ...  589.0  422.0  277.0  160.0   74.0   24.0    7.0    0.0    0.0   \n",
       "1  1590.4  ...  610.4  438.8  289.0  167.6   78.0   25.8    7.0    0.0    0.0   \n",
       "2  1642.8  ...  631.8  455.6  301.0  175.2   82.0   27.6    7.0    0.0    0.0   \n",
       "3  1695.2  ...  653.2  472.4  313.0  182.8   86.0   29.4    7.0    0.0    0.0   \n",
       "4  1747.6  ...  674.6  489.2  325.0  190.4   90.0   31.2    7.0    0.0    0.0   \n",
       "\n",
       "   100+  \n",
       "0   0.0  \n",
       "1   0.0  \n",
       "2   0.0  \n",
       "3   0.0  \n",
       "4   0.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_male_africa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>0-4</th>\n",
       "      <th>5-9</th>\n",
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       "      <th>...</th>\n",
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       "      <th>65-69</th>\n",
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       "      <th>100+</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1950</td>\n",
       "      <td>6065.0</td>\n",
       "      <td>4583.0</td>\n",
       "      <td>3819.0</td>\n",
       "      <td>3238.0</td>\n",
       "      <td>2736.0</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>1574.0</td>\n",
       "      <td>...</td>\n",
       "      <td>655.0</td>\n",
       "      <td>487.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1951</td>\n",
       "      <td>5948.6</td>\n",
       "      <td>4764.2</td>\n",
       "      <td>3949.6</td>\n",
       "      <td>3342.8</td>\n",
       "      <td>2824.4</td>\n",
       "      <td>2369.8</td>\n",
       "      <td>1973.8</td>\n",
       "      <td>1627.2</td>\n",
       "      <td>...</td>\n",
       "      <td>679.0</td>\n",
       "      <td>505.8</td>\n",
       "      <td>347.6</td>\n",
       "      <td>211.2</td>\n",
       "      <td>105.4</td>\n",
       "      <td>37.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1952</td>\n",
       "      <td>5832.2</td>\n",
       "      <td>4945.4</td>\n",
       "      <td>4080.2</td>\n",
       "      <td>3447.6</td>\n",
       "      <td>2912.8</td>\n",
       "      <td>2444.6</td>\n",
       "      <td>2036.6</td>\n",
       "      <td>1680.4</td>\n",
       "      <td>...</td>\n",
       "      <td>703.0</td>\n",
       "      <td>524.6</td>\n",
       "      <td>361.2</td>\n",
       "      <td>220.4</td>\n",
       "      <td>110.8</td>\n",
       "      <td>40.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1953</td>\n",
       "      <td>5715.8</td>\n",
       "      <td>5126.6</td>\n",
       "      <td>4210.8</td>\n",
       "      <td>3552.4</td>\n",
       "      <td>3001.2</td>\n",
       "      <td>2519.4</td>\n",
       "      <td>2099.4</td>\n",
       "      <td>1733.6</td>\n",
       "      <td>...</td>\n",
       "      <td>727.0</td>\n",
       "      <td>543.4</td>\n",
       "      <td>374.8</td>\n",
       "      <td>229.6</td>\n",
       "      <td>116.2</td>\n",
       "      <td>43.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1954</td>\n",
       "      <td>5599.4</td>\n",
       "      <td>5307.8</td>\n",
       "      <td>4341.4</td>\n",
       "      <td>3657.2</td>\n",
       "      <td>3089.6</td>\n",
       "      <td>2594.2</td>\n",
       "      <td>2162.2</td>\n",
       "      <td>1786.8</td>\n",
       "      <td>...</td>\n",
       "      <td>751.0</td>\n",
       "      <td>562.2</td>\n",
       "      <td>388.4</td>\n",
       "      <td>238.8</td>\n",
       "      <td>121.6</td>\n",
       "      <td>46.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  year     0-4     5-9   10-14   15-19   20-24   25-29   30-34  \\\n",
       "0  Djibouti  1950  6065.0  4583.0  3819.0  3238.0  2736.0  2295.0  1911.0   \n",
       "1  Djibouti  1951  5948.6  4764.2  3949.6  3342.8  2824.4  2369.8  1973.8   \n",
       "2  Djibouti  1952  5832.2  4945.4  4080.2  3447.6  2912.8  2444.6  2036.6   \n",
       "3  Djibouti  1953  5715.8  5126.6  4210.8  3552.4  3001.2  2519.4  2099.4   \n",
       "4  Djibouti  1954  5599.4  5307.8  4341.4  3657.2  3089.6  2594.2  2162.2   \n",
       "\n",
       "    35-39  ...  55-59  60-64  65-69  70-74  75-79  80-84  85-89  90-94  95-99  \\\n",
       "0  1574.0  ...  655.0  487.0  334.0  202.0  100.0   34.0   11.0    2.0    0.0   \n",
       "1  1627.2  ...  679.0  505.8  347.6  211.2  105.4   37.0   11.0    2.0    0.0   \n",
       "2  1680.4  ...  703.0  524.6  361.2  220.4  110.8   40.0   11.0    2.0    0.0   \n",
       "3  1733.6  ...  727.0  543.4  374.8  229.6  116.2   43.0   11.0    2.0    0.0   \n",
       "4  1786.8  ...  751.0  562.2  388.4  238.8  121.6   46.0   11.0    2.0    0.0   \n",
       "\n",
       "   100+  \n",
       "0   0.0  \n",
       "1   0.0  \n",
       "2   0.0  \n",
       "3   0.0  \n",
       "4   0.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_female_africa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2.3) Computing the total African population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we will compute the total population per year. This new dataframe **pop_tot** will be useful for the next section of our analysis. `obtain_total_pop` does just this, combining the population in *pop_male_africa* and *pop_female_africa*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_tot_africa = obtain_total_pop(pop_male_africa, pop_female_africa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the next analysis we will need to match this data with the `food_balance_africa`. We proceed to give to our population data the same shape as the other datasets by using the function `reshape_pop_dataframe`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_tot_africa = reshape_pop_dataframe(pop_tot_africa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>1950</th>\n",
       "      <th>1951</th>\n",
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       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
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       "      <th>Algeria</th>\n",
       "      <td>8872250.0</td>\n",
       "      <td>9052656.0</td>\n",
       "      <td>9233062.0</td>\n",
       "      <td>9413468.0</td>\n",
       "      <td>9593874.0</td>\n",
       "      <td>9774280.0</td>\n",
       "      <td>10030996.8</td>\n",
       "      <td>10287713.6</td>\n",
       "      <td>10544430.4</td>\n",
       "      <td>10801147.2</td>\n",
       "      <td>...</td>\n",
       "      <td>36727564.8</td>\n",
       "      <td>37477678.6</td>\n",
       "      <td>38227792.4</td>\n",
       "      <td>38977906.2</td>\n",
       "      <td>39728020.0</td>\n",
       "      <td>40552624.6</td>\n",
       "      <td>41377229.2</td>\n",
       "      <td>42201833.8</td>\n",
       "      <td>43026438.4</td>\n",
       "      <td>43851043.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>4548021.0</td>\n",
       "      <td>4647067.0</td>\n",
       "      <td>4746113.0</td>\n",
       "      <td>4845159.0</td>\n",
       "      <td>4944205.0</td>\n",
       "      <td>5043251.0</td>\n",
       "      <td>5125588.4</td>\n",
       "      <td>5207925.8</td>\n",
       "      <td>5290263.2</td>\n",
       "      <td>5372600.6</td>\n",
       "      <td>...</td>\n",
       "      <td>24261873.6</td>\n",
       "      <td>25167500.2</td>\n",
       "      <td>26073126.8</td>\n",
       "      <td>26978753.4</td>\n",
       "      <td>27884380.0</td>\n",
       "      <td>28880757.6</td>\n",
       "      <td>29877135.2</td>\n",
       "      <td>30873512.8</td>\n",
       "      <td>31869890.4</td>\n",
       "      <td>32866268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benin</th>\n",
       "      <td>2255222.0</td>\n",
       "      <td>2264896.0</td>\n",
       "      <td>2274570.0</td>\n",
       "      <td>2284244.0</td>\n",
       "      <td>2293918.0</td>\n",
       "      <td>2303592.0</td>\n",
       "      <td>2329197.0</td>\n",
       "      <td>2354802.0</td>\n",
       "      <td>2380407.0</td>\n",
       "      <td>2406012.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9474595.6</td>\n",
       "      <td>9749937.2</td>\n",
       "      <td>10025278.8</td>\n",
       "      <td>10300620.4</td>\n",
       "      <td>10575962.0</td>\n",
       "      <td>10885409.2</td>\n",
       "      <td>11194856.4</td>\n",
       "      <td>11504303.6</td>\n",
       "      <td>11813750.8</td>\n",
       "      <td>12123198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>412541.0</td>\n",
       "      <td>422703.4</td>\n",
       "      <td>432865.8</td>\n",
       "      <td>443028.2</td>\n",
       "      <td>453190.6</td>\n",
       "      <td>463353.0</td>\n",
       "      <td>471229.0</td>\n",
       "      <td>479105.0</td>\n",
       "      <td>486981.0</td>\n",
       "      <td>494857.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2013828.0</td>\n",
       "      <td>2040550.0</td>\n",
       "      <td>2067272.0</td>\n",
       "      <td>2093994.0</td>\n",
       "      <td>2120716.0</td>\n",
       "      <td>2166897.8</td>\n",
       "      <td>2213079.6</td>\n",
       "      <td>2259261.4</td>\n",
       "      <td>2305443.2</td>\n",
       "      <td>2351625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burkina Faso</th>\n",
       "      <td>4284455.0</td>\n",
       "      <td>4330995.0</td>\n",
       "      <td>4377535.0</td>\n",
       "      <td>4424075.0</td>\n",
       "      <td>4470615.0</td>\n",
       "      <td>4517155.0</td>\n",
       "      <td>4579581.8</td>\n",
       "      <td>4642008.6</td>\n",
       "      <td>4704435.4</td>\n",
       "      <td>4766862.2</td>\n",
       "      <td>...</td>\n",
       "      <td>16106292.0</td>\n",
       "      <td>16607373.0</td>\n",
       "      <td>17108454.0</td>\n",
       "      <td>17609535.0</td>\n",
       "      <td>18110616.0</td>\n",
       "      <td>18669148.4</td>\n",
       "      <td>19227680.8</td>\n",
       "      <td>19786213.2</td>\n",
       "      <td>20344745.6</td>\n",
       "      <td>20903278.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   1950       1951       1952       1953       1954  \\\n",
       "Country                                                               \n",
       "Algeria       8872250.0  9052656.0  9233062.0  9413468.0  9593874.0   \n",
       "Angola        4548021.0  4647067.0  4746113.0  4845159.0  4944205.0   \n",
       "Benin         2255222.0  2264896.0  2274570.0  2284244.0  2293918.0   \n",
       "Botswana       412541.0   422703.4   432865.8   443028.2   453190.6   \n",
       "Burkina Faso  4284455.0  4330995.0  4377535.0  4424075.0  4470615.0   \n",
       "\n",
       "                   1955        1956        1957        1958        1959  ...  \\\n",
       "Country                                                                  ...   \n",
       "Algeria       9774280.0  10030996.8  10287713.6  10544430.4  10801147.2  ...   \n",
       "Angola        5043251.0   5125588.4   5207925.8   5290263.2   5372600.6  ...   \n",
       "Benin         2303592.0   2329197.0   2354802.0   2380407.0   2406012.0  ...   \n",
       "Botswana       463353.0    471229.0    479105.0    486981.0    494857.0  ...   \n",
       "Burkina Faso  4517155.0   4579581.8   4642008.6   4704435.4   4766862.2  ...   \n",
       "\n",
       "                    2011        2012        2013        2014        2015  \\\n",
       "Country                                                                    \n",
       "Algeria       36727564.8  37477678.6  38227792.4  38977906.2  39728020.0   \n",
       "Angola        24261873.6  25167500.2  26073126.8  26978753.4  27884380.0   \n",
       "Benin          9474595.6   9749937.2  10025278.8  10300620.4  10575962.0   \n",
       "Botswana       2013828.0   2040550.0   2067272.0   2093994.0   2120716.0   \n",
       "Burkina Faso  16106292.0  16607373.0  17108454.0  17609535.0  18110616.0   \n",
       "\n",
       "                    2016        2017        2018        2019        2020  \n",
       "Country                                                                   \n",
       "Algeria       40552624.6  41377229.2  42201833.8  43026438.4  43851043.0  \n",
       "Angola        28880757.6  29877135.2  30873512.8  31869890.4  32866268.0  \n",
       "Benin         10885409.2  11194856.4  11504303.6  11813750.8  12123198.0  \n",
       "Botswana       2166897.8   2213079.6   2259261.4   2305443.2   2351625.0  \n",
       "Burkina Faso  18669148.4  19227680.8  19786213.2  20344745.6  20903278.0  \n",
       "\n",
       "[5 rows x 71 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_tot_africa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3) Estimantion of ideal human food demand in Africa "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we multiply each column of the population data for each matching `age_group` in the calories table (which is squeezed to enable multiplication, similar to transposing rows/columns of the dataset).  All of this is done in `get_calories_need`  \n",
    "We obtain two datasets: `male_cal_need_africa` and `female_cal_need_africa` reporting total calories needed for **each country in each year per age group per gender**. \n",
    "The unit here is **kcal/day**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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       "    country  year           0-4        5-9       10-14       15-19      20-24  \\\n",
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     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_cal_need_africa = get_calories_need(pop_male_africa, male_calories)\n",
    "male_cal_need_africa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
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       "    country  year           0-4        5-9      10-14      15-19      20-24  \\\n",
       "0  Djibouti  1950  7.682333e+06  7882760.0  8249040.0  7771200.0  6566400.0   \n",
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       "4  Djibouti  1954  7.092573e+06  9129416.0  9377424.0  8777280.0  7415040.0   \n",
       "\n",
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       "[5 rows x 23 columns]"
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     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total calories female\n",
    "female_cal_need_africa = get_calories_need(pop_female_africa, female_calories)\n",
    "female_cal_need_africa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have the calories needed for both genders, we can aggregate the total caloric need of african countries into `total_cal_need_africa`. The function `obtain_total_cal_need` does just this, returning a dataframe with the calories needed for all the population of a country in a year, in the unit **kcal/year**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_cal_need_africa = obtain_total_cal_need(male_cal_need_africa, female_cal_need_africa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the total calories dataframe **total_cal**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2413</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.679755e+14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.637743e+14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2411</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.595730e+14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2410</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.553718e+14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2409</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.511706e+14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country  year      Calories\n",
       "2413  Nigeria  2020  1.679755e+14\n",
       "2412  Nigeria  2019  1.637743e+14\n",
       "2411  Nigeria  2018  1.595730e+14\n",
       "2410  Nigeria  2017  1.553718e+14\n",
       "2409  Nigeria  2016  1.511706e+14"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_cal_need_africa.sort_values(by=\"Calories\", ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the sake of consistency, we will now reshape our dataframe `total_cal` into a new one `total_cal_final` according to the same schema seen for `food_supply_africa`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_cal_need_africa = reshape_calories_df(total_cal_need_africa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drawing a sample of the final shaped dataframe total calories `total_cal_need_africa`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>1950</th>\n",
       "      <th>1951</th>\n",
       "      <th>1952</th>\n",
       "      <th>1953</th>\n",
       "      <th>1954</th>\n",
       "      <th>1955</th>\n",
       "      <th>1956</th>\n",
       "      <th>1957</th>\n",
       "      <th>1958</th>\n",
       "      <th>1959</th>\n",
       "      <th>...</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>7.290425e+12</td>\n",
       "      <td>7.412354e+12</td>\n",
       "      <td>7.534284e+12</td>\n",
       "      <td>7.656213e+12</td>\n",
       "      <td>7.778143e+12</td>\n",
       "      <td>7.900072e+12</td>\n",
       "      <td>8.092341e+12</td>\n",
       "      <td>8.284610e+12</td>\n",
       "      <td>8.476879e+12</td>\n",
       "      <td>8.669149e+12</td>\n",
       "      <td>...</td>\n",
       "      <td>3.150748e+13</td>\n",
       "      <td>3.205546e+13</td>\n",
       "      <td>3.260345e+13</td>\n",
       "      <td>3.315144e+13</td>\n",
       "      <td>3.369942e+13</td>\n",
       "      <td>3.433776e+13</td>\n",
       "      <td>3.497610e+13</td>\n",
       "      <td>3.561444e+13</td>\n",
       "      <td>3.625278e+13</td>\n",
       "      <td>3.689112e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>3.721975e+12</td>\n",
       "      <td>3.797306e+12</td>\n",
       "      <td>3.872637e+12</td>\n",
       "      <td>3.947968e+12</td>\n",
       "      <td>4.023299e+12</td>\n",
       "      <td>4.098630e+12</td>\n",
       "      <td>4.159526e+12</td>\n",
       "      <td>4.220422e+12</td>\n",
       "      <td>4.281318e+12</td>\n",
       "      <td>4.342214e+12</td>\n",
       "      <td>...</td>\n",
       "      <td>1.941009e+13</td>\n",
       "      <td>2.013902e+13</td>\n",
       "      <td>2.086796e+13</td>\n",
       "      <td>2.159689e+13</td>\n",
       "      <td>2.232582e+13</td>\n",
       "      <td>2.315305e+13</td>\n",
       "      <td>2.398027e+13</td>\n",
       "      <td>2.480750e+13</td>\n",
       "      <td>2.563473e+13</td>\n",
       "      <td>2.646195e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benin</th>\n",
       "      <td>1.851458e+12</td>\n",
       "      <td>1.858390e+12</td>\n",
       "      <td>1.865323e+12</td>\n",
       "      <td>1.872255e+12</td>\n",
       "      <td>1.879187e+12</td>\n",
       "      <td>1.886119e+12</td>\n",
       "      <td>1.905149e+12</td>\n",
       "      <td>1.924180e+12</td>\n",
       "      <td>1.943210e+12</td>\n",
       "      <td>1.962241e+12</td>\n",
       "      <td>...</td>\n",
       "      <td>7.691320e+12</td>\n",
       "      <td>7.921509e+12</td>\n",
       "      <td>8.151697e+12</td>\n",
       "      <td>8.381886e+12</td>\n",
       "      <td>8.612074e+12</td>\n",
       "      <td>8.874543e+12</td>\n",
       "      <td>9.137011e+12</td>\n",
       "      <td>9.399479e+12</td>\n",
       "      <td>9.661947e+12</td>\n",
       "      <td>9.924415e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>3.377080e+11</td>\n",
       "      <td>3.450761e+11</td>\n",
       "      <td>3.524442e+11</td>\n",
       "      <td>3.598122e+11</td>\n",
       "      <td>3.671803e+11</td>\n",
       "      <td>3.745484e+11</td>\n",
       "      <td>3.799093e+11</td>\n",
       "      <td>3.852703e+11</td>\n",
       "      <td>3.906313e+11</td>\n",
       "      <td>3.959923e+11</td>\n",
       "      <td>...</td>\n",
       "      <td>1.688976e+12</td>\n",
       "      <td>1.709664e+12</td>\n",
       "      <td>1.730351e+12</td>\n",
       "      <td>1.751039e+12</td>\n",
       "      <td>1.771726e+12</td>\n",
       "      <td>1.812568e+12</td>\n",
       "      <td>1.853409e+12</td>\n",
       "      <td>1.894250e+12</td>\n",
       "      <td>1.935091e+12</td>\n",
       "      <td>1.975932e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burkina Faso</th>\n",
       "      <td>3.538834e+12</td>\n",
       "      <td>3.574675e+12</td>\n",
       "      <td>3.610516e+12</td>\n",
       "      <td>3.646357e+12</td>\n",
       "      <td>3.682198e+12</td>\n",
       "      <td>3.718039e+12</td>\n",
       "      <td>3.765608e+12</td>\n",
       "      <td>3.813178e+12</td>\n",
       "      <td>3.860748e+12</td>\n",
       "      <td>3.908317e+12</td>\n",
       "      <td>...</td>\n",
       "      <td>1.296491e+13</td>\n",
       "      <td>1.338135e+13</td>\n",
       "      <td>1.379779e+13</td>\n",
       "      <td>1.421422e+13</td>\n",
       "      <td>1.463066e+13</td>\n",
       "      <td>1.510462e+13</td>\n",
       "      <td>1.557857e+13</td>\n",
       "      <td>1.605253e+13</td>\n",
       "      <td>1.652648e+13</td>\n",
       "      <td>1.700044e+13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      1950          1951          1952          1953  \\\n",
       "Country                                                                \n",
       "Algeria       7.290425e+12  7.412354e+12  7.534284e+12  7.656213e+12   \n",
       "Angola        3.721975e+12  3.797306e+12  3.872637e+12  3.947968e+12   \n",
       "Benin         1.851458e+12  1.858390e+12  1.865323e+12  1.872255e+12   \n",
       "Botswana      3.377080e+11  3.450761e+11  3.524442e+11  3.598122e+11   \n",
       "Burkina Faso  3.538834e+12  3.574675e+12  3.610516e+12  3.646357e+12   \n",
       "\n",
       "                      1954          1955          1956          1957  \\\n",
       "Country                                                                \n",
       "Algeria       7.778143e+12  7.900072e+12  8.092341e+12  8.284610e+12   \n",
       "Angola        4.023299e+12  4.098630e+12  4.159526e+12  4.220422e+12   \n",
       "Benin         1.879187e+12  1.886119e+12  1.905149e+12  1.924180e+12   \n",
       "Botswana      3.671803e+11  3.745484e+11  3.799093e+11  3.852703e+11   \n",
       "Burkina Faso  3.682198e+12  3.718039e+12  3.765608e+12  3.813178e+12   \n",
       "\n",
       "                      1958          1959  ...          2011          2012  \\\n",
       "Country                                   ...                               \n",
       "Algeria       8.476879e+12  8.669149e+12  ...  3.150748e+13  3.205546e+13   \n",
       "Angola        4.281318e+12  4.342214e+12  ...  1.941009e+13  2.013902e+13   \n",
       "Benin         1.943210e+12  1.962241e+12  ...  7.691320e+12  7.921509e+12   \n",
       "Botswana      3.906313e+11  3.959923e+11  ...  1.688976e+12  1.709664e+12   \n",
       "Burkina Faso  3.860748e+12  3.908317e+12  ...  1.296491e+13  1.338135e+13   \n",
       "\n",
       "                      2013          2014          2015          2016  \\\n",
       "Country                                                                \n",
       "Algeria       3.260345e+13  3.315144e+13  3.369942e+13  3.433776e+13   \n",
       "Angola        2.086796e+13  2.159689e+13  2.232582e+13  2.315305e+13   \n",
       "Benin         8.151697e+12  8.381886e+12  8.612074e+12  8.874543e+12   \n",
       "Botswana      1.730351e+12  1.751039e+12  1.771726e+12  1.812568e+12   \n",
       "Burkina Faso  1.379779e+13  1.421422e+13  1.463066e+13  1.510462e+13   \n",
       "\n",
       "                      2017          2018          2019          2020  \n",
       "Country                                                               \n",
       "Algeria       3.497610e+13  3.561444e+13  3.625278e+13  3.689112e+13  \n",
       "Angola        2.398027e+13  2.480750e+13  2.563473e+13  2.646195e+13  \n",
       "Benin         9.137011e+12  9.399479e+12  9.661947e+12  9.924415e+12  \n",
       "Botswana      1.853409e+12  1.894250e+12  1.935091e+12  1.975932e+12  \n",
       "Burkina Faso  1.557857e+13  1.605253e+13  1.652648e+13  1.700044e+13  \n",
       "\n",
       "[5 rows x 71 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_cal_need_africa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go on with a interactive visualization of the data in order to understand the trend over countries. The following map is based on data for **2020**.  \n",
    "However, it's important also to understand what are the possible changing over time, so we plot the same map over the range from 1970 to 2020 (step of 10 years).  \n",
    "**If the maps for 2020 doesn't show click [here](https://manuleo.github.io/mADAm_files/africa_est_kcal2020.html)**  \n",
    "Link to the other maps:\n",
    "- [1970](https://manuleo.github.io/mADAm_files/africa_est_kcal1970.html)\n",
    "- [1980](https://manuleo.github.io/mADAm_files/africa_est_kcal1980.html)\n",
    "- [1990](https://manuleo.github.io/mADAm_files/africa_est_kcal1990.html)\n",
    "- [2000](https://manuleo.github.io/mADAm_files/africa_est_kcal2000.html)\n",
    "- [2010](https://manuleo.github.io/mADAm_files/africa_est_kcal2010.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
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    color_map_579596c93a5b4999822535484c619679.x = d3.scale.linear()
              .domain([8.502273548666668, 1679.7552061213335])
              .range([0, 400]);

    color_map_579596c93a5b4999822535484c619679.legend = L.control({position: 'topright'});
    color_map_579596c93a5b4999822535484c619679.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
    color_map_579596c93a5b4999822535484c619679.legend.addTo(map_2918e025fdfc4bb18e03829848329eaa);

    color_map_579596c93a5b4999822535484c619679.xAxis = d3.svg.axis()
        .scale(color_map_579596c93a5b4999822535484c619679.x)
        .orient("top")
        .tickSize(1)
        .tickValues([8.502273548666668, 287.04442897744445, 565.5865844062223, 844.1287398350001, 1122.6708952637778, 1401.2130506925555, 1679.7552061213335]);

    color_map_579596c93a5b4999822535484c619679.svg = d3.select(".legend.leaflet-control").append("svg")
        .attr("id", 'legend')
        .attr("width", 450)
        .attr("height", 40);

    color_map_579596c93a5b4999822535484c619679.g = color_map_579596c93a5b4999822535484c619679.svg.append("g")
        .attr("class", "key")
        .attr("transform", "translate(25,16)");

    color_map_579596c93a5b4999822535484c619679.g.selectAll("rect")
        .data(color_map_579596c93a5b4999822535484c619679.color.range().map(function(d, i) {
          return {
            x0: i ? color_map_579596c93a5b4999822535484c619679.x(color_map_579596c93a5b4999822535484c619679.color.domain()[i - 1]) : color_map_579596c93a5b4999822535484c619679.x.range()[0],
            x1: i < color_map_579596c93a5b4999822535484c619679.color.domain().length ? color_map_579596c93a5b4999822535484c619679.x(color_map_579596c93a5b4999822535484c619679.color.domain()[i]) : color_map_579596c93a5b4999822535484c619679.x.range()[1],
            z: d
          };
        }))
      .enter().append("rect")
        .attr("height", 10)
        .attr("x", function(d) { return d.x0; })
        .attr("width", function(d) { return d.x1 - d.x0; })
        .style("fill", function(d) { return d.z; });

    color_map_579596c93a5b4999822535484c619679.g.call(color_map_579596c93a5b4999822535484c619679.xAxis).append("text")
        .attr("class", "caption")
        .attr("y", 21)
        .text('Estimation of kcal/year [10^11 kcal/year]');
    
        function geo_json_0c24bbcbbed34770aa69a2892da6385d_styler(feature) {
            switch(feature.id) {
                default:
                    return {"color": "#000000", "fillColor": "#ffffff", "fillOpacity": 0.1, "weight": 0.1};
            }
        }
        function geo_json_0c24bbcbbed34770aa69a2892da6385d_highlighter(feature) {
            switch(feature.id) {
                default:
                    return {"color": "#000000", "fillColor": "#000000", "fillOpacity": 0.5, "weight": 0.1};
            }
        }
        function geo_json_0c24bbcbbed34770aa69a2892da6385d_onEachFeature(feature, layer) {
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        geo_json_0c24bbcbbed34770aa69a2892da6385d.bindTooltip(
            function(layer){
            // Convert non-primitive to String.
            let handleObject = (feature)=>typeof(feature)=='object' ? JSON.stringify(feature) : feature;
            let fields = ["names", "val"];
            let aliases = ["Country: ", "Estimation of kcal/year [10^11 kcal/year]"];
            return '<table style="background-color: white; color: #333333; font-family: arial; font-size: 12px; padding: 10px;">' +
            String(
                fields.map(
                columnname=>
                    `<tr style="text-align: left;">
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                    <td style="padding: 4px;">${handleObject(layer.feature.properties[columnname])
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                base_layers : {
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                layer_control_03f7a5c5e10f4824b77789c3744b22a5.overlays,
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x14ca51310>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code used just for generating the map\n",
    "for year in range(1970,2030,10):\n",
    "    legend_name = \"Estimation of kcal/year [10^11 kcal/year]\"\n",
    "    africa_kcal_est_map = plot_map(total_cal_need_africa.divide(10**11), path_dict['geoworld_json'], \\\n",
    "    african_country_kv, year, \"Greens\", legend_name, legend_name, path_dict['african_estimation_kcal'] + str(year) + \".html\")\n",
    "africa_kcal_est_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://manuleo.github.io/mADAm_files/africa_est_kcal2020.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
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      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x126ce7850>"
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     "execution_count": 58,
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   "source": [
    "IFrame(src='https://manuleo.github.io/mADAm_files/africa_est_kcal2020.html', width = 800, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_map_data(geojson_africa, african_country_kv, total_cal_need_africa.divide(10**11).T, \"docs/json/africa_need/africa_need_{}.geojson\", \"docs/json/africa_need/africa_need_ticks.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By looking at the **scale change** over the years, it's possible to note how the needs of the African population  have constantly grown (connected to the increase of population too). This is not reflected in the African food supply, that doesn't increase at the same rate as the population."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Which countries are in food deficit?\n",
    "Next, an interesting comparison is introduced between the two dataframes we have obtained in the fist two parts of our analysis. More specifically, the analysis will take into account the total population dataframe `pop_tot_africa` and the `food_supply_africa`. With regard to the FAO Dataframe of food supply, we will need to transform the unit in **kcal/year** in order to compare results appropriately."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `obtain_difference` takes into account our dataframes to compute which countris have enough caloric food supply to actually meet their needs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>1961</th>\n",
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       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>-573.589919</td>\n",
       "      <td>-620.801820</td>\n",
       "      <td>-659.153014</td>\n",
       "      <td>-644.633318</td>\n",
       "      <td>-591.233516</td>\n",
       "      <td>-612.982117</td>\n",
       "      <td>-538.630181</td>\n",
       "      <td>-481.186136</td>\n",
       "      <td>-483.657494</td>\n",
       "      <td>-515.050972</td>\n",
       "      <td>...</td>\n",
       "      <td>866.671209</td>\n",
       "      <td>928.653501</td>\n",
       "      <td>959.361778</td>\n",
       "      <td>998.680650</td>\n",
       "      <td>1009.590805</td>\n",
       "      <td>1041.856405</td>\n",
       "      <td>1058.409442</td>\n",
       "      <td>1066.538096</td>\n",
       "      <td>1083.352529</td>\n",
       "      <td>1086.631007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>-413.161895</td>\n",
       "      <td>-391.881557</td>\n",
       "      <td>-357.607490</td>\n",
       "      <td>-348.339486</td>\n",
       "      <td>-333.077345</td>\n",
       "      <td>-320.591047</td>\n",
       "      <td>-290.100518</td>\n",
       "      <td>-355.605811</td>\n",
       "      <td>-266.106978</td>\n",
       "      <td>-247.604067</td>\n",
       "      <td>...</td>\n",
       "      <td>215.152631</td>\n",
       "      <td>191.672527</td>\n",
       "      <td>280.225775</td>\n",
       "      <td>186.029318</td>\n",
       "      <td>294.259093</td>\n",
       "      <td>203.038770</td>\n",
       "      <td>292.560777</td>\n",
       "      <td>210.272579</td>\n",
       "      <td>277.137533</td>\n",
       "      <td>214.003164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benin</th>\n",
       "      <td>-492.836248</td>\n",
       "      <td>-467.466793</td>\n",
       "      <td>-519.203353</td>\n",
       "      <td>-550.041004</td>\n",
       "      <td>-403.975119</td>\n",
       "      <td>-409.528255</td>\n",
       "      <td>-378.181242</td>\n",
       "      <td>-273.928090</td>\n",
       "      <td>-335.763279</td>\n",
       "      <td>-392.681714</td>\n",
       "      <td>...</td>\n",
       "      <td>373.935633</td>\n",
       "      <td>384.061159</td>\n",
       "      <td>391.289648</td>\n",
       "      <td>386.664138</td>\n",
       "      <td>392.847960</td>\n",
       "      <td>387.777835</td>\n",
       "      <td>390.545646</td>\n",
       "      <td>388.001273</td>\n",
       "      <td>387.055618</td>\n",
       "      <td>384.440686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>-208.233159</td>\n",
       "      <td>-272.356799</td>\n",
       "      <td>-206.603106</td>\n",
       "      <td>-174.964398</td>\n",
       "      <td>-168.433620</td>\n",
       "      <td>-225.305819</td>\n",
       "      <td>-161.230054</td>\n",
       "      <td>-118.202804</td>\n",
       "      <td>-156.220859</td>\n",
       "      <td>-133.281287</td>\n",
       "      <td>...</td>\n",
       "      <td>-24.779281</td>\n",
       "      <td>46.535339</td>\n",
       "      <td>32.790121</td>\n",
       "      <td>49.163878</td>\n",
       "      <td>88.124217</td>\n",
       "      <td>68.644236</td>\n",
       "      <td>103.269109</td>\n",
       "      <td>65.282088</td>\n",
       "      <td>99.284178</td>\n",
       "      <td>67.992986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burkina Faso</th>\n",
       "      <td>-915.448084</td>\n",
       "      <td>-930.741495</td>\n",
       "      <td>-784.109191</td>\n",
       "      <td>-678.548155</td>\n",
       "      <td>-660.055530</td>\n",
       "      <td>-668.265460</td>\n",
       "      <td>-638.569119</td>\n",
       "      <td>-620.961863</td>\n",
       "      <td>-616.439348</td>\n",
       "      <td>-671.997508</td>\n",
       "      <td>...</td>\n",
       "      <td>458.632137</td>\n",
       "      <td>499.472919</td>\n",
       "      <td>510.440182</td>\n",
       "      <td>517.817483</td>\n",
       "      <td>514.063968</td>\n",
       "      <td>518.852721</td>\n",
       "      <td>521.753299</td>\n",
       "      <td>516.341587</td>\n",
       "      <td>517.861824</td>\n",
       "      <td>517.405554</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    1961        1962        1963        1964        1965  \\\n",
       "Algeria      -573.589919 -620.801820 -659.153014 -644.633318 -591.233516   \n",
       "Angola       -413.161895 -391.881557 -357.607490 -348.339486 -333.077345   \n",
       "Benin        -492.836248 -467.466793 -519.203353 -550.041004 -403.975119   \n",
       "Botswana     -208.233159 -272.356799 -206.603106 -174.964398 -168.433620   \n",
       "Burkina Faso -915.448084 -930.741495 -784.109191 -678.548155 -660.055530   \n",
       "\n",
       "                    1966        1967        1968        1969        1970  ...  \\\n",
       "Algeria      -612.982117 -538.630181 -481.186136 -483.657494 -515.050972  ...   \n",
       "Angola       -320.591047 -290.100518 -355.605811 -266.106978 -247.604067  ...   \n",
       "Benin        -409.528255 -378.181242 -273.928090 -335.763279 -392.681714  ...   \n",
       "Botswana     -225.305819 -161.230054 -118.202804 -156.220859 -133.281287  ...   \n",
       "Burkina Faso -668.265460 -638.569119 -620.961863 -616.439348 -671.997508  ...   \n",
       "\n",
       "                    2011        2012        2013        2014         2015  \\\n",
       "Algeria       866.671209  928.653501  959.361778  998.680650  1009.590805   \n",
       "Angola        215.152631  191.672527  280.225775  186.029318   294.259093   \n",
       "Benin         373.935633  384.061159  391.289648  386.664138   392.847960   \n",
       "Botswana      -24.779281   46.535339   32.790121   49.163878    88.124217   \n",
       "Burkina Faso  458.632137  499.472919  510.440182  517.817483   514.063968   \n",
       "\n",
       "                     2016         2017         2018         2019         2020  \n",
       "Algeria       1041.856405  1058.409442  1066.538096  1083.352529  1086.631007  \n",
       "Angola         203.038770   292.560777   210.272579   277.137533   214.003164  \n",
       "Benin          387.777835   390.545646   388.001273   387.055618   384.440686  \n",
       "Botswana        68.644236   103.269109    65.282088    99.284178    67.992986  \n",
       "Burkina Faso   518.852721   521.753299   516.341587   517.861824   517.405554  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caloric_difference_africa = obtain_difference(pop_tot_africa, food_supply_africa, total_cal_need_africa)\n",
    "caloric_difference_africa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1) Visualizing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by doing a simple barplot of the deficit per persona/year in each country. As our main point of interest is the present, we will start with a graph showing next year sitution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "caloric_difference_africa_sorted = caloric_difference_africa[2020].sort_values()\n",
    "p = caloric_difference_africa_sorted.plot(kind='barh', color=(caloric_difference_africa_sorted > 0).map({True: 'g', False: 'red'}),alpha=0.75, rot=0);\n",
    "p.set_xlabel(\"deficit/surplus in the african countries [kcal/persona/day]\")\n",
    "p.set_ylabel(\"African countries\")\n",
    "plt.title('Estimation of net food availability in African countries in 2020' );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot already suggests that only by smart redistribution, Africa could sustain its own food demand. However, having the capabilities and know-how to efficiently set up a food aid operation is harder than it seems. European countries on the other hand have a lot more experience in this field, and they are expected to have an even higher amount of excess food, making it easier to provide for this whole operation. Therefore They will be considered to provide the difference in African countries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a better visualization, it is convenient to see the **evolution of the kcal demand** for all the years of interest. By using the `draw_demand_bar`, we wil plot the information for every year combined with an animation to move back and forth in time.  \n",
    "**_Note_: the code below is used to generate the HTML animation. If the animation doesn't work inside the notebook, click on [this link](https://manuleo.github.io/mADAm_files/african_cal_diff_animation.html)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # Code to generate the animation\n",
    "\n",
    "\n",
    "# fig = plt.figure()\n",
    "# animator = animation.FuncAnimation(fig, draw_demand_bar, frames=range(1961, 2021),\\\n",
    "#                                    fargs=(caloric_difference_africa, ),\n",
    "#                                       repeat=False);\n",
    "# #HTML(animator.to_jshtml())\n",
    "# with open(path_dict['african_demand_anim'], \"w\") as f:\n",
    "#     print(animator.to_html5_video(), file=f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1100\"\n",
       "            height=\"600\"\n",
       "            src=\"https://manuleo.github.io/mADAm_files/african_cal_diff_animation.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x14d840f50>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(src='https://manuleo.github.io/mADAm_files/african_cal_diff_animation.html', width = 1100, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the animation shows, the African situation **sensitevely improved** over the last 60 years, starting from a share of **75%** of starving countries in 1961 to \"just\" **21%** in 2020.  \n",
    "During the years the situation improved differently. The improvement was basic up to the nineties, than dramatically changed from 2000 to now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our data story we plotted the same animation in a slider manner, by using the below function for Africa's and Europe's surplus situation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_plotly_progress(dataframe, zone, title, width=800, height=800, mini=-1100, maxi=1500, size=20):\n",
    "    fig = go.Figure()\n",
    "\n",
    "    for year in range(1961, 2021):\n",
    "        x, y, colors = colors_and_order(dataframe[year])\n",
    "        fig.add_trace(\n",
    "                go.Bar(visible=False,\n",
    "                       width=0.85,\n",
    "                       orientation=\"h\",\n",
    "                       x=x,\n",
    "                       y=y,\n",
    "                       text=y,\n",
    "                       hovertemplate=\"%{x} kcal\",\n",
    "                       textposition='outside',\n",
    "                       textfont=dict(size=100),\n",
    "                       marker_color=colors,\n",
    "                       name=str(year)))\n",
    "\n",
    "    fig.data[-1].visible=True\n",
    "\n",
    "    steps = []\n",
    "    for i in range(len(fig.data)):\n",
    "        step = dict(\n",
    "            method=\"restyle\",\n",
    "            args=[\"visible\", [False] * len(fig.data)],\n",
    "            label=str(i+1961),\n",
    "\n",
    "        )\n",
    "        step[\"args\"][1][i] = True  # Toggle i'th trace to \"visible\"\n",
    "        steps.append(step)\n",
    "\n",
    "    sliders = [dict(\n",
    "        active=59,\n",
    "        currentvalue={\"prefix\": \"Year: \"},\n",
    "        pad={\"t\": 50},\n",
    "        steps=steps,\n",
    "    )]\n",
    "\n",
    "    fig.update_layout(\n",
    "        title=dict(text=title, y=0.94, x=0.03, font=dict(size=15)),\n",
    "        sliders=sliders,\n",
    "        xaxis=dict(autorange=False, range=[mini, maxi], title=\"caloric surplus (kcal/person/day)\", showline=True),\n",
    "        yaxis=dict(autorange=True, showgrid=False, ticks='', showticklabels=False),\n",
    "        margin=dict(t=30, l=5, b=10, r=5),\n",
    "        autosize=False,\n",
    "        width=width,\n",
    "        height=height,\n",
    "        paper_bgcolor='rgba(0,0,0,0)',\n",
    "        plot_bgcolor='rgba(0,0,0,0)'\n",
    "    )\n",
    "\n",
    "    py.plot(fig, filename='docs/_includes/cal_diff_{}.html'.format(zone), include_plotlyjs=False)\n",
    "    \n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now move on to a more understandable map visualization. As usual, we are interested in **2020** for our endpoint, but also on the changing over the years.\n",
    "**_Note_: if it doesn't show, the 2020 map can be found [here](https://manuleo.github.io/mADAm_files/african_kcal_need2020.html)**  \n",
    "Links to the other years:\n",
    "- [1970](https://manuleo.github.io/mADAm_files/african_kcal_need1970.html)\n",
    "- [1980](https://manuleo.github.io/mADAm_files/african_kcal_need1980.html)\n",
    "- [1990](https://manuleo.github.io/mADAm_files/african_kcal_need1990.html)\n",
    "- [2000](https://manuleo.github.io/mADAm_files/african_kcal_need2000.html)\n",
    "- [2010](https://manuleo.github.io/mADAm_files/african_kcal_need2010.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    
        <script>
            L_NO_TOUCH = false;
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    color_map_69adfa373952453793bf2362a817eb1d.x = d3.scale.linear()
              .domain([-336.82745378672877, 1337.425136129638])
              .range([0, 400]);

    color_map_69adfa373952453793bf2362a817eb1d.legend = L.control({position: 'topright'});
    color_map_69adfa373952453793bf2362a817eb1d.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
    color_map_69adfa373952453793bf2362a817eb1d.legend.addTo(map_bacc210227c74930bb47a0402dd94c65);

    color_map_69adfa373952453793bf2362a817eb1d.xAxis = d3.svg.axis()
        .scale(color_map_69adfa373952453793bf2362a817eb1d.x)
        .orient("top")
        .tickSize(1)
        .tickValues([-336.82745378672877, 0.0, 200.0, 450.0, 1337.425136129638]);

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"Feature"}], "type": "FeatureCollection"});
        
    
        geo_json_a961652d16df4c39b4b6c624baf6271f.bindTooltip(
            function(layer){
            // Convert non-primitive to String.
            let handleObject = (feature)=>typeof(feature)=='object' ? JSON.stringify(feature) : feature;
            let fields = ["names", "val"];
            let aliases = ["Country: ", "Estimation of kcal/persona/day deficit"];
            return '<table style="background-color: white; color: #333333; font-family: arial; font-size: 12px; padding: 10px;">' +
            String(
                fields.map(
                columnname=>
                    `<tr style="text-align: left;">
                    <th style="padding: 4px; padding-right: 10px;">
                        ${aliases[fields.indexOf(columnname)]
                        }
                    </th>
                    
                    <td style="padding: 4px;">${handleObject(layer.feature.properties[columnname])
                    }</td></tr>`
                ).join(''))
                +'</table>'
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            var layer_control_99775e7270cc4517a729b0338739f29c = {
                base_layers : {
                },
                overlays :  {
                    "Choropleth" : choropleth_ae0b2990766749ffb0fc03e9b247f79f,
                },
            };
            L.control.layers(
                layer_control_99775e7270cc4517a729b0338739f29c.base_layers,
                layer_control_99775e7270cc4517a729b0338739f29c.overlays,
                {"autoZIndex": true, "collapsed": true, "position": "topright"}
            ).addTo(map_bacc210227c74930bb47a0402dd94c65);
        
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x14d711210>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legend_name = \"Estimation of kcal/persona/day deficit\"\n",
    "for year in range(1970,2030,10):\n",
    "    bins = [min(caloric_difference_africa[year]), 0, 200, 450, max(caloric_difference_africa[year])]\n",
    "    african_kcal_need_map = plot_map(caloric_difference_africa, path_dict['geoworld_json'], \\\n",
    "    african_country_kv, year, \"RdYlGn\", legend_name, legend_name, path_dict['african_kcal_need'] + str(year) + \".html\", bins)\n",
    "african_kcal_need_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://manuleo.github.io/mADAm_files/african_kcal_need2020.html\"\n",
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      "text/plain": [
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   ],
   "source": [
    "IFrame(src='https://manuleo.github.io/mADAm_files/african_kcal_need2020.html', width = 800, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/9/90/African_countries_by_GDP_%28PPP%29_per_capita_in_2017.png/1024px-African_countries_by_GDP_%28PPP%29_per_capita_in_2017.png\" height=\"400\" width=\"400\" style=\"float: left; margin-right: 10px;\">  \n",
    "\n",
    "We found an analysis <a href=\"https://en.wikipedia.org/wiki/List_of_African_countries_by_GDP_(PPP)_per_capita\">here</a> about the GDP (Gross Domestic Product) in African countries, which we think can be a good way to evaluate the correctness of our analysis. We show to the left the heat map of Africa in terms of each countries GDP per capita.\n",
    "\n",
    "Observing the evolution over the last 50 years allow us to better understand and comment the animation above. As we can see from the 1970 map, almost all Africa lived in starving condition with the lowest peak in Guinea-Bissau (at huge deficit of 700 kcal/persona/day needed).\n",
    "\n",
    "Individual case studies prove the data's accuracy. For example, the Ethiopian famine in the 80's is impressively reproduced and manifests itself as Ethiopia drops to the lowest rank. In general, a very positive development can be observed, as more and more countries manage to reach a surplus regime every single decade, with the highest increase occurring just recently in the last 10 years. \n",
    "\n",
    "Ever since 2001, more than half of the examined countries show a net surplus. As of 2019, all of the countries in red were determined to be either war-riddled or politically fragile. Exceptions to the rule are Namibia and Eswatini, which both boast a relatively high GDP per capita (ranked 10th and 11th for the African continent, respectively). Thus, the only explanation would be inequality amongst the population or insufficient distribution of available resources. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Determination of European countries with food surplus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) How much human food resources are available for European countries?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1) Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To answer this important question, we will need to import data from the **FAO Dataset**. More specifically, we will focus on the section **Food Balance Sheet** with respect to European countries only. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_balance_europe = pd.read_csv(path_dict['food_balance_europe'],encoding='latin-1', low_memory=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "European countries will be analysed following the same strategy we used for African countries in order to be consistent also in the way by which we assess whether countries are in deficit or have a surplus. To start off, we will:\n",
    "- **remove** all the columns with title **\"Y----F\"**.\n",
    "- **replace** all the **NAN** values with **0** as Item was not available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_balance_europe = clean_Fs_and_years(food_balance_europe)\n",
    "food_balance_europe = food_balance_europe.fillna(0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The third step to complete **the cleaning** of food_balance_europe consists on adapting names of countries in order to have consistency along our different dataframes.\n",
    "\n",
    "The easiest of these changes that we observe in our dataframe is **The former Yugoslav Republic of Macedonia** should become **North Macedonia**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_balance_europe = replace_names_of_countries(food_balance_europe, [(\"The former Yugoslav Republic of Macedonia\", \"North Macedonia\")])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our Dataframe looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Area Code</th>\n",
       "      <th>Area</th>\n",
       "      <th>Item Code</th>\n",
       "      <th>Item</th>\n",
       "      <th>Element Code</th>\n",
       "      <th>Element</th>\n",
       "      <th>Unit</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2501</td>\n",
       "      <td>Population</td>\n",
       "      <td>511</td>\n",
       "      <td>Total Population - Both sexes</td>\n",
       "      <td>1000 persons</td>\n",
       "      <td>1669.0</td>\n",
       "      <td>1719.0</td>\n",
       "      <td>1771.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3216.0</td>\n",
       "      <td>3196.0</td>\n",
       "      <td>3180.0</td>\n",
       "      <td>3166.0</td>\n",
       "      <td>3157.0</td>\n",
       "      <td>3151.0</td>\n",
       "      <td>3150.0</td>\n",
       "      <td>3154.0</td>\n",
       "      <td>3162.0</td>\n",
       "      <td>3173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2511</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>5511</td>\n",
       "      <td>Production</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>98.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>...</td>\n",
       "      <td>253.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2511</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>5611</td>\n",
       "      <td>Import Quantity</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>182.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>...</td>\n",
       "      <td>465.0</td>\n",
       "      <td>417.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>389.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2511</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>5072</td>\n",
       "      <td>Stock Variation</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-52.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2511</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>5911</td>\n",
       "      <td>Export Quantity</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area Code     Area  Item Code                Item  Element Code  \\\n",
       "0          3  Albania       2501          Population           511   \n",
       "1          3  Albania       2511  Wheat and products          5511   \n",
       "2          3  Albania       2511  Wheat and products          5611   \n",
       "3          3  Albania       2511  Wheat and products          5072   \n",
       "4          3  Albania       2511  Wheat and products          5911   \n",
       "\n",
       "                         Element          Unit    1961    1962    1963  ...  \\\n",
       "0  Total Population - Both sexes  1000 persons  1669.0  1719.0  1771.0  ...   \n",
       "1                     Production   1000 tonnes    98.0   146.0    62.0  ...   \n",
       "2                Import Quantity   1000 tonnes   182.0    89.0   110.0  ...   \n",
       "3                Stock Variation   1000 tonnes   -52.0    -4.0    56.0  ...   \n",
       "4                Export Quantity   1000 tonnes     0.0     0.0     0.0  ...   \n",
       "\n",
       "     2004    2005    2006    2007    2008    2009    2010    2011    2012  \\\n",
       "0  3216.0  3196.0  3180.0  3166.0  3157.0  3151.0  3150.0  3154.0  3162.0   \n",
       "1   253.0   260.0   231.0   250.0   335.0   333.0   295.0   293.0   300.0   \n",
       "2   465.0   417.0   414.0   406.0   355.0   343.0   362.0   389.0   377.0   \n",
       "3     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "4     2.0     0.0     0.0     2.0     0.0     0.0     0.0     4.0     4.0   \n",
       "\n",
       "     2013  \n",
       "0  3173.0  \n",
       "1   294.0  \n",
       "2   360.0  \n",
       "3     0.0  \n",
       "4     4.0  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_balance_europe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the European countries analysis, and since the structure of this dataset is equivalent to that one, we can again obtain the pairs **(Item Code, 2901) and (Element Code, 664)** for our Europe analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_supply_europe = obtain_supply(food_balance_europe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now group by **Area** and see the supplies derived from each item available in countries for each particular year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\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>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>1966</th>\n",
       "      <th>1967</th>\n",
       "      <th>1968</th>\n",
       "      <th>1969</th>\n",
       "      <th>1970</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>2223.0</td>\n",
       "      <td>2242.0</td>\n",
       "      <td>2156.0</td>\n",
       "      <td>2270.0</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>2262.0</td>\n",
       "      <td>2343.0</td>\n",
       "      <td>2404.0</td>\n",
       "      <td>2415.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2792.0</td>\n",
       "      <td>2874.0</td>\n",
       "      <td>2855.0</td>\n",
       "      <td>2860.0</td>\n",
       "      <td>2947.0</td>\n",
       "      <td>2993.0</td>\n",
       "      <td>3076.0</td>\n",
       "      <td>3132.0</td>\n",
       "      <td>3184.0</td>\n",
       "      <td>3193.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>3191.0</td>\n",
       "      <td>3193.0</td>\n",
       "      <td>3248.0</td>\n",
       "      <td>3270.0</td>\n",
       "      <td>3220.0</td>\n",
       "      <td>3232.0</td>\n",
       "      <td>3194.0</td>\n",
       "      <td>3221.0</td>\n",
       "      <td>3153.0</td>\n",
       "      <td>3217.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3606.0</td>\n",
       "      <td>3640.0</td>\n",
       "      <td>3719.0</td>\n",
       "      <td>3737.0</td>\n",
       "      <td>3717.0</td>\n",
       "      <td>3723.0</td>\n",
       "      <td>3724.0</td>\n",
       "      <td>3735.0</td>\n",
       "      <td>3739.0</td>\n",
       "      <td>3768.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2901.0</td>\n",
       "      <td>2987.0</td>\n",
       "      <td>3084.0</td>\n",
       "      <td>3186.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>3126.0</td>\n",
       "      <td>3196.0</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>3250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3722.0</td>\n",
       "      <td>3716.0</td>\n",
       "      <td>3716.0</td>\n",
       "      <td>3713.0</td>\n",
       "      <td>3702.0</td>\n",
       "      <td>3697.0</td>\n",
       "      <td>3707.0</td>\n",
       "      <td>3720.0</td>\n",
       "      <td>3715.0</td>\n",
       "      <td>3733.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium-Luxembourg</th>\n",
       "      <td>2923.0</td>\n",
       "      <td>2908.0</td>\n",
       "      <td>3007.0</td>\n",
       "      <td>3061.0</td>\n",
       "      <td>2966.0</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>3088.0</td>\n",
       "      <td>3032.0</td>\n",
       "      <td>3156.0</td>\n",
       "      <td>3068.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      1961    1962    1963    1964    1965    1966    1967  \\\n",
       "Area                                                                         \n",
       "Albania             2223.0  2242.0  2156.0  2270.0  2254.0  2254.0  2262.0   \n",
       "Austria             3191.0  3193.0  3248.0  3270.0  3220.0  3232.0  3194.0   \n",
       "Belarus                0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "Belgium                0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "Belgium-Luxembourg  2923.0  2908.0  3007.0  3061.0  2966.0  3067.0  3088.0   \n",
       "\n",
       "                      1968    1969    1970  ...    2004    2005    2006  \\\n",
       "Area                                        ...                           \n",
       "Albania             2343.0  2404.0  2415.0  ...  2792.0  2874.0  2855.0   \n",
       "Austria             3221.0  3153.0  3217.0  ...  3606.0  3640.0  3719.0   \n",
       "Belarus                0.0     0.0     0.0  ...  2901.0  2987.0  3084.0   \n",
       "Belgium                0.0     0.0     0.0  ...  3722.0  3716.0  3716.0   \n",
       "Belgium-Luxembourg  3032.0  3156.0  3068.0  ...     0.0     0.0     0.0   \n",
       "\n",
       "                      2007    2008    2009    2010    2011    2012    2013  \n",
       "Area                                                                        \n",
       "Albania             2860.0  2947.0  2993.0  3076.0  3132.0  3184.0  3193.0  \n",
       "Austria             3737.0  3717.0  3723.0  3724.0  3735.0  3739.0  3768.0  \n",
       "Belarus             3186.0  3200.0  3126.0  3196.0  3400.0  3400.0  3250.0  \n",
       "Belgium             3713.0  3702.0  3697.0  3707.0  3720.0  3715.0  3733.0  \n",
       "Belgium-Luxembourg     0.0     0.0     0.0     0.0     0.0     0.0     0.0  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_supply_europe = food_supply_europe.set_index(\"Area\")\n",
    "food_supply_europe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to check for anomalies in our data, we would like to analyze the **timeline**. We therefore transpose the dataframe and plot the timeline of how food supply in different countries evolved. Legend was suppressed as it is too large."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_supply_europe = food_supply_europe.transpose();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting the year from string to int\n",
    "food_supply_europe.index = food_supply_europe.index.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "timeline_supply(food_supply_europe, \"European\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe a similar situation in this graph as we did in the African countries analysis. As such, we can assume that some countries may have changed names or maybe some other situations occured, which caused countries data to stop being recorded/collected. \n",
    "\n",
    "Let's then see which countries have zeros in the values for food supplies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Belarus', 'Belgium', 'Belgium-Luxembourg',\n",
       "       'Bosnia and Herzegovina', 'Croatia', 'Czechia', 'Czechoslovakia',\n",
       "       'Estonia', 'Latvia', 'Lithuania', 'Luxembourg', 'Montenegro',\n",
       "       'Republic of Moldova', 'Russian Federation', 'Serbia',\n",
       "       'Serbia and Montenegro', 'Slovakia', 'Slovenia', 'North Macedonia',\n",
       "       'Ukraine', 'USSR', 'Yugoslav SFR'], dtype=object)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_supply_europe.columns.values[(food_supply_europe == 0).any()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Respecting the cronology, we observe that there were some countries on which data stopped being collected, as well as others from which data started being collected after the initial collection year of 1950. After some research on these countries, we found out that:\n",
    "- **USSR** (Union of Soviet Socialist Republics) was a union of a lot of countries, some of which were not even located in Europe. The Union was dissolved in 26 December 1991, and so the countries **Belarus**, **Ukraine**, **Estonia**, **Republic of Moldova**, **Russian Federation**, **Latvia**, **Lithuania** are the European countries that became independent in Europe.\n",
    "- **Yugoslav SFR** was made up of the countries that became **Bosnia and Herzegovina**, **Croatia**, **Serbia and Montenegro**, **North Macedonia**, **Slovenia**, all of which obtained their independence between 25 June 1991 - 27 April 1992\n",
    "- **Czechoslovakia** was dissoluted in 1 January 1993, after a period called The Velvet Revolution (because of the peaceful ways), and split into the two countries **Czechia** and **Slovakia**\n",
    "- **Serbia and Montenegro** separated themselves in 2006, breaking up the last union still recognized as a successor of Yougoslavia\n",
    "- **Belgium-Luxembourg** (2003) does not have a main reason to exist, as there is no major event happening in 2003 that would justify the mixing of the two countries. Our understanding lead us to believe this was probably just a grouping created in FAO when collecting the data from the two countries.\n",
    "\n",
    "Let's then take care of all these cases.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries_to_merge = {'USSR': ['Belarus', 'Ukraine', 'Estonia', 'Republic of Moldova', 'Russian Federation', 'Latvia', 'Lithuania'], \n",
    "                         'Yugoslav SFR': ['Bosnia and Herzegovina', 'Croatia', 'Serbia and Montenegro', 'North Macedonia', 'Slovenia'],\n",
    "                         'Czechoslovakia': ['Czechia', 'Slovakia'],\n",
    "                         'Serbia and Montenegro': ['Serbia', 'Montenegro'],\n",
    "                         'Belgium-Luxembourg': ['Luxembourg', 'Belgium']}\n",
    "food_supply_europe = merge_countries(food_supply_europe, countries_to_merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "timeline_supply(food_supply_europe, \"European\", \" - Cleaned dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe a drop in the *food supply* from most countries that came from the former USSR, which we find to be interesting. While it might not be the case for all of these countries, some of them are vey poor and as such, when separated from the world power that was the USSR, their ability to provide and produce for themselves may have dropped significantly, which is coherent with what we observe. As such, we continue our analysis with these values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2) Interpolation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we want to add more columns representing future years until 2020 to prepare cells for extrapolation to make predictions about possible scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_supply_europe = prepare_future(food_supply_europe, 2014, 2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_supply_europe = predict_future(food_supply_europe, path_dict['europe_supply_rnn'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "timeline_supply(food_supply_europe, \"European\", \" - Predicted dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the analysis of African countries, the same conclusions can be drawn about using the neural network method. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing our results with a interactive heatmap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>POLYGON ((20.59025 41.85540, 20.46317 41.51509...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AUT</td>\n",
       "      <td>Austria</td>\n",
       "      <td>POLYGON ((16.97967 48.12350, 16.90375 47.71487...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BEL</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>POLYGON ((3.31497 51.34578, 4.04707 51.26726, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>BGR</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>POLYGON ((22.65715 44.23492, 22.94483 43.82379...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BIH</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>POLYGON ((19.00549 44.86023, 19.36803 44.86300...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     id                    name  \\\n",
       "2   ALB                 Albania   \n",
       "9   AUT                 Austria   \n",
       "12  BEL                 Belgium   \n",
       "16  BGR                Bulgaria   \n",
       "18  BIH  Bosnia and Herzegovina   \n",
       "\n",
       "                                             geometry  \n",
       "2   POLYGON ((20.59025 41.85540, 20.46317 41.51509...  \n",
       "9   POLYGON ((16.97967 48.12350, 16.90375 47.71487...  \n",
       "12  POLYGON ((3.31497 51.34578, 4.04707 51.26726, ...  \n",
       "16  POLYGON ((22.65715 44.23492, 22.94483 43.82379...  \n",
       "18  POLYGON ((19.00549 44.86023, 19.36803 44.86300...  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "european_country_codes = [\"ALB\",\"AUT\",\"BLR\",\"BEL\",\"BIH\",\"BGR\",\"HRV\",\"CZE\",\"DNK\",\"EST\",\"FIN\",\"FRA\",\"DEU\",\"GRC\",\"HUN\",\"ISL\",\"IRL\",\n",
    "                          \"ITA\",\"LVA\",\"LTU\",\"LUX\",\"MALTAMISSING\",\"MNE\",\"NLD\",\"NOR\",\"POL\",\"PRT\",\"MDA\",\"ROU\",\"RUS\",\"SRB\",\"SWK\",\n",
    "                          \"SVN\",\"ESP\",\"SWE\",\"CHE\",\"MKD\",\"UKR\",\"GBR\"]\n",
    "european_country_names = food_supply_europe.T.index.values\n",
    "\n",
    "\n",
    "european_country_kv = pd.DataFrame({'codes': european_country_codes,\n",
    "                                    'names': european_country_names\n",
    "                                    })\n",
    "\n",
    "geojson_europe = geojson_world[geojson_world.id.isin(european_country_codes)]\n",
    "geojson_europe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice, after our manual analysis on the countries that **Malta** is not displayed in the json. Hence, we won't consider it for our analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_supply_europe = food_supply_europe.drop(columns=[\"Malta\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moving into the plot of the European countries to see their actual and past supply. \n",
    "**_Note_: the map for 2020 (in case it doesn't load) is available [here](https://manuleo.github.io/mADAm_files/european_supply_map2020.html)**  \n",
    "Links to other years:\n",
    "- [1970](https://manuleo.github.io/mADAm_files/european_supply_map1970.html)\n",
    "- [1980](https://manuleo.github.io/mADAm_files/european_supply_map1980.html)\n",
    "- [1990](https://manuleo.github.io/mADAm_files/european_supply_map1990.html)\n",
    "- [2000](https://manuleo.github.io/mADAm_files/european_supply_map2000.html)\n",
    "- [2010](https://manuleo.github.io/mADAm_files/european_supply_map2010.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
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[36.626168, 50.225591], [37.39346, 50.383953], [38.010631, 49.915662], [38.594988, 49.926462], [40.069058, 49.601055], [40.080789, 49.30743], [39.674664, 48.783818], [39.895632, 48.232405], [39.738278, 47.898937], [38.770585, 47.825608], [38.255112, 47.5464], [38.223538, 47.10219], [37.425137, 47.022221], [36.759855, 46.6987], [35.823685, 46.645964], [34.962342, 46.273197], [35.020788, 45.651219], [35.510009, 45.409993], [36.529998, 45.46999], [36.334713, 45.113216], [35.239999, 44.939996], [33.882511, 44.361479], [33.326421, 44.564877], [33.546924, 45.034771], [32.454174, 45.327466], [32.630804, 45.519186], [33.588162, 45.851569], [33.298567, 46.080598], [31.74414, 46.333348], [31.675307, 46.706245], [30.748749, 46.5831], [30.377609, 46.03241], [29.603289, 45.293308], [29.149725, 45.464925], [28.679779, 45.304031], [28.233554, 45.488283], [28.485269, 45.596907], [28.659987, 45.939987], [28.933717, 46.25883], [28.862972, 46.437889], [29.072107, 46.517678], [29.170654, 46.379262], 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51.319503], [30.619454, 51.822806], [30.927549, 52.042353], [31.785998, 52.101678]]], "type": "Polygon"}, "id": "36", "properties": {"id": "UKR", "names": "Ukraine", "val": 3146.002201067628}, "type": "Feature"}], "type": "FeatureCollection"});
        
    
        geo_json_f168c3ccd0f943f98cc01963c330afa2.bindTooltip(
            function(layer){
            // Convert non-primitive to String.
            let handleObject = (feature)=>typeof(feature)=='object' ? JSON.stringify(feature) : feature;
            let fields = ["names", "val"];
            let aliases = ["Country: ", "Food supply in Europe (kcal/person/day)"];
            return '<table style="background-color: white; color: #333333; font-family: arial; font-size: 12px; padding: 10px;">' +
            String(
                fields.map(
                columnname=>
                    `<tr style="text-align: left;">
                    <th style="padding: 4px; padding-right: 10px;">
                        ${aliases[fields.indexOf(columnname)]
                        }
                    </th>
                    
                    <td style="padding: 4px;">${handleObject(layer.feature.properties[columnname])
                    }</td></tr>`
                ).join(''))
                +'</table>'
            }, {"sticky": true});
        
    
            var layer_control_f812b2cd20fa4a64bb45c0814e95ee38 = {
                base_layers : {
                },
                overlays :  {
                    "Choropleth" : choropleth_95fb3f0c8b5f4cdebb2fe4330042c77e,
                },
            };
            L.control.layers(
                layer_control_f812b2cd20fa4a64bb45c0814e95ee38.base_layers,
                layer_control_f812b2cd20fa4a64bb45c0814e95ee38.overlays,
                {"autoZIndex": true, "collapsed": true, "position": "topright"}
            ).addTo(map_3c1e2e39da274b139936c8fdf65c14c6);
        
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x14d062590>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plotting\n",
    "legend_name = \"Food supply in Europe (kcal/person/day)\"\n",
    "for year in range(1970, 2030, 10):\n",
    "    europe_supply_map = plot_map(food_supply_europe.T, path_dict['geoworld_json'], \\\n",
    "    european_country_kv, year, \"Blues\", legend_name, legend_name, path_dict['european_supply_map'] + str(year) + \".html\", bins=9)\n",
    "europe_supply_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"600\"\n",
       "            src=\"https://manuleo.github.io/mADAm_files/european_supply_map2020.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x14e518450>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(src='https://manuleo.github.io/mADAm_files/european_supply_map2020.html', width = 1000, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_map_data(geojson_europe, european_country_kv, food_supply_europe, \"docs/json/europe_supply/europe_supply_{}.geojson\", \"docs/json/europe_supply/europe_supply_ticks.json\", bins=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyzing the data shows a trend **scaled up but mostly similar** African case. The available food supply in Europe over the last 50 years remained mostly the same, with an available supply between 2500 to 3800 kcal/persona/day.  \n",
    "Interesting to note the drop the ex USSR had in 2000 after the division, drop already emerged during the previous cleaning of the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) What is the ideal amount of kcal each European country needs?\n",
    "Once again, we do a very similar analysis on European countries as we did for the African ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1) How many people live in Europe?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1.1) Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this second part of our analysis, we load the list of **European countries**. Secondly, we load the **World Population Database** (United Nation) and therefore we obtain two dataframes: one for males and the other one for females."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open (path_dict[\"european_countries_list\"],'r') as eu_c:\n",
    "    eu_countries = [line.rstrip() for line in eu_c] #loading list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to check if the FAO Database contains data regarding **every country** in Europe. We will check the intersection with the list `eu_countries`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of countries for which no data is available: ['Channel Islands', 'Malta']\n"
     ]
    }
   ],
   "source": [
    "eu_to_remove = list(set(eu_countries) - set(food_supply_europe.columns.values))\n",
    "print(\"List of countries for which no data is available: \" + str(eu_to_remove))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because there are less countries in Europe, and also because most European countries are part of ONU, we expected most countries to be present in both the FAO database and the population database. These **Channel Islands** are a small set of islands in the English Channel, and because they are so small, we can safely remove them from our scope of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of countries for which no data is available: []\n"
     ]
    }
   ],
   "source": [
    "eu_countries = [i for i in eu_countries if not i in eu_to_remove]\n",
    "eu_to_remove = list(set(eu_countries) - set(food_supply_europe.columns.values))\n",
    "print(\"List of countries for which no data is available: \" + str(eu_to_remove))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now obtain the population for each gender in all european countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_male_europe = clean_pop_df(pop_male, eu_countries)\n",
    "pop_female_europe = clean_pop_df(pop_female, eu_countries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the final version of male population data grouped by age:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>0-4</th>\n",
       "      <th>5-9</th>\n",
       "      <th>10-14</th>\n",
       "      <th>15-19</th>\n",
       "      <th>20-24</th>\n",
       "      <th>25-29</th>\n",
       "      <th>30-34</th>\n",
       "      <th>35-39</th>\n",
       "      <th>...</th>\n",
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       "      <th>60-64</th>\n",
       "      <th>65-69</th>\n",
       "      <th>70-74</th>\n",
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       "      <th>85-89</th>\n",
       "      <th>90-94</th>\n",
       "      <th>95-99</th>\n",
       "      <th>100+</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3135</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1950</td>\n",
       "      <td>333700</td>\n",
       "      <td>282651</td>\n",
       "      <td>395174</td>\n",
       "      <td>382562</td>\n",
       "      <td>361905</td>\n",
       "      <td>237761</td>\n",
       "      <td>150660</td>\n",
       "      <td>206475</td>\n",
       "      <td>...</td>\n",
       "      <td>138445</td>\n",
       "      <td>124045</td>\n",
       "      <td>99216</td>\n",
       "      <td>68726</td>\n",
       "      <td>41415</td>\n",
       "      <td>19537</td>\n",
       "      <td>7648</td>\n",
       "      <td>2113</td>\n",
       "      <td>365</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3136</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1955</td>\n",
       "      <td>418684</td>\n",
       "      <td>315056</td>\n",
       "      <td>264730</td>\n",
       "      <td>374177</td>\n",
       "      <td>357223</td>\n",
       "      <td>337374</td>\n",
       "      <td>219617</td>\n",
       "      <td>136496</td>\n",
       "      <td>...</td>\n",
       "      <td>128674</td>\n",
       "      <td>111772</td>\n",
       "      <td>95381</td>\n",
       "      <td>70127</td>\n",
       "      <td>42393</td>\n",
       "      <td>19507</td>\n",
       "      <td>8196</td>\n",
       "      <td>2262</td>\n",
       "      <td>415</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3137</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1960</td>\n",
       "      <td>491730</td>\n",
       "      <td>406036</td>\n",
       "      <td>305601</td>\n",
       "      <td>255607</td>\n",
       "      <td>360905</td>\n",
       "      <td>342271</td>\n",
       "      <td>323037</td>\n",
       "      <td>208441</td>\n",
       "      <td>...</td>\n",
       "      <td>150864</td>\n",
       "      <td>108601</td>\n",
       "      <td>89962</td>\n",
       "      <td>70827</td>\n",
       "      <td>45805</td>\n",
       "      <td>21818</td>\n",
       "      <td>8211</td>\n",
       "      <td>2371</td>\n",
       "      <td>420</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3138</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1965</td>\n",
       "      <td>448162</td>\n",
       "      <td>482745</td>\n",
       "      <td>398975</td>\n",
       "      <td>299589</td>\n",
       "      <td>248620</td>\n",
       "      <td>350831</td>\n",
       "      <td>331524</td>\n",
       "      <td>311625</td>\n",
       "      <td>...</td>\n",
       "      <td>158297</td>\n",
       "      <td>130151</td>\n",
       "      <td>89304</td>\n",
       "      <td>68499</td>\n",
       "      <td>47682</td>\n",
       "      <td>24484</td>\n",
       "      <td>9262</td>\n",
       "      <td>2393</td>\n",
       "      <td>448</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3139</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1970</td>\n",
       "      <td>368381</td>\n",
       "      <td>450207</td>\n",
       "      <td>485335</td>\n",
       "      <td>400660</td>\n",
       "      <td>299407</td>\n",
       "      <td>247750</td>\n",
       "      <td>348188</td>\n",
       "      <td>327625</td>\n",
       "      <td>...</td>\n",
       "      <td>147426</td>\n",
       "      <td>140703</td>\n",
       "      <td>110454</td>\n",
       "      <td>70311</td>\n",
       "      <td>47732</td>\n",
       "      <td>27537</td>\n",
       "      <td>10041</td>\n",
       "      <td>2554</td>\n",
       "      <td>418</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      country  year     0-4     5-9   10-14   15-19   20-24   25-29   30-34  \\\n",
       "3135  Belarus  1950  333700  282651  395174  382562  361905  237761  150660   \n",
       "3136  Belarus  1955  418684  315056  264730  374177  357223  337374  219617   \n",
       "3137  Belarus  1960  491730  406036  305601  255607  360905  342271  323037   \n",
       "3138  Belarus  1965  448162  482745  398975  299589  248620  350831  331524   \n",
       "3139  Belarus  1970  368381  450207  485335  400660  299407  247750  348188   \n",
       "\n",
       "       35-39  ...   55-59   60-64   65-69  70-74  75-79  80-84  85-89 90-94  \\\n",
       "3135  206475  ...  138445  124045   99216  68726  41415  19537   7648  2113   \n",
       "3136  136496  ...  128674  111772   95381  70127  42393  19507   8196  2262   \n",
       "3137  208441  ...  150864  108601   89962  70827  45805  21818   8211  2371   \n",
       "3138  311625  ...  158297  130151   89304  68499  47682  24484   9262  2393   \n",
       "3139  327625  ...  147426  140703  110454  70311  47732  27537  10041  2554   \n",
       "\n",
       "     95-99 100+  \n",
       "3135   365   35  \n",
       "3136   415   47  \n",
       "3137   420   50  \n",
       "3138   448   52  \n",
       "3139   418   51  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_male_europe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1.2) Interpolation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly to the analysis performed on Africa, we once again interpolate the years with a frequency of **5 years** in *pop_male_europe*, to have a frequency of 1 year. Before doing so, we check again if the evolution in these 5 years intervals occurs in a linear manner.  \n",
    "**_Note_: as usual, if the animation is not visible in the notebook, click [here](https://manuleo.github.io/mADAm_files/european_pop_growth.html)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1200\"\n",
       "            height=\"600\"\n",
       "            src=\"https://manuleo.github.io/mADAm_files/european_pop_growth.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x14addf410>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(src='https://manuleo.github.io/mADAm_files/european_pop_growth.html', width = 1200, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Selecting countries\n",
    "# countryrand = []\n",
    "# n_countries = 2\n",
    "# for i in range(0, n_countries):\n",
    "#     countryrand.append(random.choice(pop_male_europe.country.drop_duplicates().values))\n",
    "\n",
    "# fig = plt.figure()\n",
    "# animator = animation.FuncAnimation(fig, timeline_country_gender, frames=(range(1950, 2025, 5)),\\\n",
    "#                                    fargs = (pop_male_europe, pop_female_europe, \"30-34\", countryrand), repeat=False)\n",
    "# #HTML(animator.to_jshtml())\n",
    "\n",
    "# with open(path_dict['european_pop_growth'], \"w\") as f:\n",
    "#     print(animator.to_html5_video(), file=f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even if the growth is not so linear as a whole as before, the animation clearly shows that it is linear inside each group of 5 year. As our interpolation works by interpolating on each of these groups, we can proceed with it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_male_europe = interpolate_years(pop_male_europe, 1950, 2020)\n",
    "pop_female_europe = interpolate_years(pop_female_europe, 1950, 2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how the new dataframes for males and females population look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>0-4</th>\n",
       "      <th>5-9</th>\n",
       "      <th>10-14</th>\n",
       "      <th>15-19</th>\n",
       "      <th>20-24</th>\n",
       "      <th>25-29</th>\n",
       "      <th>30-34</th>\n",
       "      <th>35-39</th>\n",
       "      <th>...</th>\n",
       "      <th>55-59</th>\n",
       "      <th>60-64</th>\n",
       "      <th>65-69</th>\n",
       "      <th>70-74</th>\n",
       "      <th>75-79</th>\n",
       "      <th>80-84</th>\n",
       "      <th>85-89</th>\n",
       "      <th>90-94</th>\n",
       "      <th>95-99</th>\n",
       "      <th>100+</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1950</td>\n",
       "      <td>333700.0</td>\n",
       "      <td>282651.0</td>\n",
       "      <td>395174.0</td>\n",
       "      <td>382562.0</td>\n",
       "      <td>361905.0</td>\n",
       "      <td>237761.0</td>\n",
       "      <td>150660.0</td>\n",
       "      <td>206475.0</td>\n",
       "      <td>...</td>\n",
       "      <td>138445.0</td>\n",
       "      <td>124045.0</td>\n",
       "      <td>99216.0</td>\n",
       "      <td>68726.0</td>\n",
       "      <td>41415.0</td>\n",
       "      <td>19537.0</td>\n",
       "      <td>7648.0</td>\n",
       "      <td>2113.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1951</td>\n",
       "      <td>350696.8</td>\n",
       "      <td>289132.0</td>\n",
       "      <td>369085.2</td>\n",
       "      <td>380885.0</td>\n",
       "      <td>360968.6</td>\n",
       "      <td>257683.6</td>\n",
       "      <td>164451.4</td>\n",
       "      <td>192479.2</td>\n",
       "      <td>...</td>\n",
       "      <td>136490.8</td>\n",
       "      <td>121590.4</td>\n",
       "      <td>98449.0</td>\n",
       "      <td>69006.2</td>\n",
       "      <td>41610.6</td>\n",
       "      <td>19531.0</td>\n",
       "      <td>7757.6</td>\n",
       "      <td>2142.8</td>\n",
       "      <td>375.0</td>\n",
       "      <td>37.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1952</td>\n",
       "      <td>367693.6</td>\n",
       "      <td>295613.0</td>\n",
       "      <td>342996.4</td>\n",
       "      <td>379208.0</td>\n",
       "      <td>360032.2</td>\n",
       "      <td>277606.2</td>\n",
       "      <td>178242.8</td>\n",
       "      <td>178483.4</td>\n",
       "      <td>...</td>\n",
       "      <td>134536.6</td>\n",
       "      <td>119135.8</td>\n",
       "      <td>97682.0</td>\n",
       "      <td>69286.4</td>\n",
       "      <td>41806.2</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>7867.2</td>\n",
       "      <td>2172.6</td>\n",
       "      <td>385.0</td>\n",
       "      <td>39.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1953</td>\n",
       "      <td>384690.4</td>\n",
       "      <td>302094.0</td>\n",
       "      <td>316907.6</td>\n",
       "      <td>377531.0</td>\n",
       "      <td>359095.8</td>\n",
       "      <td>297528.8</td>\n",
       "      <td>192034.2</td>\n",
       "      <td>164487.6</td>\n",
       "      <td>...</td>\n",
       "      <td>132582.4</td>\n",
       "      <td>116681.2</td>\n",
       "      <td>96915.0</td>\n",
       "      <td>69566.6</td>\n",
       "      <td>42001.8</td>\n",
       "      <td>19519.0</td>\n",
       "      <td>7976.8</td>\n",
       "      <td>2202.4</td>\n",
       "      <td>395.0</td>\n",
       "      <td>42.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1954</td>\n",
       "      <td>401687.2</td>\n",
       "      <td>308575.0</td>\n",
       "      <td>290818.8</td>\n",
       "      <td>375854.0</td>\n",
       "      <td>358159.4</td>\n",
       "      <td>317451.4</td>\n",
       "      <td>205825.6</td>\n",
       "      <td>150491.8</td>\n",
       "      <td>...</td>\n",
       "      <td>130628.2</td>\n",
       "      <td>114226.6</td>\n",
       "      <td>96148.0</td>\n",
       "      <td>69846.8</td>\n",
       "      <td>42197.4</td>\n",
       "      <td>19513.0</td>\n",
       "      <td>8086.4</td>\n",
       "      <td>2232.2</td>\n",
       "      <td>405.0</td>\n",
       "      <td>44.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year       0-4       5-9     10-14     15-19     20-24     25-29  \\\n",
       "0  Belarus  1950  333700.0  282651.0  395174.0  382562.0  361905.0  237761.0   \n",
       "1  Belarus  1951  350696.8  289132.0  369085.2  380885.0  360968.6  257683.6   \n",
       "2  Belarus  1952  367693.6  295613.0  342996.4  379208.0  360032.2  277606.2   \n",
       "3  Belarus  1953  384690.4  302094.0  316907.6  377531.0  359095.8  297528.8   \n",
       "4  Belarus  1954  401687.2  308575.0  290818.8  375854.0  358159.4  317451.4   \n",
       "\n",
       "      30-34     35-39  ...     55-59     60-64    65-69    70-74    75-79  \\\n",
       "0  150660.0  206475.0  ...  138445.0  124045.0  99216.0  68726.0  41415.0   \n",
       "1  164451.4  192479.2  ...  136490.8  121590.4  98449.0  69006.2  41610.6   \n",
       "2  178242.8  178483.4  ...  134536.6  119135.8  97682.0  69286.4  41806.2   \n",
       "3  192034.2  164487.6  ...  132582.4  116681.2  96915.0  69566.6  42001.8   \n",
       "4  205825.6  150491.8  ...  130628.2  114226.6  96148.0  69846.8  42197.4   \n",
       "\n",
       "     80-84   85-89   90-94  95-99  100+  \n",
       "0  19537.0  7648.0  2113.0  365.0  35.0  \n",
       "1  19531.0  7757.6  2142.8  375.0  37.4  \n",
       "2  19525.0  7867.2  2172.6  385.0  39.8  \n",
       "3  19519.0  7976.8  2202.4  395.0  42.2  \n",
       "4  19513.0  8086.4  2232.2  405.0  44.6  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_male_europe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>0-4</th>\n",
       "      <th>5-9</th>\n",
       "      <th>10-14</th>\n",
       "      <th>15-19</th>\n",
       "      <th>20-24</th>\n",
       "      <th>25-29</th>\n",
       "      <th>30-34</th>\n",
       "      <th>35-39</th>\n",
       "      <th>...</th>\n",
       "      <th>55-59</th>\n",
       "      <th>60-64</th>\n",
       "      <th>65-69</th>\n",
       "      <th>70-74</th>\n",
       "      <th>75-79</th>\n",
       "      <th>80-84</th>\n",
       "      <th>85-89</th>\n",
       "      <th>90-94</th>\n",
       "      <th>95-99</th>\n",
       "      <th>100+</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1950</td>\n",
       "      <td>325456.0</td>\n",
       "      <td>287716.0</td>\n",
       "      <td>420006.0</td>\n",
       "      <td>403120.0</td>\n",
       "      <td>424672.0</td>\n",
       "      <td>338168.0</td>\n",
       "      <td>223359.0</td>\n",
       "      <td>326549.0</td>\n",
       "      <td>...</td>\n",
       "      <td>178569.0</td>\n",
       "      <td>183039.0</td>\n",
       "      <td>152746.0</td>\n",
       "      <td>119379.0</td>\n",
       "      <td>85709.0</td>\n",
       "      <td>39512.0</td>\n",
       "      <td>20284.0</td>\n",
       "      <td>7706.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1951</td>\n",
       "      <td>338789.2</td>\n",
       "      <td>291637.0</td>\n",
       "      <td>389910.6</td>\n",
       "      <td>402225.4</td>\n",
       "      <td>415571.6</td>\n",
       "      <td>350871.6</td>\n",
       "      <td>242416.2</td>\n",
       "      <td>302527.8</td>\n",
       "      <td>...</td>\n",
       "      <td>179256.4</td>\n",
       "      <td>177849.8</td>\n",
       "      <td>153480.6</td>\n",
       "      <td>119761.2</td>\n",
       "      <td>85312.8</td>\n",
       "      <td>41588.0</td>\n",
       "      <td>19740.8</td>\n",
       "      <td>7352.0</td>\n",
       "      <td>1810.6</td>\n",
       "      <td>277.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1952</td>\n",
       "      <td>352122.4</td>\n",
       "      <td>295558.0</td>\n",
       "      <td>359815.2</td>\n",
       "      <td>401330.8</td>\n",
       "      <td>406471.2</td>\n",
       "      <td>363575.2</td>\n",
       "      <td>261473.4</td>\n",
       "      <td>278506.6</td>\n",
       "      <td>...</td>\n",
       "      <td>179943.8</td>\n",
       "      <td>172660.6</td>\n",
       "      <td>154215.2</td>\n",
       "      <td>120143.4</td>\n",
       "      <td>84916.6</td>\n",
       "      <td>43664.0</td>\n",
       "      <td>19197.6</td>\n",
       "      <td>6998.0</td>\n",
       "      <td>1697.2</td>\n",
       "      <td>262.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1953</td>\n",
       "      <td>365455.6</td>\n",
       "      <td>299479.0</td>\n",
       "      <td>329719.8</td>\n",
       "      <td>400436.2</td>\n",
       "      <td>397370.8</td>\n",
       "      <td>376278.8</td>\n",
       "      <td>280530.6</td>\n",
       "      <td>254485.4</td>\n",
       "      <td>...</td>\n",
       "      <td>180631.2</td>\n",
       "      <td>167471.4</td>\n",
       "      <td>154949.8</td>\n",
       "      <td>120525.6</td>\n",
       "      <td>84520.4</td>\n",
       "      <td>45740.0</td>\n",
       "      <td>18654.4</td>\n",
       "      <td>6644.0</td>\n",
       "      <td>1583.8</td>\n",
       "      <td>247.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1954</td>\n",
       "      <td>378788.8</td>\n",
       "      <td>303400.0</td>\n",
       "      <td>299624.4</td>\n",
       "      <td>399541.6</td>\n",
       "      <td>388270.4</td>\n",
       "      <td>388982.4</td>\n",
       "      <td>299587.8</td>\n",
       "      <td>230464.2</td>\n",
       "      <td>...</td>\n",
       "      <td>181318.6</td>\n",
       "      <td>162282.2</td>\n",
       "      <td>155684.4</td>\n",
       "      <td>120907.8</td>\n",
       "      <td>84124.2</td>\n",
       "      <td>47816.0</td>\n",
       "      <td>18111.2</td>\n",
       "      <td>6290.0</td>\n",
       "      <td>1470.4</td>\n",
       "      <td>232.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year       0-4       5-9     10-14     15-19     20-24     25-29  \\\n",
       "0  Belarus  1950  325456.0  287716.0  420006.0  403120.0  424672.0  338168.0   \n",
       "1  Belarus  1951  338789.2  291637.0  389910.6  402225.4  415571.6  350871.6   \n",
       "2  Belarus  1952  352122.4  295558.0  359815.2  401330.8  406471.2  363575.2   \n",
       "3  Belarus  1953  365455.6  299479.0  329719.8  400436.2  397370.8  376278.8   \n",
       "4  Belarus  1954  378788.8  303400.0  299624.4  399541.6  388270.4  388982.4   \n",
       "\n",
       "      30-34     35-39  ...     55-59     60-64     65-69     70-74    75-79  \\\n",
       "0  223359.0  326549.0  ...  178569.0  183039.0  152746.0  119379.0  85709.0   \n",
       "1  242416.2  302527.8  ...  179256.4  177849.8  153480.6  119761.2  85312.8   \n",
       "2  261473.4  278506.6  ...  179943.8  172660.6  154215.2  120143.4  84916.6   \n",
       "3  280530.6  254485.4  ...  180631.2  167471.4  154949.8  120525.6  84520.4   \n",
       "4  299587.8  230464.2  ...  181318.6  162282.2  155684.4  120907.8  84124.2   \n",
       "\n",
       "     80-84    85-89   90-94   95-99   100+  \n",
       "0  39512.0  20284.0  7706.0  1924.0  293.0  \n",
       "1  41588.0  19740.8  7352.0  1810.6  277.8  \n",
       "2  43664.0  19197.6  6998.0  1697.2  262.6  \n",
       "3  45740.0  18654.4  6644.0  1583.8  247.4  \n",
       "4  47816.0  18111.2  6290.0  1470.4  232.2  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_female_europe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1.3) Computing total European population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we will compute the total population per year. This new dataframe **pop_tot** will be useful for the next section of our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_tot_europe = obtain_total_pop(pop_male_europe, pop_female_europe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the next analysis we will need to match this data with the `food_balance_europe`. We proceed to give to our population data the same shape as the other dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_tot_europe = reshape_pop_dataframe(pop_tot_europe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1950</th>\n",
       "      <th>1951</th>\n",
       "      <th>1952</th>\n",
       "      <th>1953</th>\n",
       "      <th>1954</th>\n",
       "      <th>1955</th>\n",
       "      <th>1956</th>\n",
       "      <th>1957</th>\n",
       "      <th>1958</th>\n",
       "      <th>1959</th>\n",
       "      <th>...</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>1263164.0</td>\n",
       "      <td>1294529.4</td>\n",
       "      <td>1325894.8</td>\n",
       "      <td>1357260.2</td>\n",
       "      <td>1388625.6</td>\n",
       "      <td>1419991.0</td>\n",
       "      <td>1463211.0</td>\n",
       "      <td>1506431.0</td>\n",
       "      <td>1549651.0</td>\n",
       "      <td>1592871.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2936528.0</td>\n",
       "      <td>2925027.0</td>\n",
       "      <td>2913526.0</td>\n",
       "      <td>2902025.0</td>\n",
       "      <td>2890524.0</td>\n",
       "      <td>2887979.2</td>\n",
       "      <td>2885434.4</td>\n",
       "      <td>2882889.6</td>\n",
       "      <td>2880344.8</td>\n",
       "      <td>2877800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>6936442.0</td>\n",
       "      <td>6939834.4</td>\n",
       "      <td>6943226.8</td>\n",
       "      <td>6946619.2</td>\n",
       "      <td>6950011.6</td>\n",
       "      <td>6953404.0</td>\n",
       "      <td>6976877.8</td>\n",
       "      <td>7000351.6</td>\n",
       "      <td>7023825.4</td>\n",
       "      <td>7047299.2</td>\n",
       "      <td>...</td>\n",
       "      <td>8463689.4</td>\n",
       "      <td>8517433.8</td>\n",
       "      <td>8571178.2</td>\n",
       "      <td>8624922.6</td>\n",
       "      <td>8678667.0</td>\n",
       "      <td>8744213.6</td>\n",
       "      <td>8809760.2</td>\n",
       "      <td>8875306.8</td>\n",
       "      <td>8940853.4</td>\n",
       "      <td>9006400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>7745004.0</td>\n",
       "      <td>7745446.2</td>\n",
       "      <td>7745888.4</td>\n",
       "      <td>7746330.6</td>\n",
       "      <td>7746772.8</td>\n",
       "      <td>7747215.0</td>\n",
       "      <td>7822748.2</td>\n",
       "      <td>7898281.4</td>\n",
       "      <td>7973814.6</td>\n",
       "      <td>8049347.8</td>\n",
       "      <td>...</td>\n",
       "      <td>9424345.6</td>\n",
       "      <td>9428115.2</td>\n",
       "      <td>9431884.8</td>\n",
       "      <td>9435654.4</td>\n",
       "      <td>9439424.0</td>\n",
       "      <td>9441403.4</td>\n",
       "      <td>9443382.8</td>\n",
       "      <td>9445362.2</td>\n",
       "      <td>9447341.6</td>\n",
       "      <td>9449321.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>8637521.0</td>\n",
       "      <td>8687471.8</td>\n",
       "      <td>8737422.6</td>\n",
       "      <td>8787373.4</td>\n",
       "      <td>8837324.2</td>\n",
       "      <td>8887275.0</td>\n",
       "      <td>8943292.2</td>\n",
       "      <td>8999309.4</td>\n",
       "      <td>9055326.6</td>\n",
       "      <td>9111343.8</td>\n",
       "      <td>...</td>\n",
       "      <td>11008574.2</td>\n",
       "      <td>11078413.4</td>\n",
       "      <td>11148252.6</td>\n",
       "      <td>11218091.8</td>\n",
       "      <td>11287931.0</td>\n",
       "      <td>11348268.0</td>\n",
       "      <td>11408605.0</td>\n",
       "      <td>11468942.0</td>\n",
       "      <td>11529279.0</td>\n",
       "      <td>11589616.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>2661296.0</td>\n",
       "      <td>2716296.8</td>\n",
       "      <td>2771297.6</td>\n",
       "      <td>2826298.4</td>\n",
       "      <td>2881299.2</td>\n",
       "      <td>2936300.0</td>\n",
       "      <td>2994172.8</td>\n",
       "      <td>3052045.6</td>\n",
       "      <td>3109918.4</td>\n",
       "      <td>3167791.2</td>\n",
       "      <td>...</td>\n",
       "      <td>3650254.8</td>\n",
       "      <td>3595031.6</td>\n",
       "      <td>3539808.4</td>\n",
       "      <td>3484585.2</td>\n",
       "      <td>3429362.0</td>\n",
       "      <td>3399652.6</td>\n",
       "      <td>3369943.2</td>\n",
       "      <td>3340233.8</td>\n",
       "      <td>3310524.4</td>\n",
       "      <td>3280815.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             1950       1951       1952       1953       1954  \\\n",
       "Country                                                                         \n",
       "Albania                 1263164.0  1294529.4  1325894.8  1357260.2  1388625.6   \n",
       "Austria                 6936442.0  6939834.4  6943226.8  6946619.2  6950011.6   \n",
       "Belarus                 7745004.0  7745446.2  7745888.4  7746330.6  7746772.8   \n",
       "Belgium                 8637521.0  8687471.8  8737422.6  8787373.4  8837324.2   \n",
       "Bosnia and Herzegovina  2661296.0  2716296.8  2771297.6  2826298.4  2881299.2   \n",
       "\n",
       "                             1955       1956       1957       1958       1959  \\\n",
       "Country                                                                         \n",
       "Albania                 1419991.0  1463211.0  1506431.0  1549651.0  1592871.0   \n",
       "Austria                 6953404.0  6976877.8  7000351.6  7023825.4  7047299.2   \n",
       "Belarus                 7747215.0  7822748.2  7898281.4  7973814.6  8049347.8   \n",
       "Belgium                 8887275.0  8943292.2  8999309.4  9055326.6  9111343.8   \n",
       "Bosnia and Herzegovina  2936300.0  2994172.8  3052045.6  3109918.4  3167791.2   \n",
       "\n",
       "                        ...        2011        2012        2013        2014  \\\n",
       "Country                 ...                                                   \n",
       "Albania                 ...   2936528.0   2925027.0   2913526.0   2902025.0   \n",
       "Austria                 ...   8463689.4   8517433.8   8571178.2   8624922.6   \n",
       "Belarus                 ...   9424345.6   9428115.2   9431884.8   9435654.4   \n",
       "Belgium                 ...  11008574.2  11078413.4  11148252.6  11218091.8   \n",
       "Bosnia and Herzegovina  ...   3650254.8   3595031.6   3539808.4   3484585.2   \n",
       "\n",
       "                              2015        2016        2017        2018  \\\n",
       "Country                                                                  \n",
       "Albania                  2890524.0   2887979.2   2885434.4   2882889.6   \n",
       "Austria                  8678667.0   8744213.6   8809760.2   8875306.8   \n",
       "Belarus                  9439424.0   9441403.4   9443382.8   9445362.2   \n",
       "Belgium                 11287931.0  11348268.0  11408605.0  11468942.0   \n",
       "Bosnia and Herzegovina   3429362.0   3399652.6   3369943.2   3340233.8   \n",
       "\n",
       "                              2019        2020  \n",
       "Country                                         \n",
       "Albania                  2880344.8   2877800.0  \n",
       "Austria                  8940853.4   9006400.0  \n",
       "Belarus                  9447341.6   9449321.0  \n",
       "Belgium                 11529279.0  11589616.0  \n",
       "Bosnia and Herzegovina   3310524.4   3280815.0  \n",
       "\n",
       "[5 rows x 71 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_tot_europe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Estimantion of ideal human food demand in Europe "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we multiply each column of the population data for each matching `age_group` in the calories table (that here we squeeze to allow the multiplication, similar to a transpose rows/columns of the dataset).  \n",
    "We obtain two datasets: `male_cal_need_europe` and `female_cal_need_europe` reporting total calories needed for **each country in each year per age group per gender**. \n",
    "The unit here is **kcal/day**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>country</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1950</td>\n",
       "      <td>4.449333e+08</td>\n",
       "      <td>520077840.0</td>\n",
       "      <td>964224560.0</td>\n",
       "      <td>1.193593e+09</td>\n",
       "      <td>1.085715e+09</td>\n",
       "      <td>713283000.0</td>\n",
       "      <td>451980000.0</td>\n",
       "      <td>586389000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>365494800.0</td>\n",
       "      <td>322517000.0</td>\n",
       "      <td>257961600.0</td>\n",
       "      <td>178687600.0</td>\n",
       "      <td>101052600.0</td>\n",
       "      <td>46888800.0</td>\n",
       "      <td>18355200.0</td>\n",
       "      <td>5071200.0</td>\n",
       "      <td>876000.0</td>\n",
       "      <td>84000.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1951</td>\n",
       "      <td>4.675957e+08</td>\n",
       "      <td>532002880.0</td>\n",
       "      <td>900567888.0</td>\n",
       "      <td>1.188361e+09</td>\n",
       "      <td>1.082906e+09</td>\n",
       "      <td>773050800.0</td>\n",
       "      <td>493354200.0</td>\n",
       "      <td>546640928.0</td>\n",
       "      <td>...</td>\n",
       "      <td>360335712.0</td>\n",
       "      <td>316135040.0</td>\n",
       "      <td>255967400.0</td>\n",
       "      <td>179416120.0</td>\n",
       "      <td>101529864.0</td>\n",
       "      <td>46874400.0</td>\n",
       "      <td>18618240.0</td>\n",
       "      <td>5142720.0</td>\n",
       "      <td>900000.0</td>\n",
       "      <td>89760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1952</td>\n",
       "      <td>4.902581e+08</td>\n",
       "      <td>543927920.0</td>\n",
       "      <td>836911216.0</td>\n",
       "      <td>1.183129e+09</td>\n",
       "      <td>1.080097e+09</td>\n",
       "      <td>832818600.0</td>\n",
       "      <td>534728400.0</td>\n",
       "      <td>506892856.0</td>\n",
       "      <td>...</td>\n",
       "      <td>355176624.0</td>\n",
       "      <td>309753080.0</td>\n",
       "      <td>253973200.0</td>\n",
       "      <td>180144640.0</td>\n",
       "      <td>102007128.0</td>\n",
       "      <td>46860000.0</td>\n",
       "      <td>18881280.0</td>\n",
       "      <td>5214240.0</td>\n",
       "      <td>924000.0</td>\n",
       "      <td>95520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1953</td>\n",
       "      <td>5.129205e+08</td>\n",
       "      <td>555852960.0</td>\n",
       "      <td>773254544.0</td>\n",
       "      <td>1.177897e+09</td>\n",
       "      <td>1.077287e+09</td>\n",
       "      <td>892586400.0</td>\n",
       "      <td>576102600.0</td>\n",
       "      <td>467144784.0</td>\n",
       "      <td>...</td>\n",
       "      <td>350017536.0</td>\n",
       "      <td>303371120.0</td>\n",
       "      <td>251979000.0</td>\n",
       "      <td>180873160.0</td>\n",
       "      <td>102484392.0</td>\n",
       "      <td>46845600.0</td>\n",
       "      <td>19144320.0</td>\n",
       "      <td>5285760.0</td>\n",
       "      <td>948000.0</td>\n",
       "      <td>101280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1954</td>\n",
       "      <td>5.355829e+08</td>\n",
       "      <td>567778000.0</td>\n",
       "      <td>709597872.0</td>\n",
       "      <td>1.172664e+09</td>\n",
       "      <td>1.074478e+09</td>\n",
       "      <td>952354200.0</td>\n",
       "      <td>617476800.0</td>\n",
       "      <td>427396712.0</td>\n",
       "      <td>...</td>\n",
       "      <td>344858448.0</td>\n",
       "      <td>296989160.0</td>\n",
       "      <td>249984800.0</td>\n",
       "      <td>181601680.0</td>\n",
       "      <td>102961656.0</td>\n",
       "      <td>46831200.0</td>\n",
       "      <td>19407360.0</td>\n",
       "      <td>5357280.0</td>\n",
       "      <td>972000.0</td>\n",
       "      <td>107040.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year           0-4          5-9        10-14         15-19  \\\n",
       "0  Belarus  1950  4.449333e+08  520077840.0  964224560.0  1.193593e+09   \n",
       "1  Belarus  1951  4.675957e+08  532002880.0  900567888.0  1.188361e+09   \n",
       "2  Belarus  1952  4.902581e+08  543927920.0  836911216.0  1.183129e+09   \n",
       "3  Belarus  1953  5.129205e+08  555852960.0  773254544.0  1.177897e+09   \n",
       "4  Belarus  1954  5.355829e+08  567778000.0  709597872.0  1.172664e+09   \n",
       "\n",
       "          20-24        25-29        30-34        35-39  ...        55-59  \\\n",
       "0  1.085715e+09  713283000.0  451980000.0  586389000.0  ...  365494800.0   \n",
       "1  1.082906e+09  773050800.0  493354200.0  546640928.0  ...  360335712.0   \n",
       "2  1.080097e+09  832818600.0  534728400.0  506892856.0  ...  355176624.0   \n",
       "3  1.077287e+09  892586400.0  576102600.0  467144784.0  ...  350017536.0   \n",
       "4  1.074478e+09  952354200.0  617476800.0  427396712.0  ...  344858448.0   \n",
       "\n",
       "         60-64        65-69        70-74        75-79       80-84       85-89  \\\n",
       "0  322517000.0  257961600.0  178687600.0  101052600.0  46888800.0  18355200.0   \n",
       "1  316135040.0  255967400.0  179416120.0  101529864.0  46874400.0  18618240.0   \n",
       "2  309753080.0  253973200.0  180144640.0  102007128.0  46860000.0  18881280.0   \n",
       "3  303371120.0  251979000.0  180873160.0  102484392.0  46845600.0  19144320.0   \n",
       "4  296989160.0  249984800.0  181601680.0  102961656.0  46831200.0  19407360.0   \n",
       "\n",
       "       90-94     95-99      100+  \n",
       "0  5071200.0  876000.0   84000.0  \n",
       "1  5142720.0  900000.0   89760.0  \n",
       "2  5214240.0  924000.0   95520.0  \n",
       "3  5285760.0  948000.0  101280.0  \n",
       "4  5357280.0  972000.0  107040.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total calories male\n",
    "male_cal_need_europe = get_calories_need(pop_male_europe, male_calories)\n",
    "male_cal_need_europe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>0-4</th>\n",
       "      <th>5-9</th>\n",
       "      <th>10-14</th>\n",
       "      <th>15-19</th>\n",
       "      <th>20-24</th>\n",
       "      <th>25-29</th>\n",
       "      <th>30-34</th>\n",
       "      <th>35-39</th>\n",
       "      <th>...</th>\n",
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       "      <th>60-64</th>\n",
       "      <th>65-69</th>\n",
       "      <th>70-74</th>\n",
       "      <th>75-79</th>\n",
       "      <th>80-84</th>\n",
       "      <th>85-89</th>\n",
       "      <th>90-94</th>\n",
       "      <th>95-99</th>\n",
       "      <th>100+</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1950</td>\n",
       "      <td>4.122443e+08</td>\n",
       "      <td>494871520.0</td>\n",
       "      <td>907212960.0</td>\n",
       "      <td>967488000.0</td>\n",
       "      <td>1.019213e+09</td>\n",
       "      <td>811603200.0</td>\n",
       "      <td>500324160.0</td>\n",
       "      <td>718407800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>392851800.0</td>\n",
       "      <td>373399560.0</td>\n",
       "      <td>305492000.0</td>\n",
       "      <td>238758000.0</td>\n",
       "      <td>171418000.0</td>\n",
       "      <td>79024000.0</td>\n",
       "      <td>40568000.0</td>\n",
       "      <td>15412000.0</td>\n",
       "      <td>3848000.0</td>\n",
       "      <td>586000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1951</td>\n",
       "      <td>4.291330e+08</td>\n",
       "      <td>501615640.0</td>\n",
       "      <td>842206896.0</td>\n",
       "      <td>965340960.0</td>\n",
       "      <td>9.973718e+08</td>\n",
       "      <td>842091840.0</td>\n",
       "      <td>543012288.0</td>\n",
       "      <td>665561160.0</td>\n",
       "      <td>...</td>\n",
       "      <td>394364080.0</td>\n",
       "      <td>362813592.0</td>\n",
       "      <td>306961200.0</td>\n",
       "      <td>239522400.0</td>\n",
       "      <td>170625600.0</td>\n",
       "      <td>83176000.0</td>\n",
       "      <td>39481600.0</td>\n",
       "      <td>14704000.0</td>\n",
       "      <td>3621200.0</td>\n",
       "      <td>555600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1952</td>\n",
       "      <td>4.460217e+08</td>\n",
       "      <td>508359760.0</td>\n",
       "      <td>777200832.0</td>\n",
       "      <td>963193920.0</td>\n",
       "      <td>9.755309e+08</td>\n",
       "      <td>872580480.0</td>\n",
       "      <td>585700416.0</td>\n",
       "      <td>612714520.0</td>\n",
       "      <td>...</td>\n",
       "      <td>395876360.0</td>\n",
       "      <td>352227624.0</td>\n",
       "      <td>308430400.0</td>\n",
       "      <td>240286800.0</td>\n",
       "      <td>169833200.0</td>\n",
       "      <td>87328000.0</td>\n",
       "      <td>38395200.0</td>\n",
       "      <td>13996000.0</td>\n",
       "      <td>3394400.0</td>\n",
       "      <td>525200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1953</td>\n",
       "      <td>4.629104e+08</td>\n",
       "      <td>515103880.0</td>\n",
       "      <td>712194768.0</td>\n",
       "      <td>961046880.0</td>\n",
       "      <td>9.536899e+08</td>\n",
       "      <td>903069120.0</td>\n",
       "      <td>628388544.0</td>\n",
       "      <td>559867880.0</td>\n",
       "      <td>...</td>\n",
       "      <td>397388640.0</td>\n",
       "      <td>341641656.0</td>\n",
       "      <td>309899600.0</td>\n",
       "      <td>241051200.0</td>\n",
       "      <td>169040800.0</td>\n",
       "      <td>91480000.0</td>\n",
       "      <td>37308800.0</td>\n",
       "      <td>13288000.0</td>\n",
       "      <td>3167600.0</td>\n",
       "      <td>494800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>1954</td>\n",
       "      <td>4.797991e+08</td>\n",
       "      <td>521848000.0</td>\n",
       "      <td>647188704.0</td>\n",
       "      <td>958899840.0</td>\n",
       "      <td>9.318490e+08</td>\n",
       "      <td>933557760.0</td>\n",
       "      <td>671076672.0</td>\n",
       "      <td>507021240.0</td>\n",
       "      <td>...</td>\n",
       "      <td>398900920.0</td>\n",
       "      <td>331055688.0</td>\n",
       "      <td>311368800.0</td>\n",
       "      <td>241815600.0</td>\n",
       "      <td>168248400.0</td>\n",
       "      <td>95632000.0</td>\n",
       "      <td>36222400.0</td>\n",
       "      <td>12580000.0</td>\n",
       "      <td>2940800.0</td>\n",
       "      <td>464400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year           0-4          5-9        10-14        15-19  \\\n",
       "0  Belarus  1950  4.122443e+08  494871520.0  907212960.0  967488000.0   \n",
       "1  Belarus  1951  4.291330e+08  501615640.0  842206896.0  965340960.0   \n",
       "2  Belarus  1952  4.460217e+08  508359760.0  777200832.0  963193920.0   \n",
       "3  Belarus  1953  4.629104e+08  515103880.0  712194768.0  961046880.0   \n",
       "4  Belarus  1954  4.797991e+08  521848000.0  647188704.0  958899840.0   \n",
       "\n",
       "          20-24        25-29        30-34        35-39  ...        55-59  \\\n",
       "0  1.019213e+09  811603200.0  500324160.0  718407800.0  ...  392851800.0   \n",
       "1  9.973718e+08  842091840.0  543012288.0  665561160.0  ...  394364080.0   \n",
       "2  9.755309e+08  872580480.0  585700416.0  612714520.0  ...  395876360.0   \n",
       "3  9.536899e+08  903069120.0  628388544.0  559867880.0  ...  397388640.0   \n",
       "4  9.318490e+08  933557760.0  671076672.0  507021240.0  ...  398900920.0   \n",
       "\n",
       "         60-64        65-69        70-74        75-79       80-84       85-89  \\\n",
       "0  373399560.0  305492000.0  238758000.0  171418000.0  79024000.0  40568000.0   \n",
       "1  362813592.0  306961200.0  239522400.0  170625600.0  83176000.0  39481600.0   \n",
       "2  352227624.0  308430400.0  240286800.0  169833200.0  87328000.0  38395200.0   \n",
       "3  341641656.0  309899600.0  241051200.0  169040800.0  91480000.0  37308800.0   \n",
       "4  331055688.0  311368800.0  241815600.0  168248400.0  95632000.0  36222400.0   \n",
       "\n",
       "        90-94      95-99      100+  \n",
       "0  15412000.0  3848000.0  586000.0  \n",
       "1  14704000.0  3621200.0  555600.0  \n",
       "2  13996000.0  3394400.0  525200.0  \n",
       "3  13288000.0  3167600.0  494800.0  \n",
       "4  12580000.0  2940800.0  464400.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total calories female\n",
    "female_cal_need_europe = get_calories_need(pop_female_europe, female_calories)\n",
    "female_cal_need_europe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have the calories needed for both gender, we can add them together easily to achieve total calories needed for **each country in each year**, and we collect them in the dataframe `total_cal_need_europe`. The unit is **kcal/year**. All of this is done by the `obtain_total_cal_need` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_cal_need_europe = obtain_total_cal_need(male_cal_need_europe, female_cal_need_europe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at total calories dataframe **total_cal**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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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       "        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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.277543e+14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>1999</td>\n",
       "      <td>1.277413e+14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>1998</td>\n",
       "      <td>1.277284e+14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>1997</td>\n",
       "      <td>1.277154e+14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>1996</td>\n",
       "      <td>1.277024e+14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                country  year      Calories\n",
       "547  Russian Federation  2000  1.277543e+14\n",
       "546  Russian Federation  1999  1.277413e+14\n",
       "545  Russian Federation  1998  1.277284e+14\n",
       "544  Russian Federation  1997  1.277154e+14\n",
       "543  Russian Federation  1996  1.277024e+14"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_cal_need_europe.sort_values(by=\"Calories\", ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the sake of consistency, we will now reshape our dataframe `total_cal_need_europe` according to the same schema seen for `food_supply_europe`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_cal_need_europe = reshape_calories_df(total_cal_need_europe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drawing a sample of the final shaped dataframe total calories `total_cal`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "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>1950</th>\n",
       "      <th>1951</th>\n",
       "      <th>1952</th>\n",
       "      <th>1953</th>\n",
       "      <th>1954</th>\n",
       "      <th>1955</th>\n",
       "      <th>1956</th>\n",
       "      <th>1957</th>\n",
       "      <th>1958</th>\n",
       "      <th>1959</th>\n",
       "      <th>...</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>1.041791e+12</td>\n",
       "      <td>1.067157e+12</td>\n",
       "      <td>1.092523e+12</td>\n",
       "      <td>1.117889e+12</td>\n",
       "      <td>1.143255e+12</td>\n",
       "      <td>1.168621e+12</td>\n",
       "      <td>1.202214e+12</td>\n",
       "      <td>1.235807e+12</td>\n",
       "      <td>1.269400e+12</td>\n",
       "      <td>1.302993e+12</td>\n",
       "      <td>...</td>\n",
       "      <td>2.558970e+12</td>\n",
       "      <td>2.551603e+12</td>\n",
       "      <td>2.544237e+12</td>\n",
       "      <td>2.536870e+12</td>\n",
       "      <td>2.529503e+12</td>\n",
       "      <td>2.526050e+12</td>\n",
       "      <td>2.522597e+12</td>\n",
       "      <td>2.519144e+12</td>\n",
       "      <td>2.515691e+12</td>\n",
       "      <td>2.512237e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>5.906169e+12</td>\n",
       "      <td>5.912127e+12</td>\n",
       "      <td>5.918085e+12</td>\n",
       "      <td>5.924043e+12</td>\n",
       "      <td>5.930001e+12</td>\n",
       "      <td>5.935959e+12</td>\n",
       "      <td>5.950704e+12</td>\n",
       "      <td>5.965449e+12</td>\n",
       "      <td>5.980194e+12</td>\n",
       "      <td>5.994939e+12</td>\n",
       "      <td>...</td>\n",
       "      <td>7.368272e+12</td>\n",
       "      <td>7.414042e+12</td>\n",
       "      <td>7.459812e+12</td>\n",
       "      <td>7.505582e+12</td>\n",
       "      <td>7.551352e+12</td>\n",
       "      <td>7.603258e+12</td>\n",
       "      <td>7.655163e+12</td>\n",
       "      <td>7.707068e+12</td>\n",
       "      <td>7.758973e+12</td>\n",
       "      <td>7.810878e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>6.581842e+12</td>\n",
       "      <td>6.573869e+12</td>\n",
       "      <td>6.565896e+12</td>\n",
       "      <td>6.557924e+12</td>\n",
       "      <td>6.549951e+12</td>\n",
       "      <td>6.541978e+12</td>\n",
       "      <td>6.585445e+12</td>\n",
       "      <td>6.628913e+12</td>\n",
       "      <td>6.672380e+12</td>\n",
       "      <td>6.715847e+12</td>\n",
       "      <td>...</td>\n",
       "      <td>8.184568e+12</td>\n",
       "      <td>8.171641e+12</td>\n",
       "      <td>8.158714e+12</td>\n",
       "      <td>8.145787e+12</td>\n",
       "      <td>8.132860e+12</td>\n",
       "      <td>8.125860e+12</td>\n",
       "      <td>8.118860e+12</td>\n",
       "      <td>8.111861e+12</td>\n",
       "      <td>8.104861e+12</td>\n",
       "      <td>8.097861e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>7.440138e+12</td>\n",
       "      <td>7.475404e+12</td>\n",
       "      <td>7.510669e+12</td>\n",
       "      <td>7.545935e+12</td>\n",
       "      <td>7.581201e+12</td>\n",
       "      <td>7.616467e+12</td>\n",
       "      <td>7.656335e+12</td>\n",
       "      <td>7.696202e+12</td>\n",
       "      <td>7.736070e+12</td>\n",
       "      <td>7.775938e+12</td>\n",
       "      <td>...</td>\n",
       "      <td>9.508244e+12</td>\n",
       "      <td>9.565091e+12</td>\n",
       "      <td>9.621938e+12</td>\n",
       "      <td>9.678785e+12</td>\n",
       "      <td>9.735632e+12</td>\n",
       "      <td>9.787514e+12</td>\n",
       "      <td>9.839396e+12</td>\n",
       "      <td>9.891278e+12</td>\n",
       "      <td>9.943160e+12</td>\n",
       "      <td>9.995042e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>2.207555e+12</td>\n",
       "      <td>2.252332e+12</td>\n",
       "      <td>2.297108e+12</td>\n",
       "      <td>2.341885e+12</td>\n",
       "      <td>2.386662e+12</td>\n",
       "      <td>2.431439e+12</td>\n",
       "      <td>2.479545e+12</td>\n",
       "      <td>2.527652e+12</td>\n",
       "      <td>2.575759e+12</td>\n",
       "      <td>2.623865e+12</td>\n",
       "      <td>...</td>\n",
       "      <td>3.196515e+12</td>\n",
       "      <td>3.144872e+12</td>\n",
       "      <td>3.093230e+12</td>\n",
       "      <td>3.041587e+12</td>\n",
       "      <td>2.989945e+12</td>\n",
       "      <td>2.963127e+12</td>\n",
       "      <td>2.936309e+12</td>\n",
       "      <td>2.909492e+12</td>\n",
       "      <td>2.882674e+12</td>\n",
       "      <td>2.855856e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                1950          1951          1952  \\\n",
       "Country                                                            \n",
       "Albania                 1.041791e+12  1.067157e+12  1.092523e+12   \n",
       "Austria                 5.906169e+12  5.912127e+12  5.918085e+12   \n",
       "Belarus                 6.581842e+12  6.573869e+12  6.565896e+12   \n",
       "Belgium                 7.440138e+12  7.475404e+12  7.510669e+12   \n",
       "Bosnia and Herzegovina  2.207555e+12  2.252332e+12  2.297108e+12   \n",
       "\n",
       "                                1953          1954          1955  \\\n",
       "Country                                                            \n",
       "Albania                 1.117889e+12  1.143255e+12  1.168621e+12   \n",
       "Austria                 5.924043e+12  5.930001e+12  5.935959e+12   \n",
       "Belarus                 6.557924e+12  6.549951e+12  6.541978e+12   \n",
       "Belgium                 7.545935e+12  7.581201e+12  7.616467e+12   \n",
       "Bosnia and Herzegovina  2.341885e+12  2.386662e+12  2.431439e+12   \n",
       "\n",
       "                                1956          1957          1958  \\\n",
       "Country                                                            \n",
       "Albania                 1.202214e+12  1.235807e+12  1.269400e+12   \n",
       "Austria                 5.950704e+12  5.965449e+12  5.980194e+12   \n",
       "Belarus                 6.585445e+12  6.628913e+12  6.672380e+12   \n",
       "Belgium                 7.656335e+12  7.696202e+12  7.736070e+12   \n",
       "Bosnia and Herzegovina  2.479545e+12  2.527652e+12  2.575759e+12   \n",
       "\n",
       "                                1959  ...          2011          2012  \\\n",
       "Country                               ...                               \n",
       "Albania                 1.302993e+12  ...  2.558970e+12  2.551603e+12   \n",
       "Austria                 5.994939e+12  ...  7.368272e+12  7.414042e+12   \n",
       "Belarus                 6.715847e+12  ...  8.184568e+12  8.171641e+12   \n",
       "Belgium                 7.775938e+12  ...  9.508244e+12  9.565091e+12   \n",
       "Bosnia and Herzegovina  2.623865e+12  ...  3.196515e+12  3.144872e+12   \n",
       "\n",
       "                                2013          2014          2015  \\\n",
       "Country                                                            \n",
       "Albania                 2.544237e+12  2.536870e+12  2.529503e+12   \n",
       "Austria                 7.459812e+12  7.505582e+12  7.551352e+12   \n",
       "Belarus                 8.158714e+12  8.145787e+12  8.132860e+12   \n",
       "Belgium                 9.621938e+12  9.678785e+12  9.735632e+12   \n",
       "Bosnia and Herzegovina  3.093230e+12  3.041587e+12  2.989945e+12   \n",
       "\n",
       "                                2016          2017          2018  \\\n",
       "Country                                                            \n",
       "Albania                 2.526050e+12  2.522597e+12  2.519144e+12   \n",
       "Austria                 7.603258e+12  7.655163e+12  7.707068e+12   \n",
       "Belarus                 8.125860e+12  8.118860e+12  8.111861e+12   \n",
       "Belgium                 9.787514e+12  9.839396e+12  9.891278e+12   \n",
       "Bosnia and Herzegovina  2.963127e+12  2.936309e+12  2.909492e+12   \n",
       "\n",
       "                                2019          2020  \n",
       "Country                                             \n",
       "Albania                 2.515691e+12  2.512237e+12  \n",
       "Austria                 7.758973e+12  7.810878e+12  \n",
       "Belarus                 8.104861e+12  8.097861e+12  \n",
       "Belgium                 9.943160e+12  9.995042e+12  \n",
       "Bosnia and Herzegovina  2.882674e+12  2.855856e+12  \n",
       "\n",
       "[5 rows x 71 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_cal_need_europe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can proceed to plot the results inside a map visualization. The most interesting one is as usual the **2020**, but also analyzing past years is crucial to understanding how the trend changed. \n",
    "**_Note_: the link to 2020 in case it won't show up is available [here](https://manuleo.github.io/mADAm_files/europe_est_kcal2020.html)**  \n",
    "Links for other years:\n",
    "- [1970](https://manuleo.github.io/mADAm_files/europe_est_kcal1970.html)\n",
    "- [1980](https://manuleo.github.io/mADAm_files/europe_est_kcal1980.html)\n",
    "- [1990](https://manuleo.github.io/mADAm_files/europe_est_kcal1990.html)\n",
    "- [2000](https://manuleo.github.io/mADAm_files/europe_est_kcal2000.html)\n",
    "- [2010](https://manuleo.github.io/mADAm_files/europe_est_kcal2010.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
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    var color_map_3b27c4742abe4025a4b4f1482fd543d1 = {};

    
    color_map_3b27c4742abe4025a4b4f1482fd543d1.color = d3.scale.threshold()
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              .range([0, 400]);

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        .tickSize(1)
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        .attr("id", 'legend')
        .attr("width", 450)
        .attr("height", 40);

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        .attr("transform", "translate(25,16)");

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        .text('Estimation of kcal/year [10^11 kcal/year]');
    
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        geo_json_32d64240a658412198cad1a5a4986caa.bindTooltip(
            function(layer){
            // Convert non-primitive to String.
            let handleObject = (feature)=>typeof(feature)=='object' ? JSON.stringify(feature) : feature;
            let fields = ["names", "val"];
            let aliases = ["Country: ", "Estimation of kcal/year [10^11 kcal/year]"];
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            String(
                fields.map(
                columnname=>
                    `<tr style="text-align: left;">
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                    <td style="padding: 4px;">${handleObject(layer.feature.properties[columnname])
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            var layer_control_bda1bd137eb0436592167daa6dcff3f6 = {
                base_layers : {
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                overlays :  {
                    "Choropleth" : choropleth_3b939dc037a2422bbe220f492d93861b,
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                layer_control_bda1bd137eb0436592167daa6dcff3f6.base_layers,
                layer_control_bda1bd137eb0436592167daa6dcff3f6.overlays,
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x14e0ea8d0>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for year in range(1970,2030,10):\n",
    "    legend_name = \"Estimation of kcal/year [10^11 kcal/year]\"\n",
    "    europe_kcal_est_map = plot_map(total_cal_need_europe.divide(10**11), path_dict['geoworld_json'], \\\n",
    "    european_country_kv, year, \"YlGn\", legend_name, legend_name, path_dict['european_estimation_kcal'] + str(year) + \".html\", bins=9)\n",
    "europe_kcal_est_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"600\"\n",
       "            src=\"https://manuleo.github.io/mADAm_files/europe_est_kcal2020.html\"\n",
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    "IFrame(src='https://manuleo.github.io/mADAm_files/europe_est_kcal2020.html', width = 1000, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_map_data(geojson_europe, european_country_kv, total_cal_need_europe.divide(10**11).T, \"docs/json/europe_need/europe_need_{}.geojson\", \"docs/json/europe_need/europe_need_ticks.json\", bins=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the increasing in population is reflected in increasing of needed kcal in Europe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4) Which countries have more than they need? \n",
    "In this Europe evaluation, we flip the scope of our analysis completely. While in the case of Africa, we want to know which countries need most help (as in, are not producing enough to sustainably survive), for Europe we want to find out which countries are producing more food internally than what they need. The point of this analysis is to find out who could help the African countries in need, by giving away some of their production, while still keeping at least a minimum to be healthy.\n",
    "\n",
    "As with the African analysis, this analysis will take into account the total population dataframe `pop_tot` and the `food_supply_europe`. With regards to the FAO Dataframe of food supply, we will need to transform the unit into **kcal/year** in order to compare results appropriately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "caloric_difference_europe = obtain_difference(pop_tot_europe, food_supply_europe, total_cal_need_europe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>1966</th>\n",
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       "      <th>...</th>\n",
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       "      <th>2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>-13.834758</td>\n",
       "      <td>6.437203</td>\n",
       "      <td>-78.364674</td>\n",
       "      <td>36.765861</td>\n",
       "      <td>21.834368</td>\n",
       "      <td>17.419790</td>\n",
       "      <td>21.230166</td>\n",
       "      <td>98.248729</td>\n",
       "      <td>155.460337</td>\n",
       "      <td>162.851283</td>\n",
       "      <td>...</td>\n",
       "      <td>744.528672</td>\n",
       "      <td>794.041235</td>\n",
       "      <td>800.534160</td>\n",
       "      <td>815.804774</td>\n",
       "      <td>812.927833</td>\n",
       "      <td>818.292383</td>\n",
       "      <td>817.404589</td>\n",
       "      <td>821.619756</td>\n",
       "      <td>821.669430</td>\n",
       "      <td>824.708041</td>\n",
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       "      <td>870.779595</td>\n",
       "      <td>928.899367</td>\n",
       "      <td>953.978842</td>\n",
       "      <td>907.018796</td>\n",
       "      <td>919.030973</td>\n",
       "      <td>881.043010</td>\n",
       "      <td>908.054910</td>\n",
       "      <td>840.066677</td>\n",
       "      <td>904.078311</td>\n",
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       "      <td>1353.341990</td>\n",
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       "      <td>815.470694</td>\n",
       "      <td>877.200747</td>\n",
       "      <td>923.935723</td>\n",
       "      <td>925.675488</td>\n",
       "      <td>933.419914</td>\n",
       "      <td>921.544550</td>\n",
       "      <td>945.798567</td>\n",
       "      <td>999.178346</td>\n",
       "      <td>992.680403</td>\n",
       "      <td>1041.301381</td>\n",
       "      <td>...</td>\n",
       "      <td>1020.686503</td>\n",
       "      <td>1025.394254</td>\n",
       "      <td>880.098242</td>\n",
       "      <td>996.816638</td>\n",
       "      <td>857.158851</td>\n",
       "      <td>958.721120</td>\n",
       "      <td>859.469478</td>\n",
       "      <td>966.582382</td>\n",
       "      <td>931.484184</td>\n",
       "      <td>988.133970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>588.233931</td>\n",
       "      <td>574.260475</td>\n",
       "      <td>674.274851</td>\n",
       "      <td>729.277274</td>\n",
       "      <td>635.267953</td>\n",
       "      <td>734.632818</td>\n",
       "      <td>754.010615</td>\n",
       "      <td>696.401193</td>\n",
       "      <td>818.804401</td>\n",
       "      <td>729.220090</td>\n",
       "      <td>...</td>\n",
       "      <td>1353.664146</td>\n",
       "      <td>1349.523251</td>\n",
       "      <td>1368.371593</td>\n",
       "      <td>1353.634612</td>\n",
       "      <td>1357.850225</td>\n",
       "      <td>1354.710149</td>\n",
       "      <td>1355.474792</td>\n",
       "      <td>1354.426316</td>\n",
       "      <td>1354.660523</td>\n",
       "      <td>1354.457022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>777.060508</td>\n",
       "      <td>918.659699</td>\n",
       "      <td>933.346142</td>\n",
       "      <td>1007.115164</td>\n",
       "      <td>1012.962422</td>\n",
       "      <td>1111.095960</td>\n",
       "      <td>1136.325436</td>\n",
       "      <td>1102.647323</td>\n",
       "      <td>1125.058265</td>\n",
       "      <td>1124.555066</td>\n",
       "      <td>...</td>\n",
       "      <td>734.831924</td>\n",
       "      <td>653.334435</td>\n",
       "      <td>759.915028</td>\n",
       "      <td>738.663968</td>\n",
       "      <td>795.658111</td>\n",
       "      <td>749.993127</td>\n",
       "      <td>789.841469</td>\n",
       "      <td>782.711656</td>\n",
       "      <td>800.878745</td>\n",
       "      <td>800.331316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              1961        1962        1963         1964  \\\n",
       "Albania                 -13.834758    6.437203  -78.364674    36.765861   \n",
       "Austria                 865.618730  870.779595  928.899367   953.978842   \n",
       "Belarus                 815.470694  877.200747  923.935723   925.675488   \n",
       "Belgium                 588.233931  574.260475  674.274851   729.277274   \n",
       "Bosnia and Herzegovina  777.060508  918.659699  933.346142  1007.115164   \n",
       "\n",
       "                               1965         1966         1967         1968  \\\n",
       "Albania                   21.834368    17.419790    21.230166    98.248729   \n",
       "Austria                  907.018796   919.030973   881.043010   908.054910   \n",
       "Belarus                  933.419914   921.544550   945.798567   999.178346   \n",
       "Belgium                  635.267953   734.632818   754.010615   696.401193   \n",
       "Bosnia and Herzegovina  1012.962422  1111.095960  1136.325436  1102.647323   \n",
       "\n",
       "                               1969         1970  ...         2011  \\\n",
       "Albania                  155.460337   162.851283  ...   744.528672   \n",
       "Austria                  840.066677   904.078311  ...  1349.864434   \n",
       "Belarus                  992.680403  1041.301381  ...  1020.686503   \n",
       "Belgium                  818.804401   729.220090  ...  1353.664146   \n",
       "Bosnia and Herzegovina  1125.058265  1124.555066  ...   734.831924   \n",
       "\n",
       "                               2012         2013         2014         2015  \\\n",
       "Albania                  794.041235   800.534160   815.804774   812.927833   \n",
       "Austria                 1354.192008  1383.515474  1328.758610  1390.383215   \n",
       "Belarus                 1025.394254   880.098242   996.816638   857.158851   \n",
       "Belgium                 1349.523251  1368.371593  1353.634612  1357.850225   \n",
       "Bosnia and Herzegovina   653.334435   759.915028   738.663968   795.658111   \n",
       "\n",
       "                               2016         2017         2018         2019  \\\n",
       "Albania                  818.292383   817.404589   821.619756   821.669430   \n",
       "Austria                 1339.989372  1390.463523  1343.677415  1386.151286   \n",
       "Belarus                  958.721120   859.469478   966.582382   931.484184   \n",
       "Belgium                 1354.710149  1355.474792  1354.426316  1354.660523   \n",
       "Bosnia and Herzegovina   749.993127   789.841469   782.711656   800.878745   \n",
       "\n",
       "                               2020  \n",
       "Albania                  824.708041  \n",
       "Austria                 1353.341990  \n",
       "Belarus                  988.133970  \n",
       "Belgium                 1354.457022  \n",
       "Bosnia and Herzegovina   800.331316  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caloric_difference_europe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caloric_difference_europe[caloric_difference_europe[2020].values < 0].index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When running the exact same analysis on European countries as we did in African ones, it's interesting to observe that in Europe, no country at all is producing less than what they actually need. As such, every single European country's suplly is actually greater than its demand, and should in theory be able to solve the hunger issue in Africa.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1) Visualizing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now proceed to the same visualization we did before, this time with the scope of **visualizing the 2020 situation and evaluating if there has been a deficit of kcal over the past years**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "caloric_difference_europe_sorted = caloric_difference_europe[2020].sort_values()\n",
    "p = caloric_difference_europe_sorted.plot(kind='barh', color=(caloric_difference_europe_sorted > 0).map({True: 'g', False: 'red'}),alpha=0.75, rot=0);\n",
    "p.set_xlabel(\"deficit/surplus in the european countries [kcal/persona/day]\")\n",
    "p.set_ylabel(\"European countries\")\n",
    "plt.title('Estimation of net food availability in European countries in 2020' );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As already noticed before, European countries boast a very high surplus of food supplied. We can now use the animation we used before to assess the situation over the past years and see if Europe had deficits:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig = plt.figure()\n",
    "# animator = animation.FuncAnimation(fig, draw_demand_bar, frames=range(1961, 2021),\\\n",
    "#                                    fargs=(caloric_difference_europe, ),\n",
    "#                                       repeat=False);\n",
    "# #HTML(animator.to_jshtml())\n",
    "# with open(path_dict['european_demand_anim'], \"w\") as f:\n",
    "#     print(animator.to_html5_video(), file=f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1200\"\n",
       "            height=\"600\"\n",
       "            src=\"https://manuleo.github.io/mADAm_files/european_cal_diff_animation.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x14adacbd0>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "IFrame(src='https://manuleo.github.io/mADAm_files/european_cal_diff_animation.html', width = 1200, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, **Europe has experienced high affluence periods** since 1961. Over the past 50 years, only in three years some countries presented a really small deficit in calories:\n",
    "- Albania in 1961 and 1963\n",
    "- Moldova, Macedonia and Croatia directly after their independence (this situation already emerged during the food supply analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we draw a map of the European situation. Year **2020** is as usual the most important one, especially for Europe, as we are interested in their actual surplus for the rest of our analysis and because there has been basically no significant change over the last 50 years.  \n",
    "**_Note_: map for 2020 is available [here](https://manuleo.github.io/mADAm_files/european_kcal_surplus2020.html)**  \n",
    "Links for other years:\n",
    "- [1970](https://manuleo.github.io/mADAm_files/european_kcal_surplus1970.html)\n",
    "- [1980](https://manuleo.github.io/mADAm_files/european_kcal_surplus1980.html)\n",
    "- [1990](https://manuleo.github.io/mADAm_files/european_kcal_surplus1990.html)\n",
    "- [2000](https://manuleo.github.io/mADAm_files/european_kcal_surplus2000.html)\n",
    "- [2010](https://manuleo.github.io/mADAm_files/european_kcal_surplus2010.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
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    var color_map_bfafed827cc54760a993e09474ab6a12 = {};

    
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                    `<tr style="text-align: left;">
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                base_layers : {
                },
                overlays :  {
                    "Choropleth" : choropleth_64403fd14e714ec692bd9889b145d91e,
                },
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                layer_control_39118a29a3d841469c384b0bc9c7eef9.base_layers,
                layer_control_39118a29a3d841469c384b0bc9c7eef9.overlays,
                {"autoZIndex": true, "collapsed": true, "position": "topright"}
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x14afffc50>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legend_name = \"Estimation of kcal/persona/day surplus\"\n",
    "for year in range(1970,2030,10):\n",
    "    europe_kcal_surplus_map = plot_map(caloric_difference_europe, path_dict['geoworld_json'], \\\n",
    "    european_country_kv, year, \"Greens\", legend_name, legend_name, path_dict['european_kcal_surplus'] + str(year) + \".html\", bins=8)\n",
    "europe_kcal_surplus_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"600\"\n",
       "            src=\"https://manuleo.github.io/mADAm_files/european_kcal_surplus2020.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x14e3279d0>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
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    "IFrame(src='https://manuleo.github.io/mADAm_files/european_kcal_surplus2020.html', width = 1000, height=600)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a consequence of the welfare in which Europe is leaving since the post wars period, the available surplus hasn't changed a lot over the years. However, a little increase is notable since 1970 to now.  \n",
    "With the information we gathered so far, we can proceed to a **comparison between Africa and Europe**, to then move on to an analysis on which European countries could potentially help African countries in need."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Which European countries can help Africa?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have easily noticed that in Europe, every country has more food than what they need to healthily survive. We will now plot a map that shows this difference more noticeably."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
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       "        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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>MULTIPOLYGON (((16.32653 -5.87747, 16.57318 -6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>POLYGON ((20.59025 41.85540, 20.46317 41.51509...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AUT</td>\n",
       "      <td>Austria</td>\n",
       "      <td>POLYGON ((16.97967 48.12350, 16.90375 47.71487...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BEL</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>POLYGON ((3.31497 51.34578, 4.04707 51.26726, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BEN</td>\n",
       "      <td>Benin</td>\n",
       "      <td>POLYGON ((2.69170 6.25882, 1.86524 6.14216, 1....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id     name                                           geometry\n",
       "1   AGO   Angola  MULTIPOLYGON (((16.32653 -5.87747, 16.57318 -6...\n",
       "2   ALB  Albania  POLYGON ((20.59025 41.85540, 20.46317 41.51509...\n",
       "9   AUT  Austria  POLYGON ((16.97967 48.12350, 16.90375 47.71487...\n",
       "12  BEL  Belgium  POLYGON ((3.31497 51.34578, 4.04707 51.26726, ...\n",
       "13  BEN    Benin  POLYGON ((2.69170 6.25882, 1.86524 6.14216, 1...."
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "afr_eu_country_kv = european_country_kv.copy()\n",
    "afr_eu_country_kv = afr_eu_country_kv.append(african_country_kv)\n",
    "\n",
    "afr_eu_country_kv = afr_eu_country_kv.sort_values(by='names')\n",
    "\n",
    "geojson_afr_eu = geojson_world[geojson_world.id.isin(afr_eu_country_kv.codes)]\n",
    "geojson_afr_eu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "caloric_difference_world = pd.concat([caloric_difference_africa, caloric_difference_europe])\n",
    "caloric_difference_world = caloric_difference_world.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we plot the surplus/deficit of calories in Africa and Europe in the year of **2020**, for the comparison mentioned before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
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    color_map_21de3c330a884ebda68e32870125357b.x = d3.scale.linear()
              .domain([-336.82745378672877, 1354.4570220777964])
              .range([0, 400]);

    color_map_21de3c330a884ebda68e32870125357b.legend = L.control({position: 'topright'});
    color_map_21de3c330a884ebda68e32870125357b.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
    color_map_21de3c330a884ebda68e32870125357b.legend.addTo(map_120ddc27b71944e2a9d6e65421df5979);

    color_map_21de3c330a884ebda68e32870125357b.xAxis = d3.svg.axis()
        .scale(color_map_21de3c330a884ebda68e32870125357b.x)
        .orient("top")
        .tickSize(1)
        .tickValues([-336.82745378672877, 0.0, 300.0, 600.0, 900.0, 1354.4570220777964]);

    color_map_21de3c330a884ebda68e32870125357b.svg = d3.select(".legend.leaflet-control").append("svg")
        .attr("id", 'legend')
        .attr("width", 450)
        .attr("height", 40);

    color_map_21de3c330a884ebda68e32870125357b.g = color_map_21de3c330a884ebda68e32870125357b.svg.append("g")
        .attr("class", "key")
        .attr("transform", "translate(25,16)");

    color_map_21de3c330a884ebda68e32870125357b.g.selectAll("rect")
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        geo_json_e6b4abcd360840bf8b2cb838d2b58123.bindTooltip(
            function(layer){
            // Convert non-primitive to String.
            let handleObject = (feature)=>typeof(feature)=='object' ? JSON.stringify(feature) : feature;
            let fields = ["names", "val"];
            let aliases = ["Country: ", "Estimation of kcal/persona/day surplus"];
            return '<table style="background-color: white; color: #333333; font-family: arial; font-size: 12px; padding: 10px;">' +
            String(
                fields.map(
                columnname=>
                    `<tr style="text-align: left;">
                    <th style="padding: 4px; padding-right: 10px;">
                        ${aliases[fields.indexOf(columnname)]
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                    </th>
                    
                    <td style="padding: 4px;">${handleObject(layer.feature.properties[columnname])
                    }</td></tr>`
                ).join(''))
                +'</table>'
            }, {"sticky": true});
        
    
            var layer_control_7231ba95c851466ab07984cbda73be12 = {
                base_layers : {
                },
                overlays :  {
                    "Choropleth" : choropleth_6336994f14d34cd59e1e81552cceca18,
                },
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            L.control.layers(
                layer_control_7231ba95c851466ab07984cbda73be12.base_layers,
                layer_control_7231ba95c851466ab07984cbda73be12.overlays,
                {"autoZIndex": true, "collapsed": true, "position": "topright"}
            ).addTo(map_120ddc27b71944e2a9d6e65421df5979);
        
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x126be6910>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legend_name = \"Estimation of kcal/persona/day surplus\"\n",
    "bins = [min(caloric_difference_world[2020]), 0, 300, 600, 900, max(caloric_difference_world[2020])]\n",
    "afr_eu_kcal_surplus_map = plot_map(caloric_difference_world, path_dict['geoworld_json'], \\\n",
    "afr_eu_country_kv, year, \"RdYlGn\", legend_name, legend_name, path_dict['world_kcal_surplus'] + str(2020) + \".html\", bins)\n",
    "afr_eu_kcal_surplus_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1100\"\n",
       "            height=\"800\"\n",
       "            src=\"https://manuleo.github.io/mADAm_files/world/world_kcal_surplus2020.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
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      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x149bc7e50>"
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     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "IFrame(src='https://manuleo.github.io/mADAm_files/world/world_kcal_surplus2020.html', width = 1100, height=800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_map_data(geojson_afr_eu, afr_eu_country_kv, caloric_difference_world.T, \"docs/json/cal_world/cal_world_{}.geojson\", \"docs/json/cal_world/cal_world_ticks.json\",\\\n",
    "              bins=[0, 300, 600, 900], year_start = 2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This map illustrates the previously stated assumption that Europe is in a much better food situation than African countries in general. This is obviously observed by checking that there are no countries in Europe with a color \"lower\" than yellow-ish green, while in Africa we observe multiple countries colored in orange and even red! These african countries which don't have enough food to feed some of their citizens are the ones we consider to be in most dire need of help, and thus we will try to come up with opportunities of how to help them in the future.   \n",
    "Another interesting thing to observe is that there seems to be a line dividing the hunger in the world in North and South. By looking at the plots generated by the above cell (located in *visualization/world*), we notice that this line has been going more and more south over the decades, which means that the hunger situation is slowly being solved as years go by. The main reason we think that such a line exists and it's going south is that the countries in the north of Africa are developing more rapidly than their subsaharan counterparts, and are subsequently able to provide better quality of life for their citizens."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Furthermore, comparing the countries in Europe which have the highest surplus to the African countries with the highest deficit would be of interest as well. We consider the countries in need of help as those that do not have a surplus of at least **300** kcal/person/day (orange and red in the map). This selection makes sense because the values we calculate are averaged over the whole population, and if a country has a low surplus, it may mean that a big slice of the population are still starving."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The countries with deficit in calories\n",
    "african_countries_to_help = caloric_difference_africa[caloric_difference_africa[2020].values < 300][2020].sort_values(ascending = False)\n",
    "number_of_need_of_help = len(african_countries_to_help.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The countries in Europe with highest surplus in calories\n",
    "caloric_difference_highest_europe = caloric_difference_europe[2020].sort_values(ascending=False).head(number_of_need_of_help).sort_values(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = plt.GridSpec(1,2)\n",
    "\n",
    "# negative african countries\n",
    "plt.subplot(grid[0, 0]);\n",
    "p = african_countries_to_help.plot(kind='barh', color=(african_countries_to_help > 0).map({True: 'orange', False: 'red'}),alpha=0.75, rot=0);\n",
    "p.set_xlabel(\"deficit in the african countries [kcal/persona/day]\")\n",
    "p.set_ylabel(\"African countries\")\n",
    "plt.title('Lowest surplus in African Countries in 2020' )\n",
    "\n",
    "# highest surplus europe countries\n",
    "plt.subplot(grid[0, 1]);\n",
    "p1 = caloric_difference_highest_europe.plot(kind='barh', color=(caloric_difference_highest_europe > 900).map({True: 'g', False: 'lightgreen'}),alpha=0.75, rot=0);\n",
    "p1.set_xlabel(\"surplus in the european countries [kcal/persona/day]\")\n",
    "p1.set_ylabel(\"European countries\")\n",
    "plt.title('Highest surplus in European countries in 2020' )\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By looking at the difference between the country most in need **Zambia** and the country with the highest surplus **Belgium**, we see that Belgium alone has way more extra food than the lacking countries need individually. As such, we think that an interesting analysis to be made is which European countries could solve African hunger on their own. Below we analyse how much Africa needs in total, and how much Belgium alone \"can\" provide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "African countries with a deficit need 1306.82 extra kcal/person/day in total to solve hunger.\n"
     ]
    }
   ],
   "source": [
    "need_in_africa = abs(african_countries_to_help[african_countries_to_help.values < 0].sum())\n",
    "print(\"African countries with a deficit need {0:.2f} extra kcal/person/day in total to solve hunger.\".format(need_in_africa))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Belgium has 1354.46 kcal/person/day over their basic needs.\n"
     ]
    }
   ],
   "source": [
    "print(\"Belgium has {0:.2f} kcal/person/day over their basic needs.\".format(caloric_difference_highest_europe['Belgium']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These numbers need to be multiplied by the countries' population to allow for direct comparison! We can say however that each person in Belgium could help one person/day in each of the most starving countries in Africa, while still maintaining some extra kcals (around 300 kcal). We proceed to do the analysis on the total caloric need for the starving countries we picked out (less than 0 kcal/person/day)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the african countries with negative surplus\n",
    "deficit_africa = pd.DataFrame(african_countries_to_help[african_countries_to_help.values < 0])\n",
    "\n",
    "# european countries with highest surplus (per person)\n",
    "caloric_surplus_europe_high = pd.DataFrame(caloric_difference_europe[2020].sort_values(ascending=False).head(len(deficit_africa.index)).sort_values(ascending=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>Total deficit (kcal/year)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Namibia</th>\n",
       "      <td>-7.753115e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central African Republic</th>\n",
       "      <td>-1.973362e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Congo</th>\n",
       "      <td>-2.194120e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senegal</th>\n",
       "      <td>-2.408757e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chad</th>\n",
       "      <td>-1.049659e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kenya</th>\n",
       "      <td>-1.541405e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>-2.260161e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madagascar</th>\n",
       "      <td>-2.498640e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ethiopia</th>\n",
       "      <td>-5.256981e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Total deficit (kcal/year)\n",
       "Namibia                               -7.753115e+10\n",
       "Central African Republic              -1.973362e+11\n",
       "Congo                                 -2.194120e+11\n",
       "Senegal                               -2.408757e+11\n",
       "Chad                                  -1.049659e+12\n",
       "Kenya                                 -1.541405e+12\n",
       "Zambia                                -2.260161e+12\n",
       "Madagascar                            -2.498640e+12\n",
       "Ethiopia                              -5.256981e+12"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_need_africa = deficit_africa.merge(pop_tot_africa[2020], left_index=True, right_index=True)\n",
    "total_need_africa['Total deficit (kcal/year)'] = total_need_africa['2020_x'] * total_need_africa['2020_y']*365\n",
    "total_need_africa = total_need_africa.drop(columns=['2020_x', '2020_y']).sort_values(by='Total deficit (kcal/year)', ascending=False)\n",
    "total_need_africa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>Total surplus (kcal/year)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>2.603685e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>2.203824e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>2.230506e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>4.316255e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>4.321679e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>4.448890e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>5.729637e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2.754328e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2.854461e+13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total surplus (kcal/year)\n",
       "Luxembourg               2.603685e+11\n",
       "Norway                   2.203824e+12\n",
       "Ireland                  2.230506e+12\n",
       "Greece                   4.316255e+12\n",
       "Portugal                 4.321679e+12\n",
       "Austria                  4.448890e+12\n",
       "Belgium                  5.729637e+12\n",
       "Italy                    2.754328e+13\n",
       "France                   2.854461e+13"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_extra_europe = caloric_surplus_europe_high.merge(pop_tot_europe[2020], left_index=True, right_index=True)\n",
    "total_extra_europe['Total surplus (kcal/year)'] = total_extra_europe['2020_x'] * total_extra_europe['2020_y']*365\n",
    "total_extra_europe = total_extra_europe.drop(columns=['2020_x', '2020_y']).sort_values(by='Total surplus (kcal/year)')\n",
    "total_extra_europe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = plt.GridSpec(1,2);\n",
    "\n",
    "ax0 = plt.subplot(grid[0, 0]);\n",
    "total_need_africa.plot(kind='barh', color='r',alpha=0.75, rot=0, ax = ax0 );\n",
    "plt.xlabel(\"Deficit in the african countries [kcal/year]\")\n",
    "plt.ylabel(\"African countries\")\n",
    "plt.title('Highest total deficits in African countries in 2020' )\n",
    "ax1 = plt.subplot(grid[0, 1]);\n",
    "\n",
    "total_extra_europe.plot(kind='barh', color='g',alpha=0.75, rot=0, ax = ax1);\n",
    "plt.xlabel(\"Surplus in the european countries [kcal/year]\")\n",
    "plt.ylabel(\"European countries\")\n",
    "plt.title('Highest total surplus in European countries in 2020' )\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now see that **France** is actually the one with highest total surplus (of the ones with highest surplus per person). This is due to the fact that France has a much higher population than Belgium does. Following now is the repetition of the previous analysis if France alone could solve the sum of African deficits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "African countries with a deficit need 1.33E+13 extra kcal/year in total to solve hunger.\n"
     ]
    }
   ],
   "source": [
    "need_in_africa = abs(total_need_africa['Total deficit (kcal/year)'].sum())\n",
    "print(\"African countries with a deficit need {0:.2E} extra kcal/year in total to solve hunger.\".format(need_in_africa))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "France has 2.85E+13 kcal/year over their basic needs.\n"
     ]
    }
   ],
   "source": [
    "surplus_france = total_extra_europe['Total surplus (kcal/year)']['France']\n",
    "print(\"France has {0:.2E} kcal/year over their basic needs.\".format(surplus_france))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this analysis, we see that the total surplus in France is **over double** of the sum of deficits in African countries. This result shows that the European countries with a high surplus per person are indeed very capable of feeding a large percentage of starving people in Africa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final interesting analysis we want to look into is the actual total surplus in each European country. From that, we choose the countries that have the highest of total surplus (multiply the surplus per person by the country population) and then we choose the ones with the highest values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>Total surplus (kcal/year)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>6.409110e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>1.256577e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>1.312546e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>1.432861e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2.619960e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2.754328e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2.854461e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>3.301847e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russian Federation</th>\n",
       "      <td>5.501934e+13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Total surplus (kcal/year)\n",
       "Romania                          6.409110e+12\n",
       "Ukraine                          1.256577e+13\n",
       "Spain                            1.312546e+13\n",
       "Poland                           1.432861e+13\n",
       "United Kingdom                   2.619960e+13\n",
       "Italy                            2.754328e+13\n",
       "France                           2.854461e+13\n",
       "Germany                          3.301847e+13\n",
       "Russian Federation               5.501934e+13"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_europe = pd.DataFrame(caloric_difference_europe[2020]).merge(pop_tot_europe[2020], left_index=True, right_index=True)\n",
    "total_europe['Total surplus (kcal/year)'] = total_europe['2020_x'] * total_europe['2020_y']*365\n",
    "total_europe = total_europe.drop(columns=['2020_x', '2020_y']).sort_values(by='Total surplus (kcal/year)', ascending=False).head(len(deficit_africa.index)).sort_values(by='Total surplus (kcal/year)')\n",
    "total_europe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1 = total_europe.plot(kind='barh', color='g',alpha=0.75, rot=0);\n",
    "p1.set_xlabel(\"Surplus in the european countries [kcal/year]\")\n",
    "p1.set_ylabel(\"European countries\")\n",
    "plt.title('Highest total surplus in European countries in 2020' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the **Russian Federation** has such a high population, we see that, even though its surplus/person is not very high compared to other European countries, the total surplus in the full country will actually overrrule the other countries' surplus. For the sake of consistency, we print below the actual total surplus of **Russian Federation** and compare it to the total deficit in Africa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "African countries with a deficit need 1.33E+13 extra kcal/year in total to solve hunger.\n"
     ]
    }
   ],
   "source": [
    "print(\"African countries with a deficit need {0:.2E} extra kcal/year in total to solve hunger.\".format(need_in_africa))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Russian Federation has 5.50E+13 kcal/year over their basic needs.\n"
     ]
    }
   ],
   "source": [
    "total_surplus_russia = total_europe['Total surplus (kcal/year)']['Russian Federation']\n",
    "print(\"The Russian Federation has {0:.2E} kcal/year over their basic needs.\".format(total_surplus_russia))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this shift in first seeing the total country's surplus, we arrive to the conclusion that the **Russian Federation** alone has a whopping **5 times** bigger surplus than what is needed to solve hunger in the African countries with a deficit.  \n",
    "With this analysis, we're able to see that Europe is very likely able to solve the hunger issue in Africa by giving up a **small fraction** of the extra calories that are not currently being put to use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A summing up to now  \n",
    "\n",
    "The results we came up with are satisfactory as they **accurately reproduce what was expected**. They also allowed for interesting analysis. We decided to conduct an analysis on Europe too as it's interesting to see how much spare food would be available for redistribution, as European countries are more likely to be able to provide help. Furthermore, it was decided to restrict further analysis to 2020 as the interest is in the contemporary situation. **Nine African countries** were found to show an overall deficit in supply, meaning unlike other African countries, they are not capable of solving their issues themselves."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next steps until Milestone 3\n",
    "\n",
    "1. **Split the nutrition supply into macronutrients**, in order to do so we will use the FAO food supply dataset, this time by choosing the protein and fat supply (the charbohydrates will be derived by using _total_carbo (computed basing on kcal) - (fat + supply)_. Then we will compute the needed amounts of these nutrients with the use of a simple approximation on the already computed kcal (55% charbo/ 25% proteins/ 20% fat) and we will compute the difference between the two. In this sense, the analysis will be **fairly similar** to what we already did.  \n",
    "2. After achieving this information, we can compute what are the **best final products that can represent African needs** over all the three macronutrients (and the total kcals needed). In order to do so, we will produce a ranking of products that best match each of the three drivers. Once we have this, we can sample a bunch of products and decide to use them to produce a _\"simple diet\"_ for African countries.\n",
    "3. We then will decide **how Europe should send products to Africa**. As a first approximation, we will aggregate African countries and the same will be done for European ones. In this sense, the Europe as a whole won't be any problem to send products to Africa. We will need to consider the amount of food supply in the whole europe using another time the FAO dataset.\n",
    "4. Having decided which products we should send from Europe and attested that we can send them, we'll find **which countries should receive which food items and in what quantities** (considering their surplus and deficit).\n",
    "    - At this point, we will decide if proceeding with the simple approximation up to the end or not. Our plan now start to move from seeing the European countries as a whole (first approximation) to a second, more in-depth step were we will decide **how to redistribute the aid sending** among all the considered European countries. This step will be done by considering the total supply available in each country in absolute value and then scale it taking into account the population. This will allow to define **a share** for the products.\n",
    "5. **Final visualization steps and data story**\n",
    "    - Improve the **visualization methods** already used and come up with new ones. As example, we would introduce maps changing over time for the fist part of the analysis instead of sticking them to a single year. Also, the animations will be developed in Javascript, allowing the user to enjoy the visualization experience with **real-time manipulations of the data**.\n",
    "    - In parallel to the visualization improvement, we will develop a data story that **presents the issue we want to solve**, and propose **our way to solve it**. The data story we'll propose will probably follow the main points of the analysis we have done so far about Africa vs Europe supply, and from this baseline we'll move to the \"simple diet\" introduction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimizing the distribution of products from Europe to Africa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The idea of optimizing the distribution of products from Europe to Africa presupposes the knowledge of the countries that will help and the countries that will be helped. In this context, we have chosen that helper European countries will be **Italy, France, Germany, United Kingdom, Spain**. This decision is justified by different reasons:\n",
    "- The total surplus of Europe is 2 orders of magnitude higher than the deficit in Africa. We will take this results and adapt it to the scope of our analysis. More specifically, this simplification will allow to stress the fact that **the problem of African starvation can come to an end by the hand of just few countries and not necessarily the whole world**.\n",
    "- During the analysis of food prices we were not able to scrape costs of every food item in every european country. The decision is then to narrow it down.\n",
    "- The choice will be tested with a GWP analysis on the richest European countries that should be involved in the scope of helping Africa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As regards African countries, our plan is to primarily help countries showing a deficit predicted by our initial model. In addition, we will extend the scope to other African countries that shows a weak surplus. As such, we set the threshold of **300kcal/persona/day** for the food availability. If a country is below this upper bound it will be helped to reach it. For the sake of completeness, we want to let the reader know that our model is accurate in many ways but still doesn't take into account of different phenomena that can altarate our values such as **wealth distribution**, **civil wars**, **climate disasters**,etc. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) How much African countries really need to solve the hunger problem?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the countries we need to help:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Central African Republic</th>\n",
       "      <td>411.940700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ethiopia</th>\n",
       "      <td>425.280446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chad</th>\n",
       "      <td>475.076246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madagascar</th>\n",
       "      <td>547.213290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>636.827454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                2020\n",
       "Central African Republic  411.940700\n",
       "Ethiopia                  425.280446\n",
       "Chad                      475.076246\n",
       "Madagascar                547.213290\n",
       "Zambia                    636.827454"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "af_real_deficit = np.abs(300 - african_countries_to_help)\n",
    "af_real_deficit = af_real_deficit.to_frame()\n",
    "af_real_deficit.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the countries vs the amount of kilocalories they need to recevive in order to reach an desired availability of **300kcal/persona/day**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y=\"index\", x=2020, data=af_real_deficit.reset_index(), palette=\"Reds\")\n",
    "plt.xlabel(\"Amount of kilocalories needed [kcal/persona/day]\")\n",
    "plt.ylabel(\"African countries\")\n",
    "plt.title(\"Calculated amount of kcal to reach a threshold of 300kcal/persona/day in every african countries [kcal/persona/day]\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the plot above we see how the darker red coloured countries like **Zambia**, **Madagascar** and **Chad** are most in need. The situation here is flipped so the reader must pay attention to the context of this plot to fully understand the meaning. <br><br>\n",
    "Now we will scale the problem to its real dimensions,*i.e.* we will multiply these values for the population of each country respecetively and for 365 day/year. The results keep the structure but the unit of values will be **kcal/year**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>Total deficit (kcal/year)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ethiopia</th>\n",
       "      <td>1.784549e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kenya</th>\n",
       "      <td>7.429362e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Republic of Tanzania</th>\n",
       "      <td>6.083874e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madagascar</th>\n",
       "      <td>5.530806e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>4.273204e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Total deficit (kcal/year)\n",
       "Ethiopia                                  1.784549e+13\n",
       "Kenya                                     7.429362e+12\n",
       "United Republic of Tanzania               6.083874e+12\n",
       "Madagascar                                5.530806e+12\n",
       "Zambia                                    4.273204e+12"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "af_real_deficit_tot = af_real_deficit.merge(pop_tot_africa[2020], left_index=True, right_index=True)\n",
    "af_real_deficit_tot['Total deficit (kcal/year)'] = af_real_deficit_tot['2020_x'] * af_real_deficit_tot['2020_y']*365\n",
    "af_real_deficit_tot = af_real_deficit_tot.drop(columns=['2020_x', '2020_y']).sort_values(by='Total deficit (kcal/year)', ascending=False)\n",
    "af_real_deficit_tot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) GDP Analysis on European countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have set the number of countries that will be \"the helper\", now we want to understand how much they should contribute individually to the cause. In particular, the scope is to define an index by which we will weight the contribution of each European country. According to this [website](https://www.thebalance.com/gdp-per-capita-formula-u-s-compared-to-highest-and-lowest-3305848), \"the **GDP per capita is a measure of a country's economic output** that accounts for its number of people. It is a good measurement of a country's standard of living\". This looks like exactly what we are looking for. Starting from the GDP per capita, it will be relatively easy to compute an **effective weight for how prosperous a country is**. The goal here is to **determine a measure for how much countries should give up of their surplus** and the idea is to model the optimization problem in order to **penalize countries with highest GDP that should indeed contribute the most**.\n",
    "We will show initally all the countries but then narrow it down as mentioned before."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the dataset from the FAO on **Macro Statistics and Key Indicators**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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>Area Code</th>\n",
       "      <th>Area</th>\n",
       "      <th>Item Code</th>\n",
       "      <th>Item</th>\n",
       "      <th>Element Code</th>\n",
       "      <th>Element</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Y1970</th>\n",
       "      <th>Y1970F</th>\n",
       "      <th>Y1970N</th>\n",
       "      <th>...</th>\n",
       "      <th>Y2014N</th>\n",
       "      <th>Y2015</th>\n",
       "      <th>Y2015F</th>\n",
       "      <th>Y2015N</th>\n",
       "      <th>Y2016</th>\n",
       "      <th>Y2016F</th>\n",
       "      <th>Y2016N</th>\n",
       "      <th>Y2017</th>\n",
       "      <th>Y2017F</th>\n",
       "      <th>Y2017N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Albania</td>\n",
       "      <td>22008</td>\n",
       "      <td>Gross Domestic Product</td>\n",
       "      <td>6109</td>\n",
       "      <td>Value Local Currency</td>\n",
       "      <td>millions</td>\n",
       "      <td>11327.885280</td>\n",
       "      <td>X</td>\n",
       "      <td>Data from UNSD AMA</td>\n",
       "      <td>...</td>\n",
       "      <td>Data from UNSD AMA</td>\n",
       "      <td>1.434307e+06</td>\n",
       "      <td>X</td>\n",
       "      <td>Data from UNSD AMA</td>\n",
       "      <td>1.475251e+06</td>\n",
       "      <td>X</td>\n",
       "      <td>Data from UNSD AMA</td>\n",
       "      <td>1.552983e+06</td>\n",
       "      <td>X</td>\n",
       "      <td>Data from UNSD AMA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Albania</td>\n",
       "      <td>22008</td>\n",
       "      <td>Gross Domestic Product</td>\n",
       "      <td>6110</td>\n",
       "      <td>Value US$</td>\n",
       "      <td>millions</td>\n",
       "      <td>2265.577056</td>\n",
       "      <td>X</td>\n",
       "      <td>Data from UNSD AMA</td>\n",
       "      <td>...</td>\n",
       "      <td>Data from UNSD AMA</td>\n",
       "      <td>1.138692e+04</td>\n",
       "      <td>X</td>\n",
       "      <td>Data from UNSD AMA</td>\n",
       "      <td>1.188368e+04</td>\n",
       "      <td>X</td>\n",
       "      <td>Data from UNSD AMA</td>\n",
       "      <td>1.303936e+04</td>\n",
       "      <td>X</td>\n",
       "      <td>Data from UNSD AMA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 151 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area Code     Area  Item Code                    Item  Element Code  \\\n",
       "0          3  Albania      22008  Gross Domestic Product          6109   \n",
       "1          3  Albania      22008  Gross Domestic Product          6110   \n",
       "\n",
       "                Element      Unit         Y1970 Y1970F              Y1970N  \\\n",
       "0  Value Local Currency  millions  11327.885280      X  Data from UNSD AMA   \n",
       "1             Value US$  millions   2265.577056      X  Data from UNSD AMA   \n",
       "\n",
       "   ...              Y2014N         Y2015 Y2015F              Y2015N  \\\n",
       "0  ...  Data from UNSD AMA  1.434307e+06      X  Data from UNSD AMA   \n",
       "1  ...  Data from UNSD AMA  1.138692e+04      X  Data from UNSD AMA   \n",
       "\n",
       "          Y2016 Y2016F              Y2016N         Y2017 Y2017F  \\\n",
       "0  1.475251e+06      X  Data from UNSD AMA  1.552983e+06      X   \n",
       "1  1.188368e+04      X  Data from UNSD AMA  1.303936e+04      X   \n",
       "\n",
       "               Y2017N  \n",
       "0  Data from UNSD AMA  \n",
       "1  Data from UNSD AMA  \n",
       "\n",
       "[2 rows x 151 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_europe = pd.read_csv(\"data/raw/Macro-Statistics_Key_Indicators_E_Europe.csv\")\n",
    "gdp_europe.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are interested in the **Gross Domestic Product in US Dollars**. We now filter down for Element Code and Item Code. In particular **Item Code** equal to **22008** corresponds to \"Gross Domestic Product\" and **Element Code** equal **22008** corresponds to \"Value US$\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filtering element code and item code\n",
    "gdp_europe = gdp_europe[(gdp_europe[\"Element Code\"]==6110) & (gdp_europe[\"Item Code\"]==22008)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A short note on the dataset: the most recent observation is for 2017 and in our analysis many times we predicted the data up to 2020 if not available. In this case, we decide not to do it since we are not interested in the exact value of GDP, but rather to an index. Therefore, we accept the error as it will be neglegible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We clean now a little bit our dataframe in order to delete information we don't need:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP [millions US$]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>13039.355905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>416835.975860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>54441.189058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>494763.551890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>18169.075913</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        GDP [millions US$]\n",
       "Area                                      \n",
       "Albania                       13039.355905\n",
       "Austria                      416835.975860\n",
       "Belarus                       54441.189058\n",
       "Belgium                      494763.551890\n",
       "Bosnia and Herzegovina        18169.075913"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Delete all the columns but Area and Y2017, set the index as Area (name of the country)\n",
    "gdp_europe = gdp_europe[[\"Area\",\"Y2017\"]].set_index(\"Area\")\n",
    "#Rename column\n",
    "gdp_europe = gdp_europe.rename(columns={\"Y2017\": \"GDP [millions US$]\"})\n",
    "#Drop Malta as its not in our scope\n",
    "gdp_europe = gdp_europe.drop(\"Malta\")\n",
    "#Filter down to the coutries in our main list european_country_kv\n",
    "gdp_europe = gdp_europe[gdp_europe.index.isin(european_country_kv.names)]\n",
    "gdp_europe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize quickly this result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = sns.barplot(y=\"Area\", x=\"GDP [millions US$]\",\\\n",
    "            data=gdp_europe.sort_values(by=\"GDP [millions US$]\",ascending=True).reset_index()\\\n",
    "            ,palette=\"Greens\")\n",
    "p.set(xscale=\"log\") #we use log axis because we are are more interested in the order of magnitude than the exact value\n",
    "plt.xlabel(\"Log of GWP [millions US$]\")\n",
    "plt.ylabel(\"European countries\")\n",
    "plt.title(\"GWP for European countries [millions US$]\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see **Germany, United Kingdom, France, Italy and Spain are the countries with highest GDP in Europe**. This justifies perfectly our design choice of narrowing down the helper countries to these five. Since we are not taking into account of population, Russian Federation shows up in the list of richest countries. Let's analyse now what happens if we divide by population. We expect that Switzerland and Luxembourg will be at the top of the list while Russian Federation will lose positions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>GDP kUS$/capita</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>4.531015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>46.282197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>5.761386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>42.690245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>5.537976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        GDP kUS$/capita\n",
       "Area                                   \n",
       "Albania                        4.531015\n",
       "Austria                       46.282197\n",
       "Belarus                        5.761386\n",
       "Belgium                       42.690245\n",
       "Bosnia and Herzegovina         5.537976"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create dataframe gdp_capita\n",
    "gdp_capita = pd.DataFrame(index=gdp_europe.index)\n",
    "#retrieve population data\n",
    "pop_tot_2020 = pop_tot_europe[[2020]]\n",
    "#dividing gwp for population\n",
    "gdp_capita[\"GDP kUS$/capita\"] = gdp_europe.values/pop_tot_2020.values*1000\n",
    "gdp_capita.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = sns.barplot(y=\"Area\", x=\"GDP kUS$/capita\",\\\n",
    "            data=gdp_capita.sort_values(by=\"GDP kUS$/capita\",ascending=True).reset_index()\\\n",
    "            ,palette=\"Greens\")\n",
    "plt.xlabel(\"GWP per capita [kUS$/capita]\")\n",
    "plt.ylabel(\"European countries\")\n",
    "plt.title(\"GWP per capita for European countries [kUS$/capita]\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our expectations are met. Switzerland and Luxembourg are the richest countries if considered the well-being of every person. Frow now on we will consider **the GWP per capita as the weight indicating at what extent countries should contribute to the African cause**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Modelling the optmization problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this chapter we will introduce **a method to redistribute food among African countries** that are in need. The idea behind the implementation is to **minimize the amount of food that has to be delivered** by our five best european contries. While minimizing, we will have to respect the demand constraint as well as the constraint on food availability of each European country. It is very important to model our problem in a way that richest countries with an higher food availability will have to give up more and viceversa. It is now way more clear where the analysis on GWP will come in handy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The objective function to be minimized is a **quadratic non-negative weighted sum of food [kcal/year]**. More spefically, the weights we designed take into account both of GWP of a country and the food availability [kcal/year]. The modelling choice is justified by the fact that a rich country with a large surplus should contribute more than a relatively poor country with less possibilities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem we want to model is a **Quadratic Program with Linear Constraints** a.k.a **QP**. The choice of a quadratic objective function is due the fact that we will need to evenely distribute the resources and this means that weights will have to increase quadratically with the amount of food given away. It makes sense to say that the more a country gives away of its surplus, the less the same country should contribute further if other countries didn't countribute at all yet."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In particular:\n",
    "- $Y$ is a matrix in $R^{mxn}$ in which $m$ is the number of European countries and $n$ is the number of African countries. Each entry $y_{ij}$ of the matrix $Y\\in{R^{mxn}}$ is the amount [kcal/year] of kilocalories that the European country $i\\in{1,...,m}$ will have to send to the African country $j\\in{1,...,n}$\n",
    "- The weights will be in the interval $[-1,1]$. Initally we normalize the GWP per capita of every european country. Afterwards, we will calculate the gwp_inverse = (1-gwp_normalized). The final weight is computed by multiplying the food surplus for gwp_inverse. The number obtained will be the measure by which the logic \"The richest gives more\" is respected. We will name the weights $w\\in{R^{mx1}}$.\n",
    "- The objective function is $\\sum_{i=1}^{m}\\sum_{j=1}^{n}{w_jy_{ij}^2 + w_jy_{ij}}$\n",
    "- The constraints can be considered a restriction on the value that our decision variable will assume. By restricting the feasibile set we will impose the following limits: \n",
    "  - non-negativity of food [kcal/year] sent\n",
    "  - supply and demand must be met according to European country surplus and African countries deficit.\n",
    "  - even distribution of resources\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is then to normalize the GWP in order to scale them in the interval $[-1,1]$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>GDP/capita scaled</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0.025796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>0.453856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>0.038410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>0.417029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>0.036120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        GDP/capita scaled\n",
       "Area                                     \n",
       "Albania                          0.025796\n",
       "Austria                          0.453856\n",
       "Belarus                          0.038410\n",
       "Belgium                          0.417029\n",
       "Bosnia and Herzegovina           0.036120"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "scaler.fit(gdp_capita.values.reshape(-1,1)) #Min Max of sklearn used\n",
    "gdp_europe_capita_scaled = pd.DataFrame(index=gdp_capita.index) #create new dataframe\n",
    "gdp_europe_capita_scaled[\"GDP/capita scaled\"] = np.round_(scaler.transform(gdp_capita.values.reshape(-1,1)),6) #round values in order to have clean numbers\n",
    "gdp_europe_capita_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
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h=1;for(c=l(t,u,t.length,e),u=0;u<o;u++)h*=e;this.imuln(h),this.words[0]+c<67108864?this.words[0]+=c:this._iaddn(c)}},i.prototype.copy=function(t){t.words=new Array(this.length);for(var e=0;e<this.length;e++)t.words[e]=this.words[e];t.length=this.length,t.negative=this.negative,t.red=this.red},i.prototype.clone=function(){var t=new i(null);return this.copy(t),t},i.prototype._expand=function(t){for(;this.length<t;)this.words[this.length++]=0;return this},i.prototype.strip=function(){for(;this.length>1&&0===this.words[this.length-1];)this.length--;return this._normSign()},i.prototype._normSign=function(){return 1===this.length&&0===this.words[0]&&(this.negative=0),this},i.prototype.inspect=function(){return(this.red?\"<BN-R: \":\"<BN: \")+this.toString(16)+\">\"};var c=[\"\",\"0\",\"00\",\"000\",\"0000\",\"00000\",\"000000\",\"0000000\",\"00000000\",\"000000000\",\"0000000000\",\"00000000000\",\"000000000000\",\"0000000000000\",\"00000000000000\",\"000000000000000\",\"0000000000000000\",\"00000000000000000\",\"000000000000000000\",\"0000000000000000000\",\"00000000000000000000\",\"000000000000000000000\",\"0000000000000000000000\",\"00000000000000000000000\",\"000000000000000000000000\",\"0000000000000000000000000\"],u=[0,0,25,16,12,11,10,9,8,8,7,7,7,7,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5],h=[0,0,33554432,43046721,16777216,48828125,60466176,40353607,16777216,43046721,1e7,19487171,35831808,62748517,7529536,11390625,16777216,24137569,34012224,47045881,64e6,4084101,5153632,6436343,7962624,9765625,11881376,14348907,17210368,20511149,243e5,28629151,33554432,39135393,45435424,52521875,60466176];function f(t,e,r){r.negative=e.negative^t.negative;var n=t.length+e.length|0;r.length=n,n=n-1|0;var a=0|t.words[0],i=0|e.words[0],o=a*i,s=67108863&o,l=o/67108864|0;r.words[0]=s;for(var c=1;c<n;c++){for(var u=l>>>26,h=67108863&l,f=Math.min(c,e.length-1),p=Math.max(0,c-t.length+1);p<=f;p++){var d=c-p|0;u+=(o=(a=0|t.words[d])*(i=0|e.words[p])+h)/67108864|0,h=67108863&o}r.words[c]=0|h,l=0|u}return 0!==l?r.words[c]=0|l:r.length--,r.strip()}i.prototype.toString=function(t,e){var r;if(e=0|e||1,16===(t=t||10)||\"hex\"===t){r=\"\";for(var a=0,i=0,o=0;o<this.length;o++){var s=this.words[o],l=(16777215&(s<<a|i)).toString(16);r=0!==(i=s>>>24-a&16777215)||o!==this.length-1?c[6-l.length]+l+r:l+r,(a+=2)>=26&&(a-=26,o--)}for(0!==i&&(r=i.toString(16)+r);r.length%e!=0;)r=\"0\"+r;return 0!==this.negative&&(r=\"-\"+r),r}if(t===(0|t)&&t>=2&&t<=36){var f=u[t],p=h[t];r=\"\";var d=this.clone();for(d.negative=0;!d.isZero();){var g=d.modn(p).toString(t);r=(d=d.idivn(p)).isZero()?g+r:c[f-g.length]+g+r}for(this.isZero()&&(r=\"0\"+r);r.length%e!=0;)r=\"0\"+r;return 0!==this.negative&&(r=\"-\"+r),r}n(!1,\"Base 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32-Math.clz32(t)}:i.prototype._countBits=function(t){var e=t,r=0;return e>=4096&&(r+=13,e>>>=13),e>=64&&(r+=7,e>>>=7),e>=8&&(r+=4,e>>>=4),e>=2&&(r+=2,e>>>=2),r+e},i.prototype._zeroBits=function(t){if(0===t)return 26;var e=t,r=0;return 0==(8191&e)&&(r+=13,e>>>=13),0==(127&e)&&(r+=7,e>>>=7),0==(15&e)&&(r+=4,e>>>=4),0==(3&e)&&(r+=2,e>>>=2),0==(1&e)&&r++,r},i.prototype.bitLength=function(){var t=this.words[this.length-1],e=this._countBits(t);return 26*(this.length-1)+e},i.prototype.zeroBits=function(){if(this.isZero())return 0;for(var t=0,e=0;e<this.length;e++){var r=this._zeroBits(this.words[e]);if(t+=r,26!==r)break}return t},i.prototype.byteLength=function(){return Math.ceil(this.bitLength()/8)},i.prototype.toTwos=function(t){return 0!==this.negative?this.abs().inotn(t).iaddn(1):this.clone()},i.prototype.fromTwos=function(t){return this.testn(t-1)?this.notn(t).iaddn(1).ineg():this.clone()},i.prototype.isNeg=function(){return 0!==this.negative},i.prototype.neg=function(){return this.clone().ineg()},i.prototype.ineg=function(){return this.isZero()||(this.negative^=1),this},i.prototype.iuor=function(t){for(;this.length<t.length;)this.words[this.length++]=0;for(var e=0;e<t.length;e++)this.words[e]=this.words[e]|t.words[e];return this.strip()},i.prototype.ior=function(t){return n(0==(this.negative|t.negative)),this.iuor(t)},i.prototype.or=function(t){return this.length>t.length?this.clone().ior(t):t.clone().ior(this)},i.prototype.uor=function(t){return this.length>t.length?this.clone().iuor(t):t.clone().iuor(this)},i.prototype.iuand=function(t){var e;e=this.length>t.length?t:this;for(var r=0;r<e.length;r++)this.words[r]=this.words[r]&t.words[r];return this.length=e.length,this.strip()},i.prototype.iand=function(t){return n(0==(this.negative|t.negative)),this.iuand(t)},i.prototype.and=function(t){return this.length>t.length?this.clone().iand(t):t.clone().iand(this)},i.prototype.uand=function(t){return this.length>t.length?this.clone().iuand(t):t.clone().iuand(this)},i.prototype.iuxor=function(t){var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},i.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},i.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},i.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},i.prototype.inotn=function(t){n(\"number\"==typeof t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var a=0;a<e;a++)this.words[a]=67108863&~this.words[a];return r>0&&(this.words[a]=~this.words[a]&67108863>>26-r),this.strip()},i.prototype.notn=function(t){return this.clone().inotn(t)},i.prototype.setn=function(t,e){n(\"number\"==typeof t&&t>=0);var r=t/26|0,a=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]|1<<a:this.words[r]&~(1<<a),this.strip()},i.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 a=0,i=0;i<n.length;i++)e=(0|r.words[i])+(0|n.words[i])+a,this.words[i]=67108863&e,a=e>>>26;for(;0!==a&&i<r.length;i++)e=(0|r.words[i])+a,this.words[i]=67108863&e,a=e>>>26;if(this.length=r.length,0!==a)this.words[this.length]=a,this.length++;else if(r!==this)for(;i<r.length;i++)this.words[i]=r.words[i];return this},i.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)},i.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,a=this.cmp(t);if(0===a)return this.negative=0,this.length=1,this.words[0]=0,this;a>0?(r=this,n=t):(r=t,n=this);for(var i=0,o=0;o<n.length;o++)i=(e=(0|r.words[o])-(0|n.words[o])+i)>>26,this.words[o]=67108863&e;for(;0!==i&&o<r.length;o++)i=(e=(0|r.words[o])+i)>>26,this.words[o]=67108863&e;if(0===i&&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()},i.prototype.sub=function(t){return this.clone().isub(t)};var p=function(t,e,r){var n,a,i,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],h=8191&u,f=u>>>13,p=0|o[1],d=8191&p,g=p>>>13,v=0|o[2],m=8191&v,y=v>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],k=8191&w,T=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],O=8191&P,z=P>>>13,I=0|o[8],D=8191&I,R=I>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],V=8191&j,U=j>>>13,q=0|s[1],H=8191&q,G=q>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,at=0|s[6],it=8191&at,ot=at>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ht=8191&ut,ft=ut>>>13,pt=0|s[9],dt=8191&pt,gt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var vt=(c+(n=Math.imul(h,V))|0)+((8191&(a=(a=Math.imul(h,U))+Math.imul(f,V)|0))<<13)|0;c=((i=Math.imul(f,U))+(a>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(d,V),a=(a=Math.imul(d,U))+Math.imul(g,V)|0,i=Math.imul(g,U);var mt=(c+(n=n+Math.imul(h,H)|0)|0)+((8191&(a=(a=a+Math.imul(h,G)|0)+Math.imul(f,H)|0))<<13)|0;c=((i=i+Math.imul(f,G)|0)+(a>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(m,V),a=(a=Math.imul(m,U))+Math.imul(y,V)|0,i=Math.imul(y,U),n=n+Math.imul(d,H)|0,a=(a=a+Math.imul(d,G)|0)+Math.imul(g,H)|0,i=i+Math.imul(g,G)|0;var yt=(c+(n=n+Math.imul(h,W)|0)|0)+((8191&(a=(a=a+Math.imul(h,X)|0)+Math.imul(f,W)|0))<<13)|0;c=((i=i+Math.imul(f,X)|0)+(a>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,V),a=(a=Math.imul(b,U))+Math.imul(_,V)|0,i=Math.imul(_,U),n=n+Math.imul(m,H)|0,a=(a=a+Math.imul(m,G)|0)+Math.imul(y,H)|0,i=i+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,a=(a=a+Math.imul(d,X)|0)+Math.imul(g,W)|0,i=i+Math.imul(g,X)|0;var xt=(c+(n=n+Math.imul(h,J)|0)|0)+((8191&(a=(a=a+Math.imul(h,K)|0)+Math.imul(f,J)|0))<<13)|0;c=((i=i+Math.imul(f,K)|0)+(a>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(k,V),a=(a=Math.imul(k,U))+Math.imul(T,V)|0,i=Math.imul(T,U),n=n+Math.imul(b,H)|0,a=(a=a+Math.imul(b,G)|0)+Math.imul(_,H)|0,i=i+Math.imul(_,G)|0,n=n+Math.imul(m,W)|0,a=(a=a+Math.imul(m,X)|0)+Math.imul(y,W)|0,i=i+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,a=(a=a+Math.imul(d,K)|0)+Math.imul(g,J)|0,i=i+Math.imul(g,K)|0;var bt=(c+(n=n+Math.imul(h,$)|0)|0)+((8191&(a=(a=a+Math.imul(h,tt)|0)+Math.imul(f,$)|0))<<13)|0;c=((i=i+Math.imul(f,tt)|0)+(a>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,V),a=(a=Math.imul(M,U))+Math.imul(S,V)|0,i=Math.imul(S,U),n=n+Math.imul(k,H)|0,a=(a=a+Math.imul(k,G)|0)+Math.imul(T,H)|0,i=i+Math.imul(T,G)|0,n=n+Math.imul(b,W)|0,a=(a=a+Math.imul(b,X)|0)+Math.imul(_,W)|0,i=i+Math.imul(_,X)|0,n=n+Math.imul(m,J)|0,a=(a=a+Math.imul(m,K)|0)+Math.imul(y,J)|0,i=i+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,a=(a=a+Math.imul(d,tt)|0)+Math.imul(g,$)|0,i=i+Math.imul(g,tt)|0;var _t=(c+(n=n+Math.imul(h,rt)|0)|0)+((8191&(a=(a=a+Math.imul(h,nt)|0)+Math.imul(f,rt)|0))<<13)|0;c=((i=i+Math.imul(f,nt)|0)+(a>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,V),a=(a=Math.imul(L,U))+Math.imul(C,V)|0,i=Math.imul(C,U),n=n+Math.imul(M,H)|0,a=(a=a+Math.imul(M,G)|0)+Math.imul(S,H)|0,i=i+Math.imul(S,G)|0,n=n+Math.imul(k,W)|0,a=(a=a+Math.imul(k,X)|0)+Math.imul(T,W)|0,i=i+Math.imul(T,X)|0,n=n+Math.imul(b,J)|0,a=(a=a+Math.imul(b,K)|0)+Math.imul(_,J)|0,i=i+Math.imul(_,K)|0,n=n+Math.imul(m,$)|0,a=(a=a+Math.imul(m,tt)|0)+Math.imul(y,$)|0,i=i+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,a=(a=a+Math.imul(d,nt)|0)+Math.imul(g,rt)|0,i=i+Math.imul(g,nt)|0;var wt=(c+(n=n+Math.imul(h,it)|0)|0)+((8191&(a=(a=a+Math.imul(h,ot)|0)+Math.imul(f,it)|0))<<13)|0;c=((i=i+Math.imul(f,ot)|0)+(a>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(O,V),a=(a=Math.imul(O,U))+Math.imul(z,V)|0,i=Math.imul(z,U),n=n+Math.imul(L,H)|0,a=(a=a+Math.imul(L,G)|0)+Math.imul(C,H)|0,i=i+Math.imul(C,G)|0,n=n+Math.imul(M,W)|0,a=(a=a+Math.imul(M,X)|0)+Math.imul(S,W)|0,i=i+Math.imul(S,X)|0,n=n+Math.imul(k,J)|0,a=(a=a+Math.imul(k,K)|0)+Math.imul(T,J)|0,i=i+Math.imul(T,K)|0,n=n+Math.imul(b,$)|0,a=(a=a+Math.imul(b,tt)|0)+Math.imul(_,$)|0,i=i+Math.imul(_,tt)|0,n=n+Math.imul(m,rt)|0,a=(a=a+Math.imul(m,nt)|0)+Math.imul(y,rt)|0,i=i+Math.imul(y,nt)|0,n=n+Math.imul(d,it)|0,a=(a=a+Math.imul(d,ot)|0)+Math.imul(g,it)|0,i=i+Math.imul(g,ot)|0;var kt=(c+(n=n+Math.imul(h,lt)|0)|0)+((8191&(a=(a=a+Math.imul(h,ct)|0)+Math.imul(f,lt)|0))<<13)|0;c=((i=i+Math.imul(f,ct)|0)+(a>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(D,V),a=(a=Math.imul(D,U))+Math.imul(R,V)|0,i=Math.imul(R,U),n=n+Math.imul(O,H)|0,a=(a=a+Math.imul(O,G)|0)+Math.imul(z,H)|0,i=i+Math.imul(z,G)|0,n=n+Math.imul(L,W)|0,a=(a=a+Math.imul(L,X)|0)+Math.imul(C,W)|0,i=i+Math.imul(C,X)|0,n=n+Math.imul(M,J)|0,a=(a=a+Math.imul(M,K)|0)+Math.imul(S,J)|0,i=i+Math.imul(S,K)|0,n=n+Math.imul(k,$)|0,a=(a=a+Math.imul(k,tt)|0)+Math.imul(T,$)|0,i=i+Math.imul(T,tt)|0,n=n+Math.imul(b,rt)|0,a=(a=a+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,i=i+Math.imul(_,nt)|0,n=n+Math.imul(m,it)|0,a=(a=a+Math.imul(m,ot)|0)+Math.imul(y,it)|0,i=i+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,a=(a=a+Math.imul(d,ct)|0)+Math.imul(g,lt)|0,i=i+Math.imul(g,ct)|0;var Tt=(c+(n=n+Math.imul(h,ht)|0)|0)+((8191&(a=(a=a+Math.imul(h,ft)|0)+Math.imul(f,ht)|0))<<13)|0;c=((i=i+Math.imul(f,ft)|0)+(a>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(B,V),a=(a=Math.imul(B,U))+Math.imul(N,V)|0,i=Math.imul(N,U),n=n+Math.imul(D,H)|0,a=(a=a+Math.imul(D,G)|0)+Math.imul(R,H)|0,i=i+Math.imul(R,G)|0,n=n+Math.imul(O,W)|0,a=(a=a+Math.imul(O,X)|0)+Math.imul(z,W)|0,i=i+Math.imul(z,X)|0,n=n+Math.imul(L,J)|0,a=(a=a+Math.imul(L,K)|0)+Math.imul(C,J)|0,i=i+Math.imul(C,K)|0,n=n+Math.imul(M,$)|0,a=(a=a+Math.imul(M,tt)|0)+Math.imul(S,$)|0,i=i+Math.imul(S,tt)|0,n=n+Math.imul(k,rt)|0,a=(a=a+Math.imul(k,nt)|0)+Math.imul(T,rt)|0,i=i+Math.imul(T,nt)|0,n=n+Math.imul(b,it)|0,a=(a=a+Math.imul(b,ot)|0)+Math.imul(_,it)|0,i=i+Math.imul(_,ot)|0,n=n+Math.imul(m,lt)|0,a=(a=a+Math.imul(m,ct)|0)+Math.imul(y,lt)|0,i=i+Math.imul(y,ct)|0,n=n+Math.imul(d,ht)|0,a=(a=a+Math.imul(d,ft)|0)+Math.imul(g,ht)|0,i=i+Math.imul(g,ft)|0;var At=(c+(n=n+Math.imul(h,dt)|0)|0)+((8191&(a=(a=a+Math.imul(h,gt)|0)+Math.imul(f,dt)|0))<<13)|0;c=((i=i+Math.imul(f,gt)|0)+(a>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,H),a=(a=Math.imul(B,G))+Math.imul(N,H)|0,i=Math.imul(N,G),n=n+Math.imul(D,W)|0,a=(a=a+Math.imul(D,X)|0)+Math.imul(R,W)|0,i=i+Math.imul(R,X)|0,n=n+Math.imul(O,J)|0,a=(a=a+Math.imul(O,K)|0)+Math.imul(z,J)|0,i=i+Math.imul(z,K)|0,n=n+Math.imul(L,$)|0,a=(a=a+Math.imul(L,tt)|0)+Math.imul(C,$)|0,i=i+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,a=(a=a+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,i=i+Math.imul(S,nt)|0,n=n+Math.imul(k,it)|0,a=(a=a+Math.imul(k,ot)|0)+Math.imul(T,it)|0,i=i+Math.imul(T,ot)|0,n=n+Math.imul(b,lt)|0,a=(a=a+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,i=i+Math.imul(_,ct)|0,n=n+Math.imul(m,ht)|0,a=(a=a+Math.imul(m,ft)|0)+Math.imul(y,ht)|0,i=i+Math.imul(y,ft)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(a=(a=a+Math.imul(d,gt)|0)+Math.imul(g,dt)|0))<<13)|0;c=((i=i+Math.imul(g,gt)|0)+(a>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),a=(a=Math.imul(B,X))+Math.imul(N,W)|0,i=Math.imul(N,X),n=n+Math.imul(D,J)|0,a=(a=a+Math.imul(D,K)|0)+Math.imul(R,J)|0,i=i+Math.imul(R,K)|0,n=n+Math.imul(O,$)|0,a=(a=a+Math.imul(O,tt)|0)+Math.imul(z,$)|0,i=i+Math.imul(z,tt)|0,n=n+Math.imul(L,rt)|0,a=(a=a+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,i=i+Math.imul(C,nt)|0,n=n+Math.imul(M,it)|0,a=(a=a+Math.imul(M,ot)|0)+Math.imul(S,it)|0,i=i+Math.imul(S,ot)|0,n=n+Math.imul(k,lt)|0,a=(a=a+Math.imul(k,ct)|0)+Math.imul(T,lt)|0,i=i+Math.imul(T,ct)|0,n=n+Math.imul(b,ht)|0,a=(a=a+Math.imul(b,ft)|0)+Math.imul(_,ht)|0,i=i+Math.imul(_,ft)|0;var St=(c+(n=n+Math.imul(m,dt)|0)|0)+((8191&(a=(a=a+Math.imul(m,gt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((i=i+Math.imul(y,gt)|0)+(a>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),a=(a=Math.imul(B,K))+Math.imul(N,J)|0,i=Math.imul(N,K),n=n+Math.imul(D,$)|0,a=(a=a+Math.imul(D,tt)|0)+Math.imul(R,$)|0,i=i+Math.imul(R,tt)|0,n=n+Math.imul(O,rt)|0,a=(a=a+Math.imul(O,nt)|0)+Math.imul(z,rt)|0,i=i+Math.imul(z,nt)|0,n=n+Math.imul(L,it)|0,a=(a=a+Math.imul(L,ot)|0)+Math.imul(C,it)|0,i=i+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,a=(a=a+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,i=i+Math.imul(S,ct)|0,n=n+Math.imul(k,ht)|0,a=(a=a+Math.imul(k,ft)|0)+Math.imul(T,ht)|0,i=i+Math.imul(T,ft)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(a=(a=a+Math.imul(b,gt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((i=i+Math.imul(_,gt)|0)+(a>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),a=(a=Math.imul(B,tt))+Math.imul(N,$)|0,i=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,a=(a=a+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,i=i+Math.imul(R,nt)|0,n=n+Math.imul(O,it)|0,a=(a=a+Math.imul(O,ot)|0)+Math.imul(z,it)|0,i=i+Math.imul(z,ot)|0,n=n+Math.imul(L,lt)|0,a=(a=a+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,i=i+Math.imul(C,ct)|0,n=n+Math.imul(M,ht)|0,a=(a=a+Math.imul(M,ft)|0)+Math.imul(S,ht)|0,i=i+Math.imul(S,ft)|0;var Lt=(c+(n=n+Math.imul(k,dt)|0)|0)+((8191&(a=(a=a+Math.imul(k,gt)|0)+Math.imul(T,dt)|0))<<13)|0;c=((i=i+Math.imul(T,gt)|0)+(a>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),a=(a=Math.imul(B,nt))+Math.imul(N,rt)|0,i=Math.imul(N,nt),n=n+Math.imul(D,it)|0,a=(a=a+Math.imul(D,ot)|0)+Math.imul(R,it)|0,i=i+Math.imul(R,ot)|0,n=n+Math.imul(O,lt)|0,a=(a=a+Math.imul(O,ct)|0)+Math.imul(z,lt)|0,i=i+Math.imul(z,ct)|0,n=n+Math.imul(L,ht)|0,a=(a=a+Math.imul(L,ft)|0)+Math.imul(C,ht)|0,i=i+Math.imul(C,ft)|0;var Ct=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(a=(a=a+Math.imul(M,gt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((i=i+Math.imul(S,gt)|0)+(a>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,it),a=(a=Math.imul(B,ot))+Math.imul(N,it)|0,i=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,a=(a=a+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,i=i+Math.imul(R,ct)|0,n=n+Math.imul(O,ht)|0,a=(a=a+Math.imul(O,ft)|0)+Math.imul(z,ht)|0,i=i+Math.imul(z,ft)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(a=(a=a+Math.imul(L,gt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((i=i+Math.imul(C,gt)|0)+(a>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),a=(a=Math.imul(B,ct))+Math.imul(N,lt)|0,i=Math.imul(N,ct),n=n+Math.imul(D,ht)|0,a=(a=a+Math.imul(D,ft)|0)+Math.imul(R,ht)|0,i=i+Math.imul(R,ft)|0;var Ot=(c+(n=n+Math.imul(O,dt)|0)|0)+((8191&(a=(a=a+Math.imul(O,gt)|0)+Math.imul(z,dt)|0))<<13)|0;c=((i=i+Math.imul(z,gt)|0)+(a>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,n=Math.imul(B,ht),a=(a=Math.imul(B,ft))+Math.imul(N,ht)|0,i=Math.imul(N,ft);var zt=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(a=(a=a+Math.imul(D,gt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((i=i+Math.imul(R,gt)|0)+(a>>>13)|0)+(zt>>>26)|0,zt&=67108863;var It=(c+(n=Math.imul(B,dt))|0)+((8191&(a=(a=Math.imul(B,gt))+Math.imul(N,dt)|0))<<13)|0;return c=((i=Math.imul(N,gt))+(a>>>13)|0)+(It>>>26)|0,It&=67108863,l[0]=vt,l[1]=mt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=kt,l[8]=Tt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=Ot,l[17]=zt,l[18]=It,0!==c&&(l[19]=c,r.length++),r};function d(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(p=f),i.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?p(this,t,e):r<63?f(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,a=0,i=0;i<r.length-1;i++){var o=a;a=0;for(var s=67108863&n,l=Math.min(i,e.length-1),c=Math.max(0,i-t.length+1);c<=l;c++){var u=i-c,h=(0|t.words[u])*(0|e.words[c]),f=67108863&h;s=67108863&(f=f+s|0),a+=(o=(o=o+(h/67108864|0)|0)+(f>>>26)|0)>>>26,o&=67108863}r.words[i]=s,n=o,o=a}return 0!==n?r.words[i]=n:r.length--,r.strip()}(this,t,e):d(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=i.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,a=0;a<e;a++)n|=(1&t)<<e-a-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,a,i){for(var o=0;o<i;o++)n[o]=e[t[o]],a[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,a,i){this.permute(i,t,e,r,n,a);for(var o=1;o<a;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<a;u+=s)for(var h=l,f=c,p=0;p<o;p++){var d=r[u+p],g=n[u+p],v=r[u+p+o],m=n[u+p+o],y=h*v-f*m;m=h*m+f*v,v=y,r[u+p]=d+v,n[u+p]=g+m,r[u+p+o]=d-v,n[u+p+o]=g-m,p!==s&&(y=l*h-c*f,f=l*f+c*h,h=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,a=0;for(r=r/2|0;r;r>>>=1)a++;return 1<<a+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var a=t[n];t[n]=t[r-n-1],t[r-n-1]=a,a=e[n],e[n]=-e[r-n-1],e[r-n-1]=-a}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var a=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&a,r=a<67108864?0:a/67108864|0}return t},g.prototype.convert13b=function(t,e,r,a){for(var i=0,o=0;o<e;o++)i+=0|t[o],r[2*o]=8191&i,i>>>=13,r[2*o+1]=8191&i,i>>>=13;for(o=2*e;o<a;++o)r[o]=0;n(0===i),n(0==(-8192&i))},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),a=this.makeRBT(n),i=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),h=new Array(n),f=r.words;f.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,i,s,l,n,a),this.transform(c,i,u,h,n,a);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*h[p];l[p]=s[p]*h[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,f,i,n,a),this.conjugate(f,i,n),this.normalize13b(f,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},i.prototype.mul=function(t){var e=new i(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},i.prototype.mulf=function(t){var e=new i(null);return e.words=new Array(this.length+t.length),d(this,t,e)},i.prototype.imul=function(t){return this.clone().mulTo(t,this)},i.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var a=(0|this.words[r])*t,i=(67108863&a)+(67108863&e);e>>=26,e+=a/67108864|0,e+=i>>>26,this.words[r]=67108863&i}return 0!==e&&(this.words[r]=e,this.length++),this},i.prototype.muln=function(t){return this.clone().imuln(t)},i.prototype.sqr=function(){return this.mul(this)},i.prototype.isqr=function(){return this.imul(this.clone())},i.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,a=r%26;e[r]=(t.words[n]&1<<a)>>>a}return e}(t);if(0===e.length)return new i(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var a=r.sqr();n<e.length;n++,a=a.sqr())0!==e[n]&&(r=r.mul(a));return r},i.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,a=(t-r)/26,i=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&i,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!==a){for(e=this.length-1;e>=0;e--)this.words[e+a]=this.words[e];for(e=0;e<a;e++)this.words[e]=0;this.length+=a}return this.strip()},i.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},i.prototype.iushrn=function(t,e,r){var a;n(\"number\"==typeof t&&t>=0),a=e?(e-e%26)/26:0;var i=t%26,o=Math.min((t-i)/26,this.length),s=67108863^67108863>>>i<<i,l=r;if(a-=o,a=Math.max(0,a),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>=a);c--){var h=0|this.words[c];this.words[c]=u<<26-i|h>>>i,u=h&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},i.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},i.prototype.shln=function(t){return this.clone().ishln(t)},i.prototype.ushln=function(t){return this.clone().iushln(t)},i.prototype.shrn=function(t){return this.clone().ishrn(t)},i.prototype.ushrn=function(t){return this.clone().iushrn(t)},i.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,a=1<<e;return!(this.length<=r)&&!!(this.words[r]&a)},i.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 a=67108863^67108863>>>e<<e;this.words[this.length-1]&=a}return this.strip()},i.prototype.maskn=function(t){return this.clone().imaskn(t)},i.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)},i.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},i.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()},i.prototype.addn=function(t){return this.clone().iaddn(t)},i.prototype.subn=function(t){return this.clone().isubn(t)},i.prototype.iabs=function(){return this.negative=0,this},i.prototype.abs=function(){return this.clone().iabs()},i.prototype._ishlnsubmul=function(t,e,r){var a,i,o=t.length+r;this._expand(o);var s=0;for(a=0;a<t.length;a++){i=(0|this.words[a+r])+s;var l=(0|t.words[a])*e;s=((i-=67108863&l)>>26)-(l/67108864|0),this.words[a+r]=67108863&i}for(;a<this.length-r;a++)s=(i=(0|this.words[a+r])+s)>>26,this.words[a+r]=67108863&i;if(0===s)return this.strip();for(n(-1===s),s=0,a=0;a<this.length;a++)s=(i=-(0|this.words[a])+s)>>26,this.words[a]=67108863&i;return this.negative=1,this.strip()},i.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),a=t,o=0|a.words[a.length-1];0!==(r=26-this._countBits(o))&&(a=a.ushln(r),n.iushln(r),o=0|a.words[a.length-1]);var s,l=n.length-a.length;if(\"mod\"!==e){(s=new i(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(a,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var h=l-1;h>=0;h--){var f=67108864*(0|n.words[a.length+h])+(0|n.words[a.length+h-1]);for(f=Math.min(f/o|0,67108863),n._ishlnsubmul(a,f,h);0!==n.negative;)f--,n.negative=0,n._ishlnsubmul(a,1,h),n.isZero()||(n.negative^=1);s&&(s.words[h]=f)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},i.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new i(0),mod:new i(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(a=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:a,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(a=s.div.neg()),{div:a,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 i(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new i(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new i(this.modn(t.words[0]))}:this._wordDiv(t,e);var a,o,s},i.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},i.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},i.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},i.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),a=t.andln(1),i=r.cmp(n);return i<0||1===a&&0===i?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},i.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,a=this.length-1;a>=0;a--)r=(e*r+(0|this.words[a]))%t;return r},i.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var a=(0|this.words[r])+67108864*e;this.words[r]=a/t|0,e=a%t}return this.strip()},i.prototype.divn=function(t){return this.clone().idivn(t)},i.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 a=new i(1),o=new i(0),s=new i(0),l=new i(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),h=e.clone();!e.isZero();){for(var f=0,p=1;0==(e.words[0]&p)&&f<26;++f,p<<=1);if(f>0)for(e.iushrn(f);f-- >0;)(a.isOdd()||o.isOdd())&&(a.iadd(u),o.isub(h)),a.iushrn(1),o.iushrn(1);for(var d=0,g=1;0==(r.words[0]&g)&&d<26;++d,g<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(h)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),a.isub(s),o.isub(l)):(r.isub(e),s.isub(a),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},i.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 a,o=new i(1),s=new i(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 h=0,f=1;0==(r.words[0]&f)&&h<26;++h,f<<=1);if(h>0)for(r.iushrn(h);h-- >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(a=0===e.cmpn(1)?o:s).cmpn(0)<0&&a.iadd(t),a},i.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 a=e.cmp(r);if(a<0){var i=e;e=r,r=i}else if(0===a||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},i.prototype.invm=function(t){return this.egcd(t).a.umod(t)},i.prototype.isEven=function(){return 0==(1&this.words[0])},i.prototype.isOdd=function(){return 1==(1&this.words[0])},i.prototype.andln=function(t){return this.words[0]&t},i.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,a=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=a,this;for(var i=a,o=r;0!==i&&o<this.length;o++){var s=0|this.words[o];i=(s+=i)>>>26,s&=67108863,this.words[o]=s}return 0!==i&&(this.words[o]=i,this.length++),this},i.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},i.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 a=0|this.words[0];e=a===t?0:a<t?-1:1}return 0!==this.negative?0|-e:e},i.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},i.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],a=0|t.words[r];if(n!==a){n<a?e=-1:n>a&&(e=1);break}}return e},i.prototype.gtn=function(t){return 1===this.cmpn(t)},i.prototype.gt=function(t){return 1===this.cmp(t)},i.prototype.gten=function(t){return this.cmpn(t)>=0},i.prototype.gte=function(t){return this.cmp(t)>=0},i.prototype.ltn=function(t){return-1===this.cmpn(t)},i.prototype.lt=function(t){return-1===this.cmp(t)},i.prototype.lten=function(t){return this.cmpn(t)<=0},i.prototype.lte=function(t){return this.cmp(t)<=0},i.prototype.eqn=function(t){return 0===this.cmpn(t)},i.prototype.eq=function(t){return 0===this.cmp(t)},i.red=function(t){return new w(t)},i.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},i.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},i.prototype._forceRed=function(t){return this.red=t,this},i.prototype.forceRed=function(t){return n(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},i.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},i.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},i.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},i.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},i.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},i.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},i.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},i.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},i.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},i.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},i.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},i.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},i.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 m(t,e){this.name=t,this.p=new i(e,16),this.n=this.p.bitLength(),this.k=new i(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function y(){m.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function x(){m.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function b(){m.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function _(){m.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function w(t){if(\"string\"==typeof t){var e=i._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){w.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new i(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)}m.prototype._tmp=function(){var t=new i(null);return t.words=new Array(Math.ceil(this.n/13)),t},m.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):r.strip(),r},m.prototype.split=function(t,e){t.iushrn(this.n,0,e)},m.prototype.imulK=function(t){return t.imul(this.k)},a(y,m),y.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var a=t.words[9];for(e.words[e.length++]=4194303&a,n=10;n<t.length;n++){var i=0|t.words[n];t.words[n-10]=(4194303&i)<<4|a>>>22,a=i}a>>>=22,t.words[n-10]=a,0===a&&t.length>10?t.length-=10:t.length-=9},y.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},a(x,m),a(b,m),a(_,m),_.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,a=67108863&n;n>>>=26,t.words[r]=a,e=n}return 0!==e&&(t.words[t.length++]=e),t},i._prime=function(t){if(v[t])return v[t];var e;if(\"k256\"===t)e=new y;else if(\"p224\"===t)e=new x;else if(\"p192\"===t)e=new b;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new _}return v[t]=e,e},w.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},w.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},w.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},w.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},w.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},w.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},w.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},w.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},w.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},w.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},w.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},w.prototype.isqr=function(t){return this.imul(t,t.clone())},w.prototype.sqr=function(t){return this.mul(t,t)},w.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new i(1)).iushrn(2);return this.pow(t,r)}for(var a=this.m.subn(1),o=0;!a.isZero()&&0===a.andln(1);)o++,a.iushrn(1);n(!a.isZero());var s=new i(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new i(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var h=this.pow(u,a),f=this.pow(t,a.addn(1).iushrn(1)),p=this.pow(t,a),d=o;0!==p.cmp(s);){for(var g=p,v=0;0!==g.cmp(s);v++)g=g.redSqr();n(v<d);var m=this.pow(h,new i(1).iushln(d-v-1));f=f.redMul(m),h=m.redSqr(),p=p.redMul(h),d=v}return f},w.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},w.prototype.pow=function(t,e){if(e.isZero())return new i(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new i(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var a=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 h=c>>u&1;a!==r[0]&&(a=this.sqr(a)),0!==h||0!==o?(o<<=1,o|=h,(4===++s||0===n&&0===u)&&(a=this.mul(a,r[o]),s=0,o=0)):s=0}l=26}return a},w.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},w.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},i.mont=function(t){return new k(t)},a(k,w),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),a=r.isub(n).iushrn(this.shift),i=a;return a.cmp(this.m)>=0?i=a.isub(this.m):a.cmpn(0)<0&&(i=a.iadd(this.m)),i._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new i(0)._forceRed(this);var r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),a=r.isub(n).iushrn(this.shift),o=a;return a.cmp(this.m)>=0?o=a.isub(this.m):a.cmpn(0)<0&&(o=a.iadd(this.m)),o._forceRed(this)},k.prototype.invm=function(t){return this.imod(t._invmp(this.m).mul(this.r2))._forceRed(this)}}(\"undefined\"==typeof e||e,this)},{buffer:104}],96:[function(t,e,r){\"use strict\";e.exports=function(t){var e,r,n,a=t.length,i=0;for(e=0;e<a;++e)i+=t[e].length;var o=new Array(i),s=0;for(e=0;e<a;++e){var l=t[e],c=l.length;for(r=0;r<c;++r){var u=o[s++]=new Array(c-1),h=0;for(n=0;n<c;++n)n!==r&&(u[h++]=l[n]);if(1&r){var f=u[1];u[1]=u[0],u[0]=f}}}return o}},{}],97:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){switch(arguments.length){case 1:return n=[],c(a=t,a,u,!0),n;case 2:return\"function\"==typeof e?c(t,t,e,!0):function(t,e){return n=[],c(t,e,u,!1),n}(t,e);case 3:return c(t,e,r,!1);default:throw new Error(\"box-intersect: Invalid arguments\")}var a};var n,a=t(\"typedarray-pool\"),i=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 a=0,i=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[a++]=c[u];n[i++]=o}}return i}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 h,f=a.mallocDouble(2*u*s),p=a.mallocInt32(s);if((s=l(t,u,f,p))>0){if(1===u&&n)i.init(s),h=i.sweepComplete(u,r,0,s,f,p,0,s,f,p);else{var d=a.mallocDouble(2*u*c),g=a.mallocInt32(c);(c=l(e,u,d,g))>0&&(i.init(s+c),h=1===u?i.sweepBipartite(u,r,0,s,f,p,0,c,d,g):o(u,r,n,s,f,p,c,d,g),a.free(d),a.free(g))}a.free(f),a.free(p)}return h}}}function u(t,e){n.push([t,e])}},{\"./lib/intersect\":99,\"./lib/sweep\":103,\"typedarray-pool\":543}],98:[function(t,e,r){\"use strict\";var n=\"d\",a=\"ax\",i=\"vv\",o=\"fp\",s=\"es\",l=\"rs\",c=\"re\",u=\"rb\",h=\"ri\",f=\"rp\",p=\"bs\",d=\"be\",g=\"bb\",v=\"bi\",m=\"bp\",y=\"rv\",x=\"Q\",b=[n,a,i,l,c,u,h,p,d,g,v];function _(t){var e=\"bruteForce\"+(t?\"Full\":\"Partial\"),r=[],_=b.slice();t||_.splice(3,0,o);var w=[\"function \"+e+\"(\"+_.join()+\"){\"];function k(e,o){var _=function(t,e,r){var o=\"bruteForce\"+(t?\"Red\":\"Blue\")+(e?\"Flip\":\"\")+(r?\"Full\":\"\"),_=[\"function \",o,\"(\",b.join(),\"){\",\"var \",s,\"=2*\",n,\";\"],w=\"for(var i=\"+l+\",\"+f+\"=\"+s+\"*\"+l+\";i<\"+c+\";++i,\"+f+\"+=\"+s+\"){var x0=\"+u+\"[\"+a+\"+\"+f+\"],x1=\"+u+\"[\"+a+\"+\"+f+\"+\"+n+\"],xi=\"+h+\"[i];\",k=\"for(var j=\"+p+\",\"+m+\"=\"+s+\"*\"+p+\";j<\"+d+\";++j,\"+m+\"+=\"+s+\"){var y0=\"+g+\"[\"+a+\"+\"+m+\"],\"+(r?\"y1=\"+g+\"[\"+a+\"+\"+m+\"+\"+n+\"],\":\"\")+\"yi=\"+v+\"[j];\";return 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0!==(P=H?l.sweepBipartite(t,e,B,N,X,Z,R,Q,Y,W):l.sweepBipartite(t,e,R,Q,Y,W,B,N,X,Z)))return P}else T(O++,D+1,R,Q,B,N,H,-1/0,1/0),T(O++,D+1,B,N,R,Q,1^H,-1/0,1/0);if(Q<F){var $=c(t,D,B,N,X,Z),tt=X[z*$+D],et=g(t,D,$,N,X,Z,tt);if(et<N&&T(O++,D,Q,F,et,N,(4|H)+(G?16:0),tt,q),B<$&&T(O++,D,Q,F,B,$,(2|H)+(G?16:0),U,tt),$+1===et){if(void 0!==(P=G?M(t,D,e,Q,F,Y,W,$,X,Z[$]):A(t,D,e,H,Q,F,Y,W,$,X,Z[$])))return P}else if($<et){var rt;if(G){if(rt=y(t,D,Q,F,Y,W,tt),Q<rt){var nt=g(t,D,Q,rt,Y,W,tt);if(D===t-2){if(Q<nt&&void 0!==(P=l.sweepComplete(t,e,Q,nt,Y,W,$,et,X,Z)))return P;if(nt<rt&&void 0!==(P=l.sweepBipartite(t,e,nt,rt,Y,W,$,et,X,Z)))return P}else Q<nt&&T(O++,D+1,Q,nt,$,et,16,-1/0,1/0),nt<rt&&(T(O++,D+1,nt,rt,$,et,0,-1/0,1/0),T(O++,D+1,$,et,nt,rt,1,-1/0,1/0))}}else rt=H?x(t,D,Q,F,Y,W,tt):y(t,D,Q,F,Y,W,tt),Q<rt&&(D===t-2?P=H?l.sweepBipartite(t,e,$,et,X,Z,Q,rt,Y,W):l.sweepBipartite(t,e,Q,rt,Y,W,$,et,X,Z):(T(O++,D+1,Q,rt,$,et,H,-1/0,1/0),T(O++,D+1,$,et,Q,rt,1^H,-1/0,1/0)))}}}}};var n=t(\"typedarray-pool\"),a=t(\"bit-twiddle\"),i=t(\"./brute\"),o=i.partial,s=i.full,l=t(\"./sweep\"),c=t(\"./median\"),u=t(\"./partition\"),h=128,f=1<<22,p=1<<22,d=u(\"!(lo>=p0)&&!(p1>=hi)\",[\"p0\",\"p1\"]),g=u(\"lo===p0\",[\"p0\"]),v=u(\"lo<p0\",[\"p0\"]),m=u(\"hi<=p0\",[\"p0\"]),y=u(\"lo<=p0&&p0<=hi\",[\"p0\"]),x=u(\"lo<p0&&p0<=hi\",[\"p0\"]),b=6,_=2,w=n.mallocInt32(1024),k=n.mallocDouble(1024);function T(t,e,r,n,a,i,o,s,l){var c=b*t;w[c]=e,w[c+1]=r,w[c+2]=n,w[c+3]=a,w[c+4]=i,w[c+5]=o;var u=_*t;k[u]=s,k[u+1]=l}function A(t,e,r,n,a,i,o,s,l,c,u){var h=2*t,f=l*h,p=c[f+e];t:for(var d=a,g=a*h;d<i;++d,g+=h){var v=o[g+e],m=o[g+e+t];if(!(p<v||m<p)&&(!n||p!==v)){for(var y,x=s[d],b=e+1;b<t;++b){v=o[g+b],m=o[g+b+t];var _=c[f+b],w=c[f+b+t];if(m<_||w<v)continue t}if(void 0!==(y=n?r(u,x):r(x,u)))return y}}}function M(t,e,r,n,a,i,o,s,l,c){var u=2*t,h=s*u,f=l[h+e];t:for(var p=n,d=n*u;p<a;++p,d+=u){var g=o[p];if(g!==c){var v=i[d+e],m=i[d+e+t];if(!(f<v||m<f)){for(var y=e+1;y<t;++y){v=i[d+y],m=i[d+y+t];var x=l[h+y],b=l[h+y+t];if(m<x||b<v)continue t}var _=r(g,c);if(void 0!==_)return _}}}}},{\"./brute\":98,\"./median\":100,\"./partition\":101,\"./sweep\":103,\"bit-twiddle\":93,\"typedarray-pool\":543}],100:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,o,s,l){if(o<=r+1)return r;var c=r,u=o,h=o+r>>>1,f=2*t,p=h,d=s[f*h+e];for(;c<u;){if(u-c<a){i(t,e,c,u,s,l),d=s[f*h+e];break}var g=u-c,v=Math.random()*g+c|0,m=s[f*v+e],y=Math.random()*g+c|0,x=s[f*y+e],b=Math.random()*g+c|0,_=s[f*b+e];m<=x?_>=x?(p=y,d=x):m>=_?(p=v,d=m):(p=b,d=_):x>=_?(p=y,d=x):_>=m?(p=v,d=m):(p=b,d=_);for(var w=f*(u-1),k=f*p,T=0;T<f;++T,++w,++k){var A=s[w];s[w]=s[k],s[k]=A}var M=l[u-1];l[u-1]=l[p],l[p]=M,p=n(t,e,c,u-1,s,l,d);for(var w=f*(u-1),k=f*p,T=0;T<f;++T,++w,++k){var A=s[w];s[w]=s[k],s[k]=A}var M=l[u-1];if(l[u-1]=l[p],l[p]=M,h<p){for(u=p-1;c<u&&s[f*(u-1)+e]===d;)u-=1;u+=1}else{if(!(p<h))break;for(c=p+1;c<u&&s[f*c+e]===d;)c+=1}}return n(t,e,r,h,s,l,s[f*h+e])};var n=t(\"./partition\")(\"lo<p0\",[\"p0\"]),a=8;function i(t,e,r,n,a,i){for(var o=2*t,s=o*(r+1)+e,l=r+1;l<n;++l,s+=o)for(var c=a[s],u=l,h=o*(l-1);u>r&&a[h+e]>c;--u,h-=o){for(var f=h,p=h+o,d=0;d<o;++d,++f,++p){var g=a[f];a[f]=a[p],a[p]=g}var v=i[u];i[u]=i[u-1],i[u-1]=v}}},{\"./partition\":101}],101:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=\"abcdef\".split(\"\").concat(e),a=[];t.indexOf(\"lo\")>=0&&a.push(\"lo=e[k+n]\");t.indexOf(\"hi\")>=0&&a.push(\"hi=e[k+o]\");return r.push(n.replace(\"_\",a.join()).replace(\"$\",t)),Function.apply(void 0,r)};var n=\"for(var j=2*a,k=j*c,l=k,m=c,n=b,o=a+b,p=c;d>p;++p,k+=j){var _;if($)if(m===p)m+=1,l+=j;else{for(var s=0;j>s;++s){var t=e[k+s];e[k+s]=e[l],e[l++]=t}var u=f[p];f[p]=f[m],f[m++]=u}}return m\"},{}],102:[function(t,e,r){\"use strict\";e.exports=function(t,e){e<=4*n?a(0,e-1,t):function t(e,r,h){var 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c=r[l-2],u=r[l-1];if(c<i)break;if(c===i&&u<o)break;r[l]=c,r[l+1]=u,l-=2}r[l]=i,r[l+1]=o}}function i(t,e,r){e*=2;var n=r[t*=2],a=r[t+1];r[t]=r[e],r[t+1]=r[e+1],r[e]=n,r[e+1]=a}function o(t,e,r){e*=2,r[t*=2]=r[e],r[t+1]=r[e+1]}function s(t,e,r,n){e*=2,r*=2;var a=n[t*=2],i=n[t+1];n[t]=n[e],n[t+1]=n[e+1],n[e]=n[r],n[e+1]=n[r+1],n[r]=a,n[r+1]=i}function l(t,e,r,n,a){e*=2,a[t*=2]=a[e],a[e]=r,a[t+1]=a[e+1],a[e+1]=n}function c(t,e,r){e*=2;var n=r[t*=2],a=r[e];return!(n<a)&&(n!==a||r[t+1]>r[e+1])}function u(t,e,r,n){var a=n[t*=2];return a<e||a===e&&n[t+1]<r}},{}],103:[function(t,e,r){\"use strict\";e.exports={init:function(t){var e=a.nextPow2(t);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));h.length<e&&(n.free(h),h=n.mallocInt32(e));f.length<e&&(n.free(f),f=n.mallocInt32(e));var r=8*e;p.length<r&&(n.free(p),p=n.mallocDouble(r))},sweepBipartite:function(t,e,r,n,a,h,f,v,m,y){for(var x=0,b=2*t,_=t-1,w=b-1,k=r;k<n;++k){var T=h[k],A=b*k;p[x++]=a[A+_],p[x++]=-(T+1),p[x++]=a[A+w],p[x++]=T}for(var k=f;k<v;++k){var T=y[k]+o,M=b*k;p[x++]=m[M+_],p[x++]=-T,p[x++]=m[M+w],p[x++]=T}var S=x>>>1;i(p,S);for(var E=0,L=0,k=0;k<S;++k){var C=0|p[2*k+1];if(C>=o)d(c,u,L--,C=C-o|0);else if(C>=0)d(s,l,E--,C);else if(C<=-o){C=-C-o|0;for(var P=0;P<E;++P){var O=e(s[P],C);if(void 0!==O)return O}g(c,u,L++,C)}else{C=-C-1|0;for(var P=0;P<L;++P){var O=e(C,c[P]);if(void 0!==O)return O}g(s,l,E++,C)}}},sweepComplete:function(t,e,r,n,a,o,v,m,y,x){for(var b=0,_=2*t,w=t-1,k=_-1,T=r;T<n;++T){var A=o[T]+1<<1,M=_*T;p[b++]=a[M+w],p[b++]=-A,p[b++]=a[M+k],p[b++]=A}for(var T=v;T<m;++T){var A=x[T]+1<<1,S=_*T;p[b++]=y[S+w],p[b++]=1|-A,p[b++]=y[S+k],p[b++]=1|A}var E=b>>>1;i(p,E);for(var L=0,C=0,P=0,T=0;T<E;++T){var O=0|p[2*T+1],z=1&O;if(T<E-1&&O>>1==p[2*T+3]>>1&&(z=2,T+=1),O<0){for(var I=-(O>>1)-1,D=0;D<P;++D){var R=e(h[D],I);if(void 0!==R)return R}if(0!==z)for(var D=0;D<L;++D){var R=e(s[D],I);if(void 0!==R)return R}if(1!==z)for(var D=0;D<C;++D){var R=e(c[D],I);if(void 0!==R)return R}0===z?g(s,l,L++,I):1===z?g(c,u,C++,I):2===z&&g(h,f,P++,I)}else{var I=(O>>1)-1;0===z?d(s,l,L--,I):1===z?d(c,u,C--,I):2===z&&d(h,f,P--,I)}}},scanBipartite:function(t,e,r,n,a,c,u,h,f,v,m,y){var x=0,b=2*t,_=e,w=e+t,k=1,T=1;n?T=o:k=o;for(var A=a;A<c;++A){var M=A+k,S=b*A;p[x++]=u[S+_],p[x++]=-M,p[x++]=u[S+w],p[x++]=M}for(var A=f;A<v;++A){var M=A+T,E=b*A;p[x++]=m[E+_],p[x++]=-M}var L=x>>>1;i(p,L);for(var C=0,A=0;A<L;++A){var P=0|p[2*A+1];if(P<0){var M=-P,O=!1;if(M>=o?(O=!n,M-=o):(O=!!n,M-=1),O)g(s,l,C++,M);else{var z=y[M],I=b*M,D=m[I+e+1],R=m[I+e+1+t];t:for(var F=0;F<C;++F){var B=s[F],N=b*B;if(!(R<u[N+e+1]||u[N+e+1+t]<D)){for(var j=e+2;j<t;++j)if(m[I+j+t]<u[N+j]||u[N+j+t]<m[I+j])continue t;var V,U=h[B];if(void 0!==(V=n?r(z,U):r(U,z)))return V}}}}else d(s,l,C--,P-k)}},scanComplete:function(t,e,r,n,a,l,c,u,h,f,d){for(var g=0,v=2*t,m=e,y=e+t,x=n;x<a;++x){var b=x+o,_=v*x;p[g++]=l[_+m],p[g++]=-b,p[g++]=l[_+y],p[g++]=b}for(var x=u;x<h;++x){var b=x+1,w=v*x;p[g++]=f[w+m],p[g++]=-b}var k=g>>>1;i(p,k);for(var T=0,x=0;x<k;++x){var A=0|p[2*x+1];if(A<0){var b=-A;if(b>=o)s[T++]=b-o;else{var M=d[b-=1],S=v*b,E=f[S+e+1],L=f[S+e+1+t];t:for(var C=0;C<T;++C){var P=s[C],O=c[P];if(O===M)break;var z=v*P;if(!(L<l[z+e+1]||l[z+e+1+t]<E)){for(var I=e+2;I<t;++I)if(f[S+I+t]<l[z+I]||l[z+I+t]<f[S+I])continue t;var D=r(O,M);if(void 0!==D)return D}}}}else{for(var b=A-o,C=T-1;C>=0;--C)if(s[C]===b){for(var I=C+1;I<T;++I)s[I-1]=s[I];break}--T}}}};var n=t(\"typedarray-pool\"),a=t(\"bit-twiddle\"),i=t(\"./sort\"),o=1<<28,s=n.mallocInt32(1024),l=n.mallocInt32(1024),c=n.mallocInt32(1024),u=n.mallocInt32(1024),h=n.mallocInt32(1024),f=n.mallocInt32(1024),p=n.mallocDouble(8192);function d(t,e,r,n){var a=e[n],i=t[r-1];t[a]=i,e[i]=a}function 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array shape\")}else{if(\"object\"!=typeof o||!o.offset)throw new Error(\"cwise: Unknown argument type \"+r[i]);e.argTypes[i]=\"offset\",e.offsetArgs.push({array:o.array,offset:o.offset}),e.offsetArgIndex.push(i)}}if(e.arrayArgs.length<=0)throw new Error(\"cwise: No array arguments specified\");if(e.pre.args.length>r.length)throw new Error(\"cwise: Too many arguments in pre() block\");if(e.body.args.length>r.length)throw new Error(\"cwise: Too many arguments in body() block\");if(e.post.args.length>r.length)throw new Error(\"cwise: Too many arguments in post() block\");return e.debug=!!t.printCode||!!t.debug,e.funcName=t.funcName||\"cwise\",e.blockSize=t.blockSize||64,n(e)}},{\"./lib/thunk.js\":149}],148:[function(t,e,r){\"use strict\";var n=t(\"uniq\");function a(t,e,r){var 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w=n([].concat(t.pre.thisVars).concat(t.body.thisVars).concat(t.post.thisVars));for((y=y.concat(w)).length>0&&m.push(\"var \"+y.join(\",\")),l=0;l<t.arrayArgs.length;++l)m.push(\"p\"+l+\"|=0\");t.pre.body.length>3&&m.push(i(t.pre,t,s));var k=i(t.body,t,s),T=function(t){for(var e=0,r=t[0].length;e<r;){for(var n=1;n<t.length;++n)if(t[n][e]!==t[0][e])return e;++e}return e}(p);T<r?m.push(function(t,e,r,n){for(var i=e.length,o=r.arrayArgs.length,s=r.blockSize,l=r.indexArgs.length>0,c=[],u=0;u<o;++u)c.push([\"var offset\",u,\"=p\",u].join(\"\"));for(u=t;u<i;++u)c.push([\"for(var j\"+u+\"=SS[\",e[u],\"]|0;j\",u,\">0;){\"].join(\"\")),c.push([\"if(j\",u,\"<\",s,\"){\"].join(\"\")),c.push([\"s\",e[u],\"=j\",u].join(\"\")),c.push([\"j\",u,\"=0\"].join(\"\")),c.push([\"}else{s\",e[u],\"=\",s].join(\"\")),c.push([\"j\",u,\"-=\",s,\"}\"].join(\"\")),l&&c.push([\"index[\",e[u],\"]=j\",u].join(\"\"));for(u=0;u<o;++u){for(var 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n,a=t(\"es5-ext/object/copy\"),i=t(\"es5-ext/object/normalize-options\"),o=t(\"es5-ext/object/valid-callable\"),s=t(\"es5-ext/object/map\"),l=t(\"es5-ext/object/valid-callable\"),c=t(\"es5-ext/object/valid-value\"),u=Function.prototype.bind,h=Object.defineProperty,f=Object.prototype.hasOwnProperty;n=function(t,e,r){var n,i=c(e)&&l(e.value);return delete(n=a(e)).writable,delete n.value,n.get=function(){return!r.overwriteDefinition&&f.call(this,t)?i:(e.value=u.call(i,r.resolveContext?r.resolveContext(this):this),h(this,t,e),this[t])},n},e.exports=function(t){var e=i(arguments[1]);return null!=e.resolveContext&&o(e.resolveContext),s(t,function(t,r){return n(r,t,e)})}},{\"es5-ext/object/copy\":191,\"es5-ext/object/map\":200,\"es5-ext/object/normalize-options\":201,\"es5-ext/object/valid-callable\":205,\"es5-ext/object/valid-value\":207}],152:[function(t,e,r){\"use strict\";var 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h=r-1;h>=0;--h)n.push(i(l[h],c[h],n[o]+u*a[o])),a.push(0),o+=1}}},{\"binary-search-bounds\":92,\"cubic-hermite\":146}],229:[function(t,e,r){var n=t(\"dtype\");e.exports=function(t,e,r){if(!t)throw new TypeError(\"must specify data as first parameter\");if(r=0|+(r||0),Array.isArray(t)&&t[0]&&\"number\"==typeof t[0][0]){var a,i,o,s,l=t[0].length,c=t.length*l;e&&\"string\"!=typeof e||(e=new(n(e||\"float32\"))(c+r));var u=e.length-r;if(c!==u)throw new Error(\"source length \"+c+\" (\"+l+\"x\"+t.length+\") does not match destination length \"+u);for(a=0,o=r;a<t.length;a++)for(i=0;i<l;i++)e[o++]=null===t[a][i]?NaN:t[a][i]}else if(e&&\"string\"!=typeof e)e.set(t,r);else{var h=n(e||\"float32\");if(Array.isArray(t)||\"array\"===e)for(e=new h(t.length+r),a=0,o=r,s=e.length;o<s;o++,a++)e[o]=null===t[a]?NaN:t[a];else 0===r?e=new h(t):(e=new h(t.length+r)).set(t,r)}return e}},{dtype:170}],230:[function(t,e,r){\"use strict\";var n=t(\"css-font/stringify\"),a=[32,126];e.exports=function(t){var e=(t=t||{}).shape?t.shape:t.canvas?[t.canvas.width,t.canvas.height]:[512,512],r=t.canvas||document.createElement(\"canvas\"),i=t.font,o=\"number\"==typeof t.step?[t.step,t.step]:t.step||[32,32],s=t.chars||a;i&&\"string\"!=typeof i&&(i=n(i));if(Array.isArray(s)){if(2===s.length&&\"number\"==typeof s[0]&&\"number\"==typeof s[1]){for(var l=[],c=s[0],u=0;c<=s[1];c++)l[u++]=String.fromCharCode(c);s=l}}else s=String(s).split(\"\");e=e.slice(),r.width=e[0],r.height=e[1];var h=r.getContext(\"2d\");h.fillStyle=\"#000\",h.fillRect(0,0,r.width,r.height),h.font=i,h.textAlign=\"center\",h.textBaseline=\"middle\",h.fillStyle=\"#fff\";for(var f=o[0]/2,p=o[1]/2,c=0;c<s.length;c++)h.fillText(s[c],f,p),(f+=o[0])>e[0]-o[0]/2&&(f=o[0]/2,p+=o[1]);return r}},{\"css-font/stringify\":143}],231:[function(t,e,r){\"use strict\";function n(t,e){e||(e={}),(\"string\"==typeof t||Array.isArray(t))&&(e.family=t);var r=Array.isArray(e.family)?e.family.join(\", \"):e.family;if(!r)throw Error(\"`family` must be 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m=p-i(h.getImageData(0,0,p,p))-1+g;d.baseline=d.alphabetic=m,h.clearRect(0,0,p,p),h.textBaseline=\"middle\",h.fillText(\"H\",0,.5*p);var y=i(h.getImageData(0,0,p,p));d.median=d.middle=p-y-1+g-.5*p,h.clearRect(0,0,p,p),h.textBaseline=\"hanging\",h.fillText(\"H\",0,.5*p);var x=i(h.getImageData(0,0,p,p));d.hanging=p-x-1+g-.5*p,h.clearRect(0,0,p,p),h.textBaseline=\"ideographic\",h.fillText(\"H\",0,p);var b=i(h.getImageData(0,0,p,p));if(d.ideographic=p-b-1+g,f.upper&&(h.clearRect(0,0,p,p),h.textBaseline=\"top\",h.fillText(f.upper,0,0),d.upper=i(h.getImageData(0,0,p,p)),d.capHeight=d.baseline-d.upper),f.lower&&(h.clearRect(0,0,p,p),h.textBaseline=\"top\",h.fillText(f.lower,0,0),d.lower=i(h.getImageData(0,0,p,p)),d.xHeight=d.baseline-d.lower),f.tittle&&(h.clearRect(0,0,p,p),h.textBaseline=\"top\",h.fillText(f.tittle,0,0),d.tittle=i(h.getImageData(0,0,p,p))),f.ascent&&(h.clearRect(0,0,p,p),h.textBaseline=\"top\",h.fillText(f.ascent,0,0),d.ascent=i(h.getImageData(0,0,p,p))),f.descent&&(h.clearRect(0,0,p,p),h.textBaseline=\"top\",h.fillText(f.descent,0,0),d.descent=o(h.getImageData(0,0,p,p))),f.overshoot){h.clearRect(0,0,p,p),h.textBaseline=\"top\",h.fillText(f.overshoot,0,0);var _=o(h.getImageData(0,0,p,p));d.overshoot=_-m}for(var w in d)d[w]/=s;return d.em=s,n.cache[r]=d,a(d,c)}function a(t,e){var r={};for(var n in\"string\"==typeof e&&(e=t[e]),t)\"em\"!==n&&(r[n]=t[n]-e);return r}function i(t){for(var e=t.height,r=t.data,n=3;n<r.length;n+=4)if(0!==r[n])return Math.floor(.25*(n-3)/e)}function o(t){for(var e=t.height,r=t.data,n=r.length-1;n>0;n-=4)if(0!==r[n])return Math.floor(.25*(n-3)/e)}e.exports=n,n.canvas=document.createElement(\"canvas\"),n.cache={}},{}],232:[function(t,e,r){\"use strict\";e.exports=function(t){return new c(t||d,null)};var n=0,a=1;function i(t,e,r,n,a,i){this._color=t,this.key=e,this.value=r,this.left=n,this.right=a,this._count=i}function o(t){return new i(t._color,t.key,t.value,t.left,t.right,t._count)}function s(t,e){return new i(t,e.key,e.value,e.left,e.right,e._count)}function l(t){t._count=1+(t.left?t.left._count:0)+(t.right?t.right._count:0)}function c(t,e){this._compare=t,this.root=e}var u=c.prototype;function h(t,e){this.tree=t,this._stack=e}Object.defineProperty(u,\"keys\",{get:function(){var t=[];return this.forEach(function(e,r){t.push(e)}),t}}),Object.defineProperty(u,\"values\",{get:function(){var t=[];return this.forEach(function(e,r){t.push(r)}),t}}),Object.defineProperty(u,\"length\",{get:function(){return this.root?this.root._count:0}}),u.insert=function(t,e){for(var r=this._compare,o=this.root,u=[],h=[];o;){var f=r(t,o.key);u.push(o),h.push(f),o=f<=0?o.left:o.right}u.push(new i(n,t,e,null,null,1));for(var p=u.length-2;p>=0;--p){o=u[p];h[p]<=0?u[p]=new i(o._color,o.key,o.value,u[p+1],o.right,o._count+1):u[p]=new i(o._color,o.key,o.value,o.left,u[p+1],o._count+1)}for(p=u.length-1;p>1;--p){var d=u[p-1];o=u[p];if(d._color===a||o._color===a)break;var g=u[p-2];if(g.left===d)if(d.left===o){if(!(v=g.right)||v._color!==n){if(g._color=n,g.left=d.right,d._color=a,d.right=g,u[p-2]=d,u[p-1]=o,l(g),l(d),p>=3)(m=u[p-3]).left===g?m.left=d:m.right=d;break}d._color=a,g.right=s(a,v),g._color=n,p-=1}else{if(!(v=g.right)||v._color!==n){if(d.right=o.left,g._color=n,g.left=o.right,o._color=a,o.left=d,o.right=g,u[p-2]=o,u[p-1]=d,l(g),l(d),l(o),p>=3)(m=u[p-3]).left===g?m.left=o:m.right=o;break}d._color=a,g.right=s(a,v),g._color=n,p-=1}else if(d.right===o){if(!(v=g.left)||v._color!==n){if(g._color=n,g.right=d.left,d._color=a,d.left=g,u[p-2]=d,u[p-1]=o,l(g),l(d),p>=3)(m=u[p-3]).right===g?m.right=d:m.left=d;break}d._color=a,g.left=s(a,v),g._color=n,p-=1}else{var v;if(!(v=g.left)||v._color!==n){var m;if(d.left=o.right,g._color=n,g.right=o.left,o._color=a,o.right=d,o.left=g,u[p-2]=o,u[p-1]=d,l(g),l(d),l(o),p>=3)(m=u[p-3]).right===g?m.right=o:m.left=o;break}d._color=a,g.left=s(a,v),g._color=n,p-=1}}return u[0]._color=a,new c(r,u[0])},u.forEach=function(t,e,r){if(this.root)switch(arguments.length){case 1:return function t(e,r){var n;if(r.left&&(n=t(e,r.left)))return n;return(n=e(r.key,r.value))||(r.right?t(e,r.right):void 0)}(t,this.root);case 2:return function t(e,r,n,a){if(r(e,a.key)<=0){var i;if(a.left&&(i=t(e,r,n,a.left)))return i;if(i=n(a.key,a.value))return i}if(a.right)return t(e,r,n,a.right)}(e,this._compare,t,this.root);case 3:if(this._compare(e,r)>=0)return;return function t(e,r,n,a,i){var o,s=n(e,i.key),l=n(r,i.key);if(s<=0){if(i.left&&(o=t(e,r,n,a,i.left)))return o;if(l>0&&(o=a(i.key,i.value)))return o}if(l>0&&i.right)return t(e,r,n,a,i.right)}(e,r,this._compare,t,this.root)}},Object.defineProperty(u,\"begin\",{get:function(){for(var t=[],e=this.root;e;)t.push(e),e=e.left;return new h(this,t)}}),Object.defineProperty(u,\"end\",{get:function(){for(var t=[],e=this.root;e;)t.push(e),e=e.right;return new h(this,t)}}),u.at=function(t){if(t<0)return new h(this,[]);for(var e=this.root,r=[];;){if(r.push(e),e.left){if(t<e.left._count){e=e.left;continue}t-=e.left._count}if(!t)return new h(this,r);if(t-=1,!e.right)break;if(t>=e.right._count)break;e=e.right}return new h(this,[])},u.ge=function(t){for(var e=this._compare,r=this.root,n=[],a=0;r;){var i=e(t,r.key);n.push(r),i<=0&&(a=n.length),r=i<=0?r.left:r.right}return n.length=a,new h(this,n)},u.gt=function(t){for(var e=this._compare,r=this.root,n=[],a=0;r;){var i=e(t,r.key);n.push(r),i<0&&(a=n.length),r=i<0?r.left:r.right}return n.length=a,new h(this,n)},u.lt=function(t){for(var e=this._compare,r=this.root,n=[],a=0;r;){var i=e(t,r.key);n.push(r),i>0&&(a=n.length),r=i<=0?r.left:r.right}return n.length=a,new h(this,n)},u.le=function(t){for(var e=this._compare,r=this.root,n=[],a=0;r;){var i=e(t,r.key);n.push(r),i>=0&&(a=n.length),r=i<0?r.left:r.right}return n.length=a,new h(this,n)},u.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var a=e(t,r.key);if(n.push(r),0===a)return new 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i(r._color,r.key,r.value,r.left,e[u+1],r._count)}if((r=e[e.length-1]).left&&r.right){var h=e.length;for(r=r.left;r.right;)e.push(r),r=r.right;var f=e[h-1];e.push(new i(r._color,f.key,f.value,r.left,r.right,r._count)),e[h-1].key=r.key,e[h-1].value=r.value;for(u=e.length-2;u>=h;--u)r=e[u],e[u]=new i(r._color,r.key,r.value,r.left,e[u+1],r._count);e[h-1].left=e[h]}if((r=e[e.length-1])._color===n){var d=e[e.length-2];d.left===r?d.left=null:d.right===r&&(d.right=null),e.pop();for(u=0;u<e.length;++u)e[u]._count--;return new c(this.tree._compare,e[0])}if(r.left||r.right){r.left?p(r,r.left):r.right&&p(r,r.right),r._color=a;for(u=0;u<e.length-1;++u)e[u]._count--;return new c(this.tree._compare,e[0])}if(1===e.length)return new c(this.tree._compare,null);for(u=0;u<e.length;++u)e[u]._count--;var g=e[e.length-2];return function(t){for(var e,r,i,c,u=t.length-1;u>=0;--u){if(e=t[u],0===u)return void(e._color=a);if((r=t[u-1]).left===e){if((i=r.right).right&&i.right._color===n)return c=(i=r.right=o(i)).right=o(i.right),r.right=i.left,i.left=r,i.right=c,i._color=r._color,e._color=a,r._color=a,c._color=a,l(r),l(i),u>1&&((h=t[u-2]).left===r?h.left=i:h.right=i),void(t[u-1]=i);if(i.left&&i.left._color===n)return c=(i=r.right=o(i)).left=o(i.left),r.right=c.left,i.left=c.right,c.left=r,c.right=i,c._color=r._color,r._color=a,i._color=a,e._color=a,l(r),l(i),l(c),u>1&&((h=t[u-2]).left===r?h.left=c:h.right=c),void(t[u-1]=c);if(i._color===a){if(r._color===n)return r._color=a,void(r.right=s(n,i));r.right=s(n,i);continue}i=o(i),r.right=i.left,i.left=r,i._color=r._color,r._color=n,l(r),l(i),u>1&&((h=t[u-2]).left===r?h.left=i:h.right=i),t[u-1]=i,t[u]=r,u+1<t.length?t[u+1]=e:t.push(e),u+=2}else{if((i=r.left).left&&i.left._color===n)return c=(i=r.left=o(i)).left=o(i.left),r.left=i.right,i.right=r,i.left=c,i._color=r._color,e._color=a,r._color=a,c._color=a,l(r),l(i),u>1&&((h=t[u-2]).right===r?h.right=i:h.left=i),void(t[u-1]=i);if(i.right&&i.right._color===n)return c=(i=r.left=o(i)).right=o(i.right),r.left=c.right,i.right=c.left,c.right=r,c.left=i,c._color=r._color,r._color=a,i._color=a,e._color=a,l(r),l(i),l(c),u>1&&((h=t[u-2]).right===r?h.right=c:h.left=c),void(t[u-1]=c);if(i._color===a){if(r._color===n)return r._color=a,void(r.left=s(n,i));r.left=s(n,i);continue}var h;i=o(i),r.left=i.right,i.right=r,i._color=r._color,r._color=n,l(r),l(i),u>1&&((h=t[u-2]).right===r?h.right=i:h.left=i),t[u-1]=i,t[u]=r,u+1<t.length?t[u+1]=e:t.push(e),u+=2}}}(e),g.left===r?g.left=null:g.right=null,new c(this.tree._compare,e[0])},Object.defineProperty(f,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(f,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(f,\"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}),f.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(f,\"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}}),f.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),n=e[e.length-1];r[r.length-1]=new i(n._color,n.key,t,n.left,n.right,n._count);for(var a=e.length-2;a>=0;--a)(n=e[a]).left===e[a+1]?r[a]=new i(n._color,n.key,n.value,r[a+1],n.right,n._count):r[a]=new i(n._color,n.key,n.value,n.left,r[a+1],n._count);return new c(this.tree._compare,r[0])},f.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(f,\"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}})},{}],233:[function(t,e,r){var n=[.9999999999998099,676.5203681218851,-1259.1392167224028,771.3234287776531,-176.6150291621406,12.507343278686905,-.13857109526572012,9984369578019572e-21,1.5056327351493116e-7],a=607/128,i=[.9999999999999971,57.15623566586292,-59.59796035547549,14.136097974741746,-.4919138160976202,3399464998481189e-20,4652362892704858e-20,-9837447530487956e-20,.0001580887032249125,-.00021026444172410488,.00021743961811521265,-.0001643181065367639,8441822398385275e-20,-26190838401581408e-21,36899182659531625e-22];function o(t){if(t<0)return Number(\"0/0\");for(var e=i[0],r=i.length-1;r>0;--r)e+=i[r]/(t+r);var n=t+a+.5;return.5*Math.log(2*Math.PI)+(t+.5)*Math.log(n)-n+Math.log(e)-Math.log(t)}e.exports=function t(e){if(e<.5)return Math.PI/(Math.sin(Math.PI*e)*t(1-e));if(e>100)return Math.exp(o(e));e-=1;for(var r=n[0],a=1;a<9;a++)r+=n[a]/(e+a);var i=e+7+.5;return Math.sqrt(2*Math.PI)*Math.pow(i,e+.5)*Math.exp(-i)*r},e.exports.log=o},{}],234:[function(t,e,r){e.exports=function(t,e){if(\"string\"!=typeof t)throw new TypeError(\"must specify type string\");if(e=e||{},\"undefined\"==typeof document&&!e.canvas)return null;var r=e.canvas||document.createElement(\"canvas\");\"number\"==typeof e.width&&(r.width=e.width);\"number\"==typeof e.height&&(r.height=e.height);var n,a=e;try{var i=[t];0===t.indexOf(\"webgl\")&&i.push(\"experimental-\"+t);for(var o=0;o<i.length;o++)if(n=r.getContext(i[o],a))return n}catch(t){n=null}return n||null}},{}],235:[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\"),a=t(\"./lib/lines.js\"),i=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=i(t)}var h=u.prototype;function f(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}h.update=function(t){function e(e,r,n){if(n in t){var a,i=t[n],o=this[n];(e?Array.isArray(i)&&Array.isArray(i[0]):Array.isArray(i))?this[n]=a=[r(i[0]),r(i[1]),r(i[2])]:this[n]=a=[r(i),r(i),r(i)];for(var s=0;s<3;++s)if(a[s]!==o[s])return!0}return!1}t=t||{};var r,i=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,h=!1;if(\"bounds\"in t)for(var f=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)f[p][d]!==this.bounds[p][d]&&(h=!0),this.bounds[p][d]=f[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 i(\"tickSpacing\")&&(this.autoTicks=!0,h=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),h=!0,u=!0,this._firstInit=!1),h&&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),i(\"tickSize\"),i(\"tickAngle\"),i(\"tickPad\"),c(\"tickColor\");var g=l(\"labels\");l(\"labelFont\")&&(g=!0),o(\"labelEnable\"),i(\"labelSize\"),i(\"labelPad\"),c(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),i(\"lineWidth\"),c(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),i(\"lineTickLength\"),i(\"lineTickWidth\"),c(\"lineTickColor\"),o(\"gridEnable\"),i(\"gridWidth\"),c(\"gridColor\"),o(\"zeroEnable\"),c(\"zeroLineColor\"),i(\"zeroLineWidth\"),o(\"backgroundEnable\"),c(\"backgroundColor\"),this._text?this._text&&(g||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=a(this.gl,this.bounds,this.ticks))};var p=[new f,new f,new f];function d(t,e,r,n,a){for(var i=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var h=i,f=s,p=o,d=l;c&1<<u&&(h=s,f=i,p=l,d=o),h[u]=r[0][u],f[u]=r[1][u],a[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var g=[0,0,0],v={model:l,view:l,projection:l,_ortho:!1};h.isOpaque=function(){return!0},h.isTransparent=function(){return!1},h.drawTransparent=function(t){};var m=[0,0,0],y=[0,0,0],x=[0,0,0];h.draw=function(t){t=t||v;for(var e=this.gl,r=t.model||l,n=t.view||l,a=t.projection||l,i=this.bounds,s=t._ortho||!1,u=o(r,n,a,i,s),h=u.cubeEdges,f=u.axis,b=n[12],_=n[13],w=n[14],k=n[15],T=(s?2:1)*this.pixelRatio*(a[3]*b+a[7]*_+a[11]*w+a[15]*k)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=h[A],this.lastCubeProps.axis[A]=f[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,h,f);e=this.gl;var S,E=g;for(A=0;A<3;++A)this.backgroundEnable[A]?E[A]=f[A]:E[A]=0;this._background.draw(r,n,a,i,E,this.backgroundColor),this._lines.bind(r,n,a,this);for(A=0;A<3;++A){var L=[0,0,0];f[A]>0?L[A]=i[1][A]:L[A]=i[0][A];for(var C=0;C<2;++C){var P=(A+1+C)%3,O=(A+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,O,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(A+1+C)%3,O=(A+1+(1^C))%3;this.zeroEnable[O]&&Math.min(i[0][O],i[1][O])<=0&&Math.max(i[0][O],i[1][O])>=0&&this._lines.drawZero(P,O,this.bounds,L,this.zeroLineColor[O],this.zeroLineWidth[O]*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 z=c(m,M[A].primalMinor),I=c(y,M[A].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=T/r[5*C];z[C]*=D[C]*R,I[C]*=D[C]*R}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,I,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,a,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,a=(t+2)%3,i=e[n],o=e[a],s=r[n],l=r[a];i>0&&l>0?N(n):i>0&&l<0?N(n):i<0&&l>0?N(n):i<0&&l<0?N(n):o>0&&s>0?N(a):o>0&&s<0?N(a):o<0&&s>0?N(a):o<0&&s<0&&N(a)}for(A=0;A<3;++A){var V=M[A].primalMinor,U=M[A].mirrorMinor,q=c(x,M[A].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[A]&&(q[C]+=T*V[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var H=[0,0,0];if(H[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,V,U);for(C=0;C<3;++C)q[C]+=T*V[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],q,this.tickColor[A],H,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)q[C]+=T*V[C]*this.labelPad[C]/r[5*C];q[A]+=.5*(i[0][A]+i[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],q,this.labelColor[A],[0,0,0],B,S)}}this._text.unbind()},h.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\":236,\"./lib/cube.js\":237,\"./lib/lines.js\":238,\"./lib/text.js\":240,\"./lib/ticks.js\":241}],236:[function(t,e,r){\"use 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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:a,colors:i},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\":239,\"gl-buffer\":243,\"gl-vao\":328}],237:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,i,p){a(s,e,t),a(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=i[x][2];for(var b=0;b<2;++b){u[1]=i[b][1];for(var _=0;_<2;++_)u[0]=i[_][0],f(l[y],u,s),y+=1}}for(var w=-1,x=0;x<8;++x){for(var k=l[x][3],T=0;T<3;++T)c[x][T]=l[x][T]/k;p&&(c[x][2]*=-1),k<0&&(w<0?w=x: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=C<<A,O=P+(C<<M)+(1-C<<S),z=P+(1-C<<M)+(C<<S);o(c[P],c[O],c[z],h)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(var C=0;C<2;++C){var P=C<<A,O=P+(C<<M)+(1-C<<S),z=P+(1-C<<M)+(C<<S),I=d([l[P],l[O],l[z],l[P+(1<<M)+(1<<S)]]);C?E=I:L=I}L>E&&(w|=1<<A)}}}for(var D=7^w,R=-1,x=0;x<8;++x)x!==w&&x!==D&&(R<0?R=x:c[R][1]>c[x][1]&&(R=x));for(var F=-1,x=0;x<3;++x){var B=R^1<<x;if(B!==w&&B!==D){F<0&&(F=B);var S=c[B];S[0]<c[F][0]&&(F=B)}}for(var N=-1,x=0;x<3;++x){var B=R^1<<x;if(B!==w&&B!==D&&B!==F){N<0&&(N=B);var S=c[B];S[0]>c[N][0]&&(N=B)}}var j=g;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^N)]=R&N;var V=7^N;V===w||V===D?(V=7^F,j[n.log2(N^V)]=V&N):j[n.log2(F^V)]=V&F;for(var U=v,q=w,A=0;A<3;++A)U[A]=q&1<<A?-1:1;return m};var n=t(\"bit-twiddle\"),a=t(\"gl-mat4/multiply\"),i=t(\"split-polygon\"),o=t(\"robust-orientation\"),s=new Array(16),l=new Array(8),c=new Array(8),u=new Array(3),h=[0,0,0];function f(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var a=0;a<3;++a)t[n]+=e[a]*r[4*a+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=i.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],a=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]-a,h=l[0]/l[3]-n,f=l[1]/l[3]-a;o+=Math.abs(c*f-u*h)}return o}var g=[1,1,1],v=[0,0,0],m={cubeEdges:g,axis:v}},{\"bit-twiddle\":93,\"gl-mat4/multiply\":269,\"robust-orientation\":508,\"split-polygon\":525}],238:[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 h=0;h<3;++h){for(var f=o.length/3|0,d=0;d<r[h].length;++d){var g=+r[h][d].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var v=o.length/3|0;s[h]=f,l[h]=v-f;for(var f=o.length/3|0,m=0;m<r[h].length;++m){var g=+r[h][m].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var v=o.length/3|0;c[h]=f,u[h]=v-f}var y=n(t,new Float32Array(o)),x=a(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=i(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t(\"gl-buffer\"),a=t(\"gl-vao\"),i=t(\"./shaders\").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function h(t){return t[0]=t[1]=t[2]=0,t}function f(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,a,i,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=a,this.tickOffset=i,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,a){var i=h(s);this.shader.uniforms.majorAxis=s,i[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=i;var 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g=h(l);g[t]=1,this.shader.uniforms.screenAxis=g,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=a,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,a,i){var o=h(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=f(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=h(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=a,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{\"./shaders\":239,\"gl-buffer\":243,\"gl-vao\":328}],239:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),a=t(\"gl-shader\"),i=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 a(t,i,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 a(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 a(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},{\"gl-shader\":303,glslify:410}],240:[function(t,e,r){(function(r){\"use strict\";e.exports=function(t,e,r,i,s,l){var u=n(t),h=a(t,[{buffer:u,size:3}]),f=o(t);f.attributes.position.location=0;var p=new c(t,f,u,h);return p.update(e,r,i,s,l),p};var n=t(\"gl-buffer\"),a=t(\"gl-vao\"),i=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,h=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var a=this.shader.uniforms;a.model=t,a.view=e,a.projection=r,a.pixelScale=n,h[0]=this.gl.drawingBufferWidth,h[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=h},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,a){var o=[];function s(t,e,r,n,a,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return i(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:a,styletags:s}));for(var h=(n||12)/12,f=u.positions,p=u.cells,d=0,g=p.length;d<g;++d)for(var v=p[d],m=2;m>=0;--m){var y=f[v[m]];o.push(h*y[0],-h*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],h=[0,0,0],f=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){h[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),f[d]=(o.length/3|0)-h[d],c[d]=o.length/3|0;for(var g=0;g<n[d].length;++g)n[d][g].text&&s(n[d][g].x,n[d][g].text,n[d][g].font||a,n[d][g].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=h,this.labelCount=f},u.drawTicks=function(t,e,r,n,a,i,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=i,this.shader.uniforms.color=a,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,a,i,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=i,this.shader.uniforms.color=a,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,t(\"_process\"))},{\"./shaders\":239,_process:483,\"gl-buffer\":243,\"gl-vao\":328,\"vectorize-text\":548}],241:[function(t,e,r){\"use strict\";function n(t,e){var r=t+\"\",n=r.indexOf(\".\"),a=0;n>=0&&(a=r.length-n-1);var i=Math.pow(10,a),o=Math.round(t*e*i),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/i,c=o%i;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),a){for(var h=\"\"+c;h.length<a;)h=\"0\"+h;return u+\".\"+h}return u}r.create=function(t,e){for(var r=[],a=0;a<3;++a){for(var i=[],o=(t[0][a],t[1][a],0);o*e[a]<=t[1][a];++o)i.push({x:o*e[a],text:n(e[a],o)});for(var o=-1;o*e[a]>=t[0][a];--o)i.push({x:o*e[a],text:n(e[a],o)});r.push(i)}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 a=t[r][n],i=e[r][n];if(a.x!==i.x||a.text!==i.text||a.font!==i.font||a.fontColor!==i.fontColor||a.fontSize!==i.fontSize||a.dx!==i.dx||a.dy!==i.dy)return!1}}return!0}},{}],242:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,l,h){var f=e.model||c,p=e.view||c,m=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(h=h||i(f,p,m,x,y)).axis;o(u,p,f),o(u,m,u);for(var _=g,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var k=n(s(u,u));s(u,u);for(var T=0;T<3;++T){var A=(T+1)%3,M=(T+2)%3,S=v;t:for(var w=0;w<2;++w){var E=[];if(b[T]<0!=!!w){S[T]=x[w][T];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())}for(var P=y?5:4,L=P;L===P;++L){if(0===E.length)continue t;E=a.positive(E,k[L])}for(var L=0;L<E.length;++L)for(var M=E[L],O=d(v,u,M,r,l),C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==T&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(O[C])))}}}return _};var n=t(\"extract-frustum-planes\"),a=t(\"split-polygon\"),i=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 h(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var f=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,a){for(var i=0;i<3;++i){for(var o=f,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[i]+=1,l(s,s,e),s[3]<0&&(t[i]=1/0),o[i]-=1,l(o,o,e),o[3]<0&&(t[i]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,h=(o[1]/o[3]-s[1]/s[3])*a;t[i]=.25*Math.sqrt(u*u+h*h)}return t}var g=[new h(1/0,-1/0,1/0),new h(1/0,-1/0,1/0),new h(1/0,-1/0,1/0)],v=[0,0,0]},{\"./lib/cube.js\":237,\"extract-frustum-planes\":226,\"gl-mat4/multiply\":269,\"gl-mat4/transpose\":278,\"gl-vec4/transformMat4\":399,\"split-polygon\":525}],243:[function(t,e,r){\"use strict\";var n=t(\"typedarray-pool\"),a=t(\"ndarray-ops\"),i=t(\"ndarray\"),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,a){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=a}var l=s.prototype;function c(t,e,r,n,a,i){var o=a.length*a.BYTES_PER_ELEMENT;if(i<0)return t.bufferData(e,a,n),o;if(o+i>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,i,a),r}function u(t,e){for(var r=n.malloc(t.length,e),a=t.length,i=0;i<a;++i)r[i]=t[i];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&\"undefined\"!=typeof t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=i(s,t.shape);a.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 h;h=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?h:h.subarray(0,t.length),e),n.free(h)}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 a=t.createBuffer(),i=new s(t,r,a,0,n);return i.update(e),i}},{ndarray:451,\"ndarray-ops\":445,\"typedarray-pool\":543}],244:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\");e.exports=function(t,e){var r=t.positions,a=t.vectors,i={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]),i;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,h=1/0,f=-1/0,p=null,d=null,g=[],v=1/0,m=!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),h=Math.min(x[2],h),f=Math.max(x[2],f);var b=a[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(v=Math.min(v,_),m=!1):m=!0}m||(p=x,d=b),g.push(b)}var w=[s,c,h],k=[l,u,f];e&&(e[0]=w,e[1]=k),0===o&&(o=1);var T=1/o;isFinite(v)||(v=1),i.vectorScale=v;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*T),i.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=g[y],P=n.length(C)*T,O=0;O<8;O++){i.positions.push([S,E,L,M++]),i.positions.push([S,E,L,M++]),i.positions.push([S,E,L,M++]),i.positions.push([S,E,L,M++]),i.positions.push([S,E,L,M++]),i.positions.push([S,E,L,M++]),i.vectors.push(C),i.vectors.push(C),i.vectors.push(C),i.vectors.push(C),i.vectors.push(C),i.vectors.push(C),i.vertexIntensity.push(P,P,P),i.vertexIntensity.push(P,P,P);var z=i.positions.length;i.cells.push([z-6,z-5,z-4],[z-3,z-2,z-1])}return i};var a=t(\"./lib/shaders\");e.exports.createMesh=t(\"./create_mesh\"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:a,traceType:\"cone\"})}},{\"./create_mesh\":245,\"./lib/shaders\":246,\"gl-vec3\":347}],245:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),a=t(\"gl-buffer\"),i=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"gl-mat4/multiply\"),l=t(\"gl-mat4/invert\"),c=t(\"ndarray\"),u=t(\"colormap\"),h=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function f(t,e,r,n,a,i,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=a,this.triangleVectors=i,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=h,this._view=h,this._projection=h,this._resolution=[1,1]}var p=f.prototype;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 a=e[n],i=0;i<3;++i)r[4*n+i]=a[i];r[4*n+3]=255*a[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,a=t.vectors;if(n&&r&&a){var i=[],o=[],s=[],l=[],h=[];this.cells=r,this.positions=n,this.vectors=a;var f=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,g=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],g=+t.vertexIntensityBounds[1];else for(var v=0;v<p.length;++v){var m=p[v];d=Math.min(d,m),g=Math.max(g,m)}else for(v=0;v<n.length;++v){m=n[v][2];d=Math.min(d,m),g=Math.max(g,m)}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(v=0;v<n.length;++v)for(var y=n[v],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(v=0;v<r.length;++v){var _=r[v];switch(_.length){case 3:for(x=0;x<3;++x){y=n[k=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var k;y=n[k=_[2-x]];i.push(y[0],y[1],y[2],y[3]);var T=a[k];o.push(T[0],T[1],T[2],T[3]||0);var A,M=f;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[k]-d)/(g-d),0]:[(y[2]-d)/(g-d),0],l.push(A[0],A[1]),h.push(v)}b+=1}}this.triangleCount=b,this.trianglePositions.update(i),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(h))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||h,n=t.view||h,a=t.projection||h,i=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)i[0][o]=Math.max(i[0][o],this.clipBounds[0][o]),i[1][o]=Math.min(i[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:a,inverseModel:h.slice(),clipBounds:i,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 f=u[15];for(o=0;o<3;++o)f+=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/f}if(this.triangleCount>0){var g=this.triShader;g.bind(),g.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||h,n=t.view||h,a=t.projection||h,i=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)i[0][o]=Math.max(i[0][o],this.clipBounds[0][o]),i[1][o]=Math.min(i[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(a),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:a,clipBounds:i,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),a={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?a.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(a.intensity=this.intensity[r[1]],a.velocity=this.vectors[r[1]].slice(0,3),a.divergence=this.vectors[r[1]][3],a.index=e),a},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 s=r.shaders;1===arguments.length&&(t=(e=t).gl);var l=function(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}(t,s),u=function(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}(t,s),h=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));h.generateMipmap(),h.minFilter=t.LINEAR_MIPMAP_LINEAR,h.magFilter=t.LINEAR;var p=a(t),d=a(t),g=a(t),v=a(t),m=a(t),y=new f(t,h,l,u,p,d,m,g,v,i(t,[{buffer:p,type:t.FLOAT,size:4},{buffer:m,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:4}]),r.traceType||\"cone\");return y.update(e),y}},{colormap:127,\"gl-buffer\":243,\"gl-mat4/invert\":267,\"gl-mat4/multiply\":269,\"gl-shader\":303,\"gl-texture2d\":323,\"gl-vao\":328,ndarray:451}],246:[function(t,e,r){var n=t(\"glslify\"),a=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\"]),i=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:a,fragment:i,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:410}],247:[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\",34000:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORMATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}},{}],248:[function(t,e,r){var n=t(\"./1.0/numbers\");e.exports=function(t){return n[t]}},{\"./1.0/numbers\":247}],249:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),o=a(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=i(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var c=new s(e,r,o,l);return c.update(t),c};var n=t(\"gl-buffer\"),a=t(\"gl-vao\"),i=t(\"./shaders/index\"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=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.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function c(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,a=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var i=n[12],s=n[13],l=n[14],c=n[15],u=(t._ortho||!1?2:1)*this.pixelRatio*(a[3]*i+a[7]*s+a[11]*l+a[15]*c)/e.drawingBufferHeight;this.vao.bind();for(var h=0;h<3;++h)e.lineWidth(this.lineWidth[h]*this.pixelRatio),r.capSize=this.capSize[h]*u,this.lineCount[h]&&e.drawArrays(e.LINES,this.lineOffset[h],this.lineCount[h]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var a=-1;a<=1;a+=2){var i=[0,0,0];i[(n+e)%3]=a,r.push(i)}t[e]=r}return t}();function h(t,e,r,n){for(var a=u[n],i=0;i<a.length;++i){var o=a[i];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return a.length}l.update=function(t){\"lineWidth\"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),\"capSize\"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var a=[],i=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<i;++l){for(var u=r[l],f=0;f<3;++f)if(isNaN(u[f])||!isFinite(u[f]))continue t;var p=n[l],d=e[s];if(Array.isArray(d[0])&&(d=e[l]),3===d.length?d=[d[0],d[1],d[2],1]:4===d.length&&(d=[d[0],d[1],d[2],d[3]],!this.hasAlpha&&d[3]<1&&(this.hasAlpha=!0)),!isNaN(p[0][s])&&!isNaN(p[1][s])){var g;if(p[0][s]<0)(g=u.slice())[s]+=p[0][s],a.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,g[0],g[1],g[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,g),o+=2+h(a,g,d,s);if(p[1][s]>0)(g=u.slice())[s]+=p[1][s],a.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,g[0],g[1],g[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,g),o+=2+h(a,g,d,s)}}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(a)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{\"./shaders/index\":250,\"gl-buffer\":243,\"gl-vao\":328}],250:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),a=t(\"gl-shader\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, offset;\\nattribute vec4 color;\\nuniform mat4 model, view, projection;\\nuniform float capSize;\\nvarying vec4 fragColor;\\nvarying vec3 fragPosition;\\n\\nvoid main() {\\n  vec4 worldPosition  = model * vec4(position, 1.0);\\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\\n  gl_Position         = projection * view * worldPosition;\\n  fragColor           = color;\\n  fragPosition        = position;\\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 float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  gl_FragColor = opacity * fragColor;\\n}\"]);e.exports=function(t){return a(t,i,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},{\"gl-shader\":303,glslify:410}],251:[function(t,e,r){\"use strict\";var n=t(\"gl-texture2d\");e.exports=function(t,e,r,n){a||(a=t.FRAMEBUFFER_UNSUPPORTED,i=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension(\"WEBGL_draw_buffers\");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var a=new Array(r),i=0;i<n;++i)a[i]=t.COLOR_ATTACHMENT0+i;for(var i=n;i<r;++i)a[i]=t.NONE;l[n]=a}}(t,c);Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]);if(\"number\"!=typeof e)throw new Error(\"gl-fbo: Missing shape parameter\");var u=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>u||r<0||r>u)throw new Error(\"gl-fbo: Parameters are too large for FBO\");var h=1;if(\"color\"in(n=n||{})){if((h=Math.max(0|n.color,0))<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(h>1){if(!c)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(h>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+h+\" draw buffers\")}}var f=t.UNSIGNED_BYTE,p=t.getExtension(\"OES_texture_float\");if(n.float&&h>0){if(!p)throw new Error(\"gl-fbo: Context does not support floating point textures\");f=t.FLOAT}else n.preferFloat&&h>0&&p&&(f=t.FLOAT);var g=!0;\"depth\"in n&&(g=!!n.depth);var v=!1;\"stencil\"in n&&(v=!!n.stencil);return new d(t,e,r,f,h,g,v,c)};var a,i,o,s,l=null;function c(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function u(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function h(t){switch(t){case a:throw new Error(\"gl-fbo: Framebuffer unsupported\");case i:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case o:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case s:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function f(t,e,r,a,i,o){if(!a)return null;var s=n(t,e,r,i,a);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,a){var i=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,i),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,a,t.RENDERBUFFER,i),i}function d(t,e,r,n,a,i,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(a);for(var d=0;d<a;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=i,this._useStencil=o;var g=this,v=[0|e,0|r];Object.defineProperties(v,{0:{get:function(){return g._shape[0]},set:function(t){return g.width=t}},1:{get:function(){return g._shape[1]},set:function(t){return g.height=t}}}),this._shapeVector=v,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),a=t._shape[0],i=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,g=t._useDepth,v=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var m=0;m<o;++m)t.color[m]=f(r,a,i,v,r.RGBA,r.COLOR_ATTACHMENT0+m);0===o?(t._color_rb=p(r,a,i,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=f(r,a,i,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g&&(t.depth=f(r,a,i,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):g&&d?t._depth_rb=p(r,a,i,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g?t._depth_rb=p(r,a,i,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,a,i,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){for(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),m=0;m<t.color.length;++m)t.color[m].dispose(),t.color[m]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),h(x)}u(r,e)}(this)}var g=d.prototype;function v(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,a=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var i=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,i),h(s)),u(n,i)}}Object.defineProperties(g,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return v(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return v(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,v(this,this._shape[0],t),t},enumerable:!1}}),g.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},g.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":323}],252:[function(t,e,r){var n=t(\"sprintf-js\").sprintf,a=t(\"gl-constants/lookup\"),i=t(\"glsl-shader-name\"),o=t(\"add-line-numbers\");e.exports=function(t,e,r){\"use strict\";var s=i(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===a.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var c=n(\"Error compiling %s shader %s:\\n\",l,s),u=n(\"%s%s\",c,t),h=t.split(\"\\n\"),f={},p=0;p<h.length;p++){var d=h[p];if(\"\"!==d&&\"\\0\"!==d){var g=parseInt(d.split(\":\")[2]);if(isNaN(g))throw new Error(n(\"Could not parse error: %s\",d));f[g]=d}}for(var v=o(e).split(\"\\n\"),p=0;p<v.length;p++)if(f[p+3]||f[p+2]||f[p+1]){var m=v[p];if(c+=m+\"\\n\",f[p+1]){var y=f[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\":63,\"gl-constants/lookup\":248,\"glsl-shader-name\":402,\"sprintf-js\":526}],253:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),a=o(r,l.pickVertex,l.pickFragment),i=s(r),u=s(r),h=s(r),f=s(r),p=new c(t,n,a,i,u,h,f);return p.update(e),t.addObject(p),p};var n=t(\"binary-search-bounds\"),a=t(\"iota-array\"),i=t(\"typedarray-pool\"),o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"./lib/shaders\");function c(t,e,r,n,a,i,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=a,this.colorBuffer=i,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,h=c.prototype,f=[0,0,1,0,0,1,1,0,1,1,0,1];h.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 a=t.gl,i=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=i[2]-i[0],c=i[3]-i[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-i[0])/l-1,u[7]=2*(r[1]-i[1])/c-1,e.bind();var h=e.uniforms;h.viewTransform=u,h.shape=this.shape;var f=e.attributes;this.positionBuffer.bind(),f.position.pointer(),this.weightBuffer.bind(),f.weight.pointer(a.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),f.color.pointer(a.UNSIGNED_BYTE,!0),a.drawArrays(a.TRIANGLES,0,n)}}),h.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,a=this.pickShader,i=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=i[2]-i[0],u=i[3]-i[1],h=l[2]-l[0],f=l[3]-l[1];t[0]=2*c/h,t[4]=2*u/f,t[6]=2*(i[0]-l[0])/h-1,t[7]=2*(i[1]-l[1])/f-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,a.bind();var d=a.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var g=a.attributes;return this.positionBuffer.bind(),g.position.pointer(),this.weightBuffer.bind(),g.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),g.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),h.pick=function(t,e,r){var n=this.pickOffset,a=this.shape[0]*this.shape[1];if(r<n||r>=n+a)return null;var i=r-n,o=this.xData,s=this.yData;return{object:this,pointId:i,dataCoord:[o[i%this.shape[0]],s[i/this.shape[0]|0]]}},h.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||a(e[0]),o=t.y||a(e[1]),s=t.z||new Float32Array(e[0]*e[1]);this.xData=r,this.yData=o;var l=t.colorLevels||[0],c=t.colorValues||[0,0,0,1],u=l.length,h=this.bounds,p=h[0]=r[0],d=h[1]=o[0],g=1/((h[2]=r[r.length-1])-p),v=1/((h[3]=o[o.length-1])-d),m=e[0],y=e[1];this.shape=[m,y];var x=(m-1)*(y-1)*(f.length>>>1);this.numVertices=x;for(var b=i.mallocUint8(4*x),_=i.mallocFloat32(2*x),w=i.mallocUint8(2*x),k=i.mallocUint32(x),T=0,A=0;A<y-1;++A)for(var M=v*(o[A]-d),S=v*(o[A+1]-d),E=0;E<m-1;++E)for(var L=g*(r[E]-p),C=g*(r[E+1]-p),P=0;P<f.length;P+=2){var O,z,I,D,R=f[P],F=f[P+1],B=s[(A+F)*m+(E+R)],N=n.le(l,B);if(N<0)O=c[0],z=c[1],I=c[2],D=c[3];else if(N===u-1)O=c[4*u-4],z=c[4*u-3],I=c[4*u-2],D=c[4*u-1];else{var j=(B-l[N])/(l[N+1]-l[N]),V=1-j,U=4*N,q=4*(N+1);O=V*c[U]+j*c[q],z=V*c[U+1]+j*c[q+1],I=V*c[U+2]+j*c[q+2],D=V*c[U+3]+j*c[q+3]}b[4*T]=255*O,b[4*T+1]=255*z,b[4*T+2]=255*I,b[4*T+3]=255*D,_[2*T]=.5*L+.5*C,_[2*T+1]=.5*M+.5*S,w[2*T]=R,w[2*T+1]=F,k[T]=A*m+E,T+=1}this.positionBuffer.update(_),this.weightBuffer.update(w),this.colorBuffer.update(b),this.idBuffer.update(k),i.free(_),i.free(b),i.free(w),i.free(k)},h.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\":254,\"binary-search-bounds\":255,\"gl-buffer\":243,\"gl-shader\":303,\"iota-array\":417,\"typedarray-pool\":543}],254:[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:410}],255:[function(t,e,r){arguments[4][112][0].apply(r,arguments)},{dup:112}],256:[function(t,e,r){var n=t(\"glslify\"),a=t(\"gl-shader\"),i=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\\nlowp vec4 encode_float_1540259130(highp float v) {\\n  highp 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  highp vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  highp float e = floor(log2(av));\\n  highp 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  highp 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, encode_float_1540259130(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 a(t,i,o,null,l)},r.createPickShader=function(t){return a(t,i,s,null,l)}},{\"gl-shader\":303,glslify:410}],257:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=u(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),c=a(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}]),f=l(new Array(1024),[256,1,4]),p=0;p<1024;++p)f.data[p]=255;var d=i(e,f);d.wrap=e.REPEAT;var g=new v(e,r,o,s,c,d);return g.update(t),g};var n=t(\"gl-buffer\"),a=t(\"gl-vao\"),i=t(\"gl-texture2d\"),o=t(\"glsl-read-float\"),s=t(\"binary-search-bounds\"),l=t(\"ndarray\"),c=t(\"./lib/shaders\"),u=c.createShader,h=c.createPickShader,f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function p(t,e){for(var r=0,n=0;n<3;++n){var a=t[n]-e[n];r+=a*a}return Math.sqrt(r)}function d(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,a,i){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=a,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=i,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var m=v.prototype;m.isTransparent=function(){return this.hasAlpha},m.isOpaque=function(){return!this.hasAlpha},m.pickSlots=1,m.setPickBase=function(t){this.pickId=t},m.drawTransparent=m.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||f,view:t.view||f,projection:t.projection||f,clipBounds:d(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()}},m.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||f,view:t.view||f,projection:t.projection||f,pickId:this.pickId,clipBounds:d(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.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 a=[],i=[],o=[],c=0,u=0,h=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],f=t.position||t.positions;if(f){var d=t.color||t.colors||[0,0,0,1],g=t.lineWidth||1,v=!1;t:for(e=1;e<f.length;++e){var m,y,x,b=f[e-1],_=f[e];for(i.push(c),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&a.length>0){for(var w=0;w<24;++w)a.push(a[a.length-12]);u+=2,v=!0}continue t}h[0][r]=Math.min(h[0][r],b[r],_[r]),h[1][r]=Math.max(h[1][r],b[r],_[r])}Array.isArray(d[0])?(m=d.length>e-1?d[e-1]:d.length>0?d[d.length-1]:[0,0,0,1],y=d.length>e?d[e]:d.length>0?d[d.length-1]:[0,0,0,1]):m=y=d,3===m.length&&(m=[m[0],m[1],m[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&m[3]<1&&(this.hasAlpha=!0),x=Array.isArray(g)?g.length>e-1?g[e-1]:g.length>0?g[g.length-1]:[0,0,0,1]:g;var k=c;if(c+=p(b,_),v){for(r=0;r<2;++r)a.push(b[0],b[1],b[2],_[0],_[1],_[2],k,x,m[0],m[1],m[2],m[3]);u+=2,v=!1}a.push(b[0],b[1],b[2],_[0],_[1],_[2],k,x,m[0],m[1],m[2],m[3],b[0],b[1],b[2],_[0],_[1],_[2],k,-x,m[0],m[1],m[2],m[3],_[0],_[1],_[2],b[0],b[1],b[2],c,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],c,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(a),i.push(c),o.push(f[f.length-1].slice()),this.bounds=h,this.vertexCount=u,this.points=o,this.arcLength=i,\"dashes\"in t){var T=t.dashes.slice();for(T.unshift(0),e=1;e<T.length;++e)T[e]=T[e-1]+T[e];var A=l(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&s.le(T,T[T.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},m.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},m.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=o(t.value[0],t.value[1],t.value[2],0),r=s.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],a=this.points[Math.min(r+1,this.points.length-1)],i=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),l=1-i,c=[0,0,0],u=0;u<3;++u)c[u]=l*n[u]+i*a[u];var h=Math.min(i<.5?r:r+1,this.points.length-1);return new g(e,c,h,this.points[h])}},{\"./lib/shaders\":256,\"binary-search-bounds\":258,\"gl-buffer\":243,\"gl-texture2d\":323,\"gl-vao\":328,\"glsl-read-float\":401,ndarray:451}],258:[function(t,e,r){arguments[4][112][0].apply(r,arguments)},{dup:112}],259:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],a=e[2],i=e[3],o=r*i-a*n;return o?(o=1/o,t[0]=i*o,t[1]=-n*o,t[2]=-a*o,t[3]=r*o,t):null}},{}],260:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],a=e[2],i=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],h=u*o-s*c,f=-u*i+s*l,p=c*i-o*l,d=r*h+n*f+a*p;return d?(d=1/d,t[0]=h*d,t[1]=(-u*n+a*c)*d,t[2]=(s*n-a*o)*d,t[3]=f*d,t[4]=(u*r-a*l)*d,t[5]=(-s*r+a*i)*d,t[6]=p*d,t[7]=(-c*r+n*l)*d,t[8]=(o*r-n*i)*d,t):null}},{}],261:[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}},{}],262:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);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}},{}],263:[function(t,e,r){e.exports=function(t){var e=t[0],r=t[1],n=t[2],a=t[3],i=t[4],o=t[5],s=t[6],l=t[7],c=t[8],u=t[9],h=t[10],f=t[11],p=t[12],d=t[13],g=t[14],v=t[15];return(e*o-r*i)*(h*v-f*g)-(e*s-n*i)*(u*v-f*d)+(e*l-a*i)*(u*g-h*d)+(r*s-n*o)*(c*v-f*p)-(r*l-a*o)*(c*g-h*p)+(n*l-a*s)*(c*d-u*p)}},{}],264:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],a=e[2],i=e[3],o=r+r,s=n+n,l=a+a,c=r*o,u=n*o,h=n*s,f=a*o,p=a*s,d=a*l,g=i*o,v=i*s,m=i*l;return t[0]=1-h-d,t[1]=u+m,t[2]=f-v,t[3]=0,t[4]=u-m,t[5]=1-c-d,t[6]=p+g,t[7]=0,t[8]=f+v,t[9]=p-g,t[10]=1-c-h,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],265:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],a=e[1],i=e[2],o=e[3],s=n+n,l=a+a,c=i+i,u=n*s,h=n*l,f=n*c,p=a*l,d=a*c,g=i*c,v=o*s,m=o*l,y=o*c;return t[0]=1-(p+g),t[1]=h+y,t[2]=f-m,t[3]=0,t[4]=h-y,t[5]=1-(u+g),t[6]=d+v,t[7]=0,t[8]=f+m,t[9]=d-v,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],266:[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}},{}],267:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],a=e[2],i=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],h=e[9],f=e[10],p=e[11],d=e[12],g=e[13],v=e[14],m=e[15],y=r*s-n*o,x=r*l-a*o,b=r*c-i*o,_=n*l-a*s,w=n*c-i*s,k=a*c-i*l,T=u*g-h*d,A=u*v-f*d,M=u*m-p*d,S=h*v-f*g,E=h*m-p*g,L=f*m-p*v,C=y*L-x*E+b*S+_*M-w*A+k*T;if(!C)return null;return C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(a*E-n*L-i*S)*C,t[2]=(g*k-v*w+m*_)*C,t[3]=(f*w-h*k-p*_)*C,t[4]=(l*M-o*L-c*A)*C,t[5]=(r*L-a*M+i*A)*C,t[6]=(v*b-d*k-m*x)*C,t[7]=(u*k-f*b+p*x)*C,t[8]=(o*E-s*M+c*T)*C,t[9]=(n*M-r*E-i*T)*C,t[10]=(d*w-g*b+m*y)*C,t[11]=(h*b-u*w-p*y)*C,t[12]=(s*A-o*S-l*T)*C,t[13]=(r*S-n*A+a*T)*C,t[14]=(g*x-d*_-v*y)*C,t[15]=(u*_-h*x+f*y)*C,t}},{}],268:[function(t,e,r){var n=t(\"./identity\");e.exports=function(t,e,r,a){var i,o,s,l,c,u,h,f,p,d,g=e[0],v=e[1],m=e[2],y=a[0],x=a[1],b=a[2],_=r[0],w=r[1],k=r[2];if(Math.abs(g-_)<1e-6&&Math.abs(v-w)<1e-6&&Math.abs(m-k)<1e-6)return n(t);h=g-_,f=v-w,p=m-k,d=1/Math.sqrt(h*h+f*f+p*p),i=x*(p*=d)-b*(f*=d),o=b*(h*=d)-y*p,s=y*f-x*h,(d=Math.sqrt(i*i+o*o+s*s))?(i*=d=1/d,o*=d,s*=d):(i=0,o=0,s=0);l=f*s-p*o,c=p*i-h*s,u=h*o-f*i,(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]=i,t[1]=l,t[2]=h,t[3]=0,t[4]=o,t[5]=c,t[6]=f,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(i*g+o*v+s*m),t[13]=-(l*g+c*v+u*m),t[14]=-(h*g+f*v+p*m),t[15]=1,t}},{\"./identity\":266}],269:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],a=e[1],i=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],h=e[8],f=e[9],p=e[10],d=e[11],g=e[12],v=e[13],m=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*h+w*g,t[1]=x*a+b*l+_*f+w*v,t[2]=x*i+b*c+_*p+w*m,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+_*h+w*g,t[5]=x*a+b*l+_*f+w*v,t[6]=x*i+b*c+_*p+w*m,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+_*h+w*g,t[9]=x*a+b*l+_*f+w*v,t[10]=x*i+b*c+_*p+w*m,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+_*h+w*g,t[13]=x*a+b*l+_*f+w*v,t[14]=x*i+b*c+_*p+w*m,t[15]=x*o+b*u+_*d+w*y,t}},{}],270:[function(t,e,r){e.exports=function(t,e,r,n,a,i,o){var s=1/(e-r),l=1/(n-a),c=1/(i-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]=(a+n)*l,t[14]=(o+i)*c,t[15]=1,t}},{}],271:[function(t,e,r){e.exports=function(t,e,r,n,a){var i=1/Math.tan(e/2),o=1/(n-a);return t[0]=i/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=i,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(a+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*a*n*o,t[15]=0,t}},{}],272:[function(t,e,r){e.exports=function(t,e,r,n){var a,i,o,s,l,c,u,h,f,p,d,g,v,m,y,x,b,_,w,k,T,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,a=Math.sin(r),i=Math.cos(r),o=1-i,s=e[0],l=e[1],c=e[2],u=e[3],h=e[4],f=e[5],p=e[6],d=e[7],g=e[8],v=e[9],m=e[10],y=e[11],x=E*E*o+i,b=L*E*o+C*a,_=C*E*o-L*a,w=E*L*o-C*a,k=L*L*o+i,T=C*L*o+E*a,A=E*C*o+L*a,M=L*C*o-E*a,S=C*C*o+i,t[0]=s*x+h*b+g*_,t[1]=l*x+f*b+v*_,t[2]=c*x+p*b+m*_,t[3]=u*x+d*b+y*_,t[4]=s*w+h*k+g*T,t[5]=l*w+f*k+v*T,t[6]=c*w+p*k+m*T,t[7]=u*w+d*k+y*T,t[8]=s*A+h*M+g*S,t[9]=l*A+f*M+v*S,t[10]=c*A+p*M+m*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}},{}],273:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),a=Math.cos(r),i=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],h=e[10],f=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]=i*a+c*n,t[5]=o*a+u*n,t[6]=s*a+h*n,t[7]=l*a+f*n,t[8]=c*a-i*n,t[9]=u*a-o*n,t[10]=h*a-s*n,t[11]=f*a-l*n,t}},{}],274:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),a=Math.cos(r),i=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],h=e[10],f=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]=i*a-c*n,t[1]=o*a-u*n,t[2]=s*a-h*n,t[3]=l*a-f*n,t[8]=i*n+c*a,t[9]=o*n+u*a,t[10]=s*n+h*a,t[11]=l*n+f*a,t}},{}],275:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),a=Math.cos(r),i=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],h=e[6],f=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]=i*a+c*n,t[1]=o*a+u*n,t[2]=s*a+h*n,t[3]=l*a+f*n,t[4]=c*a-i*n,t[5]=u*a-o*n,t[6]=h*a-s*n,t[7]=f*a-l*n,t}},{}],276:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],a=r[1],i=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]*a,t[5]=e[5]*a,t[6]=e[6]*a,t[7]=e[7]*a,t[8]=e[8]*i,t[9]=e[9]*i,t[10]=e[10]*i,t[11]=e[11]*i,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],277:[function(t,e,r){e.exports=function(t,e,r){var n,a,i,o,s,l,c,u,h,f,p,d,g=r[0],v=r[1],m=r[2];e===t?(t[12]=e[0]*g+e[4]*v+e[8]*m+e[12],t[13]=e[1]*g+e[5]*v+e[9]*m+e[13],t[14]=e[2]*g+e[6]*v+e[10]*m+e[14],t[15]=e[3]*g+e[7]*v+e[11]*m+e[15]):(n=e[0],a=e[1],i=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],h=e[8],f=e[9],p=e[10],d=e[11],t[0]=n,t[1]=a,t[2]=i,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=h,t[9]=f,t[10]=p,t[11]=d,t[12]=n*g+s*v+h*m+e[12],t[13]=a*g+l*v+f*m+e[13],t[14]=i*g+c*v+p*m+e[14],t[15]=o*g+u*v+d*m+e[15]);return t}},{}],278:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],a=e[3],i=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]=i,t[11]=e[14],t[12]=a,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}},{}],279:[function(t,e,r){\"use strict\";e.exports=function(t,e){switch(e.length){case 0:break;case 1:t[0]=1/e[0];break;case 4:n(t,e);break;case 9:a(t,e);break;case 16:i(t,e);break;default:throw new Error(\"currently supports matrices up to 4x4\")}return t};var n=t(\"gl-mat2/invert\"),a=t(\"gl-mat3/invert\"),i=t(\"gl-mat4/invert\")},{\"gl-mat2/invert\":259,\"gl-mat3/invert\":260,\"gl-mat4/invert\":267}],280:[function(t,e,r){\"use strict\";var n=t(\"barycentric\"),a=t(\"polytope-closest-point/lib/closest_point_2d.js\");function i(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var a=0;a<4;++a)r[a]+=t[4*n+a]*e[n];return r}function o(t,e,r,n,a){for(var o=i(n,i(r,i(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*a[0]*(1+o[0]),.5*a[1]*(1-o[1])]}e.exports=function(t,e,r,i,s,l){if(1===t.length)return[0,t[0].slice()];for(var c=new Array(t.length),u=0;u<t.length;++u)c[u]=o(t[u],r,i,s,l);for(var h=0,f=1/0,u=0;u<c.length;++u){for(var p=0,d=0;d<2;++d)p+=Math.pow(c[u][d]-e[d],2);p<f&&(f=p,h=u)}for(var g=function(t,e){if(2===t.length){for(var r=0,i=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),i+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),i=Math.sqrt(i),r+i<1e-6?[1,0]:[i/(r+i),r/(i+r)]}if(3===t.length){var s=[0,0];return a(t[0],t[1],t[2],e,s),n(t,s)}return[]}(c,e),v=0,u=0;u<3;++u){if(g[u]<-.001||g[u]>1.0001)return null;v+=g[u]}if(Math.abs(v-1)>.001)return null;return[h,function(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var a=t[n],i=e[n],o=0;o<3;++o)r[o]+=i*a[o];return r}(t,g),g]}},{barycentric:74,\"polytope-closest-point/lib/closest_point_2d.js\":482}],281:[function(t,e,r){var n=t(\"glslify\"),a=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\"]),i=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}\"]),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\\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}\"]),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\\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:a,fragment:i,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:h,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:f,fragment:h,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:410}],282:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),a=t(\"gl-buffer\"),i=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),h=t(\"colormap\"),f=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./lib/shaders\"),g=t(\"./lib/closest-point\"),v=d.meshShader,m=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 k(t,e,r,n,a,i,o,s,l,c,u,h,f,p,d,g,v,m,y,x,b,_,k,T,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=a,this.pickShader=i,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=f,this.triangleUVs=h,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=v,this.edgeUVs=m,this.edgeIds=g,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=k,this.pointSizes=T,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.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 T=k.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,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 S(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function E(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 L(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}T.isOpaque=function(){return!this.hasAlpha},T.isTransparent=function(){return this.hasAlpha},T.pickSlots=1,T.setPickBase=function(t){this.pickId=t},T.highlight=function(t){if(t&&this.contourEnable){for(var e=f(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,a=e.vertexWeights,i=r.length,o=p.mallocFloat32(6*i),s=0,l=0;l<i;++l)for(var c=r[l],u=0;u<2;++u){var h=c[0];2===c.length&&(h=c[u]);for(var d=n[h][0],g=n[h][1],v=a[h],m=1-v,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=v*y[b]+m*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},T.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=h({colormap:t,nshades:256,format:\"rgba\"}),n=new Uint8Array(1024),a=0;a<256;++a){for(var i=r[a],o=0;o<3;++o)n[4*a+o]=i[o];n[4*a+3]=e?255*A(a/255,e):255*i[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 a=[],i=[],l=[],c=[],f=[],p=[],d=[],g=[],v=[],m=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,k=t.cellNormals,T=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!k&&(k=s.faceNormals(r,n,M)),k||w||(w=s.vertexNormals(r,n,T));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,O=t.cellUVs,z=t.cellIntensity,I=1/0,D=-1/0;if(!C&&!O)if(P)if(t.vertexIntensityBounds)I=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];I=Math.min(I,F),D=Math.max(D,F)}else if(z)for(R=0;R<z.length;++R){F=z[R];I=Math.min(I,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];I=Math.min(I,F),D=Math.max(D,F)}this.intensity=P||(z?function(t,e,r){for(var n=new Array(e),a=0;a<e;++a)n[a]=0;var i=t.length;for(a=0;a<i;++a)for(var o=t[a],s=0;s<o.length;++s)n[o[s]]=r[a];return n}(r,n.length,z):function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n));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],V=0;V<3;++V)!isNaN(j[V])&&isFinite(j[V])&&(this.bounds[0][V]=Math.min(this.bounds[0][V],j[V]),this.bounds[1][V]=Math.max(this.bounds[1][V],j[V]));var U=0,q=0,H=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],V=0;V<3;++V)if(isNaN(j[V])||!isFinite(j[V]))continue t;m.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?i.push(X[0],X[1],X[2],this.opacity*A((P[W]-I)/(D-I),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]-I)/(D-I),0]:O?O[R]:z?[(z[R]-I)/(D-I),0]:[(j[2]-I)/(D-I),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),H+=1;break;case 2:for(V=0;V<2;++V){j=n[W=G[V]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(V=0;V<2;++V){j=n[W=G[V]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?i.push(X[0],X[1],X[2],this.opacity*A((P[W]-I)/(D-I),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]-I)/(D-I),0]:O?O[R]:z?[(z[R]-I)/(D-I),0]:[(j[2]-I)/(D-I),0],g.push(Z[0],Z[1]),v.push(R)}q+=1;break;case 3:for(V=0;V<3;++V)for(j=n[W=G[V]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(V=0;V<3;++V){var W,X,Z,J;j=n[W=G[2-V]];a.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?i.push(X[0],X[1],X[2],this.opacity*A((P[W]-I)/(D-I),this.opacityscale)):3===X.length?i.push(X[0],X[1],X[2],this.opacity):(i.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):i.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-I)/(D-I),0]:O?O[R]:z?[(z[R]-I)/(D-I),0]:[(j[2]-I)/(D-I),0],c.push(Z[0],Z[1]),J=w?w[W]:k[R],l.push(J[0],J[1],J[2]),f.push(R)}U+=1}}this.pointCount=H,this.edgeCount=q,this.triangleCount=U,this.pointPositions.update(m),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(g),this.edgeIds.update(new Uint32Array(v)),this.trianglePositions.update(a),this.triangleColors.update(i),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(f))}},T.drawTransparent=T.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,a=t.projection||w,i=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)i[0][o]=Math.max(i[0][o],this.clipBounds[0][o]),i[1][o]=Math.min(i[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:a,inverseModel:w.slice(),clipBounds:i,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 h,f=u[15];for(o=0;o<3;++o)f+=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/f}this.triangleCount>0&&((h=this.triShader).bind(),h.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((h=this.lineShader).bind(),h.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&&((h=this.pointShader).bind(),h.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((h=this.contourShader).bind(),h.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},T.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,a=t.projection||w,i=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)i[0][o]=Math.max(i[0][o],this.clipBounds[0][o]),i[1][o]=Math.min(i[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(a),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:a,clipBounds:i,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())},T.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,a=new Array(r.length),i=0;i<r.length;++i)a[i]=n[r[i]];var o=g(a,[t.coord[0],this._resolution[1]-t.coord[1]],this._model,this._view,this._projection,this._resolution);if(!o)return null;var s=o[2],l=0;for(i=0;i<r.length;++i)l+=s[i]*this.intensity[r[i]];return{position:o[1],index:r[o[0]],cell:r,cellId:e,intensity:l,dataCoordinate:this.positions[r[o[0]]]}},T.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){if(1===arguments.length&&(t=(e=t).gl),!(t.getExtension(\"OES_standard_derivatives\")||t.getExtension(\"MOZ_OES_standard_derivatives\")||t.getExtension(\"WEBKIT_OES_standard_derivatives\")))throw new Error(\"derivatives not supported\");var r=function(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.attributes.normal.location=4,e}(t),s=function(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}(t),l=M(t),c=S(t),h=E(t),f=L(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=a(t),g=a(t),y=a(t),x=a(t),b=a(t),_=i(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:b,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:y,type:t.FLOAT,size:2},{buffer:x,type:t.FLOAT,size:3}]),w=a(t),T=a(t),A=a(t),C=a(t),P=i(t,[{buffer:w,type:t.FLOAT,size:3},{buffer:C,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:T,type:t.FLOAT,size:4},{buffer:A,type:t.FLOAT,size:2}]),O=a(t),z=a(t),I=a(t),D=a(t),R=a(t),F=i(t,[{buffer:O,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:z,type:t.FLOAT,size:4},{buffer:I,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=a(t),N=new k(t,p,r,s,l,c,h,f,d,b,g,y,x,_,w,C,T,A,P,O,R,z,I,D,F,B,i(t,[{buffer:B,type:t.FLOAT,size:3}]));return N.update(e),N}},{\"./lib/closest-point\":280,\"./lib/shaders\":281,colormap:127,\"gl-buffer\":243,\"gl-mat4/invert\":267,\"gl-mat4/multiply\":269,\"gl-shader\":303,\"gl-texture2d\":323,\"gl-vao\":328,ndarray:451,normals:454,\"simplicial-complex-contour\":516,\"typedarray-pool\":543}],283:[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=a(e,i.boxVert,i.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),a=t(\"gl-shader\"),i=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,a){var i=this.plot,o=this.shader,c=i.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=a,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":286,\"gl-buffer\":243,\"gl-shader\":303}],284:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),i=a(e,o.gridVert,o.gridFrag),l=a(e,o.tickVert,o.gridFrag);return new s(t,r,i,l)};var n=t(\"gl-buffer\"),a=t(\"gl-shader\"),i=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,h,f,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],h=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,a=t.gl,i=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,f=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,g=0;g<2;++g){var v=i[g],m=i[g+2]-v,y=.5*(o[g+2]+o[g]),x=o[g+2]-o[g];u[g]=2*m/x,c[g]=2*(v-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(g=0;g<2;++g){h[0]=h[1]=0,h[g]=1,r.uniforms.dataAxis=h,r.uniforms.lineWidth=l[g]/(s[g+2]-s[g])*d,r.uniforms.color=f[g];var _=6*n[g].length;p[g]&&_&&a.drawArrays(a.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],a=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,h=this.ticks,f=s.gl,p=s._tickBounds,d=s.dataBox,g=s.viewBox,v=s.pixelRatio,m=s.screenBox,y=m[2]-m[0],x=m[3]-m[1],b=g[2]-g[0],_=g[3]-g[1],w=0;w<2;++w){var k=p[w],T=p[w+2]-k,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*T/M,t[w]=2*(k-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*h[0].length,O=Math.min(i.ge(h[0],(d[0]-p[0])/(p[2]-p[0]),l),h[0].length),z=Math.min(i.gt(h[0],(d[2]-p[0])/(p[2]-p[0]),l),h[0].length),I=0+6*O,D=6*Math.max(0,z-O),R=Math.min(i.ge(h[1],(d[1]-p[1])/(p[3]-p[1]),l),h[1].length),F=Math.min(i.gt(h[1],(d[3]-p[1])/(p[3]-p[1]),l),h[1].length),B=P+6*R,N=6*Math.max(0,F-R);a[0]=2*(g[0]-E[1])/y-1,a[1]=(g[3]+g[1])/x-1,o[0]=E[1]*v/y,o[1]=L[1]*v/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=a,f.drawArrays(f.TRIANGLES,B,N)),a[0]=(g[2]+g[0])/y-1,a[1]=2*(g[1]-E[0])/x-1,o[0]=L[0]*v/y,o[1]=E[0]*v/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=a,f.drawArrays(f.TRIANGLES,I,D)),a[0]=2*(g[2]+E[3])/y-1,a[1]=(g[3]+g[1])/x-1,o[0]=E[3]*v/y,o[1]=L[3]*v/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=a,f.drawArrays(f.TRIANGLES,B,N)),a[0]=(g[2]+g[0])/y-1,a[1]=2*(g[3]+E[2])/x-1,o[0]=L[2]*v/y,o[1]=E[2]*v/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=a,f.drawArrays(f.TRIANGLES,I,D))}}(),d.update=(f=[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)),a=(this.plot.zeroLineEnable,0),i=[[],[]],o=0;o<2;++o)for(var s=i[o],l=e[o],c=r[o],u=r[o+2],h=0;h<l.length;++h){var d=(l[h].x-c)/(u-c);s.push(d);for(var g=0;g<6;++g)n[a++]=d,n[a++]=f[g],n[a++]=p[g]}this.ticks=i,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":286,\"binary-search-bounds\":288,\"gl-buffer\":243,\"gl-shader\":303}],285:[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=a(e,i.lineVert,i.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),a=t(\"gl-shader\"),i=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,a,i){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=a*o.pixelRatio,c.uniforms.color=i,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":286,\"gl-buffer\":243,\"gl-shader\":303}],286:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),a=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:a,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:a,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:a,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:410}],287:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),i=a(e,s.textVert,s.textFrag);return new l(t,r,i)};var n=t(\"gl-buffer\"),a=t(\"gl-shader\"),i=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,h,f,p,d,g=l.prototype;g.drawTicks=(c=[0,0],u=[0,0],h=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],a=this.tickOffset[t],i=e.gl,s=e.viewBox,l=e.dataBox,f=e.screenBox,p=e.pixelRatio,d=e.tickEnable,g=e.tickPad,v=e.tickColor,m=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],k=this.labelCount[t],T=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])/(f[2+t]-f[t])-1;var M=2/f[2+(1^t)]-f[1^t];u[1^t]=M*s[1^t]-1,d[t]&&(u[1^t]-=M*p*g[t],T<A&&a[A]>a[T]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=v[t],r.uniforms.angle=m[t],i.drawArrays(i.TRIANGLES,a[T],a[A]-a[T]))),y[t]&&k&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=h,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],i.drawArrays(i.TRIANGLES,w,k)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*g[t+2],T<A&&a[A]>a[T]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=v[t+2],r.uniforms.angle=m[t+2],i.drawArrays(i.TRIANGLES,a[T],a[A]-a[T]))),y[t+2]&&k&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=h,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],i.drawArrays(i.TRIANGLES,w,k))}),g.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,a=r.gl,i=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-i[u])/(i[2+u]-i[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,a.drawArrays(a.TRIANGLES,this.titleOffset,this.titleCount)}}}(),g.bind=(f=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,a=t.screenBox,i=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],h=i[o],g=i[o+2]-h,v=a[o],m=a[o+2]-v;p[o]=2*l/u*g/m,f[o]=2*(s-c)/u*g/m}d[1]=2*t.pixelRatio/(a[3]-a[1]),d[0]=d[1]*(a[3]-a[1])/(a[2]-a[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=f,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),g.update=function(t){var e,r,n,a,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],h=[-1/0],f=l[o];for(e=0;e<f.length;++e){var p=f[e],d=p.x,g=p.text,v=p.font||\"sans-serif\";a=p.fontSize||12;for(var m=1/(c[o+2]-c[o]),y=c[o],x=g.split(\"\\n\"),b=0;b<x.length;b++)for(n=i(v,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*a,-n[r+1]*a-b*a*1.2,(d-y)*m);u.push(Math.floor(s.length/3)),h.push(d)}this.tickOffset[o]=u,this.tickX[o]=h}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=i(t.labelFont[o],t.labels[o],{textAlign:\"center\"}).data,a=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*a,-n[e+1]*a,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=i(t.titleFont,t.title).data,a=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*a,-n[e+1]*a,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},g.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":286,\"binary-search-bounds\":288,\"gl-buffer\":243,\"gl-shader\":303,\"text-cache\":534}],288:[function(t,e,r){arguments[4][112][0].apply(r,arguments)},{dup:112}],289:[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=a(c),c.text=i(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t(\"gl-select-static\"),a=t(\"./lib/grid\"),i=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 h(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,a=this.pixelRatio,i=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),i.draw();var h=this.zeroLineEnable,f=this.zeroLineColor,p=this.zeroLineWidth;if(h[0]||h[1]){o.bind();for(var d=0;d<2;++d)if(h[d]&&n[d]<=0&&n[d+2]>=0){var g=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(g,e[1],g,e[3],p[d],f[d]):o.drawLine(e[0],g,e[2],g,p[d],f[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 v=this.borderLineEnable,m=this.borderLineWidth,y=this.borderLineColor;for(v[1]&&o.drawLine(r[0],r[1]-.5*m[1]*a,r[0],r[3]+.5*m[3]*a,m[1],y[1]),v[0]&&o.drawLine(r[0]-.5*m[0]*a,r[1],r[2]+.5*m[2]*a,r[1],m[0],y[0]),v[3]&&o.drawLine(r[2],r[1]-.5*m[1]*a,r[2],r[3]+.5*m[3]*a,m[3],y[3]),v[2]&&o.drawLine(r[0]-.5*m[0]*a,r[3],r[2]+.5*m[2]*a,r[3],m[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,a=this.viewBox,i=0|Math.round((t-a[0]/r)*n),o=0|Math.round((e-a[1]/r)*n),s=this.pickBuffer.query(i,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 h=c[u].pick(i,o,l);if(h)return h}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,a=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/a,10,10/a]),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 i=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=i[s].slice(0);0!==l.length&&(l.sort(h),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:i}),this.text.update({bounds:o,ticks:i,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\":283,\"./lib/grid\":284,\"./lib/line\":285,\"./lib/text\":287,\"gl-select-static\":302}],290:[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=a({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],h=0,f=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,a=e-2*r;c.idle(e-r),c.recalcMatrix(a),c.flush(e-(100+2*r));for(var i=!0,o=c.computedMatrix,s=0;s<16;++s)i=i&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===f&&t.clientHeight===p;return f=t.clientWidth,p=t.clientHeight,i?!l:(h=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(),a=c.computedCenter.slice();if(c.setMode(t),\"turntable\"===t){var i=n();c._active.lookAt(i,r,a,e),c._active.lookAt(i+500,r,a,[0,0,1]),c._active.flush(i)}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 h},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,a,i){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,u=\"zoom\"===o,f=!!i.control,p=!!i.alt,g=!!i.shift,v=!!(1&e),m=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(a-d._lastY),w=d.flipX?1:-1,k=d.flipY?1:-1,T=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&v&&!f&&!p&&!g||v&&!f&&!p&&g)&&c.rotate(A,w*T*b,-k*T*_,0),(l&&v&&!f&&!p&&!g||m||v&&f&&!p&&!g)&&c.pan(A,-d.translateSpeed*b*h,d.translateSpeed*_*h,0),u&&v&&!f&&!p&&!g||y||v&&!f&&p&&!g)){var M=-d.zoomSpeed*_/window.innerHeight*(A-c.lastT())*100;c.pan(A,0,0,h*(Math.exp(M)-1))}return d._lastX=r,d._lastY=a,d._lastMods=i,!0}}d.mouseListener=i(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),r.preventDefault()},!!l&&{passive:!1}),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),t.preventDefault()},!!l&&{passive:!1}),d.wheelListener=o(t,function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,a=d.flipY?1:-1,i=n();if(Math.abs(t)>Math.abs(e))c.rotate(i,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*a*e/window.innerHeight*(i-c.lastT())/20;c.pan(i,0,0,h*(Math.exp(o)-1))}}},!0)},d.enableMouseListeners(),d};var n=t(\"right-now\"),a=t(\"3d-view\"),i=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"has-passive-events\")},{\"3d-view\":54,\"has-passive-events\":412,\"mouse-change\":436,\"mouse-event-offset\":437,\"mouse-wheel\":439,\"right-now\":502}],291:[function(t,e,r){var n=t(\"glslify\"),a=t(\"gl-shader\"),i=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 a(t,i,o,null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":303,glslify:410}],292:[function(t,e,r){\"use strict\";var n=t(\"./camera.js\"),a=t(\"gl-axes3d\"),i=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\"),h=t(\"gl-mat4/perspective\"),f=t(\"gl-mat4/ortho\"),p=t(\"./lib/shader\"),d=t(\"is-mobile\")({tablet:!0});function g(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function v(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 m(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){var r=t.container;r.appendChild(e)}else document.body.appendChild(e);var y=t.gl;y||(y=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(!y)throw new Error(\"webgl not supported\");var x=t.bounds||[[-10,-10,-10],[10,10,10]],b=new g,_=l(y,[y.drawingBufferWidth,y.drawingBufferHeight],{preferFloat:!d}),w=p(y),k=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:k},A=t.axes||{},M=a(y,A);M.enable=!A.disable;var S=t.spikes||{},E=o(y,S),L=[],C=[],P=[],O=[],z=!0,I=!0,D=new Array(16),R=new Array(16),F={view:null,projection:D,model:R,_ortho:!1},I=!0,B=[y.drawingBufferWidth,y.drawingBufferHeight],N=t.cameraObject||n(e,T),j={gl:y,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:b,camera:N,axes:M,axesPixels:null,spikes:E,bounds:x,objects:L,shape:B,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:m(t.autoResize),autoBounds:m(t.autoBounds),autoScale:!!t.autoScale,autoCenter:m(t.autoCenter),clipToBounds:m(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:F,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}},V=[y.drawingBufferWidth/j.pixelRatio|0,y.drawingBufferHeight/j.pixelRatio|0];function U(){if(!j._stopped&&j.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 a=0|Math.ceil(r*j.pixelRatio),i=0|Math.ceil(n*j.pixelRatio);if(a!==e.width||i!==e.height){e.width=a,e.height=i;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",z=!0}}}j.autoResize&&U();function q(){for(var t=L.length,e=O.length,r=0;r<e;++r)P[r]=0;t:for(var r=0;r<t;++r){var n=L[r],a=n.pickSlots;if(a){for(var i=0;i<e;++i)if(P[i]+a<255){C[r]=i,n.setPickBase(P[i]+1),P[i]+=a;continue t}var o=s(y,B);C[r]=e,O.push(o),P.push(a),n.setPickBase(1),e+=1}else C[r]=-1}for(;e>0&&0===P[e-1];)P.pop(),O.pop().dispose()}function H(){if(j.contextLost)return!0;y.isContextLost()&&(j.contextLost=!0,j.mouseListener.enabled=!1,j.selection.object=null,j.oncontextloss&&j.oncontextloss())}window.addEventListener(\"resize\",U),j.update=function(t){j._stopped||(t=t||{},z=!0,I=!0)},j.add=function(t){j._stopped||(t.axes=M,L.push(t),C.push(-1),z=!0,I=!0,q())},j.remove=function(t){if(!j._stopped){var e=L.indexOf(t);e<0||(L.splice(e,1),C.pop(),z=!0,I=!0,q())}},j.dispose=function(){if(!j._stopped&&(j._stopped=!0,window.removeEventListener(\"resize\",U),e.removeEventListener(\"webglcontextlost\",H),j.mouseListener.enabled=!1,!j.contextLost)){M.dispose(),E.dispose();for(var t=0;t<L.length;++t)L[t].dispose();_.dispose();for(var t=0;t<O.length;++t)O[t].dispose();w.dispose(),y=null,M=null,E=null,L=[]}},j._mouseRotating=!1,j._prevButtons=0,j.enableMouseListeners=function(){j.mouseListener=u(e,function(t,e,r){if(!j._stopped){var n=O.length,a=L.length,i=b.object;b.distance=1/0,b.mouse[0]=e,b.mouse[1]=r,b.object=null,b.screen=null,b.dataCoordinate=b.dataPosition=null;var o=!1;if(t&&j._prevButtons)j._mouseRotating=!0;else{j._mouseRotating&&(I=!0),j._mouseRotating=!1;for(var s=0;s<n;++s){var l=O[s].query(e,V[1]-r-1,j.pickRadius);if(l){if(l.distance>b.distance)continue;for(var c=0;c<a;++c){var u=L[c];if(C[c]===s){var h=u.pick(l);h&&(b.buttons=t,b.screen=l.coord,b.distance=l.distance,b.object=u,b.index=h.distance,b.dataPosition=h.position,b.dataCoordinate=h.dataCoordinate,b.data=h,o=!0)}}}}}i&&i!==b.object&&(i.highlight&&i.highlight(null),z=!0),b.object&&(b.object.highlight&&b.object.highlight(b.data),z=!0),(o=o||b.object!==i)&&j.onselect&&j.onselect(b),1&t&&!(1&j._prevButtons)&&j.onclick&&j.onclick(b),j._prevButtons=t}})},e.addEventListener(\"webglcontextlost\",H);var G=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],Y=[G[0].slice(),G[1].slice()];function W(){if(!H()){U();var t=j.camera.tick();F.view=j.camera.matrix,z=z||t,I=I||t,M.pixelRatio=j.pixelRatio,E.pixelRatio=j.pixelRatio;var e=L.length,r=G[0],n=G[1];r[0]=r[1]=r[2]=1/0,n[0]=n[1]=n[2]=-1/0;for(var a=0;a<e;++a){var o=L[a];o.pixelRatio=j.pixelRatio,o.axes=j.axes,z=z||!!o.dirty,I=I||!!o.dirty;var s=o.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)r[p]=Math.min(r[p],l[p]),n[p]=Math.max(n[p],u[p])}var d=j.bounds;if(j.autoBounds)for(var p=0;p<3;++p){if(n[p]<r[p])r[p]=-1,n[p]=1;else{r[p]===n[p]&&(r[p]-=1,n[p]+=1);var g=.05*(n[p]-r[p]);r[p]=r[p]-g,n[p]=n[p]+g}d[0][p]=r[p],d[1][p]=n[p]}for(var m=!1,p=0;p<3;++p)m=m||Y[0][p]!==d[0][p]||Y[1][p]!==d[1][p],Y[0][p]=d[0][p],Y[1][p]=d[1][p];if(I=I||m,z=z||m){if(m){for(var x=[0,0,0],a=0;a<3;++a)x[a]=v((d[1][a]-d[0][a])/10);M.autoTicks?M.update({bounds:d,tickSpacing:x}):M.update({bounds:d})}var T=y.drawingBufferWidth,A=y.drawingBufferHeight;B[0]=T,B[1]=A,V[0]=0|Math.max(T/j.pixelRatio,1),V[1]=0|Math.max(A/j.pixelRatio,1),k?(f(D,-T/A,T/A,-1,1,j.zNear,j.zFar),F._ortho=!0):(h(D,j.fovy,T/A,j.zNear,j.zFar),F._ortho=!1);for(var a=0;a<16;++a)R[a]=0;R[15]=1;for(var S=0,a=0;a<3;++a)S=Math.max(S,d[1][a]-d[0][a]);for(var a=0;a<3;++a)j.autoScale?R[5*a]=j.aspect[a]/(d[1][a]-d[0][a]):R[5*a]=1/S,j.autoCenter&&(R[12+a]=.5*-R[5*a]*(d[0][a]+d[1][a]));for(var a=0;a<e;++a){var o=L[a];o.axesBounds=d,j.clipToBounds&&(o.clipBounds=d)}b.object&&(j.snapToData?E.position=b.dataCoordinate:E.position=b.dataPosition,E.bounds=d),I&&(I=!1,function(){if(H())return;y.colorMask(!0,!0,!0,!0),y.depthMask(!0),y.disable(y.BLEND),y.enable(y.DEPTH_TEST),y.depthFunc(y.LEQUAL);for(var t=L.length,e=O.length,r=0;r<e;++r){var n=O[r];n.shape=V,n.begin();for(var a=0;a<t;++a)if(C[a]===r){var i=L[a];i.drawPick&&(i.pixelRatio=1,i.drawPick(F))}n.end()}}()),j.axesPixels=i(j.axes,F,T,A),j.onrender&&j.onrender(),y.bindFramebuffer(y.FRAMEBUFFER,null),y.viewport(0,0,T,A);var P=j.clearColor;y.clearColor(P[0],P[1],P[2],P[3]),y.clear(y.COLOR_BUFFER_BIT|y.DEPTH_BUFFER_BIT),y.depthMask(!0),y.colorMask(!0,!0,!0,!0),y.enable(y.DEPTH_TEST),y.depthFunc(y.LEQUAL),y.disable(y.BLEND),y.disable(y.CULL_FACE);var N=!1;M.enable&&(N=N||M.isTransparent(),M.draw(F)),E.axes=M,b.object&&E.draw(F),y.disable(y.CULL_FACE);for(var a=0;a<e;++a){var o=L[a];o.axes=M,o.pixelRatio=j.pixelRatio,o.isOpaque&&o.isOpaque()&&o.draw(F),o.isTransparent&&o.isTransparent()&&(N=!0)}if(N){_.shape=B,_.bind(),y.clear(y.DEPTH_BUFFER_BIT),y.colorMask(!1,!1,!1,!1),y.depthMask(!0),y.depthFunc(y.LESS),M.enable&&M.isTransparent()&&M.drawTransparent(F);for(var a=0;a<e;++a){var o=L[a];o.isOpaque&&o.isOpaque()&&o.draw(F)}y.enable(y.BLEND),y.blendEquation(y.FUNC_ADD),y.blendFunc(y.ONE,y.ONE_MINUS_SRC_ALPHA),y.colorMask(!0,!0,!0,!0),y.depthMask(!1),y.clearColor(0,0,0,0),y.clear(y.COLOR_BUFFER_BIT),M.isTransparent()&&M.drawTransparent(F);for(var a=0;a<e;++a){var o=L[a];o.isTransparent&&o.isTransparent()&&o.drawTransparent(F)}y.bindFramebuffer(y.FRAMEBUFFER,null),y.blendFunc(y.ONE,y.ONE_MINUS_SRC_ALPHA),y.disable(y.DEPTH_TEST),w.bind(),_.color[0].bind(0),w.uniforms.accumBuffer=0,c(y),y.disable(y.BLEND)}z=!1;for(var a=0;a<e;++a)L[a].dirty=!1}}}return j.enableMouseListeners(),function t(){j._stopped||j.contextLost||(W(),requestAnimationFrame(t))}(),j.redraw=function(){j._stopped||(z=!0,W())},j},createCamera:n}},{\"./camera.js\":290,\"./lib/shader\":291,\"a-big-triangle\":61,\"gl-axes3d\":235,\"gl-axes3d/properties\":242,\"gl-fbo\":251,\"gl-mat4/ortho\":270,\"gl-mat4/perspective\":271,\"gl-select-static\":302,\"gl-spikes3d\":312,\"is-mobile\":421,\"mouse-change\":436}],293:[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:410}],294:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),a=t(\"gl-buffer\"),i=t(\"typedarray-pool\"),o=t(\"./lib/shader\");function s(t,e,r,n,a){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=a,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,i=a(r),l=a(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),h=new s(t,i,l,c,u);return h.update(e),t.addObject(h),h};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,a=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=a?s:i.mallocFloat32(s.length),c=o?t.idToIndex:i.mallocInt32(n);if(a||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),a||i.free(l),o||i.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,a=this.plot.dataBox;if(0===this.pointCount)return t;var i=a[2]-a[0],o=a[3]-a[1],s=function(t,e){var r,n=0,a=t.length>>>1;for(r=0;r<a;r++){var i=t[2*r],o=t[2*r+1];i>=e[0]&&i<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,a),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/i,l[4]=2/o,l[6]=-2*a[0]/i-1,l[7]=-2*a[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 h=n.getParameter(n.BLEND),f=n.getParameter(n.DITHER);return h&&!this.blend&&n.disable(n.BLEND),f&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),h&&!this.blend&&n.enable(n.BLEND),f&&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,a=this.pointCount;if(r<n||r>=n+a)return null;var i=r-n,o=this.points;return{object:this,pointId:i,dataCoord:[o[2*i],o[2*i+1]]}}},{\"./lib/shader\":293,\"gl-buffer\":243,\"gl-shader\":303,\"typedarray-pool\":543}],295:[function(t,e,r){e.exports=function(t,e,r,n){var a,i,o,s,l,c=e[0],u=e[1],h=e[2],f=e[3],p=r[0],d=r[1],g=r[2],v=r[3];(i=c*p+u*d+h*g+f*v)<0&&(i=-i,p=-p,d=-d,g=-g,v=-v);1-i>1e-6?(a=Math.acos(i),o=Math.sin(a),s=Math.sin((1-n)*a)/o,l=Math.sin(n*a)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*h+l*g,t[3]=s*f+l*v,t}},{}],296:[function(t,e,r){\"use strict\";e.exports=function(t){return t||0===t?t.toString():\"\"}},{}],297:[function(t,e,r){\"use strict\";var n=t(\"vectorize-text\");e.exports=function(t,e,r){var i=a[e];i||(i=a[e]={});if(t in i)return i[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 h=[[1/0,1/0],[-1/0,-1/0]],f=u.positions.length;for(l=0;l<f;++l){var p=u.positions[l];for(c=0;c<2;++c)h[0][c]=Math.min(h[0][c],p[c]),h[1][c]=Math.max(h[1][c],p[c])}return i[t]=[s,u,h]};var a={}},{\"vectorize-text\":548}],298:[function(t,e,r){var n=t(\"gl-shader\"),a=t(\"glslify\"),i=a([\"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=a([\"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=a([\"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=a([\"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=a([\"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\"}],h={vertex:i,fragment:l,attributes:u},f={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:i,fragment:c,attributes:u},g={vertex:o,fragment:c,attributes:u},v={vertex:s,fragment:c,attributes:u};function m(t,e){var r=n(t,e),a=r.attributes;return a.position.location=0,a.color.location=1,a.glyph.location=2,a.id.location=3,r}r.createPerspective=function(t){return m(t,h)},r.createOrtho=function(t){return m(t,f)},r.createProject=function(t){return m(t,p)},r.createPickPerspective=function(t){return m(t,d)},r.createPickOrtho=function(t){return m(t,g)},r.createPickProject=function(t){return m(t,v)}},{\"gl-shader\":303,glslify:410}],299:[function(t,e,r){\"use strict\";var n=t(\"is-string-blank\"),a=t(\"gl-buffer\"),i=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\"),h=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function f(t,e){var r=t[0],n=t[1],a=t[2],i=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*a+e[12]*i,t[1]=e[1]*r+e[5]*n+e[9]*a+e[13]*i,t[2]=e[2]*r+e[6]*n+e[10]*a+e[14]*i,t[3]=e[3]*r+e[7]*n+e[11]*a+e[15]*i,t}function p(t,e,r,n){return f(n,n),f(n,n),f(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function g(t){return!0===t?1:t>1?1:t}function v(t,e,r,n,a,i,o,s,l,c,u,h){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=a,this.colorBuffer=i,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=h,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),h=a(e),f=a(e),p=a(e),d=a(e),g=i(e,[{buffer:h,size:3,type:e.FLOAT},{buffer:f,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),m=new v(e,r,n,o,h,f,p,d,g,s,c,u);return m.update(t),m};var m=v.prototype;m.pickSlots=1,m.setPickBase=function(t){this.pickId=t},m.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},m.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],k=h.slice(),T=[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 a,i=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||h,u=r.view||h,f=r.projection||h,d=e.axesBounds,g=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);a=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=f,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=g,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var v=0;v<3;++v)if(i[v]){l.scale=e.projectScale[v],l.opacity=e.projectOpacity[v];for(var m=k,L=0;L<16;++L)m[L]=0;for(L=0;L<4;++L)m[5*L]=1;m[5*v]=0,a[v]<0?m[12+v]=d[0][v]:m[12+v]=d[1][v],s(m,c,m),l.model=m;var C=(v+1)%3,P=(v+2)%3,O=M(x),z=M(b);O[C]=1,z[P]=1;var I=p(0,0,0,S(_,O)),D=p(0,0,0,S(w,z));if(Math.abs(I[1])>Math.abs(D[1])){var R=I;I=D,D=R,R=O,O=z,z=R;var F=C;C=P,P=F}I[0]<0&&(O[C]=-1),D[1]>0&&(z[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);O[C]/=Math.sqrt(B),z[P]/=Math.sqrt(N),l.axes[0]=O,l.axes[1]=z,l.fragClipBounds[0]=E(T,g[0],v,-1e8),l.fragClipBounds[1]=E(T,g[1],v,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,a,i,o){var s=r.gl;if((i===r.projectHasAlpha||o)&&L(e,r,n,a),i===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||h,l.view=n.view||h,l.projection=n.projection||h,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=a,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*a),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function O(t,e,r,a){var i;i=Array.isArray(t)?e<t.length?t[e]:void 0:t,i=u(i);var o=!0;n(i)&&(i=\"\\u25bc\",o=!1);var s=c(i,r,a);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}m.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},m.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},m.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},m.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 a=0;a<3;++a)n.position[a]=n.dataCoordinate[a]=r[a];return n},m.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,a=e>>16&255;this.highlightId=[r/255,n/255,a/255,0]}else this.highlightId=[1,1,1,1]},m.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=g(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=g(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var a,i,s=t.position,l=t.font||\"normal\",c=t.alignment||[0,0];if(2===c.length)a=c[0],i=c[1];else{a=[],i=[];for(n=0;n<c.length;++n)a[n]=c[n][0],i[n]=c[n][1]}var u=[1/0,1/0,1/0],h=[-1/0,-1/0,-1/0],f=t.glyph,p=t.color,d=t.size,v=t.angle,m=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],k=0;k<3;++k)if(isNaN(w[k])||!isFinite(w[k]))continue t;var T=(N=O(f,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;x+=3*T.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 z=0,I=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(m)&&Array.isArray(m[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],k=0;k<3;++k){if(isNaN(w[k])||!isFinite(w[k]))continue t;h[k]=Math.max(h[k],w[k]),u[k]=Math.min(u[k],w[k])}T=(N=O(f,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(V=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(k=0;k<3;++k)D[k]=V[k];D[3]=1}else if(4===V.length){for(k=0;k<4;++k)D[k]=V[k];!this.hasAlpha&&V[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(m)){var V;if(3===(V=B?n<m.length?m[n]:[0,0,0,0]:m).length){for(k=0;k<3;++k)R[k]=V[k];R[k]=1}else if(4===V.length){for(k=0;k<4;++k)R[k]=V[k];!this.hasAlpha&&V[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var U=.5;j?Array.isArray(d)?U=n<d.length?+d[n]:12:d?U=+d:this.useOrtho&&(U=12):U=0;var q=0;Array.isArray(v)?q=n<v.length?+v[n]:0:v&&(q=+v);var H=Math.cos(q),G=Math.sin(q);for(w=s[n],k=0;k<3;++k)h[k]=Math.max(h[k],w[k]),u[k]=Math.min(u[k],w[k]);var Y=a,W=i;Y=0;Array.isArray(a)?Y=n<a.length?a[n]:0:a&&(Y=a);W=0;Array.isArray(i)?W=n<i.length?i[n]:0:i&&(W=i);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=T.cells||[],J=T.positions||[];for(k=0;k<Z.length;++k)for(var K=Z[k],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)L[4*z+$]=D[$];P[z]=y;var tt=J[K[Q]];C[2*z]=U*(H*tt[0]-G*tt[1]+X[0]),C[2*z+1]=U*(G*tt[0]+H*tt[1]+X[1]),z+=1}for(Z=A.edges,J=A.positions,k=0;k<Z.length;++k)for(K=Z[k],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*I+$]=w[$];for($=0;$<4;++$)L[4*I+$]=R[$];P[I]=y;tt=J[K[Q]];C[2*I]=U*(H*tt[0]-G*tt[1]+X[0]),C[2*I+1]=U*(G*tt[0]+H*tt[1]+X[1]),I+=1}}}this.bounds=[u,h],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)},m.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\":296,\"./lib/glyphs\":297,\"./lib/shaders\":298,\"gl-buffer\":243,\"gl-mat4/multiply\":269,\"gl-vao\":328,\"is-string-blank\":424,\"typedarray-pool\":543}],300:[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:410}],301:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),a=t(\"gl-buffer\"),i=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=a(r,[0,0,0,1,1,0,1,1]),l=n(r,i.boxVertex,i.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),a=(this.outerFill,this.outerColor),i=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,h=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],f=(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(h=Math.max(h,c[0]),f=Math.max(f,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<h||d<f)){o.bind();var g=s[2]-s[0],v=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,g,f,a),o.drawBox(0,f,h,d,a),o.drawBox(0,d,g,v,a),o.drawBox(p,f,g,d,a)),this.innerFill&&o.drawBox(h,f,p,d,n),r>0){var 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o}(this.gl,t.shader,e.shader,r,a),t.programs.push(i),e.programs.push(i)),o}},{\"./GLError\":304,\"gl-format-compiler-error\":252,\"weakmap-shim\":553}],310:[function(t,e,r){\"use strict\";function n(t){this.plot=t,this.enable=[!0,!0,!1,!1],this.width=[1,1,1,1],this.color=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.center=[1/0,1/0]}e.exports=function(t,e){var r=new n(t);return r.update(e),t.addOverlay(r),r};var a=n.prototype;a.update=function(t){t=t||{},this.enable=(t.enable||[!0,!0,!1,!1]).slice(),this.width=(t.width||[1,1,1,1]).slice(),this.color=(t.color||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]).map(function(t){return t.slice()}),this.center=(t.center||[1/0,1/0]).slice(),this.plot.setOverlayDirty()},a.draw=function(){var t=this.enable,e=this.width,r=this.color,n=this.center,a=this.plot,i=a.line,o=a.dataBox,s=a.viewBox;if(i.bind(),o[0]<=n[0]&&n[0]<=o[2]&&o[1]<=n[1]&&n[1]<=o[3]){var l=s[0]+(n[0]-o[0])/(o[2]-o[0])*(s[2]-s[0]),c=s[1]+(n[1]-o[1])/(o[3]-o[1])*(s[3]-s[1]);t[0]&&i.drawLine(l,c,s[0],c,e[0],r[0]),t[1]&&i.drawLine(l,c,l,s[1],e[1],r[1]),t[2]&&i.drawLine(l,c,s[2],c,e[2],r[2]),t[3]&&i.drawLine(l,c,l,s[3],e[3],r[3])}},a.dispose=function(){this.plot.removeOverlay(this)}},{}],311:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),a=t(\"gl-shader\"),i=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, color;\\nattribute float weight;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 coordinates[3];\\nuniform vec4 colors[3];\\nuniform vec2 screenShape;\\nuniform float lineWidth;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vertexPosition = mix(coordinates[0],\\n    mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));\\n\\n  vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\\n  vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\\n  vec2 delta = weight * clipOffset * screenShape;\\n  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\\n\\n  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\\n  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\\n}\\n\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  gl_FragColor = fragColor;\\n}\"]);e.exports=function(t){return a(t,i,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec3\"},{name:\"weight\",type:\"float\"}])}},{\"gl-shader\":303,glslify:410}],312:[function(t,e,r){\"use strict\";var n=t(\"gl-buffer\"),a=t(\"gl-vao\"),i=t(\"./shaders/index\");e.exports=function(t,e){var r=[];function o(t,e,n,a,i,o){var s=[t,e,n,0,0,0,1];s[a+3]=1,s[a]=i,r.push.apply(r,s),s[6]=-1,r.push.apply(r,s),s[a]=o,r.push.apply(r,s),r.push.apply(r,s),s[6]=1,r.push.apply(r,s),s[a]=i,r.push.apply(r,s)}o(0,0,0,0,0,1),o(0,0,0,1,0,1),o(0,0,0,2,0,1),o(1,0,0,1,-1,1),o(1,0,0,2,-1,1),o(0,1,0,0,-1,1),o(0,1,0,2,-1,1),o(0,0,1,0,-1,1),o(0,0,1,1,-1,1);var l=n(t,r),c=a(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),u=i(t);u.attributes.position.location=0,u.attributes.color.location=1,u.attributes.weight.location=2;var h=new s(t,l,c,u);return h.update(e),h};var o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var l=s.prototype,c=[0,0,0],u=[0,0,0],h=[0,0];l.isTransparent=function(){return!1},l.drawTransparent=function(t){},l.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var a,i=t.model||o,s=t.view||o,l=t.projection||o;this.axes&&(a=this.axes.lastCubeProps.axis);for(var f=c,p=u,d=0;d<3;++d)a&&a[d]<0?(f[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(f[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);h[0]=e.drawingBufferWidth,h[1]=e.drawingBufferHeight,n.uniforms.model=i,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,f,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=h;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\":311,\"gl-buffer\":243,\"gl-vao\":328}],313:[function(t,e,r){var n=t(\"glslify\"),a=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\"]),i=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:a,fragment:i,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:410}],314:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\"),a=t(\"gl-vec4\"),i=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e,r,i){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,i){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],h=[],f=[],p=[],d=[],g=0,v=0,m=a.create(),y=a.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),v=n.length(_)/i,m=a.create(),n.copy(m,_),m[3]=w;for(var k=0;k<8;k++)p[k]=[b[0],b[1],b[2],k];if(f.length>0)for(k=0;k<8;k++){var T=(k+1)%8;c.push(f[k],p[k],p[T],p[T],f[T],f[k]),h.push(y,m,m,m,y,y),d.push(g,v,v,v,g,g);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=f;f=p,p=M;var S=y;y=m,m=S;var E=g;g=v,v=E}return{positions:c,cells:u,vectors:h,vertexIntensity:d}}(t,r,i,o)}),h=[],f=[],p=[],d=[];for(s=0;s<u.length;s++){var g=u[s],v=h.length;h=h.concat(g.positions),p=p.concat(g.vectors),d=d.concat(g.vertexIntensity);for(c=0;c<g.cells.length;c++){var m=g.cells[c],y=[];f.push(y);for(var x=0;x<m.length;x++)y.push(m[x]+v)}}return{positions:h,cells:f,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var a=t[r];if(a===e)return r;if(a>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 a=Math.abs(t[n]-t[n-1]);a<e&&(e=a)}return e};e.exports=function(t,e){var r=t.startingPositions,a=t.maxLength||1e3,u=t.tubeSize||1,h=t.absoluteTubeSize,f=t.gridFill||\"+x+y+z\",p={};-1!==f.indexOf(\"-x\")&&(p.reversedX=!0),-1!==f.indexOf(\"-y\")&&(p.reversedY=!0),-1!==f.indexOf(\"-z\")&&(p.reversedZ=!0),p.filled=i.indexOf(f.replace(/-/g,\"\").replace(/\\+/g,\"\"));var d=t.getVelocity||function(e){return function(t,e,r){var a=e.vectors,i=e.meshgrid,o=t[0],c=t[1],u=t[2],h=i[0].length,f=i[1].length,p=i[2].length,d=s(i[0],o),g=s(i[1],c),v=s(i[2],u),m=d+1,y=g+1,x=v+1;if(d=l(d,0,h-1),m=l(m,0,h-1),g=l(g,0,f-1),y=l(y,0,f-1),v=l(v,0,p-1),x=l(x,0,p-1),d<0||g<0||v<0||m>h-1||y>f-1||x>p-1)return n.create();var b,_,w,k,T,A,M=i[0][d],S=i[0][m],E=i[1][g],L=i[1][y],C=i[2][v],P=(o-M)/(S-M),O=(c-E)/(L-E),z=(u-C)/(i[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(O)||(O=.5),isFinite(z)||(z=.5),r.reversedX&&(d=h-1-d,m=h-1-m),r.reversedY&&(g=f-1-g,y=f-1-y),r.reversedZ&&(v=p-1-v,x=p-1-x),r.filled){case 5:T=v,A=x,w=g*p,k=y*p,b=d*p*f,_=m*p*f;break;case 4:T=v,A=x,b=d*p,_=m*p,w=g*p*h,k=y*p*h;break;case 3:w=g,k=y,T=v*f,A=x*f,b=d*f*p,_=m*f*p;break;case 2:w=g,k=y,b=d*f,_=m*f,T=v*f*h,A=x*f*h;break;case 1:b=d,_=m,T=v*h,A=x*h,w=g*h*p,k=y*h*p;break;default:b=d,_=m,w=g*h,k=y*h,T=v*h*f,A=x*h*f}var I=a[b+w+T],D=a[b+w+A],R=a[b+k+T],F=a[b+k+A],B=a[_+w+T],N=a[_+w+A],j=a[_+k+T],V=a[_+k+A],U=n.create(),q=n.create(),H=n.create(),G=n.create();n.lerp(U,I,B,P),n.lerp(q,D,N,P),n.lerp(H,R,j,P),n.lerp(G,F,V,P);var Y=n.create(),W=n.create();n.lerp(Y,U,H,O),n.lerp(W,q,G,O);var X=n.create();return n.lerp(X,Y,W,z),X}(e,t,p)},g=t.getDivergence||function(t,e){var r=n.create(),a=1e-4;n.add(r,t,[a,0,0]);var i=d(r);n.subtract(i,i,e),n.scale(i,i,1e4),n.add(r,t,[0,a,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1e4),n.add(r,t,[0,0,a]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1e4),n.add(r,i,o),n.add(r,r,s),r},v=[],m=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],k=function(t){var e=t[0],r=t[1],n=t[2];return!(e<m||e>b||r<y||r>_||n<x||n>w)},T=10*n.distance(e[0],e[1])/a,A=T*T,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],a={},i={},o={},s=t.length,l=0;l<s;l++){var u=t[l],h=u[0],f=u[1],p=u[2];a[h]||(e.push(h),a[h]=!0),i[f]||(r.push(f),i[f]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),g=c(r),v=c(n),m=Math.min(d,g,v);return isFinite(m)?m:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],O=[],z=d(C),I=C;O.push(z);var D=[],R=g(C,z),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),v.push({points:P,velocities:O,divergences:D});for(var B=0;B<100*a&&P.length<a&&k(C);){B++;var N=n.clone(z),j=n.squaredLength(N);if(0===j)break;if(j>A&&n.scale(N,N,T/Math.sqrt(j)),n.add(N,N,C),z=d(N),n.squaredDistance(I,N)-A>-1e-4*A){P.push(N),I=N,O.push(z);R=g(N,z),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}C=N}}var V=o(v,t.colormap,S,M);return h?V.tubeScale=h:(0===S&&(S=1),V.tubeScale=.5*u*M/S),V};var u=t(\"./lib/shaders\"),h=t(\"gl-cone3d\").createMesh;e.exports.createTubeMesh=function(t,e){return h(t,e,{shaders:u,traceType:\"streamtube\"})}},{\"./lib/shaders\":313,\"gl-cone3d\":244,\"gl-vec3\":347,\"gl-vec4\":383}],315:[function(t,e,r){var n=t(\"gl-shader\"),a=t(\"glslify\"),i=a([\"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=a([\"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 ((kill > 0.0) ||\\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) 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=a([\"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=a([\"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,i,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,i,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\":303,glslify:410}],316:[function(t,e,r){arguments[4][112][0].apply(r,arguments)},{dup:112}],317:[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=a(e),u=i(e,[{buffer:c,size:4,stride:w,offset:0},{buffer:c,size:3,stride:w,offset:16},{buffer:c,size:3,stride:w,offset:28}]),h=a(e),f=i(e,[{buffer:h,size:4,stride:20,offset:0},{buffer:h,size:1,stride:20,offset:16}]),p=a(e),d=i(e,[{buffer:p,size:2,type:e.FLOAT}]),g=o(e,1,S,e.RGBA,e.UNSIGNED_BYTE);g.minFilter=e.LINEAR,g.magFilter=e.LINEAR;var v=new E(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,g,s,l,h,f,p,d,[0,0,0]),m={levels:[[],[],[]]};for(var k in t)m[k]=t[k];return m.colormap=m.colormap||\"jet\",v.update(m),v};var n=t(\"bit-twiddle\"),a=t(\"gl-buffer\"),i=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"typedarray-pool\"),l=t(\"colormap\"),c=t(\"ndarray-ops\"),u=t(\"ndarray-pack\"),h=t(\"ndarray\"),f=t(\"surface-nets\"),p=t(\"gl-mat4/multiply\"),d=t(\"gl-mat4/invert\"),g=t(\"binary-search-bounds\"),v=t(\"ndarray-gradient\"),m=t(\"./lib/shaders\"),y=m.createShader,x=m.createContourShader,b=m.createPickShader,_=m.createPickContourShader,w=40,k=[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]],A=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function M(t,e,r,n,a){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=a}!function(){for(var t=0;t<3;++t){var e=A[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();var S=256;function E(t,e,r,n,a,i,o,l,c,u,f,p,d,g,v){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=v,this.intensityBounds=[],this._shader=n,this._pickShader=a,this._coordinateBuffer=i,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=f,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new M([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=g,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[h(s.mallocFloat(1024),[0,0]),h(s.mallocFloat(1024),[0,0]),h(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 L=E.prototype;L.isTransparent=function(){return this.opacity<1},L.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this._contourCounts[t].length>0||this._dynamicCounts[t]>0)return!0;return!1},L.pickSlots=1,L.setPickBase=function(t){this.pickId=t};var C=[0,0,0],P={showSurface:!1,showContour:!1,projections:[k.slice(),k.slice(),k.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function O(t,e){var r,n,a,i=e.axes&&e.axes.lastCubeProps.axis||C,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=P.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[+(i[r]>0)][r],p(l,t.model,l);var c=P.clipBounds[r];for(a=0;a<2;++a)for(n=0;n<3;++n)c[a][n]=t.clipBounds[a][n];c[0][r]=-1e8,c[1][r]=1e8}return P.showSurface=o,P.showContour=s,P}var z={model:k,view:k,projection:k,inverseModel:k.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=k.slice(),D=[1,0,0,0,1,0,0,0,1];function R(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=z;n.model=t.model||k,n.view=t.view||k,n.projection=t.projection||k,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 a=0;a<2;++a)for(var i=n.clipBounds[a],o=0;o<3;++o)i[o]=Math.min(Math.max(this.clipBounds[a][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=D,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),a=0;a<3;++a)n.eyePosition[a]=s[12+a]/s[15];var l=s[15];for(a=0;a<3;++a)l+=this.lightPosition[a]*s[4*a+3];for(a=0;a<3;++a){var c=s[12+a];for(o=0;o<3;++o)c+=s[4*o+a]*this.lightPosition[o];n.lightPosition[a]=c/l}var u=O(n,this);if(u.showSurface&&e===this.opacity<1){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),a=0;a<3;++a)this.surfaceProject[a]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[a],this._shader.uniforms.clipBounds=u.clipBounds[a],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour&&!e){var h=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,h.bind(),h.uniforms=n;var f=this._contourVAO;for(f.bind(),a=0;a<3;++a)for(h.uniforms.permutation=A[a],r.lineWidth(this.contourWidth[a]*this.pixelRatio),o=0;o<this.contourLevels[a].length;++o)o===this.highlightLevel[a]?(h.uniforms.contourColor=this.highlightColor[a],h.uniforms.contourTint=this.highlightTint[a]):0!==o&&o-1!==this.highlightLevel[a]||(h.uniforms.contourColor=this.contourColor[a],h.uniforms.contourTint=this.contourTint[a]),this._contourCounts[a][o]&&(h.uniforms.height=this.contourLevels[a][o],f.draw(r.LINES,this._contourCounts[a][o],this._contourOffsets[a][o]));for(a=0;a<3;++a)for(h.uniforms.model=u.projections[a],h.uniforms.clipBounds=u.clipBounds[a],o=0;o<3;++o)if(this.contourProject[a][o]){h.uniforms.permutation=A[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var g=0;g<this.contourLevels[o].length;++g)g===this.highlightLevel[o]?(h.uniforms.contourColor=this.highlightColor[o],h.uniforms.contourTint=this.highlightTint[o]):0!==g&&g-1!==this.highlightLevel[o]||(h.uniforms.contourColor=this.contourColor[o],h.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][g]&&(h.uniforms.height=this.contourLevels[o][g],f.draw(r.LINES,this._contourCounts[o][g],this._contourOffsets[o][g]))}for(f.unbind(),(f=this._dynamicVAO).bind(),a=0;a<3;++a)if(0!==this._dynamicCounts[a])for(h.uniforms.model=n.model,h.uniforms.clipBounds=n.clipBounds,h.uniforms.permutation=A[a],r.lineWidth(this.dynamicWidth[a]*this.pixelRatio),h.uniforms.contourColor=this.dynamicColor[a],h.uniforms.contourTint=this.dynamicTint[a],h.uniforms.height=this.dynamicLevel[a],f.draw(r.LINES,this._dynamicCounts[a],this._dynamicOffsets[a]),o=0;o<3;++o)this.contourProject[o][a]&&(h.uniforms.model=u.projections[o],h.uniforms.clipBounds=u.clipBounds[o],f.draw(r.LINES,this._dynamicCounts[a],this._dynamicOffsets[a]));f.unbind()}}L.draw=function(t){return R.call(this,t,!1)},L.drawTransparent=function(t){return R.call(this,t,!0)};var F={model:k,view:k,projection:k,inverseModel:k,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 B(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function N(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 j(t){if(Array.isArray(t)){if(Array.isArray(t))return[N(t[0]),N(t[1]),N(t[2])];var e=N(t);return[e.slice(),e.slice(),e.slice()]}}L.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=F;r.model=t.model||k,r.view=t.view||k,r.projection=t.projection||k,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=D;for(var n=0;n<2;++n)for(var a=r.clipBounds[n],i=0;i<3;++i)a[i]=Math.min(Math.max(this.clipBounds[n][i],-1e8),1e8);var o=O(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(),i=0;i<3;++i)for(e.lineWidth(this.contourWidth[i]*this.pixelRatio),s.uniforms.permutation=A[i],n=0;n<this.contourLevels[i].length;++n)this._contourCounts[i][n]&&(s.uniforms.height=this.contourLevels[i][n],l.draw(e.LINES,this._contourCounts[i][n],this._contourOffsets[i][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],i=0;i<3;++i)if(this.contourProject[n][i]){s.uniforms.permutation=A[i],e.lineWidth(this.contourWidth[i]*this.pixelRatio);for(var c=0;c<this.contourLevels[i].length;++c)this._contourCounts[i][c]&&(s.uniforms.height=this.contourLevels[i][c],l.draw(e.LINES,this._contourCounts[i][c],this._contourOffsets[i][c]))}l.unbind()}},L.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,a=Math.floor(n),i=n-a,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;a+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var h=u?i:1-i,f=0;f<2;++f)for(var p=a+u,d=s+f,v=h*(f?l:1-l),m=0;m<3;++m)c[m]+=this._field[m].get(p,d)*v;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=g.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=i<.5?a:a+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],m=0;m<3;++m)r.dataCoordinate[m]=this._field[m].get(r.index[0],r.index[1]);return r},L.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))},L.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=B(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=B(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=B(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=j(t.contourColor)),\"contourProject\"in t&&(this.contourProject=B(t.contourProject,function(t){return B(t,Boolean)})),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=j(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=B(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=B(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0);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 a=(e.shape[0]+2)*(e.shape[1]+2);a>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(a))),this._field[2]=h(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 i=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]=h(this._field[o].data,[i[0]+2,i[1]+2]);if(t.coords){var p=t.coords;if(!Array.isArray(p)||3!==p.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var d=p[o];for(b=0;b<2;++b)if(d.shape[b]!==i[b])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],d)}}else if(t.ticks){var g=t.ticks;if(!Array.isArray(g)||2!==g.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var m=g[o];if((Array.isArray(m)||m.length)&&(m=h(m)),m.shape[0]!==i[o])throw new Error(\"gl-surface: invalid tick length\");var y=h(m.data,i);y.stride[o]=m.stride[0],y.stride[1^o]=0,this.padField(this._field[o],y)}}else{for(o=0;o<2;++o){var x=[0,0];x[o]=1,this._field[o]=h(this._field[o].data,[i[0]+2,i[1]+2],x,0)}this._field[0].set(0,0,0);for(var b=0;b<i[0];++b)this._field[0].set(b+1,0,b);for(this._field[0].set(i[0]+1,0,i[0]-1),this._field[1].set(0,0,0),b=0;b<i[1];++b)this._field[1].set(0,b+1,b);this._field[1].set(0,i[1]+1,i[1]-1)}var _=this._field,w=h(s.mallocFloat(3*_[2].size*2),[3,i[0]+2,i[1]+2,2]);for(o=0;o<3;++o)v(w.pick(o),_[o],\"mirror\");var k=h(s.mallocFloat(3*_[2].size),[i[0]+2,i[1]+2,3]);for(o=0;o<i[0]+2;++o)for(b=0;b<i[1]+2;++b){var A=w.get(0,o,b,0),M=w.get(0,o,b,1),E=w.get(1,o,b,0),L=w.get(1,o,b,1),C=w.get(2,o,b,0),P=w.get(2,o,b,1),O=E*P-L*C,z=C*M-P*A,I=A*L-M*E,D=Math.sqrt(O*O+z*z+I*I);D<1e-8?(D=Math.max(Math.abs(O),Math.abs(z),Math.abs(I)))<1e-8?(I=1,z=O=0,D=1):D=1/D:D=1/Math.sqrt(D),k.set(o,b,0,O*D),k.set(o,b,1,z*D),k.set(o,b,2,I*D)}s.free(w.data);var R=[1/0,1/0,1/0],F=[-1/0,-1/0,-1/0],N=1/0,V=-1/0,U=(i[0]-1)*(i[1]-1)*6,q=s.mallocFloat(n.nextPow2(10*U)),H=0,G=0;for(o=0;o<i[0]-1;++o)t:for(b=0;b<i[1]-1;++b){for(var Y=0;Y<2;++Y)for(var W=0;W<2;++W)for(var X=0;X<3;++X){var Z=this._field[X].get(1+o+Y,1+b+W);if(isNaN(Z)||!isFinite(Z))continue t}for(X=0;X<6;++X){var J=o+T[X][0],K=b+T[X][1],Q=this._field[0].get(J+1,K+1),$=this._field[1].get(J+1,K+1);Z=this._field[2].get(J+1,K+1),O=k.get(J+1,K+1,0),z=k.get(J+1,K+1,1),I=k.get(J+1,K+1,2),t.intensity&&(tt=t.intensity.get(J,K));var tt=t.intensity?t.intensity.get(J,K):Z+this.objectOffset[2];q[H++]=J,q[H++]=K,q[H++]=Q,q[H++]=$,q[H++]=Z,q[H++]=0,q[H++]=tt,q[H++]=O,q[H++]=z,q[H++]=I,R[0]=Math.min(R[0],Q+this.objectOffset[0]),R[1]=Math.min(R[1],$+this.objectOffset[1]),R[2]=Math.min(R[2],Z+this.objectOffset[2]),N=Math.min(N,tt),F[0]=Math.max(F[0],Q+this.objectOffset[0]),F[1]=Math.max(F[1],$+this.objectOffset[1]),F[2]=Math.max(F[2],Z+this.objectOffset[2]),V=Math.max(V,tt),G+=1}}for(t.intensityBounds&&(N=+t.intensityBounds[0],V=+t.intensityBounds[1]),o=6;o<H;o+=10)q[o]=(q[o]-N)/(V-N);this._vertexCount=G,this._coordinateBuffer.update(q.subarray(0,H)),s.freeFloat(q),s.free(k.data),this.bounds=[R,F],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===N&&this.intensityBounds[1]===V||(r=!0),this.intensityBounds=[N,V]}if(\"levels\"in t){var et=t.levels;for(et=Array.isArray(et[0])?et.slice():[[],[],et],o=0;o<3;++o)et[o]=et[o].slice(),et[o].sort(function(t,e){return t-e});for(o=0;o<3;++o)for(b=0;b<et[o].length;++b)et[o][b]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(et[o].length!==this.contourLevels[o].length){r=!0;break}for(b=0;b<et[o].length;++b)if(et[o][b]!==this.contourLevels[o][b]){r=!0;break t}}this.contourLevels=et}if(r){_=this._field,i=this.shape;for(var rt=[],nt=0;nt<3;++nt){var at=this.contourLevels[nt],it=[],ot=[],st=[0,0,0];for(o=0;o<at.length;++o){var lt=f(this._field[nt],at[o]);it.push(rt.length/5|0),G=0;t:for(b=0;b<lt.cells.length;++b){var ct=lt.cells[b];for(X=0;X<2;++X){var ut=lt.positions[ct[X]],ht=ut[0],ft=0|Math.floor(ht),pt=ht-ft,dt=ut[1],gt=0|Math.floor(dt),vt=dt-gt,mt=!1;e:for(var yt=0;yt<3;++yt){st[yt]=0;var xt=(nt+yt+1)%3;for(Y=0;Y<2;++Y){var bt=Y?pt:1-pt;for(J=0|Math.min(Math.max(ft+Y,0),i[0]),W=0;W<2;++W){var _t=W?vt:1-vt;if(K=0|Math.min(Math.max(gt+W,0),i[1]),Z=yt<2?this._field[xt].get(J,K):(this.intensity.get(J,K)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(Z)||isNaN(Z)){mt=!0;break e}var wt=bt*_t;st[yt]+=wt*Z}}}if(mt){if(X>0){for(var kt=0;kt<5;++kt)rt.pop();G-=1}continue t}rt.push(st[0],st[1],ut[0],ut[1],st[2]),G+=1}}ot.push(G)}this._contourOffsets[nt]=it,this._contourCounts[nt]=ot}var Tt=s.mallocFloat(rt.length);for(o=0;o<rt.length;++o)Tt[o]=rt[o];this._contourBuffer.update(Tt),s.freeFloat(Tt)}t.colormap&&this._colorMap.setPixels(function(t){var e=u([l({colormap:t,nshades:S,format:\"rgba\"}).map(function(t){return[t[0],t[1],t[2],255*t[3]]})]);return c.divseq(e,255),e}(t.colormap))},L.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)},L.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,a=this.shape,i=s.mallocFloat(12*a[0]*a[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],h=this._field[l],p=this._field[c],d=f(u,r[o]),g=d.cells,v=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<g.length;++e)for(var m=g[e],y=0;y<2;++y){var x=v[m[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,a[0]),k=b-_,T=1-k,A=+x[1],M=0|A,S=0|Math.min(M+1,a[1]),E=A-M,L=1-E,C=T*L,P=T*E,O=k*L,z=k*E,I=C*h.get(_,M)+P*h.get(_,S)+O*h.get(w,M)+z*h.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+O*p.get(w,M)+z*p.get(w,S);if(isNaN(I)||isNaN(D)){y&&(n-=1);break}i[2*n+0]=I,i[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(i.subarray(0,2*n)),s.freeFloat(i)}}},{\"./lib/shaders\":315,\"binary-search-bounds\":316,\"bit-twiddle\":93,colormap:127,\"gl-buffer\":243,\"gl-mat4/invert\":267,\"gl-mat4/multiply\":269,\"gl-texture2d\":323,\"gl-vao\":328,ndarray:451,\"ndarray-gradient\":442,\"ndarray-ops\":445,\"ndarray-pack\":446,\"surface-nets\":529,\"typedarray-pool\":543}],318:[function(t,e,r){\"use strict\";var n=t(\"css-font\"),a=t(\"pick-by-alias\"),i=t(\"regl\"),o=t(\"gl-util/context\"),s=t(\"es6-weak-map\"),l=t(\"color-normalize\"),c=t(\"font-atlas\"),u=t(\"typedarray-pool\"),h=t(\"parse-rect\"),f=t(\"is-plain-obj\"),p=t(\"parse-unit\"),d=t(\"to-px\"),g=t(\"detect-kerning\"),v=t(\"object-assign\"),m=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 k=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||i({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(f(t)?t:{})};k.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop(\"count\"),offset:t.prop(\"offset\"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this(\"sizeBuffer\")},width:{offset:0,stride:8,buffer:t.this(\"sizeBuffer\")},char:t.this(\"charBuffer\"),position:t.this(\"position\")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop(\"color\"),opacity:t.prop(\"opacity\"),viewport:t.this(\"viewportArray\"),scale:t.this(\"scale\"),align:t.prop(\"align\"),baseline:t.prop(\"baseline\"),translate:t.this(\"translate\"),positionOffset:t.prop(\"positionOffset\")},primitive:\"points\",viewport:t.this(\"viewport\"),vert:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tattribute float width, charOffset, char;\\n\\t\\t\\tattribute vec2 position;\\n\\t\\t\\tuniform float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\t\"+(k.normalViewport?\"\":\"vec2 positionOffset = vec2(positionOffset.x,- positionOffset.y);\")+\"\\n\\n\\t\\t\\t\\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ positionOffset))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\t\"+(k.normalViewport?\"position.y = 1. - position.y;\":\"\")+\"\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 255.;\\n\\t\\t\\t}\",frag:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tuniform sampler2D atlas;\\n\\t\\t\\tuniform float fontSize, charStep, opacity;\\n\\t\\t\\tuniform vec2 atlasSize;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\n\\t\\t\\tfloat lightness(vec4 color) {\\n\\t\\t\\t\\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\\n\\t\\t\\t}\\n\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\\n\\t\\t\\t\\tfloat halfCharStep = floor(charStep * .5 + .5);\\n\\n\\t\\t\\t\\t// invert y and shift by 1px (FF expecially needs that)\\n\\t\\t\\t\\tuv.y = charStep - uv.y;\\n\\n\\t\\t\\t\\t// ignore points outside of character bounding box\\n\\t\\t\\t\\tfloat halfCharWidth = ceil(charWidth * .5);\\n\\t\\t\\t\\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\\n\\t\\t\\t\\t\\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a *= maskY;\\n\\t\\t\\t\\tcolor.a *= opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},k.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=a(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=h(t.viewport),k.normalViewport&&(this.viewport.y=this.canvas.height-this.viewport.y-this.viewport.height),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&(\"number\"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=k.baseFontSize+\"px sans-serif\");var r,i=!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(k.baseFontSize+\"px \"+t)}else t=n.parse(n.stringify(t));var a=n.stringify({size:k.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]&&a==e.font[r].baseString||(i=!0,e.font[r]=k.fonts[a],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:a,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:m(c,{origin:\"top\",fontSize:k.baseFontSize,fontStyle:u.join(\" \")})},k.fonts[a]=e.font[r]}}),(i||o)&&this.font.forEach(function(r,a){var i=n.stringify({size:e.fontSize[a],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[a]=e.shader.atlas[i],!e.fontAtlas[a]){var o=r.metrics;e.shader.atlas[i]=e.fontAtlas[a]={fontString:i,step:2*Math.ceil(e.fontSize[a]*o.bottom*.5),em:e.fontSize[a],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),f=0;f<s.length;f++)s[f]=t.text;t.text=s}if(null!=t.text||i){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){k.atlasContext.font=t.baseString;for(var a=e.fontAtlas[n],i=0;i<e.text.length;i++){var o=e.text.charAt(i);if(null==a.ids[o]&&(a.ids[o]=a.chars.length,a.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=k.atlasContext.measureText(o).width/k.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);v(t.kerning,g(t.family,{pairs:s}))}}})}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,T=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++)T[M++]=t.position[2*A],T[M++]=t.position[2*A+1];else for(var L=0;L<S;L++)T[M++]=t.position[A][0],T[M++]=t.position[A][1]}this.position.call?this.position({type:\"float\",data:T}):this.position=this.regl.buffer({type:\"float\",data:T}),u.freeFloat(T)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||i){var C=u.mallocUint8(this.count),P=u.mallocFloat(2*this.count);this.textWidth=[];for(var O=0,z=0;O<this.counts.length;O++){for(var I=this.counts[O],D=this.font[O]||this.font[0],R=this.fontAtlas[O]||this.fontAtlas[0],F=0;F<I;F++){var B=this.text.charAt(z),N=this.text.charAt(z-1);if(C[z]=R.ids[B],P[2*z]=D.width[B],F){var j=P[2*z-2],V=P[2*z],U=P[2*z-1]+.5*j+.5*V;if(this.kerning){var q=D.kerning[N+B];q&&(U+=.001*q)}P[2*z+1]=U}else P[2*z+1]=.5*P[2*z];z++}this.textWidth.push(P.length?.5*P[2*z-2]+P[2*z-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],a=n.step,i=Math.floor(k.maxAtlasSize/a),o=Math.min(i,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*a),u=x(s*a);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:k.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[a,a]})})})}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,a=0;return a+=.5*n.bottom,a+=\"number\"==typeof t?t-n.baseline:-n[t],k.normalViewport||(a*=-1),a})),null!=t.color)if(t.color||(t.color=\"transparent\"),\"string\"!=typeof t.color&&isNaN(t.color)){var H;if(\"number\"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;H=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)H.set(l(Y(W,W+4),\"uint8\"),W)}else{var X=t.color.length;H=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)H.set(l(t.color[Z]||0,\"uint8\"),4*Z)}this.color=H}else this.color=l(t.color,\"uint8\");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var K=0;K<this.batch.length;K++)this.batch[K]={count:this.counts.length>1?this.counts[K]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[K]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*K,4*K+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[K]:this.opacity,baseline:null!=this.baselineOffset[K]?this.baselineOffset[K]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[K]?this.alignOffset[K]:this.alignOffset[0]:0,atlas:this.fontAtlas[K]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*K,2*K+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},k.prototype.destroy=function(){},k.prototype.kerning=!0,k.prototype.position={constant:new Float32Array(2)},k.prototype.translate=null,k.prototype.scale=null,k.prototype.font=null,k.prototype.text=\"\",k.prototype.positionOffset=[0,0],k.prototype.opacity=1,k.prototype.color=new Uint8Array([0,0,0,255]),k.prototype.alignOffset=[0,0],k.normalViewport=!1,k.maxAtlasSize=1024,k.atlasCanvas=document.createElement(\"canvas\"),k.atlasContext=k.atlasCanvas.getContext(\"2d\",{alpha:!1}),k.baseFontSize=64,k.fonts={},e.exports=k},{\"bit-twiddle\":93,\"color-normalize\":121,\"css-font\":140,\"detect-kerning\":167,\"es6-weak-map\":319,\"flatten-vertex-data\":229,\"font-atlas\":230,\"font-measure\":231,\"gl-util/context\":324,\"is-plain-obj\":423,\"object-assign\":455,\"parse-rect\":460,\"parse-unit\":462,\"pick-by-alias\":466,regl:500,\"to-px\":537,\"typedarray-pool\":543}],319:[function(t,e,r){\"use strict\";e.exports=t(\"./is-implemented\")()?WeakMap:t(\"./polyfill\")},{\"./is-implemented\":320,\"./polyfill\":322}],320:[function(t,e,r){\"use strict\";e.exports=function(){var t,e;if(\"function\"!=typeof WeakMap)return!1;try{t=new WeakMap([[e={},\"one\"],[{},\"two\"],[{},\"three\"]])}catch(t){return!1}return\"[object WeakMap]\"===String(t)&&(\"function\"==typeof t.set&&(t.set({},1)===t&&(\"function\"==typeof t.delete&&(\"function\"==typeof t.has&&\"one\"===t.get(e)))))}},{}],321:[function(t,e,r){\"use strict\";e.exports=\"function\"==typeof WeakMap&&\"[object WeakMap]\"===Object.prototype.toString.call(new WeakMap)},{}],322:[function(t,e,r){\"use strict\";var n,a=t(\"es5-ext/object/is-value\"),i=t(\"es5-ext/object/set-prototype-of\"),o=t(\"es5-ext/object/valid-object\"),s=t(\"es5-ext/object/valid-value\"),l=t(\"es5-ext/string/random-uniq\"),c=t(\"d\"),u=t(\"es6-iterator/get\"),h=t(\"es6-iterator/for-of\"),f=t(\"es6-symbol\").toStringTag,p=t(\"./is-native-implemented\"),d=Array.isArray,g=Object.defineProperty,v=Object.prototype.hasOwnProperty,m=Object.getPrototypeOf;e.exports=n=function(){var t,e=arguments[0];if(!(this instanceof n))throw new TypeError(\"Constructor requires 'new'\");return t=p&&i&&WeakMap!==n?i(new WeakMap,m(this)):this,a(e)&&(d(e)||(e=u(e))),g(t,\"__weakMapData__\",c(\"c\",\"$weakMap$\"+l())),e?(h(e,function(e){s(e),t.set(e[0],e[1])}),t):t},p&&(i&&i(n,WeakMap),n.prototype=Object.create(WeakMap.prototype,{constructor:c(n)})),Object.defineProperties(n.prototype,{delete:c(function(t){return!!v.call(o(t),this.__weakMapData__)&&(delete t[this.__weakMapData__],!0)}),get:c(function(t){if(v.call(o(t),this.__weakMapData__))return t[this.__weakMapData__]}),has:c(function(t){return v.call(o(t),this.__weakMapData__)}),set:c(function(t,e){return g(o(t),this.__weakMapData__,c(\"c\",e)),this}),toString:c(function(){return\"[object 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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=c(e)?e:e.raw;if(r)return function(t,e,r,n,a,i){var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,a,a,i,e),new f(t,o,r,n,a,i)}(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 function(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=d(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 h,p,v=0;if(2===o.length)v=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 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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 e=0;e<2;++e)if(l.indexOf(t[e])<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);this._wrapS=t[0],this._wrapT=t[1];var r=this.gl;return this.bind(),r.texParameteri(r.TEXTURE_2D,r.TEXTURE_WRAP_S,this._wrapS),r.texParameteri(r.TEXTURE_2D,r.TEXTURE_WRAP_T,this._wrapT),t}},shape:{get:function(){return 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t.style.position=\"absolute\",t.style.top=0,t.style.left=0,t}e.exports=function(t){var e;if(t?\"string\"==typeof t&&(t={container:t}):t={},i(t)?t={container:t}:t=\"string\"==typeof(e=t).nodeName&&\"function\"==typeof e.appendChild&&\"function\"==typeof e.getBoundingClientRect?{container:t}:function(t){return\"function\"==typeof t.drawArrays||\"function\"==typeof t.drawElements}(t)?{gl:t}:n(t,{container:\"container target element el canvas holder parent parentNode wrapper use ref root node\",gl:\"gl context webgl glContext\",attrs:\"attributes attrs contextAttributes\",pixelRatio:\"pixelRatio pxRatio px ratio pxratio pixelratio\",width:\"w width\",height:\"h height\"},!0),t.pixelRatio||(t.pixelRatio=r.pixelRatio||1),t.gl)return t.gl;if(t.canvas&&(t.container=t.canvas.parentNode),t.container){if(\"string\"==typeof t.container){var s=document.querySelector(t.container);if(!s)throw Error(\"Element \"+t.container+\" is not 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if(1===i.length)t.vertexAttrib1f(a,i[0]);else if(2===i.length)t.vertexAttrib2f(a,i[0],i[1]);else if(3===i.length)t.vertexAttrib3f(a,i[0],i[1],i[2]);else{if(4!==i.length)throw new Error(\"gl-vao: Invalid vertex attribute\");t.vertexAttrib4f(a,i[0],i[1],i[2],i[3])}t.disableVertexAttribArray(a)}}for(;a<n;++a)t.disableVertexAttribArray(a)}else for(t.bindBuffer(t.ARRAY_BUFFER,null),a=0;a<n;++a)t.disableVertexAttribArray(a)}},{}],326:[function(t,e,r){\"use strict\";var n=t(\"./do-bind.js\");function a(t){this.gl=t,this._elements=null,this._attributes=null,this._elementsType=t.UNSIGNED_SHORT}a.prototype.bind=function(){n(this.gl,this._elements,this._attributes)},a.prototype.update=function(t,e,r){this._elements=e,this._attributes=t,this._elementsType=r||this.gl.UNSIGNED_SHORT},a.prototype.dispose=function(){},a.prototype.unbind=function(){},a.prototype.draw=function(t,e,r){r=r||0;var 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t=0;t<this._attribs.length;++t)this._attribs[t].bind(this.gl)},i.prototype.unbind=function(){this._ext.bindVertexArrayOES(null)},i.prototype.dispose=function(){this._ext.deleteVertexArrayOES(this.handle)},i.prototype.update=function(t,e,r){if(this.bind(),n(this.gl,e,t),this.unbind(),this._attribs.length=0,t)for(var i=0;i<t.length;++i){var o=t[i];\"number\"==typeof o?this._attribs.push(new a(i,1,o)):Array.isArray(o)&&this._attribs.push(new a(i,o.length,o[0],o[1],o[2],o[3]))}this._useElements=!!e,this._elementsType=r||this.gl.UNSIGNED_SHORT},i.prototype.draw=function(t,e,r){r=r||0;var n=this.gl;this._useElements?n.drawElements(t,e,this._elementsType,r):n.drawArrays(t,r,e)},e.exports=function(t,e){return new i(t,e,e.createVertexArrayOES())}},{\"./do-bind.js\":325}],328:[function(t,e,r){\"use strict\";var n=t(\"./lib/vao-native.js\"),a=t(\"./lib/vao-emulated.js\");function 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Math.min.apply(Math,e.map(function(e){return e.evaluate(t)}))}],max:[It,or(It),function(t,e){return Math.max.apply(Math,e.map(function(e){return e.evaluate(t)}))}],abs:[It,[It],function(t,e){var r=e[0];return Math.abs(r.evaluate(t))}],round:[It,[It],function(t,e){var r=e[0].evaluate(t);return r<0?-Math.round(-r):Math.round(r)}],floor:[It,[It],function(t,e){var r=e[0];return Math.floor(r.evaluate(t))}],ceil:[It,[It],function(t,e){var r=e[0];return Math.ceil(r.evaluate(t))}],\"filter-==\":[Rt,[Dt,Nt],function(t,e){var r=e[0],n=e[1];return t.properties()[r.value]===n.value}],\"filter-id-==\":[Rt,[Nt],function(t,e){var r=e[0];return t.id()===r.value}],\"filter-type-==\":[Rt,[Dt],function(t,e){var r=e[0];return t.geometryType()===r.value}],\"filter-<\":[Rt,[Dt,Nt],function(t,e){var r=e[0],n=e[1],a=t.properties()[r.value],i=n.value;return typeof a==typeof i&&a<i}],\"filter-id-<\":[Rt,[Nt],function(t,e){var r=e[0],n=t.id(),a=r.value;return typeof n==typeof 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e[0].evaluate(t).toUpperCase()}],downcase:[Dt,[Dt],function(t,e){return e[0].evaluate(t).toLowerCase()}],concat:[Dt,or(Nt),function(t,e){return e.map(function(e){return $t(e.evaluate(t))}).join(\"\")}],\"resolved-locale\":[Dt,[jt],function(t,e){return e[0].evaluate(t).resolvedLocale()}]});var br=function(t,e){this.expression=t,this._warningHistory={},this._evaluator=new le,this._defaultValue=e?function(t){return\"color\"===t.type&&pr(t.default)?new Wt(0,0,0,0):\"color\"===t.type?Wt.parse(t.default)||null:void 0===t.default?null:t.default}(e):null,this._enumValues=e&&\"enum\"===e.type?e.values:null};function _r(t){return Array.isArray(t)&&t.length>0&&\"string\"==typeof t[0]&&t[0]in rr}function wr(t,e){var r=new ge(rr,[],e?function(t){var e={color:Ft,string:Dt,number:It,enum:Dt,boolean:Rt,formatted:Vt};return\"array\"===t.type?Ut(e[t.value]||Nt,t.length):e[t.type]}(e):void 0),n=r.parse(t,void 0,void 0,void 0,e&&\"string\"===e.type?{typeAnnotation:\"coerce\"}:void 0);return n?sr(new br(n,e)):lr(r.errors)}br.prototype.evaluateWithoutErrorHandling=function(t,e,r,n){return this._evaluator.globals=t,this._evaluator.feature=e,this._evaluator.featureState=r,this._evaluator.formattedSection=n,this.expression.evaluate(this._evaluator)},br.prototype.evaluate=function(t,e,r,n){this._evaluator.globals=t,this._evaluator.feature=e||null,this._evaluator.featureState=r||null,this._evaluator.formattedSection=n||null;try{var a=this.expression.evaluate(this._evaluator);if(null==a)return this._defaultValue;if(this._enumValues&&!(a in this._enumValues))throw new ee(\"Expected value to be one of \"+Object.keys(this._enumValues).map(function(t){return JSON.stringify(t)}).join(\", \")+\", but found \"+JSON.stringify(a)+\" instead.\");return a}catch(t){return this._warningHistory[t.message]||(this._warningHistory[t.message]=!0,\"undefined\"!=typeof console&&console.warn(t.message)),this._defaultValue}};var kr=function(t,e){this.kind=t,this._styleExpression=e,this.isStateDependent=\"constant\"!==t&&!fe(e.expression)};kr.prototype.evaluateWithoutErrorHandling=function(t,e,r,n){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n)},kr.prototype.evaluate=function(t,e,r,n){return this._styleExpression.evaluate(t,e,r,n)};var Tr=function(t,e,r,n){this.kind=t,this.zoomStops=r,this._styleExpression=e,this.isStateDependent=\"camera\"!==t&&!fe(e.expression),this.interpolationType=n};function Ar(t,e){if(\"error\"===(t=wr(t,e)).result)return t;var r=t.value.expression,n=he(r);if(!n&&!cr(e))return lr([new Pt(\"\",\"data expressions not supported\")]);var a=pe(r,[\"zoom\"]);if(!a&&!ur(e))return lr([new Pt(\"\",\"zoom expressions not supported\")]);var i=function t(e){var r=null;if(e instanceof Ve)r=t(e.result);else if(e instanceof je)for(var n=0,a=e.args;n<a.length;n+=1){var i=a[n];if(r=t(i))break}else(e instanceof me||e instanceof Be)&&e.input instanceof ce&&\"zoom\"===e.input.name&&(r=e);return r instanceof Pt?r:(e.eachChild(function(e){var n=t(e);n instanceof Pt?r=n:!r&&n?r=new Pt(\"\",'\"zoom\" expression may only be used as input to a top-level \"step\" or \"interpolate\" expression.'):r&&n&&r!==n&&(r=new Pt(\"\",'Only one zoom-based \"step\" or \"interpolate\" subexpression may be used in an expression.'))}),r)}(r);if(!i&&!a)return lr([new Pt(\"\",'\"zoom\" expression may only be used as input to a top-level \"step\" or \"interpolate\" expression.')]);if(i instanceof Pt)return lr([i]);if(i instanceof Be&&!hr(e))return lr([new Pt(\"\",'\"interpolate\" expressions cannot be used with this property')]);if(!i)return sr(new kr(n?\"constant\":\"source\",t.value));var o=i instanceof Be?i.interpolation:void 0;return sr(new Tr(n?\"camera\":\"composite\",t.value,i.labels,o))}Tr.prototype.evaluateWithoutErrorHandling=function(t,e,r,n){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n)},Tr.prototype.evaluate=function(t,e,r,n){return this._styleExpression.evaluate(t,e,r,n)},Tr.prototype.interpolationFactor=function(t,e,r){return this.interpolationType?Be.interpolationFactor(this.interpolationType,t,e,r):0};var Mr=function(t,e){this._parameters=t,this._specification=e,St(this,function t(e,r){var n,a,i,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||(hr(r)?\"exponential\":\"interval\");if(o&&((e=St({},e)).stops&&(e.stops=e.stops.map(function(t){return[t[0],Wt.parse(t[1])]})),e.default?e.default=Wt.parse(e.default):e.default=Wt.parse(r.default)),e.colorSpace&&\"rgb\"!==e.colorSpace&&!Fe[e.colorSpace])throw new Error(\"Unknown color space: \"+e.colorSpace);if(\"exponential\"===u)n=yr;else if(\"interval\"===u)n=mr;else if(\"categorical\"===u){n=vr,a=Object.create(null);for(var h=0,f=e.stops;h<f.length;h+=1){var p=f[h];a[p[0]]=p[1]}i=typeof 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s=o&&o.properties?o.properties[e.property]:void 0;return void 0===s?gr(e.default,r.default):n(e,r,s,a,i)}}}(this._parameters,this._specification))};function Sr(t){var e=t.key,r=t.value,n=t.valueSpec||{},a=t.objectElementValidators||{},i=t.style,o=t.styleSpec,s=[],l=fr(r);if(\"object\"!==l)return[new At(e,r,\"object expected, \"+l+\" found\")];for(var c in r){var u=c.split(\".\")[0],h=n[u]||n[\"*\"],f=void 0;if(a[u])f=a[u];else if(n[u])f=Kr;else if(a[\"*\"])f=a[\"*\"];else{if(!n[\"*\"]){s.push(new At(e,r[c],'unknown property \"'+c+'\"'));continue}f=Kr}s=s.concat(f({key:(e?e+\".\":e)+c,value:r[c],valueSpec:h,style:i,styleSpec:o,object:r,objectKey:c},r))}for(var p in n)a[p]||n[p].required&&void 0===n[p].default&&void 0===r[p]&&s.push(new At(e,r,'missing required property \"'+p+'\"'));return s}function Er(t){var e=t.value,r=t.valueSpec,n=t.style,a=t.styleSpec,i=t.key,o=t.arrayElementValidator||Kr;if(\"array\"!==fr(e))return[new At(i,e,\"array expected, \"+fr(e)+\" found\")];if(r.length&&e.length!==r.length)return[new At(i,e,\"array length \"+r.length+\" expected, length \"+e.length+\" found\")];if(r[\"min-length\"]&&e.length<r[\"min-length\"])return[new At(i,e,\"array length at least \"+r[\"min-length\"]+\" expected, length \"+e.length+\" found\")];var s={type:r.value,values:r.values};a.$version<7&&(s.function=r.function),\"object\"===fr(r.value)&&(s=r.value);for(var l=[],c=0;c<e.length;c++)l=l.concat(o({array:e,arrayIndex:c,value:e[c],valueSpec:s,style:n,styleSpec:a,key:i+\"[\"+c+\"]\"}));return l}function Lr(t){var e=t.key,r=t.value,n=t.valueSpec,a=fr(r);return\"number\"!==a?[new At(e,r,\"number expected, \"+a+\" found\")]:\"minimum\"in n&&r<n.minimum?[new At(e,r,r+\" is less than the minimum value \"+n.minimum)]:\"maximum\"in n&&r>n.maximum?[new At(e,r,r+\" is greater than the maximum value \"+n.maximum)]:[]}function Cr(t){var e,r,n,a=t.valueSpec,i=Lt(t.value.type),o={},s=\"categorical\"!==i&&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=Sr({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if(\"identity\"===i)return[new At(t.key,t.value,'identity function may not have a \"stops\" property')];var e=[],r=t.value;return e=e.concat(Er({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:h})),\"array\"===fr(r)&&0===r.length&&e.push(new At(t.key,r,\"array must have at least one stop\")),e},default:function(t){return Kr({key:t.key,value:t.value,valueSpec:a,style:t.style,styleSpec:t.styleSpec})}}});return\"identity\"===i&&s&&u.push(new At(t.key,t.value,'missing required property \"property\"')),\"identity\"===i||t.value.stops||u.push(new At(t.key,t.value,'missing required property \"stops\"')),\"exponential\"===i&&t.valueSpec.expression&&!hr(t.valueSpec)&&u.push(new At(t.key,t.value,\"exponential functions not supported\")),t.styleSpec.$version>=8&&(l&&!cr(t.valueSpec)?u.push(new At(t.key,t.value,\"property functions not supported\")):s&&!ur(t.valueSpec)&&u.push(new At(t.key,t.value,\"zoom functions not supported\"))),\"categorical\"!==i&&!c||void 0!==t.value.property||u.push(new At(t.key,t.value,'\"property\" property is required')),u;function h(t){var e=[],i=t.value,s=t.key;if(\"array\"!==fr(i))return[new At(s,i,\"array expected, \"+fr(i)+\" found\")];if(2!==i.length)return[new At(s,i,\"array length 2 expected, length \"+i.length+\" found\")];if(c){if(\"object\"!==fr(i[0]))return[new At(s,i,\"object expected, \"+fr(i[0])+\" found\")];if(void 0===i[0].zoom)return[new At(s,i,\"object stop key must have zoom\")];if(void 0===i[0].value)return[new At(s,i,\"object stop key must have value\")];if(n&&n>Lt(i[0].zoom))return[new At(s,i[0].zoom,\"stop zoom values must appear in ascending order\")];Lt(i[0].zoom)!==n&&(n=Lt(i[0].zoom),r=void 0,o={}),e=e.concat(Sr({key:s+\"[0]\",value:i[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:Lr,value:f}}))}else e=e.concat(f({key:s+\"[0]\",value:i[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},i));return _r(Ct(i[1]))?e.concat([new At(s+\"[1]\",i[1],\"expressions are not allowed in function stops.\")]):e.concat(Kr({key:s+\"[1]\",value:i[1],valueSpec:a,style:t.style,styleSpec:t.styleSpec}))}function f(t,n){var s=fr(t.value),l=Lt(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new At(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 At(t.key,c,\"stop domain value must be a number, string, or boolean\")];if(\"number\"!==s&&\"categorical\"!==i){var u=\"number expected, \"+s+\" found\";return cr(a)&&void 0===i&&(u+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new At(t.key,c,u)]}return\"categorical\"!==i||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==i&&\"number\"===s&&void 0!==r&&l<r?[new At(t.key,c,\"stop domain values must appear in ascending order\")]:(r=l,\"categorical\"===i&&l in o?[new At(t.key,c,\"stop domain values must be unique\")]:(o[l]=!0,[])):[new At(t.key,c,\"integer expected, found \"+l)]}}function Pr(t){var e=(\"property\"===t.expressionContext?Ar:wr)(Ct(t.value),t.valueSpec);if(\"error\"===e.result)return e.value.map(function(e){return new At(\"\"+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&&-1!==r.possibleOutputs().indexOf(void 0))return[new At(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&&!fe(r))return[new At(t.key,t.value,'\"feature-state\" data expressions are not supported with layout properties.')];if(\"filter\"===t.expressionContext&&!fe(r))return[new At(t.key,t.value,'\"feature-state\" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf(\"cluster\")){if(!pe(r,[\"zoom\",\"feature-state\"]))return[new At(t.key,t.value,'\"zoom\" and \"feature-state\" expressions are not supported with cluster properties.')];if(\"cluster-initial\"===t.expressionContext&&!he(r))return[new At(t.key,t.value,\"Feature data expressions are not supported with initial expression part of cluster properties.\")]}return[]}function Or(t){var e=t.key,r=t.value,n=t.valueSpec,a=[];return Array.isArray(n.values)?-1===n.values.indexOf(Lt(r))&&a.push(new At(e,r,\"expected one of [\"+n.values.join(\", \")+\"], 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Ir={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function Dr(t){if(null==t)return function(){return!0};zr(t)||(t=Fr(t));var e=wr(t,Ir);if(\"error\"===e.result)throw new Error(e.value.map(function(t){return t.key+\": \"+t.message}).join(\", \"));return function(t,r){return e.value.evaluate(t,r)}}function Rr(t,e){return t<e?-1:t>e?1:0}function Fr(t){if(!t)return!0;var e,r=t[0];return t.length<=1?\"any\"!==r:\"==\"===r?Br(t[1],t[2],\"==\"):\"!=\"===r?Vr(Br(t[1],t[2],\"==\")):\"<\"===r||\">\"===r||\"<=\"===r||\">=\"===r?Br(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(Fr))):\"all\"===r?[\"all\"].concat(t.slice(1).map(Fr)):\"none\"===r?[\"all\"].concat(t.slice(1).map(Fr).map(Vr)):\"in\"===r?Nr(t[1],t.slice(2)):\"!in\"===r?Vr(Nr(t[1],t.slice(2))):\"has\"===r?jr(t[1]):\"!has\"!==r||Vr(jr(t[1]))}function 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l=1;l<r.length;l++)o=o.concat(t({key:n+\"[\"+l+\"]\",value:r[l],style:e.style,styleSpec:e.styleSpec}));break;case\"has\":case\"!has\":a=fr(r[1]),2!==r.length?o.push(new At(n,r,'filter array for \"'+r[0]+'\" operator must have 2 elements')):\"string\"!==a&&o.push(new At(n+\"[1]\",r[1],\"string expected, \"+a+\" found\"))}return o}(t)}function qr(t,e){var r=t.key,n=t.style,a=t.styleSpec,i=t.value,o=t.objectKey,s=a[e+\"_\"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if(\"paint\"===e&&l&&s[l[1]]&&s[l[1]].transition)return Kr({key:r,value:i,valueSpec:a.transition,style:n,styleSpec:a});var c,u=t.valueSpec||s[o];if(!u)return[new At(r,i,'unknown property \"'+o+'\"')];if(\"string\"===fr(i)&&cr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(i)))return[new At(r,i,'\"'+o+'\" does not support interpolation syntax\\nUse an identity property function instead: `{ \"type\": \"identity\", \"property\": '+JSON.stringify(c[1])+\" }`.\")];var h=[];return\"symbol\"===t.layerType&&(\"text-field\"===o&&n&&!n.glyphs&&h.push(new At(r,i,'use of \"text-field\" requires a style \"glyphs\" property')),\"text-font\"===o&&pr(Ct(i))&&\"identity\"===Lt(i.type)&&h.push(new At(r,i,'\"text-font\" does not support identity functions'))),h.concat(Kr({key:t.key,value:i,valueSpec:u,style:n,styleSpec:a,expressionContext:\"property\",propertyType:e,propertyKey:o}))}function Hr(t){return qr(t,\"paint\")}function Gr(t){return qr(t,\"layout\")}function Yr(t){var e=[],r=t.value,n=t.key,a=t.style,i=t.styleSpec;r.type||r.ref||e.push(new At(n,r,'either \"type\" or \"ref\" is required'));var o,s=Lt(r.type),l=Lt(r.ref);if(r.id)for(var c=Lt(r.id),u=0;u<t.arrayIndex;u++){var h=a.layers[u];Lt(h.id)===c&&e.push(new At(n,r.id,'duplicate layer id \"'+r.id+'\", previously used at line '+h.id.__line__))}if(\"ref\"in r)[\"type\",\"source\",\"source-layer\",\"filter\",\"layout\"].forEach(function(t){t in r&&e.push(new At(n,r[t],'\"'+t+'\" is prohibited for ref 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Sr({key:r,value:e,valueSpec:n[\"source_\"+o.replace(\"-\",\"_\")],style:t.style,styleSpec:n});case\"geojson\":if(i=Sr({key:r,value:e,valueSpec:n.source_geojson,style:a,styleSpec:n}),e.cluster)for(var s in e.clusterProperties){var l=e.clusterProperties[s],c=l[0],u=l[1],h=\"string\"==typeof c?[c,[\"accumulated\"],[\"get\",s]]:c;i.push.apply(i,Pr({key:r+\".\"+s+\".map\",value:u,expressionContext:\"cluster-map\"})),i.push.apply(i,Pr({key:r+\".\"+s+\".reduce\",value:h,expressionContext:\"cluster-reduce\"}))}return i;case\"video\":return Sr({key:r,value:e,valueSpec:n.source_video,style:a,styleSpec:n});case\"image\":return Sr({key:r,value:e,valueSpec:n.source_image,style:a,styleSpec:n});case\"canvas\":return[new At(r,null,\"Please use runtime APIs to add canvas sources, rather than including them in stylesheets.\",\"source.canvas\")];default:return Or({key:r+\".type\",value:e.type,valueSpec:{values:[\"vector\",\"raster\",\"raster-dem\",\"geojson\",\"video\",\"image\"]},style:a,styleSpec:n})}}function Xr(t){var e=t.value,r=t.styleSpec,n=r.light,a=t.style,i=[],o=fr(e);if(void 0===e)return i;if(\"object\"!==o)return i.concat([new At(\"light\",e,\"object expected, \"+o+\" found\")]);for(var s in e){var l=s.match(/^(.*)-transition$/);i=l&&n[l[1]]&&n[l[1]].transition?i.concat(Kr({key:s,value:e[s],valueSpec:r.transition,style:a,styleSpec:r})):n[s]?i.concat(Kr({key:s,value:e[s],valueSpec:n[s],style:a,styleSpec:r})):i.concat([new At(s,e[s],'unknown property \"'+s+'\"')])}return i}function Zr(t){var e=t.value,r=t.key,n=fr(e);return\"string\"!==n?[new At(r,e,\"string expected, \"+n+\" found\")]:[]}var Jr={\"*\":function(){return[]},array:Er,boolean:function(t){var e=t.value,r=t.key,n=fr(e);return\"boolean\"!==n?[new At(r,e,\"boolean expected, \"+n+\" found\")]:[]},number:Lr,color:function(t){var e=t.key,r=t.value,n=fr(r);return\"string\"!==n?[new At(e,r,\"color expected, \"+n+\" found\")]:null===Yt(r)?[new At(e,r,'color expected, \"'+r+'\" found')]:[]},constants:Mt,enum:Or,filter:Ur,function:Cr,layer:Yr,object:Sr,source:Wr,light:Xr,string:Zr,formatted:function(t){return 0===Zr(t).length?[]:Pr(t)}};function Kr(t){var e=t.value,r=t.valueSpec,n=t.styleSpec;return r.expression&&pr(Lt(e))?Cr(t):r.expression&&_r(Ct(e))?Pr(t):r.type&&Jr[r.type]?Jr[r.type](t):Sr(St({},t,{valueSpec:r.type?n[r.type]:r}))}function Qr(t){var e=t.value,r=t.key,n=Zr(t);return n.length?n:(-1===e.indexOf(\"{fontstack}\")&&n.push(new At(r,e,'\"glyphs\" url must include a \"{fontstack}\" token')),-1===e.indexOf(\"{range}\")&&n.push(new At(r,e,'\"glyphs\" url must include a \"{range}\" token')),n)}function $r(t,e){e=e||Tt;var r=[];return r=r.concat(Kr({key:\"\",value:t,valueSpec:e.$root,styleSpec:e,style:t,objectElementValidators:{glyphs:Qr,\"*\":function(){return[]}}})),t.constants&&(r=r.concat(Mt({key:\"constants\",value:t.constants,style:t,styleSpec:e}))),tn(r)}function tn(t){return[].concat(t).sort(function(t,e){return t.line-e.line})}function en(t){return function(){for(var e=[],r=arguments.length;r--;)e[r]=arguments[r];return tn(t.apply(this,e))}}$r.source=en(Wr),$r.light=en(Xr),$r.layer=en(Yr),$r.filter=en(Ur),$r.paintProperty=en(Hr),$r.layoutProperty=en(Gr);var rn=$r,nn=rn.light,an=rn.paintProperty,on=rn.layoutProperty;function sn(t,e){var r=!1;if(e&&e.length)for(var n=0,a=e;n<a.length;n+=1){var i=a[n];t.fire(new wt(new Error(i.message))),r=!0}return r}var ln=un,cn=3;function un(t,e,r){var n=this.cells=[];if(t instanceof ArrayBuffer){this.arrayBuffer=t;var a=new Int32Array(this.arrayBuffer);t=a[0],e=a[1],r=a[2],this.d=e+2*r;for(var i=0;i<this.d*this.d;i++){var 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this._forEachCell(t,e,r,n,this._queryCell,s,{},a),s},un.prototype._queryCell=function(t,e,r,n,a,i,o,s){var l=this.cells[a];if(null!==l)for(var c=this.keys,u=this.bboxes,h=0;h<l.length;h++){var f=l[h];if(void 0===o[f]){var p=4*f;(s?s(u[p+0],u[p+1],u[p+2],u[p+3]):t<=u[p+2]&&e<=u[p+3]&&r>=u[p+0]&&n>=u[p+1])?(o[f]=!0,i.push(c[f])):o[f]=!1}}},un.prototype._forEachCell=function(t,e,r,n,a,i,o,s){for(var l=this._convertToCellCoord(t),c=this._convertToCellCoord(e),u=this._convertToCellCoord(r),h=this._convertToCellCoord(n),f=l;f<=u;f++)for(var p=c;p<=h;p++){var d=this.d*p+f;if((!s||s(this._convertFromCellCoord(f),this._convertFromCellCoord(p),this._convertFromCellCoord(f+1),this._convertFromCellCoord(p+1)))&&a.call(this,t,e,r,n,d,i,o,s))return}},un.prototype._convertFromCellCoord=function(t){return(t-this.padding)/this.scale},un.prototype._convertToCellCoord=function(t){return Math.max(0,Math.min(this.d-1,Math.floor(t*this.scale)+this.padding))},un.prototype.toArrayBuffer=function(){if(this.arrayBuffer)return this.arrayBuffer;for(var t=this.cells,e=cn+this.cells.length+1+1,r=0,n=0;n<this.cells.length;n++)r+=this.cells[n].length;var a=new Int32Array(e+r+this.keys.length+this.bboxes.length);a[0]=this.extent,a[1]=this.n,a[2]=this.padding;for(var i=e,o=0;o<t.length;o++){var s=t[o];a[cn+o]=i,a.set(s,i),i+=s.length}return a[cn+t.length]=i,a.set(this.keys,i),i+=this.keys.length,a[cn+t.length+1]=i,a.set(this.bboxes,i),i+=this.bboxes.length,a.buffer};var hn=self.ImageData,fn={};function pn(t,e,r){void 0===r&&(r={}),Object.defineProperty(e,\"_classRegistryKey\",{value:t,writeable:!1}),fn[t]={klass:e,omit:r.omit||[],shallow:r.shallow||[]}}for(var dn in pn(\"Object\",Object),ln.serialize=function(t,e){var r=t.toArrayBuffer();return e&&e.push(r),{buffer:r}},ln.deserialize=function(t){return new ln(t.buffer)},pn(\"Grid\",ln),pn(\"Color\",Wt),pn(\"Error\",Error),pn(\"StylePropertyFunction\",Mr),pn(\"StyleExpression\",br,{omit:[\"_evaluator\"]}),pn(\"ZoomDependentExpression\",Tr),pn(\"ZoomConstantExpression\",kr),pn(\"CompoundExpression\",ce,{omit:[\"_evaluate\"]}),rr)rr[dn]._classRegistryKey||pn(\"Expression_\"+dn,rr[dn]);function gn(t,e){if(null==t||\"boolean\"==typeof t||\"number\"==typeof t||\"string\"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp)return t;if(t instanceof ArrayBuffer)return e&&e.push(t),t;if(ArrayBuffer.isView(t)){var r=t;return e&&e.push(r.buffer),r}if(t instanceof hn)return e&&e.push(t.data.buffer),t;if(Array.isArray(t)){for(var n=[],a=0,i=t;a<i.length;a+=1){var o=i[a];n.push(gn(o,e))}return n}if(\"object\"==typeof t){var s=t.constructor,l=s._classRegistryKey;if(!l)throw new Error(\"can't serialize object of unregistered class\");var 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this.int16[_+0]=e,this.int16[_+1]=r,this.int16[_+2]=n,this.int16[_+3]=a,this.int16[_+4]=i,this.int16[_+5]=o,this.uint16[_+6]=s,this.uint16[_+7]=l,this.uint16[_+8]=c,this.uint16[_+9]=u,this.uint16[_+10]=h,this.uint16[_+11]=f,this.uint16[_+12]=p,this.uint16[_+13]=d,this.uint16[_+14]=g,this.uint16[_+15]=v,this.uint16[_+16]=m,this.uint32[w+9]=y,this.float32[w+10]=x,this.float32[w+11]=b,t},e}(Xn);ca.prototype.bytesPerElement=48,pn(\"StructArrayLayout6i11ui1ul2f48\",ca);var ua=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}(Xn);ua.prototype.bytesPerElement=4,pn(\"StructArrayLayout1f4\",ua);var ha=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 a=3*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,t},e}(Xn);ha.prototype.bytesPerElement=6,pn(\"StructArrayLayout3i6\",ha);var fa=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 a=2*t,i=4*t;return this.uint32[a+0]=e,this.uint16[i+2]=r,this.uint16[i+3]=n,t},e}(Xn);fa.prototype.bytesPerElement=8,pn(\"StructArrayLayout1ul2ui8\",fa);var pa=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 a=3*t;return this.uint16[a+0]=e,this.uint16[a+1]=r,this.uint16[a+2]=n,t},e}(Xn);pa.prototype.bytesPerElement=6,pn(\"StructArrayLayout3ui6\",pa);var da=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}(Xn);da.prototype.bytesPerElement=4,pn(\"StructArrayLayout2ui4\",da);var ga=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 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this._structArray.uint16[this._pos2+9]},r.bucketIndex.set=function(t){this._structArray.uint16[this._pos2+9]=t},r.radius.get=function(){return this._structArray.int16[this._pos2+10]},r.radius.set=function(t){this._structArray.int16[this._pos2+10]=t},r.signedDistanceFromAnchor.get=function(){return this._structArray.int16[this._pos2+11]},r.signedDistanceFromAnchor.set=function(t){this._structArray.int16[this._pos2+11]=t},r.anchorPoint.get=function(){return new a(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Wn);ya.prototype.size=24;var xa=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 ya(this,t)},e}(ia);pn(\"CollisionBoxArray\",xa);var ba=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var 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this._structArray.uint16[this._pos2+3]},r.numGlyphs.set=function(t){this._structArray.uint16[this._pos2+3]=t},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.vertexStartIndex.set=function(t){this._structArray.uint32[this._pos4+2]=t},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineStartIndex.set=function(t){this._structArray.uint32[this._pos4+3]=t},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.lineLength.set=function(t){this._structArray.uint32[this._pos4+4]=t},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.segment.set=function(t){this._structArray.uint16[this._pos2+10]=t},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.lowerSize.set=function(t){this._structArray.uint16[this._pos2+11]=t},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.upperSize.set=function(t){this._structArray.uint16[this._pos2+12]=t},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetX.set=function(t){this._structArray.float32[this._pos4+7]=t},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.lineOffsetY.set=function(t){this._structArray.float32[this._pos4+8]=t},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.writingMode.set=function(t){this._structArray.uint8[this._pos1+36]=t},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 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za,this._bufferOffset=0};Ja.createDynamic=function(t,e,r){var n=new Ja,a=[];for(var i in t.paint._values)if(r(i)){var o=t.paint.get(i);if(o instanceof Fn&&cr(o.property.specification)){var s=Qa(i,t.type),l=o.property.specification.type,c=o.property.useIntegerZoom;if(\"cross-faded\"===o.property.specification[\"property-type\"]||\"cross-faded-data-driven\"===o.property.specification[\"property-type\"])if(\"constant\"===o.value.kind)n.binders[i]=new Ya(o.value.value,s,l),a.push(\"/u_\"+i);else{var u=$a(i,l,\"source\");n.binders[i]=new Za(o.value,s,l,c,e,u,t.id),a.push(\"/a_\"+i)}else if(\"constant\"===o.value.kind)n.binders[i]=new Ga(o.value.value,s,l),a.push(\"/u_\"+i);else if(\"source\"===o.value.kind){var h=$a(i,l,\"source\");n.binders[i]=new Wa(o.value,s,l,h),a.push(\"/a_\"+i)}else{var f=$a(i,l,\"composite\");n.binders[i]=new Xa(o.value,s,l,c,e,f),a.push(\"/z_\"+i)}}}return n.cacheKey=a.sort().join(\"\"),n},Ja.prototype.populatePaintArrays=function(t,e,r,n,a){for(var i in this.binders)this.binders[i].populatePaintArray(t,e,n,a);void 0!==e.id&&this._featureMap.add(+e.id,r,this._bufferOffset,t),this._bufferOffset=t},Ja.prototype.setConstantPatternPositions=function(t,e){for(var r in this.binders)this.binders[r].setConstantPatternPositions(t,e)},Ja.prototype.updatePaintArrays=function(t,e,r,n){var a=!1;for(var i in t)for(var o=0,s=this._featureMap.getPositions(+i);o<s.length;o+=1){var l=s[o],c=e.feature(l.index);for(var u in this.binders){var h=this.binders[u];if(!(h instanceof Ga||h instanceof Ya)&&!0===h.expression.isStateDependent){var f=r.paint.get(u);h.expression=f.value,h.updatePaintArray(l.start,l.end,c,t[i],n),a=!0}}}return a},Ja.prototype.defines=function(){var t=[];for(var e in this.binders)t.push.apply(t,this.binders[e].defines());return t},Ja.prototype.getPaintVertexBuffers=function(){return this._buffers},Ja.prototype.getUniforms=function(t,e){var r=[];for(var n in this.binders)for(var a=this.binders[n],i=0,o=a.uniformNames;i<o.length;i+=1){var s=o[i];if(e[s]){var l=a.getBinding(t,e[s]);r.push({name:s,property:n,binding:l})}}return r},Ja.prototype.setUniforms=function(t,e,r,n){for(var a=0,i=e;a<i.length;a+=1){var o=i[a],s=o.name,l=o.property,c=o.binding;this.binders[l].setUniforms(t,c,n,r.get(l),s)}},Ja.prototype.updatePatternPaintBuffers=function(t){var e=[];for(var r in this.binders){var n=this.binders[r];if(n instanceof Za){var a=2===t.fromScale?n.zoomInPaintVertexBuffer:n.zoomOutPaintVertexBuffer;a&&e.push(a)}else(n instanceof Wa||n instanceof Xa)&&n.paintVertexBuffer&&e.push(n.paintVertexBuffer)}this._buffers=e},Ja.prototype.upload=function(t){for(var e in this.binders)this.binders[e].upload(t);var r=[];for(var n in this.binders){var a=this.binders[n];(a instanceof Wa||a instanceof Xa)&&a.paintVertexBuffer&&r.push(a.paintVertexBuffer)}this._buffers=r},Ja.prototype.destroy=function(){for(var t in this.binders)this.binders[t].destroy()};var Ka=function(t,e,r,n){void 0===n&&(n=function(){return!0}),this.programConfigurations={};for(var a=0,i=e;a<i.length;a+=1){var o=i[a];this.programConfigurations[o.id]=Ja.createDynamic(o,r,n),this.programConfigurations[o.id].layoutAttributes=t}this.needsUpload=!1};function Qa(t,e){return{\"text-opacity\":[\"opacity\"],\"icon-opacity\":[\"opacity\"],\"text-color\":[\"fill_color\"],\"icon-color\":[\"fill_color\"],\"text-halo-color\":[\"halo_color\"],\"icon-halo-color\":[\"halo_color\"],\"text-halo-blur\":[\"halo_blur\"],\"icon-halo-blur\":[\"halo_blur\"],\"text-halo-width\":[\"halo_width\"],\"icon-halo-width\":[\"halo_width\"],\"line-gap-width\":[\"gapwidth\"],\"line-pattern\":[\"pattern_to\",\"pattern_from\"],\"fill-pattern\":[\"pattern_to\",\"pattern_from\"],\"fill-extrusion-pattern\":[\"pattern_to\",\"pattern_from\"]}[t]||[t.replace(e+\"-\",\"\").replace(/-/g,\"_\")]}function $a(t,e,r){var n={color:{source:va,composite:ma},number:{source:ua,composite:va}},a=function(t){return{\"line-pattern\":{source:ea,composite:ea},\"fill-pattern\":{source:ea,composite:ea},\"fill-extrusion-pattern\":{source:ea,composite:ea}}[t]}(t);return a&&a[r]||n[e][r]}Ka.prototype.populatePaintArrays=function(t,e,r,n,a){for(var i in this.programConfigurations)this.programConfigurations[i].populatePaintArrays(t,e,r,n,a);this.needsUpload=!0},Ka.prototype.updatePaintArrays=function(t,e,r,n){for(var a=0,i=r;a<i.length;a+=1){var o=i[a];this.needsUpload=this.programConfigurations[o.id].updatePaintArrays(t,e,o,n)||this.needsUpload}},Ka.prototype.get=function(t){return this.programConfigurations[t]},Ka.prototype.upload=function(t){if(this.needsUpload){for(var e in this.programConfigurations)this.programConfigurations[e].upload(t);this.needsUpload=!1}},Ka.prototype.destroy=function(){for(var t in 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t.id}),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Kn,this.indexArray=new pa,this.segments=new Pa,this.programConfigurations=new Ka(Ca,t.layers,t.zoom),this.stateDependentLayerIds=this.layers.filter(function(t){return t.isStateDependent()}).map(function(t){return t.id})};function ii(t,e){for(var r=0;r<t.length;r++)if(di(e,t[r]))return!0;for(var n=0;n<e.length;n++)if(di(t,e[n]))return!0;return!!ci(t,e)}function oi(t,e,r){return!!di(t,e)||!!hi(e,t,r)}function si(t,e){if(1===t.length)return pi(e,t[0]);for(var r=0;r<e.length;r++)for(var n=e[r],a=0;a<n.length;a++)if(di(t,n[a]))return!0;for(var i=0;i<t.length;i++)if(pi(e,t[i]))return!0;for(var o=0;o<e.length;o++)if(ci(t,e[o]))return!0;return!1}function li(t,e,r){if(t.length>1){if(ci(t,e))return!0;for(var n=0;n<e.length;n++)if(hi(e[n],t,r))return!0}for(var a=0;a<t.length;a++)if(hi(t[a],e,r))return!0;return!1}function ci(t,e){if(0===t.length||0===e.length)return!1;for(var r=0;r<t.length-1;r++)for(var n=t[r],a=t[r+1],i=0;i<e.length-1;i++)if(ui(n,a,e[i],e[i+1]))return!0;return!1}function ui(t,e,r,n){return k(t,r,n)!==k(e,r,n)&&k(t,e,r)!==k(t,e,n)}function hi(t,e,r){var n=r*r;if(1===e.length)return t.distSqr(e[0])<n;for(var a=1;a<e.length;a++)if(fi(t,e[a-1],e[a])<n)return!0;return!1}function fi(t,e,r){var n=e.distSqr(r);if(0===n)return t.distSqr(e);var a=((t.x-e.x)*(r.x-e.x)+(t.y-e.y)*(r.y-e.y))/n;return a<0?t.distSqr(e):a>1?t.distSqr(r):t.distSqr(r.sub(e)._mult(a)._add(e))}function pi(t,e){for(var r,n,a,i=!1,o=0;o<t.length;o++)for(var s=0,l=(r=t[o]).length-1;s<r.length;l=s++)n=r[s],a=r[l],n.y>e.y!=a.y>e.y&&e.x<(a.x-n.x)*(e.y-n.y)/(a.y-n.y)+n.x&&(i=!i);return i}function di(t,e){for(var r=!1,n=0,a=t.length-1;n<t.length;a=n++){var i=t[n],o=t[a];i.y>e.y!=o.y>e.y&&e.x<(o.x-i.x)*(e.y-i.y)/(o.y-i.y)+i.x&&(r=!r)}return r}function gi(t,e,r){var n=r[0],a=r[2];if(t.x<n.x&&e.x<n.x||t.x>a.x&&e.x>a.x||t.y<n.y&&e.y<n.y||t.y>a.y&&e.y>a.y)return!1;var i=k(t,e,r[0]);return i!==k(t,e,r[1])||i!==k(t,e,r[2])||i!==k(t,e,r[3])}function vi(t,e,r){var n=e.paint.get(t).value;return\"constant\"===n.kind?n.value:r.programConfigurations.get(e.id).binders[t].maxValue}function mi(t){return Math.sqrt(t[0]*t[0]+t[1]*t[1])}function yi(t,e,r,n,i){if(!e[0]&&!e[1])return t;var o=a.convert(e)._mult(i);\"viewport\"===r&&o._rotate(-n);for(var s=[],l=0;l<t.length;l++){var c=t[l];s.push(c.sub(o))}return s}ai.prototype.populate=function(t,e){var r=this.layers[0],n=[],a=null;\"circle\"===r.type&&(a=r.layout.get(\"circle-sort-key\"));for(var i=0,o=t;i<o.length;i+=1){var s=o[i],l=s.feature,c=s.index,u=s.sourceLayerIndex;if(this.layers[0]._featureFilter(new Cn(this.zoom),l)){var h=ri(l),f=a?a.evaluate(l,{}):void 0,p={id:l.id,properties:l.properties,type:l.type,sourceLayerIndex:u,index:c,geometry:h,patterns:{},sortKey:f};n.push(p)}}a&&n.sort(function(t,e){return t.sortKey-e.sortKey});for(var d=0,g=n;d<g.length;d+=1){var v=g[d],m=v,y=m.geometry,x=m.index,b=m.sourceLayerIndex,_=t[x].feature;this.addFeature(v,y,x),e.featureIndex.insert(_,y,x,b,this.index)}},ai.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},ai.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},ai.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},ai.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Ca),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},ai.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},ai.prototype.addFeature=function(t,e,r){for(var n=0,a=e;n<a.length;n+=1)for(var i=0,o=a[n];i<o.length;i+=1){var s=o[i],l=s.x,c=s.y;if(!(l<0||l>=ti||c<0||c>=ti)){var u=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray,t.sortKey),h=u.vertexLength;ni(this.layoutVertexArray,l,c,-1,-1),ni(this.layoutVertexArray,l,c,1,-1),ni(this.layoutVertexArray,l,c,1,1),ni(this.layoutVertexArray,l,c,-1,1),this.indexArray.emplaceBack(h,h+1,h+2),this.indexArray.emplaceBack(h,h+3,h+2),u.vertexLength+=4,u.primitiveLength+=2}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,{})},pn(\"CircleBucket\",ai,{omit:[\"layers\"]});var xi,bi=new Hn({\"circle-sort-key\":new jn(Tt.layout_circle[\"circle-sort-key\"])}),_i={paint:new Hn({\"circle-radius\":new jn(Tt.paint_circle[\"circle-radius\"]),\"circle-color\":new jn(Tt.paint_circle[\"circle-color\"]),\"circle-blur\":new jn(Tt.paint_circle[\"circle-blur\"]),\"circle-opacity\":new jn(Tt.paint_circle[\"circle-opacity\"]),\"circle-translate\":new Nn(Tt.paint_circle[\"circle-translate\"]),\"circle-translate-anchor\":new 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t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new ai(t)},e.prototype.queryRadius=function(t){var e=t;return vi(\"circle-radius\",this,e)+vi(\"circle-stroke-width\",this,e)+mi(this.paint.get(\"circle-translate\"))},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,i,o,s){for(var l=yi(t,this.paint.get(\"circle-translate\"),this.paint.get(\"circle-translate-anchor\"),i.angle,o),c=this.paint.get(\"circle-radius\").evaluate(e,r)+this.paint.get(\"circle-stroke-width\").evaluate(e,r),u=\"map\"===this.paint.get(\"circle-pitch-alignment\"),h=u?l:function(t,e){return l.map(function(t){return Ai(t,e)})}(0,s),f=u?c*o:c,p=0,d=n;p<d.length;p+=1)for(var g=0,v=d[p];g<v.length;g+=1){var m=v[g],y=u?m:Ai(m,s),x=f,b=ki([],[m.x,m.y,0,1],s);if(\"viewport\"===this.paint.get(\"circle-pitch-scale\")&&\"map\"===this.paint.get(\"circle-pitch-alignment\")?x*=b[3]/i.cameraToCenterDistance:\"map\"===this.paint.get(\"circle-pitch-scale\")&&\"viewport\"===this.paint.get(\"circle-pitch-alignment\")&&(x*=i.cameraToCenterDistance/b[3]),oi(h,y,x))return!0}return!1},e}(Gn);function Ai(t,e){var r=ki([],[t.x,t.y,0,1],e);return new a(r[0]/r[3],r[1]/r[3])}var Mi=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}(ai);function Si(t,e,r,n){var a=e.width,i=e.height;if(n){if(n instanceof Uint8ClampedArray)n=new Uint8Array(n.buffer);else if(n.length!==a*i*r)throw new RangeError(\"mismatched image size\")}else n=new Uint8Array(a*i*r);return t.width=a,t.height=i,t.data=n,t}function Ei(t,e,r){var n=e.width,a=e.height;if(n!==t.width||a!==t.height){var i=Si({},{width:n,height:a},r);Li(t,i,{x:0,y:0},{x:0,y:0},{width:Math.min(t.width,n),height:Math.min(t.height,a)},r),t.width=n,t.height=a,t.data=i.data}}function Li(t,e,r,n,a,i){if(0===a.width||0===a.height)return e;if(a.width>t.width||a.height>t.height||r.x>t.width-a.width||r.y>t.height-a.height)throw new RangeError(\"out of range source coordinates for image copy\");if(a.width>e.width||a.height>e.height||n.x>e.width-a.width||n.y>e.height-a.height)throw new RangeError(\"out of range destination coordinates for image copy\");for(var o=t.data,s=e.data,l=0;l<a.height;l++)for(var c=((r.y+l)*t.width+r.x)*i,u=((n.y+l)*e.width+n.x)*i,h=0;h<a.width*i;h++)s[u+h]=o[c+h];return e}pn(\"HeatmapBucket\",Mi,{omit:[\"layers\"]});var Ci=function(t,e){Si(this,t,1,e)};Ci.prototype.resize=function(t){Ei(this,t,1)},Ci.prototype.clone=function(){return new Ci({width:this.width,height:this.height},new Uint8Array(this.data))},Ci.copy=function(t,e,r,n,a){Li(t,e,r,n,a,1)};var Pi=function(t,e){Si(this,t,4,e)};Pi.prototype.resize=function(t){Ei(this,t,4)},Pi.prototype.replace=function(t,e){e?this.data.set(t):t instanceof Uint8ClampedArray?this.data=new Uint8Array(t.buffer):this.data=t},Pi.prototype.clone=function(){return new Pi({width:this.width,height:this.height},new Uint8Array(this.data))},Pi.copy=function(t,e,r,n,a){Li(t,e,r,n,a,4)},pn(\"AlphaImage\",Ci),pn(\"RGBAImage\",Pi);var Oi={paint:new Hn({\"heatmap-radius\":new jn(Tt.paint_heatmap[\"heatmap-radius\"]),\"heatmap-weight\":new jn(Tt.paint_heatmap[\"heatmap-weight\"]),\"heatmap-intensity\":new Nn(Tt.paint_heatmap[\"heatmap-intensity\"]),\"heatmap-color\":new qn(Tt.paint_heatmap[\"heatmap-color\"]),\"heatmap-opacity\":new Nn(Tt.paint_heatmap[\"heatmap-opacity\"])})};function zi(t,e){for(var r=new Uint8Array(1024),n={},a=0,i=0;a<256;a++,i+=4){n[e]=a/255;var o=t.evaluate(n);r[i+0]=Math.floor(255*o.r/o.a),r[i+1]=Math.floor(255*o.g/o.a),r[i+2]=Math.floor(255*o.b/o.a),r[i+3]=Math.floor(255*o.a)}return new 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c=t.readVarint();n=7&c,i=c>>3}if(i--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new a(o,s));else{if(7!==n)throw new Error(\"unknown command \"+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},Mo.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,a=0,i=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)(a+=t.readSVarint())<o&&(o=a),a>s&&(s=a),(i+=t.readSVarint())<l&&(l=i),i>c&&(c=i);else if(7!==r)throw new Error(\"unknown command \"+r)}return[o,l,s,c]},Mo.prototype.toGeoJSON=function(t,e,r){var n,a,i=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=Mo.types[this.type];function u(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/i;t[e]=[360*(r.x+o)/i-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var h=[];for(n=0;n<l.length;n++)h[n]=l[n][0];u(l=h);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,a=[],i=0;i<e;i++){var o=Eo(t[i]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&a.push(r),r=[t[i]]):r.push(t[i]))}return r&&a.push(r),a}(l),n=0;n<l.length;n++)for(a=0;a<l[n].length;a++)u(l[n][a])}1===l.length?l=l[0]:c=\"Multi\"+c;var f={type:\"Feature\",geometry:{type:c,coordinates:l},properties:this.properties};return\"id\"in this&&(f.id=this.id),f};var Lo=Co;function Co(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(Po,this,e),this.length=this._features.length}function Po(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){for(var e=null,r=t.readVarint()+t.pos;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Oo(t,e,r){if(3===t){var n=new Lo(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Co.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 Ao(this._pbf,e,this.extent,this._keys,this._values)};var zo={VectorTile:function(t,e){this.layers=t.readFields(Oo,{},e)},VectorTileFeature:Ao,VectorTileLayer:Lo},Io=zo.VectorTileFeature.types,Do=Math.pow(2,13);function Ro(t,e,r,n,a,i,o,s){t.emplaceBack(e,r,2*Math.floor(n*Do)+o,a*Do*2,i*Do*2,Math.round(s))}var Fo=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 $n,this.indexArray=new pa,this.programConfigurations=new Ka(To,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 Bo(t,e){return t.x===e.x&&(t.x<0||t.x>ti)||t.y===e.y&&(t.y<0||t.y>ti)}function No(t){return t.every(function(t){return t.x<0})||t.every(function(t){return t.x>ti})||t.every(function(t){return t.y<0})||t.every(function(t){return t.y>ti})}Fo.prototype.populate=function(t,e){this.features=[],this.hasPattern=yo(\"fill-extrusion\",this.layers,e);for(var r=0,n=t;r<n.length;r+=1){var a=n[r],i=a.feature,o=a.index,s=a.sourceLayerIndex;if(this.layers[0]._featureFilter(new Cn(this.zoom),i)){var l=ri(i),c={sourceLayerIndex:s,index:o,geometry:l,properties:i.properties,type:i.type,patterns:{}};void 0!==i.id&&(c.id=i.id),this.hasPattern?this.features.push(xo(\"fill-extrusion\",this.layers,c,this.zoom,e)):this.addFeature(c,l,o,{}),e.featureIndex.insert(i,l,o,s,this.index,!0)}}},Fo.prototype.addFeatures=function(t,e){for(var r=0,n=this.features;r<n.length;r+=1){var a=n[r],i=a.geometry;this.addFeature(a,i,a.index,e)}},Fo.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Fo.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Fo.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Fo.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,To),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Fo.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Fo.prototype.addFeature=function(t,e,r,n){for(var a=0,i=vo(e,500);a<i.length;a+=1){for(var o=i[a],s=0,l=0,c=o;l<c.length;l+=1)s+=c[l].length;for(var u=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,f=o;h<f.length;h+=1){var p=f[h];if(0!==p.length&&!No(p))for(var d=0,g=0;g<p.length;g++){var v=p[g];if(g>=1){var m=p[g-1];if(!Bo(v,m)){u.vertexLength+4>Pa.MAX_VERTEX_ARRAY_LENGTH&&(u=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var y=v.sub(m)._perp()._unit(),x=m.dist(v);d+x>32768&&(d=0),Ro(this.layoutVertexArray,v.x,v.y,y.x,y.y,0,0,d),Ro(this.layoutVertexArray,v.x,v.y,y.x,y.y,0,1,d),d+=x,Ro(this.layoutVertexArray,m.x,m.y,y.x,y.y,0,0,d),Ro(this.layoutVertexArray,m.x,m.y,y.x,y.y,0,1,d);var b=u.vertexLength;this.indexArray.emplaceBack(b,b+2,b+1),this.indexArray.emplaceBack(b+1,b+2,b+3),u.vertexLength+=4,u.primitiveLength+=2}}}}if(u.vertexLength+s>Pa.MAX_VERTEX_ARRAY_LENGTH&&(u=this.segments.prepareSegment(s,this.layoutVertexArray,this.indexArray)),\"Polygon\"===Io[t.type]){for(var _=[],w=[],k=u.vertexLength,T=0,A=o;T<A.length;T+=1){var M=A[T];if(0!==M.length){M!==o[0]&&w.push(_.length/2);for(var S=0;S<M.length;S++){var E=M[S];Ro(this.layoutVertexArray,E.x,E.y,0,0,1,1,0),_.push(E.x),_.push(E.y)}}}for(var L=Bi(_,w),C=0;C<L.length;C+=3)this.indexArray.emplaceBack(k+L[C],k+L[C+2],k+L[C+1]);u.primitiveLength+=L.length/3,u.vertexLength+=s}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,n)},pn(\"FillExtrusionBucket\",Fo,{omit:[\"layers\",\"features\"]});var jo={paint:new Hn({\"fill-extrusion-opacity\":new Nn(Tt[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new jn(Tt[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new Nn(Tt[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new Nn(Tt[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new Vn(Tt[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new jn(Tt[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new jn(Tt[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new Nn(Tt[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})},Vo=function(t){function e(e){t.call(this,e,jo)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new Fo(t)},e.prototype.queryRadius=function(){return mi(this.paint.get(\"fill-extrusion-translate\"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,i,o,s,l){var c=yi(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),h=this.paint.get(\"fill-extrusion-base\").evaluate(e,r),f=function(t,e,r,n){for(var i=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,0,1];ki(c,c,e),i.push(new a(c[0]/c[3],c[1]/c[3]))}return i}(c,l),p=function(t,e,r,n){for(var i=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,h=n[8]*r,f=n[9]*r,p=n[10]*r,d=n[11]*r,g=0,v=t;g<v.length;g+=1){for(var m=[],y=[],x=0,b=v[g];x<b.length;x+=1){var _=b[x],w=_.x,k=_.y,T=n[0]*w+n[4]*k+n[12],A=n[1]*w+n[5]*k+n[13],M=n[2]*w+n[6]*k+n[14],S=n[3]*w+n[7]*k+n[15],E=M+c,L=S+u,C=T+h,P=A+f,O=M+p,z=S+d,I=new a((T+s)/L,(A+l)/L);I.z=E/L,m.push(I);var D=new a(C/z,P/z);D.z=O/z,y.push(D)}i.push(m),o.push(y)}return[i,o]}(n,h,u,l);return function(t,e,r){var n=1/0;si(r,e)&&(n=qo(r,e[0]));for(var a=0;a<e.length;a++)for(var i=e[a],o=t[a],s=0;s<i.length-1;s++){var l=i[s],c=i[s+1],u=o[s],h=[l,c,o[s+1],u,l];ii(r,h)&&(n=Math.min(n,qo(r,h)))}return n!==1/0&&n}(p[0],p[1],f)},e}(Gn);function Uo(t,e){return t.x*e.x+t.y*e.y}function qo(t,e){if(1===t.length){var r=e[0],n=e[1],a=e[3],i=t[0],o=n.sub(r),s=a.sub(r),l=i.sub(r),c=Uo(o,o),u=Uo(o,s),h=Uo(s,s),f=Uo(l,o),p=Uo(l,s),d=c*h-u*u,g=(h*f-u*p)/d,v=(c*p-u*f)/d,m=1-g-v;return r.z*m+n.z*g+a.z*v}for(var y=1/0,x=0,b=e;x<b.length;x+=1){var _=b[x];y=Math.min(y,_.z)}return y}var Ho=Zn([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4).members,Go=zo.VectorTileFeature.types,Yo=Math.cos(Math.PI/180*37.5),Wo=Math.pow(2,14)/.5,Xo=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 ta,this.indexArray=new pa,this.programConfigurations=new Ka(Ho,t.layers,t.zoom),this.segments=new Pa,this.stateDependentLayerIds=this.layers.filter(function(t){return t.isStateDependent()}).map(function(t){return t.id})};Xo.prototype.populate=function(t,e){this.hasPattern=yo(\"line\",this.layers,e);for(var r=this.layers[0].layout.get(\"line-sort-key\"),n=[],a=0,i=t;a<i.length;a+=1){var o=i[a],s=o.feature,l=o.index,c=o.sourceLayerIndex;if(this.layers[0]._featureFilter(new Cn(this.zoom),s)){var u=ri(s),h=r?r.evaluate(s,{}):void 0,f={id:s.id,properties:s.properties,type:s.type,sourceLayerIndex:c,index:l,geometry:u,patterns:{},sortKey:h};n.push(f)}}r&&n.sort(function(t,e){return t.sortKey-e.sortKey});for(var p=0,d=n;p<d.length;p+=1){var g=d[p],v=g,m=v.geometry,y=v.index,x=v.sourceLayerIndex;if(this.hasPattern){var b=xo(\"line\",this.layers,g,this.zoom,e);this.patternFeatures.push(b)}else this.addFeature(g,m,y,{});var _=t[y].feature;e.featureIndex.insert(_,m,y,x,this.index)}},Xo.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Xo.prototype.addFeatures=function(t,e){for(var r=0,n=this.patternFeatures;r<n.length;r+=1){var a=n[r];this.addFeature(a,a.geometry,a.index,e)}},Xo.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Xo.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Xo.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Ho),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Xo.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Xo.prototype.addFeature=function(t,e,r,n){for(var a=this.layers[0].layout,i=a.get(\"line-join\").evaluate(t,{}),o=a.get(\"line-cap\"),s=a.get(\"line-miter-limit\"),l=a.get(\"line-round-limit\"),c=0,u=e;c<u.length;c+=1){var h=u[c];this.addLine(h,t,i,o,s,l,r,n)}},Xo.prototype.addLine=function(t,e,r,n,a,i,o,s){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 l=0;l<t.length-1;l++)this.totalDistance+=t[l].dist(t[l+1])}for(var c=\"Polygon\"===Go[e.type],u=t.length;u>=2&&t[u-1].equals(t[u-2]);)u--;for(var h=0;h<u-1&&t[h].equals(t[h+1]);)h++;if(!(u<(c?3:2))){\"bevel\"===r&&(a=1.05);var f,p=ti/(512*this.overscaling)*15,d=this.segments.prepareSegment(10*u,this.layoutVertexArray,this.indexArray),g=void 0,v=void 0,m=void 0,y=void 0;this.e1=this.e2=-1,c&&(f=t[u-2],y=t[h].sub(f)._unit()._perp());for(var x=h;x<u;x++)if(!(v=c&&x===u-1?t[h+1]:t[x+1])||!t[x].equals(v)){y&&(m=y),f&&(g=f),f=t[x],y=v?v.sub(f)._unit()._perp():m;var b=(m=m||y).add(y);0===b.x&&0===b.y||b._unit();var _=m.x*y.x+m.y*y.y,w=b.x*y.x+b.y*y.y,k=0!==w?1/w:1/0,T=2*Math.sqrt(2-2*w),A=w<Yo&&g&&v,M=m.x*y.y-m.y*y.x>0;if(A&&x>h){var S=f.dist(g);if(S>2*p){var E=f.sub(f.sub(g)._mult(p/S)._round());this.updateDistance(g,E),this.addCurrentVertex(E,m,0,0,d),g=E}}var L=g&&v,C=L?r:c?\"butt\":n;if(L&&\"round\"===C&&(k<i?C=\"miter\":k<=2&&(C=\"fakeround\")),\"miter\"===C&&k>a&&(C=\"bevel\"),\"bevel\"===C&&(k>2&&(C=\"flipbevel\"),k<a&&(C=\"miter\")),g&&this.updateDistance(g,f),\"miter\"===C)b._mult(k),this.addCurrentVertex(f,b,0,0,d);else if(\"flipbevel\"===C){if(k>100)b=y.mult(-1);else{var P=k*m.add(y).mag()/m.sub(y).mag();b._perp()._mult(P*(M?-1:1))}this.addCurrentVertex(f,b,0,0,d),this.addCurrentVertex(f,b.mult(-1),0,0,d)}else if(\"bevel\"===C||\"fakeround\"===C){var O=-Math.sqrt(k*k-1),z=M?O:0,I=M?0:O;if(g&&this.addCurrentVertex(f,m,z,I,d),\"fakeround\"===C)for(var D=Math.round(180*T/Math.PI/20),R=1;R<D;R++){var F=R/D;if(.5!==F){var B=F-.5;F+=F*B*(F-1)*((1.0904+_*(_*(3.55645-1.43519*_)-3.2452))*B*B+(.848013+_*(.215638*_-1.06021)))}var N=y.sub(m)._mult(F)._add(m)._unit()._mult(M?-1:1);this.addHalfVertex(f,N.x,N.y,!1,M,0,d)}v&&this.addCurrentVertex(f,y,-z,-I,d)}else if(\"butt\"===C)this.addCurrentVertex(f,b,0,0,d);else if(\"square\"===C){var j=g?1:-1;this.addCurrentVertex(f,b,j,j,d)}else\"round\"===C&&(g&&(this.addCurrentVertex(f,m,0,0,d),this.addCurrentVertex(f,m,1,1,d,!0)),v&&(this.addCurrentVertex(f,y,-1,-1,d,!0),this.addCurrentVertex(f,y,0,0,d)));if(A&&x<u-1){var V=f.dist(v);if(V>2*p){var U=f.add(v.sub(f)._mult(p/V)._round());this.updateDistance(f,U),this.addCurrentVertex(U,y,0,0,d),f=U}}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,e,o,s)}},Xo.prototype.addCurrentVertex=function(t,e,r,n,a,i){void 0===i&&(i=!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,i,!1,r,a),this.addHalfVertex(t,l,c,i,!0,-n,a),this.distance>Wo/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,a,i))},Xo.prototype.addHalfVertex=function(t,e,r,n,a,i,o){var s=t.x,l=t.y,c=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(a?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===i?0:i<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++),a?this.e2=u:this.e1=u},Xo.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Wo-1):this.distance},pn(\"LineBucket\",Xo,{omit:[\"layers\",\"patternFeatures\"]});var Zo=new Hn({\"line-cap\":new Nn(Tt.layout_line[\"line-cap\"]),\"line-join\":new jn(Tt.layout_line[\"line-join\"]),\"line-miter-limit\":new Nn(Tt.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new Nn(Tt.layout_line[\"line-round-limit\"]),\"line-sort-key\":new jn(Tt.layout_line[\"line-sort-key\"])}),Jo={paint:new Hn({\"line-opacity\":new jn(Tt.paint_line[\"line-opacity\"]),\"line-color\":new jn(Tt.paint_line[\"line-color\"]),\"line-translate\":new Nn(Tt.paint_line[\"line-translate\"]),\"line-translate-anchor\":new Nn(Tt.paint_line[\"line-translate-anchor\"]),\"line-width\":new jn(Tt.paint_line[\"line-width\"]),\"line-gap-width\":new jn(Tt.paint_line[\"line-gap-width\"]),\"line-offset\":new jn(Tt.paint_line[\"line-offset\"]),\"line-blur\":new jn(Tt.paint_line[\"line-blur\"]),\"line-dasharray\":new Un(Tt.paint_line[\"line-dasharray\"]),\"line-pattern\":new Vn(Tt.paint_line[\"line-pattern\"]),\"line-gradient\":new qn(Tt.paint_line[\"line-gradient\"])}),layout:Zo},Ko=new(function(t){function e(){t.apply(this,arguments)}return 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is(t,e,r){return t.sections.forEach(function(t){t.text=function(t,e,r){var 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us(t,e,r,n,a,i,o,s,l,c,u){var h,f=cs.fromFeature(t,r);c===ls.vertical&&f.verticalizePunctuation();var p=Ln.processBidirectionalText,d=Ln.processStyledBidirectionalText;if(p&&1===f.sections.length){h=[];for(var g=0,v=p(f.toString(),vs(f,s,n,e));g<v.length;g+=1){var m=v[g],y=new cs;y.text=m,y.sections=f.sections;for(var x=0;x<m.length;x++)y.sectionIndex.push(0);h.push(y)}}else if(d){h=[];for(var b=0,_=d(f.text,f.sectionIndex,vs(f,s,n,e));b<_.length;b+=1){var w=_[b],k=new cs;k.text=w[0],k.sectionIndex=w[1],k.sections=f.sections,h.push(k)}}else h=function(t,e){for(var r=[],n=t.text,a=0,i=0,o=e;i<o.length;i+=1){var s=o[i];r.push(t.substring(a,s)),a=s}return a<n.length&&r.push(t.substring(a,n.length)),r}(f,vs(f,s,n,e));var T=[],A={positionedGlyphs:T,text:f.toString(),top:l[1],bottom:l[1],left:l[0],right:l[0],writingMode:c,lineCount:h.length,yOffset:-17};return function(t,e,r,n,a,i,o,s,l){for(var 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s=n[i],l=n[o];return\"composite\"===r.kind?{kind:\"composite\",minZoom:s,maxZoom:l,interpolationType:a}:{kind:\"camera\",minZoom:s,maxZoom:l,minSize:r.evaluate(new Cn(s)),maxSize:r.evaluate(new Cn(l)),interpolationType:a}}function ws(t,e,r){var n=e.uSize,a=e.uSizeT,i=r.lowerSize,o=r.upperSize;return\"source\"===t.kind?i/bs:\"composite\"===t.kind?ye(i/bs,o/bs,a):n}function ks(t,e){var r=0,n=0;if(\"constant\"===t.kind)n=t.layoutSize;else if(\"source\"!==t.kind){var a=t.interpolationType,i=t.minZoom,o=t.maxZoom,s=a?c(Be.interpolationFactor(a,e,i,o),0,1):0;\"camera\"===t.kind?n=ye(t.minSize,t.maxSize,s):r=s}return{uSizeT:r,uSize:n}}var Ts=Object.freeze({getSizeData:_s,evaluateSizeForFeature:ws,evaluateSizeForZoom:ks,SIZE_PACK_FACTOR:bs}),As=zo.VectorTileFeature.types,Ms=[{name:\"a_fade_opacity\",components:1,type:\"Uint8\",offset:0}];function Ss(t,e,r,n,a,i,o,s){t.emplaceBack(e,r,Math.round(32*n),Math.round(32*a),i,o,s?s[0]:0,s?s[1]:0)}function Es(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)}var Ls=function(t){this.layoutVertexArray=new ra,this.indexArray=new pa,this.programConfigurations=t,this.segments=new Pa,this.dynamicLayoutVertexArray=new na,this.opacityVertexArray=new aa,this.placedSymbolArray=new _a};Ls.prototype.upload=function(t,e,r,n){r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,ts.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,es.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,Ms,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t)},Ls.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},pn(\"SymbolBuffers\",Ls);var Cs=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new Pa,this.collisionVertexArray=new sa};Cs.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,rs.members,!0)},Cs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},pn(\"CollisionBuffers\",Cs);var Ps=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.hasPaintOverrides=!1;var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=_s(this.zoom,e[\"text-size\"]),this.iconSizeData=_s(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout,n=r.get(\"symbol-sort-key\"),a=r.get(\"symbol-z-order\");this.sortFeaturesByKey=\"viewport-y\"!==a&&void 0!==n.constantOr(1);var i=\"viewport-y\"===a||\"auto\"===a&&!this.sortFeaturesByKey;this.sortFeaturesByY=i&&(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 ls[t]})),this.stateDependentLayerIds=this.layers.filter(function(t){return t.isStateDependent()}).map(function(t){return t.id}),this.sourceID=t.sourceID};Ps.prototype.createArrays=function(){var t=this.layers[0].layout;this.hasPaintOverrides=Ds.hasPaintOverrides(t),this.text=new Ls(new Ka(ts.members,this.layers,this.zoom,function(t){return/^text/.test(t)})),this.icon=new Ls(new Ka(ts.members,this.layers,this.zoom,function(t){return/^icon/.test(t)})),this.collisionBox=new Cs(oa,ns.members,da),this.collisionCircle=new Cs(oa,as.members,pa),this.glyphOffsetArray=new Aa,this.lineVertexArray=new Sa,this.symbolInstances=new ka},Ps.prototype.calculateGlyphDependencies=function(t,e,r,n,a){for(var i=0;i<t.length;i++)if(e[t.charCodeAt(i)]=!0,(r||n)&&a){var o=os[t.charAt(i)];o&&(e[o.charCodeAt(0)]=!0)}},Ps.prototype.populate=function(t,e){var r=this.layers[0],n=r.layout,a=n.get(\"text-font\"),i=n.get(\"text-field\"),o=n.get(\"icon-image\"),s=(\"constant\"!==i.value.kind||i.value.value.toString().length>0)&&(\"constant\"!==a.value.kind||a.value.value.length>0),l=\"constant\"!==o.value.kind||o.value.value&&o.value.value.length>0,c=n.get(\"symbol-sort-key\");if(this.features=[],s||l){for(var u=e.iconDependencies,h=e.glyphDependencies,f=new Cn(this.zoom),p=0,d=t;p<d.length;p+=1){var g=d[p],v=g.feature,m=g.index,y=g.sourceLayerIndex;if(r._featureFilter(f,v)){var x=void 0;if(s){var b=r.getValueAndResolveTokens(\"text-field\",v);x=is(b instanceof Jt?b:Jt.fromString(b),r,v)}var _=void 0;if(l&&(_=r.getValueAndResolveTokens(\"icon-image\",v)),x||_){var w=this.sortFeaturesByKey?c.evaluate(v,{}):void 0,k={text:x,icon:_,index:m,sourceLayerIndex:y,geometry:ri(v),properties:v.properties,type:As[v.type],sortKey:w};if(void 0!==v.id&&(k.id=v.id),this.features.push(k),_&&(u[_]=!0),x){var T=a.evaluate(v,{}).join(\",\"),A=\"map\"===n.get(\"text-rotation-alignment\")&&\"point\"!==n.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(ls.vertical)>=0;for(var M=0,S=x.sections;M<S.length;M+=1){var E=S[M],L=xn(x.toString()),C=E.fontStack||T,P=h[C]=h[C]||{};this.calculateGlyphDependencies(E.text,P,A,this.allowVerticalPlacement,L)}}}}}\"line\"===n.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],a=0;function i(e){n.push(t[e]),a++}function o(t,e,a){var i=r[t];return delete r[t],r[e]=i,n[i].geometry[0].pop(),n[i].geometry[0]=n[i].geometry[0].concat(a[0]),i}function s(t,r,a){var i=e[r];return delete e[r],e[t]=i,n[i].geometry[0].shift(),n[i].geometry[0]=a[0].concat(n[i].geometry[0]),i}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],h=u.geometry,f=u.text?u.text.toString():null;if(f){var p=l(f,h),d=l(f,h,!0);if(p in r&&d in e&&r[p]!==e[d]){var g=s(p,d,h),v=o(p,d,n[g].geometry);delete e[p],delete r[d],r[l(f,n[v].geometry,!0)]=v,n[g].geometry=null}else p in r?o(p,d,h):d in e?s(p,d,h):(i(c),e[p]=a-1,r[d]=a-1)}else i(c)}return n.filter(function(t){return t.geometry})}(this.features)),this.sortFeaturesByKey&&this.features.sort(function(t,e){return t.sortKey-e.sortKey})}},Ps.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))},Ps.prototype.isEmpty=function(){return 0===this.symbolInstances.length},Ps.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Ps.prototype.upload=function(t){this.uploaded||(this.collisionBox.upload(t),this.collisionCircle.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},Ps.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.collisionBox.destroy(),this.collisionCircle.destroy()},Ps.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),a=t.dist(e[t.segment]),i={},o=t.segment+1;o<e.length;o++)i[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--)i[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:a},s>0&&(a+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=i[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Ps.prototype.addSymbols=function(t,e,r,n,a,i,o,s,l,c){var u=this,h=t.indexArray,f=t.layoutVertexArray,p=t.dynamicLayoutVertexArray,d=t.segments.prepareSegment(4*e.length,t.layoutVertexArray,t.indexArray,i.sortKey),g=this.glyphOffsetArray.length,v=d.vertexLength,m=this.allowVerticalPlacement&&o===ls.vertical?Math.PI/2:0,y=function(t){var e=t.tl,n=t.tr,a=t.bl,i=t.br,o=t.tex,l=d.vertexLength,c=t.glyphOffset[1];Ss(f,s.x,s.y,e.x,c+e.y,o.x,o.y,r),Ss(f,s.x,s.y,n.x,c+n.y,o.x+o.w,o.y,r),Ss(f,s.x,s.y,a.x,c+a.y,o.x,o.y+o.h,r),Ss(f,s.x,s.y,i.x,c+i.y,o.x+o.w,o.y+o.h,r),Es(p,s,m),h.emplaceBack(l,l+1,l+2),h.emplaceBack(l+1,l+2,l+3),d.vertexLength+=4,d.primitiveLength+=2,u.glyphOffsetArray.emplaceBack(t.glyphOffset[0])};if(i.text&&i.text.sections){var x=i.text.sections;if(this.hasPaintOverrides){for(var b,_=function(e,r){void 0===b||b===e&&!r||t.programConfigurations.populatePaintArrays(t.layoutVertexArray.length,i,i.index,{},x[b]),b=e},w=0,k=e;w<k.length;w+=1){var T=k[w];_(T.sectionIndex,!1),y(T)}_(b,!0)}else{for(var A=0,M=e;A<M.length;A+=1)y(M[A]);t.programConfigurations.populatePaintArrays(t.layoutVertexArray.length,i,i.index,{},x[0])}}else{for(var S=0,E=e;S<E.length;S+=1)y(E[S]);t.programConfigurations.populatePaintArrays(t.layoutVertexArray.length,i,i.index,{})}t.placedSymbolArray.emplaceBack(s.x,s.y,g,this.glyphOffsetArray.length-g,v,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0)},Ps.prototype._addCollisionDebugVertex=function(t,e,r,n,a,i){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,a,Math.round(i.x),Math.round(i.y))},Ps.prototype.addCollisionDebugVertices=function(t,e,r,n,i,o,s,l){var c=i.segments.prepareSegment(4,i.layoutVertexArray,i.indexArray),u=c.vertexLength,h=i.layoutVertexArray,f=i.collisionVertexArray,p=s.anchorX,d=s.anchorY;if(this._addCollisionDebugVertex(h,f,o,p,d,new a(t,e)),this._addCollisionDebugVertex(h,f,o,p,d,new a(r,e)),this._addCollisionDebugVertex(h,f,o,p,d,new a(r,n)),this._addCollisionDebugVertex(h,f,o,p,d,new a(t,n)),c.vertexLength+=4,l){var g=i.indexArray;g.emplaceBack(u,u+1,u+2),g.emplaceBack(u,u+2,u+3),c.primitiveLength+=2}else{var v=i.indexArray;v.emplaceBack(u,u+1),v.emplaceBack(u+1,u+2),v.emplaceBack(u+2,u+3),v.emplaceBack(u+3,u),c.primitiveLength+=4}},Ps.prototype.addDebugCollisionBoxes=function(t,e,r){for(var n=t;n<e;n++){var a=this.collisionBoxArray.get(n),i=a.x1,o=a.y1,s=a.x2,l=a.y2,c=a.radius>0;this.addCollisionDebugVertices(i,o,s,l,c?this.collisionCircle:this.collisionBox,a.anchorPoint,r,c)}},Ps.prototype.generateCollisionDebugBuffers=function(){for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e)}},Ps.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,a,i,o){for(var s={},l=e;l<r;l++){var c=t.get(l);if(0===c.radius){s.textBox={x1:c.x1,y1:c.y1,x2:c.x2,y2:c.y2,anchorPointX:c.anchorPointX,anchorPointY:c.anchorPointY},s.textFeatureIndex=c.featureIndex;break}s.textCircles||(s.textCircles=[],s.textFeatureIndex=c.featureIndex),s.textCircles.push(c.anchorPointX,c.anchorPointY,c.radius,c.signedDistanceFromAnchor,1)}for(var u=n;u<a;u++){var h=t.get(u);if(0===h.radius){s.verticalTextBox={x1:h.x1,y1:h.y1,x2:h.x2,y2:h.y2,anchorPointX:h.anchorPointX,anchorPointY:h.anchorPointY},s.verticalTextFeatureIndex=h.featureIndex;break}}for(var f=i;f<o;f++){var p=t.get(f);if(0===p.radius){s.iconBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},s.iconFeatureIndex=p.featureIndex;break}}return s},Ps.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))}},Ps.prototype.hasTextData=function(){return this.text.segments.get().length>0},Ps.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Ps.prototype.hasCollisionBoxData=function(){return this.collisionBox.segments.get().length>0},Ps.prototype.hasCollisionCircleData=function(){return this.collisionCircle.segments.get().length>0},Ps.prototype.addIndicesForPlacedTextSymbol=function(t){for(var e=this.text.placedSymbolArray.get(t),r=e.vertexStartIndex+4*e.numGlyphs,n=e.vertexStartIndex;n<r;n+=4)this.text.indexArray.emplaceBack(n,n+1,n+2),this.text.indexArray.emplaceBack(n+1,n+2,n+3)},Ps.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=[],a=[],i=[],o=0;o<this.symbolInstances.length;++o){i.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),a.push(s.featureIndex)}return i.sort(function(t,e){return n[t]-n[e]||a[e]-a[t]}),i},Ps.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 a=n[r],i=this.symbolInstances.get(a);this.featureSortOrder.push(i.featureIndex),[i.rightJustifiedTextSymbolIndex,i.centerJustifiedTextSymbolIndex,i.leftJustifiedTextSymbolIndex].forEach(function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedTextSymbol(t)}),i.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedTextSymbol(i.verticalPlacedTextSymbolIndex);var o=this.icon.placedSymbolArray.get(a);if(o.numGlyphs){var s=o.vertexStartIndex;this.icon.indexArray.emplaceBack(s,s+1,s+2),this.icon.indexArray.emplaceBack(s+1,s+2,s+3)}}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},pn(\"SymbolBucket\",Ps,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),Ps.MAX_GLYPHS=65535,Ps.addDynamicAttributes=Es;var Os=new Hn({\"symbol-placement\":new Nn(Tt.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new Nn(Tt.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new Nn(Tt.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new jn(Tt.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new Nn(Tt.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new Nn(Tt.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new Nn(Tt.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new Nn(Tt.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new Nn(Tt.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new jn(Tt.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new Nn(Tt.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new Nn(Tt.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new jn(Tt.layout_symbol[\"icon-image\"]),\"icon-rotate\":new jn(Tt.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new Nn(Tt.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new Nn(Tt.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new jn(Tt.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new jn(Tt.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new Nn(Tt.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new Nn(Tt.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new Nn(Tt.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new jn(Tt.layout_symbol[\"text-field\"]),\"text-font\":new jn(Tt.layout_symbol[\"text-font\"]),\"text-size\":new jn(Tt.layout_symbol[\"text-size\"]),\"text-max-width\":new jn(Tt.layout_symbol[\"text-max-width\"]),\"text-line-height\":new Nn(Tt.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new jn(Tt.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new jn(Tt.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new jn(Tt.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new Nn(Tt.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new jn(Tt.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new Nn(Tt.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new Nn(Tt.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new jn(Tt.layout_symbol[\"text-rotate\"]),\"text-padding\":new Nn(Tt.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new Nn(Tt.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new jn(Tt.layout_symbol[\"text-transform\"]),\"text-offset\":new jn(Tt.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new Nn(Tt.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new Nn(Tt.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new Nn(Tt.layout_symbol[\"text-optional\"])}),zs={paint:new Hn({\"icon-opacity\":new jn(Tt.paint_symbol[\"icon-opacity\"]),\"icon-color\":new jn(Tt.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new jn(Tt.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new jn(Tt.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new jn(Tt.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new Nn(Tt.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new Nn(Tt.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new jn(Tt.paint_symbol[\"text-opacity\"]),\"text-color\":new jn(Tt.paint_symbol[\"text-color\"],{runtimeType:Ft,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new jn(Tt.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new jn(Tt.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new jn(Tt.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new Nn(Tt.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new Nn(Tt.paint_symbol[\"text-translate-anchor\"])}),layout:Os},Is=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:zt,this.defaultValue=t};Is.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},Is.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},Is.prototype.possibleOutputs=function(){return[void 0]},Is.prototype.serialize=function(){return null},pn(\"FormatSectionOverride\",Is,{omit:[\"defaultValue\"]});var Ds=function(t){function e(e){t.call(this,e,zs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e){if(t.prototype.recalculate.call(this,e),\"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 r=this.layout.get(\"text-writing-mode\");if(r){for(var n=[],a=0,i=r;a<i.length;a+=1){var o=i[a];n.indexOf(o)<0&&n.push(o)}this.layout._values[\"text-writing-mode\"]=n}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e){var r=this.layout.get(t).evaluate(e,{}),n=this._unevaluatedLayout._values[t];return n.isDataDriven()||_r(n.value)?r:function(t,e){return r.replace(/{([^{}]+)}/g,function(e,r){return r in t?String(t[r]):\"\"})}(e.properties)},e.prototype.createBucket=function(t){return new Ps(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=zs.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var a,i=this.paint.get(n),o=new Is(i),s=new br(o,i.property.specification);a=\"constant\"===i.value.kind||\"source\"===i.value.kind?new kr(\"source\",s):new Tr(\"composite\",s,i.value.zoomStops,i.value._interpolationType),this.paint._values[n]=new Fn(i.property,a,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=zs.paint.properties[e],a=!1,i=function(t){for(var e=0,r=t;e<r.length;e+=1){var i=r[e];if(n.overrides&&n.overrides.hasOverride(i))return void(a=!0)}};if(\"constant\"===r.value.kind&&r.value.value instanceof Jt)i(r.value.value.sections);else if(\"source\"===r.value.kind){var o=function(t){if(!a)if(t instanceof te&&Qt(t.value)===Vt){var e=t.value;i(e.sections)}else t instanceof ae?i(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return a},e.hasPaintOverrides=function(t){for(var r=0,n=zs.paint.overridableProperties;r<n.length;r+=1){var a=n[r];if(e.hasPaintOverride(t,a))return!0}return!1},e}(Gn),Rs={paint:new Hn({\"background-color\":new Nn(Tt.paint_background[\"background-color\"]),\"background-pattern\":new Un(Tt.paint_background[\"background-pattern\"]),\"background-opacity\":new Nn(Tt.paint_background[\"background-opacity\"])})},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}(Gn),Bs={paint:new Hn({\"raster-opacity\":new Nn(Tt.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new Nn(Tt.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new Nn(Tt.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new Nn(Tt.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new Nn(Tt.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new Nn(Tt.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new Nn(Tt.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new Nn(Tt.paint_raster[\"raster-fade-duration\"])})},Ns=function(t){function e(e){t.call(this,e,Bs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Gn),js=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}(Gn),Vs={circle:Ti,heatmap:Ii,hillshade:Ri,fill:ko,\"fill-extrusion\":Vo,line:Qo,symbol:Ds,background:Fs,raster:Ns};function Us(t){for(var e=0,r=0,n=0,a=t;n<a.length;n+=1){var i=a[n];e+=i.w*i.h,r=Math.max(r,i.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 h=u[c],f=o.length-1;f>=0;f--){var p=o[f];if(!(h.w>p.w||h.h>p.h)){if(h.x=p.x,h.y=p.y,l=Math.max(l,h.y+h.h),s=Math.max(s,h.x+h.w),h.w===p.w&&h.h===p.h){var d=o.pop();f<o.length&&(o[f]=d)}else h.h===p.h?(p.x+=h.w,p.w-=h.w):h.w===p.w?(p.y+=h.h,p.h-=h.h):(o.push({x:p.x+h.w,y:p.y,w:p.w-h.w,h:h.h}),p.y+=h.h,p.h-=h.h);break}}return{w:s,h:l,fill:e/(s*l)||0}}var qs=function(t,e){var r=e.pixelRatio,n=e.version;this.paddedRect=t,this.pixelRatio=r,this.version=n},Hs={tl:{configurable:!0},br:{configurable:!0},tlbr:{configurable:!0},displaySize:{configurable:!0}};Hs.tl.get=function(){return[this.paddedRect.x+1,this.paddedRect.y+1]},Hs.br.get=function(){return[this.paddedRect.x+this.paddedRect.w-1,this.paddedRect.y+this.paddedRect.h-1]},Hs.tlbr.get=function(){return this.tl.concat(this.br)},Hs.displaySize.get=function(){return[(this.paddedRect.w-2)/this.pixelRatio,(this.paddedRect.h-2)/this.pixelRatio]},Object.defineProperties(qs.prototype,Hs);var Gs=function(t,e){var r={},n={};this.haveRenderCallbacks=[];var a=[];this.addImages(t,r,a),this.addImages(e,n,a);var i=Us(a),o=i.w,s=i.h,l=new Pi({width:o||1,height:s||1});for(var c in t){var u=t[c],h=r[c].paddedRect;Pi.copy(u.data,l,{x:0,y:0},{x:h.x+1,y:h.y+1},u.data)}for(var f in e){var p=e[f],d=n[f].paddedRect,g=d.x+1,v=d.y+1,m=p.data.width,y=p.data.height;Pi.copy(p.data,l,{x:0,y:0},{x:g,y:v},p.data),Pi.copy(p.data,l,{x:0,y:y-1},{x:g,y:v-1},{width:m,height:1}),Pi.copy(p.data,l,{x:0,y:0},{x:g,y:v+y},{width:m,height:1}),Pi.copy(p.data,l,{x:m-1,y:0},{x:g-1,y:v},{width:1,height:y}),Pi.copy(p.data,l,{x:0,y:0},{x:g+m,y:v},{width:1,height:y})}this.image=l,this.iconPositions=r,this.patternPositions=n};Gs.prototype.addImages=function(t,e,r){for(var n in t){var a=t[n],i={x:0,y:0,w:a.data.width+2,h:a.data.height+2};r.push(i),e[n]=new qs(i,a),a.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},Gs.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)},Gs.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl,a=n[0],i=n[1];r.update(e.data,void 0,{x:a,y:i})}},pn(\"ImagePosition\",qs),pn(\"ImageAtlas\",Gs);var Ys=self.HTMLImageElement,Ws=self.HTMLCanvasElement,Xs=self.HTMLVideoElement,Zs=self.ImageData,Js=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Js.prototype.update=function(t,e,r){var n=t.width,a=t.height,i=!(this.size&&this.size[0]===n&&this.size[1]===a||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)),i)this.size=[n,a],t instanceof Ys||t instanceof Ws||t instanceof Xs||t instanceof Zs?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,a,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Ys||t instanceof Ws||t instanceof Xs||t instanceof Zs?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,a,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Js.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)},Js.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Js.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Ks=function(t,e,r,n,a){var i,o,s=8*a-n-1,l=(1<<s)-1,c=l>>1,u=-7,h=r?a-1:0,f=r?-1:1,p=t[e+h];for(h+=f,i=p&(1<<-u)-1,p>>=-u,u+=s;u>0;i=256*i+t[e+h],h+=f,u-=8);for(o=i&(1<<-u)-1,i>>=-u,u+=n;u>0;o=256*o+t[e+h],h+=f,u-=8);if(0===i)i=1-c;else{if(i===l)return o?NaN:1/0*(p?-1:1);o+=Math.pow(2,n),i-=c}return(p?-1:1)*o*Math.pow(2,i-n)},Qs=function(t,e,r,n,a,i){var o,s,l,c=8*i-a-1,u=(1<<c)-1,h=u>>1,f=23===a?Math.pow(2,-24)-Math.pow(2,-77):0,p=n?0:i-1,d=n?1:-1,g=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+h>=1?f/l:f*Math.pow(2,1-h))*l>=2&&(o++,l/=2),o+h>=u?(s=0,o=u):o+h>=1?(s=(e*l-1)*Math.pow(2,a),o+=h):(s=e*Math.pow(2,h-1)*Math.pow(2,a),o=0));a>=8;t[r+p]=255&s,p+=d,s/=256,a-=8);for(o=o<<a|s,c+=a;c>0;t[r+p]=255&o,p+=d,o/=256,c-=8);t[r+p-d]|=128*g},$s=tl;function tl(t){this.buf=ArrayBuffer.isView&&ArrayBuffer.isView(t)?t:new Uint8Array(t||0),this.pos=0,this.type=0,this.length=this.buf.length}function el(t){return t.type===tl.Bytes?t.readVarint()+t.pos:t.pos+1}function rl(t,e,r){return r?4294967296*e+(t>>>0):4294967296*(e>>>0)+(t>>>0)}function nl(t,e,r){var n=e<=16383?1:e<=2097151?2:e<=268435455?3:Math.floor(Math.log(e)/(7*Math.LN2));r.realloc(n);for(var a=r.pos-1;a>=t;a--)r.buf[a+n]=r.buf[a]}function al(t,e){for(var r=0;r<t.length;r++)e.writeVarint(t[r])}function il(t,e){for(var r=0;r<t.length;r++)e.writeSVarint(t[r])}function ol(t,e){for(var r=0;r<t.length;r++)e.writeFloat(t[r])}function sl(t,e){for(var r=0;r<t.length;r++)e.writeDouble(t[r])}function ll(t,e){for(var r=0;r<t.length;r++)e.writeBoolean(t[r])}function cl(t,e){for(var r=0;r<t.length;r++)e.writeFixed32(t[r])}function ul(t,e){for(var r=0;r<t.length;r++)e.writeSFixed32(t[r])}function hl(t,e){for(var r=0;r<t.length;r++)e.writeFixed64(t[r])}function fl(t,e){for(var r=0;r<t.length;r++)e.writeSFixed64(t[r])}function pl(t,e){return(t[e]|t[e+1]<<8|t[e+2]<<16)+16777216*t[e+3]}function dl(t,e,r){t[r]=e,t[r+1]=e>>>8,t[r+2]=e>>>16,t[r+3]=e>>>24}function gl(t,e){return(t[e]|t[e+1]<<8|t[e+2]<<16)+(t[e+3]<<24)}tl.Varint=0,tl.Fixed64=1,tl.Bytes=2,tl.Fixed32=5,tl.prototype={destroy:function(){this.buf=null},readFields:function(t,e,r){for(r=r||this.length;this.pos<r;){var n=this.readVarint(),a=n>>3,i=this.pos;this.type=7&n,t(a,e,this),this.pos===i&&this.skip(n)}return e},readMessage:function(t,e){return this.readFields(t,e,this.readVarint()+this.pos)},readFixed32:function(){var t=pl(this.buf,this.pos);return this.pos+=4,t},readSFixed32:function(){var t=gl(this.buf,this.pos);return this.pos+=4,t},readFixed64:function(){var t=pl(this.buf,this.pos)+4294967296*pl(this.buf,this.pos+4);return this.pos+=8,t},readSFixed64:function(){var t=pl(this.buf,this.pos)+4294967296*gl(this.buf,this.pos+4);return this.pos+=8,t},readFloat:function(){var t=Ks(this.buf,this.pos,!0,23,4);return this.pos+=4,t},readDouble:function(){var t=Ks(this.buf,this.pos,!0,52,8);return this.pos+=8,t},readVarint:function(t){var e,r,n=this.buf;return e=127&(r=n[this.pos++]),r<128?e:(e|=(127&(r=n[this.pos++]))<<7,r<128?e:(e|=(127&(r=n[this.pos++]))<<14,r<128?e:(e|=(127&(r=n[this.pos++]))<<21,r<128?e:function(t,e,r){var n,a,i=r.buf;if(n=(112&(a=i[r.pos++]))>>4,a<128)return rl(t,n,e);if(n|=(127&(a=i[r.pos++]))<<3,a<128)return rl(t,n,e);if(n|=(127&(a=i[r.pos++]))<<10,a<128)return rl(t,n,e);if(n|=(127&(a=i[r.pos++]))<<17,a<128)return rl(t,n,e);if(n|=(127&(a=i[r.pos++]))<<24,a<128)return rl(t,n,e);if(n|=(1&(a=i[r.pos++]))<<31,a<128)return rl(t,n,e);throw new Error(\"Expected varint not more than 10 bytes\")}(e|=(15&(r=n[this.pos]))<<28,t,this))))},readVarint64:function(){return this.readVarint(!0)},readSVarint:function(){var t=this.readVarint();return t%2==1?(t+1)/-2:t/2},readBoolean:function(){return Boolean(this.readVarint())},readString:function(){var t=this.readVarint()+this.pos,e=function(t,e,r){for(var n=\"\",a=e;a<r;){var i,o,s,l=t[a],c=null,u=l>239?4:l>223?3:l>191?2:1;if(a+u>r)break;1===u?l<128&&(c=l):2===u?128==(192&(i=t[a+1]))&&(c=(31&l)<<6|63&i)<=127&&(c=null):3===u?(i=t[a+1],o=t[a+2],128==(192&i)&&128==(192&o)&&((c=(15&l)<<12|(63&i)<<6|63&o)<=2047||c>=55296&&c<=57343)&&(c=null)):4===u&&(i=t[a+1],o=t[a+2],s=t[a+3],128==(192&i)&&128==(192&o)&&128==(192&s)&&((c=(15&l)<<18|(63&i)<<12|(63&o)<<6|63&s)<=65535||c>=1114112)&&(c=null)),null===c?(c=65533,u=1):c>65535&&(c-=65536,n+=String.fromCharCode(c>>>10&1023|55296),c=56320|1023&c),n+=String.fromCharCode(c),a+=u}return n}(this.buf,this.pos,t);return this.pos=t,e},readBytes:function(){var t=this.readVarint()+this.pos,e=this.buf.subarray(this.pos,t);return this.pos=t,e},readPackedVarint:function(t,e){if(this.type!==tl.Bytes)return t.push(this.readVarint(e));var r=el(this);for(t=t||[];this.pos<r;)t.push(this.readVarint(e));return t},readPackedSVarint:function(t){if(this.type!==tl.Bytes)return t.push(this.readSVarint());var e=el(this);for(t=t||[];this.pos<e;)t.push(this.readSVarint());return t},readPackedBoolean:function(t){if(this.type!==tl.Bytes)return t.push(this.readBoolean());var e=el(this);for(t=t||[];this.pos<e;)t.push(this.readBoolean());return t},readPackedFloat:function(t){if(this.type!==tl.Bytes)return t.push(this.readFloat());var e=el(this);for(t=t||[];this.pos<e;)t.push(this.readFloat());return t},readPackedDouble:function(t){if(this.type!==tl.Bytes)return t.push(this.readDouble());var e=el(this);for(t=t||[];this.pos<e;)t.push(this.readDouble());return t},readPackedFixed32:function(t){if(this.type!==tl.Bytes)return t.push(this.readFixed32());var e=el(this);for(t=t||[];this.pos<e;)t.push(this.readFixed32());return t},readPackedSFixed32:function(t){if(this.type!==tl.Bytes)return t.push(this.readSFixed32());var e=el(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed32());return t},readPackedFixed64:function(t){if(this.type!==tl.Bytes)return t.push(this.readFixed64());var e=el(this);for(t=t||[];this.pos<e;)t.push(this.readFixed64());return t},readPackedSFixed64:function(t){if(this.type!==tl.Bytes)return t.push(this.readSFixed64());var e=el(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed64());return t},skip:function(t){var e=7&t;if(e===tl.Varint)for(;this.buf[this.pos++]>127;);else if(e===tl.Bytes)this.pos=this.readVarint()+this.pos;else if(e===tl.Fixed32)this.pos+=4;else{if(e!==tl.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 this.length=this.pos,this.pos=0,this.buf.subarray(0,this.length)},writeFixed32:function(t){this.realloc(4),dl(this.buf,t,this.pos),this.pos+=4},writeSFixed32:function(t){this.realloc(4),dl(this.buf,t,this.pos),this.pos+=4},writeFixed64:function(t){this.realloc(8),dl(this.buf,-1&t,this.pos),dl(this.buf,Math.floor(t*(1/4294967296)),this.pos+4),this.pos+=8},writeSFixed64:function(t){this.realloc(8),dl(this.buf,-1&t,this.pos),dl(this.buf,Math.floor(t*(1/4294967296)),this.pos+4),this.pos+=8},writeVarint:function(t){(t=+t||0)>268435455||t<0?function(t,e){var r,n;if(t>=0?(r=t%4294967296|0,n=t/4294967296|0):(n=~(-t/4294967296),4294967295^(r=~(-t%4294967296))?r=r+1|0:(r=0,n=n+1|0)),t>=0x10000000000000000||t<-0x10000000000000000)throw new Error(\"Given varint doesn't fit into 10 bytes\");e.realloc(10),function(t,e,r){r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos]=127&t}(r,0,e),function(t,e){var r=(7&t)<<4;e.buf[e.pos++]|=r|((t>>>=3)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(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,a,i=0;i<e.length;i++){if((n=e.charCodeAt(i))>55295&&n<57344){if(!a){n>56319||i+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):a=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,a=n;continue}n=a-55296<<10|n-56320|65536,a=null}else a&&(t[r++]=239,t[r++]=191,t[r++]=189,a=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&&nl(e,r,this),this.pos=e-1,this.writeVarint(r),this.pos+=r},writeFloat:function(t){this.realloc(4),Qs(this.buf,t,this.pos,!0,23,4),this.pos+=4},writeDouble:function(t){this.realloc(8),Qs(this.buf,t,this.pos,!0,52,8),this.pos+=8},writeBytes:function(t){var e=t.length;this.writeVarint(e),this.realloc(e);for(var r=0;r<e;r++)this.buf[this.pos++]=t[r]},writeRawMessage:function(t,e){this.pos++;var r=this.pos;t(e,this);var n=this.pos-r;n>=128&&nl(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,tl.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,al,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,il,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,ll,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,ol,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,sl,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,cl,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,ul,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,hl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,fl,e)},writeBytesField:function(t,e){this.writeTag(t,tl.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,tl.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,tl.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,tl.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,tl.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,tl.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,tl.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,tl.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,tl.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,tl.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};var vl=3;function ml(t,e,r){1===t&&r.readMessage(yl,e)}function yl(t,e,r){if(3===t){var n=r.readMessage(xl,{}),a=n.id,i=n.bitmap,o=n.width,s=n.height,l=n.left,c=n.top,u=n.advance;e.push({id:a,bitmap:new Ci({width:o+2*vl,height:s+2*vl},i),metrics:{width:o,height:s,left:l,top:c,advance:u}})}}function xl(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())}var bl=vl,_l=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()})};_l.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))};var wl=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},v([\"receive\",\"process\"],this),this.invoker=new _l(this.process),this.target.addEventListener(\"message\",this.receive,!1)};function kl(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]}wl.prototype.send=function(t,e,r,n){var a=this,i=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[i]=r);var o=[];return this.target.postMessage({id:i,type:t,hasCallback:!!r,targetMapId:n,sourceMapId:this.mapId,data:gn(e,o)},o),{cancel:function(){r&&delete a.callbacks[i],a.target.postMessage({id:i,type:\"<cancel>\",targetMapId:n,sourceMapId:a.mapId})}}},wl.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 this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()},wl.prototype.process=function(){var t=this;if(this.taskQueue.length){var e=this.taskQueue.shift(),r=this.tasks[e];if(delete this.tasks[e],this.taskQueue.length&&this.invoker.trigger(),r)if(\"<response>\"===r.type){var n=this.callbacks[e];delete this.callbacks[e],n&&(r.error?n(vn(r.error)):n(null,vn(r.data)))}else{var a=!1,i=r.hasCallback?function(r,n){a=!0,delete t.cancelCallbacks[e];var i=[];t.target.postMessage({id:e,type:\"<response>\",sourceMapId:t.mapId,error:r?gn(r):null,data:gn(n,i)},i)}:function(t){a=!0},o=null,s=vn(r.data);if(this.parent[r.type])o=this.parent[r.type](r.sourceMapId,s,i);else if(this.parent.getWorkerSource){var l=r.type.split(\".\");o=this.parent.getWorkerSource(r.sourceMapId,l[0],s.source)[l[1]](s,i)}else i(new Error(\"Could not find function \"+r.type));!a&&o&&o.cancel&&(this.cancelCallbacks[e]=o.cancel)}}},wl.prototype.remove=function(){this.target.removeEventListener(\"message\",this.receive,!1)};var Tl=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]))};Tl.prototype.setNorthEast=function(t){return this._ne=t instanceof Al?new Al(t.lng,t.lat):Al.convert(t),this},Tl.prototype.setSouthWest=function(t){return this._sw=t instanceof Al?new Al(t.lng,t.lat):Al.convert(t),this},Tl.prototype.extend=function(t){var e,r,n=this._sw,a=this._ne;if(t instanceof Al)e=t,r=t;else{if(!(t instanceof Tl))return Array.isArray(t)?t.every(Array.isArray)?this.extend(Tl.convert(t)):this.extend(Al.convert(t)):this;if(e=t._sw,r=t._ne,!e||!r)return this}return n||a?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),a.lng=Math.max(r.lng,a.lng),a.lat=Math.max(r.lat,a.lat)):(this._sw=new Al(e.lng,e.lat),this._ne=new Al(r.lng,r.lat)),this},Tl.prototype.getCenter=function(){return new Al((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Tl.prototype.getSouthWest=function(){return this._sw},Tl.prototype.getNorthEast=function(){return this._ne},Tl.prototype.getNorthWest=function(){return new Al(this.getWest(),this.getNorth())},Tl.prototype.getSouthEast=function(){return new Al(this.getEast(),this.getSouth())},Tl.prototype.getWest=function(){return this._sw.lng},Tl.prototype.getSouth=function(){return this._sw.lat},Tl.prototype.getEast=function(){return this._ne.lng},Tl.prototype.getNorth=function(){return this._ne.lat},Tl.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Tl.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Tl.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Tl.convert=function(t){return!t||t instanceof Tl?t:new Tl(t)};var Al=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\")};Al.prototype.wrap=function(){return new Al(u(this.lng,-180,180),this.lat)},Al.prototype.toArray=function(){return[this.lng,this.lat]},Al.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Al.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 Tl(new Al(this.lng-r,this.lat-e),new Al(this.lng+r,this.lat+e))},Al.convert=function(t){if(t instanceof Al)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Al(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Al(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 Ml=2*Math.PI*6378137;function Sl(t){return Ml*Math.cos(t*Math.PI/180)}function El(t){return(180+t)/360}function Ll(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Cl(t,e){return t/Sl(e)}function Pl(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var Ol=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};Ol.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Al.convert(t);return new Ol(El(r.lng),Ll(r.lat),Cl(e,r.lat))},Ol.prototype.toLngLat=function(){return new Al(360*this.x-180,Pl(this.y))},Ol.prototype.toAltitude=function(){return this.z*Sl(Pl(this.y))},Ol.prototype.meterInMercatorCoordinateUnits=function(){return 1/Ml*(t=Pl(this.y),1/Math.cos(t*Math.PI/180));var t};var zl=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=Rl(0,t,e,r)};zl.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},zl.prototype.url=function(t,e){var r,n,a,i,o,s=(r=this.x,n=this.y,a=this.z,i=kl(256*r,256*(n=Math.pow(2,a)-n-1),a),o=kl(256*(r+1),256*(n+1),a),i[0]+\",\"+i[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,a=\"\",i=t;i>0;i--)a+=(e&(n=1<<i-1)?1:0)+(r&n?2:0);return a}(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)},zl.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new a((t.x*e-this.x)*ti,(t.y*e-this.y)*ti)};var Il=function(t,e){this.wrap=t,this.canonical=e,this.key=Rl(t,e.z,e.x,e.y)},Dl=function(t,e,r,n,a){this.overscaledZ=t,this.wrap=e,this.canonical=new zl(r,+n,+a),this.key=Rl(e,t,n,a)};function Rl(t,e,r,n){(t*=2)<0&&(t=-1*t-1);var a=1<<e;return 32*(a*a*t+a*n+r)+e}Dl.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},Dl.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new Dl(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Dl(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},Dl.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},Dl.prototype.children=function(t){if(this.overscaledZ>=t)return[new Dl(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 Dl(e,this.wrap,e,r,n),new Dl(e,this.wrap,e,r+1,n),new Dl(e,this.wrap,e,r,n+1),new Dl(e,this.wrap,e,r+1,n+1)]},Dl.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))},Dl.prototype.wrapped=function(){return new Dl(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},Dl.prototype.unwrapTo=function(t){return new Dl(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},Dl.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},Dl.prototype.toUnwrapped=function(){return new Il(this.wrap,this.canonical)},Dl.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},Dl.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new Ol(t.x-this.wrap,t.y))},pn(\"CanonicalTileID\",zl),pn(\"OverscaledTileID\",Dl,{omit:[\"posMatrix\"]});var Fl=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 w('\"'+r+'\" is not a valid encoding type. Valid types include \"mapbox\" and \"terrarium\".');var n=this.dim=e.height;this.stride=this.dim+2,this.data=new Int32Array(this.stride*this.stride);for(var a=e.data,i=\"terrarium\"===r?this._unpackTerrarium:this._unpackMapbox,o=0;o<n;o++)for(var s=0;s<n;s++){var l=4*(o*n+s);this.set(s,o,i(a[l],a[l+1],a[l+2]))}for(var c=0;c<n;c++)this.set(-1,c,this.get(0,c)),this.set(n,c,this.get(n-1,c)),this.set(c,-1,this.get(c,0)),this.set(c,n,this.get(c,n-1));this.set(-1,-1,this.get(0,0)),this.set(n,-1,this.get(n-1,0)),this.set(-1,n,this.get(0,n-1)),this.set(n,n,this.get(n-1,n-1))};Fl.prototype.set=function(t,e,r){this.data[this._idx(t,e)]=r+65536},Fl.prototype.get=function(t,e){return this.data[this._idx(t,e)]-65536},Fl.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)},Fl.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},Fl.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},Fl.prototype.getPixels=function(){return new Pi({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},Fl.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,a=e*this.dim+this.dim,i=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=a-1;break;case 1:a=n+1}switch(r){case-1:i=o-1;break;case 1:o=i+1}for(var s=-e*this.dim,l=-r*this.dim,c=i;c<o;c++)for(var u=n;u<a;u++)this.set(u,c,t.get(u+s,c+l))},pn(\"DEMData\",Fl);var Bl=Zn([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),Nl=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}};Nl.prototype.encode=function(t){return this._stringToNumber[t]},Nl.prototype.decode=function(t){return this._numberToString[t]};var jl=function(t,e,r,n){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,null!=t.id&&(this.id=t.id)},Vl={geometry:{configurable:!0}};Vl.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},Vl.geometry.set=function(t){this._geometry=t},jl.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(jl.prototype,Vl);var Ul=function(){this.state={},this.stateChanges={},this.deletedStates={}};Ul.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]||{},h(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var a in this.deletedStates[t]={},this.state[t])a!==n&&(this.deletedStates[t][a]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var i in this.deletedStates[t][n]={},this.state[t][n])r[i]||(this.deletedStates[t][n][i]=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]},Ul.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&&e>=0)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e&&e>=0)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}},Ul.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},a=this.stateChanges[t]||{},i=h({},n[r],a[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 i[s]}return i},Ul.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},Ul.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var a={};for(var i in this.stateChanges[n])this.state[n][i]||(this.state[n][i]={}),h(this.state[n][i],this.stateChanges[n][i]),a[i]=this.state[n][i];r[n]=a}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 u=0,f=Object.keys(this.deletedStates[o][c]);u<f.length;u+=1){var p=f[u];delete this.state[o][c][p]}s[c]=this.state[o][c]}r[o]=r[o]||{},h(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t)t[d].setFeatureState(r,e)};var ql=function(t,e,r){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=e||new ln(ti,16,0),this.grid3D=new ln(ti,16,0),this.featureIndexArray=r||new La};function Hl(t){for(var e=1/0,r=1/0,n=-1/0,a=-1/0,i=0,o=t;i<o.length;i+=1){var s=o[i];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),a=Math.max(a,s.y)}return{minX:e,minY:r,maxX:n,maxY:a}}function Gl(t,e){return e-t}ql.prototype.insert=function(t,e,r,n,a,i){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,a);for(var s=i?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],h=0;h<c.length;h++){var f=c[h];u[0]=Math.min(u[0],f.x),u[1]=Math.min(u[1],f.y),u[2]=Math.max(u[2],f.x),u[3]=Math.max(u[3],f.y)}u[0]<ti&&u[1]<ti&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},ql.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new zo.VectorTile(new $s(this.rawTileData)).layers,this.sourceLayerCoder=new Nl(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},ql.prototype.query=function(t,e,r){var n=this;this.loadVTLayers();for(var i=t.params||{},o=ti/t.tileSize/t.scale,s=Dr(i.filter),l=t.queryGeometry,c=t.queryPadding*o,u=Hl(l),h=this.grid.query(u.minX-c,u.minY-c,u.maxX+c,u.maxY+c),f=Hl(t.cameraQueryGeometry),p=0,d=this.grid3D.query(f.minX-c,f.minY-c,f.maxX+c,f.maxY+c,function(e,r,n,i){return function(t,e,r,n,i){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&&i>=l.y)return!0}var c=[new a(e,r),new a(e,i),new a(n,i),new a(n,r)];if(t.length>2)for(var u=0,h=c;u<h.length;u+=1)if(di(t,h[u]))return!0;for(var f=0;f<t.length-1;f++)if(gi(t[f],t[f+1],c))return!0;return!1}(t.cameraQueryGeometry,e-c,r-c,n+c,i+c)});p<d.length;p+=1){var g=d[p];h.push(g)}h.sort(Gl);for(var v,m={},y=function(a){var c=h[a];if(c!==v){v=c;var u=n.featureIndexArray.get(c),f=null;n.loadMatchingFeature(m,u.bucketIndex,u.sourceLayerIndex,u.featureIndex,s,i.layers,e,function(e,a){f||(f=ri(e));var i={};return e.id&&(i=r.getState(a.sourceLayer||\"_geojsonTileLayer\",e.id)),a.queryIntersectsFeature(l,e,i,f,n.z,t.transform,o,t.pixelPosMatrix)})}},x=0;x<h.length;x++)y(x);return m},ql.prototype.loadMatchingFeature=function(t,e,r,n,a,i,o,s){var l=this.bucketLayerIDs[e];if(!i||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(i,l)){var c=this.sourceLayerCoder.decode(r),u=this.vtLayers[c].feature(n);if(a(new Cn(this.tileID.overscaledZ),u))for(var h=0;h<l.length;h++){var f=l[h];if(!(i&&i.indexOf(f)<0)){var p=o[f];if(p){var d=!s||s(u,p);if(d){var g=new jl(u,this.z,this.x,this.y);g.layer=p.serialize();var v=t[f];void 0===v&&(v=t[f]=[]),v.push({featureIndex:n,feature:g,intersectionZ:d})}}}}}},ql.prototype.lookupSymbolFeatures=function(t,e,r,n,a,i){var o={};this.loadVTLayers();for(var s=Dr(n),l=0,c=t;l<c.length;l+=1){var u=c[l];this.loadMatchingFeature(o,e,r,u,s,a,i)}return o},ql.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,a=r[e];n<a.length;n+=1)if(t===a[n])return!0;return!1},pn(\"FeatureIndex\",ql,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});var Yl=function(t,e){this.tileID=t,this.uid=p(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.expiredRequestCount=0,this.state=\"loading\"};function Wl(t,e,r,n,a){if(void 0===e.segment)return!0;for(var i=e,o=e.segment+1,s=0;s>-r/2;){if(--o<0)return!1;s-=t[o].dist(i),i=t[o]}s+=t[o].dist(t[o+1]),o++;for(var l=[],c=0;s<r/2;){var u=t[o-1],h=t[o],f=t[o+1];if(!f)return!1;var p=u.angleTo(h)-h.angleTo(f);for(p=Math.abs((p+3*Math.PI)%(2*Math.PI)-Math.PI),l.push({distance:s,angleDelta:p}),c+=p;s-l[0].distance>n;)c-=l.shift().angleDelta;if(c>a)return!1;o++,s+=h.dist(f)}return!0}function Xl(t){for(var e=0,r=0;r<t.length-1;r++)e+=t[r].dist(t[r+1]);return e}function Zl(t,e,r){return t?.6*e*r:0}function Jl(t,e){return Math.max(t?t.right-t.left:0,e?e.right-e.left:0)}function Kl(t,e,r,n,a,i){for(var o=Zl(r,a,i),s=Jl(r,n)*i,l=0,c=Xl(t)/2,u=0;u<t.length-1;u++){var h=t[u],f=t[u+1],p=h.dist(f);if(l+p>c){var d=(c-l)/p,g=ye(h.x,f.x,d),v=ye(h.y,f.y,d),m=new xs(g,v,f.angleTo(h),u);return m._round(),!o||Wl(t,m,s,o,e)?m:void 0}l+=p}}function Ql(t,e,r,n,a,i,o,s,l){var c=Zl(n,i,o),u=Jl(n,a),h=u*o,f=0===t[0].x||t[0].x===l||0===t[0].y||t[0].y===l;return e-h<e/4&&(e=h+e/4),function t(e,r,n,a,i,o,s,l,c){for(var u=o/2,h=Xl(e),f=0,p=r-n,d=[],g=0;g<e.length-1;g++){for(var v=e[g],m=e[g+1],y=v.dist(m),x=m.angleTo(v);p+n<f+y;){var b=((p+=n)-f)/y,_=ye(v.x,m.x,b),w=ye(v.y,m.y,b);if(_>=0&&_<c&&w>=0&&w<c&&p-u>=0&&p+u<=h){var k=new xs(_,w,x,g);k._round(),a&&!Wl(e,k,o,a,i)||d.push(k)}}f+=y}return l||d.length||s||(d=t(e,f/2,n,a,i,o,s,!0,c)),d}(t,f?e/2*s%e:(u/2+2*i)*o*s%e,e,c,r,h,f,!1,l)}Yl.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<I.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},Yl.prototype.wasRequested=function(){return\"errored\"===this.state||\"loaded\"===this.state||\"reloading\"===this.state},Yl.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=i[a],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}}},a=0,i=t;a<i.length;a+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var a=this.buckets[n];if(a instanceof Ps){if(this.hasSymbolBuckets=!0,!r)break;a.justReloaded=!0}}for(var i in this.queryPadding=0,this.buckets){var o=this.buckets[i];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(i).queryRadius(o))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new xa},Yl.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\"},Yl.prototype.unloadDEMData=function(){this.dem=null,this.neighboringTiles=null,this.state=\"unloaded\"},Yl.prototype.getBucket=function(t){return this.buckets[t.id]},Yl.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 Js(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Js(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},Yl.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},Yl.prototype.queryRenderedFeatures=function(t,e,r,n,a,i,o,s,l){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:r,cameraQueryGeometry:n,scale:a,tileSize:this.tileSize,pixelPosMatrix:l,transform:o,params:i,queryPadding:this.queryPadding*s},t,e):{}},Yl.prototype.querySourceFeatures=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData){var r=this.latestFeatureIndex.loadVTLayers(),n=e?e.sourceLayer:\"\",a=r._geojsonTileLayer||r[n];if(a)for(var i=Dr(e&&e.filter),o=this.tileID.canonical,s=o.z,l=o.x,c=o.y,u={z:s,x:l,y:c},h=0;h<a.length;h++){var f=a.feature(h);if(i(new Cn(this.tileID.overscaledZ),f)){var p=new jl(f,s,l,c);p.tile=u,t.push(p)}}}},Yl.prototype.clearMask=function(){this.segments&&(this.segments.destroy(),delete this.segments),this.maskedBoundsBuffer&&(this.maskedBoundsBuffer.destroy(),delete this.maskedBoundsBuffer),this.maskedIndexBuffer&&(this.maskedIndexBuffer.destroy(),delete this.maskedIndexBuffer)},Yl.prototype.setMask=function(t,e){if(!o(this.mask,t)&&(this.mask=t,this.clearMask(),!o(t,{0:!0}))){var r=new Qn,n=new pa;this.segments=new Pa,this.segments.prepareSegment(0,r,n);for(var i=Object.keys(t),s=0;s<i.length;s++){var l=t[+i[s]],c=ti>>l.z,u=new a(l.x*c,l.y*c),h=new a(u.x+c,u.y+c),f=this.segments.prepareSegment(4,r,n);r.emplaceBack(u.x,u.y,u.x,u.y),r.emplaceBack(h.x,u.y,h.x,u.y),r.emplaceBack(u.x,h.y,u.x,h.y),r.emplaceBack(h.x,h.y,h.x,h.y);var p=f.vertexLength;n.emplaceBack(p,p+1,p+2),n.emplaceBack(p+1,p+2,p+3),f.vertexLength+=4,f.primitiveLength+=2}this.maskedBoundsBuffer=e.createVertexBuffer(r,Bl.members),this.maskedIndexBuffer=e.createIndexBuffer(n)}},Yl.prototype.hasData=function(){return\"loaded\"===this.state||\"reloading\"===this.state||\"expired\"===this.state},Yl.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},Yl.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(),a=!1;if(this.expirationTime>n)a=!1;else if(e)if(this.expirationTime<e)a=!0;else{var i=this.expirationTime-e;i?this.expirationTime=n+Math.max(i,3e4):a=!0}else a=!0;a?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},Yl.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)},Yl.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){var a=this.buckets[n],i=a.layers[0].sourceLayer||\"_geojsonTileLayer\",o=r[i],s=t[i];o&&s&&0!==Object.keys(s).length&&(a.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{}),e&&e.style&&(this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(n).queryRadius(a))))}}},Yl.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},Yl.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<I.now()},Yl.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},Yl.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=I.now()+t};var $l=function(t,e,r,n,i,o,s,l,c,u,h,f){var p=s.top*l-c,d=s.bottom*l+c,g=s.left*l-c,v=s.right*l+c;if(this.boxStartIndex=t.length,u){var m=d-p,y=v-g;m>0&&(m=Math.max(10*l,m),this._addLineCollisionCircles(t,e,r,r.segment,y,m,n,i,o,h))}else{if(f){var x=new a(g,p),b=new a(v,p),_=new a(g,d),w=new a(v,d),k=f*Math.PI/180;x._rotate(k),b._rotate(k),_._rotate(k),w._rotate(k),g=Math.min(x.x,b.x,_.x,w.x),v=Math.max(x.x,b.x,_.x,w.x),p=Math.min(x.y,b.y,_.y,w.y),d=Math.max(x.y,b.y,_.y,w.y)}t.emplaceBack(r.x,r.y,g,p,v,d,n,i,o,0,0)}this.boxEndIndex=t.length};$l.prototype._addLineCollisionCircles=function(t,e,r,n,a,i,o,s,l,c){var u=i/2,h=Math.floor(a/u)||1,f=1+.4*Math.log(c)/Math.LN2,p=Math.floor(h*f/2),d=-i/2,g=r,v=n+1,m=d,y=-a/2,x=y-a/4;do{if(--v<0){if(m>y)return;v=0;break}m-=e[v].dist(g),g=e[v]}while(m>x);for(var b=e[v].dist(e[v+1]),_=-p;_<h+p;_++){var w=_*u,k=y+w;if(w<0&&(k+=w),w>a&&(k+=w-a),!(k<m)){for(;m+b<k;){if(m+=b,++v+1>=e.length)return;b=e[v].dist(e[v+1])}var T=k-m,A=e[v],M=e[v+1].sub(A)._unit()._mult(T)._add(A)._round(),S=Math.abs(k-d)<u?0:.8*(k-d);t.emplaceBack(M.x,M.y,-i/2,-i/2,i/2,i/2,o,s,l,i/2,S)}}};var tc=function(t,e){if(void 0===t&&(t=[]),void 0===e&&(e=ec),this.data=t,this.length=this.data.length,this.compare=e,this.length>0)for(var r=(this.length>>1)-1;r>=0;r--)this._down(r)};function ec(t,e){return t<e?-1:t>e?1:0}function rc(t,e,r){void 0===e&&(e=1),void 0===r&&(r=!1);for(var n=1/0,i=1/0,o=-1/0,s=-1/0,l=t[0],c=0;c<l.length;c++){var u=l[c];(!c||u.x<n)&&(n=u.x),(!c||u.y<i)&&(i=u.y),(!c||u.x>o)&&(o=u.x),(!c||u.y>s)&&(s=u.y)}var h=o-n,f=s-i,p=Math.min(h,f),d=p/2,g=new tc([],nc);if(0===p)return new a(n,i);for(var v=n;v<o;v+=p)for(var m=i;m<s;m+=p)g.push(new ac(v+d,m+d,d,t));for(var y=function(t){for(var e=0,r=0,n=0,a=t[0],i=0,o=a.length,s=o-1;i<o;s=i++){var l=a[i],c=a[s],u=l.x*c.y-c.x*l.y;r+=(l.x+c.x)*u,n+=(l.y+c.y)*u,e+=3*u}return new ac(r/e,n/e,0,t)}(t),x=g.length;g.length;){var b=g.pop();(b.d>y.d||!y.d)&&(y=b,r&&console.log(\"found best %d after %d probes\",Math.round(1e4*b.d)/1e4,x)),b.max-y.d<=e||(d=b.h/2,g.push(new ac(b.p.x-d,b.p.y-d,d,t)),g.push(new ac(b.p.x+d,b.p.y-d,d,t)),g.push(new ac(b.p.x-d,b.p.y+d,d,t)),g.push(new ac(b.p.x+d,b.p.y+d,d,t)),x+=4)}return r&&(console.log(\"num probes: 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_=t.length-3;p=t[_],d=t[_+1],g=t[_+2],(y=0===a?p:d)>=r&&y<=n&&vt(c,p,d,g),_=c.length-3,i&&_>=3&&(c[_]!==c[0]||c[_+1]!==c[1])&&vt(c,c[0],c[1],c[2]),c.length&&e.push(c)}function dt(t){var e=[];return e.size=t.size,e.start=t.start,e.end=t.end,e}function gt(t,e,r,n,a,i){for(var o=0;o<t.length;o++)pt(t[o],e,r,n,a,i,!1)}function vt(t,e,r,n){t.push(e),t.push(r),t.push(n)}function mt(t,e,r,n,a,i){var o=(i-e)/(n-e);return t.push(i),t.push(r+(a-r)*o),t.push(1),o}function yt(t,e,r,n,a,i){var o=(i-r)/(a-r);return t.push(e+(n-e)*o),t.push(i),t.push(1),o}function xt(t,e){for(var r=[],n=0;n<t.length;n++){var a,i=t[n],o=i.type;if(\"Point\"===o||\"MultiPoint\"===o||\"LineString\"===o)a=bt(i.geometry,e);else if(\"MultiLineString\"===o||\"Polygon\"===o){a=[];for(var s=0;s<i.geometry.length;s++)a.push(bt(i.geometry[s],e))}else if(\"MultiPolygon\"===o)for(a=[],s=0;s<i.geometry.length;s++){for(var l=[],c=0;c<i.geometry[s].length;c++)l.push(bt(i.geometry[s][c],e));a.push(l)}r.push(nt(i.id,o,a,i.tags))}return r}function bt(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 _t(t,e){if(t.transformed)return t;var r,n,a,i=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(wt(c[n],c[n+1],e,i,o,s));else for(n=0;n<c.length;n++){var h=[];for(a=0;a<c[n].length;a+=2)h.push(wt(c[n][a],c[n][a+1],e,i,o,s));l.geometry.push(h)}}return t.transformed=!0,t}function wt(t,e,r,n,a,i){return[Math.round(r*(t*n-a)),Math.round(r*(e*n-i))]}function kt(t,e,r,n,a){for(var i=e===a.maxZoom?0:a.tolerance/((1<<e)*a.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,Tt(o,t[s],i,a);var l=t[s].minX,c=t[s].minY,u=t[s].maxX,h=t[s].maxY;l<o.minX&&(o.minX=l),c<o.minY&&(o.minY=c),u>o.maxX&&(o.maxX=u),h>o.maxY&&(o.maxY=h)}return o}function Tt(t,e,r,n){var a=e.geometry,i=e.type,o=[];if(\"Point\"===i||\"MultiPoint\"===i)for(var s=0;s<a.length;s+=3)o.push(a[s]),o.push(a[s+1]),t.numPoints++,t.numSimplified++;else if(\"LineString\"===i)At(o,a,t,r,!1,!1);else if(\"MultiLineString\"===i||\"Polygon\"===i)for(s=0;s<a.length;s++)At(o,a[s],t,r,\"Polygon\"===i,0===s);else if(\"MultiPolygon\"===i)for(var l=0;l<a.length;l++){var c=a[l];for(s=0;s<c.length;s++)At(o,c[s],t,r,!0,0===s)}if(o.length){var u=e.tags||null;if(\"LineString\"===i&&n.lineMetrics){for(var h in u={},e.tags)u[h]=e.tags[h];u.mapbox_clip_start=a.start/a.size,u.mapbox_clip_end=a.end/a.size}var f={geometry:o,type:\"Polygon\"===i||\"MultiPolygon\"===i?3:\"LineString\"===i||\"MultiLineString\"===i?2:1,tags:u};null!==e.id&&(f.id=e.id),t.features.push(f)}}function At(t,e,r,n,a,i){var o=n*n;if(n>0&&e.size<(a?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++;a&&function(t,e){for(var r=0,n=0,a=t.length,i=a-2;n<a;i=n,n+=2)r+=(t[n]-t[i])*(t[n+1]+t[i+1]);if(r>0===e)for(n=0,a=t.length;n<a/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[a-2-n],t[n+1]=t[a-1-n],t[a-2-n]=o,t[a-1-n]=s}}(s,i),t.push(s)}}function Mt(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++)it(r,t.features[n],e,n);else\"Feature\"===t.type?it(r,t,e):it(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,a=ht(t,1,-1-r,r,0,-1,2,e),i=ht(t,1,1-r,2+r,0,-1,2,e);return(a||i)&&(n=ht(t,1,-r,1+r,0,-1,2,e)||[],a&&(n=xt(a,1).concat(n)),i&&(n=n.concat(xt(i,-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 St(t,e,r){return 32*((1<<t)*r+e)+t}function Et(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 a=new k(n.features),i=E(a);0===i.byteOffset&&i.byteLength===i.buffer.byteLength||(i=new Uint8Array(i)),e(null,{vectorTile:a,rawData:i.buffer})}Y.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,a=e.maxZoom,i=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(X(t[l],l));this.trees[a+1]=new H(s,tt,et,i,Float32Array),r&&console.timeEnd(o);for(var c=a;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new H(s,tt,et,i,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",c,s.length,+Date.now()-u)}return r&&console.timeEnd(\"total time\"),this},Y.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),a=180===t[2]?180:((t[2]+180)%360+360)%360-180,i=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,a=180;else if(r>a){var o=this.getClusters([r,n,180,i],e),s=this.getClusters([-180,n,a,i],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,h=l.range(K(r),Q(i),K(a),Q(n));u<h.length;u+=1){var f=h[u],p=l.points[f];c.push(p.numPoints?Z(p):this.points[p.index])}return c},Y.prototype.getChildren=function(t){var e=t>>5,r=t%32,n=\"No cluster with the specified id.\",a=this.trees[r];if(!a)throw new Error(n);var i=a.points[e];if(!i)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=a.within(i.x,i.y,o);l<c.length;l+=1){var u=c[l],h=a.points[u];h.parentId===t&&s.push(h.numPoints?Z(h):this.points[h.index])}if(0===s.length)throw new Error(n);return s},Y.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},Y.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],a=Math.pow(2,t),i=this.options,o=i.extent,s=i.radius/o,l=(r-s)/a,c=(r+1+s)/a,u={features:[]};return this._addTileFeatures(n.range((e-s)/a,l,(e+1+s)/a,c),n.points,e,r,a,u),0===e&&this._addTileFeatures(n.range(1-s/a,l,1,c),n.points,a,r,a,u),e===a-1&&this._addTileFeatures(n.range(0,l,s/a,c),n.points,-1,r,a,u),u.features.length?u:null},Y.prototype.getClusterExpansionZoom=function(t){for(var e=t%32-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},Y.prototype._appendLeaves=function(t,e,r,n,a){for(var i=0,o=this.getChildren(e);i<o.length;i+=1){var s=o[i],l=s.properties;if(l&&l.cluster?a+l.point_count<=n?a+=l.point_count:a=this._appendLeaves(t,l.cluster_id,r,n,a):a<n?a++:t.push(s),t.length===r)break}return a},Y.prototype._addTileFeatures=function(t,e,r,n,a,i){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c={type:1,geometry:[[Math.round(this.options.extent*(l.x*a-r)),Math.round(this.options.extent*(l.y*a-n))]],tags:l.numPoints?J(l):this.points[l.index].properties},u=l.numPoints?l.id:this.points[l.index].id;void 0!==u&&(c.id=u),i.features.push(c)}},Y.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},Y.prototype._cluster=function(t,e){for(var r=[],n=this.options,a=n.radius,i=n.extent,o=n.reduce,s=a/(i*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],h=u.within(c.x,c.y,s),f=c.numPoints||1,p=c.x*f,d=c.y*f,g=o&&f>1?this._map(c,!0):null,v=(l<<5)+(e+1),m=0,y=h;m<y.length;m+=1){var x=y[m],b=u.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,f+=_,b.parentId=v,o&&(g||(g=this._map(c,!0)),o(g,this._map(b)))}}1===f?r.push(c):(c.parentId=v,r.push(W(p/f,d/f,v,f,g)))}}return r},Y.prototype._map=function(t,e){if(t.numPoints)return e?$({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?$({},n):n},Mt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},Mt.prototype.splitTile=function(t,e,r,n,a,i,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,h=St(e,r,n),f=this.tiles[h];if(!f&&(c>1&&console.time(\"creation\"),f=this.tiles[h]=kt(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,f.numFeatures,f.numPoints,f.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(f.source=t,a){if(e===l.maxZoom||e===a)continue;var d=1<<a-e;if(r!==Math.floor(i/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||f.numPoints<=l.indexMaxPoints)continue;if(f.source=null,0!==t.length){c>1&&console.time(\"clipping\");var g,v,m,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,k=.5+_,T=1+_;g=v=m=y=null,x=ht(t,u,r-_,r+k,0,f.minX,f.maxX,l),b=ht(t,u,r+w,r+T,0,f.minX,f.maxX,l),t=null,x&&(g=ht(x,u,n-_,n+k,1,f.minY,f.maxY,l),v=ht(x,u,n+w,n+T,1,f.minY,f.maxY,l),x=null),b&&(m=ht(b,u,n-_,n+k,1,f.minY,f.maxY,l),y=ht(b,u,n+w,n+T,1,f.minY,f.maxY,l),b=null),c>1&&console.timeEnd(\"clipping\"),s.push(g||[],e+1,2*r,2*n),s.push(v||[],e+1,2*r,2*n+1),s.push(m||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},Mt.prototype.getTile=function(t,e,r){var n=this.options,a=n.extent,i=n.debug;if(t<0||t>24)return null;var o=1<<t,s=St(t,e=(e%o+o)%o,r);if(this.tiles[s])return _t(this.tiles[s],a);i>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,c=t,u=e,h=r;!l&&c>0;)c--,u=Math.floor(u/2),h=Math.floor(h/2),l=this.tiles[St(c,u,h)];return l&&l.source?(i>1&&console.log(\"found parent tile z%d-%d-%d\",c,u,h),i>1&&console.time(\"drilling down\"),this.splitTile(l.source,c,u,h,t,e,r),i>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?_t(this.tiles[s],a):null):null};var Lt=function(e){function r(t,r,n){e.call(this,t,r,Et),n&&(this.loadGeoJSON=n)}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 a=!!(n&&n.request&&n.request.collectResourceTiming)&&new l.Performance(n.request);this.loadGeoJSON(n,function(i,o){if(i||!o)return r(i);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));!function t(e,r){switch(e&&e.type||null){case\"FeatureCollection\":return e.features=e.features.map(y(t,r)),e;case\"GeometryCollection\":return e.geometries=e.geometries.map(y(t,r)),e;case\"Feature\":return e.geometry=t(e.geometry,r),e;case\"Polygon\":case\"MultiPolygon\":return function(t,e){return\"Polygon\"===t.type?t.coordinates=x(t.coordinates,e):\"MultiPolygon\"===t.type&&(t.coordinates=t.coordinates.map(y(x,e))),t}(e,r);default:return e}}(o,!0);try{e._geoJSONIndex=n.cluster?new Y(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var a={},i={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var h=u[c],f=n[h],p=f[0],d=f[1],g=t.createExpression(d),v=t.createExpression(\"string\"==typeof p?[p,[\"accumulated\"],[\"get\",h]]:p);a[h]=g.value,i[h]=v.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var i=n[r];e[i]=a[i].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 a=n[r];o.accumulated=t[a],t[a]=i[a].evaluate(o,s)}},r}(n)).load(o.features):new Mt(o,n.geojsonVtOptions)}catch(i){return r(i)}e.loaded={};var s={};if(a){var l=a.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,a=t.uid;return n&&n[a]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))},r.prototype.getClusterChildren=function(t,e){e(null,this._geoJSONIndex.getChildren(t.clusterId))},r.prototype.getClusterLeaves=function(t,e){e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))},r}(h),Ct=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.workerSourceTypes={vector:h,geojson:Lt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name \"'+t+'\" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isLoaded())throw new Error(\"RTL text plugin already registered.\");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return Ct.prototype.setReferrer=function(t,e){this.referrer=e},Ct.prototype.setLayers=function(t,e,r){this.getLayerIndex(t).replace(e),r()},Ct.prototype.updateLayers=function(t,e,r){this.getLayerIndex(t).update(e.layers,e.removedIds),r()},Ct.prototype.loadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).loadTile(e,r)},Ct.prototype.loadDEMTile=function(t,e,r){this.getDEMWorkerSource(t,e.source).loadTile(e,r)},Ct.prototype.reloadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).reloadTile(e,r)},Ct.prototype.abortTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).abortTile(e,r)},Ct.prototype.removeTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).removeTile(e,r)},Ct.prototype.removeDEMTile=function(t,e){this.getDEMWorkerSource(t,e.source).removeTile(e)},Ct.prototype.removeSource=function(t,e,r){if(this.workerSources[t]&&this.workerSources[t][e.type]&&this.workerSources[t][e.type][e.source]){var n=this.workerSources[t][e.type][e.source];delete this.workerSources[t][e.type][e.source],void 0!==n.removeSource?n.removeSource(e,r):r()}},Ct.prototype.loadWorkerSource=function(t,e,r){try{this.self.importScripts(e.url),r()}catch(t){r(t.toString())}},Ct.prototype.loadRTLTextPlugin=function(e,r,n){try{t.plugin.isLoaded()||(this.self.importScripts(r),n(t.plugin.isLoaded()?null:new Error(\"RTL Text Plugin failed to import scripts from \"+r)))}catch(t){n(t.toString())}},Ct.prototype.getLayerIndex=function(t){var e=this.layerIndexes[t];return e||(e=this.layerIndexes[t]=new n),e},Ct.prototype.getWorkerSource=function(t,e,r){var n=this;if(this.workerSources[t]||(this.workerSources[t]={}),this.workerSources[t][e]||(this.workerSources[t][e]={}),!this.workerSources[t][e][r]){var a={send:function(e,r,a){n.actor.send(e,r,a,t)}};this.workerSources[t][e][r]=new this.workerSourceTypes[e](a,this.getLayerIndex(t))}return this.workerSources[t][e][r]},Ct.prototype.getDEMWorkerSource=function(t,e){return this.demWorkerSources[t]||(this.demWorkerSources[t]={}),this.demWorkerSources[t][e]||(this.demWorkerSources[t][e]=new f),this.demWorkerSources[t][e]},Ct.prototype.enforceCacheSizeLimit=function(e,r){t.enforceCacheSizeLimit(r)},\"undefined\"!=typeof WorkerGlobalScope&&void 0!==t.window&&t.window instanceof WorkerGlobalScope&&(t.window.worker=new Ct(t.window)),Ct}),n(0,function(t){var e=t.createCommonjsModule(function(t){function e(t){return!!(\"undefined\"!=typeof window&&\"undefined\"!=typeof document&&Array.prototype&&Array.prototype.every&&Array.prototype.filter&&Array.prototype.forEach&&Array.prototype.indexOf&&Array.prototype.lastIndexOf&&Array.prototype.map&&Array.prototype.some&&Array.prototype.reduce&&Array.prototype.reduceRight&&Array.isArray&&Function.prototype&&Function.prototype.bind&&Object.keys&&Object.create&&Object.getPrototypeOf&&Object.getOwnPropertyNames&&Object.isSealed&&Object.isFrozen&&Object.isExtensible&&Object.getOwnPropertyDescriptor&&Object.defineProperty&&Object.defineProperties&&Object.seal&&Object.freeze&&Object.preventExtensions&&\"JSON\"in window&&\"parse\"in JSON&&\"stringify\"in JSON&&function(){if(!(\"Worker\"in window&&\"Blob\"in window&&\"URL\"in window))return!1;var t,e,r=new Blob([\"\"],{type:\"text/javascript\"}),n=URL.createObjectURL(r);try{e=new Worker(n),t=!0}catch(e){t=!1}return e&&e.terminate(),URL.revokeObjectURL(n),t}()&&\"Uint8ClampedArray\"in window&&ArrayBuffer.isView&&function(t){return void 0===r[t]&&(r[t]=function(t){var r=document.createElement(\"canvas\"),n=Object.create(e.webGLContextAttributes);return n.failIfMajorPerformanceCaveat=t,r.probablySupportsContext?r.probablySupportsContext(\"webgl\",n)||r.probablySupportsContext(\"experimental-webgl\",n):r.supportsContext?r.supportsContext(\"webgl\",n)||r.supportsContext(\"experimental-webgl\",n):r.getContext(\"webgl\",n)||r.getContext(\"experimental-webgl\",n)}(t)),r[t]}(t&&t.failIfMajorPerformanceCaveat))}t.exports?t.exports=e:window&&(window.mapboxgl=window.mapboxgl||{},window.mapboxgl.supported=e);var r={};e.webGLContextAttributes={antialias:!1,alpha:!0,stencil:!0,depth:!0}}),r={create:function(e,r,n){var a=t.window.document.createElement(e);return void 0!==r&&(a.className=r),n&&n.appendChild(a),a},createNS:function(e,r){return t.window.document.createElementNS(e,r)}},n=t.window.document.documentElement.style;function a(t){if(!n)return t[0];for(var e=0;e<t.length;e++)if(t[e]in n)return t[e];return t[0]}var i,o=a([\"userSelect\",\"MozUserSelect\",\"WebkitUserSelect\",\"msUserSelect\"]);r.disableDrag=function(){n&&o&&(i=n[o],n[o]=\"none\")},r.enableDrag=function(){n&&o&&(n[o]=i)};var s=a([\"transform\",\"WebkitTransform\"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var c=Object.defineProperty({},\"passive\",{get:function(){l=!0}});t.window.addEventListener(\"test\",c,c),t.window.removeEventListener(\"test\",c,c)}catch(t){l=!1}r.addEventListener=function(t,e,r,n){void 0===n&&(n={}),\"passive\"in n&&l?t.addEventListener(e,r,n):t.addEventListener(e,r,n.capture)},r.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),\"passive\"in n&&l?t.removeEventListener(e,r,n):t.removeEventListener(e,r,n.capture)};var u=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener(\"click\",u,!0)};function h(t){var e=t.userImage;return!!(e&&e.render&&e.render())&&(t.data.replace(new Uint8Array(e.data.buffer)),!0)}r.suppressClick=function(){t.window.addEventListener(\"click\",u,!0),t.window.setTimeout(function(){t.window.removeEventListener(\"click\",u,!0)},0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect(),a=t.window.TouchEvent&&r instanceof t.window.TouchEvent?r.touches[0]:r;return new t.Point(a.clientX-n.left-e.clientLeft,a.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var n=e.getBoundingClientRect(),a=[],i=\"touchend\"===r.type?r.changedTouches:r.touches,o=0;o<i.length;o++)a.push(new t.Point(i[o].clientX-n.left-e.clientLeft,i[o].clientY-n.top-e.clientTop));return a},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 f=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],a=n.ids,i=n.callback;this._notify(a,i)}this.requestors=[]}},r.prototype.getImage=function(t){return this.images[t]},r.prototype.addImage=function(t,e){this.images[t]=e},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,a=t;n<a.length;n+=1){var i=a[n];this.images[i]||(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={},a=0,i=e;a<i.length;a+=1){var o=i[a];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,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image \"'+o+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. 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o.resize({width:a||1,height:i||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,h=this.images[s].data,f=h.width,p=h.height;t.RGBAImage.copy(h,o,{x:0,y:0},{x:c,y:u},{width:f,height:p}),t.RGBAImage.copy(h,o,{x:0,y:p-1},{x:c,y:u-1},{width:f,height:1}),t.RGBAImage.copy(h,o,{x:0,y:0},{x:c,y:u+p},{width:f,height:1}),t.RGBAImage.copy(h,o,{x:f-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(h,o,{x:0,y:0},{x:c+f,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 a=this.images[n];h(a)&&this.updateImage(n,a)}}},r}(t.Evented),p=v,d=v,g=1e20;function v(t,e,r,n,a,i){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=a||\"sans-serif\",this.fontWeight=i||\"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 m(t,e,r,n,a,i,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,a,i,o,r),l=0;l<r;l++)t[l*e+s]=a[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,a,i,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(a[s])}}function y(t,e,r,n,a){r[0]=0,n[0]=-g,n[1]=+g;for(var i=1,o=0;i<a;i++){for(var s=(t[i]+i*i-(t[r[o]]+r[o]*r[o]))/(2*i-2*r[o]);s<=n[o];)o--,s=(t[i]+i*i-(t[r[o]]+r[o]*r[o]))/(2*i-2*r[o]);r[++o]=i,n[o]=s,n[o+1]=+g}for(i=0,o=0;i<a;i++){for(;n[o+1]<i;)o++;e[i]=(i-r[o])*(i-r[o])+t[r[o]]}}v.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 a=e.data[4*n+3]/255;this.gridOuter[n]=1===a?0:0===a?g:Math.pow(Math.max(0,.5-a),2),this.gridInner[n]=1===a?g:0===a?0:Math.pow(Math.max(0,a-.5),2)}for(m(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),m(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var i=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(i/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,a=[];for(var i in e)for(var o=0,s=e[i];o<s.length;o+=1){var l=s[o];a.push({stack:i,id:l})}t.asyncAll(a,function(t,e){var r=t.stack,a=t.id,i=n.entries[r];i||(i=n.entries[r]={glyphs:{},requests:{}});var o=i.glyphs[a];if(void 0===o){if(o=n._tinySDF(i,r,a))return i.glyphs[a]=o,void e(null,{stack:r,id:a,glyph:o});var s=Math.floor(a/256);if(256*s>65535)e(new Error(\"glyphs > 65535 not supported\"));else{var l=i.requests[s];l||(l=i.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,function(t,e){if(e)for(var r in e)n._doesCharSupportLocalGlyph(+r)||(i.glyphs[+r]=e[+r]);for(var a=0,o=l;a<o.length;a+=1)(0,o[a])(t,e);delete i.requests[s]})),l.push(function(t,n){t?e(t):n&&e(null,{stack:r,id:a,glyph:n[a]||null})})}}else e(null,{stack:r,id:a,glyph:o})},function(t,e){if(t)r(t);else if(e){for(var n={},a=0,i=e;a<i.length;a+=1){var o=i[a],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 a=this.localIdeographFontFamily;if(a&&this._doesCharSupportLocalGlyph(n)){var i=e.tinySDF;if(!i){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),i=e.tinySDF=new x.TinySDF(24,3,8,.25,a,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},i.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,a,i){var o=256*r,s=o+255,l=a.transformRequest(a.normalizeGlyphsURL(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,function(e,r){if(e)i(e);else if(r){for(var n={},a=0,o=t.parseGlyphPBF(r);a<o.length;a+=1){var s=o[a];n[s.id]=s}i(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 a=e[n];t.endsWith(n,\"-transition\")?this._transitionable.setTransition(n.slice(0,-\"-transition\".length),a):this._transitionable.setValue(n,a)}},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),k=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.bytes=4,this.data=new Uint8Array(this.width*this.height*this.bytes),this.positions={}};k.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.positions[r]||(this.positions[r]=this.addDash(t,e)),this.positions[r]},k.prototype.addDash=function(e,r){var n=r?7:0,a=2*n+1;if(this.nextRow+a>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var i=0,o=0;o<e.length;o++)i+=e[o];for(var s=this.width/i,l=s/2,c=e.length%2==1,u=-n;u<=n;u++)for(var h=this.nextRow+n+u,f=this.width*h,p=c?-e[e.length-1]:0,d=e[0],g=1,v=0;v<this.width;v++){for(;d<v/s;)p=d,d+=e[g],c&&g===e.length-1&&(d+=e[0]),g++;var m=Math.abs(v-p*s),y=Math.abs(v-d*s),x=Math.min(m,y),b=g%2==1,_=void 0;if(r){var w=n?u/n*(l+1):0;if(b){var k=l-Math.abs(w);_=Math.sqrt(x*x+k*k)}else _=l-Math.sqrt(x*x+w*w)}else _=(b?1:-1)*x;this.data[3+4*(f+v)]=Math.max(0,Math.min(255,_+128))}var T={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:i};return this.nextRow+=a,this.dirty=!0,T},k.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.RGBA,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.RGBA,this.width,this.height,0,e.RGBA,e.UNSIGNED_BYTE,this.data))};var T=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var a=this.workerPool.acquire(this.id),i=0;i<a.length;i++){var o=a[i],s=new e.Actor(o,n,this.id);s.name=\"Worker \"+i,this.actors.push(s)}};function A(e,r,n){var a=function(a,i){if(a)return n(a);if(i){var o=t.pick(t.extend(i,e),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);i.vector_layers&&(o.vectorLayers=i.vector_layers,o.vectorLayerIds=o.vectorLayers.map(function(t){return t.id})),e.url&&(o.tiles=r.canonicalizeTileset(o,e.url)),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),a):t.browser.frame(function(){return a(null,e)})}T.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,function(t,n){t.send(e,r,n)},n)},T.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},T.prototype.remove=function(){this.actors.forEach(function(t){t.remove()}),this.actors=[],this.workerPool.release(this.id)},T.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),a=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),i=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<i&&e.y>=a&&e.y<o};var S=function(e){function r(r,n,a,i){if(e.call(this),this.id=r,this.dispatcher=a,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\"])),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(i)}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),this.url,null),a={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};function i(n,a){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(a&&a.resourceTiming&&(e.resourceTiming=a.resourceTiming),this.map._refreshExpiredTiles&&a&&e.setExpiryData(a),e.loadVectorData(a,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}a.request.collectResourceTiming=this._collectResourceTiming,e.actor&&\"expired\"!==e.state?\"loading\"===e.state?e.reloadCallback=r:e.request=e.actor.send(\"reloadTile\",a,i.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send(\"loadTile\",a,i.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,a,i){e.call(this),this.id=r,this.dispatcher=a,this.setEventedParent(i),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,a=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.url,this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(a,t.ResourceType.Tile),function(a,i){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(a)e.state=\"errored\",r(a);else if(i){n.map._refreshExpiredTiles&&e.setExpiryData(i),delete i.cacheControl,delete i.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(i.width),e.texture?e.texture.update(i,{useMipmap:!0}):(e.texture=new t.Texture(o,i,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,a,i){e.call(this,r,n,a,i),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.url,this.tileSize);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 i=t.browser.getImageData(a),o={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:i,encoding:this.encoding};e.actor&&\"expired\"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send(\"loadDEMTile\",o,function(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}.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),a=(r.x-1+n)%n,i=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,i,r.z,a,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,i,r.z,a,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,i,r.z,a,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,a,i){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=a.getActor(),this.setEventedParent(i),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);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},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),a=this._data;\"string\"==typeof a?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(a),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(a),this.actor.send(this.type+\".loadData\",n,function(t,a){r._removed||a&&a.abandoned||(r._loaded=!0,a&&a.resourceTiming&&a.resourceTiming[r.id]&&(r._resourceTiming=a.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,a=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor;var i={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes};e.request=this.actor.send(a,i,function(t,i){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(i,n.map.painter,\"reloadTile\"===a),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=function(e){function r(t,r,n,a){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(a),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(a,i){n._loaded=!0,a?n.fire(new t.ErrorEvent(a)):i&&(n.image=i,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,a=-1/0,i=-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),a=Math.max(a,l.x),i=Math.max(i,l.y)}var c=a-r,u=i-n,h=Math.max(c,u),f=Math.max(0,Math.floor(-Math.log(h)/Math.LN2)),p=Math.pow(2,f);return new t.CanonicalTileID(f,Math.floor((r+a)/2*p),Math.floor((n+i)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var a=n.map(function(t){return r.tileID.getTilePoint(t)._round()});return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(a[0].x,a[0].y,0,0),this._boundsArray.emplaceBack(a[1].x,a[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(a[3].x,a[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(a[2].x,a[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,t.rasterBoundsAttributes.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 a=this.tiles[n];\"loaded\"!==a.state&&(a.state=\"loaded\",a.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),O=function(e){function r(t,r,n,a){e.call(this,t,r,n,a),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,a=r.urls;n<a.length;n+=1){var i=a[n];this.urls.push(this.map._requestManager.transformRequest(i,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(\"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,t.rasterBoundsAttributes.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 a=this.tiles[n];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(P),z=function(e){function r(r,n,a,i){e.call(this,r,n,a,i),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 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e(e){t.call(this,e),this.vao=e.extVertexArrayObject}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){this.vao&&(t!==this.current||this.dirty)&&(this.vao.bindVertexArrayOES(t),this.current=t,this.dirty=!1)},e}(q),yt=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.getDefault=function(){return 4},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_ALIGNMENT,t),this.current=t,this.dirty=!1}},e}(q),xt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_PREMULTIPLY_ALPHA_WEBGL,t),this.current=t,this.dirty=!1}},e}(q),bt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_FLIP_Y_WEBGL,t),this.current=t,this.dirty=!1}},e}(q),_t=function(t){function e(e,r){t.call(this,e),this.context=e,this.parent=r}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e}(q),wt=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.setDirty=function(){this.dirty=!0},e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}},e}(_t),kt=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),Tt=function(t,e,r){this.context=t,this.width=e,this.height=r;var n=t.gl,a=this.framebuffer=n.createFramebuffer();this.colorAttachment=new wt(t,a),this.depthAttachment=new kt(t,a)};Tt.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();e&&t.deleteTexture(e);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,a,i){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=a,this.pass=i};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 H(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new at(this),this.blendEquation=new it(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 ht(this),this.bindFramebuffer=new ft(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new gt(this),this.bindElementBuffer=new vt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new mt(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\")};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 U(this,t,e,r)},Lt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,a=n.createRenderbuffer();return this.bindRenderbuffer.set(a),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),a},Lt.prototype.createFramebuffer=function(t,e){return new Tt(this,t,e)},Lt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,a=0;e&&(a|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(a|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(a)},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,a){var i=this;e.call(this),this.id=r,this.dispatcher=a,this.on(\"data\",function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(i._sourceLoaded=!0),i._sourceLoaded&&!i._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(i.reload(),i.transform&&i.update(i.transform))}),this.on(\"error\",function(){i._sourceErrored=!0}),this._source=D(r,n,a,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,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 Object.keys(this._tiles).map(Number).sort(Pt)},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var a in this._tiles)this._isIdRenderable(+a,e)&&n.push(+a);return e?n.sort(function(e,n){var a=r._tiles[e].tileID,i=r._tiles[n].tileID,o=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle),s=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle);return a.overscaledZ-i.overscaledZ||s.y-o.y||s.x-o.x}):n.sort(Pt)},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,a){if(a)return e.state=\"errored\",void(404!==a.status?this._source.fire(new t.ErrorEvent(a,{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 a=this.getTileByID(n);i(t,a),i(a,t)}}function i(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,a=Math.pow(2,t.tileID.canonical.z),i=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+a)?r+=a:1===Math.abs(r-a)&&(r-=a)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[i]&&(t.neighboringTiles[i].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype.getZoom=function(t){return t.zoom+t.scaleZoom(t.tileSize/this._source.tileSize)},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var a in this._tiles){var i=this._tiles[a];if(!(n[a]||!i.hasData()||i.tileID.overscaledZ<=e||i.tileID.overscaledZ>r)){for(var o=i.tileID;i&&i.tileID.overscaledZ>e+1;){var s=i.tileID.scaledTo(i.tileID.overscaledZ-1);(i=this._tiles[s.key])&&i.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){for(var r=t.overscaledZ-1;r>=e;r--){var n=t.scaledTo(r);if(!n)return;var a=String(n.key),i=this._tiles[a];if(i&&i.hasData())return i;if(this._cache.has(n))return this._cache.get(n)}},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 a in this._tiles){var i=this._tiles[a];i.tileID=i.tileID.unwrapTo(i.tileID.wrap+r),n[i.tileID.key]=i}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 a;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?a=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)}):(a=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&&(a=a.filter(function(t){return n._source.hasTile(t)}))):a=[];var i=(this._source.roundZoom?Math.round:Math.floor)(this.getZoom(e)),o=Math.max(i-r.maxOverzooming,this._source.minzoom),s=Math.max(i+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(a,i);if(Ot(this._source.type)){for(var c={},u={},h=0,f=Object.keys(l);h<f.length;h+=1){var p=f[h],d=l[p],g=this._tiles[p];if(g&&!(g.fadeEndTime&&g.fadeEndTime<=t.browser.now())){var v=this.findLoadedParent(d,o);v&&(this._addTile(v.tileID),c[v.tileID.key]=v.tileID),u[p]=d}}for(var m in this._retainLoadedChildren(u,i,s,l),c)l[m]||(this._coveredTiles[m]=!0,l[m]=c[m])}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(_)}}},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={},a={},i=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],h=this._addTile(u);n[u.key]=u,h.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var f=0,p=t;f<p.length;f+=1){var d=p[f],g=this._tiles[d.key];if(!g.hasData()){if(e+1>this._source.maxzoom){var v=d.children(this._source.maxzoom)[0],m=this.getTile(v);if(m&&m.hasData()){n[v.key]=v;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=g.wasRequested(),b=d.overscaledZ-1;b>=i;--b){var _=d.scaledTo(b);if(a[_.key])break;if(a[_.key]=!0,!(g=this.getTile(_))&&x&&(g=this._addTile(_)),g&&(n[_.key]=_,x=g.wasRequested(),g.hasData()))break}}}return n},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 a=this,i=[],o=this.transform;if(!o)return i;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(),h=1/0,f=1/0,p=-1/0,d=-1/0,g=0,v=c;g<v.length;g+=1){var m=v[g];h=Math.min(h,m.x),f=Math.min(f,m.y),p=Math.max(p,m.x),d=Math.max(d,m.y)}for(var y=function(e){var n=a._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,g=Math.pow(2,o.zoom-n.tileID.overscaledZ),v=r*n.queryPadding*t.EXTENT/n.tileSize/g,m=[s.getTilePoint(new t.MercatorCoordinate(h,f)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(m[0].x-v<t.EXTENT&&m[0].y-v<t.EXTENT&&m[1].x+v>=0&&m[1].y+v>=0){var y=l.map(function(t){return s.getTilePoint(t)}),x=c.map(function(t){return s.getTilePoint(t)});i.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:g})}}},x=0;x<u.length;x++)y(x);return i},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map(function(t){return e._tiles[t].tileID}),n=0,a=r;n<a.length;n+=1){var i=a[n];i.posMatrix=this.transform.calculatePosMatrix(i.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(Ot(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}(t.Evented);function Pt(t,e){return t%32-e%32||e-t}function Ot(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function zt(){return new t.window.Worker(Qn.workerUrl)}Ct.maxOverzooming=10,Ct.maxUnderzooming=3;var It=function(){this.active={}};It.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<It.workerCount;)this.workers.push(new zt);return this.active[t]=!0,this.workers.slice()},It.prototype.release=function(t){delete this.active[t],0===Object.keys(this.active).length&&(this.workers.forEach(function(t){t.terminate()}),this.workers=null)};var Dt,Rt=Math.floor(t.browser.hardwareConcurrency/2);function Ft(e,r){var n={};for(var a in e)\"ref\"!==a&&(n[a]=e[a]);return t.refProperties.forEach(function(t){t in r&&(n[t]=r[t])}),n}function Bt(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]=Ft(t[n],e[t[n].ref]));return t}It.workerCount=Math.max(Math.min(Rt,6),1);var Nt={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 jt(t,e,r){r.push({command:Nt.addSource,args:[t,e[t]]})}function Vt(t,e,r){e.push({command:Nt.removeSource,args:[t]}),r[t]=!0}function Ut(t,e,r,n){Vt(t,r,n),jt(t,e,r)}function qt(e,r,n){var a;for(a in e[n])if(e[n].hasOwnProperty(a)&&\"data\"!==a&&!t.deepEqual(e[n][a],r[n][a]))return!1;for(a in r[n])if(r[n].hasOwnProperty(a)&&\"data\"!==a&&!t.deepEqual(e[n][a],r[n][a]))return!1;return!0}function Ht(e,r,n,a,i,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[a,s,r[s],i]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[a,s,r[s],i]}))}function Gt(t){return t.id}function Yt(t,e){return t[e.id]=e,t}var Wt=function(t,e,r){var n=this.boxCells=[],a=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var i=0;i<this.xCellCount*this.yCellCount;i++)n.push([]),a.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 Xt(e,r,n,a,i){var o=t.create();return r?(t.scale(o,o,[1/i,1/i,1]),n||t.rotateZ(o,o,a.angle)):t.multiply(o,a.labelPlaneMatrix,e),o}function Zt(e,r,n,a,i){if(r){var o=t.clone(e);return t.scale(o,o,[i,i,1]),n||t.rotateZ(o,o,-a.angle),o}return a.glCoordMatrix}function Jt(e,r){var n=[e.x,e.y,0,1];oe(n,n,r);var a=n[3];return{point:new t.Point(n[0]/a,n[1]/a),signedDistanceFromCamera:a}}function Kt(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 Qt(e,r,n,a,i,o,s,l){var c=a?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),h=[256/n.width*2+1,256/n.height*2+1],f=a?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;f.clear();for(var p=e.lineVertexArray,d=a?e.text.placedSymbolArray:e.icon.placedSymbolArray,g=n.transform.width/n.transform.height,v=!1,m=0;m<d.length;m++){var y=d.get(m);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!v)ie(y.numGlyphs,f);else{v=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),Kt(x,h)){var b=.5+x[3]/n.transform.cameraToCenterDistance*.5,_=t.evaluateSizeForFeature(c,u,y),w=s?_*b:_/b,k=new t.Point(y.anchorX,y.anchorY),T=Jt(k,i).point,A={},M=ee(y,w,!1,l,r,i,o,e.glyphOffsetArray,p,f,T,k,A,g);v=M.useVertical,(M.notEnoughRoom||v||M.needsFlipping&&ee(y,w,!0,l,r,i,o,e.glyphOffsetArray,p,f,T,k,A,g).notEnoughRoom)&&ie(y.numGlyphs,f)}else ie(y.numGlyphs,f)}}a?e.text.dynamicLayoutVertexBuffer.updateData(f):e.icon.dynamicLayoutVertexBuffer.updateData(f)}function $t(t,e,r,n,a,i,o,s,l,c,u,h){var f=s.glyphStartIndex+s.numGlyphs,p=s.lineStartIndex,d=s.lineStartIndex+s.lineLength,g=e.getoffsetX(s.glyphStartIndex),v=e.getoffsetX(f-1),m=ne(t*g,r,n,a,i,o,s.segment,p,d,l,c,u,h);if(!m)return null;var y=ne(t*v,r,n,a,i,o,s.segment,p,d,l,c,u,h);return y?{first:m,last:y}:null}function te(e,r,n,a){return e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*a?{useVertical:!0}:(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function ee(e,r,n,a,i,o,s,l,c,u,h,f,p,d){var g,v=r/24,m=e.lineOffsetX*v,y=e.lineOffsetY*v;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=$t(v,l,m,y,n,h,f,e,c,o,p,!1);if(!w)return{notEnoughRoom:!0};var k=Jt(w.first.point,s).point,T=Jt(w.last.point,s).point;if(a&&!n){var A=te(e.writingMode,k,T,d);if(A)return A}g=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)g.push(ne(v*l.getoffsetX(M),m,y,n,h,f,e.segment,b,_,c,o,p,!1));g.push(w.last)}else{if(a&&!n){var S=Jt(f,i).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(c.getx(E),c.gety(E)),C=Jt(L,i),P=C.signedDistanceFromCamera>0?C.point:re(f,L,S,1,i),O=te(e.writingMode,S,P,d);if(O)return O}var z=ne(v*l.getoffsetX(e.glyphStartIndex),m,y,n,h,f,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p,!1);if(!z)return{notEnoughRoom:!0};g=[z]}for(var I=0,D=g;I<D.length;I+=1){var R=D[I];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function re(t,e,r,n,a){var i=Jt(t.add(t.sub(e)._unit()),a).point,o=r.sub(i);return r.add(o._mult(n/o.mag()))}function ne(e,r,n,a,i,o,s,l,c,u,h,f,p){var d=a?e-r:e+r,g=d>0?1:-1,v=0;a&&(g*=-1,v=Math.PI),g<0&&(v+=Math.PI);for(var m=g>0?l+s:l+s+1,y=m,x=i,b=i,_=0,w=0,k=Math.abs(d);_+w<=k;){if((m+=g)<l||m>=c)return null;if(b=x,void 0===(x=f[m])){var T=new t.Point(u.getx(m),u.gety(m)),A=Jt(T,h);if(A.signedDistanceFromCamera>0)x=f[m]=A.point;else{var M=m-g;x=re(0===_?o:new t.Point(u.getx(M),u.gety(M)),T,b,k-_+1,h)}}_+=w,w=b.dist(x)}var S=(k-_)/w,E=x.sub(b),L=E.mult(S)._add(b);return L._add(E._unit()._perp()._mult(n*g)),{point:L,angle:v+Math.atan2(x.y-b.y,x.x-b.x),tileDistance:p?{prevTileDistance:m-g===y?0:u.gettileUnitDistanceFromAnchor(m-g),lastSegmentViewportDistance:k-_}:null}}Wt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Wt.prototype.insert=function(t,e,r,n,a){this._forEachCell(e,r,n,a,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(a)},Wt.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)},Wt.prototype._insertBoxCell=function(t,e,r,n,a,i){this.boxCells[a].push(i)},Wt.prototype._insertCircleCell=function(t,e,r,n,a,i){this.circleCells[a].push(i)},Wt.prototype._query=function(t,e,r,n,a,i){if(r<0||t>this.width||n<0||e>this.height)return!a&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(a)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],h=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-h,y1:u-h,x2:c+h,y2:u+h})}return i?o.filter(i):o}var f={hitTest:a,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,f,i),a?o.length>0:o},Wt.prototype._queryCircle=function(t,e,r,n,a){var i=t-r,o=t+r,s=e-r,l=e+r;if(o<0||i>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(i,s,o,l,this._queryCellCircle,c,u,a),n?c.length>0:c},Wt.prototype.query=function(t,e,r,n,a){return this._query(t,e,r,n,!1,a)},Wt.prototype.hitTest=function(t,e,r,n,a){return this._query(t,e,r,n,!0,a)},Wt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Wt.prototype._queryCell=function(t,e,r,n,a,i,o,s){var l=o.seenUids,c=this.boxCells[a];if(null!==c)for(var u=this.bboxes,h=0,f=c;h<f.length;h+=1){var p=f[h];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 i.push(!0),!0;i.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var g=this.circleCells[a];if(null!==g)for(var v=this.circles,m=0,y=g;m<y.length;m+=1){var x=y[m];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(v[b],v[b+1],v[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return i.push(!0),!0;var _=v[b],w=v[b+1],k=v[b+2];i.push({key:this.circleKeys[x],x1:_-k,y1:w-k,x2:_+k,y2:w+k})}}}},Wt.prototype._queryCellCircle=function(t,e,r,n,a,i,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[a];if(null!==u)for(var h=this.bboxes,f=0,p=u;f<p.length;f+=1){var d=p[f];if(!c.box[d]){c.box[d]=!0;var g=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,h[g+0],h[g+1],h[g+2],h[g+3])&&(!s||s(this.boxKeys[d])))return i.push(!0),!0}}var v=this.circleCells[a];if(null!==v)for(var m=this.circles,y=0,x=v;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(m[_],m[_+1],m[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return i.push(!0),!0}}},Wt.prototype._forEachCell=function(t,e,r,n,a,i,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),h=this._convertToYCellCoord(n),f=l;f<=u;f++)for(var p=c;p<=h;p++){var d=this.xCellCount*p+f;if(a.call(this,t,e,r,n,d,i,o,s))return}},Wt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Wt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Wt.prototype._circlesCollide=function(t,e,r,n,a,i){var o=n-t,s=a-e,l=r+i;return l*l>o*o+s*s},Wt.prototype._circleAndRectCollide=function(t,e,r,n,a,i,o){var s=(i-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-a)/2,u=Math.abs(e-(a+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var h=l-s,f=u-c;return h*h+f*f<=r*r};var ae=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ie(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(ae,3*n)}}function oe(t,e,r){var n=e[0],a=e[1];return t[0]=r[0]*n+r[4]*a+r[12],t[1]=r[1]*n+r[5]*a+r[13],t[3]=r[3]*n+r[7]*a+r[15],t}var se=function(t,e,r){void 0===e&&(e=new Wt(t.width+200,t.height+200,25)),void 0===r&&(r=new Wt(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 le(t,e,r){t[e+4]=r?1:0}function ce(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}se.prototype.placeCollisionBox=function(t,e,r,n,a){var i=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*i.perspectiveRatio,s=t.x1*o+i.point.x,l=t.y1*o+i.point.y,c=t.x2*o+i.point.x,u=t.y2*o+i.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,a)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},se.prototype.approximateTileDistance=function(t,e,r,n,a){var i=a?1:n/this.pitchfactor,o=t.lastSegmentViewportDistance*r;return t.prevTileDistance+o+(i-1)*o*Math.abs(Math.sin(e))},se.prototype.placeCollisionCircles=function(e,r,n,a,i,o,s,l,c,u,h,f,p){var d=[],g=this.projectAnchor(c,i.anchorX,i.anchorY),v=l/24,m=i.lineOffsetX*l,y=i.lineOffsetY*l,x=new t.Point(i.anchorX,i.anchorY),b=$t(v,s,m,y,!1,Jt(x,u).point,x,i,o,u,{},!0),_=!1,w=!1,k=!0,T=g.perspectiveRatio*a,A=1/(a*n),M=0,S=0;b&&(M=this.approximateTileDistance(b.first.tileDistance,b.first.angle,A,g.cameraDistance,f),S=this.approximateTileDistance(b.last.tileDistance,b.last.angle,A,g.cameraDistance,f));for(var E=0;E<e.length;E+=5){var L=e[E],C=e[E+1],P=e[E+2],O=e[E+3];if(!b||O<-M||O>S)le(e,E,!1);else{var z=this.projectPoint(c,L,C),I=P*T;if(d.length>0){var D=z.x-d[d.length-4],R=z.y-d[d.length-3];if(I*I*2>D*D+R*R&&E+8<e.length){var F=e[E+8];if(F>-M&&F<S){le(e,E,!1);continue}}}var B=E/5;d.push(z.x,z.y,I,B),le(e,E,!0);var N=z.x-I,j=z.y-I,V=z.x+I,U=z.y+I;if(k=k&&this.isOffscreen(N,j,V,U),w=w||this.isInsideGrid(N,j,V,U),!r&&this.grid.hitTestCircle(z.x,z.y,I,p)){if(!h)return{circles:[],offscreen:!1};_=!0}}}return{circles:_||!w?[]:d,offscreen:k}},se.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,a=1/0,i=-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),a=Math.min(a,u.y),i=Math.max(i,u.x),o=Math.max(o,u.y),r.push(u)}for(var h={},f={},p=0,d=this.grid.query(n,a,i,o).concat(this.ignoredGrid.query(n,a,i,o));p<d.length;p+=1){var g=d[p],v=g.key;if(void 0===h[v.bucketInstanceId]&&(h[v.bucketInstanceId]={}),!h[v.bucketInstanceId][v.featureIndex]){var m=[new t.Point(g.x1,g.y1),new t.Point(g.x2,g.y1),new t.Point(g.x2,g.y2),new t.Point(g.x1,g.y2)];t.polygonIntersectsPolygon(r,m)&&(h[v.bucketInstanceId][v.featureIndex]=!0,void 0===f[v.bucketInstanceId]&&(f[v.bucketInstanceId]=[]),f[v.bucketInstanceId].push(v.featureIndex))}}return f},se.prototype.insertCollisionBox=function(t,e,r,n,a){var i={bucketInstanceId:r,featureIndex:n,collisionGroupID:a};(e?this.ignoredGrid:this.grid).insert(i,t[0],t[1],t[2],t[3])},se.prototype.insertCollisionCircles=function(t,e,r,n,a){for(var i=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:a},s=0;s<t.length;s+=4)i.insertCircle(o,t[s],t[s+1],t[s+2])},se.prototype.projectAnchor=function(t,e,r){var n=[e,r,0,1];return oe(n,n,t),{perspectiveRatio:.5+this.transform.cameraToCenterDistance/n[3]*.5,cameraDistance:n[3]}},se.prototype.projectPoint=function(e,r,n){var a=[r,n,0,1];return oe(a,a,e),new t.Point((a[0]/a[3]+1)/2*this.transform.width+100,(-a[1]/a[3]+1)/2*this.transform.height+100)},se.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var a=[r,n,0,1];return oe(a,a,e),{point:new t.Point((a[0]/a[3]+1)/2*this.transform.width+100,(-a[1]/a[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/a[3]*.5}},se.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},se.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary};var ue=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};ue.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var he=function(t,e,r,n,a){this.text=new ue(t?t.text:null,e,r,a),this.icon=new ue(t?t.icon:null,e,n,a)};he.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var fe=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},pe=function(t,e,r,n,a){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=a},de=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function ge(e,r,n,a,i){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateRadialOffset(e,a);return new t.Point(s+c[0]*i,l+c[1]*i)}de.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 ve=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new se(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new de(r),this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function me(t,e,r,n,a){t.emplaceBack(e?1:0,r?1:0,n||0,a||0),t.emplaceBack(e?1:0,r?1:0,n||0,a||0),t.emplaceBack(e?1:0,r?1:0,n||0,a||0),t.emplaceBack(e?1:0,r?1:0,n||0,a||0)}ve.prototype.placeLayerTile=function(e,r,n,a){var i=r.getBucket(e),o=r.latestFeatureIndex;if(i&&o&&e.id===i.layerIds[0]){var s=r.collisionBoxArray,l=i.layers[0].layout,c=Math.pow(2,this.transform.zoom-r.tileID.overscaledZ),u=r.tileSize/t.EXTENT,h=this.transform.calculatePosMatrix(r.tileID.toUnwrapped()),f=Xt(h,\"map\"===l.get(\"text-pitch-alignment\"),\"map\"===l.get(\"text-rotation-alignment\"),this.transform,ce(r,1,this.transform.zoom)),p=Xt(h,\"map\"===l.get(\"icon-pitch-alignment\"),\"map\"===l.get(\"icon-rotation-alignment\"),this.transform,ce(r,1,this.transform.zoom));this.retainedQueryData[i.bucketInstanceId]=new pe(i.bucketInstanceId,o,i.sourceLayerIndex,i.index,r.tileID),this.placeLayerBucket(i,h,f,p,c,u,n,r.holdingForFade(),a,s)}},ve.prototype.attemptAnchorPlacement=function(e,r,n,a,i,o,s,l,c,u,h,f,p,d,g){var v,m=ge(e,n,a,i,o),y=this.collisionIndex.placeCollisionBox(function(e,r,n,a,i,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,h=e.anchorPointX,f=e.anchorPointY,p=new t.Point(r,n);return a&&p._rotate(i?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:h,anchorPointY:f}}(r,m.x,m.y,s,l,this.transform.angle),f,c,u,h.predicate);if(y.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[p.crossTileID]&&this.prevPlacement.placements[p.crossTileID]&&this.prevPlacement.placements[p.crossTileID].text&&(v=this.prevPlacement.variableOffsets[p.crossTileID].anchor),this.variableOffsets[p.crossTileID]={radialOffset:i,width:n,height:a,anchor:e,textBoxScale:o,prevAnchor:v},this.markUsedJustification(d,e,p,g),d.allowVerticalPlacement&&(this.markUsedOrientation(d,g,p),this.placedOrientations[p.crossTileID]=g),y},ve.prototype.placeLayerBucket=function(e,r,n,a,i,o,s,l,c,u){var h=this,f=e.layers[0].layout,p=t.evaluateSizeForZoom(e.textSizeData,this.transform.zoom),d=f.get(\"text-optional\"),g=f.get(\"icon-optional\"),v=f.get(\"text-allow-overlap\"),m=f.get(\"icon-allow-overlap\"),y=v&&(m||!e.hasIconData()||g),x=m&&(v||!e.hasTextData()||d),b=this.collisionGroups.get(e.sourceID),_=\"map\"===f.get(\"text-rotation-alignment\"),w=\"map\"===f.get(\"text-pitch-alignment\"),k=\"viewport-y\"===f.get(\"symbol-z-order\");!e.collisionArrays&&u&&e.deserializeCollisionBoxes(u);var T=function(a,u){if(!c[a.crossTileID])if(l)h.placements[a.crossTileID]=new fe(!1,!1,!1);else{var m,k=!1,T=!1,A=!0,M={box:null,offscreen:null},S={box:null,offscreen:null},E=null,L=null,C=0,P=0,O=0;u.textFeatureIndex&&(C=u.textFeatureIndex),u.verticalTextFeatureIndex&&(P=u.verticalTextFeatureIndex);var z=u.textBox;if(z){var I=function(r){var n=t.WritingMode.horizontal;if(e.allowVerticalPlacement&&!r&&h.prevPlacement){var i=h.prevPlacement.placedOrientations[a.crossTileID];i&&(h.placedOrientations[a.crossTileID]=i,n=i,h.markUsedOrientation(e,n,a))}return n},D=function(r,n){if(e.allowVerticalPlacement&&a.numVerticalGlyphVertices>0&&u.verticalTextBox)for(var i=0,o=e.writingModes;i<o.length&&(o[i]===t.WritingMode.vertical?(M=n(),S=M):M=r(),!(M&&M.box&&M.box.length));i+=1);else M=r()};if(f.get(\"text-variable-anchor\")){var R=f.get(\"text-variable-anchor\");if(h.prevPlacement&&h.prevPlacement.variableOffsets[a.crossTileID]){var F=h.prevPlacement.variableOffsets[a.crossTileID];R.indexOf(F.anchor)>0&&(R=R.filter(function(t){return t!==F.anchor})).unshift(F.anchor)}var B=function(t,n){for(var i=t.x2-t.x1,s=t.y2-t.y1,l=a.textBoxScale,c={box:[],offscreen:!1},u=v?2*R.length:R.length,f=0;f<u;++f){var p=R[f%R.length],d=f>=R.length;if((c=h.attemptAnchorPlacement(p,t,i,s,a.radialTextOffset,l,_,w,o,r,b,d,a,e,n))&&c.box&&c.box.length){k=!0;break}}return c};D(function(){return B(z,t.WritingMode.horizontal)},function(){var r=u.verticalTextBox,n=M&&M.box&&M.box.length;return e.allowVerticalPlacement&&!n&&a.numVerticalGlyphVertices>0&&r?B(r,t.WritingMode.vertical):{box:null,offscreen:null}}),M&&(k=M.box,A=M.offscreen);var N=I(M&&M.box);if(!k&&h.prevPlacement){var j=h.prevPlacement.variableOffsets[a.crossTileID];j&&(h.variableOffsets[a.crossTileID]=j,h.markUsedJustification(e,j.anchor,a,N))}}else{var V=function(t,n){var i=h.collisionIndex.placeCollisionBox(t,f.get(\"text-allow-overlap\"),o,r,b.predicate);return i&&i.box&&i.box.length&&(h.markUsedOrientation(e,n,a),h.placedOrientations[a.crossTileID]=n),i};D(function(){return V(z,t.WritingMode.horizontal)},function(){var r=u.verticalTextBox;return e.allowVerticalPlacement&&a.numVerticalGlyphVertices>0&&r?V(r,t.WritingMode.vertical):{box:null,offscreen:null}}),I(M&&M.box&&M.box.length)}}k=(m=M)&&m.box&&m.box.length>0,A=m&&m.offscreen;var U=u.textCircles;if(U){var q=e.text.placedSymbolArray.get(a.centerJustifiedTextSymbolIndex),H=t.evaluateSizeForFeature(e.textSizeData,p,q);E=h.collisionIndex.placeCollisionCircles(U,f.get(\"text-allow-overlap\"),i,o,q,e.lineVertexArray,e.glyphOffsetArray,H,r,n,s,w,b.predicate),k=f.get(\"text-allow-overlap\")||E.circles.length>0,A=A&&E.offscreen}u.iconFeatureIndex&&(O=u.iconFeatureIndex),u.iconBox&&(T=(L=h.collisionIndex.placeCollisionBox(u.iconBox,f.get(\"icon-allow-overlap\"),o,r,b.predicate)).box.length>0,A=A&&L.offscreen);var G=d||0===a.numHorizontalGlyphVertices&&0===a.numVerticalGlyphVertices,Y=g||0===a.numIconVertices;G||Y?Y?G||(T=T&&k):k=T&&k:T=k=T&&k,k&&m&&m.box&&(S&&S.box&&P?h.collisionIndex.insertCollisionBox(m.box,f.get(\"text-ignore-placement\"),e.bucketInstanceId,P,b.ID):h.collisionIndex.insertCollisionBox(m.box,f.get(\"text-ignore-placement\"),e.bucketInstanceId,C,b.ID)),T&&L&&h.collisionIndex.insertCollisionBox(L.box,f.get(\"icon-ignore-placement\"),e.bucketInstanceId,O,b.ID),k&&E&&h.collisionIndex.insertCollisionCircles(E.circles,f.get(\"text-ignore-placement\"),e.bucketInstanceId,C,b.ID),h.placements[a.crossTileID]=new fe(k||y,T||x,A||e.justReloaded),c[a.crossTileID]=!0}};if(k)for(var A=e.getSortedSymbolIndexes(this.transform.angle),M=A.length-1;M>=0;--M){var S=A[M];T(e.symbolInstances.get(S),e.collisionArrays[S])}else for(var E=0;E<e.symbolInstances.length;++E)T(e.symbolInstances.get(E),e.collisionArrays[E]);e.justReloaded=!1},ve.prototype.markUsedJustification=function(e,r,n,a){var i,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};i=a===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=i>=0&&c!==i?0:n.crossTileID)}},ve.prototype.markUsedOrientation=function(e,r,n){for(var a=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,i=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=a}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=i)},ve.prototype.commit=function(t){this.commitTime=t;var e=this.prevPlacement,r=!1,n=e&&0!==this.fadeDuration?(this.commitTime-e.commitTime)/this.fadeDuration:1,a=e?e.opacities:{},i=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=a[s];c?(this.opacities[s]=new he(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new he(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in a){var h=a[u];if(!this.opacities[u]){var f=new he(h,n,!1,!1);f.isHidden()||(this.opacities[u]=f,r=r||h.text.placed||h.icon.placed)}}for(var p in i)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=i[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)},ve.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,a=e;n<a.length;n+=1){var i=a[n],o=i.getBucket(t);o&&i.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,i.collisionBoxArray)}},ve.prototype.updateBucketOpacities=function(e,r,n){e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasCollisionBoxData()&&e.collisionBox.collisionVertexArray.clear(),e.hasCollisionCircleData()&&e.collisionCircle.collisionVertexArray.clear();var a=e.layers[0].layout,i=new he(null,0,!1,!1,!0),o=a.get(\"text-allow-overlap\"),s=a.get(\"icon-allow-overlap\"),l=a.get(\"text-variable-anchor\"),c=\"map\"===a.get(\"text-rotation-alignment\"),u=\"map\"===a.get(\"text-pitch-alignment\"),h=new he(null,0,o&&(s||!e.hasIconData()||a.get(\"icon-optional\")),s&&(o||!e.hasTextData()||a.get(\"text-optional\")),!0);!e.collisionArrays&&n&&(e.hasCollisionBoxData()||e.hasCollisionCircleData())&&e.deserializeCollisionBoxes(n);for(var f=0;f<e.symbolInstances.length;f++){var p=e.symbolInstances.get(f),d=p.numHorizontalGlyphVertices,g=p.numVerticalGlyphVertices,v=p.crossTileID,m=r[v],y=this.opacities[v];m?y=i:y||(y=h,this.opacities[v]=y),r[v]=!0;var x=d>0||g>0,b=p.numIconVertices>0;if(x){for(var _=Ae(y.text),w=(d+g)/4,k=0;k<w;k++)e.text.opacityVertexArray.emplaceBack(_);var T=y.text.isHidden()?1:0,A=this.placedOrientations[p.crossTileID],M=A===t.WritingMode.horizontal||A===t.WritingMode.horizontalOnly?1:0,S=A===t.WritingMode.vertical?1:0;[p.rightJustifiedTextSymbolIndex,p.centerJustifiedTextSymbolIndex,p.leftJustifiedTextSymbolIndex].forEach(function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=T||S)}),p.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(p.verticalPlacedTextSymbolIndex).hidden=T||M);var E=this.variableOffsets[p.crossTileID];E&&this.markUsedJustification(e,E.anchor,p,A);var L=this.placedOrientations[p.crossTileID];L&&(this.markUsedJustification(e,\"left\",p,L),this.markUsedOrientation(e,L,p))}if(b){for(var C=Ae(y.icon),P=0;P<p.numIconVertices/4;P++)e.icon.opacityVertexArray.emplaceBack(C);e.icon.placedSymbolArray.get(f).hidden=y.icon.isHidden()}if(e.hasCollisionBoxData()||e.hasCollisionCircleData()){var O=e.collisionArrays[f];if(O){if(O.textBox){var z=new t.Point(0,0),I=!0;if(l){var D=this.variableOffsets[v];D?(z=ge(D.anchor,D.width,D.height,D.radialOffset,D.textBoxScale),c&&z._rotate(u?this.transform.angle:-this.transform.angle)):I=!1}me(e.collisionBox.collisionVertexArray,y.text.placed,!I,z.x,z.y)}O.iconBox&&me(e.collisionBox.collisionVertexArray,y.icon.placed,!1);var R=O.textCircles;if(R&&e.hasCollisionCircleData())for(var F=0;F<R.length;F+=5){var B=m||0===R[F+4];me(e.collisionCircle.collisionVertexArray,y.text.placed,B)}}}}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.hasCollisionBoxData()&&e.collisionBox.collisionVertexBuffer&&e.collisionBox.collisionVertexBuffer.updateData(e.collisionBox.collisionVertexArray),e.hasCollisionCircleData()&&e.collisionCircle.collisionVertexBuffer&&e.collisionCircle.collisionVertexBuffer.updateData(e.collisionCircle.collisionVertexArray)},ve.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration},ve.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},ve.prototype.stillRecent=function(t){return this.commitTime+this.fadeDuration>t},ve.prototype.setStale=function(){this.stale=!0};var ye=Math.pow(2,25),xe=Math.pow(2,24),be=Math.pow(2,17),_e=Math.pow(2,16),we=Math.pow(2,9),ke=Math.pow(2,8),Te=Math.pow(2,1);function Ae(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*ye+e*xe+r*be+e*_e+r*we+e*ke+r*Te+e}var Me=function(){this._currentTileIndex=0,this._seenCrossTileIDs={}};Me.prototype.continuePlacement=function(t,e,r,n,a){for(;this._currentTileIndex<t.length;){var i=t[this._currentTileIndex];if(e.placeLayerTile(n,i,r,this._seenCrossTileIDs),this._currentTileIndex++,a())return!0}};var Se=function(t,e,r,n,a,i,o){this.placement=new ve(t,a,i,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};Se.prototype.isDone=function(){return this._done},Se.prototype.continuePlacement=function(e,r,n){for(var a=this,i=t.browser.now(),o=function(){var e=t.browser.now()-i;return!a._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 Me),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},Se.prototype.commit=function(t){return this.placement.commit(t),this.placement};var Ee=512/t.EXTENT/2,Le=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var a=e.get(n),i=a.key;this.indexedSymbolInstances[i]||(this.indexedSymbolInstances[i]=[]),this.indexedSymbolInstances[i].push({crossTileID:a.crossTileID,coord:this.getScaledCoordinates(a,t)})}};Le.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,a=Ee/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*a),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*a)}},Le.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),a=0;a<t.length;a++){var i=t.get(a);if(!i.crossTileID){var o=this.indexedSymbolInstances[i.key];if(o)for(var s=this.getScaledCoordinates(i,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,i.crossTileID=u.crossTileID;break}}}}};var Ce=function(){this.maxCrossTileID=0};Ce.prototype.generate=function(){return++this.maxCrossTileID};var Pe=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Pe.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],a={};for(var i in n){var o=n[i];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),a[o.tileID.key]=o}this.indexes[r]=a}this.lng=t},Pe.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 a=this.usedCrossTileIDs[t.overscaledZ];for(var i in this.indexes){var o=this.indexes[i];if(Number(i)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,a)}else{var c=o[t.scaledTo(Number(i)).key];c&&c.findMatches(e.symbolInstances,t,a)}}for(var u=0;u<e.symbolInstances.length;u++){var h=e.symbolInstances.get(u);h.crossTileID||(h.crossTileID=r.generate(),a[h.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Le(t,e.symbolInstances,e.bucketInstanceId),!0},Pe.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,a=e.indexedSymbolInstances[r];n<a.length;n+=1){var i=a[n];delete this.usedCrossTileIDs[t][i.crossTileID]}},Pe.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var a in n)t[n[a].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[a]),delete n[a],e=!0)}return e};var Oe=function(){this.layerIndexes={},this.crossTileIDs=new Ce,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Oe.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Pe);var a=!1,i={};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)&&(a=!0),i[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(i)&&(a=!0),a},Oe.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 ze=function(e,r){return t.emitValidationErrors(e,r&&r.filter(function(t){return\"source.canvas\"!==t.identifier}))},Ie=t.pick(Nt,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),De=t.pick(Nt,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),Re=function(e){function r(n,a){var i=this;void 0===a&&(a={}),e.call(this),this.map=n,this.dispatcher=new T((Dt||(Dt=new It),Dt),this),this.imageManager=new f,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,a.localIdeographFontFamily),this.lineAtlas=new k(256,512),this.crossTileSymbolIndex=new Oe,this._layers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginAvailability(function(t){for(var e in o.dispatcher.broadcast(\"loadRTLTextPlugin\",t.pluginURL,t.completionCallback),o.sourceCaches)o.sourceCaches[e].reload()}),this.on(\"data\",function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=i.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in i._layers){var a=i._layers[n];a.source===r.id&&i._validateLayer(a)}}}})}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 a=\"boolean\"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var i=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(i,function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,a)})},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._load=function(e,r){var n=this;if(!r||!ze(this,t.validateStyle(e))){for(var a in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(a,e.sources[a],{validate:!1});e.sprite?this._spriteRequest=function(e,r,n){var a,i,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,a=e,u())}),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),function(t,e){c=null,o||(o=t,i=e,u())});function u(){if(o)n(o);else if(a&&i){var e=t.browser.getImageData(i),r={};for(var s in a){var l=a[s],c=l.width,u=l.height,h=l.x,f=l.y,p=l.sdf,d=l.pixelRatio,g=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,g,{x:h,y:f},{x:0,y:0},{width:c,height:u}),r[s]={data:g,pixelRatio:d,sdf:p}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e.sprite,this.map._requestManager,function(e,r){if(n._spriteRequest=null,e)n.fire(new t.ErrorEvent(e));else if(r)for(var a in r)n.imageManager.addImage(a,r[a]);n.imageManager.setLoaded(!0),n.fire(new t.Event(\"data\",{dataType:\"style\"}))}):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=Bt(this.stylesheet.layers);this._order=i.map(function(t){return t.id}),this._layers={};for(var o=0,s=i;o<s.length;o+=1){var l=s[o];(l=t.createStyleLayer(l)).setEventedParent(this,{layer:{id:l.id}}),this._layers[l.id]=l}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._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var a=r.getSource();(\"geojson\"===a.type||a.vectorLayerIds&&-1===a.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+a.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 a=n[r],i=this._layers[a];\"custom\"!==i.type&&e.push(i.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),a=Object.keys(this._removedLayers);for(var i in(n.length||a.length)&&this._updateWorkerLayers(n,a),this._updatedSources){var o=this._updatedSources[i];\"reload\"===o?this._reloadSource(i):\"clear\"===o&&this._clearSource(i)}for(var s in 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 h=u[c],f=this._layers[h];f.recalculate(e),!f.isHidden(e.zoom)&&f.source&&(this.sourceCaches[f.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event(\"data\",{dataType:\"style\"}))}},r.prototype._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={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),ze(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=Bt(e.layers);var n=function(e,r){if(!e)return[{command:Nt.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Nt.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Nt.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Nt.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Nt.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Nt.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Nt.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Nt.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Nt.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Nt.setLight,args:[r.light]});var a={},i=[];!function(e,r,n,a){var i;for(i in r=r||{},e=e||{})e.hasOwnProperty(i)&&(r.hasOwnProperty(i)||Vt(i,n,a));for(i in r)r.hasOwnProperty(i)&&(e.hasOwnProperty(i)?t.deepEqual(e[i],r[i])||(\"geojson\"===e[i].type&&\"geojson\"===r[i].type&&qt(e,r,i)?n.push({command:Nt.setGeoJSONSourceData,args:[i,r[i].data]}):Ut(i,r,n,a)):jt(i,r,n))}(e.sources,r.sources,i,a);var o=[];e.layers&&e.layers.forEach(function(t){a[t.source]?n.push({command:Nt.removeLayer,args:[t.id]}):o.push(t)}),n=n.concat(i),function(e,r,n){r=r||[];var a,i,o,s,l,c,u,h=(e=e||[]).map(Gt),f=r.map(Gt),p=e.reduce(Yt,{}),d=r.reduce(Yt,{}),g=h.slice(),v=Object.create(null);for(a=0,i=0;a<h.length;a++)o=h[a],d.hasOwnProperty(o)?i++:(n.push({command:Nt.removeLayer,args:[o]}),g.splice(g.indexOf(o,i),1));for(a=0,i=0;a<f.length;a++)o=f[f.length-1-a],g[g.length-1-a]!==o&&(p.hasOwnProperty(o)?(n.push({command:Nt.removeLayer,args:[o]}),g.splice(g.lastIndexOf(o,g.length-i),1)):i++,c=g[g.length-a],n.push({command:Nt.addLayer,args:[d[o],c]}),g.splice(g.length-a,0,o),v[o]=!0);for(a=0;a<f.length;a++)if(s=p[o=f[a]],l=d[o],!v[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 Ht(s.layout,l.layout,n,o,null,Nt.setLayoutProperty),Ht(s.paint,l.paint,n,o,null,Nt.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Nt.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Nt.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.\")?Ht(s[u],l[u],n,o,u.slice(6),Nt.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Nt.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.\")?Ht(s[u],l[u],n,o,u.slice(6),Nt.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Nt.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Nt.removeLayer,args:[o]}),c=g[g.lastIndexOf(o)+1],n.push({command:Nt.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:Nt.setStyle,args:[r]}]}return n}(this.serialize(),e).filter(function(t){return!(t.command in De)});if(0===n.length)return!1;var a=n.filter(function(t){return!(t.command in Ie)});if(a.length>0)throw new Error(\"Unimplemented: \"+a.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.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.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 a=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 i=this.sourceCaches[e]=new Ct(e,r,this.dispatcher);i.style=this,i.setEventedParent(this,function(){return{isSourceLoaded:a.loaded(),source:i.serialize(),sourceId:e}}),i.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 a=e.id;if(this.getLayer(a))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+a+'\" already exists on this map')));else{var i;if(\"custom\"===e.type){if(ze(this,t.validateCustomStyleLayer(e)))return;i=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(a,e.source),e=t.clone$1(e),e=t.extend(e,{source:a})),this._validate(t.validateStyle.layer,\"layers.\"+a,e,{arrayIndex:-1},n))return;i=t.createStyleLayer(e),this._validateLayer(i),i.setEventedParent(this,{layer:{id:a}})}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,a),this._layerOrderChanged=!0,this._layers[a]=i,this._removedLayers[a]&&i.source&&\"custom\"!==i.type){var s=this._removedLayers[a];delete this._removedLayers[a],s.type!==i.type?this._updatedSources[i.source]=\"clear\":(this._updatedSources[i.source]=\"reload\",this.sourceCaches[i.source].pause())}this._updateLayer(i),i.onAdd&&i.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 a=r?this._order.indexOf(r):this._order.length;r&&-1===a?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(a,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._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.setLayerZoomRange=function(e,r,n){this._checkLoaded();var a=this.getLayer(e);a?a.minzoom===r&&a.maxzoom===n||(null!=r&&(a.minzoom=r),null!=n&&(a.maxzoom=n),this._updateLayer(a)):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 a=this.getLayer(e);if(a){if(!t.deepEqual(a.filter,r))return null==r?(a.filter=void 0,void this._updateLayer(a)):void(this._validate(t.validateStyle.filter,\"layers.\"+a.id+\".filter\",r,null,n)||(a.filter=t.clone$1(r),this._updateLayer(a)))}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,a){void 0===a&&(a={}),this._checkLoaded();var i=this.getLayer(e);i?t.deepEqual(i.getLayoutProperty(r),n)||(i.setLayoutProperty(r,n,a),this._updateLayer(i)):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,a){void 0===a&&(a={}),this._checkLoaded();var i=this.getLayer(e);i?t.deepEqual(i.getPaintProperty(r),n)||(i.setPaintProperty(r,n,a)&&this._updateLayer(i),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,a=e.sourceLayer,i=this.sourceCaches[n],o=parseInt(e.id,10);if(void 0!==i){var s=i.getSource().type;\"geojson\"===s&&a?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==s||a?isNaN(o)||o<0?this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided and non-negative.\"))):i.setFeatureState(a,o,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,a=this.sourceCaches[n];if(void 0!==a){var i=a.getSource().type,o=\"vector\"===i?e.sourceLayer:void 0,s=parseInt(e.id,10);\"vector\"!==i||o?void 0!==e.id&&isNaN(s)||s<0?this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be non-negative.\"))):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.\"))):a.removeFeatureState(o,s,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,a=this.sourceCaches[r],i=parseInt(e.id,10);if(void 0!==a)if(\"vector\"!==a.getSource().type||n){if(!(isNaN(i)||i<0))return a.getFeatureState(n,i);this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided and non-negative.\")))}else 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]&&(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={},a=[],i=this._order.length-1;i>=0;i--){var o=this._order[i];if(r(o)){n[o]=i;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,h=c;u<h.length;u+=1){var f=h[u];a.push(f)}}}}a.sort(function(t,e){return e.intersectionZ-t.intersectionZ});for(var p=[],d=this._order.length-1;d>=0;d--){var g=this._order[d];if(r(g))for(var v=a.length-1;v>=0;v--){var m=a[v].feature;if(n[m.layer.id]<d)break;p.push(m),a.pop()}else for(var y=0,x=t;y<x.length;y+=1){var b=x[y][g];if(b)for(var _=0,w=b;_<w.length;_+=1){var k=w[_];p.push(k.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 a={};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 i=0,o=r.layers;i<o.length;i+=1){var s=o[i],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.\"))),[];a[l.source]=!0}}var c=[];for(var u in this.sourceCaches)r.layers&&!a[u]||c.push(F(this.sourceCaches[u],this._layers,e,r,n));return this.placement&&c.push(function(t,e,r,n,a,i){for(var o={},s=a.queryRenderedSymbols(r),l=[],c=0,u=Object.keys(s).map(Number);c<u.length;c+=1){var h=u[c];l.push(i[h])}l.sort(B);for(var f=function(){var e=d[p],r=e.featureIndex.lookupSymbolFeatures(s[e.bucketInstanceId],e.bucketIndex,e.sourceLayerIndex,n.filter,n.layers,t);for(var a in r){var i=o[a]=o[a]||[],l=r[a];l.sort(function(t,r){var n=e.featureSortOrder;if(n){var a=n.indexOf(t.featureIndex);return n.indexOf(r.featureIndex)-a}return r.featureIndex-t.featureIndex});for(var c=0,u=l;c<u.length;c+=1){var h=u[c];i.push(h)}}},p=0,d=l;p<d.length;p+=1)f();var g=function(r){o[r].forEach(function(n){var a=n.feature,i=t[r],o=e[i.source].getFeatureState(a.layer[\"source-layer\"],a.id);a.source=a.layer.source,a.layer[\"source-layer\"]&&(a.sourceLayer=a.layer[\"source-layer\"]),a.state=o})};for(var v in o)g(v);return o}(this._layers,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=[],a={},i=0;i<r.length;i++){var o=r[i],s=o.tileID.canonical.key;a[s]||(a[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(),a=!1;for(var i in e)if(!t.deepEqual(e[i],n[i])){a=!0;break}if(a){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,a,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&ze(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},a)))},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(\"pluginAvailable\",this._rtlTextPluginCallback),this.sourceCaches)this.sourceCaches[e].clearTiles();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,a){for(var i=!1,o=!1,s={},l=0,c=this._order;l<c.length;l+=1){var u=c[l],h=this._layers[u];if(\"symbol\"===h.type){if(!s[h.source]){var f=this.sourceCaches[h.source];s[h.source]=f.getRenderableIds(!0).map(function(t){return f.getTileByID(t)}).sort(function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)})}var p=this.crossTileSymbolIndex.addLayer(h,s[h.source],e.center.lng);i=i||p}}this.crossTileSymbolIndex.pruneUnusedLayers(this._order);var d=this._layerOrderChanged||0===n;if((d||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now()))&&(this.pauseablePlacement=new Se(e,this._order,d,r,n,a,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,s),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),o=!0),i&&this.pauseablePlacement.placement.setStale()),o||i)for(var g=0,v=this._order;g<v.length;g+=1){var m=v[g],y=this._layers[m];\"symbol\"===y.type&&this.placement.updateLayerOpacities(y,s[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)},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);Re.getSourceType=function(t){return I[t]},Re.setSourceType=function(t,e){I[t]=e},Re.registerForPluginAvailability=t.registerForPluginAvailability;var Fe=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Be=cr(\"#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;}\"),Ne=cr(\"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);}\"),je=cr(\"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);}\"),Ve=cr(\"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);}\"),Ue=cr(\"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);}\"),qe=cr(\"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;}\"),He=cr(\"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;}\"),Ge=cr(\"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;}\"),Ye=cr(\"uniform float u_overscale_factor;varying float v_placed;varying float v_notUsed;varying float v_radius;varying vec2 v_extrude;varying vec2 v_extrude_scale;void main() {float alpha=0.5;vec4 color=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {color=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {color*=.2;}float extrude_scale_length=length(v_extrude_scale);float extrude_length=length(v_extrude)*extrude_scale_length;float stroke_width=15.0*extrude_scale_length/u_overscale_factor;float radius=v_radius*extrude_scale_length;float distance_to_edge=abs(extrude_length-radius);float opacity_t=smoothstep(-stroke_width,0.0,-distance_to_edge);gl_FragColor=opacity_t*color;}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;varying float v_radius;varying vec2 v_extrude;varying vec2 v_extrude_scale;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);highp float padding_factor=1.2;gl_Position.xy+=a_extrude*u_extrude_scale*padding_factor*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;v_radius=abs(a_extrude.y);v_extrude=a_extrude*padding_factor;v_extrude_scale=u_extrude_scale*u_camera_to_center_distance*collision_perspective_ratio;}\"),We=cr(\"uniform highp vec4 u_color;void main() {gl_FragColor=u_color;}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Xe=cr(\"#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);}\"),Ze=cr(\"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;}\"),Je=cr(\"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 vec4 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\\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;float pixelRatio=u_scale.x;float tileRatio=u_scale.y;float fromScale=u_scale.z;float toScale=u_scale.w;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=vec2((pattern_br_a.x-pattern_tl_a.x)/pixelRatio,(pattern_br_a.y-pattern_tl_a.y)/pixelRatio);vec2 display_size_b=vec2((pattern_br_b.x-pattern_tl_b.x)/pixelRatio,(pattern_br_b.y-pattern_tl_b.y)/pixelRatio);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;}\"),Ke=cr(\"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 vec4 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\\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;float pixelRatio=u_scale.x;float tileZoomRatio=u_scale.y;float fromScale=u_scale.z;float toScale=u_scale.w;vec2 display_size_a=vec2((pattern_br_a.x-pattern_tl_a.x)/pixelRatio,(pattern_br_a.y-pattern_tl_a.y)/pixelRatio);vec2 display_size_b=vec2((pattern_br_b.x-pattern_tl_b.x)/pixelRatio,(pattern_br_b.y-pattern_tl_b.y)/pixelRatio);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);}\"),Qe=cr(\"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;}\"),$e=cr(\"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\\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\\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 vec4 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\\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\\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 pixelRatio=u_scale.x;float tileRatio=u_scale.y;float fromScale=u_scale.z;float toScale=u_scale.w;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=vec2((pattern_br_a.x-pattern_tl_a.x)/pixelRatio,(pattern_br_a.y-pattern_tl_a.y)/pixelRatio);vec2 display_size_b=vec2((pattern_br_b.x-pattern_tl_b.x)/pixelRatio,(pattern_br_b.y-pattern_tl_b.y)/pixelRatio);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;}\"),tr=cr(\"#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;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;return (data.r+data.g*256.0+data.b*256.0*256.0)/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;}\"),er=cr(\"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;}\"),rr=cr(\"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);}\"),nr=cr(\"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);}\"),ar=cr(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec4 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_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 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 pixelRatio=u_scale.x;float tileZoomRatio=u_scale.y;float fromScale=u_scale.z;float toScale=u_scale.w;vec2 display_size_a=vec2((pattern_br_a.x-pattern_tl_a.x)/pixelRatio,(pattern_br_a.y-pattern_tl_a.y)/pixelRatio);vec2 display_size_b=vec2((pattern_br_b.x-pattern_tl_b.x)/pixelRatio,(pattern_br_b.y-pattern_tl_b.y)/pixelRatio);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 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,1.0);float x_b=mod(v_linesofar/pattern_size_b.x,1.0);float y_a=0.5+(v_normal.y*clamp(v_width2.s,0.0,(pattern_size_a.y+2.0)/2.0)/pattern_size_a.y);float y_b=0.5+(v_normal.y*clamp(v_width2.s,0.0,(pattern_size_b.y+2.0)/2.0)/pattern_size_b.y);vec2 pos_a=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,vec2(x_a,y_a));vec2 pos_b=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,vec2(x_b,y_b));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;\\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 vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_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 mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_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);}\"),ir=cr(\"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);}\"),or=cr(\"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;}\"),sr=cr(\"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 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;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[0],a_size[1],u_size_t)/256.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size[0]/256.0;} else if (!u_is_size_zoom_constant && u_is_size_feature_constant) {size=u_size;} 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),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));}\"),lr=cr(\"#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 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;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[0],a_size[1],u_size_t)/256.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size[0]/256.0;} else if (!u_is_size_zoom_constant && u_is_size_feature_constant) {size=u_size;} 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),0.0,1.0);float gamma_scale=gl_Position.w;vec2 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;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=vec2(tex.x,tex.y);v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\");function cr(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n={};return{fragmentSource:t=t.replace(r,function(t,e,r,a,i){return n[i]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\nvarying \"+r+\" \"+a+\" \"+i+\";\\n#else\\nuniform \"+r+\" \"+a+\" u_\"+i+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+i+\"\\n    \"+r+\" \"+a+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\"}),vertexSource:e=e.replace(r,function(t,e,r,a,i){var o=\"float\"===a?\"vec2\":\"vec4\",s=i.match(/color/)?\"color\":o;return n[i]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\nuniform lowp float u_\"+i+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+i+\";\\nvarying \"+r+\" \"+a+\" \"+i+\";\\n#else\\nuniform \"+r+\" \"+a+\" u_\"+i+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n    \"+i+\" = a_\"+i+\";\\n#else\\n    \"+r+\" \"+a+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n    \"+i+\" = unpack_mix_\"+s+\"(a_\"+i+\", u_\"+i+\"_t);\\n#else\\n    \"+r+\" \"+a+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\nuniform lowp float u_\"+i+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+i+\";\\n#else\\nuniform \"+r+\" \"+a+\" u_\"+i+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n    \"+r+\" \"+a+\" \"+i+\" = a_\"+i+\";\\n#else\\n    \"+r+\" \"+a+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n    \"+r+\" \"+a+\" \"+i+\" = unpack_mix_\"+s+\"(a_\"+i+\", u_\"+i+\"_t);\\n#else\\n    \"+r+\" \"+a+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\"})}}var ur=Object.freeze({prelude:Be,background:Ne,backgroundPattern:je,circle:Ve,clippingMask:Ue,heatmap:qe,heatmapTexture:He,collisionBox:Ge,collisionCircle:Ye,debug:We,fill:Xe,fillOutline:Ze,fillOutlinePattern:Je,fillPattern:Ke,fillExtrusion:Qe,fillExtrusionPattern:$e,hillshadePrepare:tr,hillshade:er,line:rr,lineGradient:nr,linePattern:ar,lineSDF:ir,raster:or,symbolIcon:sr,symbolSDF:lr}),hr=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};hr.prototype.bind=function(t,e,r,n,a,i,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!==a||this.boundVertexOffset!==i||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,a,i,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),a&&a.dynamicDraw&&a.bind(),s&&s.bind())},hr.prototype.freshBind=function(t,e,r,n,a,i,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=a,this.boundDynamicVertexBuffer=i,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var h=l;h<s;h++)u.disableVertexAttribArray(h)}e.enableAttributes(u,t);for(var f=0,p=r;f<p.length;f+=1)p[f].enableAttributes(u,t);i&&i.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,a);for(var d=0,g=r;d<g.length;d+=1){var v=g[d];v.bind(),v.setVertexAttribPointers(u,t,a)}i&&(i.bind(),i.setVertexAttribPointers(u,t,a)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,a)),c.currentNumAttributes=l},hr.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var fr=function(t,e,r,n,a){var i=t.gl;this.program=i.createProgram();var o=r.defines();a&&o.push(\"#define OVERDRAW_INSPECTOR;\");var s=o.concat(Be.fragmentSource,e.fragmentSource).join(\"\\n\"),l=o.concat(Be.vertexSource,e.vertexSource).join(\"\\n\"),c=i.createShader(i.FRAGMENT_SHADER);i.shaderSource(c,s),i.compileShader(c),i.attachShader(this.program,c);var u=i.createShader(i.VERTEX_SHADER);i.shaderSource(u,l),i.compileShader(u),i.attachShader(this.program,u);for(var h=r.layoutAttributes||[],f=0;f<h.length;f++)i.bindAttribLocation(this.program,f,h[f].name);i.linkProgram(this.program),this.numAttributes=i.getProgramParameter(this.program,i.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var g=i.getActiveAttrib(this.program,d);g&&(this.attributes[g.name]=i.getAttribLocation(this.program,g.name))}for(var v=i.getProgramParameter(this.program,i.ACTIVE_UNIFORMS),m=0;m<v;m++){var y=i.getActiveUniform(this.program,m);y&&(p[y.name]=i.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r.getUniforms(t,p)};function pr(e,r,n){var a=1/ce(n,1,r.transform.tileZoom),i=Math.pow(2,n.tileID.overscaledZ),o=n.tileSize*Math.pow(2,r.transform.tileZoom)/i,s=o*(n.tileID.canonical.x+n.tileID.wrap*i),l=o*n.tileID.canonical.y;return{u_image:0,u_texsize:n.imageAtlasTexture.size,u_scale:[t.browser.devicePixelRatio,a,e.fromScale,e.toScale],u_fade:e.t,u_pixel_coord_upper:[s>>16,l>>16],u_pixel_coord_lower:[65535&s,65535&l]}}fr.prototype.draw=function(t,e,r,n,a,i,o,s,l,c,u,h,f,p,d,g){var v,m=t.gl;for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(a),t.setCullFace(i),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,h,{zoom:f});for(var x=(v={},v[m.LINES]=2,v[m.TRIANGLES]=3,v[m.LINE_STRIP]=1,v)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],k=w.vaos||(w.vaos={});(k[s]||(k[s]=new hr)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,g),m.drawElements(e,w.primitiveLength*x,m.UNSIGNED_SHORT,w.primitiveOffset*x*2)}};var dr=function(e,r,n,a){var i=r.style.light,o=i.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===i.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=i.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:i.properties.get(\"intensity\"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:a}},gr=function(e,r,n,a,i,o,s){return t.extend(dr(e,r,n,a),pr(o,r,s),{u_height_factor:-Math.pow(2,i.overscaledZ)/s.tileSize/8})},vr=function(t){return{u_matrix:t}},mr=function(e,r,n,a){return t.extend(vr(e),pr(n,r,a))},yr=function(t,e){return{u_matrix:t,u_world:e}},xr=function(e,r,n,a,i){return t.extend(mr(e,r,n,a),{u_world:i})},br=function(e,r,n,a){var i,o,s=e.transform;if(\"map\"===a.paint.get(\"circle-pitch-alignment\")){var l=ce(n,1,s.zoom);i=!0,o=[l,l]}else i=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===a.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,a.paint.get(\"circle-translate\"),a.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+i,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},_r=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)}},wr=function(t,e,r){var n=ce(r,1,e.zoom),a=Math.pow(2,e.zoom-r.tileID.overscaledZ),i=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*a),e.pixelsToGLUnits[1]/(n*a)],u_overscale_factor:i}},kr=function(t,e){return{u_matrix:t,u_color:e}},Tr=function(t){return{u_matrix:t}},Ar=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:ce(e,1,r),u_intensity:n}},Mr=function(t,e,r){var n=r.paint.get(\"hillshade-shadow-color\"),a=r.paint.get(\"hillshade-highlight-color\"),i=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:Er(t,e.tileID),u_light:[r.paint.get(\"hillshade-exaggeration\"),o],u_shadow:n,u_highlight:a,u_accent:i}},Sr=function(e,r){var n=e.dem.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:[n,n],u_zoom:e.tileID.overscaledZ,u_maxzoom:r}};function Er(e,r){var n=Math.pow(2,r.canonical.z),a=r.canonical.y;return[new t.MercatorCoordinate(0,a/n).toLngLat().lat,new t.MercatorCoordinate(0,(a+1)/n).toLngLat().lat]}var Lr=function(e,r,n){var a=e.transform;return{u_matrix:Ir(e,r,n),u_ratio:1/ce(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Cr=function(e,r,n){return t.extend(Lr(e,r,n),{u_image:0})},Pr=function(e,r,n,a){var i=e.transform,o=zr(r,i);return{u_matrix:Ir(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/ce(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[t.browser.devicePixelRatio,o,a.fromScale,a.toScale],u_fade:a.t,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Or=function(e,r,n,a,i){var o=e.transform,s=e.lineAtlas,l=zr(r,o),c=\"round\"===n.layout.get(\"line-cap\"),u=s.getDash(a.from,c),h=s.getDash(a.to,c),f=u.width*i.fromScale,p=h.width*i.toScale;return t.extend(Lr(e,r,n),{u_patternscale_a:[l/f,-u.height/2],u_patternscale_b:[l/p,-h.height/2],u_sdfgamma:s.width/(256*Math.min(f,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:h.y,u_mix:i.t})};function zr(t,e){return 1/ce(t,1,e.tileZoom)}function Ir(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var Dr=function(t,e,r,n,a){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*a.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:a.paint.get(\"raster-brightness-min\"),u_brightness_high:a.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=a.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(i=a.paint.get(\"raster-contrast\"),i>0?1/(1-i):1+i),u_spin_weights:function(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]}(a.paint.get(\"raster-hue-rotate\"))};var i,o};var Rr=function(t,e,r,n,a,i,o,s,l,c){var u=a.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:a.options.fadeDuration?a.symbolFadeChange:1,u_matrix:i,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Fr=function(e,r,n,a,i,o,s,l,c,u,h){var f=i.transform;return t.extend(Rr(e,r,n,a,i,o,s,l,c,u),{u_gamma_scale:a?Math.cos(f._pitch)*f.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+h})},Br=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Nr=function(e,r,n,a,i,o){return t.extend(function(t,e,r,n){var a=r.imageManager.getPattern(t.from),i=r.imageManager.getPattern(t.to),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,h=u*(n.tileID.canonical.x+n.tileID.wrap*c),f=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:a.tl,u_pattern_br_a:a.br,u_pattern_tl_b:i.tl,u_pattern_br_b:i.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:a.displaySize,u_pattern_size_b:i.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/ce(n,1,r.transform.tileZoom),u_pixel_coord_upper:[h>>16,f>>16],u_pixel_coord_lower:[65535&h,65535&f]}}(a,o,n,i),{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.Uniform4f(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.Uniform4f(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.Uniform4f(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:_r,collisionCircle:_r,debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},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)}},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.Uniform4f(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.Uniform1f(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.Uniform1f(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.Uniform1f(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 Vr(e,r){for(var n=e.sort(function(t,e){return t.tileID.isLessThan(e.tileID)?-1:e.tileID.isLessThan(t.tileID)?1:0}),a=0;a<n.length;a++){var i={},o=n[a],s=n.slice(a+1);Ur(o.tileID.wrapped(),o.tileID,s,new t.OverscaledTileID(0,o.tileID.wrap+1,0,0,0),i),o.setMask(i,r)}}function Ur(e,r,n,a,i){for(var o=0;o<n.length;o++){var s=n[o];if(a.isLessThan(s.tileID))break;if(r.key===s.tileID.key)return;if(s.tileID.isChildOf(r)){for(var l=r.children(1/0),c=0;c<l.length;c++)Ur(e,l[c],n.slice(o),a,i);return}}var u=r.overscaledZ-e.overscaledZ,h=new t.CanonicalTileID(u,r.canonical.x-(e.canonical.x<<u),r.canonical.y-(e.canonical.y<<u));i[h.key]=i[h.key]||h}function qr(t,e,r,n,a){for(var i=t.context,o=i.gl,s=a?t.useProgram(\"collisionCircle\"):t.useProgram(\"collisionBox\"),l=0;l<n.length;l++){var c=n[l],u=e.getTile(c),h=u.getBucket(r);if(h){var f=a?h.collisionCircle:h.collisionBox;f&&s.draw(i,a?o.TRIANGLES:o.LINES,At.disabled,Mt.disabled,t.colorModeForRenderPass(),Et.disabled,wr(c.posMatrix,t.transform,u),r.id,f.layoutVertexBuffer,f.indexBuffer,f.segments,null,t.transform.zoom,null,null,f.collisionVertexBuffer)}}}var Hr=t.identity(new Float32Array(16));function Gr(e,r,n,a,i,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateRadialOffset(e,a);return new t.Point((l/i+u[0])*o,(c/i+u[1])*o)}function Yr(e,r,n,a,i,o,s,l,c,u){var h=e.text.placedSymbolArray,f=e.text.dynamicLayoutVertexArray;f.clear();for(var p=0;p<h.length;p++){var d=h.get(p),g=e.allowVerticalPlacement&&!d.placedOrientation,v=d.hidden||!d.crossTileID||g?null:a[d.crossTileID];if(v){var m=new t.Point(d.anchorX,d.anchorY),y=Jt(m,n?l:s),x=.5+o.cameraToCenterDistance/y.signedDistanceFromCamera*.5,b=i.evaluateSizeForFeature(e.textSizeData,u,d)*x/t.ONE_EM;n&&(b*=e.tilePixelRatio/c);for(var _=v.width,w=v.height,k=v.radialOffset,T=v.textBoxScale,A=Gr(v.anchor,_,w,k,T,b),M=n?Jt(m.add(A),s).point:y.point.add(r?A.rotate(-o.angle):A),S=e.allowVerticalPlacement&&d.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,E=0;E<d.numGlyphs;E++)t.addDynamicAttributes(f,M,S)}else ie(d.numGlyphs,f)}e.text.dynamicLayoutVertexBuffer.updateData(f)}function Wr(e){var r=e.text.placedSymbolArray,n=e.text.dynamicLayoutVertexArray;n.clear();for(var a=0;a<r.length;a++){var i=r.get(a);if(i.hidden||!i.placedOrientation)ie(i.numGlyphs,n);else for(var o=new t.Point(i.anchorX,i.anchorY),s=e.allowVerticalPlacement&&i.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,l=0;l<i.numGlyphs;l++)t.addDynamicAttributes(n,o,s)}e.text.dynamicLayoutVertexBuffer.updateData(n)}function Xr(e,r,n,a,i,o,s,l,c,u,h,f,p){for(var d,g,v=e.context,m=v.gl,y=e.transform,x=\"map\"===l,b=\"map\"===c,_=x&&\"point\"!==n.layout.get(\"symbol-placement\"),w=x&&!b&&!_,k=void 0!==n.layout.get(\"symbol-sort-key\").constantOr(1),T=e.depthModeForSublayer(0,At.ReadOnly),A=n.layout.get(\"text-variable-anchor\"),M=[],S=0,E=a;S<E.length;S+=1){var L=E[S],C=r.getTile(L),P=C.getBucket(n);if(P){var O=i?P.text:P.icon;if(O&&O.segments.get().length){var z=O.programConfigurations.get(n.id),I=i||P.sdfIcons,D=i?P.textSizeData:P.iconSizeData;d||(d=e.useProgram(I?\"symbolSDF\":\"symbolIcon\",z),g=t.evaluateSizeForZoom(D,y.zoom)),v.activeTexture.set(m.TEXTURE0);var R=void 0,F=void 0,B=void 0;if(i)F=C.glyphAtlasTexture,B=m.LINEAR,R=C.glyphAtlasTexture.size;else{var N=1!==n.layout.get(\"icon-size\").constantOr(0)||P.iconsNeedLinear,j=b||0!==y.pitch;F=C.imageAtlasTexture,B=I||e.options.rotating||e.options.zooming||N||j?m.LINEAR:m.NEAREST,R=C.imageAtlasTexture.size}var V=ce(C,1,e.transform.zoom),U=Xt(L.posMatrix,b,x,e.transform,V),q=Zt(L.posMatrix,b,x,e.transform,V);if(_)Qt(P,L.posMatrix,e,i,U,q,b,u);else if(i&&g&&A){var H=Math.pow(2,y.zoom-C.tileID.overscaledZ);Yr(P,x,b,p,t.symbolSize,y,U,L.posMatrix,H,g)}else i&&g&&P.allowVerticalPlacement&&Wr(P);var G=e.translatePosMatrix(L.posMatrix,C,o,s),Y=_||i&&A?Hr:U,W=e.translatePosMatrix(q,C,o,s,!0),X=I&&0!==n.paint.get(i?\"text-halo-width\":\"icon-halo-width\").constantOr(1),Z={program:d,buffers:O,uniformValues:I?Fr(D.kind,g,w,b,e,G,Y,W,i,R,!0):Rr(D.kind,g,w,b,e,G,Y,W,i,R),atlasTexture:F,atlasInterpolation:B,isSDF:I,hasHalo:X};if(k)for(var J=0,K=O.segments.get();J<K.length;J+=1){var Q=K[J];M.push({segments:new t.SegmentVector([Q]),sortKey:Q.sortKey,state:Z})}else M.push({segments:O.segments,sortKey:0,state:Z})}}}k&&M.sort(function(t,e){return t.sortKey-e.sortKey});for(var $=0,tt=M;$<tt.length;$+=1){var et=tt[$],rt=et.state;if(rt.atlasTexture.bind(rt.atlasInterpolation,m.CLAMP_TO_EDGE),rt.isSDF){var nt=rt.uniformValues;rt.hasHalo&&(nt.u_is_halo=1,Zr(rt.buffers,et.segments,n,e,rt.program,T,h,f,nt)),nt.u_is_halo=0}Zr(rt.buffers,et.segments,n,e,rt.program,T,h,f,rt.uniformValues)}}function Zr(t,e,r,n,a,i,o,s,l){var c=n.context,u=c.gl;a.draw(c,u.TRIANGLES,i,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 Jr(t,e,r,n,a,i,o){var s,l,c,u,h,f=t.context.gl,p=r.paint.get(\"fill-pattern\"),d=p&&p.constantOr(1),g=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",s=f.LINES):(l=d?\"fillPattern\":\"fill\",s=f.TRIANGLES);for(var v=0,m=n;v<m.length;v+=1){var y=m[v],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(f.TEXTURE0),x.imageAtlasTexture.bind(f.LINEAR,f.CLAMP_TO_EDGE),_.updatePatternPaintBuffers(g));var k=p.constantOr(null);if(k&&x.imageAtlas){var T=x.imageAtlas.patternPositions[k.to],A=x.imageAtlas.patternPositions[k.from];T&&A&&_.setConstantPatternPositions(T,A)}var M=t.translatePosMatrix(y.posMatrix,x,r.paint.get(\"fill-translate\"),r.paint.get(\"fill-translate-anchor\"));if(o){u=b.indexBuffer2,h=b.segments2;var S=[f.drawingBufferWidth,f.drawingBufferHeight];c=\"fillOutlinePattern\"===l&&d?xr(M,t,g,x,S):yr(M,S)}else u=b.indexBuffer,h=b.segments,c=d?mr(M,t,g,x):vr(M);w.draw(t.context,s,a,t.stencilModeForClipping(y),i,Et.disabled,c,r.id,b.layoutVertexBuffer,u,h,r.paint,t.transform.zoom,_)}}}}function Kr(t,e,r,n,a,i,o){for(var s=t.context,l=s.gl,c=r.paint.get(\"fill-extrusion-pattern\"),u=c.constantOr(1),h=r.getCrossfadeParameters(),f=r.paint.get(\"fill-extrusion-opacity\"),p=0,d=n;p<d.length;p+=1){var g=d[p],v=e.getTile(g),m=v.getBucket(r);if(m){var y=m.programConfigurations.get(r.id),x=t.useProgram(u?\"fillExtrusionPattern\":\"fillExtrusion\",y);u&&(t.context.activeTexture.set(l.TEXTURE0),v.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePatternPaintBuffers(h));var b=c.constantOr(null);if(b&&v.imageAtlas){var _=v.imageAtlas.patternPositions[b.to],w=v.imageAtlas.patternPositions[b.from];_&&w&&y.setConstantPatternPositions(_,w)}var k=t.translatePosMatrix(g.posMatrix,v,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\")),T=r.paint.get(\"fill-extrusion-vertical-gradient\"),A=u?gr(k,t,T,f,g,h,v):dr(k,t,T,f);x.draw(s,s.gl.TRIANGLES,a,i,o,Et.backCCW,A,r.id,m.layoutVertexBuffer,m.indexBuffer,m.segments,r.paint,t.transform.zoom,y)}}}function Qr(t,e,r,n,a,i){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=Mr(t,e,r);e.maskedBoundsBuffer&&e.maskedIndexBuffer&&e.segments?c.draw(o,s.TRIANGLES,n,a,i,Et.disabled,u,r.id,e.maskedBoundsBuffer,e.maskedIndexBuffer,e.segments):c.draw(o,s.TRIANGLES,n,a,i,Et.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function $r(e,r,n,a,i,o,s){var l=e.context,c=l.gl;if(r.dem&&r.dem.data){var u=r.dem.dim,h=r.dem.stride,f=r.dem.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var p=r.demTexture;p.update(f,{premultiply:!1}),p.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,f,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var d=r.fbo;if(!d){var g=new t.Texture(l,{width:u,height:u,data:null},c.RGBA);g.bind(c.LINEAR,c.CLAMP_TO_EDGE),(d=r.fbo=l.createFramebuffer(u,u)).colorAttachment.set(g.texture)}l.bindFramebuffer.set(d.framebuffer),l.viewport.set([0,0,u,u]),e.useProgram(\"hillshadePrepare\").draw(l,c.TRIANGLES,i,o,s,Et.disabled,Sr(r,a),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function tn(e,r,n,a,i){var o=a.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(),h=i.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),f=!r||Math.abs(r.tileID.overscaledZ-h)>Math.abs(e.tileID.overscaledZ-h),p=f&&e.refreshedUponExpiration?1:t.clamp(f?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}}function en(e,r,n){var 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c=e.context,u=c.gl,h=e.depthModeForSublayer(0,At.ReadOnly),f=Mt.disabled,p=e.colorModeForRenderPass(),d=[],g=0;g<a.length;g++){var v=a[g],m=r.getTile(v),y=m.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:br(e,v,m,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var k=w[_];d.push({segments:new t.SegmentVector([k]),sortKey:k.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 T=0,A=d;T<A.length;T+=1){var M=A[T],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,O=S.uniformValues,z=M.segments;L.draw(c,u.TRIANGLES,h,f,p,Et.disabled,O,n.id,C,P,z,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,a){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===e.renderPass){var i=e.context,o=i.gl,s=e.depthModeForSublayer(0,At.ReadOnly),l=Mt.disabled,c=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 a=r.heatmapFbo;if(a)n.bindTexture(n.TEXTURE_2D,a.colorAttachment.get()),t.bindFramebuffer.set(a.framebuffer);else{var i=n.createTexture();n.bindTexture(n.TEXTURE_2D,i),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),a=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4),function t(e,r,n,a){var i=e.gl;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,r.width/4,r.height/4,0,i.RGBA,e.extTextureHalfFloat?e.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE,null),a.colorAttachment.set(n),e.extTextureHalfFloat&&i.checkFramebufferStatus(i.FRAMEBUFFER)!==i.FRAMEBUFFER_COMPLETE&&(e.extTextureHalfFloat=null,a.colorAttachment.setDirty(),t(e,r,n,a))}(t,e,i,a)}}(i,e,n),i.clear({color:t.Color.transparent});for(var u=0;u<a.length;u++){var h=a[u];if(!r.hasRenderableParent(h)){var f=r.getTile(h),p=f.getBucket(n);if(p){var d=p.programConfigurations.get(n.id),g=e.useProgram(\"heatmap\",d),v=e.transform.zoom;g.draw(i,o.TRIANGLES,s,l,c,Et.disabled,Ar(h.posMatrix,f,v,n.paint.get(\"heatmap-intensity\")),n.id,p.layoutVertexBuffer,p.indexBuffer,p.segments,n.paint,e.transform.zoom,d)}}}i.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,a=n.gl,i=r.heatmapFbo;if(i){n.activeTexture.set(a.TEXTURE0),a.bindTexture(a.TEXTURE_2D,i.colorAttachment.get()),n.activeTexture.set(a.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,a.RGBA)),o.bind(a.LINEAR,a.CLAMP_TO_EDGE),e.useProgram(\"heatmapTexture\").draw(n,a.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,a){var i=t.create();t.ortho(i,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:i,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:a,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,a){if(\"translucent\"===e.renderPass){var i=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0!==i.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\"),h=u.constantOr(1),f=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=c?\"lineSDF\":h?\"linePattern\":f?\"lineGradient\":\"line\",g=e.context,v=g.gl,m=!0;if(f){g.activeTexture.set(v.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(g,n.gradient,v.RGBA)),y.bind(v.LINEAR,v.CLAMP_TO_EDGE)}for(var x=0,b=a;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!h||w.patternsLoaded()){var k=w.getBucket(n);if(k){var T=k.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,T),S=m||M.program!==A,E=u.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas.patternPositions[E.to],C=w.imageAtlas.patternPositions[E.from];L&&C&&T.setConstantPatternPositions(L,C)}var P=c?Or(e,w,n,c,p):h?Pr(e,w,n,p):f?Cr(e,w,n):Lr(e,w,n);c&&(S||e.lineAtlas.dirty)?(g.activeTexture.set(v.TEXTURE0),e.lineAtlas.bind(g)):h&&(g.activeTexture.set(v.TEXTURE0),w.imageAtlasTexture.bind(v.LINEAR,v.CLAMP_TO_EDGE),T.updatePatternPaintBuffers(p)),M.draw(g,v.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,P,n.id,k.layoutVertexBuffer,k.indexBuffer,k.segments,n.paint,e.transform.zoom,T),m=!1}}}}}},fill:function(e,r,n,a){var i=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===i.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);Jr(e,r,n,a,u,s,!1)}if(\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")){var h=e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,At.ReadOnly);Jr(e,r,n,a,h,s,!0)}}},\"fill-extrusion\":function(t,e,r,n){var a=r.paint.get(\"fill-extrusion-opacity\");if(0!==a&&\"translucent\"===t.renderPass){var i=new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D);if(1!==a||r.paint.get(\"fill-extrusion-pattern\").constantOr(1))Kr(t,e,r,n,i,Mt.disabled,St.disabled),Kr(t,e,r,n,i,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();Kr(t,e,r,n,i,Mt.disabled,o)}}},hillshade:function(t,e,r,n){if(\"offscreen\"===t.renderPass||\"translucent\"===t.renderPass){for(var a=t.context,i=e.getSource().maxzoom,o=t.depthModeForSublayer(0,At.ReadOnly),s=Mt.disabled,l=t.colorModeForRenderPass(),c=0,u=n;c<u.length;c+=1){var h=u[c],f=e.getTile(h);f.needsHillshadePrepare&&\"offscreen\"===t.renderPass?$r(t,f,r,i,o,s,l):\"translucent\"===t.renderPass&&Qr(t,f,r,o,s,l)}a.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\"))for(var a=t.context,i=a.gl,o=e.getSource(),s=t.useProgram(\"raster\"),l=Mt.disabled,c=t.colorModeForRenderPass(),u=n.length&&n[0].overscaledZ,h=!t.options.moving,f=0,p=n;f<p.length;f+=1){var d=p[f],g=t.depthModeForSublayer(d.overscaledZ-u,1===r.paint.get(\"raster-opacity\")?At.ReadWrite:At.ReadOnly,i.LESS),v=e.getTile(d),m=t.transform.calculatePosMatrix(d.toUnwrapped(),h);v.registerFadeDuration(r.paint.get(\"raster-fade-duration\"));var y=e.findLoadedParent(d,0),x=tn(v,y,e,r,t.transform),b=void 0,_=void 0,w=\"nearest\"===r.paint.get(\"raster-resampling\")?i.NEAREST:i.LINEAR;a.activeTexture.set(i.TEXTURE0),v.texture.bind(w,i.CLAMP_TO_EDGE,i.LINEAR_MIPMAP_NEAREST),a.activeTexture.set(i.TEXTURE1),y?(y.texture.bind(w,i.CLAMP_TO_EDGE,i.LINEAR_MIPMAP_NEAREST),b=Math.pow(2,y.tileID.overscaledZ-v.tileID.overscaledZ),_=[v.tileID.canonical.x*b%1,v.tileID.canonical.y*b%1]):v.texture.bind(w,i.CLAMP_TO_EDGE,i.LINEAR_MIPMAP_NEAREST);var k=Dr(m,_||[0,0],b||1,x,r);o instanceof P?s.draw(a,i.TRIANGLES,g,l,c,Et.disabled,k,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):v.maskedBoundsBuffer&&v.maskedIndexBuffer&&v.segments?s.draw(a,i.TRIANGLES,g,l,c,Et.disabled,k,r.id,v.maskedBoundsBuffer,v.maskedIndexBuffer,v.segments,r.paint,t.transform.zoom):s.draw(a,i.TRIANGLES,g,l,c,Et.disabled,k,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get(\"background-color\"),a=r.paint.get(\"background-opacity\");if(0!==a){var i=t.context,o=i.gl,s=t.transform,l=s.tileSize,c=r.paint.get(\"background-pattern\");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===a&&t.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(t.renderPass===u){var h=Mt.disabled,f=t.depthModeForSublayer(0,\"opaque\"===u?At.ReadWrite:At.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?\"backgroundPattern\":\"background\"),g=s.coveringTiles({tileSize:l});c&&(i.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var v=r.getCrossfadeParameters(),m=0,y=g;m<y.length;m+=1){var x=y[m],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Nr(b,a,t,c,{tileID:x,tileSize:l},v):Br(b,a,n);d.draw(i,o.TRIANGLES,f,h,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)en(t,e,r[n])},custom:function(t,e,r){var n=t.context,a=r.implementation;if(\"offscreen\"===t.renderPass){var i=a.prerender;i&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),i.call(a,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\"===a.renderingMode?new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,At.ReadOnly);n.setDepthMode(o),a.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},an=function(e,r){this.context=new Lt(e),this.transform=r,this._tileTextures={},this.setup(),this.numSublayers=Ct.maxUnderzooming+Ct.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.depthRboNeedsClear=!0,this.emptyProgramConfiguration=new t.ProgramConfiguration,this.crossTileSymbolIndex=new Oe};function on(t,e){if(t.y>e.y){var r=t;t=e,e=r}return{x0:t.x,y0:t.y,x1:e.x,y1:e.y,dx:e.x-t.x,dy:e.y-t.y}}function sn(t,e,r,n,a){var i=Math.max(r,Math.floor(e.y0)),o=Math.min(n,Math.ceil(e.y1));if(t.x0===e.x0&&t.y0===e.y0?t.x0+e.dy/t.dy*t.dx<e.x1:t.x1-e.dy/t.dy*t.dx<e.x0){var s=t;t=e,e=s}for(var l=t.dx/t.dy,c=e.dx/e.dy,u=t.dx>0,h=e.dx<0,f=i;f<o;f++){var p=l*Math.max(0,Math.min(t.dy,f+u-t.y0))+t.x0,d=c*Math.max(0,Math.min(e.dy,f+h-e.y0))+e.x0;a(Math.floor(d),Math.ceil(p),f)}}function ln(t,e,r,n,a,i){var o,s=on(t,e),l=on(e,r),c=on(r,t);s.dy>l.dy&&(o=s,s=l,l=o),s.dy>c.dy&&(o=s,s=c,c=o),l.dy>c.dy&&(o=l,l=c,c=o),s.dy&&sn(c,s,n,a,i),l.dy&&sn(c,l,n,a,i)}an.prototype.resize=function(e,r){var n=this.context.gl;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 a=0,i=this.style._order;a<i.length;a+=1){var o=i[a];this.style._layers[o].resize()}this.depthRbo&&(n.deleteRenderbuffer(this.depthRbo),this.depthRbo=null)},an.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,Fe.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,Fe.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var a=new t.StructArrayLayout4i8;a.emplaceBack(0,0,0,0),a.emplaceBack(t.EXTENT,0,t.EXTENT,0),a.emplaceBack(0,t.EXTENT,0,t.EXTENT),a.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(a,t.rasterBoundsAttributes.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var i=new t.StructArrayLayout2i4;i.emplaceBack(0,0),i.emplaceBack(1,0),i.emplaceBack(0,1),i.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(i,Fe.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);var l=this.context.gl;this.stencilClearMode=new Mt({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},an.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,Tr(n),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},an.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(At.disabled);var a=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var i=0,o=e;i<o.length;i+=1){var s=o[i],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;a.draw(r,n.TRIANGLES,At.disabled,new Mt({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Tr(s.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},an.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)},an.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)},an.prototype.colorModeForRenderPass=function(){var e=this.context.gl;return this._showOverdrawInspector?new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0]):\"opaque\"===this.renderPass?St.unblended:St.alphaBlended},an.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])},an.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},an.prototype.render=function(e,r){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 n=this.style._order,a=this.style.sourceCaches;for(var i in a){var o=a[i];o.used&&o.prepare(this.context)}var s={},l={},c={};for(var u in a){var h=a[u];s[u]=h.getVisibleCoordinates(),l[u]=s[u].slice().reverse(),c[u]=h.getVisibleCoordinates(!0).reverse()}for(var f in a){var p=a[f],d=p.getSource();if(\"raster\"===d.type||\"raster-dem\"===d.type){for(var g=[],v=0,m=s[f];v<m.length;v+=1){var y=m[v];g.push(p.getTile(y))}Vr(g,this.context)}}this.opaquePassCutoff=1/0;for(var x=0;x<n.length;x++){var b=n[x];if(this.style._layers[b].is3D()){this.opaquePassCutoff=x;break}}this.renderPass=\"offscreen\",this.depthRboNeedsClear=!0;for(var _=0,w=n;_<w.length;_+=1){var k=w[_],T=this.style._layers[k];if(T.hasOffscreenPass()&&!T.isHidden(this.transform.zoom)){var A=l[T.source];(\"custom\"===T.type||A.length)&&this.renderLayer(this,a[T.source],T,A)}}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=n.length-1;this.currentLayer>=0;this.currentLayer--){var M=this.style._layers[n[this.currentLayer]],S=a[M.source],E=s[M.source];this._renderTileClippingMasks(M,E),this.renderLayer(this,S,M,E)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<n.length;this.currentLayer++){var L=this.style._layers[n[this.currentLayer]],C=a[L.source],P=(\"symbol\"===L.type?c:l)[L.source];this._renderTileClippingMasks(L,s[L.source]),this.renderLayer(this,C,L,P)}if(this.options.showTileBoundaries)for(var O in a){nn.debug(this,a[O],s[O]);break}this.context.setDefault()},an.prototype.setupOffscreenDepthRenderbuffer=function(){var t=this.context;this.depthRbo||(this.depthRbo=t.createRenderbuffer(t.gl.DEPTH_COMPONENT16,this.width,this.height))},an.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||\"custom\"===r.type||n.length)&&(this.id=r.id,nn[r.type](t,e,r,n,this.style.placement.variableOffsets))},an.prototype.translatePosMatrix=function(e,r,n,a,i){if(!n[0]&&!n[1])return e;var o=i?\"map\"===a?this.transform.angle:0:\"viewport\"===a?-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=[i?n[0]:ce(r,n[0],this.transform.zoom),i?n[1]:ce(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},an.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},an.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},an.prototype.isPatternMissing=function(t){if(!t)return!1;var e=this.imageManager.getPattern(t.from),r=this.imageManager.getPattern(t.to);return!e||!r},an.prototype.useProgram=function(t,e){void 0===e&&(e=this.emptyProgramConfiguration),this.cache=this.cache||{};var r=\"\"+t+(e.cacheKey||\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new fr(this.context,ur[t],e,jr[t],this._showOverdrawInspector)),this.cache[r]},an.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()},an.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)};var cn=function(e,r,n){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===n||n,this._minZoom=e||0,this._maxZoom=r||22,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._posMatrixCache={},this._alignedPosMatrixCache={}},un={minZoom:{configurable:!0},maxZoom:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerPoint:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};cn.prototype.clone=function(){var t=new cn(this._minZoom,this._maxZoom,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._calcMatrices(),t},un.minZoom.get=function(){return this._minZoom},un.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},un.maxZoom.get=function(){return this._maxZoom},un.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},un.renderWorldCopies.get=function(){return this._renderWorldCopies},un.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},un.worldSize.get=function(){return this.tileSize*this.scale},un.centerPoint.get=function(){return this.size._div(2)},un.size.get=function(){return new t.Point(this.width,this.height)},un.bearing.get=function(){return-this.angle/Math.PI*180},un.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))},un.pitch.get=function(){return this._pitch/Math.PI*180},un.pitch.set=function(e){var r=t.clamp(e,0,60)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},un.fov.get=function(){return this._fov/Math.PI*180},un.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())},un.zoom.get=function(){return this._zoom},un.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())},un.center.get=function(){return this._center},un.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},cn.prototype.coveringZoomLevel=function(t){return(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize))},cn.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),a=this.pointCoordinate(new t.Point(this.width,0)),i=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,a.x,i.x,o.x)),l=Math.floor(Math.max(n.x,a.x,i.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},cn.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 a=t.MercatorCoordinate.fromLngLat(this.center),i=Math.pow(2,r),o=new t.Point(i*a.x-.5,i*a.y-.5);return function(e,r,n,a){void 0===a&&(a=!0);var i=1<<e,o={};function s(r,s,l){var c,u,h,f;if(l>=0&&l<=i)for(c=r;c<s;c++)u=Math.floor(c/i),h=(c%i+i)%i,0!==u&&!0!==a||(f=new t.OverscaledTileID(n,u,e,h,l),o[f.key]=f)}var l=r.map(function(e){return new t.Point(e.x,e.y)._mult(i)});return ln(l[0],l[1],l[2],0,i,s),ln(l[2],l[3],l[0],0,i,s),Object.keys(o).map(function(t){return o[t]})}(r,[this.pointCoordinate(new t.Point(0,0)),this.pointCoordinate(new t.Point(this.width,0)),this.pointCoordinate(new t.Point(this.width,this.height)),this.pointCoordinate(new t.Point(0,this.height))],e.reparseOverscaled?n:r,this._renderWorldCopies).sort(function(t,e){return o.dist(t.canonical)-o.dist(e.canonical)})},cn.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},un.unmodified.get=function(){return this._unmodified},cn.prototype.zoomScale=function(t){return Math.pow(2,t)},cn.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},cn.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)},cn.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},un.point.get=function(){return this.project(this.center)},cn.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),a=this.pointCoordinate(this.centerPoint),i=this.locationCoordinate(e),o=new t.MercatorCoordinate(i.x-(n.x-a.x),i.y-(n.y-a.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},cn.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},cn.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},cn.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},cn.prototype.coordinateLocation=function(t){return t.toLngLat()},cn.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 a=r[3],i=n[3],o=r[0]/a,s=n[0]/i,l=r[1]/a,c=n[1]/i,u=r[2]/a,h=n[2]/i,f=u===h?0:(0-u)/(h-u);return new t.MercatorCoordinate(t.number(o,s,f)/this.worldSize,t.number(l,c,f)/this.worldSize)},cn.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])},cn.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)))},cn.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},cn.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])},cn.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,a=r?this._alignedPosMatrixCache:this._posMatrixCache;if(a[n])return a[n];var i=e.canonical,o=this.worldSize/this.zoomScale(i.z),s=i.x+Math.pow(2,i.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,i.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),a[n]=new Float32Array(l),a[n]},cn.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},cn.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,a,i=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var h=this.latRange;i=t.mercatorYfromLat(h[1])*this.worldSize,e=(o=t.mercatorYfromLat(h[0])*this.worldSize)-i<c.y?c.y/(o-i):0}if(this.lngRange){var f=this.lngRange;s=t.mercatorXfromLng(f[0])*this.worldSize,r=(l=t.mercatorXfromLng(f[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+i)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var g=p.y,v=c.y/2;g-v<i&&(a=i+v),g+v>o&&(a=o-v)}if(this.lngRange){var m=p.x,y=c.x/2;m-y<s&&(n=s+y),m+y>l&&(n=l-y)}void 0===n&&void 0===a||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==a?a:p.y))),this._unmodified=u,this._constraining=!1}},cn.prototype._calcMatrices=function(){if(this.height){this.cameraToCenterDistance=.5/Math.tan(this._fov/2)*this.height;var e=this._fov/2,r=Math.PI/2+this._pitch,n=Math.sin(e)*this.cameraToCenterDistance/Math.sin(Math.PI-r-e),a=this.point,i=a.x,o=a.y,s=1.01*(Math.cos(Math.PI/2-this._pitch)*n+this.cameraToCenterDistance),l=this.height/50,c=new Float64Array(16);t.perspective(c,this._fov,this.width/this.height,l,s),t.scale(c,c,[1,-1,1]),t.translate(c,c,[0,0,-this.cameraToCenterDistance]),t.rotateX(c,c,this._pitch),t.rotateZ(c,c,this.angle),t.translate(c,c,[-i,-o,0]),this.mercatorMatrix=t.scale([],c,[this.worldSize,this.worldSize,this.worldSize]),t.scale(c,c,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=c;var u=this.width%2/2,h=this.height%2/2,f=Math.cos(this.angle),p=Math.sin(this.angle),d=i-Math.round(i)+f*u+p*h,g=o-Math.round(o)+f*h+p*u,v=new Float64Array(c);if(t.translate(v,v,[d>.5?d-1:d,g>.5?g-1:g,0]),this.alignedProjMatrix=v,c=t.create(),t.scale(c,c,[this.width/2,-this.height/2,1]),t.translate(c,c,[1,-1,0]),this.labelPlaneMatrix=c,c=t.create(),t.scale(c,c,[1,-1,1]),t.translate(c,c,[-1,-1,0]),t.scale(c,c,[2/this.width,2/this.height,1]),this.glCoordMatrix=c,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(c=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=c,this._posMatrixCache={},this._alignedPosMatrixCache={}}},cn.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},cn.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},cn.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,a=r.y,i=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),a=Math.min(a,c.y),i=Math.max(i,c.x),o=Math.max(o,c.y)}return[new t.Point(n,a),new t.Point(i,a),new t.Point(i,o),new t.Point(n,o),new t.Point(n,a)]},Object.defineProperties(cn.prototype,un);var hn=function(){var e,r,n,a;t.bindAll([\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(e=this._updateHashUnthrottled.bind(this),300,r=!1,n=null,a=function(){n=null,r&&(e(),n=setTimeout(a,300),r=!1)},function(){return r=!0,n||a(),n})};hn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},hn.prototype.remove=function(){return t.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},hn.prototype.getHashString=function(t){var e=this._map.getCenter(),r=Math.round(100*this._map.getZoom())/100,n=Math.ceil((r*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,n),i=Math.round(e.lng*a)/a,o=Math.round(e.lat*a)/a,s=this._map.getBearing(),l=this._map.getPitch(),c=\"\";return c+=t?\"#/\"+i+\"/\"+o+\"/\"+r:\"#\"+r+\"/\"+o+\"/\"+i,(s||l)&&(c+=\"/\"+Math.round(10*s)/10),l&&(c+=\"/\"+Math.round(l)),c},hn.prototype._onHashChange=function(){var e=t.window.location.hash.replace(\"#\",\"\").split(\"/\");return e.length>=3&&(this._map.jumpTo({center:[+e[2],+e[1]],zoom:+e[0],bearing:+(e[3]||0),pitch:+(e[4]||0)}),!0)},hn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,\"\",e)}catch(t){}};var fn=function(e){function n(n,a,i,o){void 0===o&&(o={});var s=r.mousePos(a.getCanvasContainer(),i),l=a.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:i},o)),this._defaultPrevented=!1,this.target=a}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var a={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},a.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,a),n}(t.Event),pn=function(e){function n(n,a,i){var o=r.touchPos(a.getCanvasContainer(),i),s=o.map(function(t){return a.unproject(t)}),l=o.reduce(function(t,e,r,n){return t.add(e.div(n.length))},new t.Point(0,0)),c=a.unproject(l);e.call(this,n,{points:o,point:l,lngLats:s,lngLat:c,originalEvent:i}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var a={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},a.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,a),n}(t.Event),dn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),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),gn=function(e){this._map=e,this._el=e.getCanvasContainer(),this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};gn.prototype.setZoomRate=function(t){this._defaultZoomRate=t},gn.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},gn.prototype.isEnabled=function(){return!!this._enabled},gn.prototype.isActive=function(){return!!this._active},gn.prototype.isZooming=function(){return!!this._zooming},gn.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},gn.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},gn.prototype.onWheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),a=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\":a>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(a*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.isActive()||this._start(e)),e.preventDefault()}},gn.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this.isActive()||this._start(t)},gn.prototype._start=function(e){if(this._delta){this._frameId&&(this._map._cancelRenderFrame(this._frameId),this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0,this._map.fire(new t.Event(\"movestart\",{originalEvent:e})),this._map.fire(new t.Event(\"zoomstart\",{originalEvent:e}))),this._finishTimeout&&clearTimeout(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=this._map._requestRenderFrame(this._onScrollFrame))}},gn.prototype._onScrollFrame=function(){var e=this;if(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,a=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==a&&(a=1/a);var i=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(i*a))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,s=this._startZoom,l=this._easing,c=!1;if(\"wheel\"===this._type&&s&&l){var u=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=l(u);r.zoom=t.number(s,o,h),u<1?this._frameId||(this._frameId=this._map._requestRenderFrame(this._onScrollFrame)):c=!0}else r.zoom=o,c=!0;r.setLocationAtPoint(this._around,this._aroundPoint),this._map.fire(new t.Event(\"move\",{originalEvent:this._lastWheelEvent})),this._map.fire(new t.Event(\"zoom\",{originalEvent:this._lastWheelEvent})),c&&(this._active=!1,this._finishTimeout=setTimeout(function(){e._zooming=!1,e._map.fire(new t.Event(\"zoomend\",{originalEvent:e._lastWheelEvent})),e._map.fire(new t.Event(\"moveend\",{originalEvent:e._lastWheelEvent})),delete e._targetZoom},200))}},gn.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,a=(t.browser.now()-n.start)/n.duration,i=n.easing(a+.01)-n.easing(a),o=.27/Math.sqrt(i*i+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};var vn=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._container=e.getContainer(),this._clickTolerance=r.clickTolerance||1,t.bindAll([\"_onMouseMove\",\"_onMouseUp\",\"_onKeyDown\"],this)};vn.prototype.isEnabled=function(){return!!this._enabled},vn.prototype.isActive=function(){return!!this._active},vn.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},vn.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},vn.prototype.onMouseDown=function(e){this.isEnabled()&&e.shiftKey&&0===e.button&&(t.window.document.addEventListener(\"mousemove\",this._onMouseMove,!1),t.window.document.addEventListener(\"keydown\",this._onKeyDown,!1),t.window.document.addEventListener(\"mouseup\",this._onMouseUp,!1),r.disableDrag(),this._startPos=this._lastPos=r.mousePos(this._el,e),this._active=!0)},vn.prototype._onMouseMove=function(t){var e=r.mousePos(this._el,t);if(!(this._lastPos.equals(e)||!this._box&&e.dist(this._startPos)<this._clickTolerance)){var n=this._startPos;this._lastPos=e,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(n.x,e.x),i=Math.max(n.x,e.x),o=Math.min(n.y,e.y),s=Math.max(n.y,e.y);r.setTransform(this._box,\"translate(\"+a+\"px,\"+o+\"px)\"),this._box.style.width=i-a+\"px\",this._box.style.height=s-o+\"px\"}},vn.prototype._onMouseUp=function(e){if(0===e.button){var n=this._startPos,a=r.mousePos(this._el,e);this._finish(),r.suppressClick(),n.x===a.x&&n.y===a.y?this._fireEvent(\"boxzoomcancel\",e):this._map.fitScreenCoordinates(n,a,this._map.getBearing(),{linear:!0}).fire(new t.Event(\"boxzoomend\",{originalEvent:e}))}},vn.prototype._onKeyDown=function(t){27===t.keyCode&&(this._finish(),this._fireEvent(\"boxzoomcancel\",t))},vn.prototype._finish=function(){this._active=!1,t.window.document.removeEventListener(\"mousemove\",this._onMouseMove,!1),t.window.document.removeEventListener(\"keydown\",this._onKeyDown,!1),t.window.document.removeEventListener(\"mouseup\",this._onMouseUp,!1),this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},vn.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var mn=t.bezier(0,0,.25,1),yn=function(e,r){this._map=e,this._el=r.element||e.getCanvasContainer(),this._state=\"disabled\",this._button=r.button||\"right\",this._bearingSnap=r.bearingSnap||0,this._pitchWithRotate=!1!==r.pitchWithRotate,t.bindAll([\"onMouseDown\",\"_onMouseMove\",\"_onMouseUp\",\"_onBlur\",\"_onDragFrame\"],this)};yn.prototype.isEnabled=function(){return\"disabled\"!==this._state},yn.prototype.isActive=function(){return\"active\"===this._state},yn.prototype.enable=function(){this.isEnabled()||(this._state=\"enabled\")},yn.prototype.disable=function(){if(this.isEnabled())switch(this._state){case\"active\":this._state=\"disabled\",this._unbind(),this._deactivate(),this._fireEvent(\"rotateend\"),this._pitchWithRotate&&this._fireEvent(\"pitchend\"),this._fireEvent(\"moveend\");break;case\"pending\":this._state=\"disabled\",this._unbind();break;default:this._state=\"disabled\"}},yn.prototype.onMouseDown=function(e){if(\"enabled\"===this._state){var n=\"touchstart\"===e.type;if(n)this._startTime=Date.now();else if(\"right\"===this._button){if(this._eventButton=r.mouseButton(e),this._eventButton!==(e.ctrlKey?0:2))return}else{if(e.ctrlKey||0!==r.mouseButton(e))return;this._eventButton=0}r.disableDrag(),n?(t.window.document.addEventListener(\"touchmove\",this._onMouseMove,{capture:!0}),t.window.document.addEventListener(\"touchend\",this._onMouseUp)):(t.window.document.addEventListener(\"mousemove\",this._onMouseMove,{capture:!0}),t.window.document.addEventListener(\"mouseup\",this._onMouseUp)),t.window.addEventListener(\"blur\",this._onBlur),this._state=\"pending\",this._inertia=[[t.browser.now(),this._map.getBearing()]],this._startPos=this._prevPos=this._lastPos=r.mousePos(this._el,e),this._center=this._map.transform.centerPoint,e.preventDefault()}},yn.prototype._onMouseMove=function(t){var e=r.mousePos(this._el,t);this._lastPos.equals(e)||(this._lastMoveEvent=t,this._lastPos=e,\"pending\"===this._state&&(this._state=\"active\",this._fireEvent(\"rotatestart\",t),this._fireEvent(\"movestart\",t),this._pitchWithRotate&&this._fireEvent(\"pitchstart\",t)),this._frameId||(this._frameId=this._map._requestRenderFrame(this._onDragFrame)))},yn.prototype._onDragFrame=function(){this._frameId=null;var e=this._lastMoveEvent;if(e){var r=this._map.transform,n=this._prevPos,a=this._lastPos,i=.8*(n.x-a.x),o=-.5*(n.y-a.y),s=r.bearing-i,l=r.pitch-o,c=this._inertia,u=c[c.length-1];this._drainInertiaBuffer(),c.push([t.browser.now(),this._map._normalizeBearing(s,u[1])]),r.bearing=s,this._pitchWithRotate&&(this._fireEvent(\"pitch\",e),r.pitch=l),this._fireEvent(\"rotate\",e),this._fireEvent(\"move\",e),delete this._lastMoveEvent,this._prevPos=this._lastPos}},yn.prototype._onMouseUp=function(t){if(\"touchend\"===t.type&&this._startPos===this._lastPos&&Date.now()-this._startTime<300&&this._el.click(),r.mouseButton(t)===this._eventButton)switch(this._state){case\"active\":this._state=\"enabled\",r.suppressClick(),this._unbind(),this._deactivate(),this._inertialRotate(t);break;case\"pending\":this._state=\"enabled\",this._unbind()}},yn.prototype._onBlur=function(t){switch(this._state){case\"active\":this._state=\"enabled\",this._unbind(),this._deactivate(),this._fireEvent(\"rotateend\",t),this._pitchWithRotate&&this._fireEvent(\"pitchend\",t),this._fireEvent(\"moveend\",t);break;case\"pending\":this._state=\"enabled\",this._unbind()}},yn.prototype._unbind=function(){t.window.document.removeEventListener(\"mousemove\",this._onMouseMove,{capture:!0}),t.window.document.removeEventListener(\"mouseup\",this._onMouseUp),t.window.document.removeEventListener(\"touchmove\",this._onMouseMove,{capture:!0}),t.window.document.removeEventListener(\"touchend\",this._onMouseUp),t.window.removeEventListener(\"blur\",this._onBlur),r.enableDrag()},yn.prototype._deactivate=function(){this._frameId&&(this._map._cancelRenderFrame(this._frameId),this._frameId=null),delete this._lastMoveEvent,delete this._startPos,delete this._prevPos,delete this._lastPos},yn.prototype._inertialRotate=function(t){var e=this;this._fireEvent(\"rotateend\",t),this._drainInertiaBuffer();var r=this._map,n=r.getBearing(),a=this._inertia,i=function(){Math.abs(n)<e._bearingSnap?r.resetNorth({noMoveStart:!0},{originalEvent:t}):e._fireEvent(\"moveend\",t),e._pitchWithRotate&&e._fireEvent(\"pitchend\",t)};if(a.length<2)i();else{var o=a[0],s=a[a.length-1],l=a[a.length-2],c=r._normalizeBearing(n,l[1]),u=s[1]-o[1],h=u<0?-1:1,f=(s[0]-o[0])/1e3;if(0!==u&&0!==f){var p=Math.abs(u*(.25/f));p>180&&(p=180);var d=p/180;c+=h*p*(d/2),Math.abs(r._normalizeBearing(c,0))<this._bearingSnap&&(c=r._normalizeBearing(0,c)),r.rotateTo(c,{duration:1e3*d,easing:mn,noMoveStart:!0},{originalEvent:t})}else i()}},yn.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},yn.prototype._drainInertiaBuffer=function(){for(var e=this._inertia,r=t.browser.now();e.length>0&&r-e[0][0]>160;)e.shift()};var xn=t.bezier(0,0,.3,1),bn=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._state=\"disabled\",this._clickTolerance=r.clickTolerance||1,t.bindAll([\"_onMove\",\"_onMouseUp\",\"_onTouchEnd\",\"_onBlur\",\"_onDragFrame\"],this)};bn.prototype.isEnabled=function(){return\"disabled\"!==this._state},bn.prototype.isActive=function(){return\"active\"===this._state},bn.prototype.enable=function(){this.isEnabled()||(this._el.classList.add(\"mapboxgl-touch-drag-pan\"),this._state=\"enabled\")},bn.prototype.disable=function(){if(this.isEnabled())switch(this._el.classList.remove(\"mapboxgl-touch-drag-pan\"),this._state){case\"active\":this._state=\"disabled\",this._unbind(),this._deactivate(),this._fireEvent(\"dragend\"),this._fireEvent(\"moveend\");break;case\"pending\":this._state=\"disabled\",this._unbind();break;default:this._state=\"disabled\"}},bn.prototype.onMouseDown=function(e){\"enabled\"===this._state&&(e.ctrlKey||0!==r.mouseButton(e)||(r.addEventListener(t.window.document,\"mousemove\",this._onMove,{capture:!0}),r.addEventListener(t.window.document,\"mouseup\",this._onMouseUp),this._start(e)))},bn.prototype.onTouchStart=function(e){\"enabled\"===this._state&&(e.touches.length>1||(r.addEventListener(t.window.document,\"touchmove\",this._onMove,{capture:!0,passive:!1}),r.addEventListener(t.window.document,\"touchend\",this._onTouchEnd),this._start(e)))},bn.prototype._start=function(e){t.window.addEventListener(\"blur\",this._onBlur),this._state=\"pending\",this._startPos=this._mouseDownPos=this._prevPos=this._lastPos=r.mousePos(this._el,e),this._inertia=[[t.browser.now(),this._startPos]]},bn.prototype._onMove=function(e){e.preventDefault();var n=r.mousePos(this._el,e);this._lastPos.equals(n)||\"pending\"===this._state&&n.dist(this._mouseDownPos)<this._clickTolerance||(this._lastMoveEvent=e,this._lastPos=n,this._drainInertiaBuffer(),this._inertia.push([t.browser.now(),this._lastPos]),\"pending\"===this._state&&(this._state=\"active\",this._fireEvent(\"dragstart\",e),this._fireEvent(\"movestart\",e)),this._frameId||(this._frameId=this._map._requestRenderFrame(this._onDragFrame)))},bn.prototype._onDragFrame=function(){this._frameId=null;var t=this._lastMoveEvent;if(t){var e=this._map.transform;e.setLocationAtPoint(e.pointLocation(this._prevPos),this._lastPos),this._fireEvent(\"drag\",t),this._fireEvent(\"move\",t),this._prevPos=this._lastPos,delete this._lastMoveEvent}},bn.prototype._onMouseUp=function(t){if(0===r.mouseButton(t))switch(this._state){case\"active\":this._state=\"enabled\",r.suppressClick(),this._unbind(),this._deactivate(),this._inertialPan(t);break;case\"pending\":this._state=\"enabled\",this._unbind()}},bn.prototype._onTouchEnd=function(t){switch(this._state){case\"active\":this._state=\"enabled\",this._unbind(),this._deactivate(),this._inertialPan(t);break;case\"pending\":this._state=\"enabled\",this._unbind()}},bn.prototype._onBlur=function(t){switch(this._state){case\"active\":this._state=\"enabled\",this._unbind(),this._deactivate(),this._fireEvent(\"dragend\",t),this._fireEvent(\"moveend\",t);break;case\"pending\":this._state=\"enabled\",this._unbind()}},bn.prototype._unbind=function(){r.removeEventListener(t.window.document,\"touchmove\",this._onMove,{capture:!0,passive:!1}),r.removeEventListener(t.window.document,\"touchend\",this._onTouchEnd),r.removeEventListener(t.window.document,\"mousemove\",this._onMove,{capture:!0}),r.removeEventListener(t.window.document,\"mouseup\",this._onMouseUp),r.removeEventListener(t.window,\"blur\",this._onBlur)},bn.prototype._deactivate=function(){this._frameId&&(this._map._cancelRenderFrame(this._frameId),this._frameId=null),delete this._lastMoveEvent,delete this._startPos,delete this._prevPos,delete this._mouseDownPos,delete this._lastPos},bn.prototype._inertialPan=function(t){this._fireEvent(\"dragend\",t),this._drainInertiaBuffer();var e=this._inertia;if(e.length<2)this._fireEvent(\"moveend\",t);else{var r=e[e.length-1],n=e[0],a=r[1].sub(n[1]),i=(r[0]-n[0])/1e3;if(0===i||r[1].equals(n[1]))this._fireEvent(\"moveend\",t);else{var o=a.mult(.3/i),s=o.mag();s>1400&&(s=1400,o._unit()._mult(s));var l=s/750,c=o.mult(-l/2);this._map.panBy(c,{duration:1e3*l,easing:xn,noMoveStart:!0},{originalEvent:t})}}},bn.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},bn.prototype._drainInertiaBuffer=function(){for(var e=this._inertia,r=t.browser.now();e.length>0&&r-e[0][0]>160;)e.shift()};var _n=function(e){this._map=e,this._el=e.getCanvasContainer(),t.bindAll([\"_onKeyDown\"],this)};function wn(t){return t*(2-t)}_n.prototype.isEnabled=function(){return!!this._enabled},_n.prototype.enable=function(){this.isEnabled()||(this._el.addEventListener(\"keydown\",this._onKeyDown,!1),this._enabled=!0)},_n.prototype.disable=function(){this.isEnabled()&&(this._el.removeEventListener(\"keydown\",this._onKeyDown),this._enabled=!1)},_n.prototype._onKeyDown=function(t){if(!(t.altKey||t.ctrlKey||t.metaKey)){var e=0,r=0,n=0,a=0,i=0;switch(t.keyCode){case 61:case 107:case 171:case 187:e=1;break;case 189:case 109:case 173:e=-1;break;case 37:t.shiftKey?r=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?r=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?n=1:(t.preventDefault(),i=-1);break;case 40:t.shiftKey?n=-1:(i=1,t.preventDefault());break;default:return}var o=this._map,s=o.getZoom(),l={duration:300,delayEndEvents:500,easing:wn,zoom:e?Math.round(s)+e*(t.shiftKey?2:1):s,bearing:o.getBearing()+15*r,pitch:o.getPitch()+10*n,offset:[100*-a,100*-i],center:o.getCenter()};o.easeTo(l,{originalEvent:t})}};var kn=function(e){this._map=e,t.bindAll([\"_onDblClick\",\"_onZoomEnd\"],this)};kn.prototype.isEnabled=function(){return!!this._enabled},kn.prototype.isActive=function(){return!!this._active},kn.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},kn.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},kn.prototype.onTouchStart=function(t){var e=this;if(this.isEnabled()&&!(t.points.length>1))if(this._tapped){var r=t.points[0],n=this._tappedPoint;if(n&&n.dist(r)<=30){t.originalEvent.preventDefault();var a=function(){e._tapped&&e._zoom(t),e._map.off(\"touchcancel\",i),e._resetTapped()},i=function(){e._map.off(\"touchend\",a),e._resetTapped()};this._map.once(\"touchend\",a),this._map.once(\"touchcancel\",i)}else this._resetTapped()}else this._tappedPoint=t.points[0],this._tapped=setTimeout(function(){e._tapped=null,e._tappedPoint=null},300)},kn.prototype._resetTapped=function(){clearTimeout(this._tapped),this._tapped=null,this._tappedPoint=null},kn.prototype.onDblClick=function(t){this.isEnabled()&&(t.originalEvent.preventDefault(),this._zoom(t))},kn.prototype._zoom=function(t){this._active=!0,this._map.on(\"zoomend\",this._onZoomEnd),this._map.zoomTo(this._map.getZoom()+(t.originalEvent.shiftKey?-1:1),{around:t.lngLat},t)},kn.prototype._onZoomEnd=function(){this._active=!1,this._map.off(\"zoomend\",this._onZoomEnd)};var Tn=t.bezier(0,0,.15,1),An=function(e){this._map=e,this._el=e.getCanvasContainer(),t.bindAll([\"_onMove\",\"_onEnd\",\"_onTouchFrame\"],this)};An.prototype.isEnabled=function(){return!!this._enabled},An.prototype.enable=function(t){this.isEnabled()||(this._el.classList.add(\"mapboxgl-touch-zoom-rotate\"),this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around)},An.prototype.disable=function(){this.isEnabled()&&(this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\"),this._enabled=!1)},An.prototype.disableRotation=function(){this._rotationDisabled=!0},An.prototype.enableRotation=function(){this._rotationDisabled=!1},An.prototype.onStart=function(e){if(this.isEnabled()&&2===e.touches.length){var n=r.mousePos(this._el,e.touches[0]),a=r.mousePos(this._el,e.touches[1]),i=n.add(a).div(2);this._startVec=n.sub(a),this._startAround=this._map.transform.pointLocation(i),this._gestureIntent=void 0,this._inertia=[],r.addEventListener(t.window.document,\"touchmove\",this._onMove,{passive:!1}),r.addEventListener(t.window.document,\"touchend\",this._onEnd)}},An.prototype._getTouchEventData=function(t){var e=r.mousePos(this._el,t.touches[0]),n=r.mousePos(this._el,t.touches[1]),a=e.sub(n);return{vec:a,center:e.add(n).div(2),scale:a.mag()/this._startVec.mag(),bearing:this._rotationDisabled?0:180*a.angleWith(this._startVec)/Math.PI}},An.prototype._onMove=function(e){if(2===e.touches.length){var r=this._getTouchEventData(e),n=r.vec,a=r.scale,i=r.bearing;if(!this._gestureIntent){var o=this._rotationDisabled&&1!==a||Math.abs(1-a)>.15;Math.abs(i)>10?this._gestureIntent=\"rotate\":o&&(this._gestureIntent=\"zoom\"),this._gestureIntent&&(this._map.fire(new t.Event(this._gestureIntent+\"start\",{originalEvent:e})),this._map.fire(new t.Event(\"movestart\",{originalEvent:e})),this._startVec=n)}this._lastTouchEvent=e,this._frameId||(this._frameId=this._map._requestRenderFrame(this._onTouchFrame)),e.preventDefault()}},An.prototype._onTouchFrame=function(){this._frameId=null;var e=this._gestureIntent;if(e){var r=this._map.transform;this._startScale||(this._startScale=r.scale,this._startBearing=r.bearing);var n=this._getTouchEventData(this._lastTouchEvent),a=n.center,i=n.bearing,o=n.scale,s=r.pointLocation(a),l=r.locationPoint(s);\"rotate\"===e&&(r.bearing=this._startBearing+i),r.zoom=r.scaleZoom(this._startScale*o),r.setLocationAtPoint(this._startAround,l),this._map.fire(new t.Event(e,{originalEvent:this._lastTouchEvent})),this._map.fire(new t.Event(\"move\",{originalEvent:this._lastTouchEvent})),this._drainInertiaBuffer(),this._inertia.push([t.browser.now(),o,a])}},An.prototype._onEnd=function(e){r.removeEventListener(t.window.document,\"touchmove\",this._onMove,{passive:!1}),r.removeEventListener(t.window.document,\"touchend\",this._onEnd);var n=this._gestureIntent,a=this._startScale;if(this._frameId&&(this._map._cancelRenderFrame(this._frameId),this._frameId=null),delete this._gestureIntent,delete this._startScale,delete this._startBearing,delete this._lastTouchEvent,n){this._map.fire(new t.Event(n+\"end\",{originalEvent:e})),this._drainInertiaBuffer();var i=this._inertia,o=this._map;if(i.length<2)o.snapToNorth({},{originalEvent:e});else{var s=i[i.length-1],l=i[0],c=o.transform.scaleZoom(a*s[1]),u=o.transform.scaleZoom(a*l[1]),h=c-u,f=(s[0]-l[0])/1e3,p=s[2];if(0!==f&&c!==u){var d=.15*h/f;Math.abs(d)>2.5&&(d=d>0?2.5:-2.5);var g=1e3*Math.abs(d/(12*.15)),v=c+d*g/2e3;v<0&&(v=0),o.easeTo({zoom:v,duration:g,easing:Tn,around:this._aroundCenter?o.getCenter():o.unproject(p),noMoveStart:!0},{originalEvent:e})}else o.snapToNorth({},{originalEvent:e})}}},An.prototype._drainInertiaBuffer=function(){for(var e=this._inertia,r=t.browser.now();e.length>2&&r-e[0][0]>160;)e.shift()};var Mn={scrollZoom:gn,boxZoom:vn,dragRotate:yn,dragPan:bn,keyboard:_n,doubleClickZoom:kn,touchZoomRotate:An},Sn=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.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,a){if(\"number\"==typeof(a=t.extend({padding:{top:0,bottom:0,right:0,left:0},offset:[0,0],maxZoom:this.transform.maxZoom},a)).padding){var i=a.padding;a.padding={top:i,bottom:i,right:i,left:i}}if(t.deepEqual(Object.keys(a.padding).sort(function(t,e){return t<e?-1:t>e?1:0}),[\"bottom\",\"left\",\"right\",\"top\"])){var o=this.transform,s=o.project(t.LngLat.convert(e)),l=o.project(t.LngLat.convert(r)),c=s.rotate(-n*Math.PI/180),u=l.rotate(-n*Math.PI/180),h=new t.Point(Math.max(c.x,u.x),Math.max(c.y,u.y)),f=new t.Point(Math.min(c.x,u.x),Math.min(c.y,u.y)),p=h.sub(f),d=(o.width-a.padding.left-a.padding.right)/p.x,g=(o.height-a.padding.top-a.padding.bottom)/p.y;if(!(g<0||d<0)){var v=Math.min(o.scaleZoom(o.scale*Math.min(d,g)),a.maxZoom),m=t.Point.convert(a.offset),y=(a.padding.left-a.padding.right)/2,x=(a.padding.top-a.padding.bottom)/2,b=new t.Point(m.x+y,m.y+x).mult(o.scale/o.zoomScale(v));return{center:o.unproject(s.add(l).div(2).sub(b)),zoom:v,bearing:n}}t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\")}else t.warnOnce(\"options.padding must be a positive number, or an Object with keys 'bottom', 'left', 'right', 'top'\")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,a,i){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,a),a,i)},r.prototype._fitInternal=function(e,r,n){return e?(r=t.extend(e,r)).linear?this.easeTo(r,n):this.flyTo(r,n):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,a=!1,i=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(a=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(i=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),a&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),i&&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=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||t.browser.prefersReducedMotion)&&(e.duration=0);var a=this.transform,i=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=\"zoom\"in e?+e.zoom:i,c=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,u=\"pitch\"in e?+e.pitch:s,h=a.centerPoint.add(t.Point.convert(e.offset)),f=a.pointLocation(h),p=t.LngLat.convert(e.center||f);this._normalizeCenter(p);var d,g,v=a.project(f),m=a.project(p).sub(v),y=a.zoomScale(l-i);return e.around&&(d=t.LngLat.convert(e.around),g=a.locationPoint(d)),this._zooming=l!==i,this._rotating=o!==c,this._pitching=u!==s,this._prepareEase(r,e.noMoveStart),clearTimeout(this._easeEndTimeoutID),this._ease(function(e){if(n._zooming&&(a.zoom=t.number(i,l,e)),n._rotating&&(a.bearing=t.number(o,c,e)),n._pitching&&(a.pitch=t.number(s,u,e)),d)a.setLocationAtPoint(d,g);else{var f=a.zoomScale(a.zoom-i),p=l>i?Math.min(2,y):Math.max(.5,y),x=Math.pow(p,1-e),b=a.unproject(v.add(m.mult(e*x)).mult(f));a.setLocationAtPoint(a.renderWorldCopies?b.wrap():b,h)}n._fireMoveEvents(r)},function(){e.delayEndEvents?n._easeEndTimeoutID=setTimeout(function(){return n._afterEase(r)},e.delayEndEvents):n._afterEase(r)},e),this},r.prototype._prepareEase=function(e,r){this._moving=!0,r||this.fire(new t.Event(\"movestart\",e)),this._zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._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){var r=this._zooming,n=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,r&&this.fire(new t.Event(\"zoomend\",e)),n&&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(t.browser.prefersReducedMotion){var a=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(a,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var i=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=\"zoom\"in e?t.clamp(+e.zoom,i.minZoom,i.maxZoom):o,u=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,f=i.zoomScale(c-o),p=i.centerPoint.add(t.Point.convert(e.offset)),d=i.pointLocation(p),g=t.LngLat.convert(e.center||d);this._normalizeCenter(g);var v=i.project(d),m=i.project(g).sub(v),y=e.curve,x=Math.max(i.width,i.height),b=x/f,_=m.mag();if(\"minZoom\"in e){var w=t.clamp(Math.min(e.minZoom,o,c),i.minZoom,i.maxZoom),k=x/i.zoomScale(w-o);y=Math.sqrt(k/_*2)}var T=y*y;function A(t){var e=(b*b-x*x+(t?-1:1)*T*T*_*_)/(2*(t?b:x)*T*_);return Math.log(Math.sqrt(e*e+1)-e)}function M(t){return(Math.exp(t)-Math.exp(-t))/2}function S(t){return(Math.exp(t)+Math.exp(-t))/2}var E=A(0),L=function(t){return S(E)/S(E+y*t)},C=function(t){return x*((S(E)*(M(e=E+y*t)/S(e))-M(E))/T)/_;var e},P=(A(1)-E)/y;if(Math.abs(_)<1e-6||!isFinite(P)){if(Math.abs(x-b)<1e-6)return this.easeTo(e,r);var O=b<x?-1:1;P=Math.abs(Math.log(b/x))/y,C=function(){return 0},L=function(t){return Math.exp(O*y*t)}}if(\"duration\"in e)e.duration=+e.duration;else{var z=\"screenSpeed\"in e?+e.screenSpeed/y:+e.speed;e.duration=1e3*P/z}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==u,this._pitching=h!==l,this._prepareEase(r,!1),this._ease(function(e){var a=e*P,f=1/L(a);i.zoom=1===e?c:o+i.scaleZoom(f),n._rotating&&(i.bearing=t.number(s,u,e)),n._pitching&&(i.pitch=t.number(l,h,e));var d=1===e?g:i.unproject(v.add(m.mult(C(a))).mult(f));i.setLocationAtPoint(i.renderWorldCopies?d.wrap():d,p),n._fireMoveEvents(r)},function(){return n._afterEase(r)},e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var t=this._onEaseEnd;delete this._onEaseEnd,t.call(this)}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),En=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};En.prototype.getDefaultPosition=function(){return\"bottom-right\"},En.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},En.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},En.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\"}},En.prototype._updateData=function(t){!t||\"metadata\"!==t.sourceDataType&&\"style\"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},En.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 a=r[n];if(a.used){var i=a.getSource();i.attribution&&t.indexOf(i.attribution)<0&&t.push(i.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)}},En.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var Ln=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};Ln.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\",\"Mapbox logo\"),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},Ln.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},Ln.prototype.getDefaultPosition=function(){return\"bottom-left\"},Ln.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},Ln.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}},Ln.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 Cn=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};Cn.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},Cn.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 a=n[r];if(a.id===t)return void(a.cancelled=!0)}},Cn.prototype.run=function(){var t=this._currentlyRunning=this._queue;this._queue=[];for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!n.cancelled&&(n.callback(),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},Cn.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Pn=t.window.HTMLImageElement,On=t.window.HTMLElement,zn={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:0,maxZoom:22,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,bearingSnap:7,clickTolerance:3,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},In=function(n){function a(e){var a=this;if(null!=(e=t.extend({},zn,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than minZoom\");var i=new cn(e.minZoom,e.maxZoom,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 Cn,this._controls=[],this._mapId=t.uniqueId(),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 On))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 a._update(!1)}),this.on(\"moveend\",function(){return a._update(!1)}),this.on(\"zoom\",function(){return a._update(!0)}),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1)),function(t,e){var n=t.getCanvasContainer(),a=null,i=!1,o=null;for(var s in Mn)t[s]=new Mn[s](t,e),e.interactive&&e[s]&&t[s].enable(e[s]);r.addEventListener(n,\"mouseout\",function(e){t.fire(new fn(\"mouseout\",t,e))}),r.addEventListener(n,\"mousedown\",function(a){i=!0,o=r.mousePos(n,a);var s=new fn(\"mousedown\",t,a);t.fire(s),s.defaultPrevented||(e.interactive&&!t.doubleClickZoom.isActive()&&t.stop(),t.boxZoom.onMouseDown(a),t.boxZoom.isActive()||t.dragPan.isActive()||t.dragRotate.onMouseDown(a),t.boxZoom.isActive()||t.dragRotate.isActive()||t.dragPan.onMouseDown(a))}),r.addEventListener(n,\"mouseup\",function(e){var r=t.dragRotate.isActive();a&&!r&&t.fire(new fn(\"contextmenu\",t,a)),a=null,i=!1,t.fire(new fn(\"mouseup\",t,e))}),r.addEventListener(n,\"mousemove\",function(e){if(!t.dragPan.isActive()&&!t.dragRotate.isActive()){for(var r=e.target;r&&r!==n;)r=r.parentNode;r===n&&t.fire(new fn(\"mousemove\",t,e))}}),r.addEventListener(n,\"mouseover\",function(e){for(var r=e.target;r&&r!==n;)r=r.parentNode;r===n&&t.fire(new fn(\"mouseover\",t,e))}),r.addEventListener(n,\"touchstart\",function(r){var n=new pn(\"touchstart\",t,r);t.fire(n),n.defaultPrevented||(e.interactive&&t.stop(),t.boxZoom.isActive()||t.dragRotate.isActive()||t.dragPan.onTouchStart(r),t.touchZoomRotate.onStart(r),t.doubleClickZoom.onTouchStart(n))},{passive:!1}),r.addEventListener(n,\"touchmove\",function(e){t.fire(new pn(\"touchmove\",t,e))},{passive:!1}),r.addEventListener(n,\"touchend\",function(e){t.fire(new pn(\"touchend\",t,e))}),r.addEventListener(n,\"touchcancel\",function(e){t.fire(new pn(\"touchcancel\",t,e))}),r.addEventListener(n,\"click\",function(a){var i=r.mousePos(n,a);(!o||i.equals(o)||i.dist(o)<e.clickTolerance)&&t.fire(new fn(\"click\",t,a))}),r.addEventListener(n,\"dblclick\",function(e){var r=new fn(\"dblclick\",t,e);t.fire(r),r.defaultPrevented||t.doubleClickZoom.onDblClick(r)}),r.addEventListener(n,\"contextmenu\",function(e){var r=t.dragRotate.isActive();i||r?i&&(a=e):t.fire(new fn(\"contextmenu\",t,e)),(t.dragRotate.isEnabled()||t.listens(\"contextmenu\"))&&e.preventDefault()}),r.addEventListener(n,\"wheel\",function(r){e.interactive&&t.stop();var n=new dn(\"wheel\",t,r);t.fire(n),n.defaultPrevented||t.scrollZoom.onWheel(r)},{passive:!1})}(this,e),this._hash=e.hash&&(new hn).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 En({customAttribution:e.customAttribution})),this.addControl(new Ln,e.logoPosition),this.on(\"style.load\",function(){a.transform.unmodified&&a.jumpTo(a.style.stylesheet)}),this.on(\"data\",function(e){a._update(\"style\"===e.dataType),a.fire(new t.Event(e.dataType+\"data\",e))}),this.on(\"dataloading\",function(e){a.fire(new t.Event(e.dataType+\"dataloading\",e))})}n&&(a.__proto__=n),a.prototype=Object.create(n&&n.prototype),a.prototype.constructor=a;var i={showTileBoundaries:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return a.prototype._getMapId=function(){return this._mapId},a.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 a=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?a.insertBefore(n,a.firstChild):a.appendChild(n),this},a.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},a.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],a=r[1];return this._resizeCanvas(n,a),this.transform.resize(n,a),this.painter.resize(n,a),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e)).fire(new t.Event(\"resize\",e)).fire(new t.Event(\"moveend\",e)),this},a.prototype.getBounds=function(){return this.transform.getBounds()},a.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},a.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},a.prototype.setMinZoom=function(t){if((t=null==t?0:t)>=0&&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 0 and the current maxZoom, inclusive\")},a.prototype.getMinZoom=function(){return this.transform.minZoom},a.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\")},a.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},a.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},a.prototype.getMaxZoom=function(){return this.transform.maxZoom},a.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},a.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},a.prototype.isMoving=function(){return this._moving||this.dragPan.isActive()||this.dragRotate.isActive()||this.scrollZoom.isActive()},a.prototype.isZooming=function(){return this._zooming||this.scrollZoom.isZooming()},a.prototype.isRotating=function(){return this._rotating||this.dragRotate.isActive()},a.prototype.on=function(t,e,r){var a=this;if(void 0===r)return n.prototype.on.call(this,t,e);var i=function(){var n;if(\"mouseenter\"===t||\"mouseover\"===t){var i=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=a.getLayer(e)?a.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?i||(i=!0,r.call(a,new fn(t,a,n.originalEvent,{features:o}))):i=!1},mouseout:function(){i=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(a.getLayer(e)?a.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(a,new fn(t,a,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(a,new fn(t,a,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=a.getLayer(e)?a.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(a,t),delete t.features)},n)}}();for(var o in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(o,i.delegates[o]);return this},a.prototype.off=function(t,e,r){if(void 0===r)return n.prototype.off.call(this,t,e);if(this._delegatedListeners&&this._delegatedListeners[t])for(var a=this._delegatedListeners[t],i=0;i<a.length;i++){var o=a[i];if(o.layer===e&&o.listener===r){for(var s in o.delegates)this.off(s,o.delegates[s]);return a.splice(i,1),this}}return this},a.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 a=t.Point.convert(e[0]),i=t.Point.convert(e[1]);n=[a,new t.Point(i.x,a.y),i,new t.Point(a.x,i.y),a]}return this.style.queryRenderedFeatures(n,r,this.transform)},a.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},a.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))},a.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new Re(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)},a.prototype._diffStyle=function(e,r){var n=this;if(\"string\"==typeof e){var a=this._requestManager.normalizeStyleURL(e),i=this._requestManager.transformRequest(a,t.ResourceType.Style);t.getJSON(i,function(e,a){e?n.fire(new t.ErrorEvent(e)):a&&n._updateDiff(a,r)})}else\"object\"==typeof e&&this._updateDiff(e,r)},a.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)}},a.prototype.getStyle=function(){if(this.style)return this.style.serialize()},a.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce(\"There is no style added to the map.\")},a.prototype.addSource=function(t,e){return this.style.addSource(t,e),this._update(!0)},a.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+\"'\")))},a.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 a=r[n];if(\"loaded\"!==a.state&&\"errored\"!==a.state)return!1}}return!0},a.prototype.addSourceType=function(t,e,r){return this.style.addSourceType(t,e,r)},a.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},a.prototype.getSource=function(t){return this.style.getSource(t)},a.prototype.addImage=function(e,r,n){void 0===n&&(n={});var a=n.pixelRatio;void 0===a&&(a=1);var i=n.sdf;if(void 0===i&&(i=!1),r instanceof Pn){var o=t.browser.getImageData(r),s=o.width,l=o.height,c=o.data;this.style.addImage(e,{data:new t.RGBAImage({width:s,height:l},c),pixelRatio:a,sdf:i,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`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var u=r.width,h=r.height,f=r.data,p=r;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:h},new Uint8Array(f)),pixelRatio:a,sdf:i,version:0,userImage:p}),p.onAdd&&p.onAdd(this,e)}},a.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 a=r instanceof Pn?t.browser.getImageData(r):r,i=a.width,o=a.height,s=a.data;if(void 0===i||void 0===o)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(i!==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 Pn);n.data.replace(s,l),this.style.updateImage(e,n)},a.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},a.prototype.removeImage=function(t){this.style.removeImage(t)},a.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},a.prototype.listImages=function(){return this.style.listImages()},a.prototype.addLayer=function(t,e){return this.style.addLayer(t,e),this._update(!0)},a.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},a.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},a.prototype.getLayer=function(t){return this.style.getLayer(t)},a.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},a.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},a.prototype.getFilter=function(t){return this.style.getFilter(t)},a.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},a.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},a.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},a.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},a.prototype.setLight=function(t,e){return void 0===e&&(e={}),this.style.setLight(t,e),this._update(!0)},a.prototype.getLight=function(){return this.style.getLight()},a.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},a.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},a.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},a.prototype.getContainer=function(){return this._container},a.prototype.getCanvasContainer=function(){return this._canvasContainer},a.prototype.getCanvas=function(){return this._canvas},a.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},a.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/.\")},a.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.style.position=\"absolute\",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 a=this._controlContainer=r.create(\"div\",\"mapboxgl-control-container\",t),i=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach(function(t){i[t]=r.create(\"div\",\"mapboxgl-ctrl-\"+t,a)})},a.prototype._resizeCanvas=function(e,r){var n=t.window.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},a.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 an(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},a.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},a.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},a.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},a.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},a.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},a.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},a.prototype._render=function(){this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run();var e=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var r=this.transform.zoom,n=t.browser.now();this.style.zoomHistory.update(r,n);var a=new t.EvaluationParameters(r,{now:n,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),i=a.crossFadingFactor();1===i&&i===this._crossFadingFactor||(e=!0,this._crossFadingFactor=i),this.style.update(a)}return 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}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()||e)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this._sourcesDirty||this._repaint||this._styleDirty||this._placementDirty?this.triggerRepaint():!this.isMoving()&&this.loaded()&&this.fire(new t.Event(\"idle\")),this},a.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.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(),Dn(this._canvasContainer),Dn(this._controlContainer),Dn(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this.fire(new t.Event(\"remove\"))},a.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame(function(){e._frame=null,e._render()}))},a.prototype._onWindowOnline=function(){this._update()},a.prototype._onWindowResize=function(){this._trackResize&&this.resize()._update()},i.showTileBoundaries.get=function(){return!!this._showTileBoundaries},i.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},i.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},i.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},i.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},i.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},i.repaint.get=function(){return!!this._repaint},i.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},i.vertices.get=function(){return!!this._vertices},i.vertices.set=function(t){this._vertices=t,this._update()},a.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},i.version.get=function(){return t.version},Object.defineProperties(a.prototype,i),a}(Sn);function Dn(t){t.parentNode&&t.parentNode.removeChild(t)}var Rn={showCompass:!0,showZoom:!0,visualizePitch:!1},Fn=function(e){var n=this;this.options=t.extend({},Rn,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([\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-icon mapboxgl-ctrl-zoom-in\",\"Zoom in\",function(){return n._map.zoomIn()}),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-icon mapboxgl-ctrl-zoom-out\",\"Zoom out\",function(){return n._map.zoomOut()})),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-icon mapboxgl-ctrl-compass\",\"Reset bearing to north\",function(){n.options.visualizePitch?n._map.resetNorthPitch():n._map.resetNorth()}),this._compassArrow=r.create(\"span\",\"mapboxgl-ctrl-compass-arrow\",this._compass))};function Bn(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var a=new t.LngLat(e.lng-360,e.lat),i=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(a).distSqr(r)<o?e=a:n.locationPoint(i).distSqr(r)<o&&(e=i)}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}Fn.prototype._updateZoomButtons=function(){var t=this._map.getZoom();t===this._map.getMaxZoom()?this._zoomInButton.classList.add(\"mapboxgl-ctrl-icon-disabled\"):this._zoomInButton.classList.remove(\"mapboxgl-ctrl-icon-disabled\"),t===this._map.getMinZoom()?this._zoomOutButton.classList.add(\"mapboxgl-ctrl-icon-disabled\"):this._zoomOutButton.classList.remove(\"mapboxgl-ctrl-icon-disabled\")},Fn.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._compassArrow.style.transform=t},Fn.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new yn(t,{button:\"left\",element:this._compass}),r.addEventListener(this._compass,\"mousedown\",this._handler.onMouseDown),r.addEventListener(this._compass,\"touchstart\",this._handler.onMouseDown,{passive:!1}),this._handler.enable()),this._container},Fn.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),r.removeEventListener(this._compass,\"mousedown\",this._handler.onMouseDown),r.removeEventListener(this._compass,\"touchstart\",this._handler.onMouseDown,{passive:!1}),this._handler.disable(),delete this._handler),delete this._map},Fn.prototype._createButton=function(t,e,n){var a=r.create(\"button\",t,this._container);return a.type=\"button\",a.title=e,a.setAttribute(\"aria-label\",e),a.addEventListener(\"click\",n),a};var Nn={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 jn(t,e,r){var n=t.classList;for(var a in Nn)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+a);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var Vn,Un=function(e){function n(n,a){if(e.call(this),(n instanceof t.window.HTMLElement||a)&&(n=t.extend({element:n},a)),t.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\"],this),this._anchor=n&&n.anchor||\"center\",this._color=n&&n.color||\"#3FB1CE\",this._draggable=n&&n.draggable||!1,this._state=\"inactive\",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\");var i=r.createNS(\"http://www.w3.org/2000/svg\",\"svg\");i.setAttributeNS(null,\"display\",\"block\"),i.setAttributeNS(null,\"height\",\"41px\"),i.setAttributeNS(null,\"width\",\"27px\"),i.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var o=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");o.setAttributeNS(null,\"stroke\",\"none\"),o.setAttributeNS(null,\"stroke-width\",\"1\"),o.setAttributeNS(null,\"fill\",\"none\"),o.setAttributeNS(null,\"fill-rule\",\"evenodd\");var s=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"fill-rule\",\"nonzero\");var l=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),l.setAttributeNS(null,\"fill\",\"#000000\");for(var c=0,u=[{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\"}];c<u.length;c+=1){var h=u[c],f=r.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");f.setAttributeNS(null,\"opacity\",\"0.04\"),f.setAttributeNS(null,\"cx\",\"10.5\"),f.setAttributeNS(null,\"cy\",\"5.80029008\"),f.setAttributeNS(null,\"rx\",h.rx),f.setAttributeNS(null,\"ry\",h.ry),l.appendChild(f)}var p=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");p.setAttributeNS(null,\"fill\",this._color);var d=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");d.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\"),p.appendChild(d);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 m=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");m.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),m.setAttributeNS(null,\"fill\",\"#FFFFFF\");var y=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");y.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var x=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");x.setAttributeNS(null,\"fill\",\"#000000\"),x.setAttributeNS(null,\"opacity\",\"0.25\"),x.setAttributeNS(null,\"cx\",\"5.5\"),x.setAttributeNS(null,\"cy\",\"5.5\"),x.setAttributeNS(null,\"r\",\"5.4999962\");var b=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");b.setAttributeNS(null,\"fill\",\"#FFFFFF\"),b.setAttributeNS(null,\"cx\",\"5.5\"),b.setAttributeNS(null,\"cy\",\"5.5\"),b.setAttributeNS(null,\"r\",\"5.4999962\"),y.appendChild(x),y.appendChild(b),s.appendChild(l),s.appendChild(p),s.appendChild(g),s.appendChild(m),s.appendChild(y),i.appendChild(s),this._element.appendChild(i),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",function(t){t.preventDefault()}),jn(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),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)}return this},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){this._map&&(this._map.transform.renderWorldCopies&&(this._lngLat=Bn(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset),t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,Nn[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px)\"))},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}(t.Evented),qn={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showUserLocation:!0},Hn=function(e){function n(r){e.call(this),this.options=t.extend({},qn,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_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!==Vn?n(Vn):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then(function(t){Vn=\"denied\"!==t.state,n(Vn)}):(Vn=!!t.window.navigator.geolocation,n(Vn)),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(),r.remove(this._container),this._map=void 0},n.prototype._onSuccess=function(e){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,a=this._map.getBearing(),i=t.extend({bearing:a},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),i,{geolocateSource:!0})},n.prototype._updateMarker=function(t){t?this._userLocationDotMarker.setLngLat([t.coords.longitude,t.coords.latitude]).addTo(this._map):this._userLocationDotMarker.remove()},n.prototype._onError=function(e){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\"),void 0!==this._geolocationWatchID&&this._clearWatch();else 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\")}\"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;!1!==e?(this._container.addEventListener(\"contextmenu\",function(t){return t.preventDefault()}),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-icon mapboxgl-ctrl-geolocate\",this._container),this._geolocateButton.type=\"button\",this._geolocateButton.setAttribute(\"aria-label\",\"Geolocate\"),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 Un(this._dotElement),this.options.trackUserLocation&&(this._watchState=\"OFF\")),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",function(e){e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||(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\")))})):t.warnOnce(\"Geolocation support is not available, the GeolocateControl will not be visible.\")},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\":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\")}\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID?this._clearWatch():void 0===this._geolocationWatchID&&(this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,this.options.positionOptions))}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),Gn={maxWidth:100,unit:\"metric\"},Yn=function(e){this.options=t.extend({},Gn,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function Wn(t,e,r){var n,a,i,o,s,l,c=r&&r.maxWidth||100,u=t._container.clientHeight/2,h=(n=t.unproject([0,u]),a=t.unproject([c,u]),i=Math.PI/180,o=n.lat*i,s=a.lat*i,l=Math.sin(o)*Math.sin(s)+Math.cos(o)*Math.cos(s)*Math.cos((a.lng-n.lng)*i),6371e3*Math.acos(Math.min(l,1)));if(r&&\"imperial\"===r.unit){var f=3.2808*h;f>5280?Xn(e,c,f/5280,\"mi\"):Xn(e,c,f,\"ft\")}else r&&\"nautical\"===r.unit?Xn(e,c,h/1852,\"nm\"):Xn(e,c,h,\"m\")}function Xn(t,e,r,n){var a,i,o,s=(a=r,(i=Math.pow(10,(\"\"+Math.floor(a)).length-1))*(o=(o=a/i)>=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))),l=s/r;\"m\"===n&&s>=1e3&&(s/=1e3,n=\"km\"),t.style.width=e*l+\"px\",t.innerHTML=s+n}Yn.prototype.getDefaultPosition=function(){return\"bottom-left\"},Yn.prototype._onMove=function(){Wn(this._map,this._container,this.options)},Yn.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},Yn.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},Yn.prototype.setUnit=function(t){this.options.unit=t,Wn(this._map,this._container,this.options)};var Zn=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\"),this._className=\"mapboxgl-ctrl\"};Zn.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",this._className+\" mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},Zn.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Zn.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Zn.prototype._setupUI=function(){(this._fullscreenButton=r.create(\"button\",this._className+\"-icon \"+this._className+\"-fullscreen\",this._controlContainer)).type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Zn.prototype._updateTitle=function(){var t=this._isFullscreen()?\"Exit fullscreen\":\"Enter fullscreen\";this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},Zn.prototype._isFullscreen=function(){return this._fullscreen},Zn.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(this._className+\"-shrink\"),this._fullscreenButton.classList.toggle(this._className+\"-fullscreen\"),this._updateTitle())},Zn.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 Jn={closeButton:!0,closeOnClick:!0,className:\"\",maxWidth:\"240px\"},Kn=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Jn),r),t.bindAll([\"_update\",\"_onClickClose\",\"remove\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){var r=this;return this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClickClose),this._map.on(\"remove\",this.remove),this._update(),this._trackPointer?(this._map.on(\"mousemove\",function(t){r._update(t.point)}),this._map.on(\"mouseup\",function(t){r._update(t.point)}),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(\"click\",this._onClickClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\"),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._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){var t=this;return this._trackPointer=!0,this._pos=null,this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",function(e){t._update(e.point)}),this._map.on(\"drag\",function(e){t._update(e.point)}),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(),a=t.window.document.createElement(\"body\");for(a.innerHTML=e;r=a.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return 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n=t(\"color-normalize\"),a=t(\"array-bounds\"),i=t(\"object-assign\"),o=t(\"glslify\"),s=t(\"pick-by-alias\"),l=t(\"flatten-vertex-data\"),c=t(\"earcut\"),u=t(\"array-normalize\"),h=t(\"to-float32\"),f=h.float32,p=h.fract32,d=t(\"es6-weak-map\"),g=t(\"parse-rect\");function v(t,e){if(!(this instanceof v))return new v(t,e);if(\"function\"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=v.shaders.has(t)?v.shaders.get(t):v.shaders.set(t,v.createShaders(t)).get(t),this.update(e)}e.exports=v,v.dashMult=2,v.maxPatternLength=256,v.precisionThreshold=3e6,v.maxPoints=1e4,v.maxLines=2048,v.shaders=new d,v.createShaders=function(t){var e,r=t.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),n={primitive:\"triangle strip\",instances:t.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:function(t,e){return\"round\"===e.join?2:1},miterLimit:t.prop(\"miterLimit\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),thickness:t.prop(\"thickness\"),dashPattern:t.prop(\"dashTexture\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),dashSize:t.prop(\"dashLength\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]},depth:t.prop(\"depth\")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:function(t,e){return!e.overlay}},stencil:{enable:!1},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\")},a=t(i({vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\\nattribute vec4 color;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\\n\\t// the order is important\\n\\treturn position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n}\\n\\nvoid main() {\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineOffset = lineTop * 2. - 1.;\\n\\n\\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\\n\\ttangent = normalize(diff * scale * viewport.zw);\\n\\tvec2 normal = vec2(-tangent.y, tangent.x);\\n\\n\\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\\n\\t\\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\\n\\n\\t\\t+ thickness * normal * .5 * lineOffset / viewport.zw;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D dashPattern;\\n\\nuniform float dashSize, pixelRatio, thickness, opacity, id;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvoid main() {\\n\\tfloat alpha = 1.;\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashSize) * .5 + .25;\\n\\tfloat dash = texture2D(dashPattern, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},n));try{e=t(i({cull:{enable:!0,face:\"back\"},vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\\nattribute vec4 aColor, bColor;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, translate;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\nuniform float miterLimit, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 tangent;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nconst float REVERSE_THRESHOLD = -.875;\\nconst float MIN_DIFF = 1e-6;\\n\\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\\n// TODO: precalculate dot products, normalize things beforehead etc.\\n// TODO: refactor to rectangular algorithm\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nbool isNaN( float val ){\\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\\n}\\n\\nvoid main() {\\n\\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\\n\\n  vec2 adjustedScale;\\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\\n\\n  vec2 scaleRatio = adjustedScale * viewport.zw;\\n\\tvec2 normalWidth = thickness / scaleRatio;\\n\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineBot = 1. - lineTop;\\n\\n\\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\\n\\n\\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\\n\\n\\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\\n\\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\\n\\n\\tvec2 prevDiff = aCoord - prevCoord;\\n\\tvec2 currDiff = bCoord - aCoord;\\n\\tvec2 nextDiff = nextCoord - bCoord;\\n\\n\\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\\n\\tvec2 currTangent = normalize(currDiff * scaleRatio);\\n\\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\\n\\n\\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\\n\\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\\n\\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\\n\\n\\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\\n\\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\\n\\n\\t// collapsed/unidirectional segment cases\\n\\t// FIXME: there should be more elegant solution\\n\\tvec2 prevTanDiff = abs(prevTangent - currTangent);\\n\\tvec2 nextTanDiff = abs(nextTangent - currTangent);\\n\\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\\n\\t\\tstartJoinDirection = currNormal;\\n\\t}\\n\\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\\n\\t\\tendJoinDirection = currNormal;\\n\\t}\\n\\tif (aCoord == bCoord) {\\n\\t\\tendJoinDirection = startJoinDirection;\\n\\t\\tcurrNormal = prevNormal;\\n\\t\\tcurrTangent = prevTangent;\\n\\t}\\n\\n\\ttangent = currTangent;\\n\\n\\t//calculate join shifts relative to normals\\n\\tfloat startJoinShift = dot(currNormal, startJoinDirection);\\n\\tfloat endJoinShift = dot(currNormal, endJoinDirection);\\n\\n\\tfloat startMiterRatio = abs(1. / startJoinShift);\\n\\tfloat endMiterRatio = abs(1. / endJoinShift);\\n\\n\\tvec2 startJoin = startJoinDirection * startMiterRatio;\\n\\tvec2 endJoin = endJoinDirection * endMiterRatio;\\n\\n\\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\\n\\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\\n\\tstartBotJoin = -startTopJoin;\\n\\n\\tendTopJoin = sign(endJoinShift) * endJoin * .5;\\n\\tendBotJoin = -endTopJoin;\\n\\n\\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\\n\\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\\n\\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\\n\\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\\n\\n\\t//miter anti-clipping\\n\\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\\n\\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\\n\\n\\t//prevent close to reverse direction switch\\n\\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\n\\tif (prevReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\\n\\t\\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D dashPattern;\\nuniform float dashSize, pixelRatio, thickness, opacity, id, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashSize) * .5 + .25;\\n\\tfloat dash = texture2D(dashPattern, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=a}return{fill:t({primitive:\"triangle\",elements:function(t,e){return e.triangles},offset:0,vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position, positionFract;\\n\\nuniform vec4 color;\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio, id;\\nuniform vec4 viewport;\\nuniform float opacity;\\n\\nvarying vec4 fragColor;\\n\\nconst float MAX_LINES = 256.;\\n\\nvoid main() {\\n\\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\\n\\n\\tvec2 position = position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n\\tfragColor.a *= opacity;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n\\tgl_FragColor = fragColor;\\n}\\n\"]),uniforms:{scale:t.prop(\"scale\"),color:t.prop(\"fill\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8},positionFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:a,miter:e}},v.defaults={dashes:null,join:\"miter\",miterLimit:1,thickness:10,cap:\"square\",color:\"black\",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},v.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},v.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach(function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>v.precisionThreshold||e.scale[1]*e.viewport.height>v.precisionThreshold?t.shaders.rect(e):\"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,h){var d=e.passes[h];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 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r}(t,e)||function(){throw new TypeError(\"Invalid attempt to destructure non-iterable instance\")}()}function a(t){return function(t){if(Array.isArray(t)){for(var e=0,r=new Array(t.length);e<t.length;e++)r[e]=t[e];return r}}(t)||function(t){if(Symbol.iterator in Object(t)||\"[object Arguments]\"===Object.prototype.toString.call(t))return Array.from(t)}(t)||function(){throw new TypeError(\"Invalid attempt to spread non-iterable instance\")}()}var i=t(\"color-normalize\"),o=t(\"array-bounds\"),s=t(\"color-id\"),l=t(\"point-cluster\"),c=t(\"object-assign\"),u=t(\"glslify\"),h=t(\"pick-by-alias\"),f=t(\"update-diff\"),p=t(\"flatten-vertex-data\"),d=t(\"is-iexplorer\"),g=t(\"to-float32\"),v=t(\"parse-rect\"),m=y;function y(t,e){var r=this;if(!(this instanceof y))return new y(t,e);\"function\"==typeof t?(e||(e={}),e.regl=t):(e=t,t=null),e&&e.length&&(e.positions=e);var n,a=(t=e.regl)._gl,i=[];this.tooManyColors=d,n=t.texture({data:new 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{\\n  return isDirect ? id / 255. : texture2D(palette,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  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 * pixelRatio;\\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=c({},o);l.frag=u([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\n\\nuniform float opacity;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat smoothStep(float edge0, float edge1, float x) {\\n\\tfloat t;\\n\\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\\n\\treturn t * t * (3.0 - 2.0 * t);\\n}\\n\\nvoid main() {\\n\\tfloat radius, alpha = 1.0, delta = fragWidth;\\n\\n\\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\\n\\n\\tif (radius > 1.0 + delta) {\\n\\t\\tdiscard;\\n\\t}\\n\\n\\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\\n\\n\\tfloat borderRadius = fragBorderRadius;\\n\\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\\n\\tvec4 color = mix(fragColor, fragBorderColor, ratio);\\n\\tcolor.a *= alpha * opacity;\\n\\tgl_FragColor = color;\\n}\\n\"]),l.vert=u([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio;\\nuniform sampler2D palette;\\nuniform vec2 paletteSize;\\n\\nconst float maxSize = 100.;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(palette,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = (size + borderSize) * 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  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\"]),d&&(l.frag=l.frag.replace(\"smoothstep\",\"smoothStep\"),s.frag=s.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(l)}y.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},y.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},y.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var a=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 i=0;i<r.length;i++)this.drawItem(i,r[i]);else a.forEach(function(e,r){t.drawItem(r)});return this},y.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 i=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&i.push.apply(i,a(this.getMarkerDrawOptions(o,n,e)));i.length&&this.drawMarker(i)}},y.prototype.getMarkerDrawOptions=function(t,e,r){var a=e.range,i=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,u=e.count;this.regl;if(!i)return r?[c({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[c({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var h=[],f=i.range(a,{lod:!0,px:[(a[2]-a[0])/o.width,(a[3]-a[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(u),g=0;g<r.length;g++){var v=r[g];d[v]=p?p[v]:1}l.subdata(d)}for(var m=f.length;m--;){var y=n(f[m],2),x=y[0],b=y[1];h.push(c({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return h},y.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 a=this.groups,i=this.gl,s=this.regl,u=this.maxSize,d=this.maxColors,m=this.palette;this.groups=a=r.map(function(e,r){var n=a[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=h(e,{positions:\"positions data 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t.x.length?e.xAttr={buffer:s.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:s.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=p(t,\"float64\");var u=e.count=Math.floor(t.length/2),h=e.bounds=u?o(t,2):null;if(r.range||e.range||(delete e.range,r.range=h),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||u>n)?e.tree=l(t,{bounds:h}):n&&n.length&&(e.tree=n),e.tree){var f={primitive:\"points\",usage:\"static\",data:e.tree,type:\"uint32\"};e.elements?e.elements(f):e.elements=s.elements(f)}return a({data:g.float(t),usage:\"dynamic\"}),i({data:g.fract(t),usage:\"dynamic\"}),c({data:new Uint8Array(u),type:\"uint8\",usage:\"stream\"}),t}},{marker:function(e,r,n){var a=r.activation;if(a.forEach(function(t){return 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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 x=t(\"object-assign\");e.exports=function(t,e){var r=new m(t,e),n=r.render.bind(r);return x(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}},{\"array-bounds\":67,\"color-id\":119,\"color-normalize\":121,\"flatten-vertex-data\":229,glslify:410,\"is-iexplorer\":420,\"object-assign\":455,\"parse-rect\":460,\"pick-by-alias\":466,\"point-cluster\":470,\"to-float32\":536,\"update-diff\":547}],499:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),a=t(\"pick-by-alias\"),i=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 h(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function f(t,e,r){var n,a,i,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],a=s[3]):s.length?(n=s[0],a=s[1]):(s.x,n=s.y,s.x+s.width,a=s.y+s.height),l.length>2?(i=l[0],o=l[2],l[1],l[3]):l.length?(i=l[0],o=l[1]):(i=l.x,l.y,o=l.x+l.width,l.y+l.height),[i,n,o,a]}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 a=[],i=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++)a.push(this.passes[l[c]]);s.passOffset=i,i+=s.passes.length}return(t=this.scatter).update.apply(t,a),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=a(e,{data:\"data items columns rows values dimensions samples x\",snap:\"snap cluster\",size:\"sizes size radius\",color:\"colors color fill fill-color fillColor\",opacity:\"opacity alpha transparency 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o=this.computedUp,s=this.computedRight,l=this.computedToward,c=0;c<3;++c)a[4*c]=o[c],a[4*c+1]=s[c],a[4*c+2]=l[c];i(a,a,n,d);for(c=0;c<3;++c)o[c]=a[4*c],s[c]=a[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 a=this.computedMatrix,i=(Math.exp(this.computedRadius[0]),a[1]),o=a[5],s=a[9],l=c(i,o,s);i/=l,o/=l,s/=l;var u=a[0],h=a[4],f=a[8],p=u*i+h*o+f*s,d=c(u-=i*p,h-=o*p,f-=s*p),g=(u/=d)*e+i*r,v=(h/=d)*e+o*r,m=(f/=d)*e+s*r;this.center.move(t,g,v,m);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 i=1;\"number\"==typeof r&&(i=0|r),(i<0||i>3)&&(i=1);var o=(i+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[i],l=e[i+4],h=e[i+8];if(n){var f=Math.abs(s),p=Math.abs(l),d=Math.abs(h),g=Math.max(f,p,d);f===g?(s=s<0?-1:1,l=h=0):d===g?(h=h<0?-1:1,s=l=0):(l=l<0?-1:1,s=h=0)}else{var v=c(s,l,h);s/=v,l/=v,h/=v}var m,y,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*h,k=c(x-=s*w,b-=l*w,_-=h*w),T=l*(_/=k)-h*(b/=k),A=h*(x/=k)-s*_,M=s*b-l*x,S=c(T,A,M);if(T/=S,A/=S,M/=S,this.center.jump(t,H,G,Y),this.radius.idle(t),this.up.jump(t,s,l,h),this.right.jump(t,x,b,_),2===i){var E=e[1],L=e[5],C=e[9],P=E*x+L*b+C*_,O=E*T+L*A+C*M;m=R<0?-Math.PI/2:Math.PI/2,y=Math.atan2(O,P)}else{var z=e[2],I=e[6],D=e[10],R=z*s+I*l+D*h,F=z*x+I*b+D*_,B=z*T+I*A+D*M;m=Math.asin(u(R)),y=Math.atan2(B,F)}this.angle.jump(t,y,m),this.recalcMatrix(t);var N=e[2],j=e[6],V=e[10],U=this.computedMatrix;a(U,e);var q=U[15],H=U[12]/q,G=U[13]/q,Y=U[14]/q,W=Math.exp(this.computedRadius[0]);this.center.jump(t,H-N*W,G-j*W,Y-V*W)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(t){this.center.idle(t),this.up.idle(t),this.right.idle(t),this.radius.idle(t),this.angle.idle(t)},p.flush=function(t){this.center.flush(t),this.up.flush(t),this.right.flush(t),this.radius.flush(t),this.angle.flush(t)},p.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},p.lookAt=function(t,e,r,n){this.recalcMatrix(t),e=e||this.computedEye,r=r||this.computedCenter;var a=(n=n||this.computedUp)[0],i=n[1],o=n[2],s=c(a,i,o);if(!(s<1e-6)){a/=s,i/=s,o/=s;var l=e[0]-r[0],h=e[1]-r[1],f=e[2]-r[2],p=c(l,h,f);if(!(p<1e-6)){l/=p,h/=p,f/=p;var d=this.computedRight,g=d[0],v=d[1],m=d[2],y=a*g+i*v+o*m,x=c(g-=y*a,v-=y*i,m-=y*o);if(!(x<.01&&(x=c(g=i*f-o*h,v=o*l-a*f,m=a*h-i*l))<1e-6)){g/=x,v/=x,m/=x,this.up.set(t,a,i,o),this.right.set(t,g,v,m),this.center.set(t,r[0],r[1],r[2]),this.radius.set(t,Math.log(p));var b=i*m-o*v,_=o*g-a*m,w=a*v-i*g,k=c(b,_,w),T=a*l+i*h+o*f,A=g*l+v*h+m*f,M=(b/=k)*l+(_/=k)*h+(w/=k)*f,S=Math.asin(u(T)),E=Math.atan2(M,A),L=this.angle._state,C=L[L.length-1],P=L[L.length-2];C%=2*Math.PI;var O=Math.abs(C+2*Math.PI-E),z=Math.abs(C-E),I=Math.abs(C-2*Math.PI-E);O<z&&(C+=2*Math.PI),I<z&&(C-=2*Math.PI),this.angle.jump(this.angle.lastT(),C,P),this.angle.set(t,E,S)}}}}},{\"filtered-vector\":228,\"gl-mat4/invert\":267,\"gl-mat4/rotate\":272,\"gl-vec3/cross\":335,\"gl-vec3/dot\":340,\"gl-vec3/normalize\":357}],541:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var a=t*e,i=n*t,o=i-(i-t),s=t-o,l=n*e,c=l-(l-e),u=e-c,h=s*u-(a-o*c-s*c-o*u);if(r)return r[0]=h,r[1]=a,r;return[h,a]};var n=+(Math.pow(2,27)+1)},{}],542:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var n=t+e,a=n-t,i=e-a,o=t-(n-a);if(r)return r[0]=o+i,r[1]=n,r;return[o+i,n]}},{}],543:[function(t,e,r){(function(e,n){\"use strict\";var 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n=t(\"../main\"),a=t(\"object-assign\");function i(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}i.prototype=new n.baseCalendar,a(i.prototype,{name:\"Coptic\",jdEpoch:1825029.5,daysPerMonth:[30,30,30,30,30,30,30,30,30,30,30,30,5],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Coptic\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Thout\",\"Paopi\",\"Hathor\",\"Koiak\",\"Tobi\",\"Meshir\",\"Paremhat\",\"Paremoude\",\"Pashons\",\"Paoni\",\"Epip\",\"Mesori\",\"Pi Kogi Enavot\"],monthNamesShort:[\"Tho\",\"Pao\",\"Hath\",\"Koi\",\"Tob\",\"Mesh\",\"Pat\",\"Pad\",\"Pash\",\"Pao\",\"Epi\",\"Meso\",\"PiK\"],dayNames:[\"Tkyriaka\",\"Pesnau\",\"Pshoment\",\"Peftoou\",\"Ptiou\",\"Psoou\",\"Psabbaton\"],dayNamesShort:[\"Tky\",\"Pes\",\"Psh\",\"Pef\",\"Pti\",\"Pso\",\"Psa\"],dayNamesMin:[\"Tk\",\"Pes\",\"Psh\",\"Pef\",\"Pt\",\"Pso\",\"Psa\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3||t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var a=this._validate(t,e,r,n.local.invalidDate);return(t=a.year())<0&&t++,a.day()+30*(a.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,a=e-30*(n-1)+1;return this.newDate(r,n,a)}}),n.calendars.coptic=i},{\"../main\":569,\"object-assign\":455}],557:[function(t,e,r){var n=t(\"../main\"),a=t(\"object-assign\");function i(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}i.prototype=new n.baseCalendar,a(i.prototype,{name:\"Discworld\",jdEpoch:1721425.5,daysPerMonth:[16,32,32,32,32,32,32,32,32,32,32,32,32],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Discworld\",epochs:[\"BUC\",\"UC\"],monthNames:[\"Ick\",\"Offle\",\"February\",\"March\",\"April\",\"May\",\"June\",\"Grune\",\"August\",\"Spune\",\"Sektober\",\"Ember\",\"December\"],monthNamesShort:[\"Ick\",\"Off\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Gru\",\"Aug\",\"Spu\",\"Sek\",\"Emb\",\"Dec\"],dayNames:[\"Sunday\",\"Octeday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Oct\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Oc\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:2,isRTL:!1}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),13},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),400},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/8)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]},daysInWeek:function(){return 8},dayOfWeek:function(t,e,r){return(this._validate(t,e,r,n.local.invalidDate).day()+1)%8},weekDay:function(t,e,r){var n=this.dayOfWeek(t,e,r);return n>=2&&n<=6},extraInfo:function(t,e,r){var a=this._validate(t,e,r,n.local.invalidDate);return{century:o[Math.floor((a.year()-1)/100)+1]||\"\"}},toJD:function(t,e,r){var a=this._validate(t,e,r,n.local.invalidDate);return t=a.year()+(a.year()<0?1:0),e=a.month(),(r=a.day())+(e>1?16:0)+(e>2?32*(e-2):0)+400*(t-1)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t+.5)-Math.floor(this.jdEpoch)-1;var e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=i},{\"../main\":569,\"object-assign\":455}],558:[function(t,e,r){var n=t(\"../main\"),a=t(\"object-assign\");function i(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}i.prototype=new n.baseCalendar,a(i.prototype,{name:\"Ethiopian\",jdEpoch:1724220.5,daysPerMonth:[30,30,30,30,30,30,30,30,30,30,30,30,5],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Ethiopian\",epochs:[\"BEE\",\"EE\"],monthNames:[\"Meskerem\",\"Tikemet\",\"Hidar\",\"Tahesas\",\"Tir\",\"Yekatit\",\"Megabit\",\"Miazia\",\"Genbot\",\"Sene\",\"Hamle\",\"Nehase\",\"Pagume\"],monthNamesShort:[\"Mes\",\"Tik\",\"Hid\",\"Tah\",\"Tir\",\"Yek\",\"Meg\",\"Mia\",\"Gen\",\"Sen\",\"Ham\",\"Neh\",\"Pag\"],dayNames:[\"Ehud\",\"Segno\",\"Maksegno\",\"Irob\",\"Hamus\",\"Arb\",\"Kidame\"],dayNamesShort:[\"Ehu\",\"Seg\",\"Mak\",\"Iro\",\"Ham\",\"Arb\",\"Kid\"],dayNamesMin:[\"Eh\",\"Se\",\"Ma\",\"Ir\",\"Ha\",\"Ar\",\"Ki\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3||t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var a=this._validate(t,e,r,n.local.invalidDate);return(t=a.year())<0&&t++,a.day()+30*(a.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,a=e-30*(n-1)+1;return this.newDate(r,n,a)}}),n.calendars.ethiopian=i},{\"../main\":569,\"object-assign\":455}],559:[function(t,e,r){var n=t(\"../main\"),a=t(\"object-assign\");function i(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}i.prototype=new n.baseCalendar,a(i.prototype,{name:\"Hebrew\",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{\"\":{name:\"Hebrew\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Nisan\",\"Iyar\",\"Sivan\",\"Tammuz\",\"Av\",\"Elul\",\"Tishrei\",\"Cheshvan\",\"Kislev\",\"Tevet\",\"Shevat\",\"Adar\",\"Adar II\"],monthNamesShort:[\"Nis\",\"Iya\",\"Siv\",\"Tam\",\"Av\",\"Elu\",\"Tis\",\"Che\",\"Kis\",\"Tev\",\"She\",\"Ada\",\"Ad2\"],dayNames:[\"Yom Rishon\",\"Yom Sheni\",\"Yom Shlishi\",\"Yom Revi'i\",\"Yom Chamishi\",\"Yom Shishi\",\"Yom Shabbat\"],dayNamesShort:[\"Ris\",\"She\",\"Shl\",\"Rev\",\"Cha\",\"Shi\",\"Sha\"],dayNamesMin:[\"Ri\",\"She\",\"Shl\",\"Re\",\"Ch\",\"Shi\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7*(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)?30:8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var a=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(a)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(a)%10-3]}},toJD:function(t,e,r){var a=this._validate(t,e,r,n.local.invalidDate);t=a.year(),e=a.month(),r=a.day();var i=t<=0?t+1:t,o=this.jdEpoch+this._delay1(i)+this._delay2(i)+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=i},{\"../main\":569,\"object-assign\":455}],560:[function(t,e,r){var n=t(\"../main\"),a=t(\"object-assign\");function i(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}i.prototype=new n.baseCalendar,a(i.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 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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=i},{\"../main\":569,\"object-assign\":455}],561:[function(t,e,r){var n=t(\"../main\"),a=t(\"object-assign\");function i(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}i.prototype=new 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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 a=this._validate(t,e,r,n.local.invalidDate);return t=a.year(),e=a.month(),r=a.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),a=Math.floor((e-n)/30.6001),i=a-Math.floor(a<14?1:13),o=r-Math.floor(i>2?4716:4715),s=e-n-Math.floor(30.6001*a);return o<=0&&o--,this.newDate(o,i,s)}}),n.calendars.julian=i},{\"../main\":569,\"object-assign\":455}],562:[function(t,e,r){var n=t(\"../main\"),a=t(\"object-assign\");function i(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return 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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 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n.baseCalendar;var o=n.instance(\"gregorian\");a(i.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 a=this._validate(t,e,r,n.local.invalidMonth);(t=a.year())<0&&t++;for(var i=a.day(),s=1;s<a.month();s++)i+=this.daysPerMonth[s-1];return i+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=i},{\"../main\":569,\"object-assign\":455}],564:[function(t,e,r){var n=t(\"../main\"),a=t(\"object-assign\");function i(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}i.prototype=new n.baseCalendar,a(i.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(),\"undefined\"==typeof 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),\"undefined\"==typeof 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 a=this._validate(t,e,r,n.local.invalidDate);t=a.year(),e=a.month(),r=a.day();var i=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+=i.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],i.newDate(c,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),a=e.dayOfYear(),i=r+56;this._createMissingCalendarData(i);for(var o=9,s=this.NEPALI_CALENDAR_DATA[i][0],l=this.NEPALI_CALENDAR_DATA[i][o]-s+1;a>l;)++o>12&&(o=1,i++),l+=this.NEPALI_CALENDAR_DATA[i][o];var c=this.NEPALI_CALENDAR_DATA[i][o]-(l-a);return this.newDate(i,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)\"undefined\"==typeof this.NEPALI_CALENDAR_DATA[r]&&(this.NEPALI_CALENDAR_DATA[r]=e)},NEPALI_CALENDAR_DATA:{1970:[18,31,31,32,31,31,31,30,29,30,29,30,30],1971:[18,31,31,32,31,32,30,30,29,30,29,30,30],1972:[17,31,32,31,32,31,30,30,30,29,29,30,30],1973:[19,30,32,31,32,31,30,30,30,29,30,29,31],1974:[19,31,31,32,30,31,31,30,29,30,29,30,30],1975:[18,31,31,32,32,30,31,30,29,30,29,30,30],1976:[17,31,32,31,32,31,30,30,30,29,29,30,31],1977:[18,31,32,31,32,31,31,29,30,29,30,29,31],1978:[18,31,31,32,31,31,31,30,29,30,29,30,30],1979:[18,31,31,32,32,31,30,30,29,30,29,30,30],1980:[17,31,32,31,32,31,30,30,30,29,29,30,31],1981:[18,31,31,31,32,31,31,29,30,30,29,30,30],1982:[18,31,31,32,31,31,31,30,29,30,29,30,30],1983:[18,31,31,32,32,31,30,30,29,30,29,30,30],1984:[17,31,32,31,32,31,30,30,30,29,29,30,31],1985:[18,31,31,31,32,31,31,29,30,30,29,30,30],1986:[18,31,31,32,31,31,31,30,29,30,29,30,30],1987:[18,31,32,31,32,31,30,30,29,30,29,30,30],1988:[17,31,32,31,32,31,30,30,30,29,29,30,31],1989:[18,31,31,31,32,31,31,30,29,30,29,30,30],1990:[18,31,31,32,31,31,31,30,29,30,29,30,30],1991:[18,31,32,31,32,31,30,30,29,30,29,30,30],1992:[17,31,32,31,32,31,30,30,30,29,30,29,31],1993:[18,31,31,31,32,31,31,30,29,30,29,30,30],1994:[18,31,31,32,31,31,31,30,29,30,29,30,30],1995:[17,31,32,31,32,31,30,30,30,29,29,30,30],1996:[17,31,32,31,32,31,30,30,30,29,30,29,31],1997:[18,31,31,32,31,31,31,30,29,30,29,30,30],1998:[18,31,31,32,31,31,31,30,29,30,29,30,30],1999:[17,31,32,31,32,31,30,30,30,29,29,30,31],2000:[17,30,32,31,32,31,30,30,30,29,30,29,31],2001:[18,31,31,32,31,31,31,30,29,30,29,30,30],2002:[18,31,31,32,32,31,30,30,29,30,29,30,30],2003:[17,31,32,31,32,31,30,30,30,29,29,30,31],2004:[17,30,32,31,32,31,30,30,30,29,30,29,31],2005:[18,31,31,32,31,31,31,30,29,30,29,30,30],2006:[18,31,31,32,32,31,30,30,29,30,29,30,30],2007:[17,31,32,31,32,31,30,30,30,29,29,30,31],2008:[17,31,31,31,32,31,31,29,30,30,29,29,31],2009:[18,31,31,32,31,31,31,30,29,30,29,30,30],2010:[18,31,31,32,32,31,30,30,29,30,29,30,30],2011:[17,31,32,31,32,31,30,30,30,29,29,30,31],2012:[17,31,31,31,32,31,31,29,30,30,29,30,30],2013:[18,31,31,32,31,31,31,30,29,30,29,30,30],2014:[18,31,31,32,32,31,30,30,29,30,29,30,30],2015:[17,31,32,31,32,31,30,30,30,29,29,30,31],2016:[17,31,31,31,32,31,31,29,30,30,29,30,30],2017:[18,31,31,32,31,31,31,30,29,30,29,30,30],2018:[18,31,32,31,32,31,30,30,29,30,29,30,30],2019:[17,31,32,31,32,31,30,30,30,29,30,29,31],2020:[17,31,31,31,32,31,31,30,29,30,29,30,30],2021:[18,31,31,32,31,31,31,30,29,30,29,30,30],2022:[17,31,32,31,32,31,30,30,30,29,29,30,30],2023:[17,31,32,31,32,31,30,30,30,29,30,29,31],2024:[17,31,31,31,32,31,31,30,29,30,29,30,30],2025:[18,31,31,32,31,31,31,30,29,30,29,30,30],2026:[17,31,32,31,32,31,30,30,30,29,29,30,31],2027:[17,30,32,31,32,31,30,30,30,29,30,29,31],2028:[17,31,31,32,31,31,31,30,29,30,29,30,30],2029:[18,31,31,32,31,32,30,30,29,30,29,30,30],2030:[17,31,32,31,32,31,30,30,30,30,30,30,31],2031:[17,31,32,31,32,31,31,31,31,31,31,31,31],2032:[17,32,32,32,32,32,32,32,32,32,32,32,32],2033:[18,31,31,32,32,31,30,30,29,30,29,30,30],2034:[17,31,32,31,32,31,30,30,30,29,29,30,31],2035:[17,30,32,31,32,31,31,29,30,30,29,29,31],2036:[17,31,31,32,31,31,31,30,29,30,29,30,30],2037:[18,31,31,32,32,31,30,30,29,30,29,30,30],2038:[17,31,32,31,32,31,30,30,30,29,29,30,31],2039:[17,31,31,31,32,31,31,29,30,30,29,30,30],2040:[17,31,31,32,31,31,31,30,29,30,29,30,30],2041:[18,31,31,32,32,31,30,30,29,30,29,30,30],2042:[17,31,32,31,32,31,30,30,30,29,29,30,31],2043:[17,31,31,31,32,31,31,29,30,30,29,30,30],2044:[17,31,31,32,31,31,31,30,29,30,29,30,30],2045:[18,31,32,31,32,31,30,30,29,30,29,30,30],2046:[17,31,32,31,32,31,30,30,30,29,29,30,31],2047:[17,31,31,31,32,31,31,30,29,30,29,30,30],2048:[17,31,31,32,31,31,31,30,29,30,29,30,30],2049:[17,31,32,31,32,31,30,30,30,29,29,30,30],2050:[17,31,32,31,32,31,30,30,30,29,30,29,31],2051:[17,31,31,31,32,31,31,30,29,30,29,30,30],2052:[17,31,31,32,31,31,31,30,29,30,29,30,30],2053:[17,31,32,31,32,31,30,30,30,29,29,30,30],2054:[17,31,32,31,32,31,30,30,30,29,30,29,31],2055:[17,31,31,32,31,31,31,30,29,30,30,29,30],2056:[17,31,31,32,31,32,30,30,29,30,29,30,30],2057:[17,31,32,31,32,31,30,30,30,29,29,30,31],2058:[17,30,32,31,32,31,30,30,30,29,30,29,31],2059:[17,31,31,32,31,31,31,30,29,30,29,30,30],2060:[17,31,31,32,32,31,30,30,29,30,29,30,30],2061:[17,31,32,31,32,31,30,30,30,29,29,30,31],2062:[17,30,32,31,32,31,31,29,30,29,30,29,31],2063:[17,31,31,32,31,31,31,30,29,30,29,30,30],2064:[17,31,31,32,32,31,30,30,29,30,29,30,30],2065:[17,31,32,31,32,31,30,30,30,29,29,30,31],2066:[17,31,31,31,32,31,31,29,30,30,29,29,31],2067:[17,31,31,32,31,31,31,30,29,30,29,30,30],2068:[17,31,31,32,32,31,30,30,29,30,29,30,30],2069:[17,31,32,31,32,31,30,30,30,29,29,30,31],2070:[17,31,31,31,32,31,31,29,30,30,29,30,30],2071:[17,31,31,32,31,31,31,30,29,30,29,30,30],2072:[17,31,32,31,32,31,30,30,29,30,29,30,30],2073:[17,31,32,31,32,31,30,30,30,29,29,30,31],2074:[17,31,31,31,32,31,31,30,29,30,29,30,30],2075:[17,31,31,32,31,31,31,30,29,30,29,30,30],2076:[16,31,32,31,32,31,30,30,30,29,29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n=t(\"../main\"),a=t(\"object-assign\");function i(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}i.prototype=new n.baseCalendar,a(i.prototype,{name:\"Persian\",jdEpoch:1948320.5,daysPerMonth:[31,31,31,31,31,31,30,30,30,30,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Persian\",epochs:[\"BP\",\"AP\"],monthNames:[\"Farvardin\",\"Ordibehesht\",\"Khordad\",\"Tir\",\"Mordad\",\"Shahrivar\",\"Mehr\",\"Aban\",\"Azar\",\"Day\",\"Bahman\",\"Esfand\"],monthNamesShort:[\"Far\",\"Ord\",\"Kho\",\"Tir\",\"Mor\",\"Sha\",\"Meh\",\"Aba\",\"Aza\",\"Day\",\"Bah\",\"Esf\"],dayNames:[\"Yekshambe\",\"Doshambe\",\"Seshambe\",\"Ch\\xe6harshambe\",\"Panjshambe\",\"Jom'e\",\"Shambe\"],dayNamesShort:[\"Yek\",\"Do\",\"Se\",\"Ch\\xe6\",\"Panj\",\"Jom\",\"Sha\"],dayNamesMin:[\"Ye\",\"Do\",\"Se\",\"Ch\",\"Pa\",\"Jo\",\"Sh\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!1}},leapYear:function(t){var 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 a=this._validate(t,e,r,n.local.invalidDate);t=a.year(),e=a.month(),r=a.day();var i=t-(t>=0?474:473),s=474+o(i,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(i/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),a=2820;if(1029982!==n){var i=Math.floor(n/366),s=o(n,366);a=Math.floor((2134*i+2816*s+2815)/1028522)+i+1}var l=a+2820*r+474;l=l<=0?l-1:l;var c=t-this.toJD(l,1,1)+1,u=c<=186?Math.ceil(c/31):Math.ceil((c-6)/30),h=t-this.toJD(l,u,1)+1;return this.newDate(l,u,h)}}),n.calendars.persian=i,n.calendars.jalali=i},{\"../main\":569,\"object-assign\":455}],566:[function(t,e,r){var n=t(\"../main\"),a=t(\"object-assign\"),i=n.instance();function o(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}o.prototype=new n.baseCalendar,a(o.prototype,{name:\"Taiwan\",jdEpoch:2419402.5,yearsOffset:1911,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Taiwan\",epochs:[\"BROC\",\"ROC\"],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:\"yyyy/mm/dd\",firstDay:1,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(e.year());return i.leapYear(t)},weekOfYear:function(t,e,r){var a=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(a.year());return i.weekOfYear(t,a.month(),a.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 a=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(a.year());return i.toJD(t,a.month(),a.day())},fromJD:function(t){var e=i.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\":569,\"object-assign\":455}],567:[function(t,e,r){var n=t(\"../main\"),a=t(\"object-assign\"),i=n.instance();function o(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}o.prototype=new n.baseCalendar,a(o.prototype,{name:\"Thai\",jdEpoch:1523098.5,yearsOffset:543,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Thai\",epochs:[\"BBE\",\"BE\"],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:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(e.year());return i.leapYear(t)},weekOfYear:function(t,e,r){var a=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(a.year());return i.weekOfYear(t,a.month(),a.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 a=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(a.year());return i.toJD(t,a.month(),a.day())},fromJD:function(t){var e=i.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\":569,\"object-assign\":455}],568:[function(t,e,r){var n=t(\"../main\"),a=t(\"object-assign\");function i(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}i.prototype=new n.baseCalendar,a(i.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,a=0,i=0;i<o.length;i++){if(o[i]>r)return o[a]-o[a-1];a++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var a=this._validate(t,e,r,n.local.invalidDate),i=12*(a.year()-1)+a.month()-15292;return a.day()+o[i-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 a=r+15292,i=Math.floor((a-1)/12),s=i+1,l=a-12*i,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var a=n.baseCalendar.prototype.isValid.apply(this,arguments);return a&&(a=(t=null!=t.year?t.year:t)>=1276&&t<=1500),a},_validate:function(t,e,r,a){var i=n.baseCalendar.prototype._validate.apply(this,arguments);if(i.year<1276||i.year>1500)throw a.replace(/\\{0\\}/,this.local.name);return i}}),n.calendars.ummalqura=i;var 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n=t(\"object-assign\");function a(){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 i(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(a.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,a){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,a):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=\"\",a=0;r>0;){var i=r%10;n=(0===i?\"\":t[i]+e[a])+n,a++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(i.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 i(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),a=t.calendar().fromJD(n);return this._validateLevel--,[a.year(),a.month(),a.day()]}try{var i=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0);a=t.day();\"y\"===r?(t.month()!==this.fromMonthOfYear(i,o)&&(o=this.newDate(i,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(i)),a=Math.min(a,this.daysInMonth(i,this.fromMonthOfYear(i,o)))):\"m\"===r&&(!function(t){for(;o<t.minMonth;)i--,o+=t.monthsInYear(i);for(var e=t.monthsInYear(i);o>e-1+t.minMonth;)i++,o-=e,e=t.monthsInYear(i)}(this),a=Math.min(a,this.daysInMonth(i,this.fromMonthOfYear(i,o))));var s=[i,this.fromMonthOfYear(i,o),a];return this._validateLevel--,s}catch(t){throw 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j=e.font,V=x._meta?o.templateString(e.text,x._meta):e.text,U=z.append(\"text\").classed(\"annotation-text\",!0).text(V);_.annotationText?U.call(h.makeEditable,{delegate:z,gd:t}).call(H).on(\"edit\",function(r){e.text=r,this.call(H),T(\"text\",r),s&&s.autorange&&k(s._name+\".autorange\",!0),v&&v.autorange&&k(v._name+\".autorange\",!0),a.call(\"_guiRelayout\",t,A())}):U.call(H)}else n.selectAll(\"#\"+M).remove();function q(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return i&&(n.subplotId=i),n}function H(r){return r.call(c.font,j).attr({\"text-anchor\":{left:\"start\",right:\"end\"}[e.align]||\"middle\"}),h.convertToTspans(r,t,G),r}function G(){var r=U.selectAll(\"a\");1===r.size()&&r.text()===U.text()&&z.insert(\"a\",\":first-child\").attr({\"xlink:xlink:href\":r.attr(\"xlink:href\"),\"xlink:xlink:show\":r.attr(\"xlink:show\")}).style({cursor:\"pointer\"}).node().appendChild(F.node());var n=z.select(\".annotation-text-math-group\"),u=!n.empty(),d=c.bBox((u?n:U).node()),m=d.width,y=d.height,w=e.width||m,O=e.height||y,D=Math.round(w+2*R),j=Math.round(O+2*R);function V(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 H=!1,G=[\"x\",\"y\"],Y=0;Y<G.length;Y++){var W,X,Z,J,K,Q=G[Y],$=e[Q+\"ref\"]||Q,tt=e[\"a\"+Q+\"ref\"],et={x:s,y:v}[Q],rt=(E+(\"x\"===Q?0:-90))*Math.PI/180,nt=D*Math.cos(rt),at=j*Math.sin(rt),it=Math.abs(nt)+Math.abs(at),ot=e[Q+\"anchor\"],st=e[Q+\"shift\"]*(\"x\"===Q?1:-1),lt=S[Q];if(et){var ct=et.r2fraction(e[Q]);(ct<0||ct>1)&&(tt===$?((ct=et.r2fraction(e[\"a\"+Q]))<0||ct>1)&&(H=!0):H=!0),W=et._offset+et.r2p(e[Q]),J=.5}else\"x\"===Q?(Z=e[Q],W=b.l+b.w*Z):(Z=1-e[Q],W=b.t+b.h*Z),J=e.showarrow?.5:Z;if(e.showarrow){lt.head=W;var ut=e[\"a\"+Q];K=nt*V(.5,e.xanchor)-at*V(.5,e.yanchor),tt===$?(lt.tail=et._offset+et.r2p(ut),X=K):(lt.tail=W+ut,X=K+ut),lt.text=lt.tail+K;var ht=x[\"x\"===Q?\"width\":\"height\"];if(\"paper\"===$&&(lt.head=o.constrain(lt.head,1,ht-1)),\"pixel\"===tt){var ft=-Math.max(lt.tail-3,lt.text),pt=Math.min(lt.tail+3,lt.text)-ht;ft>0?(lt.tail+=ft,lt.text+=ft):pt>0&&(lt.tail-=pt,lt.text-=pt)}lt.tail+=st,lt.head+=st}else X=K=it*V(J,ot),lt.text=W+K;lt.text+=st,K+=st,X+=st,e[\"_\"+Q+\"padplus\"]=it/2+X,e[\"_\"+Q+\"padminus\"]=it/2-X,e[\"_\"+Q+\"size\"]=it,e[\"_\"+Q+\"shift\"]=K}if(H)z.remove();else{var dt=0,gt=0;if(\"left\"!==e.align&&(dt=(w-m)*(\"center\"===e.align?.5:1)),\"top\"!==e.valign&&(gt=(O-y)*(\"middle\"===e.valign?.5:1)),u)n.select(\"svg\").attr({x:R+dt-1,y:R+gt}).call(c.setClipUrl,B?M:null,t);else{var vt=R+gt-d.top,mt=R+dt-d.left;U.call(h.positionText,mt,vt).call(c.setClipUrl,B?M:null,t)}N.select(\"rect\").call(c.setRect,R,R,w,O),F.call(c.setRect,I/2,I/2,D-I,j-I),z.call(c.setTranslate,Math.round(S.x.text-D/2),Math.round(S.y.text-j/2)),C.attr({transform:\"rotate(\"+E+\",\"+S.x.text+\",\"+S.y.text+\")\"});var yt,xt=function(r,n){L.selectAll(\".annotation-arrow-g\").remove();var u=S.x.head,h=S.y.head,f=S.x.tail+r,d=S.y.tail+n,m=S.x.text+r,y=S.y.text+n,x=o.rotationXYMatrix(E,m,y),w=o.apply2DTransform(x),M=o.apply2DTransform2(x),P=+F.attr(\"width\"),O=+F.attr(\"height\"),I=m-.5*P,D=I+P,R=y-.5*O,B=R+O,N=[[I,R,I,B],[I,B,D,B],[D,B,D,R],[D,R,I,R]].map(M);if(!N.reduce(function(t,e){return t^!!o.segmentsIntersect(u,h,u+1e6,h+1e6,e[0],e[1],e[2],e[3])},!1)){N.forEach(function(t){var e=o.segmentsIntersect(f,d,u,h,t[0],t[1],t[2],t[3]);e&&(f=e.x,d=e.y)});var j=e.arrowwidth,V=e.arrowcolor,U=e.arrowside,q=L.append(\"g\").style({opacity:l.opacity(V)}).classed(\"annotation-arrow-g\",!0),H=q.append(\"path\").attr(\"d\",\"M\"+f+\",\"+d+\"L\"+u+\",\"+h).style(\"stroke-width\",j+\"px\").call(l.stroke,l.rgb(V));if(g(H,U,e),_.annotationPosition&&H.node().parentNode&&!i){var G=u,Y=h;if(e.standoff){var W=Math.sqrt(Math.pow(u-f,2)+Math.pow(h-d,2));G+=e.standoff*(f-u)/W,Y+=e.standoff*(d-h)/W}var X,Z,J=q.append(\"path\").classed(\"annotation-arrow\",!0).classed(\"anndrag\",!0).classed(\"cursor-move\",!0).attr({d:\"M3,3H-3V-3H3ZM0,0L\"+(f-G)+\",\"+(d-Y),transform:\"translate(\"+G+\",\"+Y+\")\"}).style(\"stroke-width\",j+6+\"px\").call(l.stroke,\"rgba(0,0,0,0)\").call(l.fill,\"rgba(0,0,0,0)\");p.init({element:J.node(),gd:t,prepFn:function(){var t=c.getTranslate(z);X=t.x,Z=t.y,s&&s.autorange&&k(s._name+\".autorange\",!0),v&&v.autorange&&k(v._name+\".autorange\",!0)},moveFn:function(t,r){var n=w(X,Z),a=n[0]+t,i=n[1]+r;z.call(c.setTranslate,a,i),T(\"x\",s?s.p2r(s.r2p(e.x)+t):e.x+t/b.w),T(\"y\",v?v.p2r(v.r2p(e.y)+r):e.y-r/b.h),e.axref===e.xref&&T(\"ax\",s.p2r(s.r2p(e.ax)+t)),e.ayref===e.yref&&T(\"ay\",v.p2r(v.r2p(e.ay)+r)),q.attr(\"transform\",\"translate(\"+t+\",\"+r+\")\"),C.attr({transform:\"rotate(\"+E+\",\"+a+\",\"+i+\")\"})},doneFn:function(){a.call(\"_guiRelayout\",t,A());var 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P,O,z=i.readability(C.color,g)<1.5?h.contrast(g):C.color,I=n.spikemode,D=n.spikethickness,R=n.spikecolor||z,F=p.getPxPosition(t,n);if(-1!==I.indexOf(\"toaxis\")||-1!==I.indexOf(\"across\")){if(-1!==I.indexOf(\"toaxis\")&&(P=F,O=L),-1!==I.indexOf(\"across\")){var B=n._counterDomainMin,N=n._counterDomainMax;\"free\"===n.anchor&&(B=Math.min(B,n.position),N=Math.max(N,n.position)),P=l.t+(1-N)*l.h,O=l.t+(1-B)*l.h}o.insert(\"line\",\":first-child\").attr({x1:E,x2:E,y1:P,y2:O,\"stroke-width\":D,stroke:R,\"stroke-dasharray\":u.dashStyle(n.spikedash,D)}).classed(\"spikeline\",!0).classed(\"crisp\",!0),o.insert(\"line\",\":first-child\").attr({x1:E,x2:E,y1:P,y2:O,\"stroke-width\":D+2,stroke:g}).classed(\"spikeline\",!0).classed(\"crisp\",!0)}-1!==I.indexOf(\"marker\")&&o.insert(\"circle\",\":first-child\").attr({cx:E,cy:F-(\"top\"!==n.side?D:-D),r:D,fill:R}).classed(\"spikeline\",!0)}}}function 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e=a.isD3Selection(t)?t:n.select(t);e.selectAll(\"g.hovertext\").remove(),e.selectAll(\".spikeline\").remove()},click:t(\"./click\")}},{\"../../lib\":716,\"../dragelement\":609,\"./attributes\":621,\"./calc\":622,\"./click\":623,\"./constants\":624,\"./defaults\":625,\"./helpers\":626,\"./hover\":627,\"./layout_attributes\":630,\"./layout_defaults\":631,\"./layout_global_defaults\":632,d3:164}],630:[function(t,e,r){\"use strict\";var n=t(\"./constants\"),a=t(\"../../plots/font_attributes\")({editType:\"none\"});a.family.dflt=n.HOVERFONT,a.size.dflt=n.HOVERFONTSIZE,e.exports={clickmode:{valType:\"flaglist\",flags:[\"event\",\"select\"],dflt:\"event\",editType:\"plot\",extras:[\"none\"]},dragmode:{valType:\"enumerated\",values:[\"zoom\",\"pan\",\"select\",\"lasso\",\"orbit\",\"turntable\",!1],dflt:\"zoom\",editType:\"modebar\"},hovermode:{valType:\"enumerated\",values:[\"x\",\"y\",\"closest\",!1],editType:\"modebar\"},hoverdistance:{valType:\"integer\",min:-1,dflt:20,editType:\"none\"},spikedistance:{valType:\"integer\",min:-1,dflt:20,editType:\"none\"},hoverlabel:{bgcolor:{valType:\"color\",editType:\"none\"},bordercolor:{valType:\"color\",editType:\"none\"},font:a,align:{valType:\"enumerated\",values:[\"left\",\"right\",\"auto\"],dflt:\"auto\",editType:\"none\"},namelength:{valType:\"integer\",min:-1,dflt:15,editType:\"none\"},editType:\"none\"},selectdirection:{valType:\"enumerated\",values:[\"h\",\"v\",\"d\",\"any\"],dflt:\"any\",editType:\"none\"}}},{\"../../plots/font_attributes\":790,\"./constants\":624}],631:[function(t,e,r){\"use 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e._shapeUpperLayer.selectAll(\"path\").remove(),e._shapeLowerLayer.selectAll(\"path\").remove(),e._plots){var n=e._plots[r].shapelayer;n&&n.selectAll(\"path\").remove()}for(var a=0;a<e.shapes.length;a++)e.shapes[a].visible&&p(t,a)},drawOne:p}},{\"../../lib\":716,\"../../lib/setcursor\":736,\"../../plot_api/plot_template\":754,\"../../plots/cartesian/axes\":764,\"../../registry\":845,\"../color\":591,\"../dragelement\":609,\"../drawing\":612,\"./constants\":668,\"./helpers\":671}],671:[function(t,e,r){\"use strict\";var n=t(\"./constants\"),a=t(\"../../lib\");r.rangeToShapePosition=function(t){return\"log\"===t.type?t.r2d:function(t){return t}},r.shapePositionToRange=function(t){return\"log\"===t.type?t.d2r:function(t){return t}},r.decodeDate=function(t){return function(e){return e.replace&&(e=e.replace(\"_\",\" \")),t(e)}},r.encodeDate=function(t){return function(e){return t(e).replace(\" \",\"_\")}},r.extractPathCoords=function(t,e){var r=[];return 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n=t(\"./draw\");e.exports={moduleType:\"component\",name:\"shapes\",layoutAttributes:t(\"./attributes\"),supplyLayoutDefaults:t(\"./defaults\"),includeBasePlot:t(\"../../plots/cartesian/include_components\")(\"shapes\"),calcAutorange:t(\"./calc_autorange\"),draw:n.draw,drawOne:n.drawOne}},{\"../../plots/cartesian/include_components\":774,\"./attributes\":666,\"./calc_autorange\":667,\"./defaults\":669,\"./draw\":670}],673:[function(t,e,r){\"use strict\";var n=t(\"../../plots/font_attributes\"),a=t(\"../../plots/pad_attributes\"),i=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:i(a({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\":707,\"../../plot_api/edit_types\":747,\"../../plot_api/plot_template\":754,\"../../plots/animation_attributes\":759,\"../../plots/font_attributes\":790,\"../../plots/pad_attributes\":824,\"./constants\":674}],674:[function(t,e,r){\"use strict\";e.exports={name:\"sliders\",containerClassName:\"slider-container\",groupClassName:\"slider-group\",inputAreaClass:\"slider-input-area\",railRectClass:\"slider-rail-rect\",railTouchRectClass:\"slider-rail-touch-rect\",gripRectClass:\"slider-grip-rect\",tickRectClass:\"slider-tick-rect\",inputProxyClass:\"slider-input-proxy\",labelsClass:\"slider-labels\",labelGroupClass:\"slider-label-group\",labelClass:\"slider-label\",currentValueClass:\"slider-current-value\",railHeight:5,menuIndexAttrName:\"slider-active-index\",autoMarginIdRoot:\"slider-\",minWidth:30,minHeight:30,textPadX:40,arrowOffsetX:4,railRadius:2,railWidth:5,railBorder:4,railBorderWidth:1,railBorderColor:\"#bec8d9\",railBgColor:\"#f8fafc\",railInset:8,stepInset:10,gripRadius:10,gripWidth:20,gripHeight:20,gripBorder:20,gripBorderWidth:1,gripBorderColor:\"#bec8d9\",gripBgColor:\"#f6f8fa\",gripBgActiveColor:\"#dbdde0\",labelPadding:8,labelOffset:0,tickWidth:1,tickColor:\"#333\",tickOffset:25,tickLength:7,minorTickOffset:25,minorTickColor:\"#333\",minorTickLength:4,currentValuePadding:8,currentValueInset:0}},{}],675:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"../../plots/array_container_defaults\"),i=t(\"./attributes\"),o=t(\"./constants\").name,s=i.steps;function l(t,e,r){function o(r,a){return n.coerce(t,e,i,r,a)}for(var s=a(t,e,{name:\"steps\",handleItemDefaults:c}),l=0,u=0;u<s.length;u++)s[u].visible&&l++;if(l<2?e.visible=!1:o(\"visible\")){e._stepCount=l;var h=e._visibleSteps=n.filterVisible(s);(s[o(\"active\")]||{}).visible||(e.active=h[0]._index),o(\"x\"),o(\"y\"),n.noneOrAll(t,e,[\"x\",\"y\"]),o(\"xanchor\"),o(\"yanchor\"),o(\"len\"),o(\"lenmode\"),o(\"pad.t\"),o(\"pad.r\"),o(\"pad.b\"),o(\"pad.l\"),n.coerceFont(o,\"font\",r.font),o(\"currentvalue.visible\")&&(o(\"currentvalue.xanchor\"),o(\"currentvalue.prefix\"),o(\"currentvalue.suffix\"),o(\"currentvalue.offset\"),n.coerceFont(o,\"currentvalue.font\",e.font)),o(\"transition.duration\"),o(\"transition.easing\"),o(\"bgcolor\"),o(\"activebgcolor\"),o(\"bordercolor\"),o(\"borderwidth\"),o(\"ticklen\"),o(\"tickwidth\"),o(\"tickcolor\"),o(\"minorticklen\")}}function c(t,e){function r(r,a){return n.coerce(t,e,s,r,a)}if(\"skip\"===t.method||Array.isArray(t.args)?r(\"visible\"):e.visible=!1){r(\"method\"),r(\"args\");var 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f=t._fullLayout._size;h.lx=f.l+f.w*e.x,h.ly=f.t+f.h*(1-e.y),\"fraction\"===e.lenmode?h.outerLength=Math.round(f.w*e.len):h.outerLength=e.len,h.inputAreaStart=0,h.inputAreaLength=Math.round(h.outerLength-e.pad.l-e.pad.r);var v=(h.inputAreaLength-2*u.stepInset)/(e._stepCount-1),m=i+u.labelPadding;if(h.labelStride=Math.max(1,Math.ceil(m/v)),h.labelHeight=c,h.currentValueMaxWidth=0,h.currentValueHeight=0,h.currentValueTotalHeight=0,h.currentValueMaxLines=1,e.currentvalue.visible){var x=o.tester.append(\"g\");r.each(function(t){var r=y(x,e,t.label),n=r.node()&&o.bBox(r.node())||{width:0,height:0},a=l.lineCount(r);h.currentValueMaxWidth=Math.max(h.currentValueMaxWidth,Math.ceil(n.width)),h.currentValueHeight=Math.max(h.currentValueHeight,Math.ceil(n.height)),h.currentValueMaxLines=Math.max(h.currentValueMaxLines,a)}),h.currentValueTotalHeight=h.currentValueHeight+e.currentvalue.offset,x.remove()}h.height=h.currentValueTotalHeight+u.tickOffset+e.ticklen+u.labelOffset+h.labelHeight+e.pad.t+e.pad.b;var _=\"left\";s.isRightAnchor(e)&&(h.lx-=h.outerLength,_=\"right\"),s.isCenterAnchor(e)&&(h.lx-=h.outerLength/2,_=\"center\");var w=\"top\";s.isBottomAnchor(e)&&(h.ly-=h.height,w=\"bottom\"),s.isMiddleAnchor(e)&&(h.ly-=h.height/2,w=\"middle\"),h.outerLength=Math.ceil(h.outerLength),h.height=Math.ceil(h.height),h.lx=Math.round(h.lx),h.ly=Math.round(h.ly);var k={y:e.y,b:h.height*d[w],t:h.height*p[w]};\"fraction\"===e.lenmode?(k.l=0,k.xl=e.x-e.len*p[_],k.r=0,k.xr=e.x+e.len*d[_]):(k.x=e.x,k.l=h.outerLength*p[_],k.r=h.outerLength*d[_]),a.autoMargin(t,g(e),k)}function y(t,e,r){if(e.currentvalue.visible){var n,a,i=e._dims;switch(e.currentvalue.xanchor){case\"right\":n=i.inputAreaLength-u.currentValueInset-i.currentValueMaxWidth,a=\"left\";break;case\"center\":n=.5*i.inputAreaLength,a=\"middle\";break;default:n=u.currentValueInset,a=\"left\"}var c=s.ensureSingle(t,\"text\",u.labelClass,function(t){t.classed(\"user-select-none\",!0).attr({\"text-anchor\":a,\"data-notex\":1})}),h=e.currentvalue.prefix?e.currentvalue.prefix:\"\";if(\"string\"==typeof r)h+=r;else{var p=e.steps[e.active].label,d=e._gd._fullLayout._meta;d&&(p=s.templateString(p,d)),h+=p}e.currentvalue.suffix&&(h+=e.currentvalue.suffix),c.call(o.font,e.currentvalue.font).text(h).call(l.convertToTspans,e._gd);var 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s=t.r2l(t[i][0],o),l=t.r2l(t[i][1],o);if(\"y\"===h?(t._offset=n.t+(1-t.domain[1])*n.h,t._length=n.h*(t.domain[1]-t.domain[0]),t._m=t._length/(s-l),t._b=-t._m*l):(t._offset=n.l+t.domain[0]*n.w,t._length=n.w*(t.domain[1]-t.domain[0]),t._m=t._length/(l-s),t._b=-t._m*s),!isFinite(t._m)||!isFinite(t._b)||t._length<0)throw e._replotting=!1,new Error(\"Something went wrong with axis scaling\")},t.makeCalcdata=function(e,r){var n,a,o,s,l=t.type,c=\"date\"===l&&e[r+\"calendar\"];if(r in e){if(n=e[r],s=e._length||i.minRowLength(n),i.isTypedArray(n)&&(\"linear\"===l||\"log\"===l)){if(s===n.length)return n;if(n.subarray)return n.subarray(0,s)}if(\"multicategory\"===l)return function(t,e){for(var r=new Array(e),n=0;n<e;n++){var a=(t[0]||[])[n],i=(t[1]||[])[n];r[n]=T([a,i])}return r}(n,s);for(a=new Array(s),o=0;o<s;o++)a[o]=t.d2c(n[o],0,c)}else{var u=r+\"0\"in e?t.d2c(e[r+\"0\"],0,c):0,h=e[\"d\"+r]?Number(e[\"d\"+r]):1;for(n=e[{x:\"y\",y:\"x\"}[r]],s=e._length||n.length,a=new 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s=e[v.id2name(o)];s._categories=t._categories,s._categoriesMap=t._categoriesMap,n=n.concat(s._traceIndices)}return n};var E=e._d3locale;\"date\"===t.type&&(t._dateFormat=E?E.timeFormat.utc:n.time.format.utc,t._extraFormat=e._extraFormat),t._separators=e.separators,t._numFormat=E?E.numberFormat:n.format,delete t._minDtick,delete t._forceTick0}},{\"../../constants/numerical\":692,\"../../lib\":716,\"./axis_ids\":767,\"./constants\":770,d3:164,\"fast-isnumeric\":227}],783:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./layout_attributes\"),i=t(\"../array_container_defaults\");function o(t){var e=[\"showexponent\",\"showtickprefix\",\"showticksuffix\"].filter(function(e){return void 0!==t[e]});if(e.every(function(r){return t[r]===t[e[0]]})||1===e.length)return t[e[0]]}function s(t,e){function r(r,i){return n.coerce(t,e,a.tickformatstops,r,i)}r(\"enabled\")&&(r(\"dtickrange\"),r(\"value\"))}e.exports=function(t,e,r,l,c,u){u&&1!==u.pass||function(t,e,r,n,a){var 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a=Math.min(r,n),i=Math.max(r,n);a!==i?(l[e]=a,l[e+2]=i,c.dataBox=l,t.setRanges(l)):(t.selectBox.selectBox=[0,0,1,1],t.glplot.setDirty())}switch(n*=r.pixelRatio,a*=r.pixelRatio,a=h[3]-h[1]-a,t.fullLayout.dragmode){case\"zoom\":if(e){var m=n/(h[2]-h[0])*(l[2]-l[0])+l[0],y=a/(h[3]-h[1])*(l[3]-l[1])+l[1];c.boxInited||(c.boxStart[0]=m,c.boxStart[1]=y,c.dragStart[0]=n,c.dragStart[1]=a),c.boxEnd[0]=m,c.boxEnd[1]=y,c.boxInited=!0,c.boxEnabled||c.boxStart[0]===c.boxEnd[0]&&c.boxStart[1]===c.boxEnd[1]||(c.boxEnabled=!0);var x=Math.abs(c.dragStart[0]-n)<g,b=Math.abs(c.dragStart[1]-a)<g;if(!function(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,a=0;a<e.length;a++)if(-1!==e[a][r]){if(-1!==e[a][n])return!0;break}return!1}()||x&&b)x&&(c.boxEnd[0]=c.boxStart[0]),b&&(c.boxEnd[1]=c.boxStart[1]);else{i=c.boxEnd[0]-c.boxStart[0],s=c.boxEnd[1]-c.boxStart[1];var _=(l[3]-l[1])/(l[2]-l[0]);Math.abs(i*_)>Math.abs(s)?(c.boxEnd[1]=c.boxStart[1]+Math.abs(i)*_*(s>=0?1:-1),c.boxEnd[1]<l[1]?(c.boxEnd[1]=l[1],c.boxEnd[0]=c.boxStart[0]+(l[1]-c.boxStart[1])/Math.abs(_)):c.boxEnd[1]>l[3]&&(c.boxEnd[1]=l[3],c.boxEnd[0]=c.boxStart[0]+(l[3]-c.boxStart[1])/Math.abs(_))):(c.boxEnd[0]=c.boxStart[0]+Math.abs(s)/_*(i>=0?1:-1),c.boxEnd[0]<l[0]?(c.boxEnd[0]=l[0],c.boxEnd[1]=c.boxStart[1]+(l[0]-c.boxStart[0])*Math.abs(_)):c.boxEnd[0]>l[2]&&(c.boxEnd[0]=l[2],c.boxEnd[1]=c.boxStart[1]+(l[2]-c.boxStart[0])*Math.abs(_)))}}else c.boxEnabled?(i=c.boxStart[0]!==c.boxEnd[0],s=c.boxStart[1]!==c.boxEnd[1],i||s?(i&&(v(0,c.boxStart[0],c.boxEnd[0]),t.xaxis.autorange=!1),s&&(v(1,c.boxStart[1],c.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),c.boxEnabled=!1,c.boxInited=!1):c.boxInited&&(c.boxInited=!1);break;case\"pan\":c.boxEnabled=!1,c.boxInited=!1,e?(c.panning||(c.dragStart[0]=n,c.dragStart[1]=a),Math.abs(c.dragStart[0]-n)<d&&(n=c.dragStart[0]),Math.abs(c.dragStart[1]-a)<d&&(a=c.dragStart[1]),i=(f-n)*(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-a)*(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=i,l[2]+=i,l[1]+=s,l[3]+=s,t.setRanges(l),c.panning=!0,c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations()):c.panning&&(c.panning=!1,t.relayoutCallback())}c.lastPos[0]=n,c.lastPos[1]=a}return c.mouseListener=n(e,h),e.addEventListener(\"touchstart\",function(t){var r=i(t.changedTouches[0],e);h(0,r[0],r[1]),h(1,r[0],r[1]),t.preventDefault()},!!s&&{passive:!1}),e.addEventListener(\"touchmove\",function(t){t.preventDefault();var r=i(t.changedTouches[0],e);h(1,r[0],r[1]),t.preventDefault()},!!s&&{passive:!1}),e.addEventListener(\"touchend\",function(t){h(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()},!!s&&{passive:!1}),c.wheelListener=a(e,function(e,n){if(!t.scrollZoom)return!1;var a=t.calcDataBox(),i=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5*n/(i[3]-i[1])),h=o/(i[2]-i[0])*(a[2]-a[0])+a[0],f=s/(i[3]-i[1])*(a[3]-a[1])+a[1];return a[0]=(a[0]-h)*l+h,a[2]=(a[2]-h)*l+h,a[1]=(a[1]-f)*l+f,a[3]=(a[3]-f)*l+f,t.setRanges(a),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0},!0),c}},{\"../cartesian/constants\":770,\"has-passive-events\":412,\"mouse-change\":436,\"mouse-event-offset\":437,\"mouse-wheel\":439}],801:[function(t,e,r){\"use strict\";var n=t(\"../cartesian/axes\"),a=t(\"../../lib/str2rgbarray\");function i(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=i.prototype,s=[\"xaxis\",\"yaxis\"];o.merge=function(t){var e,r,n,i,o,l,c,u,h,f,p;for(this.titleEnable=!1,this.backgroundColor=a(t.plot_bgcolor),f=0;f<2;++f){var d=(e=s[f]).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[f+p]=!1,this.labels[f+p]=n,this.labelColor[f+p]=a(r.title.font.color),this.labelFont[f+p]=r.title.font.family,this.labelSize[f+p]=r.title.font.size,this.labelPad[f+p]=this.getLabelPad(e,r),this.tickEnable[f+p]=!1,this.tickColor[f+p]=a((r.tickfont||{}).color),this.tickAngle[f+p]=\"auto\"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[f+p]=this.getTickPad(r),this.tickMarkLength[f+p]=0,this.tickMarkWidth[f+p]=r.tickwidth||0,this.tickMarkColor[f+p]=a(r.tickcolor),this.borderLineEnable[f+p]=!1,this.borderLineColor[f+p]=a(r.linecolor),this.borderLineWidth[f+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,i=r.mirror||!1,u=c?-1!==String(i).indexOf(\"all\"):!!i,h=c?\"allticks\"===i:-1!==String(i).indexOf(\"ticks\"),o?this.labelEnable[f]=!0:l&&(this.labelEnable[f+2]=!0),o?this.tickEnable[f]=r.showticklabels:l&&(this.tickEnable[f+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[f]=r.showline),(l||u)&&(this.borderLineEnable[f+2]=r.showline),(o||h)&&(this.tickMarkLength[f]=this.getTickMarkLength(r)),(l||h)&&(this.tickMarkLength[f+2]=this.getTickMarkLength(r)),this.gridLineEnable[f]=r.showgrid,this.gridLineColor[f]=a(r.gridcolor),this.gridLineWidth[f]=r.gridwidth,this.zeroLineEnable[f]=r.zeroline,this.zeroLineColor[f]=a(r.zerolinecolor),this.zeroLineWidth[f]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},o.getLabelPad=function(t,e){var r=e.title.font.size,n=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},o.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},e.exports=function(t){return new i(t)}},{\"../../lib/str2rgbarray\":739,\"../cartesian/axes\":764}],802:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,a=t(\"./scene2d\"),i=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:i.plot_bgcolor,hoverlabel:c.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,i=0;i<n.length;i++){var o=n[i],s=e._plots[o],l=u(r,\"gl2d\",o),c=s._scene2d;void 0===c&&(c=new a({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 a=n._subplots.gl2d||[],i=0;i<a.length;i++){var o=a[i],s=n._plots[o];if(s._scene2d)0===u(t,\"gl2d\",o).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 a=e._plots[r[n]]._scene2d,i=a.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":i,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),a.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\":630,\"../../constants/xmlns_namespaces\":693,\"../../plot_api/edit_types\":747,\"../cartesian\":775,\"../cartesian/attributes\":762,\"../cartesian/constants\":770,\"../get_data\":799,\"../layout_attributes\":816,\"./scene2d\":803}],803:[function(t,e,r){\"use strict\";var n,a,i=t(\"../../registry\"),o=t(\"../../plots/cartesian/axes\"),s=t(\"../../components/fx\"),l=t(\"gl-plot2d\"),c=t(\"gl-spikes2d\"),u=t(\"gl-select-box\"),h=t(\"webgl-context\"),f=t(\"./convert\"),p=t(\"./camera\"),d=t(\"../../lib/show_no_webgl_msg\"),g=t(\"../cartesian/constraints\"),v=g.enforce,m=g.clean,y=t(\"../cartesian/autorange\").doAutoRange,x=[\"xaxis\",\"yaxis\"],b=t(\"../cartesian/constants\").SUBPLOT_PATTERN;function _(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=f(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=_;var w=_.prototype;w.makeFramework=function(){if(this.staticPlot){if(!(a||(n=document.createElement(\"canvas\"),a=h({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=n,this.gl=a}else{var t=this.container.querySelector(\".gl-canvas-focus\"),e=h({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),r.className+=\" user-select-none\";var i=this.svgContainer=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\";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(i),s.appendChild(o);var l=this;o.addEventListener(\"mouseout\",function(){l.isMouseOver=!1,l.unhover()}),o.addEventListener(\"mouseover\",function(){l.isMouseOver=!0})},w.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,a=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 i=new Uint8Array(r*a*4);e.readPixels(0,0,r,a,e.RGBA,e.UNSIGNED_BYTE,i);for(var o=0,s=a-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=i[4*(r*o+l)+c];i[4*(r*o+l)+c]=i[4*(r*s+l)+c],i[4*(r*s+l)+c]=u}var h=document.createElement(\"canvas\");h.width=r,h.height=a;var f,p=h.getContext(\"2d\"),d=p.createImageData(r,a);switch(d.data.set(i),p.putImageData(d,0,0),t){case\"jpeg\":f=h.toDataURL(\"image/jpeg\");break;case\"webp\":f=h.toDataURL(\"image/webp\");break;default:f=h.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(n),f},w.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,a=r.height,i=0|Math.ceil(e*n),o=0|Math.ceil(e*a);return t.width===i&&t.height===o||(t.width=i,t.height=o),t},w.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},w.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(b),r=\"xaxis\"+e[1],n=\"yaxis\"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},w.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,a={},o=a[e._name+\".range\"]=e.range.slice(),s=a[r._name+\".range\"]=r.range.slice();a[e._name+\".autorange\"]=e.autorange,a[r._name+\".autorange\"]=r.autorange,i.call(\"_storeDirectGUIEdit\",t.layout,t._fullLayout._preGUI,a);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,a.lastInputTime=this.camera.lastInputTime,t.emit(\"plotly_relayout\",a)},w.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],a=e[r];if(n.length!==a.length)return!0;for(var i=0;i<n.length;++i)if(n[i].x!==a[i].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())},w.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&&i.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},w.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}},w.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 a=r.width,i=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,a,i];var s={_fullLayout:{_axisConstraintGroups:this.graphDiv._fullLayout._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis}};m(s,this.xaxis),m(s,this.yaxis);var l,c,u=r._size,h=this.xaxis.domain,f=this.yaxis.domain;for(o.viewBox=[u.l+h[0]*u.w,u.b+f[0]*u.h,a-u.r-(1-h[1])*u.w,i-u.t-(1-f[1])*u.h],this.mouseContainer.style.width=u.w*(h[1]-h[0])+\"px\",this.mouseContainer.style.height=u.h*(f[1]-f[0])+\"px\",this.mouseContainer.height=u.h*(f[1]-f[0]),this.mouseContainer.style.left=u.l+h[0]*u.w+\"px\",this.mouseContainer.style.top=u.t+(1-f[1])*u.h+\"px\",c=0;c<2;++c)(l=this[x[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();v(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},w.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,a=t.r2l,i=e.r2l;return[a(r[0]),i(n[0]),a(r[1]),i(n[1])]},w.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,a=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[a(t[1]),a(t[3])]},w.updateTraces=function(t,e){var r,n,a,i=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<i.length;r++){var o=i[r],s=this.traces[o];for(n=0;n<t.length;n++)if((a=t[n]).uid===o&&a.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){a=t[r];var l=e[r],c=this.traces[a.uid];c?c.update(a,l):(c=a._module.plot(this,a,l),this.traces[a.uid]=c)}this.glplot.objects.sort(function(t,e){return t._trace.index-e._trace.index})},w.updateFx=function(t){\"lasso\"===t||\"select\"===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},w.emitPointAction=function(t,e){for(var r,n=t.trace.uid,a=t.pointIndex,i=0;i<this.fullData.length;i++)this.fullData[i].uid===n&&(r=this.fullData[i]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:a,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,a),this.graphDiv.emit(e,{points:[o]})},w.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,a=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var i,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===a.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 h=a._size,f=this.xaxis.domain,p=this.yaxis.domain,d=(i=t.pick(o/t.pixelRatio+h.l+f[0]*h.w,l/t.pixelRatio-(h.t+(1-p[1])*h.h)))&&i.object._trace.handlePick(i);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),i&&\"skip\"!==i.object._trace.hoverinfo&&a.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var g=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:i.dataCoord}),g.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(i.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])*(i.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var v=this.fullData[g.trace.index]||{},m=g.pointIndex,y=s.castHoverinfo(v,a,m);if(y&&\"all\"!==y){var x=y.split(\"+\");-1===x.indexOf(\"x\")&&(g.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(g.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(g.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(g.textLabel=void 0),-1===x.indexOf(\"name\")&&(g.name=void 0)}s.loneHover({x:g.screenCoord[0],y:g.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",g.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",g.traceCoord[1]),zLabel:g.traceCoord[2],text:g.textLabel,name:g.name,color:s.castHoverOption(v,m,\"bgcolor\")||g.color,borderColor:s.castHoverOption(v,m,\"bordercolor\"),fontFamily:s.castHoverOption(v,m,\"font.family\"),fontSize:s.castHoverOption(v,m,\"font.size\"),fontColor:s.castHoverOption(v,m,\"font.color\"),nameLength:s.castHoverOption(v,m,\"namelength\"),textAlign:s.castHoverOption(v,m,\"align\")},{container:this.svgContainer,gd:this.graphDiv})}}i||this.unhover(),t.draw()}},w.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},w.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../components/fx\":629,\"../../lib/show_no_webgl_msg\":737,\"../../plots/cartesian/axes\":764,\"../../registry\":845,\"../cartesian/autorange\":763,\"../cartesian/constants\":770,\"../cartesian/constraints\":771,\"./camera\":800,\"./convert\":801,\"gl-plot2d\":289,\"gl-select-box\":301,\"gl-spikes2d\":310,\"webgl-context\":554}],804:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,a=t(\"../../components/fx/layout_attributes\"),i=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:a.hoverlabel},\"plot\",\"nested\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,a=0;a<n.length;a++){var s=n[a],l=o(r,\"gl3d\",s),c=e[s],u=c.camera,h=c._scene;h||(h=new i({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=h),h.viewInitial||(h.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}}),h.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var a=n._subplots.gl3d||[],i=0;i<a.length;i++){var o=a[i];!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,a=0;a<r.length;a++){var i=e[r[a]],o=i.domain,s=i._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\":630,\"../../constants/xmlns_namespaces\":693,\"../../lib\":716,\"../../plot_api/edit_types\":747,\"../get_data\":799,\"./layout/attributes\":805,\"./layout/defaults\":809,\"./layout/layout_attributes\":810,\"./scene\":814}],805:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],806:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),a=t(\"../../cartesian/layout_attributes\"),i=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=o({visible:a.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:a.color,categoryorder:a.categoryorder,categoryarray:a.categoryarray,title:{text:a.title.text,font:a.title.font},type:i({},a.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autorange:a.autorange,rangemode:a.rangemode,range:i({},a.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:a.tickmode,nticks:a.nticks,tick0:a.tick0,dtick:a.dtick,tickvals:a.tickvals,ticktext:a.ticktext,ticks:a.ticks,mirror:a.mirror,ticklen:a.ticklen,tickwidth:a.tickwidth,tickcolor:a.tickcolor,showticklabels:a.showticklabels,tickfont:a.tickfont,tickangle:a.tickangle,tickprefix:a.tickprefix,showtickprefix:a.showtickprefix,ticksuffix:a.ticksuffix,showticksuffix:a.showticksuffix,showexponent:a.showexponent,exponentformat:a.exponentformat,separatethousands:a.separatethousands,tickformat:a.tickformat,tickformatstops:a.tickformatstops,hoverformat:a.hoverformat,showline:a.showline,linecolor:a.linecolor,linewidth:a.linewidth,showgrid:a.showgrid,gridcolor:i({},a.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:a.gridwidth,zeroline:a.zeroline,zerolinecolor:a.zerolinecolor,zerolinewidth:a.zerolinewidth,_deprecated:{title:a._deprecated.title,titlefont:a._deprecated.titlefont}},\"plot\",\"from-root\")},{\"../../../components/color\":591,\"../../../lib/extend\":707,\"../../../plot_api/edit_types\":747,\"../../cartesian/layout_attributes\":776}],807:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,a=t(\"../../../lib\"),i=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,h;function f(t,e){return a.coerce(u,h,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(h=i.newContainer(e,d))._id=d[0]+r.scene,h._name=d,s(u,h,f,r),l(u,h,f,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),f(\"gridcolor\",n(h.color,r.bgColor,13600/187).toRgbString()),f(\"title.text\",d[0]),h.setScale=a.noop,f(\"showspikes\")&&(f(\"spikesides\"),f(\"spikethickness\"),f(\"spikecolor\",h.color)),f(\"showaxeslabels\"),f(\"showbackground\")&&f(\"backgroundcolor\")}}},{\"../../../lib\":716,\"../../../plot_api/plot_template\":754,\"../../cartesian/axis_defaults\":766,\"../../cartesian/type_defaults\":787,\"./axis_attributes\":806,tinycolor2:535}],808:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),a=t(\"../../../lib\"),i=[\"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[i[r]];o.visible?(this.labels[r]=t._meta?a.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\":716,\"../../../lib/str2rgbarray\":739}],809:[function(t,e,r){\"use strict\";var n=t(\"../../../lib\"),a=t(\"../../../components/color\"),i=t(\"../../../registry\"),o=t(\"../../subplot_defaults\"),s=t(\"./axis_defaults\"),l=t(\"./layout_attributes\"),c=t(\"../../get_data\").getSubplotData,u=\"gl3d\";function h(t,e,r,n){for(var o=r(\"bgcolor\"),l=a.combine(o,n.paper_bgcolor),h=[\"up\",\"center\",\"eye\"],f=0;f<h.length;f++)r(\"camera.\"+h[f]+\".x\"),r(\"camera.\"+h[f]+\".y\"),r(\"camera.\"+h[f]+\".z\");r(\"camera.projection.type\");var p=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),d=r(\"aspectmode\",p?\"manual\":\"auto\");p||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===d&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode);var g=c(n.fullData,u,n.id);s(t,e,{font:n.font,scene:n.id,data:g,bgColor:l,calendar:n.calendar,fullLayout:n.fullLayout}),i.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n);var v=n.getDfltFromLayout(\"dragmode\");if(!1!==v&&!v)if(v=\"orbit\",t.camera&&t.camera.up){var m=t.camera.up.x,y=t.camera.up.y,x=t.camera.up.z;0!==x&&(m&&y&&x?x/Math.sqrt(m*m+y*y+x*x)>.999&&(v=\"turntable\"):v=\"turntable\")}else v=\"turntable\";r(\"dragmode\",v),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}e.exports=function(t,e,r){var a=e._basePlotModules.length>1;o(t,e,r,{type:u,attributes:l,handleDefaults:h,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!a)return n.validate(t[e],l[e])?t[e]:void 0},paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":591,\"../../../lib\":716,\"../../../registry\":845,\"../../get_data\":799,\"../../subplot_defaults\":839,\"./axis_defaults\":807,\"./layout_attributes\":810}],810:[function(t,e,r){\"use strict\";var n=t(\"./axis_attributes\"),a=t(\"../../domain\").attributes,i=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:i(s(0,0,1),{}),center:i(s(0,0,0),{}),eye:i(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:a({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\":716,\"../../../lib/extend\":707,\"../../domain\":789,\"./axis_attributes\":806}],811:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"];function i(){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]}i.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[a[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 i;return e.merge(t),e}},{\"../../../lib/str2rgbarray\":739}],812:[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[i[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 h=u.tickmode;if(\"auto\"===u.tickmode){u.tickmode=\"linear\";var f=u.nticks||a.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/f)}for(var p=n.calcTicks(u),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=h}}e.ticks=l;for(var c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(var 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],a=new Array(n.length),i=0;i<n.length;++i)a[i]=n[i].x;e[r]=a}return e}(l)};var n=t(\"../../cartesian/axes\"),a=t(\"../../../lib\"),i=[\"xaxis\",\"yaxis\",\"zaxis\"],o=[0,0,0]},{\"../../../lib\":716,\"../../cartesian/axes\":764}],813:[function(t,e,r){\"use strict\";function n(t,e){var r,n,a=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)a[n]+=t[4*r+n]*e[r];return a}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],814:[function(t,e,r){\"use strict\";var n,a,i=t(\"gl-plot3d\"),o=i.createCamera,s=i.createScene,l=t(\"webgl-context\"),c=t(\"has-passive-events\"),u=t(\"../../registry\"),h=t(\"../../lib\"),f=t(\"../../plots/cartesian/axes\"),p=t(\"../../components/fx\"),d=t(\"../../lib/str2rgbarray\"),g=t(\"../../lib/show_no_webgl_msg\"),v=t(\"./project\"),m=t(\"./layout/convert\"),y=t(\"./layout/spikes\"),x=t(\"./layout/tick_marks\");function b(t,e,r){if(t.initializeGLCamera(),!function(t,e,r,i,o){var c={canvas:i,gl:o,container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:e,pixelRatio:r};if(t.staticMode){if(!(a||(n=document.createElement(\"canvas\"),a=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");c.pixelRatio=t.pixelRatio,c.gl=a,c.canvas=n}var u=0;try{t.glplot=s(c)}catch(e){u++;try{t.glplot=s(c)}catch(t){u++}}return u<2}(t,t.camera,t.pixelRatio,e,r))return g(t);var i=t.graphDiv,o=i.layout,u=function(){var e={};return t.isCameraChanged(o)&&(e[t.id+\".camera\"]=t.getCamera()),t.isAspectChanged(o)&&(e[t.id+\".aspectratio\"]=t.glplot.getAspectratio()),e},d=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=u();t.saveLayout(o),t.graphDiv.emit(\"plotly_relayout\",e)}};return t.glplot.canvas.addEventListener(\"mouseup\",function(){d(t)}),t.glplot.canvas.addEventListener(\"wheel\",function(e){if(i._context._scrollZoom.gl3d){if(t.glplot.camera._ortho){var r=e.deltaX>e.deltaY?1.1:1/1.1,n=t.glplot.getAspectratio();t.glplot.setAspectratio({x:r*n.x,y:r*n.y,z:r*n.z})}d(t)}},!!c&&{passive:!1}),t.glplot.canvas.addEventListener(\"mousemove\",function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=u();t.graphDiv.emit(\"plotly_relayouting\",e)}}),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",function(e){i&&i.emit&&i.emit(\"plotly_webglcontextlost\",{event:e,layer:t.id})},!1),t.glplot.camera=t.camera,t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(t){var e,r=t.graphDiv,n=t.svgContainer,a=t.container.getBoundingClientRect(),i=a.width,o=a.height;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+i+\" \"+o),n.setAttributeNS(null,\"width\",i),n.setAttributeNS(null,\"height\",o),x(t),t.glplot.axes.update(t.axesOptions);for(var s,l=Object.keys(t.traces),c=null,u=t.glplot.selection,d=0;d<l.length;++d)\"skip\"!==(e=t.traces[l[d]]).data.hoverinfo&&e.handlePick(u)&&(c=e),e.setContourLevels&&e.setContourLevels();function g(e,r){var n=t.fullSceneLayout[e];return f.tickText(n,n.d2l(r),\"hover\").text}if(null!==c){var m=v(t.glplot.cameraParams,u.dataCoordinate);e=c.data;var y,b=r._fullData[e.index],_=u.index,w={xLabel:g(\"xaxis\",u.traceCoordinate[0]),yLabel:g(\"yaxis\",u.traceCoordinate[1]),zLabel:g(\"zaxis\",u.traceCoordinate[2])},k=p.castHoverinfo(b,t.fullLayout,_),T=(k||\"\").split(\"+\"),A=k&&\"all\"===k;b.hovertemplate||A||(-1===T.indexOf(\"x\")&&(w.xLabel=void 0),-1===T.indexOf(\"y\")&&(w.yLabel=void 0),-1===T.indexOf(\"z\")&&(w.zLabel=void 0),-1===T.indexOf(\"text\")&&(u.textLabel=void 0),-1===T.indexOf(\"name\")&&(c.name=void 0));var M=[];\"cone\"===e.type||\"streamtube\"===e.type?(w.uLabel=g(\"xaxis\",u.traceCoordinate[3]),(A||-1!==T.indexOf(\"u\"))&&M.push(\"u: \"+w.uLabel),w.vLabel=g(\"yaxis\",u.traceCoordinate[4]),(A||-1!==T.indexOf(\"v\"))&&M.push(\"v: \"+w.vLabel),w.wLabel=g(\"zaxis\",u.traceCoordinate[5]),(A||-1!==T.indexOf(\"w\"))&&M.push(\"w: \"+w.wLabel),w.normLabel=u.traceCoordinate[6].toPrecision(3),(A||-1!==T.indexOf(\"norm\"))&&M.push(\"norm: \"+w.normLabel),\"streamtube\"===e.type&&(w.divergenceLabel=u.traceCoordinate[7].toPrecision(3),(A||-1!==T.indexOf(\"divergence\"))&&M.push(\"divergence: \"+w.divergenceLabel)),u.textLabel&&M.push(u.textLabel),y=M.join(\"<br>\")):\"isosurface\"===e.type||\"volume\"===e.type?(w.valueLabel=f.tickText(t.mockAxis,t.mockAxis.d2l(u.traceCoordinate[3]),\"hover\").text,M.push(\"value: \"+w.valueLabel),u.textLabel&&M.push(u.textLabel),y=M.join(\"<br>\")):y=u.textLabel;var S={x:u.traceCoordinate[0],y:u.traceCoordinate[1],z:u.traceCoordinate[2],data:b._input,fullData:b,curveNumber:b.index,pointNumber:_};p.appendArrayPointValue(S,b,_),e._module.eventData&&(S=b._module.eventData(S,u,b,{},_));var E={points:[S]};t.fullSceneLayout.hovermode&&p.loneHover({trace:b,x:(.5+.5*m[0]/m[3])*i,y:(.5-.5*m[1]/m[3])*o,xLabel:w.xLabel,yLabel:w.yLabel,zLabel:w.zLabel,text:y,name:c.name,color:p.castHoverOption(b,_,\"bgcolor\")||c.color,borderColor:p.castHoverOption(b,_,\"bordercolor\"),fontFamily:p.castHoverOption(b,_,\"font.family\"),fontSize:p.castHoverOption(b,_,\"font.size\"),fontColor:p.castHoverOption(b,_,\"font.color\"),nameLength:p.castHoverOption(b,_,\"namelength\"),textAlign:p.castHoverOption(b,_,\"align\"),hovertemplate:h.castOption(b,_,\"hovertemplate\"),hovertemplateLabels:h.extendFlat({},S,w),eventData:[S]},{container:n,gd:r}),u.buttons&&u.distance<5?r.emit(\"plotly_click\",E):r.emit(\"plotly_hover\",E),s=E}else p.loneUnhover(n),r.emit(\"plotly_unhover\",s);t.drawAnnotations(t)}.bind(null,t),t.traces={},t.make4thDimension(),!0}function _(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var a=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\",r.appendChild(a),this.svgContainer=a,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=m(e,e[this.id]),this.spikeOptions=y(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=u.getComponentMethod(\"annotations3d\",\"draw\"),b(this)}var w=_.prototype;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.recoverContext=function(){var t=this,e=this.glplot.gl,r=this.glplot.canvas;this.glplot.dispose(),requestAnimationFrame(function n(){e.isContextLost()?requestAnimationFrame(n):b(t,r,e)?t.plot.apply(t,t.plotArgs):h.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")})};var k=[\"xaxis\",\"yaxis\",\"zaxis\"];function T(t,e,r){for(var n=t.fullSceneLayout,a=0;a<3;a++){var i=k[a],o=i.charAt(0),s=n[i],l=e[o],c=e[o+\"calendar\"],u=e[\"_\"+o+\"length\"];if(h.isArrayOrTypedArray(l))for(var f,p=0;p<(u||l.length);p++)if(h.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)f=s.d2l(l[p][d],0,c),!isNaN(f)&&isFinite(f)&&(r[0][a]=Math.min(r[0][a],f),r[1][a]=Math.max(r[1][a],f));else f=s.d2l(l[p],0,c),!isNaN(f)&&isFinite(f)&&(r[0][a]=Math.min(r[0][a],f),r[1][a]=Math.max(r[1][a],f));else r[0][a]=Math.min(r[0][a],0),r[1][a]=Math.max(r[1][a],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,a,i,o,s,l,c=e[this.id],u=r[this.id];c.bgcolor?this.glplot.clearColor=d(c.bgcolor):this.glplot.clearColor=[0,0,0,0],this.glplot.snapToData=!0,this.fullLayout=e,this.fullSceneLayout=c,this.glplotLayout=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.update({}),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var h=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(i=0;i<t.length;++i)!0===(n=t[i]).visible&&0!==n._length&&T(this,n,h);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],a=0;a<3;a++)for(var i=k[a],o=i.charAt(0),s=r[i],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][a]=Math.min(e[0][a],u),e[1][a]=Math.max(e[1][a],u))}}}(this,h);var f=[1,1,1];for(o=0;o<3;++o)h[1][o]===h[0][o]?f[o]=1:f[o]=1/(h[1][o]-h[0][o]);for(this.dataScale=f,this.convertAnnotations(this),i=0;i<t.length;++i)!0===(n=t[i]).visible&&0!==n._length&&((a=this.traces[n.uid])?a.data.type===n.type?a.update(n):(a.dispose(),a=n._module.plot(this,n),this.traces[n.uid]=a):(a=n._module.plot(this,n),this.traces[n.uid]=a),a.name=n.name);var p=Object.keys(this.traces);t:for(i=0;i<p.length;++i){for(o=0;o<t.length;++o)if(t[o].uid===p[i]&&!0===t[o].visible&&0!==t[o]._length)continue t;(a=this.traces[p[i]]).dispose(),delete this.traces[p[i]]}this.glplot.objects.sort(function(t,e){return t._trace.data.index-e._trace.data.index});var g=[[0,0,0],[0,0,0]],v=[],m={};for(i=0;i<3;++i){if((l=(s=c[k[i]]).type)in m?(m[l].acc*=f[i],m[l].count+=1):m[l]={acc:f[i],count:1},s.autorange){g[0][i]=1/0,g[1][i]=-1/0;var y=this.glplot.objects,x=this.fullSceneLayout.annotations||[],b=s._name.charAt(0);for(o=0;o<y.length;o++){var _=y[o],w=_.bounds,A=_._trace.data._pad||0;\"ErrorBars\"===_.constructor.name&&s._lowerLogErrorBound?g[0][i]=Math.min(g[0][i],s._lowerLogErrorBound):g[0][i]=Math.min(g[0][i],w[0][i]/f[i]-A),g[1][i]=Math.max(g[1][i],w[1][i]/f[i]+A)}for(o=0;o<x.length;o++){var M=x[o];if(M.visible){var S=s.r2l(M[b]);g[0][i]=Math.min(g[0][i],S),g[1][i]=Math.max(g[1][i],S)}}if(\"rangemode\"in s&&\"tozero\"===s.rangemode&&(g[0][i]=Math.min(g[0][i],0),g[1][i]=Math.max(g[1][i],0)),g[0][i]>g[1][i])g[0][i]=-1,g[1][i]=1;else{var E=g[1][i]-g[0][i];g[0][i]-=E/32,g[1][i]+=E/32}if(\"reversed\"===s.autorange){var L=g[0][i];g[0][i]=g[1][i],g[1][i]=L}}else{var C=s.range;g[0][i]=s.r2l(C[0]),g[1][i]=s.r2l(C[1])}g[0][i]===g[1][i]&&(g[0][i]-=1,g[1][i]+=1),v[i]=g[1][i]-g[0][i],this.glplot.bounds[0][i]=g[0][i]*f[i],this.glplot.bounds[1][i]=g[1][i]*f[i]}var P=[1,1,1];for(i=0;i<3;++i){var O=m[l=(s=c[k[i]]).type];P[i]=Math.pow(O.acc,1/O.count)/f[i]}var z;if(\"auto\"===c.aspectmode)z=Math.max.apply(null,P)/Math.min.apply(null,P)<=4?P:[1,1,1];else if(\"cube\"===c.aspectmode)z=[1,1,1];else if(\"data\"===c.aspectmode)z=P;else{if(\"manual\"!==c.aspectmode)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var I=c.aspectratio;z=[I.x,I.y,I.z]}c.aspectratio.x=u.aspectratio.x=z[0],c.aspectratio.y=u.aspectratio.y=z[1],c.aspectratio.z=u.aspectratio.z=z[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z});var D=c.domain||null,R=e._size||null;if(D&&R){var F=this.container.style;F.position=\"absolute\",F.left=R.l+D.x[0]*R.w+\"px\",F.top=R.t+(1-D.y[1])*R.h+\"px\",F.width=R.w*(D.x[1]-D.x[0])+\"px\",F.height=R.h*(D.y[1]-D.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=this.glplot.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){return this.glplot.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.glplot.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\"}};var t},w.setViewport=function(t){var e,r=t.camera;this.glplot.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);var n=\"orthographic\"===r.projection.type;if(n!==this.glplot.camera._ortho){this.glplot.redraw();var a=this.glplot.clearColor;this.glplot.gl.clearColor(a[0],a[1],a[2],a[3]),this.glplot.gl.clear(this.glplot.gl.DEPTH_BUFFER_BIT|this.glplot.gl.COLOR_BUFFER_BIT),this.glplot.dispose(),b(this),this.glplot.camera._ortho=n}},w.isCameraChanged=function(t){var e=this.getCamera(),r=h.nestedProperty(t,this.id+\".camera\").get();function n(t,e,r,n){var a=[\"up\",\"center\",\"eye\"],i=[\"x\",\"y\",\"z\"];return e[a[r]]&&t[a[r]][i[n]]===e[a[r]][i[n]]}var a=!1;if(void 0===r)a=!0;else{for(var i=0;i<3;i++)for(var o=0;o<3;o++)if(!n(e,r,i,o)){a=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(a=!0)}return a},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=h.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,a,i,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),f=l||c;if(f){var p={};if(l&&(e=this.getCamera(),n=(r=h.nestedProperty(t,this.id+\".camera\")).get(),p[this.id+\".camera\"]=n),c&&(a=this.glplot.getAspectratio(),o=(i=h.nestedProperty(t,this.id+\".aspectratio\")).get(),p[this.id+\".aspectratio\"]=o),u.call(\"_storeDirectGUIEdit\",t,s._preGUI,p),l)r.set(e),h.nestedProperty(s,this.id+\".camera\").set(e);if(c)i.set(a),h.nestedProperty(s,this.id+\".aspectratio\").set(a),this.glplot.redraw()}return f},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 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e.type}e.exports=function(t,e,r){o(t,e,r,{type:v.name,attributes:d,handleDefaults:y,font:e.font,paper_bgcolor:e.paper_bgcolor,fullData:r,layoutOut:e})}},{\"../../components/color\":591,\"../../lib\":716,\"../../plot_api/plot_template\":754,\"../cartesian/axis_autotype\":765,\"../cartesian/category_order_defaults\":768,\"../cartesian/line_grid_defaults\":778,\"../cartesian/tick_label_defaults\":783,\"../cartesian/tick_mark_defaults\":784,\"../cartesian/tick_value_defaults\":785,\"../get_data\":799,\"../subplot_defaults\":839,\"./constants\":826,\"./layout_attributes\":829,\"./set_convert\":838}],831:[function(t,e,r){\"use strict\";var n=t(\"../../../traces/scatter/attributes\"),a=n.marker,i=t(\"../../../lib/extend\").extendFlat;[\"Area traces are deprecated!\",\"Please switch to the *barpolar* trace type.\"].join(\" 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a({},e,{showline:{valType:\"boolean\"},showticklabels:{valType:\"boolean\"},tickorientation:{valType:\"enumerated\",values:[\"horizontal\",\"vertical\"]},ticklen:{valType:\"number\",min:0},tickcolor:{valType:\"color\"},ticksuffix:{valType:\"string\"},endpadding:{valType:\"number\",description:o},visible:{valType:\"boolean\"}})}e.exports=i({radialaxis:l(0,{range:{valType:\"info_array\",items:[{valType:\"number\"},{valType:\"number\"}]},domain:s,orientation:{valType:\"number\"}}),angularaxis:l(0,{range:{valType:\"info_array\",items:[{valType:\"number\",dflt:0},{valType:\"number\",dflt:360}]},domain:s}),layout:{direction:{valType:\"enumerated\",values:[\"clockwise\",\"counterclockwise\"]},orientation:{valType:\"angle\"}}},\"plot\",\"nested\")},{\"../../../lib/extend\":707,\"../../../plot_api/edit_types\":747,\"../../cartesian/layout_attributes\":776}],833:[function(t,e,r){\"use strict\";(e.exports=t(\"./micropolar\")).manager=t(\"./micropolar_manager\")},{\"./micropolar\":834,\"./micropolar_manager\":835}],834:[function(t,e,r){var n=t(\"d3\"),a=t(\"../../../lib\").extendDeepAll,i=t(\"../../../constants/alignment\").MID_SHIFT,o=e.exports={version:\"0.2.2\"};o.Axis=function(){var t,e,r,s,l={data:[],layout:{}},c={},u={},h=n.dispatch(\"hover\"),f={};return f.render=function(c){return function(c){e=c||e;var h=l.data,f=l.layout;(\"string\"==typeof e||e.nodeName)&&(e=n.select(e)),e.datum(h).each(function(e,l){var c=e.slice();u={data:o.util.cloneJson(c),layout:o.util.cloneJson(f)};var h=0;c.forEach(function(t,e){t.color||(t.color=f.defaultColorRange[h],h=(h+1)%f.defaultColorRange.length),t.strokeColor||(t.strokeColor=\"LinePlot\"===t.geometry?t.color:n.rgb(t.color).darker().toString()),u.data[e].color=t.color,u.data[e].strokeColor=t.strokeColor,u.data[e].strokeDash=t.strokeDash,u.data[e].strokeSize=t.strokeSize});var p=c.filter(function(t,e){var r=t.visible;return\"undefined\"==typeof r||!0===r}),d=!1,g=p.map(function(t,e){return d=d||\"undefined\"!=typeof t.groupId,t});if(d){var v=n.nest().key(function(t,e){return\"undefined\"!=typeof t.groupId?t.groupId:\"unstacked\"}).entries(g),m=[],y=v.map(function(t,e){if(\"unstacked\"===t.key)return t.values;var r=t.values[0].r.map(function(t,e){return 0});return t.values.forEach(function(t,e,n){t.yStack=[r],m.push(r),r=o.util.sumArrays(t.r,r)}),t.values});p=n.merge(y)}p.forEach(function(t,e){t.t=Array.isArray(t.t[0])?t.t:[t.t],t.r=Array.isArray(t.r[0])?t.r:[t.r]});var x=Math.min(f.width-f.margin.left-f.margin.right,f.height-f.margin.top-f.margin.bottom)/2;x=Math.max(10,x);var b,_=[f.margin.left+x,f.margin.top+x];b=d?[0,n.max(o.util.sumArrays(o.util.arrayLast(p).r[0],o.util.arrayLast(m)))]:n.extent(o.util.flattenArray(p.map(function(t,e){return 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O=n.range.apply(this,L);if(O=O.map(function(t,e){return parseFloat(t.toPrecision(12))}),s=n.scale.linear().domain(L.slice(0,2)).range(\"clockwise\"===f.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=s.domain(),u.layout.angularAxis.endPadding=M?E:0,\"undefined\"==typeof(t=n.select(this).select(\"svg.chart-root\"))||t.empty()){var z=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g class='legend-group'></g>' + '<g class='tooltips-group'></g>' + '<g class='title-group'><text></text></g>' + '</g>' + 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arguments.length?(a(t,e),this):t},n.rebind(r,e,\"on\"),r},o.Legend.defaultConfig=function(t,e){return{data:[\"a\",\"b\",\"c\"],legendConfig:{elements:[{symbol:\"line\",color:\"red\"},{symbol:\"square\",color:\"yellow\"},{symbol:\"diamond\",color:\"limegreen\"}],height:150,colorBandWidth:30,fontSize:12,container:\"body\",isContinuous:null,textColor:\"grey\",reverseOrder:!1}}},o.tooltipPanel=function(){var t,e,r,i={container:null,hasTick:!1,fontSize:12,color:\"white\",padding:5},s=\"tooltip-\"+o.tooltipPanel.uid++,l=10,c=function(){var n=(t=i.container.selectAll(\"g.\"+s).data([0])).enter().append(\"g\").classed(s,!0).style({\"pointer-events\":\"none\",display:\"none\"});return r=n.append(\"path\").style({fill:\"white\",\"fill-opacity\":.9}).attr({d:\"M0 0\"}),e=n.append(\"text\").attr({dx:i.padding+l,dy:.3*+i.fontSize}),c};return c.text=function(a){var o=n.hsl(i.color).l,s=o>=.5?\"#aaa\":\"white\",u=o>=.5?\"black\":\"white\",h=a||\"\";e.style({fill:u,\"font-size\":i.fontSize+\"px\"}).text(h);var f=i.padding,p=e.node().getBBox(),d={fill:i.color,stroke:s,\"stroke-width\":\"2px\"},g=p.width+2*f+l,v=p.height+2*f;return r.attr({d:\"M\"+[[l,-v/2],[l,-v/4],[i.hasTick?0:l,0],[l,v/4],[l,v/2],[g,v/2],[g,-v/2]].join(\"L\")+\"Z\"}).style(d),t.attr({transform:\"translate(\"+[l,-v/2+2*f]+\")\"}),t.style({display:\"block\"}),c},c.move=function(e){if(t)return t.attr({transform:\"translate(\"+[e[0],e[1]]+\")\"}).style({display:\"block\"}),c},c.hide=function(){if(t)return t.style({display:\"none\"}),c},c.show=function(){if(t)return t.style({display:\"block\"}),c},c.config=function(t){return a(i,t),c},c},o.tooltipPanel.uid=1,o.adapter={},o.adapter.plotly=function(){var t={convert:function(t,e){var r={};if(t.data&&(r.data=t.data.map(function(t,r){var n=a({},t);return[[n,[\"marker\",\"color\"],[\"color\"]],[n,[\"marker\",\"opacity\"],[\"opacity\"]],[n,[\"marker\",\"line\",\"color\"],[\"strokeColor\"]],[n,[\"marker\",\"line\",\"dash\"],[\"strokeDash\"]],[n,[\"marker\",\"line\",\"width\"],[\"strokeSize\"]],[n,[\"marker\",\"symbol\"],[\"dotType\"]],[n,[\"marker\",\"size\"],[\"dotSize\"]],[n,[\"marker\",\"barWidth\"],[\"barWidth\"]],[n,[\"line\",\"interpolation\"],[\"lineInterpolation\"]],[n,[\"showlegend\"],[\"visibleInLegend\"]]].forEach(function(t,r){o.util.translator.apply(null,t.concat(e))}),e||delete n.marker,e&&delete n.groupId,e?(\"LinePlot\"===n.geometry?(n.type=\"scatter\",!0===n.dotVisible?(delete n.dotVisible,n.mode=\"lines+markers\"):n.mode=\"lines\"):\"DotPlot\"===n.geometry?(n.type=\"scatter\",n.mode=\"markers\"):\"AreaChart\"===n.geometry?n.type=\"area\":\"BarChart\"===n.geometry&&(n.type=\"bar\"),delete n.geometry):(\"scatter\"===n.type?\"lines\"===n.mode?n.geometry=\"LinePlot\":\"markers\"===n.mode?n.geometry=\"DotPlot\":\"lines+markers\"===n.mode&&(n.geometry=\"LinePlot\",n.dotVisible=!0):\"area\"===n.type?n.geometry=\"AreaChart\":\"bar\"===n.type&&(n.geometry=\"BarChart\"),delete n.mode,delete n.type),n}),!e&&t.layout&&\"stack\"===t.layout.barmode)){var i=o.util.duplicates(r.data.map(function(t,e){return t.geometry}));r.data.forEach(function(t,e){var n=i.indexOf(t.geometry);-1!=n&&(r.data[e].groupId=n)})}if(t.layout){var s=a({},t.layout);if([[s,[\"plot_bgcolor\"],[\"backgroundColor\"]],[s,[\"showlegend\"],[\"showLegend\"]],[s,[\"radialaxis\"],[\"radialAxis\"]],[s,[\"angularaxis\"],[\"angularAxis\"]],[s.angularaxis,[\"showline\"],[\"gridLinesVisible\"]],[s.angularaxis,[\"showticklabels\"],[\"labelsVisible\"]],[s.angularaxis,[\"nticks\"],[\"ticksCount\"]],[s.angularaxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.angularaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.angularaxis,[\"range\"],[\"domain\"]],[s.angularaxis,[\"endpadding\"],[\"endPadding\"]],[s.radialaxis,[\"showline\"],[\"gridLinesVisible\"]],[s.radialaxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.radialaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.radialaxis,[\"range\"],[\"domain\"]],[s.angularAxis,[\"showline\"],[\"gridLinesVisible\"]],[s.angularAxis,[\"showticklabels\"],[\"labelsVisible\"]],[s.angularAxis,[\"nticks\"],[\"ticksCount\"]],[s.angularAxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.angularAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.angularAxis,[\"range\"],[\"domain\"]],[s.angularAxis,[\"endpadding\"],[\"endPadding\"]],[s.radialAxis,[\"showline\"],[\"gridLinesVisible\"]],[s.radialAxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.radialAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.radialAxis,[\"range\"],[\"domain\"]],[s.font,[\"outlinecolor\"],[\"outlineColor\"]],[s.legend,[\"traceorder\"],[\"reverseOrder\"]],[s,[\"labeloffset\"],[\"labelOffset\"]],[s,[\"defaultcolorrange\"],[\"defaultColorRange\"]]].forEach(function(t,r){o.util.translator.apply(null,t.concat(e))}),e?(\"undefined\"!=typeof s.tickLength&&(s.angularaxis.ticklen=s.tickLength,delete s.tickLength),s.tickColor&&(s.angularaxis.tickcolor=s.tickColor,delete s.tickColor)):(s.angularAxis&&\"undefined\"!=typeof s.angularAxis.ticklen&&(s.tickLength=s.angularAxis.ticklen),s.angularAxis&&\"undefined\"!=typeof s.angularAxis.tickcolor&&(s.tickColor=s.angularAxis.tickcolor)),s.legend&&\"boolean\"!=typeof s.legend.reverseOrder&&(s.legend.reverseOrder=\"normal\"!=s.legend.reverseOrder),s.legend&&\"boolean\"==typeof s.legend.traceorder&&(s.legend.traceorder=s.legend.traceorder?\"reversed\":\"normal\",delete s.legend.reverseOrder),s.margin&&\"undefined\"!=typeof s.margin.t){var l=[\"t\",\"r\",\"b\",\"l\",\"pad\"],c=[\"top\",\"right\",\"bottom\",\"left\",\"pad\"],u={};n.entries(s.margin).forEach(function(t,e){u[c[l.indexOf(t.key)]]=t.value}),s.margin=u}e&&(delete s.needsEndSpacing,delete s.minorTickColor,delete s.minorTicks,delete s.angularaxis.ticksCount,delete s.angularaxis.ticksCount,delete s.angularaxis.ticksStep,delete s.angularaxis.rewriteTicks,delete s.angularaxis.nticks,delete s.radialaxis.ticksCount,delete s.radialaxis.ticksCount,delete s.radialaxis.ticksStep,delete s.radialaxis.rewriteTicks,delete s.radialaxis.nticks),r.layout=s}return r}};return t}},{\"../../../constants/alignment\":685,\"../../../lib\":716,d3:164}],835:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"../../../lib\"),i=t(\"../../../components/color\"),o=t(\"./micropolar\"),s=t(\"./undo_manager\"),l=a.extendDeepAll,c=e.exports={};c.framework=function(t){var e,r,a,i,u,h=new s;function f(r,s){return s&&(u=s),n.select(n.select(u).node().parentNode).selectAll(\".svg-container>*:not(.chart-root)\").remove(),e=e?l(e,r):r,a||(a=o.Axis()),i=o.adapter.plotly().convert(e),a.config(i).render(u),t.data=e.data,t.layout=e.layout,c.fillLayout(t),e}return f.isPolar=!0,f.svg=function(){return a.svg()},f.getConfig=function(){return e},f.getLiveConfig=function(){return 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n=t(\"d3\"),a=t(\"tinycolor2\"),i=t(\"../../registry\"),o=t(\"../../lib\"),s=t(\"../../components/color\"),l=t(\"../../components/drawing\"),c=t(\"../plots\"),u=t(\"../../plots/cartesian/axes\"),h=t(\"../cartesian/set_convert\"),f=t(\"./set_convert\"),p=t(\"../cartesian/autorange\").doAutoRange,d=t(\"../cartesian/dragbox\"),g=t(\"../../components/dragelement\"),v=t(\"../../components/fx\"),m=t(\"../../components/titles\"),y=t(\"../cartesian/select\").prepSelect,x=t(\"../cartesian/select\").selectOnClick,b=t(\"../cartesian/select\").clearSelect,_=t(\"../../lib/setcursor\"),w=t(\"../../lib/clear_gl_canvases\"),k=t(\"../../plot_api/subroutines\").redrawReglTraces,T=t(\"../../constants/alignment\").MID_SHIFT,A=t(\"./constants\"),M=t(\"./helpers\"),S=o._,E=o.mod,L=o.deg2rad,C=o.rad2deg;function P(t,e){this.id=e,this.gd=t,this._hasClipOnAxisFalse=null,this.vangles=null,this.radialAxisAngle=null,this.traceHash={},this.layers={},this.clipPaths={},this.clipIds={},this.viewInitial={};var r=t._fullLayout,n=\"clip\"+r._uid+e;this.clipIds.forTraces=n+\"-for-traces\",this.clipPaths.forTraces=r._clips.append(\"clipPath\").attr(\"id\",this.clipIds.forTraces),this.clipPaths.forTraces.append(\"path\"),this.framework=r._polarlayer.append(\"g\").attr(\"class\",e),this.radialTickLayout=null,this.angularTickLayout=null}var O=P.prototype;function z(t){var e=t.ticks+String(t.ticklen)+String(t.showticklabels);return\"side\"in t&&(e+=t.side),e}function I(t,e){return e[o.findIndexOfMin(e,function(e){return o.angleDist(t,e)})]}function D(t,e,r){return e?(t.attr(\"display\",null),t.attr(r)):t&&t.attr(\"display\",\"none\"),t}function R(t,e){return\"translate(\"+t+\",\"+e+\")\"}function F(t){return\"rotate(\"+t+\")\"}e.exports=function(t,e){return new P(t,e)},O.plot=function(t,e){var r=e[this.id];this._hasClipOnAxisFalse=!1;for(var 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e,r,n,a,i=t[0],o=t[1]-i,s=E(i,360),l=s+o,c=Math.cos(L(s)),u=Math.sin(L(s)),h=Math.cos(L(l)),f=Math.sin(L(l));a=s<=90&&l>=90||s>90&&l>=450?1:u<=0&&f<=0?0:Math.max(u,f);e=s<=180&&l>=180||s>180&&l>=540?-1:c>=0&&h>=0?0:Math.min(c,h);r=s<=270&&l>=270||s>270&&l>=630?-1:u>=0&&f>=0?0:Math.min(u,f);n=l>=360?1:c<=0&&h<=0?0:Math.max(c,h);return[e,r,n,a]}(f),x=y[2]-y[0],b=y[3]-y[1],_=h/u,w=Math.abs(b/x);_>w?(p=u,m=(h-(d=u*w))/n.h/2,g=[o[0],o[1]],v=[c[0]+m,c[1]-m]):(d=h,m=(u-(p=h/w))/n.w/2,g=[o[0]+m,o[1]-m],v=[c[0],c[1]]),this.xLength2=p,this.yLength2=d,this.xDomain2=g,this.yDomain2=v;var k=this.xOffset2=n.l+n.w*g[0],T=this.yOffset2=n.t+n.h*(1-v[1]),A=this.radius=p/x,M=this.innerRadius=e.hole*A,S=this.cx=k-A*y[0],C=this.cy=T+A*y[3],P=this.cxx=S-k,O=this.cyy=C-T;this.radialAxis=this.mockAxis(t,e,a,{_id:\"x\",side:{counterclockwise:\"top\",clockwise:\"bottom\"}[a.side],domain:[M/n.w,A/n.w]}),this.angularAxis=this.mockAxis(t,e,i,{side:\"right\",domain:[0,Math.PI],autorange:!1}),this.doAutoRange(t,e),this.updateAngularAxis(t,e),this.updateRadialAxis(t,e),this.updateRadialAxisTitle(t,e),this.xaxis=this.mockCartesianAxis(t,e,{_id:\"x\",domain:g}),this.yaxis=this.mockCartesianAxis(t,e,{_id:\"y\",domain:v});var z=this.pathSubplot();this.clipPaths.forTraces.select(\"path\").attr(\"d\",z).attr(\"transform\",R(P,O)),r.frontplot.attr(\"transform\",R(k,T)).call(l.setClipUrl,this._hasClipOnAxisFalse?null:this.clipIds.forTraces,this.gd),r.bg.attr(\"d\",z).attr(\"transform\",R(S,C)).call(s.fill,e.bgcolor)},O.mockAxis=function(t,e,r,n){var a=o.extendFlat({},r,n);return f(a,e,t),a},O.mockCartesianAxis=function(t,e,r){var n=this,a=r._id,i=o.extendFlat({type:\"linear\"},r);h(i,t);var s={x:[0,2],y:[1,3]};return i.setRange=function(){var t=n.sectorBBox,r=s[a],o=n.radialAxis._rl,l=(o[1]-o[0])/(1-e.hole);i.range=[t[r[0]]*l,t[r[1]]*l]},i.isPtWithinRange=\"x\"===a?function(t){return n.isPtInside(t)}:function(){return!0},i.setRange(),i.setScale(),i},O.doAutoRange=function(t,e){var r=this.gd,n=this.radialAxis,a=e.radialaxis;n.setScale(),p(r,n);var i=n.range;a.range=i.slice(),a._input.range=i.slice(),n._rl=[n.r2l(i[0],null,\"gregorian\"),n.r2l(i[1],null,\"gregorian\")]},O.updateRadialAxis=function(t,e){var r=this,n=r.gd,a=r.layers,i=r.radius,l=r.innerRadius,c=r.cx,h=r.cy,f=e.radialaxis,p=E(e.sector[0],360),d=r.radialAxis,g=l<i;r.fillViewInitialKey(\"radialaxis.angle\",f.angle),r.fillViewInitialKey(\"radialaxis.range\",d.range.slice()),d.setGeometry(),\"auto\"===d.tickangle&&p>90&&p<=270&&(d.tickangle=180);var v=function(t){return\"translate(\"+(d.l2p(t.x)+l)+\",0)\"},m=z(f);if(r.radialTickLayout!==m&&(a[\"radial-axis\"].selectAll(\".xtick\").remove(),r.radialTickLayout=m),g){d.setScale();var y=u.calcTicks(d),x=u.clipEnds(d,y),b=u.getTickSigns(d)[2];u.drawTicks(n,d,{vals:y,layer:a[\"radial-axis\"],path:u.makeTickPath(d,0,b),transFn:v,crisp:!1}),u.drawGrid(n,d,{vals:x,layer:a[\"radial-grid\"],path:function(t){return r.pathArc(d.r2p(t.x)+l)},transFn:o.noop,crisp:!1}),u.drawLabels(n,d,{vals:y,layer:a[\"radial-axis\"],transFn:v,labelFns:u.makeLabelFns(d,0)})}var _=r.radialAxisAngle=r.vangles?C(I(L(f.angle),r.vangles)):f.angle,w=R(c,h),k=w+F(-_);D(a[\"radial-axis\"],g&&(f.showticklabels||f.ticks),{transform:k}),D(a[\"radial-grid\"],g&&f.showgrid,{transform:w}),D(a[\"radial-line\"].select(\"line\"),g&&f.showline,{x1:l,y1:0,x2:i,y2:0,transform:k}).attr(\"stroke-width\",f.linewidth).call(s.stroke,f.linecolor)},O.updateRadialAxisTitle=function(t,e,r){var n=this.gd,a=this.radius,i=this.cx,o=this.cy,s=e.radialaxis,c=this.id+\"title\",u=void 0!==r?r:this.radialAxisAngle,h=L(u),f=Math.cos(h),p=Math.sin(h),d=0;if(s.title){var g=l.bBox(this.layers[\"radial-axis\"].node()).height,v=s.title.font.size;d=\"counterclockwise\"===s.side?-g-.4*v:g+.8*v}this.layers[\"radial-axis-title\"]=m.draw(n,c,{propContainer:s,propName:this.id+\".radialaxis.title\",placeholder:S(n,\"Click to enter radial axis title\"),attributes:{x:i+a/2*f+d*p,y:o-a/2*p+d*f,\"text-anchor\":\"middle\"},transform:{rotate:-u}})},O.updateAngularAxis=function(t,e){var r=this,n=r.gd,a=r.layers,i=r.radius,l=r.innerRadius,c=r.cx,h=r.cy,f=e.angularaxis,p=r.angularAxis;r.fillViewInitialKey(\"angularaxis.rotation\",f.rotation),p.setGeometry(),p.setScale();var d=function(t){return p.t2g(t.x)};\"linear\"===p.type&&\"radians\"===p.thetaunit&&(p.tick0=C(p.tick0),p.dtick=C(p.dtick));var g=function(t){return R(c+i*Math.cos(t),h-i*Math.sin(t))},v=u.makeLabelFns(p,0).labelStandoff,m={xFn:function(t){var e=d(t);return Math.cos(e)*v},yFn:function(t){var e=d(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(v+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*T)},anchorFn:function(t){var e=d(t),r=Math.cos(e);return Math.abs(r)<.1?\"middle\":r>0?\"start\":\"end\"},heightFn:function(t,e,r){var n=d(t);return-.5*(1+Math.sin(n))*r}},y=z(f);r.angularTickLayout!==y&&(a[\"angular-axis\"].selectAll(\".\"+p._id+\"tick\").remove(),r.angularTickLayout=y);var x,b=u.calcTicks(p);if(\"linear\"===e.gridshape?(x=b.map(d),o.angleDelta(x[0],x[1])<0&&(x=x.slice().reverse())):x=null,r.vangles=x,\"category\"===p.type&&(b=b.filter(function(t){return o.isAngleInsideSector(d(t),r.sectorInRad)})),p.visible){var _=\"inside\"===p.ticks?-1:1,w=(p.linewidth||1)/2;u.drawTicks(n,p,{vals:b,layer:a[\"angular-axis\"],path:\"M\"+_*w+\",0h\"+_*p.ticklen,transFn:function(t){var e=d(t);return g(e)+F(-C(e))},crisp:!1}),u.drawGrid(n,p,{vals:b,layer:a[\"angular-grid\"],path:function(t){var e=d(t),r=Math.cos(e),n=Math.sin(e);return\"M\"+[c+l*r,h-l*n]+\"L\"+[c+i*r,h-i*n]},transFn:o.noop,crisp:!1}),u.drawLabels(n,p,{vals:b,layer:a[\"angular-axis\"],repositionOnUpdate:!0,transFn:function(t){return g(d(t))},labelFns:m})}D(a[\"angular-line\"].select(\"path\"),f.showline,{d:r.pathSubplot(),transform:R(c,h)}).attr(\"stroke-width\",f.linewidth).call(s.stroke,f.linecolor)},O.updateFx=function(t,e){this.gd._context.staticPlot||(this.updateAngularDrag(t),this.updateRadialDrag(t,e,0),this.updateRadialDrag(t,e,1),this.updateMainDrag(t))},O.updateMainDrag=function(t){var e=this,r=e.gd,o=e.layers,s=t._zoomlayer,l=A.MINZOOM,c=A.OFFEDGE,u=e.radius,h=e.innerRadius,f=e.cx,p=e.cy,m=e.cxx,_=e.cyy,w=e.sectorInRad,k=e.vangles,T=e.radialAxis,S=M.clampTiny,E=M.findXYatLength,L=M.findEnclosingVertexAngles,C=A.cornerHalfWidth,P=A.cornerLen/2,O=d.makeDragger(o,\"path\",\"maindrag\",\"crosshair\");n.select(O).attr(\"d\",e.pathSubplot()).attr(\"transform\",R(f,p));var z,I,D,F,B,N,j,V,U,q={element:O,gd:r,subplot:e.id,plotinfo:{id:e.id,xaxis:e.xaxis,yaxis:e.yaxis},xaxes:[e.xaxis],yaxes:[e.yaxis]};function H(t,e){return Math.sqrt(t*t+e*e)}function G(t,e){return H(t-m,e-_)}function Y(t,e){return Math.atan2(_-e,t-m)}function W(t,e){return[t*Math.cos(e),t*Math.sin(-e)]}function X(t,r){if(0===t)return e.pathSector(2*C);var n=P/t,a=r-n,i=r+n,o=Math.max(0,Math.min(t,u)),s=o-C,l=o+C;return\"M\"+W(s,a)+\"A\"+[s,s]+\" 0,0,0 \"+W(s,i)+\"L\"+W(l,i)+\"A\"+[l,l]+\" 0,0,1 \"+W(l,a)+\"Z\"}function Z(t,r,n){if(0===t)return e.pathSector(2*C);var a,i,o=W(t,r),s=W(t,n),l=S((o[0]+s[0])/2),c=S((o[1]+s[1])/2);if(l&&c){var u=c/l,h=-1/u,f=E(C,u,l,c);a=E(P,h,f[0][0],f[0][1]),i=E(P,h,f[1][0],f[1][1])}else{var p,d;c?(p=P,d=C):(p=C,d=P),a=[[l-p,c-d],[l+p,c-d]],i=[[l-p,c+d],[l+p,c+d]]}return\"M\"+a.join(\"L\")+\"L\"+i.reverse().join(\"L\")+\"Z\"}function J(t,e){return e=Math.max(Math.min(e,u),h),t<c?t=0:u-t<c?t=u:e<c?e=0:u-e<c&&(e=u),Math.abs(e-t)>l?(t<e?(D=t,F=e):(D=e,F=t),!0):(D=null,F=null,!1)}function K(t,e){t=t||B,e=e||\"M0,0Z\",V.attr(\"d\",t),U.attr(\"d\",e),d.transitionZoombox(V,U,N,j),N=!0;var n={};rt(n),r.emit(\"plotly_relayouting\",n)}function Q(t,r){var n,a,i=z+t,o=I+r,s=G(z,I),l=Math.min(G(i,o),u),c=Y(z,I);J(s,l)&&(n=B+e.pathSector(F),D&&(n+=e.pathSector(D)),a=X(D,c)+X(F,c)),K(n,a)}function $(t,e,r,n){var a=M.findIntersectionXY(r,n,r,[t-m,_-e]);return H(a[0],a[1])}function tt(t,r){var n,a,i=z+t,o=I+r,s=Y(z,I),l=Y(i,o),c=L(s,k),h=L(l,k);J($(z,I,c[0],c[1]),Math.min($(i,o,h[0],h[1]),u))&&(n=B+e.pathSector(F),D&&(n+=e.pathSector(D)),a=[Z(D,c[0],c[1]),Z(F,c[0],c[1])].join(\" \")),K(n,a)}function et(){if(d.removeZoombox(r),null!==D&&null!==F){var t={};rt(t),d.showDoubleClickNotifier(r),i.call(\"_guiRelayout\",r,t)}}function rt(t){var r=T._rl,n=(r[1]-r[0])/(1-h/u)/u,a=[r[0]+(D-h)*n,r[0]+(F-h)*n];t[e.id+\".radialaxis.range\"]=a}function nt(t,n){var a=r._fullLayout.clickmode;if(d.removeZoombox(r),2===t){var o={};for(var s in e.viewInitial)o[e.id+\".\"+s]=e.viewInitial[s];r.emit(\"plotly_doubleclick\",null),i.call(\"_guiRelayout\",r,o)}a.indexOf(\"select\")>-1&&1===t&&x(n,r,[e.xaxis],[e.yaxis],e.id,q),a.indexOf(\"event\")>-1&&v.click(r,n,e.id)}q.prepFn=function(t,n,i){var o=r._fullLayout.dragmode,l=O.getBoundingClientRect();if(z=n-l.left,I=i-l.top,k){var c=M.findPolygonOffset(u,w[0],w[1],k);z+=m+c[0],I+=_+c[1]}switch(o){case\"zoom\":q.moveFn=k?tt:Q,q.clickFn=nt,q.doneFn=et,function(){D=null,F=null,B=e.pathSubplot(),N=!1;var t=r._fullLayout[e.id];j=a(t.bgcolor).getLuminance(),(V=d.makeZoombox(s,j,f,p,B)).attr(\"fill-rule\",\"evenodd\"),U=d.makeCorners(s,f,p),b(r)}();break;case\"select\":case\"lasso\":y(t,n,i,q,o)}},O.onmousemove=function(t){v.hover(r,t,e.id),r._fullLayout._lasthover=O,r._fullLayout._hoversubplot=e.id},O.onmouseout=function(t){r._dragging||g.unhover(r,t)},g.init(q)},O.updateRadialDrag=function(t,e,r){var a=this,s=a.gd,l=a.layers,c=a.radius,u=a.innerRadius,h=a.cx,f=a.cy,p=a.radialAxis,v=A.radialDragBoxSize,m=v/2;if(p.visible){var y,x,_,T=L(a.radialAxisAngle),M=p._rl,S=M[0],E=M[1],P=M[r],O=.75*(M[1]-M[0])/(1-e.hole)/c;r?(y=h+(c+m)*Math.cos(T),x=f-(c+m)*Math.sin(T),_=\"radialdrag\"):(y=h+(u-m)*Math.cos(T),x=f-(u-m)*Math.sin(T),_=\"radialdrag-inner\");var z,B,N,j=d.makeRectDragger(l,_,\"crosshair\",-m,-m,v,v),V={element:j,gd:s};D(n.select(j),p.visible&&u<c,{transform:R(y,x)}),V.prepFn=function(){z=null,B=null,N=null,V.moveFn=U,V.doneFn=q,b(s)},V.clampFn=function(t,e){return Math.sqrt(t*t+e*e)<A.MINDRAG&&(t=0,e=0),[t,e]},g.init(V)}function U(t,e){if(z)z(t,e);else{var n=[t,-e],i=[Math.cos(T),Math.sin(T)],l=Math.abs(o.dot(n,i)/Math.sqrt(o.dot(n,n)));isNaN(l)||(z=l<.5?H:G)}var c={};!function(t){null!==B?t[a.id+\".radialaxis.angle\"]=B:null!==N&&(t[a.id+\".radialaxis.range[\"+r+\"]\"]=N)}(c),s.emit(\"plotly_relayouting\",c)}function q(){null!==B?i.call(\"_guiRelayout\",s,a.id+\".radialaxis.angle\",B):null!==N&&i.call(\"_guiRelayout\",s,a.id+\".radialaxis.range[\"+r+\"]\",N)}function H(t,e){if(0!==r){var n=y+t,i=x+e;B=Math.atan2(f-i,n-h),a.vangles&&(B=I(B,a.vangles)),B=C(B);var o=R(h,f)+F(-B);l[\"radial-axis\"].attr(\"transform\",o),l[\"radial-line\"].select(\"line\").attr(\"transform\",o);var 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d=e.gd._fullLayout,g=d[e.id],v=x(T+t,M+s),m=C(v-P);if(E=S+m,a.frontplot.attr(\"transform\",R(e.xOffset2,e.yOffset2)+F([-m,f,p])),e.vangles){L=e.radialAxisAngle+m;var y=R(u,h)+F(-m),b=R(u,h)+F(-L);a.bg.attr(\"transform\",y),a[\"radial-grid\"].attr(\"transform\",y),a[\"radial-axis\"].attr(\"transform\",b),a[\"radial-line\"].select(\"line\").attr(\"transform\",b),e.updateRadialAxisTitle(d,g,L)}else e.clipPaths.forTraces.select(\"path\").attr(\"transform\",R(f,p)+F(m));z.each(function(){var t=n.select(this),e=l.getTranslate(t);t.attr(\"transform\",R(e.x,e.y)+F([m]))}),I.each(function(){var t=n.select(this),e=t.select(\"text\"),r=l.getTranslate(t);t.attr(\"transform\",F([m,e.attr(\"x\"),e.attr(\"y\")])+R(r.x,r.y))}),c.rotation=o.modHalf(E,360),e.updateAngularAxis(d,g),e._hasClipOnAxisFalse&&!o.isFullCircle(e.sectorInRad)&&O.call(l.hideOutsideRangePoints,e);var _=!1;for(var A in e.traceHash)if(i.traceIs(A,\"gl\")){var 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n=t(\"d3\"),a=t(\"tinycolor2\"),i=t(\"../../registry\"),o=t(\"../../lib\"),s=o._,l=t(\"../../components/color\"),c=t(\"../../components/drawing\"),u=t(\"../cartesian/set_convert\"),h=t(\"../../lib/extend\").extendFlat,f=t(\"../plots\"),p=t(\"../cartesian/axes\"),d=t(\"../../components/dragelement\"),g=t(\"../../components/fx\"),v=t(\"../../components/titles\"),m=t(\"../cartesian/select\").prepSelect,y=t(\"../cartesian/select\").selectOnClick,x=t(\"../cartesian/select\").clearSelect,b=t(\"../cartesian/constants\");function _(t,e){this.id=t.id,this.graphDiv=t.graphDiv,this.init(e),this.makeFramework(e),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}e.exports=_;var w=_.prototype;w.init=function(t){this.container=t._ternarylayer,this.defs=t._defs,this.layoutId=t._uid,this.traceHash={},this.layers={}},w.plot=function(t,e){var r=e[this.id],n=e._size;this._hasClipOnAxisFalse=!1;for(var 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y=h[\"violin\"===t?\"_numViolins\":\"_numBoxes\"],x=\"group\"===h[t+\"mode\"]&&y>1,b=1-h[t+\"gap\"],_=1-h[t+\"groupgap\"];for(s=0;s<r.length;s++){var w,k,T,A,M,S,E=(c=u[r[s]])[0].trace,L=c[0].t,C=E.width,P=E.side;if(C)w=k=A=C/2,T=0;else if(w=m,x){var O=i(h,o._id)+E.orientation,z=(h._alignmentOpts[O]||{})[E.alignmentgroup]||{},I=Object.keys(z.offsetGroups||{}).length,D=I||y;k=w*b*_/D,T=2*w*(((I?E._offsetIndex:L.num)+.5)/D-.5)*b,A=w*b/D}else k=w*b*_,T=0,A=w;L.dPos=w,L.bPos=T,L.bdPos=k,L.wHover=A;var R,F,B,N,j,V,U=T+k,q=Boolean(C);if(\"positive\"===P?(M=w*(C?1:.5),R=U,S=R=T):\"negative\"===P?(M=R=T,S=w*(C?1:.5),F=U):(M=S=w,R=F=U),(E.boxpoints||E.points)&&g>0){var H=E.pointpos,G=E.jitter,Y=E.marker.size/2,W=0;H+G>=0&&((W=U*(H+G))>M?(q=!0,j=Y,B=W):W>R&&(j=Y,B=M)),W<=M&&(B=M);var X=0;H-G<=0&&((X=-U*(H-G))>S?(q=!0,V=Y,N=X):X>F&&(V=Y,N=S)),X<=S&&(N=S)}else B=M,N=S;var Z=new Array(c.length);for(l=0;l<c.length;l++)Z[l]=c[l].pos;E._extremes[f]=n.findExtremes(o,Z,{padded:q,vpadminus:N,vpadplus:B,vpadLinearized:!0,ppadminus:{x:V,y:j}[p],ppadplus:{x:j,y:V}[p]})}}}e.exports={crossTraceCalc:function(t,e){for(var r=t.calcdata,n=e.xaxis,a=e.yaxis,i=0;i<o.length;i++){for(var l=o[i],c=\"h\"===l?a:n,u=[],h=0;h<r.length;h++){var f=r[h],p=f[0].t,d=f[0].trace;!0!==d.visible||\"box\"!==d.type&&\"candlestick\"!==d.type||p.empty||(d.orientation||\"v\")!==l||d.xaxis!==n._id||d.yaxis!==a._id||u.push(h)}s(\"box\",t,u,c)}},setPositionOffset:s}},{\"../../lib\":716,\"../../plots/cartesian/axes\":764,\"../../plots/cartesian/axis_ids\":767}],882:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"../../registry\"),i=t(\"../../components/color\"),o=t(\"../bar/defaults\").handleGroupingDefaults,s=t(\"./attributes\");function l(t,e,r,i){var o,s,l=r(\"y\"),c=r(\"x\"),u=c&&c.length;if(l&&l.length)o=\"v\",u?s=Math.min(n.minRowLength(c),n.minRowLength(l)):(r(\"x0\"),s=n.minRowLength(l));else{if(!u)return void(e.visible=!1);o=\"h\",r(\"y0\"),s=n.minRowLength(c)}e._length=s,a.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],i),r(\"orientation\",o)}function c(t,e,r,a){var i=a.prefix,o=n.coerce2(t,e,s,\"marker.outliercolor\"),l=r(\"marker.line.outliercolor\"),c=r(i+\"points\",o||l?\"suspectedoutliers\":void 0);c?(r(\"jitter\",\"all\"===c?.3:0),r(\"pointpos\",\"all\"===c?-1.5:0),r(\"marker.symbol\"),r(\"marker.opacity\"),r(\"marker.size\"),r(\"marker.color\",e.line.color),r(\"marker.line.color\"),r(\"marker.line.width\"),\"suspectedoutliers\"===c&&(r(\"marker.line.outliercolor\",e.marker.color),r(\"marker.line.outlierwidth\")),r(\"selected.marker.color\"),r(\"unselected.marker.color\"),r(\"selected.marker.size\"),r(\"unselected.marker.size\"),r(\"text\"),r(\"hovertext\")):delete e.marker;var u=r(\"hoveron\");\"all\"!==u&&-1===u.indexOf(\"points\")||r(\"hovertemplate\"),n.coerceSelectionMarkerOpacity(e,r)}e.exports={supplyDefaults:function(t,e,r,a){function o(r,a){return n.coerce(t,e,s,r,a)}l(t,e,o,a),!1!==e.visible&&(o(\"line.color\",(t.marker||{}).color||r),o(\"line.width\"),o(\"fillcolor\",i.addOpacity(e.line.color,.5)),o(\"whiskerwidth\"),o(\"boxmean\"),o(\"width\"),o(\"notched\",void 0!==t.notchwidth)&&o(\"notchwidth\"),c(t,e,o,{prefix:\"box\"}))},crossTraceDefaults:function(t,e){var r,a;function i(t){return n.coerce(a._input,a,s,t)}for(var l=0;l<t.length;l++){var c=(a=t[l]).type;\"box\"!==c&&\"violin\"!==c||(r=a._input,\"group\"===e[c+\"mode\"]&&o(r,a,e,i))}},handleSampleDefaults:l,handlePointsDefaults:c}},{\"../../components/color\":591,\"../../lib\":716,\"../../registry\":845,\"../bar/defaults\":859,\"./attributes\":879}],883:[function(t,e,r){\"use strict\";e.exports=function(t,e){return e.hoverOnBox&&(t.hoverOnBox=e.hoverOnBox),\"xVal\"in e&&(t.x=e.xVal),\"yVal\"in e&&(t.y=e.yVal),e.xa&&(t.xaxis=e.xa),e.ya&&(t.yaxis=e.ya),t}},{}],884:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axes\"),a=t(\"../../lib\"),i=t(\"../../components/fx\"),o=t(\"../../components/color\"),s=a.fillText;function l(t,e,r,s){var l,c,u,h,f,p,d,g,v,m,y,x,b,_,w=t.cd,k=t.xa,T=t.ya,A=w[0].trace,M=w[0].t,S=\"violin\"===A.type,E=[],L=M.bdPos,C=M.wHover,P=function(t){return u.c2l(t.pos)+M.bPos-u.c2l(p)};S&&\"both\"!==A.side?(\"positive\"===A.side&&(v=function(t){var e=P(t);return i.inbox(e,e+C,m)},x=L,b=0),\"negative\"===A.side&&(v=function(t){var e=P(t);return i.inbox(e-C,e,m)},x=0,b=L)):(v=function(t){var e=P(t);return i.inbox(e-C,e+C,m)},x=b=L),_=S?function(t){return i.inbox(t.span[0]-f,t.span[1]-f,m)}:function(t){return i.inbox(t.min-f,t.max-f,m)},\"h\"===A.orientation?(f=e,p=r,d=_,g=v,l=\"y\",u=T,c=\"x\",h=k):(f=r,p=e,d=v,g=_,l=\"x\",u=k,c=\"y\",h=T);var O=Math.min(1,L/Math.abs(u.r2c(u.range[1])-u.r2c(u.range[0])));function z(t){return(d(t)+g(t))/2}m=t.maxHoverDistance-O,y=t.maxSpikeDistance-O;var I=i.getDistanceFunction(s,d,g,z);if(i.getClosest(w,I,t),!1===t.index)return[];var D=w[t.index],R=A.line.color,F=(A.marker||{}).color;o.opacity(R)&&A.line.width?t.color=R:o.opacity(F)&&A.boxpoints?t.color=F:t.color=A.fillcolor,t[l+\"0\"]=u.c2p(D.pos+M.bPos-b,!0),t[l+\"1\"]=u.c2p(D.pos+M.bPos+x,!0),t[l+\"LabelVal\"]=D.pos;var B=l+\"Spike\";t.spikeDistance=z(D)*y/m,t[B]=u.c2p(D.pos,!0);var N={},j=[\"med\",\"q1\",\"q3\",\"min\",\"max\"];(A.boxmean||(A.meanline||{}).visible)&&j.push(\"mean\"),(A.boxpoints||A.points)&&j.push(\"lf\",\"uf\");for(var V=0;V<j.length;V++){var U=j[V];if(U in D&&!(D[U]in N)){N[D[U]]=!0;var q=D[U],H=h.c2p(q,!0),G=a.extendFlat({},t);G.attr=U,G[c+\"0\"]=G[c+\"1\"]=H,G[c+\"LabelVal\"]=q,G[c+\"Label\"]=(M.labels?M.labels[U]+\" \":\"\")+n.hoverLabelText(h,q),G.hoverOnBox=!0,\"mean\"===U&&\"sd\"in D&&\"sd\"===A.boxmean&&(G[c+\"err\"]=D.sd),t.name=\"\",t.spikeDistance=void 0,t[B]=void 0,G.hovertemplate=!1,E.push(G)}}return E}function c(t,e,r){for(var n,o,l,c=t.cd,u=t.xa,h=t.ya,f=c[0].trace,p=u.c2p(e),d=h.c2p(r),g=i.quadrature(function(t){var e=Math.max(3,t.mrc||0);return Math.max(Math.abs(u.c2p(t.x)-p)-e,1-3/e)},function(t){var e=Math.max(3,t.mrc||0);return Math.max(Math.abs(h.c2p(t.y)-d)-e,1-3/e)}),v=!1,m=0;m<c.length;m++){o=c[m];for(var y=0;y<(o.pts||[]).length;y++){var x=g(l=o.pts[y]);x<=t.distance&&(t.distance=x,v=[m,y])}}if(!v)return!1;l=(o=c[v[0]]).pts[v[1]];var b,_=u.c2p(l.x,!0),w=h.c2p(l.y,!0),k=l.mrc||1;return n=a.extendFlat({},t,{index:l.i,color:(f.marker||{}).color,name:f.name,x0:_-k,x1:_+k,y0:w-k,y1:w+k,spikeDistance:t.distance,hovertemplate:f.hovertemplate}),\"h\"===f.orientation?(b=h,n.xLabelVal=l.x,n.yLabelVal=o.pos):(b=u,n.xLabelVal=o.pos,n.yLabelVal=l.y),n[b._id.charAt(0)+\"Spike\"]=b.c2p(o.pos,!0),s(l,f,n),n}e.exports={hoverPoints:function(t,e,r,n){var a,i=t.cd[0].trace.hoveron,o=[];return-1!==i.indexOf(\"boxes\")&&(o=o.concat(l(t,e,r,n))),-1!==i.indexOf(\"points\")&&(a=c(t,e,r)),\"closest\"===n?a?[a]:o:a?(o.push(a),o):o},hoverOnBoxes:l,hoverOnPoints:c}},{\"../../components/color\":591,\"../../components/fx\":629,\"../../lib\":716,\"../../plots/cartesian/axes\":764}],885:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),layoutAttributes:t(\"./layout_attributes\"),supplyDefaults:t(\"./defaults\").supplyDefaults,crossTraceDefaults:t(\"./defaults\").crossTraceDefaults,supplyLayoutDefaults:t(\"./layout_defaults\").supplyLayoutDefaults,calc:t(\"./calc\"),crossTraceCalc:t(\"./cross_trace_calc\").crossTraceCalc,plot:t(\"./plot\").plot,style:t(\"./style\").style,styleOnSelect:t(\"./style\").styleOnSelect,hoverPoints:t(\"./hover\").hoverPoints,eventData:t(\"./event_data\"),selectPoints:t(\"./select\"),moduleType:\"trace\",name:\"box\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"cartesian\",\"svg\",\"symbols\",\"oriented\",\"box-violin\",\"showLegend\",\"boxLayout\",\"zoomScale\"],meta:{}}},{\"../../plots/cartesian\":775,\"./attributes\":879,\"./calc\":880,\"./cross_trace_calc\":881,\"./defaults\":882,\"./event_data\":883,\"./hover\":884,\"./layout_attributes\":886,\"./layout_defaults\":887,\"./plot\":888,\"./select\":889,\"./style\":890}],886:[function(t,e,r){\"use strict\";e.exports={boxmode:{valType:\"enumerated\",values:[\"group\",\"overlay\"],dflt:\"overlay\",editType:\"calc\"},boxgap:{valType:\"number\",min:0,max:1,dflt:.3,editType:\"calc\"},boxgroupgap:{valType:\"number\",min:0,max:1,dflt:.3,editType:\"calc\"}}},{}],887:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),a=t(\"../../lib\"),i=t(\"./layout_attributes\");function o(t,e,r,a,i){for(var o=i+\"Layout\",s=!1,l=0;l<r.length;l++){var c=r[l];if(n.traceIs(c,o)){s=!0;break}}s&&(a(i+\"mode\"),a(i+\"gap\"),a(i+\"groupgap\"))}e.exports={supplyLayoutDefaults:function(t,e,r){o(0,0,r,function(r,n){return a.coerce(t,e,i,r,n)},\"box\")},_supply:o}},{\"../../lib\":716,\"../../registry\":845,\"./layout_attributes\":886}],888:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"../../lib\"),i=t(\"../../components/drawing\"),o=5,s=.01;function l(t,e,r,i){var o,s,l=e.pos,c=e.val,u=i.bPos,h=i.wdPos||0,f=i.bPosPxOffset||0,p=r.whiskerwidth||0,d=r.notched||!1,g=d?1-2*r.notchwidth:1;Array.isArray(i.bdPos)?(o=i.bdPos[0],s=i.bdPos[1]):(o=i.bdPos,s=i.bdPos);var v=t.selectAll(\"path.box\").data(\"violin\"!==r.type||r.box.visible?a.identity:[]);v.enter().append(\"path\").style(\"vector-effect\",\"non-scaling-stroke\").attr(\"class\",\"box\"),v.exit().remove(),v.each(function(t){if(t.empty)return\"M0,0Z\";var e=l.c2l(t.pos+u,!0),i=l.l2p(e)+f,v=l.l2p(e-o)+f,m=l.l2p(e+s)+f,y=l.l2p(e-h)+f,x=l.l2p(e+h)+f,b=l.l2p(e-o*g)+f,_=l.l2p(e+s*g)+f,w=c.c2p(t.q1,!0),k=c.c2p(t.q3,!0),T=a.constrain(c.c2p(t.med,!0),Math.min(w,k)+1,Math.max(w,k)-1),A=void 0===t.lf||!1===r.boxpoints,M=c.c2p(A?t.min:t.lf,!0),S=c.c2p(A?t.max:t.uf,!0),E=c.c2p(t.ln,!0),L=c.c2p(t.un,!0);\"h\"===r.orientation?n.select(this).attr(\"d\",\"M\"+T+\",\"+b+\"V\"+_+\"M\"+w+\",\"+v+\"V\"+m+(d?\"H\"+E+\"L\"+T+\",\"+_+\"L\"+L+\",\"+m:\"\")+\"H\"+k+\"V\"+v+(d?\"H\"+L+\"L\"+T+\",\"+b+\"L\"+E+\",\"+v:\"\")+\"ZM\"+w+\",\"+i+\"H\"+M+\"M\"+k+\",\"+i+\"H\"+S+(0===p?\"\":\"M\"+M+\",\"+y+\"V\"+x+\"M\"+S+\",\"+y+\"V\"+x)):n.select(this).attr(\"d\",\"M\"+b+\",\"+T+\"H\"+_+\"M\"+v+\",\"+w+\"H\"+m+(d?\"V\"+E+\"L\"+_+\",\"+T+\"L\"+m+\",\"+L:\"\")+\"V\"+k+\"H\"+v+(d?\"V\"+L+\"L\"+b+\",\"+T+\"L\"+v+\",\"+E:\"\")+\"ZM\"+i+\",\"+w+\"V\"+M+\"M\"+i+\",\"+k+\"V\"+S+(0===p?\"\":\"M\"+y+\",\"+M+\"H\"+x+\"M\"+y+\",\"+S+\"H\"+x))})}function c(t,e,r,n){var l=e.x,c=e.y,u=n.bdPos,h=n.bPos,f=r.boxpoints||r.points;a.seedPseudoRandom();var p=t.selectAll(\"g.points\").data(f?function(t){return t.forEach(function(t){t.t=n,t.trace=r}),t}:[]);p.enter().append(\"g\").attr(\"class\",\"points\"),p.exit().remove();var d=p.selectAll(\"path\").data(function(t){var e,n,i=t.pts2,l=Math.max((t.max-t.min)/10,t.q3-t.q1),c=1e-9*l,p=l*s,d=[],g=0;if(r.jitter){if(0===l)for(g=1,d=new Array(i.length),e=0;e<i.length;e++)d[e]=1;else for(e=0;e<i.length;e++){var v=Math.max(0,e-o),m=i[v].v,y=Math.min(i.length-1,e+o),x=i[y].v;\"all\"!==f&&(i[e].v<t.lf?x=Math.min(x,t.lf):m=Math.max(m,t.uf));var b=Math.sqrt(p*(y-v)/(x-m+c))||0;b=a.constrain(Math.abs(b),0,1),d.push(b),g=Math.max(b,g)}n=2*r.jitter/(g||1)}for(e=0;e<i.length;e++){var _=i[e],w=_.v,k=r.jitter?n*d[e]*(a.pseudoRandom()-.5):0,T=t.pos+h+u*(r.pointpos+k);\"h\"===r.orientation?(_.y=T,_.x=w):(_.x=T,_.y=w),\"suspectedoutliers\"===f&&w<t.uo&&w>t.lo&&(_.so=!0)}return i});d.enter().append(\"path\").classed(\"point\",!0),d.exit().remove(),d.call(i.translatePoints,l,c)}function u(t,e,r,i){var 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u&&\"\"!==u||n.isPlainObject(u))?(e._length=Math.min(l.length,c.length),s(\"below\"),s(\"text\"),s(\"hovertext\"),s(\"hovertemplate\"),s(\"marker.line.width\")&&s(\"marker.line.color\"),s(\"marker.opacity\"),a(t,e,o,s,{prefix:\"\",cLetter:\"z\"}),n.coerceSelectionMarkerOpacity(e,s)):e.visible=!1}},{\"../../components/colorscale/defaults\":601,\"../../lib\":716,\"./attributes\":931}],934:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),colorbar:t(\"../heatmap/colorbar\"),calc:t(\"../choropleth/calc\"),plot:t(\"./plot\"),hoverPoints:t(\"../choropleth/hover\"),eventData:t(\"../choropleth/event_data\"),selectPoints:t(\"../choropleth/select\"),styleOnSelect:function(t,e){e&&e[0].trace._glTrace.updateOnSelect(e)},getBelow:function(t,e){for(var r=e.getMapLayers(),n=r.length-2;n>=0;n--){var a=r[n].id;if(\"string\"==typeof a&&0===a.indexOf(\"water\"))for(var 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h,A,M,S,E,L=e[0].trace,C=n.select(this),P=L._hasGauge,O=L._isAngular,z=L._isBullet,I=L.domain,D={w:p._size.w*(I.x[1]-I.x[0]),h:p._size.h*(I.y[1]-I.y[0]),l:p._size.l+p._size.w*I.x[0],r:p._size.r+p._size.w*(1-I.x[1]),t:p._size.t+p._size.h*(1-I.y[1]),b:p._size.b+p._size.h*I.y[0]},R=D.l+D.w/2,F=D.t+D.h/2,B=Math.min(D.w/2,D.h),N=l.innerRadius*B,j=L.align||\"center\";if(A=F,P){if(O&&(h=R,A=F+B/2,M=function(t){return e=t,r=.9*N,n=Math.sqrt(e.width/2*(e.width/2)+e.height*e.height),[r/n,e,r];var e,r,n}),z){var V=l.bulletPadding,U=1-l.bulletNumberDomainSize+V;h=D.l+(U+(1-U)*v[j])*D.w,M=function(t){return w(t,(l.bulletNumberDomainSize-V)*D.w,D.h)}}}else h=D.l+v[j]*D.w,M=function(t){return w(t,D.w,D.h)};!function(t,e,r,i){var o,l,h,f=r[0].trace,p=i.numbersX,x=i.numbersY,w=f.align||\"center\",A=g[w],M=i.transitionOpts,S=i.onComplete,E=a.ensureSingle(e,\"g\",\"numbers\"),L=[];f._hasNumber&&L.push(\"number\");f._hasDelta&&(L.push(\"delta\"),\"left\"===f.delta.position&&L.reverse());var 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I=e.dimensions[P]._categoryarray[O],D=e.dimensions[P]._ticktext[O];z[O]={dimensionInd:x,categoryInd:L=O,categoryValue:I,displayInd:L,categoryLabel:D,valueInds:[],count:0,dragY:null}}c(z[O],y,p)}return n(function(t,e,r){var n=t.map(function(t){return t.categories.length}).reduce(function(t,e){return Math.max(t,e)});return{dimensions:t,paths:e,trace:void 0,maxCats:n,count:r}}(C,T,d))}},{\"../../components/colorscale/calc\":599,\"../../components/colorscale/helpers\":602,\"../../components/drawing\":612,\"../../lib\":716,\"../../lib/filter_unique.js\":708,\"../../lib/gup\":714}],1076:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"../../components/colorscale/helpers\").hasColorscale,i=t(\"../../components/colorscale/defaults\"),o=t(\"../../plots/domain\").defaults,s=t(\"../../plots/array_container_defaults\"),l=t(\"./attributes\"),c=t(\"../parcoords/merge_length\");function u(t,e){function r(r,a){return n.coerce(t,e,l.dimensions,r,a)}var a=r(\"values\"),i=r(\"visible\");if(a&&a.length||(i=e.visible=!1),i){r(\"label\"),r(\"displayindex\",e._index);var o,s=t.categoryarray,c=Array.isArray(s)&&s.length>0;c&&(o=\"array\");var u=r(\"categoryorder\",o);\"array\"===u?(r(\"categoryarray\"),r(\"ticktext\")):(delete t.categoryarray,delete t.ticktext),c||\"array\"!==u||(e.categoryorder=\"trace\")}}e.exports=function(t,e,r,h){function f(r,a){return n.coerce(t,e,l,r,a)}var p=s(t,e,{name:\"dimensions\",handleItemDefaults:u}),d=function(t,e,r,o,s){s(\"line.shape\"),s(\"line.hovertemplate\");var l=s(\"line.color\",o.colorway[0]);if(a(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),i(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,h,f);o(e,h,f),Array.isArray(p)&&p.length||(e.visible=!1),c(e,p,\"values\",d),f(\"hoveron\"),f(\"hovertemplate\"),f(\"arrangement\"),f(\"bundlecolors\"),f(\"sortpaths\"),f(\"counts\");var g={family:h.font.family,size:Math.round(h.font.size),color:h.font.color};n.coerceFont(f,\"labelfont\",g);var v={family:h.font.family,size:Math.round(h.font.size/1.2),color:h.font.color};n.coerceFont(f,\"tickfont\",v)}},{\"../../components/colorscale/defaults\":601,\"../../components/colorscale/helpers\":602,\"../../lib\":716,\"../../plots/array_container_defaults\":760,\"../../plots/domain\":789,\"../parcoords/merge_length\":1089,\"./attributes\":1073}],1077:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcats\",basePlotModule:t(\"./base_plot\"),categories:[\"noOpacity\"],meta:{}}},{\"./attributes\":1073,\"./base_plot\":1074,\"./calc\":1075,\"./defaults\":1076,\"./plot\":1079}],1078:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"../../plot_api/plot_api\"),i=t(\"../../components/fx\"),o=t(\"../../lib\"),s=t(\"../../components/drawing\"),l=t(\"tinycolor2\"),c=t(\"../../lib/svg_text_utils\");function u(t,e,r,a){var i=t.map(function(t,e,r){var n,a=r[0],i=e.margin||{l:80,r:80,t:100,b:80},o=a.trace,s=o.domain,l=e.width,c=e.height,u=Math.floor(l*(s.x[1]-s.x[0])),h=Math.floor(c*(s.y[1]-s.y[0])),f=s.x[0]*l+i.l,p=e.height-s.y[1]*e.height+i.t,d=o.line.shape;n=\"all\"===o.hoverinfo?[\"count\",\"probability\"]:(o.hoverinfo||\"\").split(\"+\");var g={trace:o,key:o.uid,model:a,x:f,y:p,width:u,height:h,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:i,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};a.dimensions&&(F(g),R(g));return g}.bind(0,e,r)),l=a.selectAll(\"g.parcatslayer\").data([null]);l.enter().append(\"g\").attr(\"class\",\"parcatslayer\").style(\"pointer-events\",\"all\");var u=l.selectAll(\"g.trace.parcats\").data(i,h),v=u.enter().append(\"g\").attr(\"class\",\"trace parcats\");u.attr(\"transform\",function(t){return\"translate(\"+t.x+\", \"+t.y+\")\"}),v.append(\"g\").attr(\"class\",\"paths\");var m=u.select(\"g.paths\").selectAll(\"path.path\").data(function(t){return t.paths},h);m.attr(\"fill\",function(t){return t.model.color});var b=m.enter().append(\"path\").attr(\"class\",\"path\").attr(\"stroke-opacity\",0).attr(\"fill\",function(t){return t.model.color}).attr(\"fill-opacity\",0);x(b),m.attr(\"d\",function(t){return t.svgD}),b.empty()||m.sort(p),m.exit().remove(),m.on(\"mouseover\",d).on(\"mouseout\",g).on(\"click\",y),v.append(\"g\").attr(\"class\",\"dimensions\");var k=u.select(\"g.dimensions\").selectAll(\"g.dimension\").data(function(t){return t.dimensions},h);k.enter().append(\"g\").attr(\"class\",\"dimension\"),k.attr(\"transform\",function(t){return\"translate(\"+t.x+\", 0)\"}),k.exit().remove();var T=k.selectAll(\"g.category\").data(function(t){return t.categories},h),A=T.enter().append(\"g\").attr(\"class\",\"category\");T.attr(\"transform\",function(t){return\"translate(0, \"+t.y+\")\"}),A.append(\"rect\").attr(\"class\",\"catrect\").attr(\"pointer-events\",\"none\"),T.select(\"rect.catrect\").attr(\"fill\",\"none\").attr(\"width\",function(t){return t.width}).attr(\"height\",function(t){return t.height}),_(A);var M=T.selectAll(\"rect.bandrect\").data(function(t){return t.bands},h);M.each(function(){o.raiseToTop(this)}),M.attr(\"fill\",function(t){return t.color});var O=M.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"stroke-opacity\",0).attr(\"fill\",function(t){return t.color}).attr(\"fill-opacity\",0);M.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\"}),w(O),M.exit().remove(),A.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var z=e._fullLayout.paper_bgcolor;T.select(\"text.catlabel\").attr(\"text-anchor\",function(t){return f(t)?\"start\":\"end\"}).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",z+\" -1px  1px 2px, \"+z+\" 1px  1px 2px, \"+z+\"  1px -1px 2px, \"+z+\" -1px -1px 2px\").style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",function(t){return f(t)?t.width+5:-5}).attr(\"y\",function(t){return t.height/2}).text(function(t){return t.model.categoryLabel}).each(function(t){s.font(n.select(this),t.parcatsViewModel.categorylabelfont),c.convertToTspans(n.select(this),e)}),A.append(\"text\").attr(\"class\",\"dimlabel\"),T.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){s.font(n.select(this),t.parcatsViewModel.labelfont)}),T.selectAll(\"rect.bandrect\").on(\"mouseover\",S).on(\"mouseout\",E),T.exit().remove(),k.call(n.behavior.drag().origin(function(t){return{x:t.x,y:0}}).on(\"dragstart\",L).on(\"drag\",C).on(\"dragend\",P)),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 h(t){return t.key}function f(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function p(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function d(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){o.raiseToTop(this),b(n.select(this));var e=v(t),r=m(t);if(t.parcatsViewModel.graphDiv.emit(\"plotly_hover\",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\")){var a,s,c,u=n.mouse(this)[0],h=t.parcatsViewModel.graphDiv,f=t.parcatsViewModel.trace,p=h._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),g=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(c=0;c<t.leftXs.length-1;c++)if(t.leftXs[c]+t.dimWidths[c]-2<=u&&u<=t.leftXs[c+1]+2){var y=t.parcatsViewModel.dimensions[c],x=t.parcatsViewModel.dimensions[c+1];a=(y.x+y.width+x.x)/2,s=(t.topYs[c]+t.topYs[c+1]+t.height)/2;break}var _=t.parcatsViewModel.x+a,w=t.parcatsViewModel.y+s,k=l.mostReadable(t.model.color,[\"black\",\"white\"]),T=t.model.count,A=T/t.parcatsViewModel.model.count,M={countLabel:T,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(h)[0];i.loneHover({trace:f,x:_-d.left+g.left,y:w-d.top+g.top,text:E,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:k,idealAlign:L<_?\"right\":\"left\",hovertemplate:(f.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:f._input,fullData:f,count:T,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:h})}}}function g(t){if(!t.parcatsViewModel.dragDimension&&(x(n.select(this)),i.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(p),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=v(t),r=m(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event,constraints:r})}}function v(t){for(var e=[],r=O(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var a=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:a})}return e}function m(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var a=r[n],i=a.categories[t.model.categoryInds[n]];e[a.containerInd]=i.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function y(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){var e=v(t),r=m(t);t.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:e,event:n.event,constraints:r})}}function x(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 b(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",function(t){return l.mostReadable(t.model.color,[\"black\",\"white\"])}).attr(\"stroke-width\",.3)}function _(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function w(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function k(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter(function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color})}function T(t,e,r){var a=n.select(t).datum(),i=a.categoryViewModel.model,o=a.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll(\"rect.bandrect\"),l=[];s.each(function(t){k(t).each(function(t){Array.prototype.push.apply(l,v(t))})});var c={};c[i.dimensionInd]=i.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function A(t,e,r){var a=n.select(t).datum(),i=a.categoryViewModel.model,o=a.parcatsViewModel.graphDiv,s=k(a),l=[];s.each(function(t){Array.prototype.push.apply(l,v(t))});var c={};c[i.dimensionInd]=i.categoryValue,void 0!==a.rawColor&&(c.color=a.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function M(t,e){var r,a,i=n.select(e.parentNode).select(\"rect.catrect\"),o=i.node().getBoundingClientRect(),s=i.datum(),l=s.parcatsViewModel,c=l.model.dimensions[s.model.dimensionInd],u=l.trace,h=o.top+o.height/2;l.dimensions.length>1&&c.displayInd===l.dimensions.length-1?(r=o.left,a=\"left\"):(r=o.left+o.width,a=\"right\");var f=s.model.count,p=s.model.categoryLabel,d=f/s.parcatsViewModel.model.count,g={countLabel:f,categoryLabel:p,probabilityLabel:d.toFixed(3)},v=[];-1!==s.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&v.push([\"Count:\",g.countLabel].join(\" \")),-1!==s.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&v.push([\"P(\"+g.categoryLabel+\"):\",g.probabilityLabel].join(\" \"));var m=v.join(\"<br>\");return{trace:u,x:r-t.left,y:h-t.top,text:m,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:12,fontColor:\"black\",idealAlign:a,hovertemplate:u.hovertemplate,hovertemplateLabels:g,eventData:[{data:u._input,fullData:u,count:f,category:p,probability:d}]}}function S(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,a=r._fullLayout,s=a._paperdiv.node().getBoundingClientRect(),c=t.parcatsViewModel.hoveron;if(\"color\"===c?(!function(t){var e=n.select(t).datum(),r=k(e);b(r),r.each(function(){o.raiseToTop(this)}),n.select(t.parentNode).selectAll(\"rect.bandrect\").filter(function(t){return t.color===e.color}).each(function(){o.raiseToTop(this),n.select(this).attr(\"stroke\",\"black\").attr(\"stroke-width\",1.5)})}(this),A(this,\"plotly_hover\",n.event)):(!function(t){n.select(t.parentNode).selectAll(\"rect.bandrect\").each(function(t){var e=k(t);b(e),e.each(function(){o.raiseToTop(this)})}),n.select(t.parentNode).select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}(this),T(this,\"plotly_hover\",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\"))\"category\"===c?e=M(s,this):\"color\"===c?e=function(t,e){var r,a,i=e.getBoundingClientRect(),o=n.select(e).datum(),s=o.categoryViewModel,c=s.parcatsViewModel,u=c.model.dimensions[s.model.dimensionInd],h=c.trace,f=i.y+i.height/2;c.dimensions.length>1&&u.displayInd===c.dimensions.length-1?(r=i.left,a=\"left\"):(r=i.left+i.width,a=\"right\");var p=s.model.categoryLabel,d=o.parcatsViewModel.model.count,g=0;o.categoryViewModel.bands.forEach(function(t){t.color===o.color&&(g+=t.count)});var v=s.model.count,m=0;c.pathSelection.each(function(t){t.model.color===o.color&&(m+=t.model.count)});var y=g/d,x=g/m,b=g/v,_={countLabel:d,categoryLabel:p,probabilityLabel:y.toFixed(3)},w=[];-1!==s.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&w.push([\"Count:\",_.countLabel].join(\" \")),-1!==s.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&(w.push(\"P(color \\u2229 \"+p+\"): \"+_.probabilityLabel),w.push(\"P(\"+p+\" | color): \"+x.toFixed(3)),w.push(\"P(color | \"+p+\"): \"+b.toFixed(3)));var k=w.join(\"<br>\"),T=l.mostReadable(o.color,[\"black\",\"white\"]);return{trace:h,x:r-t.left,y:f-t.top,text:k,color:o.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:T,fontSize:10,idealAlign:a,hovertemplate:h.hovertemplate,hovertemplateLabels:_,eventData:[{data:h._input,fullData:h,category:p,count:d,probability:y,categorycount:v,colorcount:m,bandcolorcount:g}]}}(s,this):\"dimension\"===c&&(e=function(t,e){var r=[];return n.select(e.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each(function(){r.push(M(t,this))}),r}(s,this)),e&&i.loneHover(e,{container:a._hoverlayer.node(),outerContainer:a._paper.node(),gd:r})}}function E(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(x(e.pathSelection),_(e.dimensionSelection.selectAll(\"g.category\")),w(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),i.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(p),-1===e.hoverinfoItems.indexOf(\"skip\"))){\"color\"===t.parcatsViewModel.hoveron?A(this,\"plotly_unhover\",n.event):T(this,\"plotly_unhover\",n.event)}}function L(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map(function(t){return t.displayInd}),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each(function(e){var r=n.mouse(this)[0],a=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=a&&a<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map(function(t){return t.displayInd}),e.model.dragY=e.y,o.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each(function(e){e.y<a&&a<=e.y+e.height&&(t.potentialClickBand=this)}))}),t.parcatsViewModel.dragDimension=t,i.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function C(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,a=e+1,i=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=i.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=i.categories,u=c[l-1],h=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==h&&s+o.height>h.y+h.height/2&&(o.model.displayInd=h.model.displayInd,h.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||\"freeform\"===t.parcatsViewModel.arrangement){i.model.dragX=n.event.x;var f=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[a];void 0!==f&&i.model.dragX<f.x+f.width&&(i.model.displayInd=f.model.displayInd,f.model.displayInd=e),void 0!==p&&i.model.dragX+i.width>p.x&&(i.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=i.model.displayInd}F(t.parcatsViewModel),R(t.parcatsViewModel),I(t.parcatsViewModel),z(t.parcatsViewModel)}}function P(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=O(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 a=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+a+\"].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}),h=c.map(function(t){return t.categoryLabel});e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[u],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[h],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?A(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):T(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,F(t.parcatsViewModel),R(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each(function(){I(t.parcatsViewModel,!0),z(t.parcatsViewModel,!0)}).each(\"end\",function(){(o||s)&&a.restyle(t.parcatsViewModel.graphDiv,e,[r])})}}function O(t){for(var e,r=t.graphDiv._fullData,n=0;n<r.length;n++)if(t.key===r[n].uid){e=n;break}return e}function z(t,e){var r;void 0===e&&(e=!1),t.pathSelection.data(function(t){return t.paths},h),(r=t.pathSelection,e?r.transition():r).attr(\"d\",function(t){return t.svgD})}function I(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data(function(t){return t.dimensions},h);var a=t.dimensionSelection.selectAll(\"g.category\").data(function(t){return t.categories},h);r(t.dimensionSelection).attr(\"transform\",function(t){return\"translate(\"+t.x+\", 0)\"}),r(a).attr(\"transform\",function(t){return\"translate(0, \"+t.y+\")\"}),a.select(\".dimlabel\").text(function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null}),a.select(\".catlabel\").attr(\"text-anchor\",function(t){return f(t)?\"start\":\"end\"}).attr(\"x\",function(t){return f(t)?t.width+5:-5}).each(function(t){var e,r;f(t)?(e=t.width+5,r=\"start\"):(e=-5,r=\"end\"),n.select(this).selectAll(\"tspan\").attr(\"x\",e).attr(\"text-anchor\",r)});var i=a.selectAll(\"rect.bandrect\").data(function(t){return t.bands},h),s=i.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"cursor\",\"move\").attr(\"stroke-opacity\",0).attr(\"fill\",function(t){return t.color}).attr(\"fill-opacity\",0);i.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}),w(s),i.each(function(){o.raiseToTop(this)}),i.exit().remove()}function D(t,e,r,a,i){var o,s,l=[],c=[];for(s=0;s<r.length-1;s++)o=n.interpolateNumber(r[s]+t[s],t[s+1]),l.push(o(i)),c.push(o(1-i));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,\"+a+\" \",u+=\"l -\"+r[r.length-1]+\",0 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Array(c.length),p=e[0].model.count,d=e[0].categories.map(function(t){return t.height}).reduce(function(t,e){return t+e}),g=0;g<c.length;g++){var v,m=c[g];v=p>0?d*(m.count/p):0;for(var y,x=new Array(n.length),b=0;b<m.categoryInds.length;b++){var _=m.categoryInds[b],w=a[b][_],k=i[b];x[k]=n[k][w],n[k][w]+=v;var T=t.dimensions[k].categories[w],A=T.bands.length,M=T.bands[A-1];if(void 0===M||m.rawColor!==M.rawColor){var S=void 0===M?0:M.y+M.height;T.bands.push({key:S,color:m.color,rawColor:m.rawColor,height:v,width:T.width,count:m.count,y:S,categoryViewModel:T,parcatsViewModel:t})}else{var E=T.bands[A-1];E.height+=v,E.count+=m.count}}y=\"hspline\"===t.pathShape?D(s,x,l,v,.5):D(s,x,l,v,0),f[g]={key:m.valueInds[0],model:m,height:v,leftXs:s,topYs:x,dimWidths:l,svgD:y,parcatsViewModel:t}}t.paths=f}function F(t){var e=t.model.dimensions.map(function(t){return{displayInd:t.displayInd,dimensionInd:t.dimensionInd}});e.sort(function(t,e){return t.displayInd-e.displayInd});var r=[];for(var n in e){var a=e[n].dimensionInd,i=t.model.dimensions[a];r.push(B(t,i))}t.dimensions=r}function B(t,e){var r,n=t.model.dimensions.length,a=e.displayInd;r=40+(n>1?(t.width-80-16)/(n-1):0)*a;var i,o,s,l,c,u=[],h=t.model.maxCats,f=e.categories.length,p=e.count,d=t.height-8*(h-1),g=8*(h-f)/2,v=e.categories.map(function(t){return{displayInd:t.displayInd,categoryInd:t.categoryInd}});for(v.sort(function(t,e){return t.displayInd-e.displayInd}),c=0;c<f;c++)l=v[c].categoryInd,o=e.categories[l],i=p>0?o.count/p*d:0,s={key:o.valueInds[0],model:o,width:16,height:i,y:null!==o.dragY?o.dragY:g,bands:[],parcatsViewModel:t},g=g+i+8,u.push(s);return{key:e.dimensionInd,x:null!==e.dragX?e.dragX:r,y:0,width:16,model:e,categories:u,parcatsViewModel:t,dragCategoryDisplayInd:null,dragDimensionDisplayInd:null,initialDragDimensionDisplayInds:null,initialDragCategoryDisplayInds:null,dragHasMoved:null,potentialClickBand:null}}e.exports=function(t,e,r,n){u(r,t,n,e)}},{\"../../components/drawing\":612,\"../../components/fx\":629,\"../../lib\":716,\"../../lib/svg_text_utils\":740,\"../../plot_api/plot_api\":751,d3:164,tinycolor2:535}],1079:[function(t,e,r){\"use strict\";var n=t(\"./parcats\");e.exports=function(t,e,r,a){var i=t._fullLayout,o=i._paper,s=i._size;n(t,o,e,{width:s.w,height:s.h,margin:{t:s.t,r:s.r,b:s.b,l:s.l}},r,a)}},{\"./parcats\":1078}],1080:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/attributes\"),a=t(\"../../plots/cartesian/layout_attributes\"),i=t(\"../../plots/font_attributes\"),o=t(\"../../plots/domain\").attributes,s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/plot_template\").templatedArray;e.exports={domain:o({name:\"parcoords\",trace:!0,editType:\"plot\"}),labelangle:{valType:\"angle\",dflt:0,editType:\"plot\"},labelside:{valType:\"enumerated\",values:[\"top\",\"bottom\"],dflt:\"top\",editType:\"plot\"},labelfont:i({editType:\"plot\"}),tickfont:i({editType:\"plot\"}),rangefont:i({editType:\"plot\"}),dimensions:l(\"dimension\",{label:{valType:\"string\",editType:\"plot\"},tickvals:s({},a.tickvals,{editType:\"plot\"}),ticktext:s({},a.ticktext,{editType:\"plot\"}),tickformat:s({},a.tickformat,{editType:\"plot\"}),visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},range:{valType:\"info_array\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},constraintrange:{valType:\"info_array\",freeLength:!0,dimensions:\"1-2\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},multiselect:{valType:\"boolean\",dflt:!0,editType:\"plot\"},values:{valType:\"data_array\",editType:\"calc\"},editType:\"calc\"}),line:s({editType:\"calc\"},n(\"line\",{colorscaleDflt:\"Viridis\",autoColorDflt:!1,editTypeOverride:\"calc\"}))}},{\"../../components/colorscale/attributes\":598,\"../../lib/extend\":707,\"../../plot_api/plot_template\":754,\"../../plots/cartesian/layout_attributes\":776,\"../../plots/domain\":789,\"../../plots/font_attributes\":790}],1081:[function(t,e,r){\"use strict\";var n=t(\"./constants\"),a=t(\"d3\"),i=t(\"../../lib/gup\").keyFun,o=t(\"../../lib/gup\").repeat,s=t(\"../../lib\").sorterAsc,l=n.bar.snapRatio;function c(t,e){return t*(1-l)+e*l}var u=n.bar.snapClose;function h(t,e){return t*(1-u)+e*u}function f(t,e,r,n){if(function(t,e){for(var r=0;r<e.length;r++)if(t>=e[r][0]&&t<=e[r][1])return!0;return!1}(r,n))return r;var a=t?-1:1,i=0,o=e.length-1;if(a<0){var s=i;i=o,o=s}for(var l=e[i],u=l,f=i;a*f<a*o;f+=a){var p=f+a,d=e[p];if(a*r<a*h(l,d))return c(l,u);if(a*r<a*d||p===o)return c(d,l);u=l,l=d}}function p(t){t.attr(\"x\",-n.bar.captureWidth/2).attr(\"width\",n.bar.captureWidth)}function d(t){t.attr(\"visibility\",\"visible\").style(\"visibility\",\"visible\").attr(\"fill\",\"yellow\").attr(\"opacity\",0)}function g(t){if(!t.brush.filterSpecified)return\"0,\"+t.height;for(var e,r,n,a=v(t.brush.filter.getConsolidated(),t.height),i=[0],o=a.length?a[0][0]:null,s=0;s<a.length;s++)r=(e=a[s])[1]-e[0],i.push(o),i.push(r),(n=s+1)<a.length&&(o=a[n][0]-e[1]);return i.push(t.height),i}function v(t,e){return t.map(function(t){return t.map(function(t){return Math.max(0,t*e)}).sort(s)})}function m(){a.select(document.body).style(\"cursor\",null)}function y(t){t.attr(\"stroke-dasharray\",g)}function x(t,e){var r=a.select(t).selectAll(\".highlight, .highlight-shadow\");y(e?r.transition().duration(n.bar.snapDuration).each(\"end\",e):r)}function b(t,e){var r,a=t.brush,i=NaN,o={};if(a.filterSpecified){var s=t.height,l=a.filter.getConsolidated(),c=v(l,s),u=NaN,h=NaN,f=NaN;for(r=0;r<=c.length;r++){var p=c[r];if(p&&p[0]<=e&&e<=p[1]){u=r;break}if(h=r?r-1:NaN,p&&p[0]>e){f=r;break}}if(i=u,isNaN(i)&&(i=isNaN(h)||isNaN(f)?isNaN(h)?f:h:e-c[h][1]<c[f][0]-e?h:f),!isNaN(i)){var d=c[i],g=function(t,e){var r=n.bar.handleHeight;if(!(e>t[1]+r||e<t[0]-r))return e>=.9*t[1]+.1*t[0]?\"n\":e<=.9*t[0]+.1*t[1]?\"s\":\"ns\"}(d,e);g&&(o.interval=l[i],o.intervalPix=d,o.region=g)}}if(t.ordinal&&!o.region){var m=t.unitTickvals,y=t.unitToPaddedPx.invert(e);for(r=0;r<m.length;r++){var x=[.25*m[Math.max(r-1,0)]+.75*m[r],.25*m[Math.min(r+1,m.length-1)]+.75*m[r]];if(y>=x[0]&&y<=x[1]){o.clickableOrdinalRange=x;break}}}return o}function _(t,e){a.event.sourceEvent.stopPropagation();var r=e.height-a.mouse(t)[1]-2*n.verticalPadding,i=e.brush.svgBrush;i.wasDragged=!0,i._dragging=!0,i.grabbingBar?i.newExtent=[r-i.grabPoint,r+i.barLength-i.grabPoint].map(e.unitToPaddedPx.invert):i.newExtent=[i.startExtent,e.unitToPaddedPx.invert(r)].sort(s),e.brush.filterSpecified=!0,i.extent=i.stayingIntervals.concat([i.newExtent]),i.brushCallback(e),x(t.parentNode)}function w(t,e){var r=b(e,e.height-a.mouse(t)[1]-2*n.verticalPadding),i=\"crosshair\";r.clickableOrdinalRange?i=\"pointer\":r.region&&(i=r.region+\"-resize\"),a.select(document.body).style(\"cursor\",i)}function k(t){t.on(\"mousemove\",function(t){a.event.preventDefault(),t.parent.inBrushDrag||w(this,t)}).on(\"mouseleave\",function(t){t.parent.inBrushDrag||m()}).call(a.behavior.drag().on(\"dragstart\",function(t){!function(t,e){a.event.sourceEvent.stopPropagation();var r=e.height-a.mouse(t)[1]-2*n.verticalPadding,i=e.unitToPaddedPx.invert(r),o=e.brush,s=b(e,r),l=s.interval,c=o.svgBrush;if(c.wasDragged=!1,c.grabbingBar=\"ns\"===s.region,c.grabbingBar){var u=l.map(e.unitToPaddedPx);c.grabPoint=r-u[0]-n.verticalPadding,c.barLength=u[1]-u[0]}c.clickableOrdinalRange=s.clickableOrdinalRange,c.stayingIntervals=e.multiselect&&o.filterSpecified?o.filter.getConsolidated():[],l&&(c.stayingIntervals=c.stayingIntervals.filter(function(t){return t[0]!==l[0]&&t[1]!==l[1]})),c.startExtent=s.region?l[\"s\"===s.region?1:0]:i,e.parent.inBrushDrag=!0,c.brushStartCallback()}(this,t)}).on(\"drag\",function(t){_(this,t)}).on(\"dragend\",function(t){!function(t,e){var r=e.brush,n=r.filter,i=r.svgBrush;i._dragging||(w(t,e),_(t,e),e.brush.svgBrush.wasDragged=!1),i._dragging=!1,a.event.sourceEvent.stopPropagation();var o=i.grabbingBar;if(i.grabbingBar=!1,i.grabLocation=void 0,e.parent.inBrushDrag=!1,m(),!i.wasDragged)return i.wasDragged=void 0,i.clickableOrdinalRange?r.filterSpecified&&e.multiselect?i.extent.push(i.clickableOrdinalRange):(i.extent=[i.clickableOrdinalRange],r.filterSpecified=!0):o?(i.extent=i.stayingIntervals,0===i.extent.length&&A(r)):A(r),i.brushCallback(e),x(t.parentNode),void i.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);var s=function(){n.set(n.getConsolidated())};if(e.ordinal){var l=e.unitTickvals;l[l.length-1]<l[0]&&l.reverse(),i.newExtent=[f(0,l,i.newExtent[0],i.stayingIntervals),f(1,l,i.newExtent[1],i.stayingIntervals)];var c=i.newExtent[1]>i.newExtent[0];i.extent=i.stayingIntervals.concat(c?[i.newExtent]:[]),i.extent.length||A(r),i.brushCallback(e),c?x(t.parentNode,s):(s(),x(t.parentNode))}else s();i.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}(this,t)}))}function T(t,e){return t[0]-e[0]}function A(t){t.filterSpecified=!1,t.svgBrush.extent=[[-1/0,1/0]]}function M(t){for(var e,r=t.slice(),n=[],a=r.shift();a;){for(e=a.slice();(a=r.shift())&&a[0]<=e[1];)e[1]=Math.max(e[1],a[1]);n.push(e)}return n}e.exports={makeBrush:function(t,e,r,n,a,i){var o,l=function(){var t,e,r=[];return{set:function(n){1===(r=n.map(function(t){return t.slice().sort(s)}).sort(T)).length&&r[0][0]===-1/0&&r[0][1]===1/0&&(r=[[0,-1]]),t=M(r),e=r.reduce(function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]},[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return l.set(r),{filter:l,filterSpecified:e,svgBrush:{extent:[],brushStartCallback:n,brushCallback:(o=a,function(t){var e=t.brush,r=function(t){return t.svgBrush.extent.map(function(t){return 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a=t.selectAll(\".highlight\").data(o);a.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width-n.bar.strokeWidth).attr(\"stroke\",n.bar.fillColor).attr(\"opacity\",n.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),a.attr(\"y1\",function(t){return t.height}).call(y)}(e)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map(function(t){return t.sort(s)}),t=e.multiselect?M(t.sort(T)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map(function(t){var e=[f(0,r,t[0],[]),f(1,r,t[1],[])];if(e[1]>e[0])return e}).filter(function(t){return t})).length)return}return t.length>1?t:t[0]}}},{\"../../lib\":716,\"../../lib/gup\":714,\"./constants\":1084,d3:164}],1082:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"../../plots/get_data\").getModuleCalcData,i=t(\"./plot\"),o=t(\"../../constants/xmlns_namespaces\");r.name=\"parcoords\",r.plot=function(t){var e=a(t.calcdata,\"parcoords\")[0];e.length&&i(t,e)},r.clean=function(t,e,r,n){var a=n._has&&n._has(\"parcoords\"),i=e._has&&e._has(\"parcoords\");a&&!i&&(n._paperdiv.selectAll(\".parcoords\").remove(),n._glimages.selectAll(\"*\").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(\".svg-container\");r.filter(function(t,e){return e===r.size()-1}).selectAll(\".gl-canvas-context, .gl-canvas-focus\").each(function(){var t=this.toDataURL(\"image/png\");e.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":t,preserveAspectRatio:\"none\",x:0,y:0,width:this.width,height:this.height})}),window.setTimeout(function(){n.selectAll(\"#filterBarPattern\").attr(\"id\",\"filterBarPattern\")},60)}},{\"../../constants/xmlns_namespaces\":693,\"../../plots/get_data\":799,\"./plot\":1091,d3:164}],1083:[function(t,e,r){\"use strict\";var n=t(\"../../lib\").isArrayOrTypedArray,a=t(\"../../components/colorscale\"),i=t(\"../../lib/gup\").wrap;e.exports=function(t,e){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\":716}],1087:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcoords\",basePlotModule:t(\"./base_plot\"),categories:[\"gl\",\"regl\",\"noOpacity\",\"noHover\"],meta:{}}},{\"./attributes\":1080,\"./base_plot\":1082,\"./calc\":1083,\"./defaults\":1085,\"./plot\":1091}],1088:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nattribute vec4 p01_04, p05_08, p09_12, p13_16,\\n               p17_20, p21_24, p25_28, p29_32,\\n               p33_36, p37_40, p41_44, p45_48,\\n               p49_52, p53_56, 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hi) {\\n    return (lo < hi) && (lo > p || p > hi);\\n}\\n\\nbool vOutside(vec4 p, vec4 lo, vec4 hi) {\\n    return (\\n        fOutside(p[0], lo[0], hi[0]) ||\\n        fOutside(p[1], lo[1], hi[1]) ||\\n        fOutside(p[2], lo[2], hi[2]) ||\\n        fOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool mOutside(mat4 p, mat4 lo, mat4 hi) {\\n    return (\\n        vOutside(p[0], lo[0], hi[0]) ||\\n        vOutside(p[1], lo[1], hi[1]) ||\\n        vOutside(p[2], lo[2], hi[2]) ||\\n        vOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    return mOutside(A, loA, hiA) ||\\n           mOutside(B, loB, hiB) ||\\n           mOutside(C, loC, hiC) ||\\n           mOutside(D, loD, hiD);\\n}\\n\\nbool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    mat4 pnts[4];\\n    pnts[0] = A;\\n    pnts[1] = B;\\n    pnts[2] = C;\\n    pnts[3] = D;\\n\\n    for(int i = 0; i < 4; ++i) {\\n        for(int j = 0; j < 4; ++j) {\\n            for(int k = 0; k < 4; ++k) {\\n                if(0 == iMod(\\n                    int(255.0 * texture2D(mask,\\n                        vec2(\\n                            (float(i * 2 + j / 2) + 0.5) / 8.0,\\n                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\\n                        ))[3]\\n                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\\n                    2\\n                )) return true;\\n            }\\n        }\\n    }\\n    return false;\\n}\\n\\nvec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float x = 0.5 * sign(v) + 0.5;\\n    float y = axisY(x, A, B, C, D);\\n    float z = 1.0 - abs(v);\\n\\n    z += isContext ? 0.0 : 2.0 * float(\\n        outsideBoundingBox(A, B, C, D) ||\\n        outsideRasterMask(A, B, C, D)\\n    );\\n\\n    return vec4(\\n        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\\n        z,\\n        1.0\\n    );\\n}\\n\\nvoid main() {\\n    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\\n    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\\n    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\\n    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\\n\\n    float v = colors[3];\\n\\n    gl_Position = position(isContext, v, A, B, C, D);\\n\\n    fragColor =\\n        isContext ? vec4(contextColor) :\\n        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\\n}\\n\"]),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n    gl_FragColor = fragColor;\\n}\\n\"]),o=t(\"./constants\").maxDimensionCount,s=t(\"../../lib\"),l=1e-6,c=2048,u=new Uint8Array(4),h=new Uint8Array(4),f={shape:[256,1],format:\"rgba\",type:\"uint8\",mag:\"nearest\",min:\"nearest\"};function p(t,e,r,n,a){var i=t._gl;i.enable(i.SCISSOR_TEST),i.scissor(e,r,n,a),t.clear({color:[0,0,0,0],depth:1})}function d(t,e,r,n,a,i){var 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r,i,o=[],s=t._fullLayout,c=s.hiddenlabels||[],u=e.labels,h=e.marker.colors||[],f=e.values,p=a(f)&&f.length;if(e.dlabel)for(u=new Array(f.length),r=0;r<f.length;r++)u[r]=String(e.label0+r*e.dlabel);var d={},g=l(s[\"_\"+e.type+\"colormap\"]),v=(p?f:u).length,m=0,y=!1;for(r=0;r<v;r++){var x,b,_;if(p){if(x=f[r],!n(x))continue;if((x=+x)<0)continue}else x=1;void 0!==(b=u[r])&&\"\"!==b||(b=r);var w=d[b=String(b)];void 0===w?(d[b]=o.length,(_=-1!==c.indexOf(b))||(m+=x),o.push({v:x,label:b,color:g(h[r],b),i:r,pts:[r],hidden:_})):(y=!0,(i=o[w]).v+=x,i.pts.push(r),i.hidden||(m+=x),!1===i.color&&h[r]&&(i.color=g(h[r],b)))}return(\"funnelarea\"===e.type?y:e.sort)&&o.sort(function(t,e){return e.v-t.v}),o[0]&&(o[0].vTotal=m),o},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,a=t.calcdata,i=n[r+\"colorway\"],o=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(i=c(i,s));for(var l=0,u=0;u<a.length;u++){var h=a[u];if(h[0].trace.type===r)for(var f=0;f<h.length;f++){var p=h[f];!1===p.color&&(o[p.label]?p.color=o[p.label]:(o[p.label]=p.color=i[l%i.length],l++))}}},makePullColorFn:l,generateExtendedColors:c}},{\"../../components/color\":591,\"../../lib\":716,\"fast-isnumeric\":227,tinycolor2:535}],1095:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./attributes\"),i=t(\"../../plots/domain\").defaults,o=t(\"../bar/defaults\").handleText;e.exports=function(t,e,r,s){function l(r,i){return n.coerce(t,e,a,r,i)}var c,u=l(\"values\"),h=n.isArrayOrTypedArray(u),f=l(\"labels\");if(Array.isArray(f)?(c=f.length,h&&(c=Math.min(c,u.length))):h&&(c=u.length,l(\"label0\"),l(\"dlabel\")),c){e._length=c,l(\"marker.line.width\")&&l(\"marker.line.color\"),l(\"marker.colors\"),l(\"scalegroup\");var p,d=l(\"text\"),g=l(\"texttemplate\");if(g||(p=l(\"textinfo\",Array.isArray(d)?\"text+percent\":\"percent\")),l(\"hovertext\"),l(\"hovertemplate\"),g||p&&\"none\"!==p){var v=l(\"textposition\");o(t,e,s,l,v,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(v)||\"auto\"===v||\"outside\"===v)&&l(\"automargin\")}i(e,s,l);var m=l(\"hole\");if(l(\"title.text\")){var y=l(\"title.position\",m?\"middle center\":\"top center\");m||\"middle center\"!==y||(e.title.position=\"top center\"),n.coerceFont(l,\"title.font\",s.font)}l(\"sort\"),l(\"direction\"),l(\"rotation\"),l(\"pull\")}else e.visible=!1}},{\"../../lib\":716,\"../../plots/domain\":789,\"../bar/defaults\":859,\"./attributes\":1092}],1096:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx/helpers\").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},{\"../../components/fx/helpers\":626}],1097:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");r.formatPiePercent=function(t,e){var r=(100*t).toPrecision(3);return-1!==r.lastIndexOf(\".\")&&(r=r.replace(/[.]?0+$/,\"\")),n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=t.toPrecision(10);return-1!==r.lastIndexOf(\".\")&&(r=r.replace(/[.]?0+$/,\"\")),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}},{\"../../lib\":716}],1098:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),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\":1092,\"./base_plot\":1093,\"./calc\":1094,\"./defaults\":1095,\"./layout_attributes\":1099,\"./layout_defaults\":1100,\"./plot\":1101,\"./style\":1102,\"./style_one\":1103}],1099:[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\"}}},{}],1100:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,i){return n.coerce(t,e,a,r,i)}r(\"hiddenlabels\"),r(\"piecolorway\",e.colorway),r(\"extendpiecolors\")}},{\"../../lib\":716,\"./layout_attributes\":1099}],1101:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"../../plots/plots\"),i=t(\"../../components/fx\"),o=t(\"../../components/color\"),s=t(\"../../components/drawing\"),l=t(\"../../lib\"),c=t(\"../../lib/svg_text_utils\"),u=t(\"./helpers\"),h=t(\"./event_data\"),f=t(\"../../lib\").isValidTextValue;function p(t,e,r){var a=r[0],o=a.trace,s=a.cx,c=a.cy;\"_hasHoverLabel\"in o||(o._hasHoverLabel=!1),\"_hasHoverEvent\"in o||(o._hasHoverEvent=!1),t.on(\"mouseover\",function(t){var r=e._fullLayout,f=e._fullData[o.index];if(!e._dragging&&!1!==r.hovermode){var p=f.hoverinfo;if(Array.isArray(p)&&(p=i.castHoverinfo({hoverinfo:[u.castOption(p,t.pts)],_module:o._module},r,0)),\"all\"===p&&(p=\"label+text+value+percent+name\"),f.hovertemplate||\"none\"!==p&&\"skip\"!==p&&p){var d=t.rInscribed||0,g=s+t.pxmid[0]*(1-d),v=c+t.pxmid[1]*(1-d),m=r.separators,y=[];if(p&&-1!==p.indexOf(\"label\")&&y.push(t.label),t.text=u.castOption(f.hovertext||f.text,t.pts),p&&-1!==p.indexOf(\"text\")){var x=t.text;l.isValidTextValue(x)&&y.push(x)}t.value=t.v,t.valueLabel=u.formatPieValue(t.v,m),p&&-1!==p.indexOf(\"value\")&&y.push(t.valueLabel),t.percent=t.v/a.vTotal,t.percentLabel=u.formatPiePercent(t.percent,m),p&&-1!==p.indexOf(\"percent\")&&y.push(t.percentLabel);var b=f.hoverlabel,_=b.font;i.loneHover({trace:o,x0:g-d*a.r,x1:g+d*a.r,y:v,text:y.join(\"<br>\"),name:f.hovertemplate||-1!==p.indexOf(\"name\")?f.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:u.castOption(b.bgcolor,t.pts)||t.color,borderColor:u.castOption(b.bordercolor,t.pts),fontFamily:u.castOption(_.family,t.pts),fontSize:u.castOption(_.size,t.pts),fontColor:u.castOption(_.color,t.pts),nameLength:u.castOption(b.namelength,t.pts),textAlign:u.castOption(b.align,t.pts),hovertemplate:u.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[h(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e}),o._hasHoverLabel=!0}o._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[h(t,f)],event:n.event})}}),t.on(\"mouseout\",function(t){var r=e._fullLayout,a=e._fullData[o.index],s=n.select(this).datum();o._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[h(s,a)],event:n.event}),o._hasHoverEvent=!1),o._hasHoverLabel&&(i.loneUnhover(r._hoverlayer.node()),o._hasHoverLabel=!1)}),t.on(\"click\",function(t){var r=e._fullLayout,a=e._fullData[o.index];e._dragging||!1===r.hovermode||(e._hoverdata=[h(t,a)],i.click(e,n.event))})}function d(t,e,r){var n=u.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=u.castOption(t._input.textfont.color,e.pts));var a=u.castOption(t.insidetextfont.family,e.pts)||u.castOption(t.textfont.family,e.pts)||r.family,i=u.castOption(t.insidetextfont.size,e.pts)||u.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:a,size:i}}function g(t,e){for(var r,n,a=0;a<t.length;a++)if((n=(r=t[a][0]).trace).title.text){var i=n.title.text;n._meta&&(i=l.templateString(i,n._meta));var o=s.tester.append(\"text\").attr(\"data-notex\",1).text(i).call(s.font,n.title.font).call(c.convertToTspans,e),u=s.bBox(o.node(),!0);r.titleBox={width:u.width,height:u.height},o.remove()}}function v(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),a=t.width/t.height,i=e.halfangle,o=e.ring,s=e.rInscribed,l=r.r||e.rpx1,c={scale:s*l*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=a+1/(2*Math.tan(i)),h=l*Math.min(1/(Math.sqrt(u*u+.5)+u),o/(Math.sqrt(a*a+o/2)+a)),f={scale:2*h/t.height,rCenter:Math.cos(h/l)-h*a/l,rotate:(180/Math.PI*e.midangle+720)%180-90},p=1/a,d=p+1/(2*Math.tan(i)),g=l*Math.min(1/(Math.sqrt(d*d+.5)+d),o/(Math.sqrt(p*p+o/2)+p)),v={scale:2*g/t.width,rCenter:Math.cos(g/l)-g/a/l,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>f.scale?v:f;return c.scale<1&&m.scale>c.scale?m:c}function m(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function y(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,i=t.height/2;return r<0&&(a*=-1),n<0&&(i*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(i)*(a>0?1:-1)/2,y:i/(1+r*r/(n*n)),outside:!0}}function x(t,e){var r,n,a,i=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=i.title.font.size,a=_(i),-1!==i.title.position.indexOf(\"top\")?(o.y-=(1+a)*t.r,s.ty-=t.titleBox.height):-1!==i.title.position.indexOf(\"bottom\")&&(o.y+=(1+a)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),h=e.w*(i.domain.x[1]-i.domain.x[0])/2;return-1!==i.title.position.indexOf(\"left\")?(h+=u,o.x-=(1+a)*u,s.tx+=t.titleBox.width/2):-1!==i.title.position.indexOf(\"center\")?h*=2:-1!==i.title.position.indexOf(\"right\")&&(h+=u,o.x+=(1+a)*u,s.tx-=t.titleBox.width/2),r=h/t.titleBox.width,n=b(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function b(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 _(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 w(t,e){for(var r=[],n=0;n<t.length;n++){var a=t[n][0],i=a.trace,o=i.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);i.title.text&&\"middle center\"!==i.title.position&&(l-=b(a,e));var c=s/2,u=l/2;\"funnelarea\"!==i.type||i.scalegroup||(u/=i.aspectratio),a.r=Math.min(c,u)/(1+_(i)),a.cx=e.l+e.w*(i.domain.x[1]+i.domain.x[0])/2,a.cy=e.t+e.h*(1-i.domain.y[0])-l/2,i.title.text&&-1!==i.title.position.indexOf(\"bottom\")&&(a.cy-=b(a,e)),i.scalegroup&&-1===r.indexOf(i.scalegroup)&&r.push(i.scalegroup)}!function(t,e){for(var r,n,a,i=0;i<e.length;i++){var o=1/0,s=e[i];for(n=0;n<t.length;n++)if(r=t[n][0],(a=r.trace).scalegroup===s){var l;if(\"pie\"===a.type)l=r.r*r.r;else if(\"funnelarea\"===a.type){var c,u;a.aspectratio>1?(c=r.r,u=c/a.aspectratio):(u=r.r,c=u*a.aspectratio),c*=(1+a.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(a=r.trace).scalegroup===s){var h=o*r.vTotal;\"funnelarea\"===a.type&&(h/=(1+a.baseratio)/2,h/=a.aspectratio),r.r=Math.sqrt(h)}}}(t,r)}function k(t,e,r){var n=t._fullLayout,a=r.trace,i=a.texttemplate,o=a.textinfo;if(!i&&o&&\"none\"!==o){var s,c=o.split(\"+\"),h=function(t){return-1!==c.indexOf(t)},p=h(\"label\"),d=h(\"text\"),g=h(\"value\"),v=h(\"percent\"),m=n.separators;if(s=p?[e.label]:[],d){var y=u.getFirstFilled(a.text,e.pts);f(y)&&s.push(y)}g&&s.push(u.formatPieValue(e.v,m)),v&&s.push(u.formatPiePercent(e.v/r.vTotal,m)),e.text=s.join(\"<br>\")}if(i){var x=l.castOption(a,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:u.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:u.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(a,t.i,\"customdata\")}}(e),_=u.getFirstFilled(a.text,e.pts);(f(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,a._meta||{})}else e.text=\"\"}}e.exports={plot:function(t,e){var r=t._fullLayout,i=r._size;g(e,t),w(e,i);var h=l.makeTraceGroups(r._pielayer,e,\"trace\").each(function(e){var r=n.select(this),h=e[0],f=h.trace;!function(t){var e,r,n,a=t[0],i=a.trace,o=i.rotation*Math.PI/180,s=2*Math.PI/a.vTotal,l=\"px0\",c=\"px1\";if(\"counterclockwise\"===i.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;o+=s*t[e].v,s*=-1,l=\"px1\",c=\"px0\"}function u(t){return[a.r*Math.sin(t),-a.r*Math.cos(t)]}for(n=u(o),e=0;e<t.length;e++)(r=t[e]).hidden||(r[l]=n,o+=s*r.v/2,r.pxmid=u(o),r.midangle=o,o+=s*r.v/2,n=u(o),r[c]=n,r.largeArc=r.v>a.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/a.vTotal,.5),r.ring=1-i.hole,r.rInscribed=m(r,a))}(e),r.attr(\"stroke-linejoin\",\"round\"),r.each(function(){var g=n.select(this).selectAll(\"g.slice\").data(e);g.enter().append(\"g\").classed(\"slice\",!0),g.exit().remove();var m=[[[],[]],[[],[]]],b=!1;g.each(function(r){if(r.hidden)n.select(this).selectAll(\"path,g\").remove();else{r.pointNumber=r.i,r.curveNumber=f.index,m[r.pxmid[1]<0?0:1][r.pxmid[0]<0?0:1].push(r);var a=h.cx,i=h.cy,o=n.select(this),g=o.selectAll(\"path.surface\").data([r]);if(g.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),o.call(p,t,e),f.pull){var x=+u.castOption(f.pull,r.pts)||0;x>0&&(a+=x*r.pxmid[0],i+=x*r.pxmid[1])}r.cxFinal=a,r.cyFinal=i;var _=f.hole;if(r.v===h.vTotal){var w=\"M\"+(a+r.px0[0])+\",\"+(i+r.px0[1])+E(r.px0,r.pxmid,!0,1)+E(r.pxmid,r.px0,!0,1)+\"Z\";_?g.attr(\"d\",\"M\"+(a+_*r.px0[0])+\",\"+(i+_*r.px0[1])+E(r.px0,r.pxmid,!1,_)+E(r.pxmid,r.px0,!1,_)+\"Z\"+w):g.attr(\"d\",w)}else{var T=E(r.px0,r.px1,!0,1);if(_){var A=1-_;g.attr(\"d\",\"M\"+(a+_*r.px1[0])+\",\"+(i+_*r.px1[1])+E(r.px1,r.px0,!1,_)+\"l\"+A*r.px0[0]+\",\"+A*r.px0[1]+T+\"Z\")}else g.attr(\"d\",\"M\"+a+\",\"+i+\"l\"+r.px0[0]+\",\"+r.px0[1]+T+\"Z\")}k(t,r,h);var M=u.castOption(f.textposition,r.pts),S=o.selectAll(\"g.slicetext\").data(r.text&&\"none\"!==M?[0]:[]);S.enter().append(\"g\").classed(\"slicetext\",!0),S.exit().remove(),S.each(function(){var e=l.ensureSingle(n.select(this),\"text\",\"\",function(t){t.attr(\"data-notex\",1)});e.text(r.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,\"outside\"===M?function(t,e,r){var n=u.castOption(t.outsidetextfont.color,e.pts)||u.castOption(t.textfont.color,e.pts)||r.color,a=u.castOption(t.outsidetextfont.family,e.pts)||u.castOption(t.textfont.family,e.pts)||r.family,i=u.castOption(t.outsidetextfont.size,e.pts)||u.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:a,size:i}}(f,r,t._fullLayout.font):d(f,r,t._fullLayout.font)).call(c.convertToTspans,t);var o,p=s.bBox(e.node());\"outside\"===M?o=y(p,r):(o=v(p,r,h),\"auto\"===M&&o.scale<1&&(e.call(s.font,f.outsidetextfont),f.outsidetextfont.family===f.insidetextfont.family&&f.outsidetextfont.size===f.insidetextfont.size||(p=s.bBox(e.node())),o=y(p,r)));var 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n=t(\"d3\"),a=t(\"./style_one\");e.exports=function(t){t._fullLayout._pielayer.selectAll(\".trace\").each(function(t){var e=t[0].trace,r=n.select(this);r.style({opacity:e.opacity}),r.selectAll(\"path.surface\").each(function(t){n.select(this).call(a,t,e)})})}},{\"./style_one\":1103,d3:164}],1103:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),a=t(\"./helpers\").castOption;e.exports=function(t,e,r){var i=r.marker.line,o=a(i.color,e.pts)||n.defaultLine,s=a(i.width,e.pts)||0;t.style(\"stroke-width\",s).call(n.fill,e.color).call(n.stroke,o)}},{\"../../components/color\":591,\"./helpers\":1097}],1104:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\");e.exports={x:n.x,y:n.y,xy:{valType:\"data_array\",editType:\"calc\"},indices:{valType:\"data_array\",editType:\"calc\"},xbounds:{valType:\"data_array\",editType:\"calc\"},ybounds:{valType:\"data_array\",editType:\"calc\"},text:n.text,marker:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,arrayOk:!1,editType:\"calc\"},blend:{valType:\"boolean\",dflt:null,editType:\"calc\"},sizemin:{valType:\"number\",min:.1,max:2,dflt:.5,editType:\"calc\"},sizemax:{valType:\"number\",min:.1,dflt:20,editType:\"calc\"},border:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},arearatio:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},editType:\"calc\"},editType:\"calc\"},transforms:void 0}},{\"../scatter/attributes\":1118}],1105:[function(t,e,r){\"use strict\";var n=t(\"gl-pointcloud2d\"),a=t(\"../../lib/str2rgbarray\"),i=t(\"../../plots/cartesian/autorange\").findExtremes,o=t(\"../scatter/get_trace_color\");function s(t,e){this.scene=t,this.uid=e,this.type=\"pointcloud\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=n(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}var l=s.prototype;l.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*e],this.pickXYData[2*e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:Array.isArray(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},l.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=o(t,{})},l.updateFast=function(t){var e,r,n,o,s,l,c=this.xData=this.pickXData=t.x,u=this.yData=this.pickYData=t.y,h=this.pickXYData=t.xy,f=t.xbounds&&t.ybounds,p=t.indices,d=this.bounds;if(h){if(n=h,e=h.length>>>1,f)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(l=0;l<e;l++)o=n[2*l],s=n[2*l+1],o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);if(p)r=p;else for(r=new Int32Array(e),l=0;l<e;l++)r[l]=l}else for(e=c.length,n=new Float32Array(2*e),r=new Int32Array(e),l=0;l<e;l++)o=c[l],s=u[l],r[l]=l,n[2*l]=o,n[2*l+1]=s,o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);this.idToIndex=r,this.pointcloudOptions.idToIndex=r,this.pointcloudOptions.positions=n;var g=a(t.marker.color),v=a(t.marker.border.color),m=t.opacity*t.marker.opacity;g[3]*=m,this.pointcloudOptions.color=g;var y=t.marker.blend;if(null===y){y=c.length<100||u.length<100}this.pointcloudOptions.blend=y,v[3]*=m,this.pointcloudOptions.borderColor=v;var x=t.marker.sizemin,b=Math.max(t.marker.sizemax,t.marker.sizemin);this.pointcloudOptions.sizeMin=x,this.pointcloudOptions.sizeMax=b,this.pointcloudOptions.areaRatio=t.marker.border.arearatio,this.pointcloud.update(this.pointcloudOptions);var _=this.scene.xaxis,w=this.scene.yaxis,k=b/2||.5;t._extremes[_._id]=i(_,[d[0],d[2]],{ppad:k}),t._extremes[w._id]=i(w,[d[1],d[3]],{ppad:k})},l.dispose=function(){this.pointcloud.dispose()},e.exports=function(t,e){var r=new s(t,e.uid);return r.update(e),r}},{\"../../lib/str2rgbarray\":739,\"../../plots/cartesian/autorange\":763,\"../scatter/get_trace_color\":1128,\"gl-pointcloud2d\":294}],1106:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./attributes\");e.exports=function(t,e,r){function i(r,i){return n.coerce(t,e,a,r,i)}i(\"x\"),i(\"y\"),i(\"xbounds\"),i(\"ybounds\"),t.xy&&t.xy instanceof Float32Array&&(e.xy=t.xy),t.indices&&t.indices instanceof Int32Array&&(e.indices=t.indices),i(\"text\"),i(\"marker.color\",r),i(\"marker.opacity\"),i(\"marker.blend\"),i(\"marker.sizemin\"),i(\"marker.sizemax\"),i(\"marker.border.color\",r),i(\"marker.border.arearatio\"),e._length=null}},{\"../../lib\":716,\"./attributes\":1104}],1107:[function(t,e,r){\"use 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n=t(\"../../plots/font_attributes\"),a=t(\"../../plots/attributes\"),i=t(\"../../components/color/attributes\"),o=t(\"../../components/fx/attributes\"),s=t(\"../../plots/domain\").attributes,l=t(\"../../plots/template_attributes\").hovertemplateAttrs,c=t(\"../../components/colorscale/attributes\"),u=t(\"../../plot_api/plot_template\").templatedArray,h=t(\"../../lib/extend\").extendFlat,f=t(\"../../plot_api/edit_types\").overrideAll;t(\"../../constants/docs\").FORMAT_LINK;(e.exports=f({hoverinfo:h({},a.hoverinfo,{flags:[],arrayOk:!1}),hoverlabel:o.hoverlabel,domain:s({name:\"sankey\",trace:!0}),orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],dflt:\"h\"},valueformat:{valType:\"string\",dflt:\".3s\"},valuesuffix:{valType:\"string\",dflt:\"\"},arrangement:{valType:\"enumerated\",values:[\"snap\",\"perpendicular\",\"freeform\",\"fixed\"],dflt:\"snap\"},textfont:n({}),node:{label:{valType:\"data_array\",dflt:[]},groups:{valType:\"info_array\",impliedEdits:{x:[],y:[]},dimensions:2,freeLength:!0,dflt:[],items:{valType:\"number\",editType:\"calc\"}},x:{valType:\"data_array\",dflt:[]},y:{valType:\"data_array\",dflt:[]},color:{valType:\"color\",arrayOk:!0},line:{color:{valType:\"color\",dflt:i.defaultLine,arrayOk:!0},width:{valType:\"number\",min:0,dflt:.5,arrayOk:!0}},pad:{valType:\"number\",arrayOk:!1,min:0,dflt:20},thickness:{valType:\"number\",arrayOk:!1,min:1,dflt:20},hoverinfo:{valType:\"enumerated\",values:[\"all\",\"none\",\"skip\"],dflt:\"all\"},hoverlabel:o.hoverlabel,hovertemplate:l({},{keys:[\"value\",\"label\"]})},link:{label:{valType:\"data_array\",dflt:[]},color:{valType:\"color\",arrayOk:!0},line:{color:{valType:\"color\",dflt:i.defaultLine,arrayOk:!0},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0}},source:{valType:\"data_array\",dflt:[]},target:{valType:\"data_array\",dflt:[]},value:{valType:\"data_array\",dflt:[]},hoverinfo:{valType:\"enumerated\",values:[\"all\",\"none\",\"skip\"],dflt:\"all\"},hoverlabel:o.hoverlabel,hovertemplate:l({},{keys:[\"value\",\"label\"]}),colorscales:u(\"concentrationscales\",{editType:\"calc\",label:{valType:\"string\",editType:\"calc\",dflt:\"\"},cmax:{valType:\"number\",editType:\"calc\",dflt:1},cmin:{valType:\"number\",editType:\"calc\",dflt:0},colorscale:h(c().colorscale,{dflt:[[0,\"white\"],[1,\"black\"]]})})}},\"calc\",\"nested\")).transforms=void 0},{\"../../components/color/attributes\":590,\"../../components/colorscale/attributes\":598,\"../../components/fx/attributes\":621,\"../../constants/docs\":687,\"../../lib/extend\":707,\"../../plot_api/edit_types\":747,\"../../plot_api/plot_template\":754,\"../../plots/attributes\":761,\"../../plots/domain\":789,\"../../plots/font_attributes\":790,\"../../plots/template_attributes\":840}],1109:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,a=t(\"../../plots/get_data\").getModuleCalcData,i=t(\"./plot\"),o=t(\"../../components/fx/layout_attributes\"),s=t(\"../../lib/setcursor\"),l=t(\"../../components/dragelement\"),c=t(\"../../plots/cartesian/select\").prepSelect,u=t(\"../../lib\"),h=t(\"../../registry\");function f(t,e){var r=t._fullData[e],n=t._fullLayout,a=n.dragmode,i=\"pan\"===n.dragmode?\"move\":\"crosshair\",o=r._bgRect;if(\"pan\"!==a&&\"zoom\"!==a){s(o,i);var f={_id:\"x\",c2p:u.identity,_offset:r._sankey.translateX,_length:r._sankey.width},p={_id:\"y\",c2p:u.identity,_offset:r._sankey.translateY,_length:r._sankey.height},d={gd:t,element:o.node(),plotinfo:{id:e,xaxis:f,yaxis:p,fillRangeItems:u.noop},subplot:e,xaxes:[f],yaxes:[p],doneFnCompleted:function(r){var n,a=t._fullData[e],i=a.node.groups.slice(),o=[];function s(t){for(var e=a._sankey.graph.nodes,r=0;r<e.length;r++)if(e[r].pointNumber===t)return e[r]}for(var l=0;l<r.length;l++){var c=s(r[l].pointNumber);if(c)if(c.group){for(var u=0;u<c.childrenNodes.length;u++)o.push(c.childrenNodes[u].pointNumber);i[c.pointNumber-a.node._count]=!1}else o.push(c.pointNumber)}n=i.filter(Boolean).concat([o]),h.call(\"_guiRestyle\",t,{\"node.groups\":[n]},e)},prepFn:function(t,e,r){c(t,e,r,d,a)}};l.init(d)}}r.name=\"sankey\",r.baseLayoutAttrOverrides=n({hoverlabel:o.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){var e=a(t.calcdata,\"sankey\")[0];i(t,e),r.updateFx(t)},r.clean=function(t,e,r,n){var 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t.dy/2+r.nodePad/2}).strength(1).iterations(n.forceIterations)).force(\"constrain\",function(t,e,r,a){return function(){for(var t=0,i=0;i<r.length;i++){var o=r[i];o===a.interactionState.dragInProgress?(o.x=o.lastDraggedX,o.y=o.lastDraggedY):(o.vx=(o.originalX-o.x)/n.forceTicksPerFrame,o.y=Math.min(a.size-o.dy/2,Math.max(o.dy/2,o.y))),t=Math.max(t,Math.abs(o.vx),Math.abs(o.vy))}!a.interactionState.dragInProgress&&t<.1&&a.forceLayouts[e].alpha()>0&&a.forceLayouts[e].alpha(0)}}(0,e,i,r)).stop()}(0,o,a),function(t,e,r,a,i){window.requestAnimationFrame(function o(){var s;for(s=0;s<n.forceTicksPerFrame;s++)r.forceLayouts[a].tick();var l=r.graph.nodes;if(function(t){for(var e=0;e<t.length;e++)t[e].y0=t[e].y-t[e].dy/2,t[e].y1=t[e].y0+t[e].dy,t[e].x0=t[e].x-t[e].dx/2,t[e].x1=t[e].x0+t[e].dx}(l),r.sankey.update(r.graph),_(t.filter(D(r)),e),r.forceLayouts[a].alpha()>0)window.requestAnimationFrame(o);else{var 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n=\"bgsankey-\"+e.trace.uid+\"-\"+r;h.ensureSingle(t._fullLayout._draggers,\"rect\",n),t._fullData[r]._bgRect=a.select(\".\"+n),t._fullData[r]._bgRect.style(\"pointer-events\",\"all\").attr(\"width\",e.width).attr(\"height\",e.height).attr(\"x\",e.translateX).attr(\"y\",e.translateY).classed(\"bgsankey\",!0).style({fill:\"transparent\",\"stroke-width\":0})}),_.transition().ease(n.ease).duration(n.duration).attr(\"transform\",T);var z=_.selectAll(\".\"+n.cn.sankeyLinks).data(d,p);z.enter().append(\"g\").classed(n.cn.sankeyLinks,!0).style(\"fill\",\"none\");var I=z.selectAll(\".\"+n.cn.sankeyLink).data(function(t){return t.graph.links.filter(function(t){return t.value}).map(function(t,e,r){var n=i(e.color),a=e.source.label+\"|\"+e.target.label+\"__\"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:a,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:o.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:y,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}.bind(null,t))},p);I.enter().append(\"path\").classed(n.cn.sankeyLink,!0).call(P,_,f.linkEvents),I.style(\"stroke\",function(t){return k(t)?o.tinyRGB(i(t.linkLineColor)):t.tinyColorHue}).style(\"stroke-opacity\",function(t){return k(t)?o.opacity(t.linkLineColor):t.tinyColorAlpha}).style(\"fill\",function(t){return t.tinyColorHue}).style(\"fill-opacity\",function(t){return t.tinyColorAlpha}).style(\"stroke-width\",function(t){return k(t)?t.linkLineWidth:1}).attr(\"d\",y()),I.style(\"opacity\",function(){return t._context.staticPlot||v||m?1:0}).transition().ease(n.ease).duration(n.duration).style(\"opacity\",1),I.exit().transition().ease(n.ease).duration(n.duration).style(\"opacity\",0).remove();var D=_.selectAll(\".\"+n.cn.sankeyNodeSet).data(d,p);D.enter().append(\"g\").classed(n.cn.sankeyNodeSet,!0),D.style(\"cursor\",function(t){switch(t.arrangement){case\"fixed\":return\"default\";case\"perpendicular\":return\"ns-resize\";default:return\"move\"}});var R=D.selectAll(\".\"+n.cn.sankeyNode).data(function(t){var e=t.graph.nodes;return function(t){var e,r=[];for(e=0;e<t.length;e++)t[e].originalX=(t[e].x0+t[e].x1)/2,t[e].originalY=(t[e].y0+t[e].y1)/2,-1===r.indexOf(t[e].originalX)&&r.push(t[e].originalX);for(r.sort(function(t,e){return t-e}),e=0;e<t.length;e++)t[e].originalLayerIndex=r.indexOf(t[e].originalX),t[e].originalLayer=t[e].originalLayerIndex/(r.length-1)}(e),e.map(function(t,e){var r=i(e.color),a=n.nodePadAcross,s=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var l=e.dx,c=Math.max(.5,e.dy),u=\"node_\"+e.pointNumber;return e.group&&(u=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:u,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(l),visibleHeight:c,zoneX:-a,zoneY:-s,zoneWidth:l+2*a,zoneHeight:c+2*s,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:o.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,u].join(\"_\"),interactionState:t.interactionState,figure:t}}.bind(null,t))},p);R.enter().append(\"g\").classed(n.cn.sankeyNode,!0).call(x).style(\"opacity\",function(e){return!t._context.staticPlot&&!v||e.partOfGroup?0:1}),R.call(P,_,f.nodeEvents).call(O,I,f,t),R.transition().ease(n.ease).duration(n.duration).call(x).style(\"opacity\",function(t){return 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n=e[0].trace;n.selectedpoints?(a.selectedPointStyle(r.selectAll(\"path.point\"),n),a.selectedTextStyle(r.selectAll(\"text\"),n)):(o(r,n,t),s(r,n,t))}}},{\"../../components/drawing\":612,\"../../registry\":845,d3:164}],1142:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports={hasLines:function(t){return t.visible&&t.mode&&-1!==t.mode.indexOf(\"lines\")},hasMarkers:function(t){return t.visible&&(t.mode&&-1!==t.mode.indexOf(\"markers\")||\"splom\"===t.type)},hasText:function(t){return t.visible&&t.mode&&-1!==t.mode.indexOf(\"text\")},isBubble:function(t){return n.isPlainObject(t.marker)&&n.isArrayOrTypedArray(t.marker.size)}}},{\"../../lib\":716}],1143:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e,r,a,i){i=i||{},a(\"textposition\"),n.coerceFont(a,\"textfont\",r.font),i.noSelect||(a(\"selected.textfont.color\"),a(\"unselected.textfont.color\"))}},{\"../../lib\":716}],1144:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"../../registry\");e.exports=function(t,e,r,i){var o,s=i(\"x\"),l=i(\"y\");if(a.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),s){var c=n.minRowLength(s);l?o=Math.min(c,n.minRowLength(l)):(o=c,i(\"y0\"),i(\"dy\"))}else{if(!l)return 0;o=n.minRowLength(l),i(\"x0\"),i(\"dx\")}return e._length=o,o}},{\"../../lib\":716,\"../../registry\":845}],1145:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),a=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../../plots/template_attributes\").texttemplateAttrs,s=t(\"../../plots/attributes\"),l=t(\"../../constants/gl3d_dashes\"),c=t(\"../../constants/gl3d_markers\"),u=t(\"../../lib/extend\").extendFlat,h=t(\"../../plot_api/edit_types\").overrideAll,f=n.line,p=n.marker,d=p.line,g=u({width:f.width,dash:{valType:\"enumerated\",values:Object.keys(l),dflt:\"solid\"}},a(\"line\"));var v=e.exports=h({x:n.x,y:n.y,z:{valType:\"data_array\"},text:u({},n.text,{}),texttemplate:o({},{}),hovertext:u({},n.hovertext,{}),hovertemplate:i(),mode:u({},n.mode,{dflt:\"lines+markers\"}),surfaceaxis:{valType:\"enumerated\",values:[-1,0,1,2],dflt:-1},surfacecolor:{valType:\"color\"},projection:{x:{show:{valType:\"boolean\",dflt:!1},opacity:{valType:\"number\",min:0,max:1,dflt:1},scale:{valType:\"number\",min:0,max:10,dflt:2/3}},y:{show:{valType:\"boolean\",dflt:!1},opacity:{valType:\"number\",min:0,max:1,dflt:1},scale:{valType:\"number\",min:0,max:10,dflt:2/3}},z:{show:{valType:\"boolean\",dflt:!1},opacity:{valType:\"number\",min:0,max:1,dflt:1},scale:{valType:\"number\",min:0,max:10,dflt:2/3}}},connectgaps:n.connectgaps,line:g,marker:u({symbol:{valType:\"enumerated\",values:Object.keys(c),dflt:\"circle\",arrayOk:!0},size:u({},p.size,{dflt:8}),sizeref:p.sizeref,sizemin:p.sizemin,sizemode:p.sizemode,opacity:u({},p.opacity,{arrayOk:!1}),colorbar:p.colorbar,line:u({width:u({},d.width,{arrayOk:!1})},a(\"marker.line\"))},a(\"marker\")),textposition:u({},n.textposition,{dflt:\"top 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Array(t.length),s=0;s<t.length;s++){var l=i(+t[s],s);if(\"log\"===a.type){var c=a.c2l(t[s]),u=t[s]-l[0],h=t[s]+l[1];if(o[s]=[(a.c2l(u,!0)-c)*r,(a.c2l(h,!0)-c)*r],u>0){var f=a.c2l(u);a._lowerLogErrorBound||(a._lowerLogErrorBound=f),a._lowerErrorBound=Math.min(a._lowerLogErrorBound,f)}}else o[s]=[-l[0]*r,l[1]*r]}return o}e.exports=function(t,e,r){var n=[a(t.x,t.error_x,e[0],r.xaxis),a(t.y,t.error_y,e[1],r.yaxis),a(t.z,t.error_z,e[2],r.zaxis)],i=function(t){for(var e=0;e<t.length;e++)if(t[e])return t[e].length;return 0}(n);if(0===i)return null;for(var o=new Array(i),s=0;s<i;s++){for(var l=[[0,0,0],[0,0,0]],c=0;c<3;c++)if(n[c])for(var u=0;u<2;u++)l[u][c]=n[c][s][u];o[s]=l}return o}},{\"../../registry\":845}],1148:[function(t,e,r){\"use strict\";var n=t(\"gl-line3d\"),a=t(\"gl-scatter3d\"),i=t(\"gl-error3d\"),o=t(\"gl-mesh3d\"),s=t(\"delaunay-triangulate\"),l=t(\"../../lib\"),c=t(\"../../lib/str2rgbarray\"),u=t(\"../../lib/gl_format_color\").formatColor,h=t(\"../scatter/make_bubble_size_func\"),f=t(\"../../constants/gl3d_dashes\"),p=t(\"../../constants/gl3d_markers\"),d=t(\"../../plots/cartesian/axes\"),g=t(\"../../components/fx/helpers\").appendArrayPointValue,v=t(\"./calc_errors\");function m(t,e){this.scene=t,this.uid=e,this.linePlot=null,this.scatterPlot=null,this.errorBars=null,this.textMarkers=null,this.delaunayMesh=null,this.color=null,this.mode=\"\",this.dataPoints=[],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.textLabels=null,this.data=null}var y=m.prototype;function x(t){return null==t?0:t.indexOf(\"left\")>-1?-1:t.indexOf(\"right\")>-1?1:0}function b(t){return null==t?0:t.indexOf(\"top\")>-1?-1:t.indexOf(\"bottom\")>-1?1:0}function _(t,e){return e(4*t)}function w(t){return p[t]}function k(t,e,r,n,a){var 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e=[0,0,0],r=[[0,0,0],[0,0,0],[0,0,0]],n=[1,1,1],a=0;a<3;a++){var i=t[a];i&&!1!==i.copy_zstyle&&!1!==t[2].visible&&(i=t[2]),i&&i.visible&&(e[a]=i.width/2,r[a]=c(i.color),n[a]=i.thickness)}return{capSize:e,color:r,lineWidth:n}}([e.error_x,e.error_y,e.error_z]);return i.errorColor=X.color,i.errorLineWidth=X.lineWidth,i.errorCapSize=X.capSize,i.delaunayAxis=e.surfaceaxis,i.delaunayColor=c(e.surfacecolor),i}function A(t){if(Array.isArray(t)){var e=t[0];return Array.isArray(e)&&(t=e),\"rgb(\"+t.slice(0,3).map(function(t){return Math.round(255*t)})+\")\"}return null}y.handlePick=function(t){if(t.object&&(t.object===this.linePlot||t.object===this.delaunayMesh||t.object===this.textMarkers||t.object===this.scatterPlot)){var e=t.index=t.data.index;return t.object.highlight&&t.object.highlight(null),this.scatterPlot&&(t.object=this.scatterPlot,this.scatterPlot.highlight(t.data)),t.textLabel=\"\",this.textLabels&&(Array.isArray(this.textLabels)?(this.textLabels[e]||0===this.textLabels[e])&&(t.textLabel=this.textLabels[e]):t.textLabel=this.textLabels),t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]],!0}},y.update=function(t){var e,r,l,c,u=this.scene.glplot.gl,h=f.solid;this.data=t;var p=T(this.scene,t);\"mode\"in p&&(this.mode=p.mode),\"lineDashes\"in p&&p.lineDashes in f&&(h=f[p.lineDashes]),this.color=A(p.scatterColor)||A(p.lineColor),this.dataPoints=p.position,e={gl:this.scene.glplot.gl,position:p.position,color:p.lineColor,lineWidth:p.lineWidth||1,dashes:h[0],dashScale:h[1],opacity:t.opacity,connectGaps:t.connectgaps},-1!==this.mode.indexOf(\"lines\")?this.linePlot?this.linePlot.update(e):(this.linePlot=n(e),this.linePlot._trace=this,this.scene.glplot.add(this.linePlot)):this.linePlot&&(this.scene.glplot.remove(this.linePlot),this.linePlot.dispose(),this.linePlot=null);var d=t.opacity;if(t.marker&&t.marker.opacity&&(d*=t.marker.opacity),r={gl:this.scene.glplot.gl,position:p.position,color:p.scatterColor,size:p.scatterSize,glyph:p.scatterMarker,opacity:d,orthographic:!0,lineWidth:p.scatterLineWidth,lineColor:p.scatterLineColor,project:p.project,projectScale:p.projectScale,projectOpacity:p.projectOpacity},-1!==this.mode.indexOf(\"markers\")?this.scatterPlot?this.scatterPlot.update(r):(this.scatterPlot=a(r),this.scatterPlot._trace=this,this.scatterPlot.highlightScale=1,this.scene.glplot.add(this.scatterPlot)):this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose(),this.scatterPlot=null),c={gl:this.scene.glplot.gl,position:p.position,glyph:p.text,color:p.textColor,size:p.textSize,angle:p.textAngle,alignment:p.textOffset,font:p.textFont,orthographic:!0,lineWidth:0,project:!1,opacity:t.opacity},this.textLabels=t.hovertext||t.text,-1!==this.mode.indexOf(\"text\")?this.textMarkers?this.textMarkers.update(c):(this.textMarkers=a(c),this.textMarkers._trace=this,this.textMarkers.highlightScale=1,this.scene.glplot.add(this.textMarkers)):this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose(),this.textMarkers=null),l={gl:this.scene.glplot.gl,position:p.position,color:p.errorColor,error:p.errorBounds,lineWidth:p.errorLineWidth,capSize:p.errorCapSize,opacity:t.opacity},this.errorBars?p.errorBounds?this.errorBars.update(l):(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose(),this.errorBars=null):p.errorBounds&&(this.errorBars=i(l),this.errorBars._trace=this,this.scene.glplot.add(this.errorBars)),p.delaunayAxis>=0){var g=function(t,e,r){var n,a=(r+1)%3,i=(r+2)%3,o=[],l=[];for(n=0;n<t.length;++n){var c=t[n];!isNaN(c[a])&&isFinite(c[a])&&!isNaN(c[i])&&isFinite(c[i])&&(o.push([c[a],c[i]]),l.push(n))}var u=s(o);for(n=0;n<u.length;++n)for(var h=u[n],f=0;f<h.length;++f)h[f]=l[h[f]];return{positions:t,cells:u,meshColor:e}}(p.position,p.delaunayColor,p.delaunayAxis);g.opacity=t.opacity,this.delaunayMesh?this.delaunayMesh.update(g):(g.gl=u,this.delaunayMesh=o(g),this.delaunayMesh._trace=this,this.scene.glplot.add(this.delaunayMesh))}else this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose(),this.delaunayMesh=null)},y.dispose=function(){this.linePlot&&(this.scene.glplot.remove(this.linePlot),this.linePlot.dispose()),this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose()),this.errorBars&&(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose()),this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose()),this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose())},e.exports=function(t,e){var r=new m(t,e.uid);return r.update(e),r}},{\"../../components/fx/helpers\":626,\"../../constants/gl3d_dashes\":689,\"../../constants/gl3d_markers\":690,\"../../lib\":716,\"../../lib/gl_format_color\":713,\"../../lib/str2rgbarray\":739,\"../../plots/cartesian/axes\":764,\"../scatter/make_bubble_size_func\":1135,\"./calc_errors\":1147,\"delaunay-triangulate\":166,\"gl-error3d\":249,\"gl-line3d\":257,\"gl-mesh3d\":282,\"gl-scatter3d\":299}],1149:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),a=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/text_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function h(r,n){return a.coerce(t,e,c,r,n)}if(function(t,e,r,a){var i=0,o=r(\"x\"),s=r(\"y\"),l=r(\"z\");n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\",\"z\"],a),o&&s&&l&&(i=Math.min(o.length,s.length,l.length),e._length=e._xlength=e._ylength=e._zlength=i);return i}(t,e,h,u)){h(\"text\"),h(\"hovertext\"),h(\"hovertemplate\"),h(\"mode\"),i.hasLines(e)&&(h(\"connectgaps\"),s(t,e,r,u,h)),i.hasMarkers(e)&&o(t,e,r,u,h,{noSelect:!0}),i.hasText(e)&&(h(\"texttemplate\"),l(t,e,u,h,{noSelect:!0}));var f=(e.line||{}).color,p=(e.marker||{}).color;h(\"surfaceaxis\")>=0&&h(\"surfacecolor\",f||p);for(var d=[\"x\",\"y\",\"z\"],g=0;g<3;++g){var v=\"projection.\"+d[g];h(v+\".show\")&&(h(v+\".opacity\"),h(v+\".scale\"))}var m=n.getComponentMethod(\"errorbars\",\"supplyDefaults\");m(t,e,f||p||r,{axis:\"z\"}),m(t,e,f||p||r,{axis:\"y\",inherit:\"z\"}),m(t,e,f||p||r,{axis:\"x\",inherit:\"z\"})}else e.visible=!1}},{\"../../lib\":716,\"../../registry\":845,\"../scatter/line_defaults\":1131,\"../scatter/marker_defaults\":1137,\"../scatter/subtypes\":1142,\"../scatter/text_defaults\":1143,\"./attributes\":1145}],1150:[function(t,e,r){\"use 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n=t(\"../scatter/attributes\"),a=t(\"../../plots/attributes\"),i=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,h=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:h.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({},a.hoverinfo,{flags:[\"a\",\"b\",\"text\",\"name\"]}),hoveron:n.hoveron,hovertemplate:i()}},{\"../../components/colorscale/attributes\":598,\"../../lib/extend\":707,\"../../plots/attributes\":761,\"../../plots/template_attributes\":840,\"../scatter/attributes\":1118}],1152:[function(t,e,r){\"use 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n=t(\"../../plots/template_attributes\").hovertemplateAttrs,a=t(\"../../plots/template_attributes\").texttemplateAttrs,i=t(\"../scatter/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../components/colorscale/attributes\"),l=t(\"../../components/drawing/attributes\").dash,c=t(\"../../lib/extend\").extendFlat,u=t(\"../../plot_api/edit_types\").overrideAll,h=i.marker,f=i.line,p=h.line;e.exports=u({lon:{valType:\"data_array\"},lat:{valType:\"data_array\"},locations:{valType:\"data_array\"},locationmode:{valType:\"enumerated\",values:[\"ISO-3\",\"USA-states\",\"country 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a={opacity:1,text:e.text,texttemplate:e.texttemplate,textposition:e.textposition,textfont:s.extendFlat({},e.textfont)};r.textfont&&s.extendFlat(a.textfont,r.textfont),n=m(t,a)}return n}function _(t,e){var r={capSize:2*e.width,lineWidth:e.thickness,color:e.color};return e.copy_ystyle&&(r=t.error_y),r}var w=p.SYMBOL_SDF_SIZE,k=p.SYMBOL_SIZE,T=p.SYMBOL_STROKE,A={},M=l.symbolFuncs[0](.05*k);function S(t){if(\"circle\"===t)return null;var e,r,n=l.symbolNumber(t),i=l.symbolFuncs[n%100],o=!!l.symbolNoDot[n%100],s=!!l.symbolNoFill[n%100],c=p.DOT_RE.test(t);return A[t]?A[t]:(e=c&&!o?i(1.1*k)+M:i(k),r=a(e,{w:w,h:w,viewBox:[-k,-k,k,k],stroke:s?T:-T}),A[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};if(!0!==e.visible)return n;if(h.hasText(e)&&(n.text=m(t,e),n.textSel=b(t,e,e.selected),n.textUnsel=b(t,e,e.unselected)),h.hasMarkers(e)&&(n.marker=y(e),n.markerSel=x(e,e.selected),n.markerUnsel=x(e,e.unselected),!e.unselected&&Array.isArray(e.marker.opacity))){var a=e.marker.opacity;for(n.markerUnsel.opacity=new Array(a.length),r=0;r<a.length;r++)n.markerUnsel.opacity[r]=d*a[r]}if(h.hasLines(e)){n.line={overlay:!0,thickness:e.line.width,color:e.line.color,opacity:e.opacity};var i=(p.DASHES[e.line.dash]||[1]).slice();for(r=0;r<i.length;++r)i[r]*=e.line.width;n.line.dashes=i}return e.error_x&&e.error_x.visible&&(n.errorX=_(e,e.error_x)),e.error_y&&e.error_y.visible&&(n.errorY=_(e,e.error_y)),e.fill&&\"none\"!==e.fill&&(n.fill={closed:!0,fill:e.fillcolor,thickness:0}),n},markerStyle:y,markerSelection:x,linePositions:function(t,e,r){var 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x=r[2*v+c],b=l(y,v),_=b[0],w=b[1];if(n(_)&&n(w)){var k=y-_,T=y+w;p[m+u[0]]=x-a.c2l(k),p[m+u[1]]=a.c2l(T)-x,p[m+u[2]]=0,p[m+u[3]]=0,d=Math.min(d,y-_),g=Math.max(g,y+w)}}}f[i]={positions:r,errors:p,_bnds:[d,g]}}}return p(a,l),p(i,u),f},textPosition:function(t,e,r,n){var a,i=e._length,o={};if(h.hasMarkers(e)){var s=r.font,l=r.align,c=r.baseline;for(o.offset=new Array(i),a=0;a<i;a++){var u=n.sizes?n.sizes[a]:n.size,f=Array.isArray(s)?s[a].size:s.size,p=Array.isArray(l)?l.length>1?l[a]:l[0]:l,d=Array.isArray(c)?c.length>1?c[a]:c[0]:c,v=g[p],m=g[d],y=u?u/.8+1:0,x=-m*y-.5*m;o.offset[a]=[v*y/f,x/f]}}return o}}},{\"../../components/drawing\":612,\"../../components/fx/helpers\":626,\"../../constants/interactions\":691,\"../../lib\":716,\"../../lib/gl_format_color\":713,\"../../plots/cartesian/axis_ids\":767,\"../../registry\":845,\"../scatter/make_bubble_size_func\":1135,\"../scatter/subtypes\":1142,\"./constants\":1171,\"color-normalize\":121,\"fast-isnumeric\":227,\"svg-path-sdf\":533}],1173:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"../../registry\"),i=t(\"./attributes\"),o=t(\"../scatter/constants\"),s=t(\"../scatter/subtypes\"),l=t(\"../scatter/xy_defaults\"),c=t(\"../scatter/marker_defaults\"),u=t(\"../scatter/line_defaults\"),h=t(\"../scatter/fillcolor_defaults\"),f=t(\"../scatter/text_defaults\");e.exports=function(t,e,r,p){function d(r,a){return n.coerce(t,e,i,r,a)}var g=!!t.marker&&/-open/.test(t.marker.symbol),v=s.isBubble(t),m=l(t,e,p,d);if(m){var y=m<o.PTS_LINESONLY?\"lines+markers\":\"lines\";d(\"text\"),d(\"hovertext\"),d(\"hovertemplate\"),d(\"mode\",y),s.hasLines(e)&&(d(\"connectgaps\"),u(t,e,r,p,d),d(\"line.shape\")),s.hasMarkers(e)&&(c(t,e,r,p,d),d(\"marker.line.width\",g||v?1:0)),s.hasText(e)&&(d(\"texttemplate\"),f(t,e,p,d));var x=(e.line||{}).color,b=(e.marker||{}).color;d(\"fill\"),\"none\"!==e.fill&&h(t,e,r,d);var _=a.getComponentMethod(\"errorbars\",\"supplyDefaults\");_(t,e,x||b||r,{axis:\"y\"}),_(t,e,x||b||r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,d)}else e.visible=!1}},{\"../../lib\":716,\"../../registry\":845,\"../scatter/constants\":1122,\"../scatter/fillcolor_defaults\":1126,\"../scatter/line_defaults\":1131,\"../scatter/marker_defaults\":1137,\"../scatter/subtypes\":1142,\"../scatter/text_defaults\":1143,\"../scatter/xy_defaults\":1144,\"./attributes\":1169}],1174:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"../../components/color\"),i=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,h=c.selectBatch[u],f=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},g=c.textUnselectedOptions[u]||{},v=n.extendFlat({},p);if(h.length||f.length){var m=d.color,y=g.color,x=p.color,b=Array.isArray(x);for(v.color=new Array(s._length),e=0;e<h.length;e++)r=h[e],v.color[r]=m||(b?x[r]:x);for(e=0;e<f.length;e++){r=f[e];var _=b?x[r]:x;v.color[r]=y||(m?_:a.addOpacity(_,i))}}c.glText[u].update(v)}}},{\"../../components/color\":591,\"../../constants/interactions\":691,\"../../lib\":716}],1175:[function(t,e,r){\"use strict\";var n=t(\"../scatter/format_labels\");e.exports=function(t,e,r){var a=t.i;return\"x\"in t||(t.x=e._x[a]),\"y\"in t||(t.y=e._y[a]),n(t,e,r)}},{\"../scatter/format_labels\":1127}],1176:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),a=t(\"../../lib\"),i=t(\"../scatter/get_trace_color\");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,h=t.index,f={pointNumber:h,x:e[h],y:r[h]};f.tx=Array.isArray(o.text)?o.text[h]:o.text,f.htx=Array.isArray(o.hovertext)?o.hovertext[h]:o.hovertext,f.data=Array.isArray(o.customdata)?o.customdata[h]:o.customdata,f.tp=Array.isArray(o.textposition)?o.textposition[h]:o.textposition;var p=o.textfont;p&&(f.ts=Array.isArray(p.size)?p.size[h]:p.size,f.tc=Array.isArray(p.color)?p.color[h]:p.color,f.tf=Array.isArray(p.family)?p.family[h]:p.family);var d=o.marker;d&&(f.ms=a.isArrayOrTypedArray(d.size)?d.size[h]:d.size,f.mo=a.isArrayOrTypedArray(d.opacity)?d.opacity[h]:d.opacity,f.mx=Array.isArray(d.symbol)?d.symbol[h]:d.symbol,f.mc=a.isArrayOrTypedArray(d.color)?d.color[h]:d.color);var g=d&&d.line;g&&(f.mlc=Array.isArray(g.color)?g.color[h]:g.color,f.mlw=a.isArrayOrTypedArray(g.width)?g.width[h]:g.width);var v=d&&d.gradient;v&&\"none\"!==v.type&&(f.mgt=Array.isArray(v.type)?v.type[h]:v.type,f.mgc=Array.isArray(v.color)?v.color[h]:v.color);var m=s.c2p(f.x,!0),y=l.c2p(f.y,!0),x=f.mrc||1,b=o.hoverlabel;b&&(f.hbg=Array.isArray(b.bgcolor)?b.bgcolor[h]:b.bgcolor,f.hbc=Array.isArray(b.bordercolor)?b.bordercolor[h]:b.bordercolor,f.hts=Array.isArray(b.font.size)?b.font.size[h]:b.font.size,f.htc=Array.isArray(b.font.color)?b.font.color[h]:b.font.color,f.htf=Array.isArray(b.font.family)?b.font.family[h]:b.font.family,f.hnl=Array.isArray(b.namelength)?b.namelength[h]:b.namelength);var _=o.hoverinfo;_&&(f.hi=Array.isArray(_)?_[h]:_);var w=o.hovertemplate;w&&(f.ht=Array.isArray(w)?w[h]:w);var k={};k[t.index]=f;var T=a.extendFlat({},t,{color:i(o,f),x0:m-x,x1:m+x,xLabelVal:f.x,y0:y-x,y1:y+x,yLabelVal:f.y,cd:k,distance:c,spikeDistance:u,hovertemplate:f.ht});return f.htx?T.text=f.htx:f.tx?T.text=f.tx:o.text&&(T.text=o.text),a.fillText(f,o,T),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(f,o,T),T}e.exports={hoverPoints:function(t,e,r,n){var a,i,s,l,c,u,h,f,p,d=t.cd,g=d[0].t,v=d[0].trace,m=t.xa,y=t.ya,x=g.x,b=g.y,_=m.c2p(e),w=y.c2p(r),k=t.distance;if(g.tree){var T=m.p2c(_-k),A=m.p2c(_+k),M=y.p2c(w-k),S=y.p2c(w+k);a=\"x\"===n?g.tree.range(Math.min(T,A),Math.min(y._rl[0],y._rl[1]),Math.max(T,A),Math.max(y._rl[0],y._rl[1])):g.tree.range(Math.min(T,A),Math.min(M,S),Math.max(T,A),Math.max(M,S))}else a=g.ids;var E=k;if(\"x\"===n)for(c=0;c<a.length;c++)s=x[a[c]],(u=Math.abs(m.c2p(s)-_))<E&&(E=u,h=y.c2p(b[a[c]])-w,p=Math.sqrt(u*u+h*h),i=a[c]);else for(c=a.length-1;c>-1;c--)s=x[a[c]],l=b[a[c]],u=m.c2p(s)-_,h=y.c2p(l)-w,(f=Math.sqrt(u*u+h*h))<E&&(E=p=f,i=a[c]);return t.index=i,t.distance=E,t.dxy=p,void 0===i?[t]:[o(t,x,b,v)]},calcHover:o}},{\"../../lib\":716,\"../../registry\":845,\"../scatter/get_trace_color\":1128}],1177:[function(t,e,r){\"use strict\";var n=t(\"./hover\");e.exports={moduleType:\"trace\",name:\"scattergl\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"gl\",\"regl\",\"cartesian\",\"symbols\",\"errorBarsOK\",\"showLegend\",\"scatter-like\"],attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),crossTraceDefaults:t(\"../scatter/cross_trace_defaults\"),colorbar:t(\"../scatter/marker_colorbar\"),formatLabels:t(\"./format_labels\"),calc:t(\"./calc\"),plot:t(\"./plot\"),hoverPoints:n.hoverPoints,selectPoints:t(\"./select\"),meta:{}}},{\"../../plots/cartesian\":775,\"../scatter/cross_trace_defaults\":1124,\"../scatter/marker_colorbar\":1136,\"./attributes\":1169,\"./calc\":1170,\"./defaults\":1173,\"./format_labels\":1175,\"./hover\":1176,\"./plot\":1178,\"./select\":1180}],1178:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),a=t(\"regl-line2d\"),i=t(\"regl-error2d\"),o=t(\"gl-text\"),s=t(\"../../lib\"),l=t(\"../../lib/prepare_regl\"),c=t(\"../scatter/subtypes\"),u=t(\"../scatter/link_traces\"),h=t(\"./edit_style\").styleTextSelection;e.exports=function(t,e,r){if(r.length){var f,p,d=t._fullLayout,g=e._scene,v=e.xaxis,m=e.yaxis;if(g)if(l(t,[\"ANGLE_instanced_arrays\",\"OES_element_index_uint\"])){var y=g.count,x=d._glcanvas.data()[0].regl;if(u(t,e,r),g.dirty){if(!0===g.error2d&&(g.error2d=i(x)),!0===g.line2d&&(g.line2d=a(x)),!0===g.scatter2d&&(g.scatter2d=n(x)),!0===g.fill2d&&(g.fill2d=a(x)),!0===g.glText)for(g.glText=new Array(y),f=0;f<y;f++)g.glText[f]=new o(x);if(g.glText){if(y>g.glText.length){var b=y-g.glText.length;for(f=0;f<b;f++)g.glText.push(new o(x))}else if(y<g.glText.length){var _=g.glText.length-y;g.glText.splice(y,_).forEach(function(t){t.destroy()})}for(f=0;f<y;f++)g.glText[f].update(g.textOptions[f])}if(g.line2d&&(g.line2d.update(g.lineOptions),g.lineOptions=g.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}),g.line2d.update(g.lineOptions)),g.error2d){var w=(g.errorXOptions||[]).concat(g.errorYOptions||[]);g.error2d.update(w)}g.scatter2d&&g.scatter2d.update(g.markerOptions),g.fillOrder=s.repeat(null,y),g.fill2d&&(g.fillOptions=g.fillOptions.map(function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var a,i,o=n[0],s=o.trace,l=o.t,c=g.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(g.fillOrder[e]=u);var h,f,p=[],d=c&&c.positions||l.positions;if(\"tozeroy\"===s.fill){for(h=0;h<d.length&&isNaN(d[h+1]);)h+=2;for(f=d.length-2;f>h&&isNaN(d[f+1]);)f-=2;0!==d[h+1]&&(p=[d[h],0]),p=p.concat(d.slice(h,f+2)),0!==d[f+1]&&(p=p.concat([d[f],0]))}else if(\"tozerox\"===s.fill){for(h=0;h<d.length&&isNaN(d[h]);)h+=2;for(f=d.length-2;f>h&&isNaN(d[f]);)f-=2;0!==d[h]&&(p=[0,d[h+1]]),p=p.concat(d.slice(h,f+2)),0!==d[f]&&(p=p.concat([0,d[f+1]]))}else if(\"toself\"===s.fill||\"tonext\"===s.fill){for(p=[],a=0,i=0;i<d.length;i+=2)(isNaN(d[i])||isNaN(d[i+1]))&&((p=p.concat(d.slice(a,i))).push(d[a],d[a+1]),a=i+2);p=p.concat(d.slice(a)),a&&p.push(d[a],d[a+1])}else{var v=s._nexttrace;if(v){var m=g.lineOptions[e+1];if(m){var y=m.positions;if(\"tonexty\"===s.fill){for(p=d.slice(),e=Math.floor(y.length/2);e--;){var x=y[2*e],b=y[2*e+1];isNaN(x)||isNaN(b)||p.push(x,b)}t.fill=v.fillcolor}}}}if(s._prevtrace&&\"tonext\"===s._prevtrace.fill){var _=g.lineOptions[e-1].positions,w=p.length/2,k=[a=w];for(i=0;i<_.length;i+=2)(isNaN(_[i])||isNaN(_[i+1]))&&(k.push(i/2+w+1),a=i+2);p=p.concat(_),t.hole=k}return t.fillmode=s.fill,t.opacity=s.opacity,t.positions=p,t}}),g.fill2d.update(g.fillOptions))}var k=d.dragmode,T=\"lasso\"===k||\"select\"===k,A=d.clickmode.indexOf(\"select\")>-1;for(f=0;f<y;f++){var M=r[f][0],S=M.trace,E=M.t,L=E.index,C=S._length,P=E.x,O=E.y;if(S.selectedpoints||T||A){if(T||(T=!0),S.selectedpoints){var z=g.selectBatch[L]=s.selIndices2selPoints(S),I={};for(p=0;p<z.length;p++)I[z[p]]=1;var D=[];for(p=0;p<C;p++)I[p]||D.push(p);g.unselectBatch[L]=D}var R=E.xpx=new Array(C),F=E.ypx=new Array(C);for(p=0;p<C;p++)R[p]=v.c2p(P[p]),F[p]=m.c2p(O[p])}else E.xpx=E.ypx=null}if(T){if(g.select2d||(g.select2d=n(d._glcanvas.data()[1].regl)),g.scatter2d){var B=new Array(y);for(f=0;f<y;f++)B[f]=g.selectBatch[f].length||g.unselectBatch[f].length?g.markerUnselectedOptions[f]:{};g.scatter2d.update(B)}g.select2d&&(g.select2d.update(g.markerOptions),g.select2d.update(g.markerSelectedOptions)),g.glText&&r.forEach(function(t){var e=((t||[])[0]||{}).trace||{};c.hasText(e)&&h(t)})}else g.scatter2d&&g.scatter2d.update(g.markerOptions);var N={viewport:function(t,e,r){var n=t._size,a=t.width,i=t.height;return[n.l+e.domain[0]*n.w,n.b+r.domain[0]*n.h,a-n.r-(1-e.domain[1])*n.w,i-n.t-(1-r.domain[1])*n.h]}(d,v,m),range:[(v._rl||v.range)[0],(m._rl||m.range)[0],(v._rl||v.range)[1],(m._rl||m.range)[1]]},j=s.repeat(N,g.count);g.fill2d&&g.fill2d.update(j),g.line2d&&g.line2d.update(j),g.error2d&&g.error2d.update(j.concat(j)),g.scatter2d&&g.scatter2d.update(j),g.select2d&&g.select2d.update(j),g.glText&&g.glText.forEach(function(t){t.update(N)})}else g.init()}}},{\"../../lib\":716,\"../../lib/prepare_regl\":729,\"../scatter/link_traces\":1134,\"../scatter/subtypes\":1142,\"./edit_style\":1174,\"gl-text\":318,\"regl-error2d\":491,\"regl-line2d\":492,\"regl-scatter2d\":498}],1179:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){var r=e._scene,a={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},i={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,i,a)},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 a=0;a<r.count;a++)r.glText[a].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,a=r.error2d,i=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,c=r.selectBatch,u=r.unselectBatch,h=0;h<t;h++){if(e&&r.fillOrder[h]&&e.draw(r.fillOrder[h]),i&&r.lineOptions[h]&&i.draw(h),a&&(r.errorXOptions[h]&&a.draw(h),r.errorYOptions[h]&&a.draw(h+t)),o&&r.markerOptions[h])if(u[h].length){var f=n.repeat([],r.count);f[h]=u[h],o.draw(f)}else c[h].length||o.draw(h);s[h]&&r.textOptions[h]&&s[h].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,a),r}},{\"../../lib\":716}],1180:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\"),a=t(\"./edit_style\").styleTextSelection;e.exports=function(t,e){var r=t.cd,i=[],o=r[0].trace,s=r[0].t,l=o._length,c=s.x,u=s.y,h=s._scene,f=s.index;if(!h)return i;var p=n.hasText(o),d=n.hasMarkers(o),g=!d&&!p;if(!0!==o.visible||g)return i;var v=[],m=[];if(!1!==e&&!e.degenerate)for(var y=0;y<l;y++)e.contains([s.xpx[y],s.ypx[y]],!1,y,t)?(v.push(y),i.push({pointNumber:y,x:c[y],y:u[y]})):m.push(y);if(d){var x=h.scatter2d;if(v.length||m.length){if(!h.selectBatch[f].length&&!h.unselectBatch[f].length){var b=new Array(h.count);b[f]=h.markerUnselectedOptions[f],x.update.apply(x,b)}}else{var _=new Array(h.count);_[f]=h.markerOptions[f],x.update.apply(x,_)}}return h.selectBatch[f]=v,h.unselectBatch[f]=m,p&&a(r),i}},{\"../scatter/subtypes\":1142,\"./edit_style\":1174}],1181:[function(t,e,r){\"use strict\";var n=t(\"../../plots/template_attributes\").hovertemplateAttrs,a=t(\"../../plots/template_attributes\").texttemplateAttrs,i=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,h=t(\"../../plot_api/edit_types\").overrideAll,f=i.line,p=i.marker;e.exports=h({lon:i.lon,lat:i.lat,mode:u({},o.mode,{dflt:\"markers\"}),text:u({},o.text,{}),texttemplate:a({editType:\"plot\"},{keys:[\"lat\",\"lon\",\"text\"]}),hovertext:u({},o.hovertext,{}),line:{color:f.color,width:f.width},connectgaps:o.connectgaps,marker:u({symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},opacity:p.opacity,size:p.size,sizeref:p.sizeref,sizemin:p.sizemin,sizemode:p.sizemode},c(\"marker\")),fill:i.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\":598,\"../../lib/extend\":707,\"../../plot_api/edit_types\":747,\"../../plots/attributes\":761,\"../../plots/mapbox/layout_attributes\":821,\"../../plots/template_attributes\":840,\"../scatter/attributes\":1118,\"../scattergeo/attributes\":1159}],1182:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),a=t(\"../../lib\"),i=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\"),h=t(\"../../plots/mapbox/convert_text_opts\"),f=t(\"../../components/fx/helpers\").appendArrayPointValue,p=t(\"../../lib/svg_text_utils\").NEWLINES,d=t(\"../../lib/svg_text_utils\").BR_TAG_ALL;function g(){return{geojson:o.makeBlank(),layout:{visibility:\"none\"},paint:{}}}function v(t){return a.isArrayOrTypedArray(t)?function(t){return t}:t?function(){return t}:m}function m(){return\"\"}function y(t){return t[0]===i}e.exports=function(t,e){var r,i=e[0].trace,x=!0===i.visible&&0!==i._length,b=\"none\"!==i.fill,_=u.hasLines(i),w=u.hasMarkers(i),k=u.hasText(i),T=w&&\"circle\"===i.marker.symbol,A=w&&\"circle\"!==i.marker.symbol,M=g(),S=g(),E=g(),L=g(),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\",a.extendFlat(M.paint,{\"fill-color\":i.fillcolor})),_&&(S.geojson=o.makeLine(r),S.layout.visibility=\"visible\",a.extendFlat(S.paint,{\"line-width\":i.line.width,\"line-color\":i.line.color,\"line-opacity\":i.opacity})),T){var P=function(t){var e,r,i,o,u=t[0].trace,h=u.marker,f=u.selectedpoints,p=a.isArrayOrTypedArray(h.color),d=a.isArrayOrTypedArray(h.size),g=a.isArrayOrTypedArray(h.opacity);function v(t){return u.opacity*t}p&&(r=s.hasColorscale(u,\"marker\")?s.makeColorScaleFuncFromTrace(h):a.identity);d&&(i=c(u));g&&(o=function(t){var e=n(t)?+a.constrain(t,0,1):0;return v(e)});var m,x=[];for(e=0;e<t.length;e++){var b=t[e],_=b.lonlat;if(!y(_)){var w={};r&&(w.mcc=b.mcc=r(b.mc)),i&&(w.mrc=b.mrc=i(b.ms)),o&&(w.mo=o(b.mo)),f&&(w.selected=b.selected||0),x.push({type:\"Feature\",geometry:{type:\"Point\",coordinates:_},properties:w})}}if(f)for(m=l.makeSelectedPointStyleFns(u),e=0;e<x.length;e++){var k=x[e].properties;m.selectedOpacityFn&&(k.mo=v(m.selectedOpacityFn(k))),m.selectedColorFn&&(k.mcc=m.selectedColorFn(k)),m.selectedSizeFn&&(k.mrc=m.selectedSizeFn(k))}return{geojson:{type:\"FeatureCollection\",features:x},mcc:p||m&&m.selectedColorFn?{type:\"identity\",property:\"mcc\"}:h.color,mrc:d||m&&m.selectedSizeFn?{type:\"identity\",property:\"mrc\"}:(T=h.size,T/2),mo:g||m&&m.selectedOpacityFn?{type:\"identity\",property:\"mo\"}:v(h.opacity)};var T}(e);E.geojson=P.geojson,E.layout.visibility=\"visible\",a.extendFlat(E.paint,{\"circle-color\":P.mcc,\"circle-radius\":P.mrc,\"circle-opacity\":P.mo})}if((A||k)&&(L.geojson=function(t,e){for(var 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z.lightPosition=[D.x,D.y,D.z],z.ambient=e.lighting.ambient,z.diffuse=e.lighting.diffuse,z.specular=e.lighting.specular,z.roughness=e.lighting.roughness,z.fresnel=e.lighting.fresnel,z.opacity=e.opacity,e._pad=z.tubeScale*e.sizeref*2,z}h.handlePick=function(t){var e=this.scene.fullSceneLayout,r=this.scene.dataScale;function n(t,n){var a=e[n],i=r[c[n]];return a.l2c(t)/i}if(t.object===this.mesh){var a=t.data.position,i=t.data.velocity;return t.traceCoordinate=[n(a[0],\"xaxis\"),n(a[1],\"yaxis\"),n(a[2],\"zaxis\"),n(i[0],\"xaxis\"),n(i[1],\"yaxis\"),n(i[2],\"zaxis\"),t.data.intensity*this.data._normMax,t.data.divergence],t.textLabel=this.data.hovertext||this.data.text,!0}},h.update=function(t){this.data=t;var e=d(this.scene,t);this.mesh.update(e)},h.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},e.exports=function(t,e){var r=t.glplot.gl,n=d(t,e),i=a(r,n),o=new u(t,e.uid);return 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l=s(\"u\"),c=s(\"v\"),u=s(\"w\"),h=s(\"x\"),f=s(\"y\"),p=s(\"z\");l&&l.length&&c&&c.length&&u&&u.length&&h&&h.length&&f&&f.length&&p&&p.length?(s(\"starts.x\"),s(\"starts.y\"),s(\"starts.z\"),s(\"maxdisplayed\"),s(\"sizeref\"),s(\"lighting.ambient\"),s(\"lighting.diffuse\"),s(\"lighting.specular\"),s(\"lighting.roughness\"),s(\"lighting.fresnel\"),s(\"lightposition.x\"),s(\"lightposition.y\"),s(\"lightposition.z\"),a(t,e,o,s,{prefix:\"\",cLetter:\"c\"}),s(\"text\"),s(\"hovertext\"),s(\"hovertemplate\"),e._length=null):e.visible=!1}},{\"../../components/colorscale/defaults\":601,\"../../lib\":716,\"./attributes\":1223}],1227:[function(t,e,r){\"use strict\";e.exports={moduleType:\"trace\",name:\"streamtube\",basePlotModule:t(\"../../plots/gl3d\"),categories:[\"gl3d\"],attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),colorbar:{min:\"cmin\",max:\"cmax\"},calc:t(\"./calc\"),plot:t(\"./convert\"),eventData:function(t,e){return t.tubex=t.x,t.tubey=t.y,t.tubez=t.z,t.tubeu=e.traceCoordinate[3],t.tubev=e.traceCoordinate[4],t.tubew=e.traceCoordinate[5],t.norm=e.traceCoordinate[6],t.divergence=e.traceCoordinate[7],delete t.x,delete t.y,delete t.z,t},meta:{}}},{\"../../plots/gl3d\":804,\"./attributes\":1223,\"./calc\":1224,\"./convert\":1225,\"./defaults\":1226}],1228:[function(t,e,r){\"use strict\";var n=t(\"../../plots/attributes\"),a=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,o=t(\"../../components/colorscale/attributes\"),s=t(\"../../plots/domain\").attributes,l=t(\"../pie/attributes\"),c=t(\"./constants\"),u=t(\"../../lib/extend\").extendFlat;e.exports={labels:{valType:\"data_array\",editType:\"calc\"},parents:{valType:\"data_array\",editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},branchvalues:{valType:\"enumerated\",values:[\"remainder\",\"total\"],dflt:\"remainder\",editType:\"calc\"},count:{valType:\"flaglist\",flags:[\"branches\",\"leaves\"],dflt:\"leaves\",editType:\"calc\"},level:{valType:\"any\",editType:\"plot\",anim:!0},maxdepth:{valType:\"integer\",editType:\"plot\",dflt:-1},marker:u({colors:{valType:\"data_array\",editType:\"calc\"},line:{color:u({},l.marker.line.color,{dflt:null}),width:u({},l.marker.line.width,{dflt:1}),editType:\"calc\"},editType:\"calc\"},o(\"marker\",{colorAttr:\"colors\",anim:!1})),leaf:{opacity:{valType:\"number\",editType:\"style\",min:0,max:1},editType:\"plot\"},text:l.text,textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"current 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n=t(\"../../plots/plots\");r.name=\"sunburst\",r.plot=function(t,e,a,i){n.plotBasePlot(r.name,t,e,a,i)},r.clean=function(t,e,a,i){n.cleanBasePlot(r.name,t,e,a,i)}},{\"../../plots/plots\":825}],1230:[function(t,e,r){\"use strict\";var n=t(\"d3-hierarchy\"),a=t(\"fast-isnumeric\"),i=t(\"../../lib\"),o=t(\"../../components/colorscale\").makeColorScaleFuncFromTrace,s=t(\"../pie/calc\").makePullColorFn,l=t(\"../pie/calc\").generateExtendedColors,c=t(\"../../components/colorscale\").calc,u={},h={};r.calc=function(t,e){var r,l,u,h,f,p,d=t._fullLayout,g=e.ids,v=i.isArrayOrTypedArray(g),m=e.labels,y=e.parents,x=e.values,b=i.isArrayOrTypedArray(x),_=[],w={},k={},T=function(t){return t||\"number\"==typeof t},A=function(t){return!b||a(x[t])&&x[t]>=0};v?(r=Math.min(g.length,y.length),l=function(t){return T(g[t])&&A(t)},u=function(t){return String(g[t])}):(r=Math.min(m.length,y.length),l=function(t){return T(m[t])&&A(t)},u=function(t){return String(m[t])}),b&&(r=Math.min(r,x.length));for(var 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r=n.formatPercent(t,0);return\"0%\"===r&&(r=o.formatPiePercent(t,e)),r}},{\"../../components/color\":591,\"../../lib\":716,\"../../lib/setcursor\":736,\"../pie/helpers\":1097}],1235:[function(t,e,r){\"use strict\";e.exports={moduleType:\"trace\",name:\"sunburst\",basePlotModule:t(\"./base_plot\"),categories:[],animatable:!0,attributes:t(\"./attributes\"),layoutAttributes:t(\"./layout_attributes\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./calc\").crossTraceCalc,plot:t(\"./plot\").plot,style:t(\"./style\").style,colorbar:t(\"../scatter/marker_colorbar\"),meta:{}}},{\"../scatter/marker_colorbar\":1136,\"./attributes\":1228,\"./base_plot\":1229,\"./calc\":1230,\"./defaults\":1232,\"./layout_attributes\":1236,\"./layout_defaults\":1237,\"./plot\":1238,\"./style\":1239}],1236:[function(t,e,r){\"use strict\";e.exports={sunburstcolorway:{valType:\"colorlist\",editType:\"calc\"},extendsunburstcolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{}],1237:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,i){return n.coerce(t,e,a,r,i)}r(\"sunburstcolorway\",e.colorway),r(\"extendsunburstcolors\")}},{\"../../lib\":716,\"./layout_attributes\":1236}],1238:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"d3-hierarchy\"),i=t(\"../../components/drawing\"),o=t(\"../../lib\"),s=t(\"../../lib/svg_text_utils\"),l=t(\"../pie/plot\").transformInsideText,c=t(\"./style\").styleOne,u=t(\"./fx\"),h=t(\"./constants\"),f=t(\"./helpers\");function p(t,e,p,d){var g=t._fullLayout,v=f.hasTransition(d),m=n.select(p).selectAll(\"g.slice\"),y=e[0],x=y.trace,b=y.hierarchy,_=f.findEntryWithLevel(b,x.level),w=f.getMaxDepth(x),k=g._size,T=x.domain,A=k.w*(T.x[1]-T.x[0]),M=k.h*(T.y[1]-T.y[0]),S=.5*Math.min(A,M),E=y.cx=k.l+k.w*(T.x[1]+T.x[0])/2,L=y.cy=k.t+k.h*(1-T.y[0])-M/2;if(!_)return m.remove();var C=null,P={};v&&m.each(function(t){P[f.getPtId(t)]={rpx0:t.rpx0,rpx1:t.rpx1,x0:t.x0,x1:t.x1,transform:t.transform},!C&&f.isEntry(t)&&(C=t)});var O=function(t){return a.partition().size([2*Math.PI,t.height+1])(t)}(_).descendants(),z=_.height+1,I=0,D=w;y.hasMultipleRoots&&f.isHierarchyRoot(_)&&(O=O.slice(1),z-=1,I=1,D+=1),O=O.filter(function(t){return t.y1<=D});var R=Math.min(z,w),F=function(t){return(t-I)/R*S},B=function(t,e){return[t*Math.cos(e),-t*Math.sin(e)]},N=function(t){return o.pathAnnulus(t.rpx0,t.rpx1,t.x0,t.x1,E,L)},j=function(t){return E+t.pxmid[0]*t.transform.rCenter+(t.transform.x||0)},V=function(t){return L+t.pxmid[1]*t.transform.rCenter+(t.transform.y||0)};(m=m.data(O,f.getPtId)).enter().append(\"g\").classed(\"slice\",!0),v?m.exit().transition().each(function(){var t=n.select(this);t.select(\"path.surface\").transition().attrTween(\"d\",function(t){var e=function(t){var e,r=f.getPtId(t),a=P[r],i=P[f.getPtId(_)];if(i){var o=t.x1>i.x1?2*Math.PI:0;e=t.rpx1<i.rpx1?{rpx0:0,rpx1:0}:{x0:o,x1:o}}else{var s,l=f.getPtId(t.parent);m.each(function(t){if(f.getPtId(t)===l)return s=t});var c,u=s.children;u.forEach(function(t,e){if(f.getPtId(t)===r)return c=e});var h=u.length,p=n.interpolate(s.x0,s.x1);e={rpx0:S,rpx1:S,x0:p(c/h),x1:p((c+1)/h)}}return n.interpolate(a,e)}(t);return function(t){return N(e(t))}}),t.select(\"g.slicetext\").attr(\"opacity\",0)}).remove():m.exit().remove(),m.order();var U=null;if(v&&C){var q=f.getPtId(C);m.each(function(t){null===U&&f.getPtId(t)===q&&(U=t.x1)})}var H=m;function G(t){var e=t.parent,r=P[f.getPtId(e)],a={};if(r){var i=e.children,o=i.indexOf(t),s=i.length,l=n.interpolate(r.x0,r.x1);a.x0=l(o/s),a.x1=l(o/s)}else a.x0=a.x1=0;return a}v&&(H=H.transition().each(\"end\",function(){var e=n.select(this);f.setSliceCursor(e,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:!1})})),H.each(function(a){var p=n.select(this),d=o.ensureSingle(p,\"path\",\"surface\",function(t){t.style(\"pointer-events\",\"all\")});a.rpx0=F(a.y0),a.rpx1=F(a.y1),a.xmid=(a.x0+a.x1)/2,a.pxmid=B(a.rpx1,a.xmid),a.midangle=-(a.xmid-Math.PI/2),a.halfangle=.5*Math.min(o.angleDelta(a.x0,a.x1)||Math.PI,Math.PI),a.ring=1-a.rpx0/a.rpx1,a.rInscribed=function(t){return 0===t.rpx0&&o.isFullCircle([t.x0,t.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2))}(a),v?d.transition().attrTween(\"d\",function(t){var e=function(t){var e,r=P[f.getPtId(t)],a={x0:t.x0,x1:t.x1,rpx0:t.rpx0,rpx1:t.rpx1};if(r)e=r;else if(C)if(t.parent)if(U){var i=t.x1>U?2*Math.PI:0;e={x0:i,x1:i}}else e={rpx0:S,rpx1:S},o.extendFlat(e,G(t));else e={rpx0:0,rpx1:0};else e={x0:0,x1:0};return n.interpolate(e,a)}(t);return function(t){return N(e(t))}}):d.attr(\"d\",N),p.call(u,_,t,e,{eventDataKeys:h.eventDataKeys,transitionTime:h.CLICK_TRANSITION_TIME,transitionEasing:h.CLICK_TRANSITION_EASING}).call(f.setSliceCursor,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:t._transitioning}),d.call(c,a,x);var m=o.ensureSingle(p,\"g\",\"slicetext\"),b=o.ensureSingle(m,\"text\",\"\",function(t){t.attr(\"data-notex\",1)});b.text(r.formatSliceLabel(a,_,x,e,g)).classed(\"slicetext\",!0).attr(\"text-anchor\",\"middle\").call(i.font,f.determineTextFont(x,a,g.font)).call(s.convertToTspans,t);var w=i.bBox(b.node());a.transform=l(w,a,y),a.translateX=j(a),a.translateY=V(a);var k=function(t,e){return\"translate(\"+t.translateX+\",\"+t.translateY+\")\"+(t.transform.scale<1?\"scale(\"+t.transform.scale+\")\":\"\")+(t.transform.rotate?\"rotate(\"+t.transform.rotate+\")\":\"\")+\"translate(\"+-(e.left+e.right)/2+\",\"+-(e.top+e.bottom)/2+\")\"};v?b.transition().attrTween(\"transform\",function(t){var e=function(t){var e,r=P[f.getPtId(t)],a=t.transform;if(r)e=r;else if(e={rpx1:t.rpx1,transform:{scale:0,rotate:a.rotate,rCenter:a.rCenter,x:a.x,y:a.y}},C)if(t.parent)if(U){var i=t.x1>U?2*Math.PI:0;e.x0=e.x1=i}else o.extendFlat(e,G(t));else e.x0=e.x1=0;else e.x0=e.x1=0;var s=n.interpolate(e.rpx1,t.rpx1),l=n.interpolate(e.x0,t.x0),c=n.interpolate(e.x1,t.x1),u=n.interpolate(e.transform.scale,a.scale),h=n.interpolate(e.transform.rotate,a.rotate),p=0===a.rCenter?3:0===e.transform.rCenter?1/3:1,d=n.interpolate(e.transform.rCenter,a.rCenter);return function(t){var e=s(t),r=l(t),n=c(t),i=function(t){return d(Math.pow(t,p))}(t),o={pxmid:B(e,(r+n)/2),transform:{rCenter:i,x:a.x,y:a.y}},f={rpx1:s(t),translateX:j(o),translateY:V(o),transform:{scale:u(t),rotate:h(t),rCenter:i}};return f}}(t);return function(t){return k(e(t),w)}}):b.attr(\"transform\",k(a,w))})}r.plot=function(t,e,r,a){var i,o,s=t._fullLayout._sunburstlayer,l=!r,c=f.hasTransition(r);((i=s.selectAll(\"g.trace.sunburst\").data(e,function(t){return t[0].trace.uid})).enter().append(\"g\").classed(\"trace\",!0).classed(\"sunburst\",!0).attr(\"stroke-linejoin\",\"round\"),i.order(),c)?(a&&(o=a()),n.transition().duration(r.duration).ease(r.easing).each(\"end\",function(){o&&o()}).each(\"interrupt\",function(){o&&o()}).each(function(){s.selectAll(\"g.trace\").each(function(e){p(t,e,this,r)})})):i.each(function(e){p(t,e,this,r)});l&&i.exit().remove()},r.formatSliceLabel=function(t,e,r,n,a){var i=r.texttemplate,s=r.textinfo;if(!(i||s&&\"none\"!==s))return\"\";var l=a.separators,c=n[0],u=t.data.data,h=c.hierarchy,p=f.isHierarchyRoot(t),d=f.getParent(h,t),g=f.getValue(t);if(!i){var v,m=s.split(\"+\"),y=function(t){return-1!==m.indexOf(t)},x=[];if(y(\"label\")&&u.label&&x.push(u.label),u.hasOwnProperty(\"v\")&&y(\"value\")&&x.push(f.formatValue(u.v,l)),!p){y(\"current path\")&&x.push(f.getPath(t.data));var b=0;y(\"percent parent\")&&b++,y(\"percent entry\")&&b++,y(\"percent root\")&&b++;var _=b>1;if(b){var w,k=function(t){v=f.formatPercent(w,l),_&&(v+=\" of \"+t),x.push(v)};y(\"percent parent\")&&!p&&(w=g/f.getValue(d),k(\"parent\")),y(\"percent entry\")&&(w=g/f.getValue(e),k(\"entry\")),y(\"percent root\")&&(w=g/f.getValue(h),k(\"root\"))}}return y(\"text\")&&(v=o.castOption(r,u.i,\"text\"),o.isValidTextValue(v)&&x.push(v)),x.join(\"<br>\")}var T=o.castOption(r,u.i,\"texttemplate\");if(!T)return\"\";var A={};u.label&&(A.label=u.label),u.hasOwnProperty(\"v\")&&(A.value=u.v,A.valueLabel=f.formatValue(u.v,l)),A.currentPath=f.getPath(t.data),p||(A.percentParent=g/f.getValue(d),A.percentParentLabel=f.formatPercent(A.percentParent,l),A.parent=f.getPtLabel(d)),A.percentEntry=g/f.getValue(e),A.percentEntryLabel=f.formatPercent(A.percentEntry,l),A.entry=f.getPtLabel(e),A.percentRoot=g/f.getValue(h),A.percentRootLabel=f.formatPercent(A.percentRoot,l),A.root=f.getPtLabel(h),u.hasOwnProperty(\"color\")&&(A.color=u.color);var M=o.castOption(r,u.i,\"text\");return(o.isValidTextValue(M)||\"\"===M)&&(A.text=M),A.customdata=o.castOption(r,u.i,\"customdata\"),o.texttemplateString(T,A,a._d3locale,A,r._meta||{})}},{\"../../components/drawing\":612,\"../../lib\":716,\"../../lib/svg_text_utils\":740,\"../pie/plot\":1101,\"./constants\":1231,\"./fx\":1233,\"./helpers\":1234,\"./style\":1239,d3:164,\"d3-hierarchy\":158}],1239:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"../../components/color\"),i=t(\"../../lib\");function o(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=i.castOption(r,s,\"marker.line.color\")||a.defaultLine,c=i.castOption(r,s,\"marker.line.width\")||0;t.style(\"stroke-width\",c).call(a.fill,n.color).call(a.stroke,l).style(\"opacity\",o?r.leaf.opacity:null)}e.exports={style:function(t){t._fullLayout._sunburstlayer.selectAll(\".trace\").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(o,t,r)})})},styleOne:o}},{\"../../components/color\":591,\"../../lib\":716,d3:164}],1240:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),a=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../../plots/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll;function 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0},{\"../../components/color\":591,\"../../components/colorscale/attributes\":598,\"../../lib/extend\":707,\"../../plot_api/edit_types\":747,\"../../plots/attributes\":761,\"../../plots/template_attributes\":840}],1241:[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\":599}],1242:[function(t,e,r){\"use strict\";var n=t(\"gl-surface3d\"),a=t(\"ndarray\"),i=t(\"ndarray-homography\"),o=t(\"ndarray-fill\"),s=t(\"../../lib\").isArrayOrTypedArray,l=t(\"../../lib/gl_format_color\").parseColorScale,c=t(\"../../lib/str2rgbarray\"),u=t(\"../../components/colorscale\").extractOpts,h=t(\"../heatmap/interp2d\"),f=t(\"../heatmap/find_empties\");function p(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 d=p.prototype;d.getXat=function(t,e,r,n){var a=s(this.data.x)?s(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?a:n.d2l(a,0,r)},d.getYat=function(t,e,r,n){var a=s(this.data.y)?s(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?a:n.d2l(a,0,r)},d.getZat=function(t,e,r,n){var a=this.data.z[e][t];return null===a&&this.data.connectgaps&&this.data._interpolatedZ&&(a=this.data._interpolatedZ[e][t]),void 0===r?a:n.d2l(a,0,r)},d.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),a=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,a],t.traceCoordinate=[this.getXat(n,a),this.getYat(n,a),this.getZat(n,a)],t.dataCoordinate=[this.getXat(n,a,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,a,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,a,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var i=0;i<3;i++){var o=t.dataCoordinate[i];null!=o&&(t.dataCoordinate[i]*=this.scene.dataScale[i])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[a]&&void 0!==s[a][n]?t.textLabel=s[a][n]:t.textLabel=s||\"\",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var g=[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 v(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function m(t){for(var e=[],r=0;r<g.length;r++){var n=g[r];e.push(v(t,n))}return e}function y(t){for(var e=m(t),r=t,n=0;n<g.length;n++)if(e[n]>0){r=g[n];break}return r}function x(t,e){if(!(t<1||e<1)){for(var r=m(t),n=m(e),a=1,i=0;i<g.length;i++)a*=Math.pow(g[i],Math.max(r[i],n[i]));return a}}d.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),a=this.getXat(e,0);r[e-1]=a!==n&&null!=n&&null!=a?Math.abs(a-n):0}var i=0;for(e=1;e<t;e++)i+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(i/r[e-1]);return r},d.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),a=this.getYat(0,e);r[e-1]=a!==n&&null!=n&&null!=a?Math.abs(a-n):0}var i=0;for(e=1;e<t;e++)i+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(i/r[e-1]);return r};var b=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],_=b[9],w=b[13];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)}d.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=x(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<_;)r*=2;for(;r>w;)r--,r/=y(r),++r<_&&(r=w);var n=Math.round(r/t);return n>1?n:1},d.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],o=t[0].shape[1],s=0|Math.floor(t[0].shape[0]*e+1),l=0|Math.floor(t[0].shape[1]*r+1),c=1+n+1,u=1+o+1,h=a(new Float32Array(c*u),[c,u]),f=0;f<t.length;++f){this.surface.padField(h,t[f]);var p=a(new Float32Array(s*l),[s,l]);i(p,h,[e,0,0,0,r,0,0,0,1]),t[f]=p}},d.setContourLevels=function(){var t,e,r,n=[[],[],[]],a=[!1,!1,!1],i=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(i=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(a[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(i){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=a[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},d.update=function(t){var e,r,n,i,s=this.scene,p=s.fullSceneLayout,d=this.surface,g=t.opacity,v=l(t,g),m=s.dataScale,y=t.z[0].length,x=t._ylength,b=s.contourLevels;this.data=t;var _=[];for(e=0;e<3;e++)for(_[e]=[],r=0;r<y;r++)_[e][r]=[];for(r=0;r<y;r++)for(n=0;n<x;n++)_[0][r][n]=this.getXat(r,n,t.xcalendar,p.xaxis),_[1][r][n]=this.getYat(r,n,t.ycalendar,p.yaxis),_[2][r][n]=this.getZat(r,n,t.zcalendar,p.zaxis);if(t.connectgaps)for(t._emptypoints=f(_[2]),h(_[2],t._emptypoints),t._interpolatedZ=[],r=0;r<y;r++)for(t._interpolatedZ[r]=[],n=0;n<x;n++)t._interpolatedZ[r][n]=_[2][r][n];for(e=0;e<3;e++)for(r=0;r<y;r++)for(n=0;n<x;n++)null==(i=_[e][r][n])?_[e][r][n]=NaN:i=_[e][r][n]*=m[e];for(e=0;e<3;e++)for(r=0;r<y;r++)for(n=0;n<x;n++)null!=(i=_[e][r][n])&&(this.minValues[e]>i&&(this.minValues[e]=i),this.maxValues[e]<i&&(this.maxValues[e]=i));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<y;r++)for(n=0;n<x;n++)null!=(i=_[e][r][n])&&(_[e][r][n]-=this.objectOffset[e]);var k=[a(new Float32Array(y*x),[y,x]),a(new Float32Array(y*x),[y,x]),a(new Float32Array(y*x),[y,x])];o(k[0],function(t,e){return _[0][t][e]}),o(k[1],function(t,e){return _[1][t][e]}),o(k[2],function(t,e){return _[2][t][e]}),_=[];var T={colormap:v,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],opacity:t.opacity},A=u(t);if(T.intensityBounds=[A.min,A.max],t.surfacecolor){var M=a(new Float32Array(y*x),[y,x]);o(M,function(e,r){return t.surfacecolor[r][e]}),k.push(M)}else T.intensityBounds[0]*=m[2],T.intensityBounds[1]*=m[2];(w<k[0].shape[0]||w<k[0].shape[1])&&(this.refineData=!1),!0===this.refineData&&(this.dataScaleX=this.estimateScale(k[0].shape[0],0),this.dataScaleY=this.estimateScale(k[0].shape[1],1),1===this.dataScaleX&&1===this.dataScaleY||this.refineCoords(k)),t.surfacecolor&&(T.intensity=k.pop());var S=[!0,!0,!0],E=[\"x\",\"y\",\"z\"];for(e=0;e<3;++e){var L=t.contours[E[e]];S[e]=L.highlight,T.showContour[e]=L.show||L.highlight,T.showContour[e]&&(T.contourProject[e]=[L.project.x,L.project.y,L.project.z],L.show?(this.showContour[e]=!0,T.levels[e]=b[e],d.highlightColor[e]=T.contourColor[e]=c(L.color),L.usecolormap?d.highlightTint[e]=T.contourTint[e]=0:d.highlightTint[e]=T.contourTint[e]=1,T.contourWidth[e]=L.width,this.contourStart[e]=L.start,this.contourEnd[e]=L.end,this.contourSize[e]=L.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),L.highlight&&(T.dynamicColor[e]=c(L.highlightcolor),T.dynamicWidth[e]=L.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]})(v)&&(T.vertexColor=!0),T.objectOffset=this.objectOffset,T.coords=k,d.update(T),d.visible=t.visible,d.enableDynamic=S,d.enableHighlight=S,d.snapToData=!0,\"lighting\"in t&&(d.ambientLight=t.lighting.ambient,d.diffuseLight=t.lighting.diffuse,d.specularLight=t.lighting.specular,d.roughness=t.lighting.roughness,d.fresnel=t.lighting.fresnel),\"lightposition\"in t&&(d.lightPosition=[t.lightposition.x,t.lightposition.y,t.lightposition.z]),g&&g<1&&(d.supportsTransparency=!0)},d.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()},e.exports=function(t,e){var r=t.glplot.gl,a=n({gl:r}),i=new p(t,a,e.uid);return a._trace=i,i.update(e),t.glplot.add(a),i}},{\"../../components/colorscale\":603,\"../../lib\":716,\"../../lib/gl_format_color\":713,\"../../lib/str2rgbarray\":739,\"../heatmap/find_empties\":1004,\"../heatmap/interp2d\":1007,\"gl-surface3d\":317,ndarray:451,\"ndarray-fill\":441,\"ndarray-homography\":443}],1243:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),a=t(\"../../lib\"),i=t(\"../../components/colorscale/defaults\"),o=t(\"./attributes\");function s(t,e,r){e in t&&!(r in t)&&(t[r]=t[e])}e.exports=function(t,e,r,l){var c,u;function h(r,n){return a.coerce(t,e,o,r,n)}var f=h(\"x\"),p=h(\"y\"),d=h(\"z\");if(!d||!d.length||f&&f.length<1||p&&p.length<1)e.visible=!1;else{e._xlength=Array.isArray(f)&&a.isArrayOrTypedArray(f[0])?d.length:d[0].length,e._ylength=d.length,n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\",\"z\"],l),h(\"text\"),h(\"hovertext\"),h(\"hovertemplate\"),[\"lighting.ambient\",\"lighting.diffuse\",\"lighting.specular\",\"lighting.roughness\",\"lighting.fresnel\",\"lightposition.x\",\"lightposition.y\",\"lightposition.z\",\"hidesurface\",\"connectgaps\",\"opacity\"].forEach(function(t){h(t)});var g=h(\"surfacecolor\"),v=[\"x\",\"y\",\"z\"];for(c=0;c<3;++c){var m=\"contours.\"+v[c],y=h(m+\".show\"),x=h(m+\".highlight\");if(y||x)for(u=0;u<3;++u)h(m+\".project.\"+v[u]);y&&(h(m+\".color\"),h(m+\".width\"),h(m+\".usecolormap\")),x&&(h(m+\".highlightcolor\"),h(m+\".highlightwidth\")),h(m+\".start\"),h(m+\".end\"),h(m+\".size\")}g||(s(t,\"zmin\",\"cmin\"),s(t,\"zmax\",\"cmax\"),s(t,\"zauto\",\"cauto\")),i(t,e,l,h,{prefix:\"\",cLetter:\"c\"}),e._length=null}}},{\"../../components/colorscale/defaults\":601,\"../../lib\":716,\"../../registry\":845,\"./attributes\":1240}],1244:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),colorbar:{min:\"cmin\",max:\"cmax\"},calc:t(\"./calc\"),plot:t(\"./convert\"),moduleType:\"trace\",name:\"surface\",basePlotModule:t(\"../../plots/gl3d\"),categories:[\"gl3d\",\"2dMap\"],meta:{}}},{\"../../plots/gl3d\":804,\"./attributes\":1240,\"./calc\":1241,\"./convert\":1242,\"./defaults\":1243}],1245:[function(t,e,r){\"use strict\";var n=t(\"../../components/annotations/attributes\"),a=t(\"../../lib/extend\").extendFlat,i=t(\"../../plot_api/edit_types\").overrideAll,o=t(\"../../plots/font_attributes\"),s=t(\"../../plots/domain\").attributes;t(\"../../constants/docs\").FORMAT_LINK;(e.exports=i({domain:s({name:\"table\",trace:!0}),columnwidth:{valType:\"number\",arrayOk:!0,dflt:null},columnorder:{valType:\"data_array\"},header:{values:{valType:\"data_array\",dflt:[]},format:{valType:\"data_array\",dflt:[]},prefix:{valType:\"string\",arrayOk:!0,dflt:null},suffix:{valType:\"string\",arrayOk:!0,dflt:null},height:{valType:\"number\",dflt:28},align:a({},n.align,{arrayOk:!0}),line:{width:{valType:\"number\",arrayOk:!0,dflt:1},color:{valType:\"color\",arrayOk:!0,dflt:\"grey\"}},fill:{color:{valType:\"color\",arrayOk:!0,dflt:\"white\"}},font:a({},o({arrayOk:!0}))},cells:{values:{valType:\"data_array\",dflt:[]},format:{valType:\"data_array\",dflt:[]},prefix:{valType:\"string\",arrayOk:!0,dflt:null},suffix:{valType:\"string\",arrayOk:!0,dflt:null},height:{valType:\"number\",dflt:20},align:a({},n.align,{arrayOk:!0}),line:{width:{valType:\"number\",arrayOk:!0,dflt:1},color:{valType:\"color\",arrayOk:!0,dflt:\"grey\"}},fill:{color:{valType:\"color\",arrayOk:!0,dflt:\"white\"}},font:a({},o({arrayOk:!0}))}},\"calc\",\"from-root\")).transforms=void 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strict\";e.exports={cellPad:8,columnExtentOffset:10,columnTitleOffset:28,emptyHeaderHeight:16,latexCheck:/^\\$.*\\$$/,goldenRatio:1.618,lineBreaker:\"<br>\",maxDimensionCount:60,overdrag:45,releaseTransitionDuration:120,releaseTransitionEase:\"cubic-out\",scrollbarCaptureWidth:18,scrollbarHideDelay:1e3,scrollbarHideDuration:1e3,scrollbarOffset:5,scrollbarWidth:8,transitionDuration:100,transitionEase:\"cubic-out\",uplift:5,wrapSpacer:\" \",wrapSplitCharacter:\" \",cn:{table:\"table\",tableControlView:\"table-control-view\",scrollBackground:\"scroll-background\",yColumn:\"y-column\",columnBlock:\"column-block\",scrollAreaClip:\"scroll-area-clip\",scrollAreaClipRect:\"scroll-area-clip-rect\",columnBoundary:\"column-boundary\",columnBoundaryClippath:\"column-boundary-clippath\",columnBoundaryRect:\"column-boundary-rect\",columnCells:\"column-cells\",columnCell:\"column-cell\",cellRect:\"cell-rect\",cellText:\"cell-text\",cellTextHolder:\"cell-text-holder\",scrollbarKit:\"scrollbar-kit\",scrollbar:\"scrollbar\",scrollbarSlider:\"scrollbar-slider\",scrollbarGlyph:\"scrollbar-glyph\",scrollbarCaptureZone:\"scrollbar-capture-zone\"}}},{}],1249:[function(t,e,r){\"use strict\";var n=t(\"./constants\"),a=t(\"../../lib/extend\").extendFlat,i=t(\"fast-isnumeric\");function o(t){if(Array.isArray(t)){for(var e=0,r=0;r<t.length;r++)e=Math.max(e,o(t[r]));return e}return t}function s(t,e){return t+e}function l(t){var e,r=t.slice(),n=1/0,a=0;for(e=0;e<r.length;e++)Array.isArray(r[e])||(r[e]=[r[e]]),n=Math.min(n,r[e].length),a=Math.max(a,r[e].length);if(n!==a)for(e=0;e<r.length;e++){var i=a-r[e].length;i&&(r[e]=r[e].concat(c(i)))}return r}function c(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=\"\";return e}function u(t){return t.calcdata.columns.reduce(function(e,r){return r.xIndex<t.xIndex?e+r.columnWidth:e},0)}function h(t,e){return Object.keys(t).map(function(r){return a({},t[r],{auxiliaryBlocks:e})})}function f(t,e){for(var r,n={},a=0,i=0,o={firstRowIndex:null,lastRowIndex:null,rows:[]},s=0,l=0,c=0;c<t.length;c++)r=t[c],o.rows.push({rowIndex:c,rowHeight:r}),((i+=r)>=e||c===t.length-1)&&(n[a]=o,o.key=l++,o.firstRowIndex=s,o.lastRowIndex=c,o={firstRowIndex:null,lastRowIndex:null,rows:[]},a+=i,s=c+1,i=0);return n}e.exports=function(t,e){var r=l(e.cells.values),p=function(t){return t.slice(e.header.values.length,t.length)},d=l(e.header.values);d.length&&!d[0].length&&(d[0]=[\"\"],d=l(d));var g=d.concat(p(r).map(function(){return c((d[0]||[\"\"]).length)})),v=e.domain,m=Math.floor(t._fullLayout._size.w*(v.x[1]-v.x[0])),y=Math.floor(t._fullLayout._size.h*(v.y[1]-v.y[0])),x=e.header.values.length?g[0].map(function(){return e.header.height}):[n.emptyHeaderHeight],b=r.length?r[0].map(function(){return e.cells.height}):[],_=x.reduce(s,0),w=f(b,y-_+n.uplift),k=h(f(x,_),[]),T=h(w,k),A={},M=e._fullInput.columnorder.concat(p(r.map(function(t,e){return e}))),S=g.map(function(t,r){var n=Array.isArray(e.columnwidth)?e.columnwidth[Math.min(r,e.columnwidth.length-1)]:e.columnwidth;return i(n)?Number(n):1}),E=S.reduce(s,0);S=S.map(function(t){return t/E*m});var L=Math.max(o(e.header.line.width),o(e.cells.line.width)),C={key:e.uid+t._context.staticPlot,translateX:v.x[0]*t._fullLayout._size.w,translateY:t._fullLayout._size.h*(1-v.y[1]),size:t._fullLayout._size,width:m,maxLineWidth:L,height:y,columnOrder:M,groupHeight:y,rowBlocks:T,headerRowBlocks:k,scrollY:0,cells:a({},e.cells,{values:r}),headerCells:a({},e.header,{values:g}),gdColumns:g.map(function(t){return t[0]}),gdColumnsOriginalOrder:g.map(function(t){return t[0]}),prevPages:[0,0],scrollbarState:{scrollbarScrollInProgress:!1},columns:g.map(function(t,e){var r=A[t];return A[t]=(r||0)+1,{key:t+\"__\"+A[t],label:t,specIndex:e,xIndex:M[e],xScale:u,x:void 0,calcdata:void 0,columnWidth:S[e]}})};return C.columns.forEach(function(t){t.calcdata=C,t.x=u(t)}),C}},{\"../../lib/extend\":707,\"./constants\":1248,\"fast-isnumeric\":227}],1250:[function(t,e,r){\"use strict\";var n=t(\"../../lib/extend\").extendFlat;r.splitToPanels=function(t){var 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n=t(\"./constants\"),a=t(\"d3\"),i=t(\"../../lib/gup\"),o=t(\"../../components/drawing\"),s=t(\"../../lib/svg_text_utils\"),l=t(\"../../lib\").raiseToTop,c=t(\"../../lib\").cancelTransition,u=t(\"./data_preparation_helper\"),h=t(\"./data_split_helpers\"),f=t(\"../../components/color\");function p(t){return Math.ceil(t.calcdata.maxLineWidth/2)}function d(t,e){return\"clip\"+t._fullLayout._uid+\"_scrollAreaBottomClip_\"+e.key}function g(t,e){return\"clip\"+t._fullLayout._uid+\"_columnBoundaryClippath_\"+e.calcdata.key+\"_\"+e.specIndex}function v(t){return[].concat.apply([],t.map(function(t){return t})).map(function(t){return t.__data__})}function m(t,e,r){var o=t.selectAll(\".\"+n.cn.scrollbarKit).data(i.repeat,i.keyFun);o.enter().append(\"g\").classed(n.cn.scrollbarKit,!0).style(\"shape-rendering\",\"geometricPrecision\"),o.each(function(t){var e=t.scrollbarState;e.totalHeight=function(t){var e=t.rowBlocks;return 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n=a.event.y,i=this.getBoundingClientRect(),o=r.scrollbarState,s=n-i.top,l=a.scale.linear().domain([0,o.scrollableAreaHeight]).range([0,o.totalHeight]).clamp(!0);o.topY<=s&&s<=o.bottomY||S(e,t,null,l(s-o.barLength/2))(r)}).call(a.behavior.drag().origin(function(t){return a.event.stopPropagation(),t.scrollbarState.scrollbarScrollInProgress=!0,t}).on(\"drag\",S(e,t)).on(\"dragend\",function(){})),u.attr(\"y2\",function(t){return t.scrollbarState.scrollableAreaHeight}),e._context.staticPlot&&(c.remove(),u.remove())}function y(t,e,r,s){var l=function(t){var e=t.selectAll(\".\"+n.cn.columnCell).data(h.splitToCells,function(t){return t.keyWithinBlock});return e.enter().append(\"g\").classed(n.cn.columnCell,!0),e.exit().remove(),e}(function(t){var e=t.selectAll(\".\"+n.cn.columnCells).data(i.repeat,i.keyFun);return e.enter().append(\"g\").classed(n.cn.columnCells,!0),e.exit().remove(),e}(r));!function(t){t.each(function(t,e){var 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n=t(\"d3\"),a=t(\"../../lib\"),i=t(\"../../components/drawing\"),o=t(\"../../lib/svg_text_utils\"),s=t(\"./partition\"),l=t(\"./style\").styleOne,c=t(\"./constants\"),u=t(\"../sunburst/helpers\"),h=t(\"../sunburst/fx\"),f=t(\"../sunburst/plot\").formatSliceLabel;e.exports=function(t,e,r,p,d){var g=d.width,v=d.height,m=d.viewX,y=d.viewY,x=d.pathSlice,b=d.toMoveInsideSlice,_=d.strTransform,w=d.hasTransition,k=d.handleSlicesExit,T=d.makeUpdateSliceInterpolator,A=d.makeUpdateTextInterpolator,M=d.prevEntry,S=t._fullLayout,E=e[0].trace,L=-1!==E.textposition.indexOf(\"left\"),C=-1!==E.textposition.indexOf(\"right\"),P=-1!==E.textposition.indexOf(\"bottom\"),O=!P&&!E.marker.pad.t||P&&!E.marker.pad.b,z=s(r,[g,v],{packing:E.tiling.packing,squarifyratio:E.tiling.squarifyratio,flipX:E.tiling.flip.indexOf(\"x\")>-1,flipY:E.tiling.flip.indexOf(\"y\")>-1,pad:{inner:E.tiling.pad,top:E.marker.pad.t,left:E.marker.pad.l,right:E.marker.pad.r,bottom:E.marker.pad.b}}).descendants(),I=1/0,D=-1/0;z.forEach(function(t){var e=t.depth;e>=E._maxDepth?(t.x0=t.x1=(t.x0+t.x1)/2,t.y0=t.y1=(t.y0+t.y1)/2):(I=Math.min(I,e),D=Math.max(D,e))}),p=p.data(z,u.getPtId),E._maxVisibleLayers=isFinite(D)?D-I+1:0,p.enter().append(\"g\").classed(\"slice\",!0),k(p,!1,{},[g,v],x),p.order();var R=null;if(w&&M){var 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M=a.ensureSingle(d,\"g\",\"slicetext\"),z=a.ensureSingle(M,\"text\",\"\",function(t){t.attr(\"data-notex\",1)});z.text(s._text||\" \").classed(\"slicetext\",!0).attr(\"text-anchor\",C?\"end\":L||p?\"start\":\"middle\").call(i.font,u.determineTextFont(E,s,S.font)).call(o.convertToTspans,t),s.textBB=i.bBox(z.node()),s.transform=b(s,{isHeader:p}),w?z.transition().attrTween(\"transform\",function(t){var e=A(t,!1,B(),[g,v]);return function(t){return _(e(t))}}):z.attr(\"transform\",_(s))}),R}},{\"../../components/drawing\":612,\"../../lib\":716,\"../../lib/svg_text_utils\":740,\"../sunburst/fx\":1233,\"../sunburst/helpers\":1234,\"../sunburst/plot\":1238,\"./constants\":1257,\"./partition\":1264,\"./style\":1266,d3:164}],1261:[function(t,e,r){\"use 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       "        \n",
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       "            <script type=\"text/javascript\">\n",
       "                require([\"plotly\"], function(Plotly) {\n",
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       "                        '28ed4f03-cd42-4c36-92b7-9bf283b6c939',\n",
       "                        [{\"alignmentgroup\": \"True\", \"hoverlabel\": {\"namelength\": 0}, \"hovertemplate\": \"Area=%{x}<br>GDP/capita scaled=%{y}\", \"legendgroup\": \"\", \"marker\": {\"color\": \"#636efa\"}, \"name\": \"\", \"offsetgroup\": \"\", \"orientation\": \"v\", \"showlegend\": false, \"textposition\": \"auto\", \"type\": \"bar\", \"x\": [\"Albania\", \"Austria\", \"Belarus\", \"Belgium\", \"Bosnia and Herzegovina\", \"Bulgaria\", \"Croatia\", \"Czechia\", \"Denmark\", \"Estonia\", \"Finland\", \"France\", \"Germany\", \"Greece\", \"Hungary\", \"Iceland\", \"Ireland\", \"Italy\", \"Latvia\", \"Lithuania\", \"Luxembourg\", \"Montenegro\", \"Netherlands\", \"North Macedonia\", \"Norway\", \"Poland\", \"Portugal\", \"Republic of Moldova\", \"Romania\", \"Russian Federation\", \"Serbia\", \"Slovakia\", \"Slovenia\", \"Spain\", \"Sweden\", \"Switzerland\", \"Ukraine\", \"United Kingdom\"], \"xaxis\": \"x\", \"y\": [0.025796, 0.453856, 0.03841, 0.417029, 0.03612, 0.06525, 0.117202, 0.185969, 0.563229, 0.179682, 0.446103, 0.384979, 0.431279, 0.179106, 0.127671, 0.714738, 0.66751, 0.308962, 0.144927, 0.158402, 1.0, 0.058425, 0.476314, 0.034849, 0.734819, 0.121892, 0.175452, 0.0, 0.092221, 0.09017, 0.028143, 0.158901, 0.218311, 0.267551, 0.523083, 0.783642, 0.005633, 0.376729], \"yaxis\": \"y\"}],\n",
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('28ed4f03-cd42-4c36-92b7-9bf283b6c939');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
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       "\n",
       "                        })\n",
       "                };\n",
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       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(gdp_europe_capita_scaled.reset_index(), x='Area', y='GDP/capita scaled')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We narrow down to the countries shown below in `best_countries`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_countries = [\"Italy\",\"France\",\"Spain\",\"United Kingdom\",\"Germany\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the scaled GDPs of our best countries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "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",
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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>GDP/capita scaled</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>0.308962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>0.384979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>0.267551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>0.376729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>0.431279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                GDP/capita scaled\n",
       "Area                             \n",
       "Italy                    0.308962\n",
       "France                   0.384979\n",
       "Spain                    0.267551\n",
       "United Kingdom           0.376729\n",
       "Germany                  0.431279"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_europe_capita_scaled.loc[best_countries]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the code below, we present how the bubble plot used for helper country selection was generated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "maximum = max(total_cal_need_europe[2020])\n",
    "sizes = [x/maximum*100 for x in total_cal_need_europe[2020]]\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=gdp_capita[\"GDP 100kUS$/capita\"],\n",
    "    y=caloric_difference_europe[2020],\n",
    "    mode=\"markers\",\n",
    "    marker=dict(size=sizes,\n",
    "                color=total_cal_need_europe[2020],\n",
    "                colorbar=dict(title=\"Total caloric surplus [kcal/year]\")\n",
    "               ),\n",
    "    text=total_cal_need_europe.index,\n",
    "    name=\"\",\n",
    "    hovertemplate=\"<b>%{text}</b><br><br>GDP per capita: %{x:.2f}e+3 US$<br>Surplus per capita: %{y:.2f} kcal/person/day<br>Total surplus: %{marker.color:.2e} kcal/year\"))\n",
    "\n",
    "fig.update_layout(\n",
    "    title = dict(text=\"Viability of European donor nations\", font=dict(size=20)), \n",
    "    plot_bgcolor='rgb(256,256,256)',\n",
    "    xaxis=dict(type=\"log\", gridcolor=\"#eee\", title=\"GDP per capita [thousands US$]\"),\n",
    "    yaxis=dict(gridcolor=\"#eee\", title=\"Caloric surplus [kcal/person/day]\"),\n",
    "    )\n",
    "\n",
    "py.plot(fig, filename='docs/_includes/gdp_surplus.html', include_plotlyjs=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"600\"\n",
       "            src=\"https://manuleo.github.io/mADAm_files/gdp_surplus.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x15b7e6790>"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(src='https://manuleo.github.io/mADAm_files/gdp_surplus.html', width = 1000, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note that the design choice of selecting 5 best european countries is on a large extent justified**. We can observe that the countries represented in the top right hand side of the plot are the ones with the higher Surplus and GDP. Moreover, we see that Italy, UK, Spain, Germany and France are actually the countries that have the most in terms of total surplus (considering the population). Russia wil not be considered because the position on the plot suggest that even tough Total surplus is high, the GDP is relatively not compared to other countries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create auxiliary dataframes to store the coefficients:\n",
    "- `europe_plus_val` and `europe_plus_index` will store respectively the european surplus and the corresponding names of the countries\n",
    "- `africa_deficit_val` and `africa_deficit_indx` will store respectively the african defict and the corresponding names of the countries\n",
    "- `gdp_europe_val` and `gdp_europe_index` will store respectively the gwp normalized of european countries and the corresponding names of the countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "europe_plus_val = total_europe.loc[best_countries].astype(float).values\n",
    "europe_plus_index = total_europe.loc[best_countries].index\n",
    "africa_deficit_val= np.abs(af_real_deficit_tot.values).astype(float)\n",
    "africa_deficit_index = af_real_deficit_tot.index\n",
    "gdp_europe_val = gdp_europe_capita_scaled.loc[best_countries].astype(float).values\n",
    "gdp_europe_index = gdp_europe_capita_scaled.loc[best_countries].index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the [documentation](https://picos-api.gitlab.io/picos/tutorial.html) of the library **PICOS** we adopted to solve the problem, **QUADRATIC PROGRAMS** can't be implemented yet. As a consequence, we will model the problem with a matlab function **QPSolverEuropeAfrica** that is located on the repository. It is possibile to run matlab functions on jupyter notebooks by importing **matlab.engine**. The function is then called and executed by a temporary matlab kernel created in the jupyter session.\n",
    "In Matlab, it is possibile to use YALMIP which is a very useful translator of the problem to different solvers. We have an academic free license of GUROBI which is a quite famous solver and we will go for it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matlab.engine\n",
    "euc = europe_plus_val; #european countries surplus matrix size 1xm\n",
    "afc = africa_deficit_val; #african countries deficit matrix size 1xn\n",
    "gwp = 1-gdp_europe_val; # gwp\n",
    "#Casting to matlab variables\n",
    "euc = matlab.double(euc.tolist())\n",
    "afc = matlab.double(afc.tolist())\n",
    "gwp = matlab.double(gwp.tolist())\n",
    "#Start matlab engine\n",
    "eng = matlab.engine.start_matlab()\n",
    "#Initializing QPSolverEuropeAfrica that will store the outputs of the matlab function\n",
    "QPSolverEuropeAfrica = eng.QPSolverEuropeAfrica(euc,afc,gwp,nargout=1)\n",
    "#Quitting the matlab engine\n",
    "eng.quit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PICKLE\n",
    "#food_opt_distribution_df.to_pickle('data/processed/food_opt_distribution_df.pkl') #create pickle\n",
    "food_opt_distribution_df = pd.read_pickle('data/processed/food_opt_distribution_df.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The optimization was successful. We will store them in the dataframe `food_opt_distribution_df`. Let's have a look at them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ethiopia</th>\n",
       "      <th>Kenya</th>\n",
       "      <th>United Republic of Tanzania</th>\n",
       "      <th>Madagascar</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Sudan</th>\n",
       "      <th>Uganda</th>\n",
       "      <th>Chad</th>\n",
       "      <th>Mozambique</th>\n",
       "      <th>Senegal</th>\n",
       "      <th>...</th>\n",
       "      <th>Congo</th>\n",
       "      <th>Central African Republic</th>\n",
       "      <th>Liberia</th>\n",
       "      <th>Namibia</th>\n",
       "      <th>Togo</th>\n",
       "      <th>Botswana</th>\n",
       "      <th>Guinea-Bissau</th>\n",
       "      <th>Eswatini</th>\n",
       "      <th>Lesotho</th>\n",
       "      <th>Djibouti</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>3.309959e+12</td>\n",
       "      <td>1.377777e+12</td>\n",
       "      <td>1.128190e+12</td>\n",
       "      <td>1.025596e+12</td>\n",
       "      <td>7.923125e+11</td>\n",
       "      <td>6.504120e+11</td>\n",
       "      <td>5.984561e+11</td>\n",
       "      <td>5.279925e+11</td>\n",
       "      <td>4.332332e+11</td>\n",
       "      <td>3.844247e+11</td>\n",
       "      <td>...</td>\n",
       "      <td>1.524224e+11</td>\n",
       "      <td>1.343460e+11</td>\n",
       "      <td>7.543230e+10</td>\n",
       "      <td>6.563087e+10</td>\n",
       "      <td>6.366816e+10</td>\n",
       "      <td>3.657805e+10</td>\n",
       "      <td>3.001846e+10</td>\n",
       "      <td>2.303602e+10</td>\n",
       "      <td>8.877370e+09</td>\n",
       "      <td>8.567958e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>3.719690e+12</td>\n",
       "      <td>1.548689e+12</td>\n",
       "      <td>1.268253e+12</td>\n",
       "      <td>1.152979e+12</td>\n",
       "      <td>8.908609e+11</td>\n",
       "      <td>7.314214e+11</td>\n",
       "      <td>6.730436e+11</td>\n",
       "      <td>5.938707e+11</td>\n",
       "      <td>4.873991e+11</td>\n",
       "      <td>4.325579e+11</td>\n",
       "      <td>...</td>\n",
       "      <td>1.718800e+11</td>\n",
       "      <td>1.515692e+11</td>\n",
       "      <td>8.537378e+10</td>\n",
       "      <td>7.436089e+10</td>\n",
       "      <td>7.215559e+10</td>\n",
       "      <td>4.171712e+10</td>\n",
       "      <td>3.434677e+10</td>\n",
       "      <td>2.650129e+10</td>\n",
       "      <td>1.059262e+10</td>\n",
       "      <td>1.024497e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>3.122538e+12</td>\n",
       "      <td>1.299598e+12</td>\n",
       "      <td>1.064122e+12</td>\n",
       "      <td>9.673289e+11</td>\n",
       "      <td>7.472343e+11</td>\n",
       "      <td>6.133565e+11</td>\n",
       "      <td>5.643380e+11</td>\n",
       "      <td>4.978583e+11</td>\n",
       "      <td>4.084565e+11</td>\n",
       "      <td>3.624075e+11</td>\n",
       "      <td>...</td>\n",
       "      <td>1.435221e+11</td>\n",
       "      <td>1.264677e+11</td>\n",
       "      <td>7.088484e+10</td>\n",
       "      <td>6.163756e+10</td>\n",
       "      <td>5.978582e+10</td>\n",
       "      <td>3.422732e+10</td>\n",
       "      <td>2.803860e+10</td>\n",
       "      <td>2.145092e+10</td>\n",
       "      <td>8.092775e+09</td>\n",
       "      <td>7.800856e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>3.670387e+12</td>\n",
       "      <td>1.528123e+12</td>\n",
       "      <td>1.251399e+12</td>\n",
       "      <td>1.137651e+12</td>\n",
       "      <td>8.790027e+11</td>\n",
       "      <td>7.216737e+11</td>\n",
       "      <td>6.640686e+11</td>\n",
       "      <td>5.859437e+11</td>\n",
       "      <td>4.808814e+11</td>\n",
       "      <td>4.267661e+11</td>\n",
       "      <td>...</td>\n",
       "      <td>1.695387e+11</td>\n",
       "      <td>1.494968e+11</td>\n",
       "      <td>8.417754e+10</td>\n",
       "      <td>7.331042e+10</td>\n",
       "      <td>7.113431e+10</td>\n",
       "      <td>4.109874e+10</td>\n",
       "      <td>3.382595e+10</td>\n",
       "      <td>2.608432e+10</td>\n",
       "      <td>1.038623e+10</td>\n",
       "      <td>1.004317e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>4.022919e+12</td>\n",
       "      <td>1.675176e+12</td>\n",
       "      <td>1.371909e+12</td>\n",
       "      <td>1.247251e+12</td>\n",
       "      <td>9.637936e+11</td>\n",
       "      <td>7.913740e+11</td>\n",
       "      <td>7.282437e+11</td>\n",
       "      <td>6.426252e+11</td>\n",
       "      <td>5.274857e+11</td>\n",
       "      <td>4.681798e+11</td>\n",
       "      <td>...</td>\n",
       "      <td>1.862799e+11</td>\n",
       "      <td>1.643157e+11</td>\n",
       "      <td>9.273118e+10</td>\n",
       "      <td>8.082172e+10</td>\n",
       "      <td>7.843688e+10</td>\n",
       "      <td>4.552040e+10</td>\n",
       "      <td>3.755002e+10</td>\n",
       "      <td>2.906583e+10</td>\n",
       "      <td>1.186203e+10</td>\n",
       "      <td>1.148607e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Ethiopia         Kenya  United Republic of Tanzania  \\\n",
       "Italy           3.309959e+12  1.377777e+12                 1.128190e+12   \n",
       "France          3.719690e+12  1.548689e+12                 1.268253e+12   \n",
       "Spain           3.122538e+12  1.299598e+12                 1.064122e+12   \n",
       "United Kingdom  3.670387e+12  1.528123e+12                 1.251399e+12   \n",
       "Germany         4.022919e+12  1.675176e+12                 1.371909e+12   \n",
       "\n",
       "                  Madagascar        Zambia         Sudan        Uganda  \\\n",
       "Italy           1.025596e+12  7.923125e+11  6.504120e+11  5.984561e+11   \n",
       "France          1.152979e+12  8.908609e+11  7.314214e+11  6.730436e+11   \n",
       "Spain           9.673289e+11  7.472343e+11  6.133565e+11  5.643380e+11   \n",
       "United Kingdom  1.137651e+12  8.790027e+11  7.216737e+11  6.640686e+11   \n",
       "Germany         1.247251e+12  9.637936e+11  7.913740e+11  7.282437e+11   \n",
       "\n",
       "                        Chad    Mozambique       Senegal  ...         Congo  \\\n",
       "Italy           5.279925e+11  4.332332e+11  3.844247e+11  ...  1.524224e+11   \n",
       "France          5.938707e+11  4.873991e+11  4.325579e+11  ...  1.718800e+11   \n",
       "Spain           4.978583e+11  4.084565e+11  3.624075e+11  ...  1.435221e+11   \n",
       "United Kingdom  5.859437e+11  4.808814e+11  4.267661e+11  ...  1.695387e+11   \n",
       "Germany         6.426252e+11  5.274857e+11  4.681798e+11  ...  1.862799e+11   \n",
       "\n",
       "                Central African Republic       Liberia       Namibia  \\\n",
       "Italy                       1.343460e+11  7.543230e+10  6.563087e+10   \n",
       "France                      1.515692e+11  8.537378e+10  7.436089e+10   \n",
       "Spain                       1.264677e+11  7.088484e+10  6.163756e+10   \n",
       "United Kingdom              1.494968e+11  8.417754e+10  7.331042e+10   \n",
       "Germany                     1.643157e+11  9.273118e+10  8.082172e+10   \n",
       "\n",
       "                        Togo      Botswana  Guinea-Bissau      Eswatini  \\\n",
       "Italy           6.366816e+10  3.657805e+10   3.001846e+10  2.303602e+10   \n",
       "France          7.215559e+10  4.171712e+10   3.434677e+10  2.650129e+10   \n",
       "Spain           5.978582e+10  3.422732e+10   2.803860e+10  2.145092e+10   \n",
       "United Kingdom  7.113431e+10  4.109874e+10   3.382595e+10  2.608432e+10   \n",
       "Germany         7.843688e+10  4.552040e+10   3.755002e+10  2.906583e+10   \n",
       "\n",
       "                     Lesotho      Djibouti  \n",
       "Italy           8.877370e+09  8.567958e+09  \n",
       "France          1.059262e+10  1.024497e+10  \n",
       "Spain           8.092775e+09  7.800856e+09  \n",
       "United Kingdom  1.038623e+10  1.004317e+10  \n",
       "Germany         1.186203e+10  1.148607e+10  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_opt_distribution =  np.round(np.asarray(QPSolverEuropeAfrica),4)\n",
    "food_opt_distribution_df= pd.DataFrame(data=food_opt_distribution,index = europe_plus_index, columns=africa_deficit_index)\n",
    "food_opt_distribution_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compute now the total amount that every European country has to give up to save Africa:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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>Total giveup [kcal/year]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>1.201073e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1.351072e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>1.132461e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>1.333023e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1.462081e+13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Total giveup [kcal/year]\n",
       "Italy                       1.201073e+13\n",
       "France                      1.351072e+13\n",
       "Spain                       1.132461e+13\n",
       "United Kingdom              1.333023e+13\n",
       "Germany                     1.462081e+13"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#summing over rows of optimal_distribution_df \n",
    "eu_countries_giveup = food_opt_distribution.sum(axis=1)\n",
    "#storing results in a new dataframe eu_countries_giveup\n",
    "eu_countries_giveup = pd.DataFrame({'Total giveup [kcal/year]': eu_countries_giveup}, index=best_countries)\n",
    "eu_countries_giveup.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding the right ingredients to provide"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) A first look inside the possible food"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following dataframe we obtained has nutritional information about the most varied food products there are. The information we are most interested in for our analysis. per product, are:\n",
    "* **Food Group** - A generalized group in which the product is inserted\n",
    "* **Food Name** - The name of the product itself\n",
    "* **Protein (g)** - The amount of grams of proteins in a 100g serving\n",
    "* **Carbohydrates (g)** - The amount of grams of carbohydrates in a 100g serving\n",
    "* **Fat (g)** - The amount of grams of fat in a 100g serving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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>Food Group</th>\n",
       "      <th>Food Name</th>\n",
       "      <th>Protein (g)</th>\n",
       "      <th>Carbohydrates (g)</th>\n",
       "      <th>Fat (g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>Butter, salted</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.06</td>\n",
       "      <td>81.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>Butter, whipped, with salt</td>\n",
       "      <td>0.49</td>\n",
       "      <td>2.87</td>\n",
       "      <td>78.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>Butter oil, anhydrous</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>99.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>Cheese, blue</td>\n",
       "      <td>21.40</td>\n",
       "      <td>2.34</td>\n",
       "      <td>28.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>Cheese, brick</td>\n",
       "      <td>23.24</td>\n",
       "      <td>2.79</td>\n",
       "      <td>29.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Food Group                   Food Name  Protein (g)  \\\n",
       "0  Dairy and Egg Products              Butter, salted         0.85   \n",
       "1  Dairy and Egg Products  Butter, whipped, with salt         0.49   \n",
       "2  Dairy and Egg Products       Butter oil, anhydrous         0.28   \n",
       "3  Dairy and Egg Products                Cheese, blue        21.40   \n",
       "4  Dairy and Egg Products               Cheese, brick        23.24   \n",
       "\n",
       "   Carbohydrates (g)  Fat (g)  \n",
       "0               0.06    81.11  \n",
       "1               2.87    78.30  \n",
       "2               0.00    99.48  \n",
       "3               2.34    28.74  \n",
       "4               2.79    29.68  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load dataset USDA-Food\n",
    "usda_foods = pd.read_excel(\"data/raw/USDA-Food.xlsx\", sheet_name=0)\n",
    "food_properties = pd.DataFrame(usda_foods[['Food Group', 'Food Name', 'Protein (g)', 'Carbohydrates (g)', 'Fat (g)']])\n",
    "food_properties.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6347"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking size of the database\n",
    "food_properties.index.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a lot of possible product inside this database, but many of them could be unsuitable for our purpose. As for the first task, we check the available _'**Food Group'**_ and remove the ones we won't need.  \n",
    "We plot them for a clear understanding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "food_properties[\"Food Group\"].value_counts().plot(kind=\"barh\")\n",
    "plt.title(\"Distribution of Food Group inside the USDA database\")\n",
    "plt.xlabel(\"Counts\")\n",
    "plt.ylabel(\"Food Group\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, **not all most viable products are necessarily healthy. Also, other are not suitable for our purpose** (like, as example *\"Baby Food\"*). Therefore, we will no longer consider the following items:\n",
    "- Sweets\n",
    "- Beverages\n",
    "- Snacks \n",
    "- Baked Products\n",
    "- Spices and Herbs  \n",
    "- Nut and Seed Products\n",
    "- Dairy and Egg Products\n",
    "- Breakfast Cereals\n",
    "- Baby Foods\n",
    "- Soups, Sauces, and Gravies\n",
    "- Spices and Herbs\n",
    "- Nut and Seed Products\n",
    "- Fats and Oils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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>Food Group</th>\n",
       "      <th>Food Name</th>\n",
       "      <th>Protein (g)</th>\n",
       "      <th>Carbohydrates (g)</th>\n",
       "      <th>Fat (g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broiler, rotisserie, BBQ, breast meat...</td>\n",
       "      <td>28.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, meat and skin and...</td>\n",
       "      <td>18.33</td>\n",
       "      <td>0.13</td>\n",
       "      <td>14.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, meat and skin and...</td>\n",
       "      <td>22.84</td>\n",
       "      <td>9.03</td>\n",
       "      <td>17.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, meat and skin and...</td>\n",
       "      <td>28.57</td>\n",
       "      <td>3.27</td>\n",
       "      <td>15.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, meat and skin and...</td>\n",
       "      <td>26.78</td>\n",
       "      <td>0.06</td>\n",
       "      <td>13.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Food Group                                          Food Name  \\\n",
       "0  Poultry Products  Chicken, broiler, rotisserie, BBQ, breast meat...   \n",
       "1  Poultry Products  Chicken, broilers or fryers, meat and skin and...   \n",
       "2  Poultry Products  Chicken, broilers or fryers, meat and skin and...   \n",
       "3  Poultry Products  Chicken, broilers or fryers, meat and skin and...   \n",
       "4  Poultry Products  Chicken, broilers or fryers, meat and skin and...   \n",
       "\n",
       "   Protein (g)  Carbohydrates (g)  Fat (g)  \n",
       "0        28.04               0.00     3.57  \n",
       "1        18.33               0.13    14.83  \n",
       "2        22.84               9.03    17.53  \n",
       "3        28.57               3.27    15.27  \n",
       "4        26.78               0.06    13.27  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_suit = [\"Sweets\",\"Snacks\",\"Beverages\", \"Baked Products\",\"Spices and Herbs\", \"Nut and Seed Products\", \\\n",
    "            \"Dairy and Egg Products\", \"Breakfast Cereals\", \"Baby Foods\", \"Soups, Sauces, and Gravies\", \\\n",
    "           \"Spices and Herbs\", \"Nut and Seed Products\", \"Fats and Oils\"]\n",
    "food_properties = food_properties[~food_properties[\"Food Group\"].isin(not_suit)].reset_index(drop=True)\n",
    "food_properties.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New check on dimension:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4240"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_properties.index.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By a first look to the head of the dataset we also understand that the `USDA-Food` contains many processed products. As we are interested in *raw* products for our analysis, we filter only this one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1283"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_properties = food_properties[food_properties[\"Food Name\"].str.contains(\"raw\")]\n",
    "food_properties.index.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a set of **1283** products from which we have to choose the possible diet.  \n",
    "We keep this dataset as it is for now and move to the next part."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Consider the producer prices\n",
    "An important feature in our next choice of products will be **the cost of producing the products** inside the European countries. For this reason, we introduce now **two new datasets** and we use them to filter more the `USDA-Food` ones.  \n",
    "The new datasets are:\n",
    "- [EU_prices_for_representative](https://ec.europa.eu/info/food-farming-fisheries/farming/facts-and-figures/markets/prices/price-monitoring-sector/eu-prices-selected-representative-products_en) by the European Commission\n",
    "- [FAO Producer prices](http://www.fao.org/faostat/en/#data/PP) (in particular we use the data for cereal and vegetables prices) \n",
    "\n",
    "We use the two of them because just the one from FAO is not complete enough for our purpose.  \n",
    "*Note*: FAO data are from 2014 to 2018, while EU_prices will go from 2014 to 2019. We assume these prices to be ok even if they are from a different period because there are no available data online. We simply decide to pick the most recent price for each available product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "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>Category</th>\n",
       "      <th>Product</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Country</th>\n",
       "      <th>Period</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Animal Products</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>€/100 kg carcass weight</td>\n",
       "      <td>AT</td>\n",
       "      <td>201910</td>\n",
       "      <td>268.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Category  Product                     Unit Country  Period   Price\n",
       "0  Animal Products  Chicken  €/100 kg carcass weight      AT  201910  268.11"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading the new datasets\n",
    "eu_prices = pd.read_csv(\"data/raw/europe_food_prices.csv\", usecols=[\"Category\", \"Product desc\", \"Unit\", \"Country\", \"Period\", \"MP Market Price\"])\\\n",
    "            .rename(columns={\"Product desc\": \"Product\", \"MP Market Price\":\"Price\"})\n",
    "eu_prices.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to have reasonable prices, we consider only the last five years, from 2014 to october 2019:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "eu_prices = eu_prices[eu_prices.Period.between(201400, 201910)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the availability inside this dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Chicken', 'Raw Milk', 'SMP', 'WMP', 'Whey Powder', 'Butter',\n",
       "       'Butter Oil', 'Emmental', 'Cheddar', 'Edam', 'Gouda', 'Apricots',\n",
       "       'Avocados', 'Cherries', 'Lemons', 'Clementines', 'Strawberries',\n",
       "       'Kiwis', 'Mandarins', 'Melons', 'Nectarines', 'Oranges',\n",
       "       'Watermelons', 'Peaches', 'Pears', 'Apples Braeburn',\n",
       "       'Apples Boskoop', 'Apples Cox', 'Apples Elstar', 'Apples Fuji',\n",
       "       'Apples Gala', 'Apples Golden', 'Apples Granny', 'Apples Idared',\n",
       "       'Apples Jonagold', 'Apples Red', 'Apples Shampion', 'Plums',\n",
       "       'Grapes', 'Satsumas', 'Garlic', 'Asparagus', 'Eggplants',\n",
       "       'Cabbages', 'Carrots', 'Mushrooms', 'Cauliflowers', 'Cucumbers',\n",
       "       'Courgettes', 'Beans', 'Lettuces', 'Leeks', 'Onions', 'Peppers',\n",
       "       'Tomatoes Cherry', 'Tomatoes Trusses', 'Tomatoes Round',\n",
       "       'Feed Oats', 'Milling Oats', 'Feed Wheat', 'Bread Wheat',\n",
       "       'Durum Wheat', 'Feed Maize', 'Malting Barley', 'Feed Barley',\n",
       "       'Feed Rye', 'Bread Rye', 'Lampante Olive Oil',\n",
       "       'Extra Virgin Olive Oil', 'Virgin Olive Oil', 'White Sugar'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eu_prices.Product.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a good number of representatives from all the categories we have in the `USDA-Food`, but we miss an important source of carbohydrates: rice. Also, there is no meat present in the database.  \n",
    "For this reason, we go to scrape these information from FAO:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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>Area</th>\n",
       "      <th>Item</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Barley</td>\n",
       "      <td>2014</td>\n",
       "      <td>431.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Barley</td>\n",
       "      <td>2015</td>\n",
       "      <td>341.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Barley</td>\n",
       "      <td>2016</td>\n",
       "      <td>319.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Barley</td>\n",
       "      <td>2017</td>\n",
       "      <td>303.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Barley</td>\n",
       "      <td>2018</td>\n",
       "      <td>302.2</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>1976</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Wheat</td>\n",
       "      <td>2014</td>\n",
       "      <td>256.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Wheat</td>\n",
       "      <td>2015</td>\n",
       "      <td>189.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Wheat</td>\n",
       "      <td>2016</td>\n",
       "      <td>162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Wheat</td>\n",
       "      <td>2017</td>\n",
       "      <td>158.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Wheat</td>\n",
       "      <td>2018</td>\n",
       "      <td>218.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1981 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Area    Item  Year  Value\n",
       "0            Albania  Barley  2014  431.4\n",
       "1            Albania  Barley  2015  341.4\n",
       "2            Albania  Barley  2016  319.7\n",
       "3            Albania  Barley  2017  303.6\n",
       "4            Albania  Barley  2018  302.2\n",
       "...              ...     ...   ...    ...\n",
       "1976  United Kingdom   Wheat  2014  256.7\n",
       "1977  United Kingdom   Wheat  2015  189.4\n",
       "1978  United Kingdom   Wheat  2016  162.0\n",
       "1979  United Kingdom   Wheat  2017  158.3\n",
       "1980  United Kingdom   Wheat  2018  218.8\n",
       "\n",
       "[1981 rows x 4 columns]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fao_prices = pd.read_csv(\"data/raw/fao_cereal_meat_prices_201418.csv\", usecols=[\"Area\", \"Item\", \"Value\", \"Year\"])\n",
    "fao_prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note*: FAO data are in USD/ton \n",
    "\n",
    "At this point we need to restrict our product analysis. In the **_Which European countries can help Africa?_** section we defined the countries with the highest surplus in Europe, that are able to help Africa only by giving away a small fraction of their surplus.  \n",
    "From the previous classification we remove now the *Russian Federation*, because it has a huge surplus and would be able to solve the African problem (and we want a fair share between the richest European countries), together with *Poland, Ukraine* and *Romania*, because they are smaller nations.  \n",
    "At this point we have build the set of **top 5 countries that will help Africa: France, Italy, United Kingdom, Germany** and **Spain**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining countries\n",
    "best_countries = [\"France\", \"Italy\", \"United Kingdom\", \"Germany\", \"Spain\"]\n",
    "best_countries_code = [\"FR\", \"IT\", \"UK\", \"DE\", \"ES\"] # codes for the eu_prices\n",
    "\n",
    "# filtering our datasets\n",
    "eu_prices = eu_prices[eu_prices.Country.isin(best_countries_code)]\n",
    "fao_prices = fao_prices[fao_prices.Area.isin(best_countries)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considering only the most recent prices in the FAO cereal dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "fao_prices = fao_prices.sort_values(\"Year\", ascending=False).groupby([\"Area\", \"Item\"])\\\n",
    "                      .first().reset_index().drop(columns=\"Year\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look to the products in the FAO **cereal** dataset now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fao_prices.Item.value_counts().plot(kind=\"barh\")\n",
    "plt.title(\"Distribution of products inside the FAO cereal and meat dataset\")\n",
    "plt.xlabel(\"Number of countries covered\")\n",
    "plt.ylabel(\"Products\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two fundamental problems emerge here: we don't have the price for rice and meat (**cattle**) in all the countries of interest, **we need to impute them**  \n",
    "We start with the rice.  \n",
    "In order to do so, what we do now is try to find a correlation between the product we have in **all the countries** (*Wheat* and *Oats*) and the rice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "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>Area</th>\n",
       "      <th>Item_x</th>\n",
       "      <th>Value_x</th>\n",
       "      <th>Item_y</th>\n",
       "      <th>Value_y</th>\n",
       "      <th>Coeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>France</td>\n",
       "      <td>Rice, paddy</td>\n",
       "      <td>372.0</td>\n",
       "      <td>Oats</td>\n",
       "      <td>145.7</td>\n",
       "      <td>2.553191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Rice, paddy</td>\n",
       "      <td>403.7</td>\n",
       "      <td>Oats</td>\n",
       "      <td>272.3</td>\n",
       "      <td>1.482556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Rice, paddy</td>\n",
       "      <td>344.6</td>\n",
       "      <td>Oats</td>\n",
       "      <td>171.3</td>\n",
       "      <td>2.011675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>France</td>\n",
       "      <td>Rice, paddy</td>\n",
       "      <td>372.0</td>\n",
       "      <td>Wheat</td>\n",
       "      <td>195.0</td>\n",
       "      <td>1.907692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Rice, paddy</td>\n",
       "      <td>403.7</td>\n",
       "      <td>Wheat</td>\n",
       "      <td>233.9</td>\n",
       "      <td>1.725951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Rice, paddy</td>\n",
       "      <td>344.6</td>\n",
       "      <td>Wheat</td>\n",
       "      <td>217.3</td>\n",
       "      <td>1.585826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Area       Item_x  Value_x Item_y  Value_y     Coeff\n",
       "11  France  Rice, paddy    372.0   Oats    145.7  2.553191\n",
       "19   Italy  Rice, paddy    403.7   Oats    272.3  1.482556\n",
       "37   Spain  Rice, paddy    344.6   Oats    171.3  2.011675\n",
       "15  France  Rice, paddy    372.0  Wheat    195.0  1.907692\n",
       "21   Italy  Rice, paddy    403.7  Wheat    233.9  1.725951\n",
       "42   Spain  Rice, paddy    344.6  Wheat    217.3  1.585826"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rice_df = fao_prices[fao_prices.Item==\"Rice, paddy\"]\n",
    "rice_df = rice_df.merge(fao_prices, on=\"Area\")\n",
    "rice_df = rice_df[rice_df.Item_y.str.contains(\"Wheat|Oats\")]\n",
    "rice_df[\"Coeff\"] = rice_df.Value_x/rice_df.Value_y\n",
    "rice_df.sort_values(\"Item_y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean coefficient for oats is 2.0158076390008994\n",
      "The mean coefficient for wheat is 1.7398232052849225\n"
     ]
    }
   ],
   "source": [
    "mean_coeff_oats = rice_df[rice_df[\"Item_y\"] == \"Oats\"].Coeff.mean()\n",
    "mean_coeff_wheat = rice_df[rice_df[\"Item_y\"] == \"Wheat\"].Coeff.mean()\n",
    "print(\"The mean coefficient for oats is\", mean_coeff_oats)\n",
    "print(\"The mean coefficient for wheat is\", mean_coeff_wheat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the wheat seems to be the product with less fluctuation, we take this one for our imputation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "wheat_germany = fao_prices[(fao_prices.Area == \"Germany\") & (fao_prices.Item==\"Wheat\")].Value.values[0]\n",
    "row_germ = pd.DataFrame(np.array([[\"Germany\", \"Rice, paddy\", '%.1f'%(mean_coeff_wheat*wheat_germany)]]), columns=[\"Area\", \"Item\", \"Value\"])\n",
    "wheat_uk = fao_prices[(fao_prices.Area == \"United Kingdom\") & (fao_prices.Item==\"Wheat\")].Value.values[0]\n",
    "row_uk = pd.DataFrame(np.array([[\"United Kingdom\", \"Rice, paddy\", '%.1f'%(mean_coeff_wheat*wheat_uk)]]), columns=[\"Area\", \"Item\", \"Value\"])\n",
    "fao_prices = fao_prices.append(row_germ)\n",
    "fao_prices = fao_prices.append(row_uk).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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>Area</th>\n",
       "      <th>Item</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>Barley</td>\n",
       "      <td>195.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Barley</td>\n",
       "      <td>195.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Barley</td>\n",
       "      <td>203.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Barley</td>\n",
       "      <td>197.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Area    Item  Value\n",
       "0           France  Barley  195.1\n",
       "16         Germany  Barley  195.4\n",
       "39           Spain  Barley  203.7\n",
       "60  United Kingdom  Barley  197.4"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fao_prices[fao_prices.Item==\"Barley\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the meat case, we first look to the countries for which we have data (considering the most general case: cattle, that we consider to be beef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "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>Area</th>\n",
       "      <th>Item</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>France</td>\n",
       "      <td>Meat, cattle</td>\n",
       "      <td>4549.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Meat, cattle</td>\n",
       "      <td>4033.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Meat, cattle</td>\n",
       "      <td>2774.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Meat, cattle</td>\n",
       "      <td>5328.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Area          Item   Value\n",
       "2           France  Meat, cattle  4549.5\n",
       "18         Germany  Meat, cattle  4033.1\n",
       "43           Spain  Meat, cattle  2774.8\n",
       "61  United Kingdom  Meat, cattle  5328.2"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fao_prices[fao_prices.Item==\"Meat, cattle\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to impute the price for Italy:\n",
    "- For Italy we can look [here](https://www.bordbia.ie/farmers-growers/farmers/prices-markets/eu-world-cattle-prices/?country=Italy). Taking a price average we have a price of 3.42€/kg\n",
    "\n",
    "We need to multiply this price by **1.10** (actual exchange EUR/USD) and 1000 (to consider tons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "meat_italy = pd.DataFrame(np.array([[\"Italy\", \"Meat, cattle\", '%.1f'%(3.42*1.10*1000)]]), columns=[\"Area\", \"Item\", \"Value\"])\n",
    "fao_prices = fao_prices.append(meat_italy).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For pork, we already have all the data so no need to impute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reprint the plot to see the final product in the FAO dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fao_prices.Item.value_counts().plot(kind=\"barh\")\n",
    "plt.title(\"Distribution of products inside the FAO cereal and meat dataset\")\n",
    "plt.xlabel(\"Number of countries covered\")\n",
    "plt.ylabel(\"Products\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to report all in a single unit in `eu_prices`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['€/100 kg carcass weight', '€/100 kg', '€/100 kg net weight',\n",
       "       '€/t', '€/100 Kg'], dtype=object)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eu_prices.Unit.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we are working with large amount of data and FAO as data in USD/tons, we take everything as EUR/ton (multiply 10) and multiply by **1.10** (EUR/USD change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "eu_prices.loc[eu_prices['Unit'].str.contains('100'), 'Price'] = eu_prices.loc[eu_prices['Unit'].str.contains('100'), 'Price']\\\n",
    "                                                                .apply(lambda x: x*1.10*10)\n",
    "eu_prices = eu_prices.drop(columns=\"Unit\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now assess, as we did for the `fao_prices`, if the prices we have are correctly represented among all our countries.  \n",
    "First step we do to this end is to keep only the most recent data for each product inside each country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "eu_prices = eu_prices.sort_values(\"Period\", ascending=False).groupby([\"Country\", \"Category\", \"Product\"])\\\n",
    "                      .first().reset_index().drop(columns=\"Period\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 15))\n",
    "eu_prices.Product.value_counts().plot(kind=\"barh\")\n",
    "plt.title(\"Distribution of products inside the eu_countries dataset\")\n",
    "plt.xlabel(\"Number of countries covered\")\n",
    "plt.ylabel(\"Products\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "eu_prices.Product = eu_prices.Product.apply(lambda x: \"Apples\" if \"Apples\" in x else x)\n",
    "eu_prices.Product = eu_prices.Product.apply(lambda x: \"Tomatoes\" if \"Tomatoes\" in x else x)\n",
    "eu_prices.Product = eu_prices.Product.apply(lambda x: \"Bread\" if \"Bread\" in x else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping and readd them\n",
    "apples_eu = eu_prices[eu_prices.Product==\"Apples\"].groupby([\"Country\", \"Category\", \"Product\"]).agg(\"mean\").reset_index()\n",
    "eu_prices = eu_prices[~(eu_prices[\"Product\"]==\"Apples\")].reset_index(drop=True)\n",
    "tomatoes_eu = eu_prices[eu_prices.Product==\"Tomatoes\"].groupby([\"Country\", \"Category\", \"Product\"]).agg(\"mean\").reset_index()\n",
    "eu_prices = eu_prices[~(eu_prices[\"Product\"]==\"Tomatoes\")].reset_index(drop=True)\n",
    "bread_eu = eu_prices[eu_prices.Product==\"Bread\"].groupby([\"Country\", \"Category\", \"Product\"]).agg(\"mean\").reset_index()\n",
    "eu_prices = eu_prices[~(eu_prices[\"Product\"]==\"Bread\")].reset_index(drop=True)\n",
    "eu_prices = eu_prices.append(apples_eu)\n",
    "eu_prices = eu_prices.append(tomatoes_eu)\n",
    "eu_prices = eu_prices.append(bread_eu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 15))\n",
    "eu_prices.Product.value_counts().plot(kind=\"barh\")\n",
    "plt.title(\"Distribution of products inside the eu_countries dataset\")\n",
    "plt.xlabel(\"Number of countries covered\")\n",
    "plt.ylabel(\"Products\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Merging didn't solve the situation.**  \n",
    "As a consequence, we decide to **drop all the products with not at least 4 counts** and put our *imputing efforts* just on them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts_eu = eu_prices.Product.value_counts()\n",
    "possible_products = counts_eu[counts_eu >= len(best_countries) - 1].index\n",
    "eu_prices = eu_prices[eu_prices.Product.isin(possible_products)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# replot to zoom on our interested products\n",
    "eu_prices.Product.value_counts().plot(kind=\"barh\")\n",
    "plt.title(\"Distribution of products inside the eu_countries dataset\")\n",
    "plt.xlabel(\"Number of countries covered\")\n",
    "plt.ylabel(\"Products\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Printing the counts to understand which countries to impute. A country with all products should count **13 products**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ES    13\n",
       "DE    12\n",
       "IT    12\n",
       "FR    11\n",
       "UK    10\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eu_prices.Country.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only Spain has all the needed products. We need to impute all the others. We therefore proceed to print all the missing points in the other countries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing product in Italy: {'Malting Barley'}\n",
      "Missing product in Germany: {'Onions'}\n",
      "Missing product in France: {'Butter', 'Lettuces'}\n",
      "Missing product in United Kingdom: {'Apples', 'Cherries', 'Feed Maize'}\n"
     ]
    }
   ],
   "source": [
    "# set difference between the products\n",
    "need_it = set(possible_products) - set(eu_prices[eu_prices.Country==\"IT\"].Product.values)\n",
    "need_de = set(possible_products) - set(eu_prices[eu_prices.Country==\"DE\"].Product.values)\n",
    "need_fr = set(possible_products) - set(eu_prices[eu_prices.Country==\"FR\"].Product.values)\n",
    "need_uk = set(possible_products) - set(eu_prices[eu_prices.Country==\"UK\"].Product.values)\n",
    "\n",
    "# printing\n",
    "print(\"Missing product in Italy:\", need_it)\n",
    "print(\"Missing product in Germany:\", need_de)\n",
    "print(\"Missing product in France:\", need_fr)\n",
    "print(\"Missing product in United Kingdom:\", need_uk)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Italy\n",
    "We need to impute the **Malting Barley**. The actual italian price can be found [here](https://www.clal.it/en/index.php?section=conf_cereali#orzo), where the price (on the day of writing) is 189 EUR/ton. We multiply by 1.10 to have the price in USD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "barley_italy = pd.DataFrame(np.array([[\"IT\", \"Vegetal Products\", \"Malting Barley\", '%.2f'%(189*1.10)]]), columns=[\"Country\", \"Category\", \"Product\", \"Price\"])\n",
    "eu_prices = eu_prices.append(barley_italy).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Germany\n",
    "We need to impute the **Onions**. We can easily expect that onions won't be part of our diet, so we can drop them for all of the countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "eu_prices = eu_prices[~(eu_prices.Product==\"Onions\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### France\n",
    "We need to impute **Lettuce** and **Butter**. For the second one, we can drop the aliment because it won't be part of the diet for sure. For the Lettuces, we can look at the FAO data directly from the [website](http://www.fao.org/faostat/en/#data/PP): 1296.3 in 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "eu_prices = eu_prices[~(eu_prices.Product==\"Butter\")]\n",
    "lettuce_france = pd.DataFrame(np.array([[\"FR\", \"Vegetable Products\", \"Lettuces\", '%.2f'%(1269.3)]]), columns=[\"Country\", \"Category\", \"Product\", \"Price\"])\n",
    "eu_prices = eu_prices.append(lettuce_france).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "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>Country</th>\n",
       "      <th>Category</th>\n",
       "      <th>Product</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DE</td>\n",
       "      <td>Vegetal Products</td>\n",
       "      <td>Feed Maize</td>\n",
       "      <td>161.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ES</td>\n",
       "      <td>Vegetal Products</td>\n",
       "      <td>Feed Maize</td>\n",
       "      <td>170.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>FR</td>\n",
       "      <td>Vegetal Products</td>\n",
       "      <td>Feed Maize</td>\n",
       "      <td>153.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>IT</td>\n",
       "      <td>Vegetal Products</td>\n",
       "      <td>Feed Maize</td>\n",
       "      <td>149.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country          Category     Product   Price\n",
       "7       DE  Vegetal Products  Feed Maize  161.88\n",
       "16      ES  Vegetal Products  Feed Maize  170.38\n",
       "24      FR  Vegetal Products  Feed Maize  153.65\n",
       "33      IT  Vegetal Products  Feed Maize  149.64"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eu_prices[(eu_prices.Product==\"Feed Maize\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### United Kingdom\n",
    "We need to impute **Apples**, **Cherries** and **Feed Maize**. For Apples and cherries we can use FAO data: 1343.4 and 4574.9 respectively. The price of Maize can be found [here](https://ahdb.org.uk/cereals-oilseeds-markets), 195\\$ per ton."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "apples_uk = pd.DataFrame(np.array([[\"UK\", \"Fruit Products\", \"Apples\", '%.2f'%(1343.4)]]), columns=[\"Country\", \"Category\", \"Product\", \"Price\"])\n",
    "cherries_uk = pd.DataFrame(np.array([[\"UK\", \"Fruit Products\", \"Cherries\", '%.2f'%(4574.9)]]), columns=[\"Country\", \"Category\", \"Product\", \"Price\"])\n",
    "maize_uk = pd.DataFrame(np.array([[\"UK\", \"Vegetal Products\", \"Feed Maize\", '%.2f'%(195)]]), columns=[\"Country\", \"Category\", \"Product\", \"Price\"])\n",
    "\n",
    "eu_prices = eu_prices.append(apples_uk)\n",
    "eu_prices = eu_prices.append(cherries_uk)\n",
    "eu_prices = eu_prices.append(maize_uk).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if everything went right:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# replot to zoom on our interested products\n",
    "eu_prices.Product.value_counts().plot(kind=\"barh\")\n",
    "plt.title(\"Distribution of products inside the eu_countries dataset\")\n",
    "plt.xlabel(\"Number of countries covered\")\n",
    "plt.ylabel(\"Products\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the **Vegetables** category is not well represented by our actual dataset. For this reason, we consider **FAO data for vegetables** in order to fill the gaps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fao_vegetables_prc = pd.read_csv(\"data/raw/fao_vegetables_prices_201418.csv\", usecols=[\"Area\", \"Item\", \"Value\", \"Year\"])\n",
    "fao_vegetables_prc = fao_vegetables_prc[fao_vegetables_prc.Area.isin(best_countries)]\n",
    "\n",
    "fao_vegetables_prc = fao_vegetables_prc.sort_values(\"Year\", ascending=False).groupby([\"Area\", \"Item\"])\\\n",
    "                      .first().reset_index().drop(columns=\"Year\")\n",
    "\n",
    "# plotting\n",
    "fao_vegetables_prc.Item.value_counts().plot(kind=\"barh\")\n",
    "plt.title(\"Distribution of products inside the FAO vegetables dataset\")\n",
    "plt.xlabel(\"Number of countries covered\")\n",
    "plt.ylabel(\"Products\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we did before, we take only the products present in at least **4 countries** for the next analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts_fao_veg = fao_vegetables_prc.Item.value_counts()\n",
    "possible_products_veg = counts_fao_veg[counts_fao_veg >= len(best_countries) - 1].index\n",
    "fao_vegetables_prc = fao_vegetables_prc[fao_vegetables_prc.Item.isin(possible_products_veg)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# replot to zoom on our interested products\n",
    "fao_vegetables_prc.Item.value_counts().plot(kind=\"barh\")\n",
    "plt.title(\"Distribution of products inside the FAO vegetables dataset\")\n",
    "plt.xlabel(\"Number of countries covered\")\n",
    "plt.ylabel(\"Products\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also drop **Carrots** and **Lettuce** before already present in the precedent dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "fao_vegetables_prc = fao_vegetables_prc[~(fao_vegetables_prc.Item.isin([\"Carrots and turnips\", \"Lettuce and chicory\"]))]\n",
    "possible_products_veg = possible_products_veg[~(possible_products_veg.isin([\"Carrots and turnips\", \"Lettuce and chicory\"]))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# replot to zoom on our interested products\n",
    "fao_vegetables_prc.Item.value_counts().plot(kind=\"barh\")\n",
    "plt.title(\"Distribution of products inside the FAO vegetables dataset\")\n",
    "plt.xlabel(\"Number of countries covered\")\n",
    "plt.ylabel(\"Products\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how these products are represented:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spain             8\n",
       "United Kingdom    8\n",
       "France            8\n",
       "Germany           8\n",
       "Italy             2\n",
       "Name: Area, dtype: int64"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fao_vegetables_prc.Area.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only country in which products are missing is **Italy**. We need to impute manually the prices of these products:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing product in Italy: {'Cabbages and other brassicas', 'Cauliflowers and broccoli', 'Mushrooms and truffles', 'Cucumbers and gherkins', 'Beans, green', 'Peas, green'}\n"
     ]
    }
   ],
   "source": [
    "# set difference between the products\n",
    "need_it = set(possible_products_veg) - set(fao_vegetables_prc[fao_vegetables_prc.Area==\"Italy\"].Item.values)\n",
    "\n",
    "# printing\n",
    "print(\"Missing product in Italy:\", need_it)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing Italy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The price for the **cucumbers** and the **cauliflowers** can be found in the `eu_prices` dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "eu_prices2 = pd.read_csv(\"data/raw/europe_food_prices.csv\", usecols=[\"Category\", \"Product desc\", \"Unit\", \"Country\", \"Period\", \"MP Market Price\"])\\\n",
    "            .rename(columns={\"Product desc\": \"Product\", \"MP Market Price\":\"Price\"})\n",
    "eu_prices2 = eu_prices2[eu_prices2.Country.isin(best_countries_code)]\n",
    "eu_prices2 = eu_prices2[eu_prices2.Period.between(201400, 201910)]\n",
    "eu_prices2 = eu_prices2.sort_values(\"Period\", ascending=False).groupby([\"Country\", \"Category\", \"Product\"])\\\n",
    "                      .first().reset_index().drop(columns=\"Period\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "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>Country</th>\n",
       "      <th>Category</th>\n",
       "      <th>Product</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>ES</td>\n",
       "      <td>Vegetable Products</td>\n",
       "      <td>Cucumbers</td>\n",
       "      <td>€/100 kg net weight</td>\n",
       "      <td>49.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>FR</td>\n",
       "      <td>Vegetable Products</td>\n",
       "      <td>Cucumbers</td>\n",
       "      <td>€/100 kg net weight</td>\n",
       "      <td>114.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>IT</td>\n",
       "      <td>Vegetable Products</td>\n",
       "      <td>Cucumbers</td>\n",
       "      <td>€/100 kg net weight</td>\n",
       "      <td>68.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country            Category    Product                 Unit   Price\n",
       "63       ES  Vegetable Products  Cucumbers  €/100 kg net weight   49.81\n",
       "101      FR  Vegetable Products  Cucumbers  €/100 kg net weight  114.55\n",
       "144      IT  Vegetable Products  Cucumbers  €/100 kg net weight   68.00"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eu_prices2[eu_prices2.Product==\"Cucumbers\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "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>Country</th>\n",
       "      <th>Category</th>\n",
       "      <th>Product</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>ES</td>\n",
       "      <td>Vegetable Products</td>\n",
       "      <td>Cauliflowers</td>\n",
       "      <td>€/100 kg net weight</td>\n",
       "      <td>25.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>FR</td>\n",
       "      <td>Vegetable Products</td>\n",
       "      <td>Cauliflowers</td>\n",
       "      <td>€/100 kg net weight</td>\n",
       "      <td>40.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>IT</td>\n",
       "      <td>Vegetable Products</td>\n",
       "      <td>Cauliflowers</td>\n",
       "      <td>€/100 kg net weight</td>\n",
       "      <td>62.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country            Category       Product                 Unit  Price\n",
       "61       ES  Vegetable Products  Cauliflowers  €/100 kg net weight  25.20\n",
       "99       FR  Vegetable Products  Cauliflowers  €/100 kg net weight  40.68\n",
       "142      IT  Vegetable Products  Cauliflowers  €/100 kg net weight  62.71"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eu_prices2[eu_prices2.Product==\"Cauliflowers\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prices are in Eur/100 kg, we need usd/tonnes\n",
    "cucumbers = pd.DataFrame(np.array([[\"Italy\", \"Cucumbers and gherkins\", 68.00*1.10*10]]), columns=[\"Area\", \"Item\", \"Value\"])\n",
    "cauliflowers = pd.DataFrame(np.array([[\"Italy\", \"Cauliflowers and broccoli\", 62.71*1.10*10]]), columns=[\"Area\", \"Item\", \"Value\"])\n",
    "\n",
    "\n",
    "fao_vegetables_prc = fao_vegetables_prc.append(cucumbers)\n",
    "fao_vegetables_prc = fao_vegetables_prc.append(cauliflowers).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the **peas** and the **beans** of 2017 in Italy can be found [here](http://www.codima.info/trunk/nor_file_107_decreto-ministeriale-n.-0031908-del-29-dicembre-2016-prezzi_parte_1-.pdf) (page in Italian).  \n",
    "The price is **120.33€/100kg** for the peas and **175.56€/100kg** for the beans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prices are in Eur/100 kg, we need usd/tonnes\n",
    "peas = pd.DataFrame(np.array([[\"Italy\", \"Peas, green\", 120.33*1.10*10]]), columns=[\"Area\", \"Item\", \"Value\"])\n",
    "beans = pd.DataFrame(np.array([[\"Italy\", \"Beans, green\", 175.56*1.10*10]]), columns=[\"Area\", \"Item\", \"Value\"])\n",
    "\n",
    "\n",
    "fao_vegetables_prc = fao_vegetables_prc.append(peas)\n",
    "fao_vegetables_prc = fao_vegetables_prc.append(beans).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, we didn't find any reliable price for **Cabbages and mushrooms**, so we drop them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "fao_vegetables_prc = fao_vegetables_prc[~(fao_vegetables_prc.Item.isin([\"Cabbages and other brassicas\", \"Mushrooms and truffles\"]))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final check on these products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fao_vegetables_prc.Item.value_counts().plot(kind=\"barh\")\n",
    "plt.title(\"Distribution of products inside the FAO vegetable dataset\")\n",
    "plt.xlabel(\"Number of countries covered\")\n",
    "plt.ylabel(\"Products\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also now rename the codes in  `eu_prices` to proper names of nations before merging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_country = dict(zip(best_countries_code, best_countries))\n",
    "eu_prices = eu_prices.replace({\"Country\":dict_country})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final step is combining the three dataframe together. We modify the needed products from the FAO datasets and adapt it to `eu_prices`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts_fao = fao_prices.Item.value_counts()\n",
    "fao_products = counts_fao[counts_fao == len(best_countries)].index\n",
    "fao_prices = fao_prices[fao_prices.Item.isin(fao_products)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping the no longer needed category column from eu\n",
    "eu_prices.drop(columns=\"Category\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "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>Country</th>\n",
       "      <th>Product</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Apples</td>\n",
       "      <td>1343.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Apples</td>\n",
       "      <td>597.443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Apples</td>\n",
       "      <td>898.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>France</td>\n",
       "      <td>Apples</td>\n",
       "      <td>1029.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Apples</td>\n",
       "      <td>713.743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country Product    Price\n",
       "0  United Kingdom  Apples  1343.40\n",
       "1         Germany  Apples  597.443\n",
       "2           Spain  Apples   898.04\n",
       "3          France  Apples  1029.79\n",
       "4           Italy  Apples  713.743"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fao_prices = fao_prices.replace({\"Item\":{\"Rice, paddy\": \"Rice\", \"Meat, cattle\": \"Meat\"}})\n",
    "fao_prices = fao_prices[[\"Area\", \"Item\", \"Value\"]]\n",
    "fao_prices = fao_prices.rename(columns={\"Item\": \"Product\", \"Area\":\"Country\", \"Value\":\"Price\"})\n",
    "fao_vegetables_prc = fao_vegetables_prc.replace({\"Item\":{\"Cauliflowers and broccoli\": \"Cauliflowers\", \"Peas, green\": \"Peas\", \"Beans, green\": \"Beans\", \"Cucumbers and gherkins\": \"Cucumbers\"}})\n",
    "fao_vegetables_prc = fao_vegetables_prc[[\"Area\", \"Item\", \"Value\"]]\n",
    "fao_vegetables_prc = fao_vegetables_prc.rename(columns={\"Item\": \"Product\", \"Area\":\"Country\", \"Value\":\"Price\"})\n",
    "prices = eu_prices.append(fao_prices).append(fao_vegetables_prc).sort_values(by=\"Product\").reset_index(drop=True)\n",
    "prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The final products present in the dataset are: ['Apples' 'Beans' 'Bread' 'Carrots' 'Cauliflowers' 'Cherries' 'Chicken'\n",
      " 'Cucumbers' 'Feed Barley' 'Feed Maize' 'Lettuces' 'Malting Barley' 'Meat'\n",
      " 'Meat, pig' 'Oats' 'Peas' 'Potatoes' 'Raw Milk' 'Rice' 'Strawberries'\n",
      " 'Tomatoes' 'Wheat']\n"
     ]
    }
   ],
   "source": [
    "print(\"The final products present in the dataset are: {}\".format(prices.Product.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have finally build our **final prices dataset!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Filtering the food dataset  \n",
    "Now that we know the set of products between we can choose, it's time to filter the `USDA_food` accordingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining the set of foods between we can choose\n",
    "foods = prices.Product.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "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>Food Group</th>\n",
       "      <th>Food Name</th>\n",
       "      <th>Protein (g)</th>\n",
       "      <th>Carbohydrates (g)</th>\n",
       "      <th>Fat (g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, meat and skin and...</td>\n",
       "      <td>18.33</td>\n",
       "      <td>0.13</td>\n",
       "      <td>14.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, meat and skin, raw</td>\n",
       "      <td>18.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, meat only, raw</td>\n",
       "      <td>21.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, skin only, raw</td>\n",
       "      <td>13.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>32.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, giblets, raw</td>\n",
       "      <td>17.88</td>\n",
       "      <td>1.80</td>\n",
       "      <td>4.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4052</th>\n",
       "      <td>Beef Products</td>\n",
       "      <td>Beef, ground, 95% lean meat / 5% fat, raw</td>\n",
       "      <td>21.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4057</th>\n",
       "      <td>Beef Products</td>\n",
       "      <td>Beef, ground, 90% lean meat / 10% fat, raw</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4062</th>\n",
       "      <td>Beef Products</td>\n",
       "      <td>Beef, ground, 85% lean meat / 15% fat, raw</td>\n",
       "      <td>18.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4067</th>\n",
       "      <td>Beef Products</td>\n",
       "      <td>Beef, ground, 80% lean meat / 20% fat, raw</td>\n",
       "      <td>17.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4072</th>\n",
       "      <td>Beef Products</td>\n",
       "      <td>Beef, ground, 75% lean meat / 25% fat, raw</td>\n",
       "      <td>15.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>269 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Food Group                                          Food Name  \\\n",
       "1     Poultry Products  Chicken, broilers or fryers, meat and skin and...   \n",
       "6     Poultry Products    Chicken, broilers or fryers, meat and skin, raw   \n",
       "11    Poultry Products        Chicken, broilers or fryers, meat only, raw   \n",
       "15    Poultry Products        Chicken, broilers or fryers, skin only, raw   \n",
       "20    Poultry Products          Chicken, broilers or fryers, giblets, raw   \n",
       "...                ...                                                ...   \n",
       "4052     Beef Products          Beef, ground, 95% lean meat / 5% fat, raw   \n",
       "4057     Beef Products         Beef, ground, 90% lean meat / 10% fat, raw   \n",
       "4062     Beef Products         Beef, ground, 85% lean meat / 15% fat, raw   \n",
       "4067     Beef Products         Beef, ground, 80% lean meat / 20% fat, raw   \n",
       "4072     Beef Products         Beef, ground, 75% lean meat / 25% fat, raw   \n",
       "\n",
       "      Protein (g)  Carbohydrates (g)  Fat (g)  \n",
       "1           18.33               0.13    14.83  \n",
       "6           18.60               0.00    15.06  \n",
       "11          21.39               0.00     3.08  \n",
       "15          13.33               0.00    32.35  \n",
       "20          17.88               1.80     4.47  \n",
       "...           ...                ...      ...  \n",
       "4052        21.41               0.00     5.00  \n",
       "4057        20.00               0.00    10.00  \n",
       "4062        18.59               0.00    15.00  \n",
       "4067        17.17               0.00    20.00  \n",
       "4072        15.76               0.00    25.00  \n",
       "\n",
       "[269 rows x 5 columns]"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_properties[food_properties[\"Food Name\"].str.contains('|'.join(foods), case=False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "269 rows, but there are multiple matches. Let's look to the elements that don't have a match:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the set of total foods and set to lower\n",
    "total_foods = food_properties[\"Food Name\"].unique()\n",
    "total_foods = [t.lower() for t in total_foods]\n",
    "\n",
    "# consider also foods to lower fo check the match\n",
    "foods = [f.lower()for f in foods]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find set of not matching\n",
    "not_matching = [f for f in foods if not any(f in t for t in total_foods)]\n",
    "len(not_matching)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cauliflowers',\n",
       " 'cucumbers',\n",
       " 'feed barley',\n",
       " 'feed maize',\n",
       " 'lettuces',\n",
       " 'malting barley',\n",
       " 'meat, pig',\n",
       " 'oats',\n",
       " 'raw milk',\n",
       " 'wheat']"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_matching"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can try to divide the multiple words, also remove duplicates after that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "foods = [f.replace(\",\",\"\").replace(\"(\", \"\").replace(\")\", \"\").split() for f in foods]\n",
    "foods = [l for sublist in foods for l in sublist]\n",
    "foods = list(set(foods))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second step is removing all generated stopwords and add singulars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = stopwords.words('english')\n",
    "stop_words += [\"feed\", \"raw\"]\n",
    "foods = [f for f in foods if f not in stop_words]\n",
    "lem = WordNetLemmatizer()\n",
    "singular = [lem.lemmatize(f) for f in foods]\n",
    "foods += singular\n",
    "foods = list(set(foods))\n",
    "foods = [f for f in foods if f not in stop_words]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bean',\n",
       " 'carrots',\n",
       " 'lettuces',\n",
       " 'cauliflower',\n",
       " 'strawberries',\n",
       " 'oats',\n",
       " 'barley',\n",
       " 'pig',\n",
       " 'lettuce',\n",
       " 'milk',\n",
       " 'rice',\n",
       " 'cucumbers',\n",
       " 'maize',\n",
       " 'tomato',\n",
       " 'beans',\n",
       " 'cherries',\n",
       " 'meat',\n",
       " 'tomatoes',\n",
       " 'cucumber',\n",
       " 'cherry',\n",
       " 'potato',\n",
       " 'carrot',\n",
       " 'cauliflowers',\n",
       " 'malting',\n",
       " 'chicken',\n",
       " 'apples',\n",
       " 'apple',\n",
       " 'peas',\n",
       " 'strawberry',\n",
       " 'pea',\n",
       " 'wheat',\n",
       " 'potatoes',\n",
       " 'oat',\n",
       " 'bread']"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing the set of food\n",
    "foods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's possible to note that _\"meat\"_ is too generic for our purpose, as we considered cow or beef in analyzing the prices. For this reason, we remove meat and add \"cow\" and \"beef\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "foods = [f for f in foods if f!=\"meat\"]\n",
    "foods += [\"beef\", \"cow\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New matching check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "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>Food Group</th>\n",
       "      <th>Food Name</th>\n",
       "      <th>Protein (g)</th>\n",
       "      <th>Carbohydrates (g)</th>\n",
       "      <th>Fat (g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, meat and skin and...</td>\n",
       "      <td>18.33</td>\n",
       "      <td>0.13</td>\n",
       "      <td>14.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, meat and skin, raw</td>\n",
       "      <td>18.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, meat only, raw</td>\n",
       "      <td>21.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, skin only, raw</td>\n",
       "      <td>13.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>32.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, giblets, raw</td>\n",
       "      <td>17.88</td>\n",
       "      <td>1.80</td>\n",
       "      <td>4.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4142</th>\n",
       "      <td>Beef Products</td>\n",
       "      <td>Beef, round, tip round, roast, separable lean ...</td>\n",
       "      <td>21.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4145</th>\n",
       "      <td>Beef Products</td>\n",
       "      <td>Beef, flank, steak, separable lean only, trimm...</td>\n",
       "      <td>21.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4146</th>\n",
       "      <td>Beef Products</td>\n",
       "      <td>Beef, flank, steak, separable lean only, trimm...</td>\n",
       "      <td>21.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4147</th>\n",
       "      <td>Beef Products</td>\n",
       "      <td>Beef, brisket, flat half, separable lean and f...</td>\n",
       "      <td>18.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>22.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4148</th>\n",
       "      <td>Beef Products</td>\n",
       "      <td>Beef, brisket, flat half, separable lean and f...</td>\n",
       "      <td>17.77</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>602 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Food Group                                          Food Name  \\\n",
       "1     Poultry Products  Chicken, broilers or fryers, meat and skin and...   \n",
       "6     Poultry Products    Chicken, broilers or fryers, meat and skin, raw   \n",
       "11    Poultry Products        Chicken, broilers or fryers, meat only, raw   \n",
       "15    Poultry Products        Chicken, broilers or fryers, skin only, raw   \n",
       "20    Poultry Products          Chicken, broilers or fryers, giblets, raw   \n",
       "...                ...                                                ...   \n",
       "4142     Beef Products  Beef, round, tip round, roast, separable lean ...   \n",
       "4145     Beef Products  Beef, flank, steak, separable lean only, trimm...   \n",
       "4146     Beef Products  Beef, flank, steak, separable lean only, trimm...   \n",
       "4147     Beef Products  Beef, brisket, flat half, separable lean and f...   \n",
       "4148     Beef Products  Beef, brisket, flat half, separable lean and f...   \n",
       "\n",
       "      Protein (g)  Carbohydrates (g)  Fat (g)  \n",
       "1           18.33               0.13    14.83  \n",
       "6           18.60               0.00    15.06  \n",
       "11          21.39               0.00     3.08  \n",
       "15          13.33               0.00    32.35  \n",
       "20          17.88               1.80     4.47  \n",
       "...           ...                ...      ...  \n",
       "4142        21.38               0.00     3.35  \n",
       "4145        21.57               0.00     5.47  \n",
       "4146        21.43               0.00     5.00  \n",
       "4147        18.12               0.12    22.15  \n",
       "4148        17.77               0.00    22.21  \n",
       "\n",
       "[602 rows x 5 columns]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_properties[food_properties[\"Food Name\"].str.contains('|'.join(foods), case=False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a good set of products now, so we can stop here for the next considerations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "poss_diet = food_properties[food_properties[\"Food Name\"].str.contains('|'.join(foods), case=False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check of our residue groups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Poultry Products', 'Fruits and Fruit Juices',\n",
       "       'Vegetables and Vegetable Products', 'Beef Products',\n",
       "       'Finfish and Shellfish Products', 'Legumes and Legume Products',\n",
       "       'Lamb, Veal, and Game Products', 'Cereal Grains and Pasta'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poss_diet[\"Food Group\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fish products seems a bit strange for what we analyzed so far, check on it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "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>Food Group</th>\n",
       "      <th>Food Name</th>\n",
       "      <th>Protein (g)</th>\n",
       "      <th>Carbohydrates (g)</th>\n",
       "      <th>Fat (g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2312</th>\n",
       "      <td>Finfish and Shellfish Products</td>\n",
       "      <td>Fish, anchovy, european, raw</td>\n",
       "      <td>20.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2364</th>\n",
       "      <td>Finfish and Shellfish Products</td>\n",
       "      <td>Fish, milkfish, raw</td>\n",
       "      <td>20.53</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2437</th>\n",
       "      <td>Finfish and Shellfish Products</td>\n",
       "      <td>Fish, turbot, european, raw</td>\n",
       "      <td>16.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Food Group                     Food Name  \\\n",
       "2312  Finfish and Shellfish Products  Fish, anchovy, european, raw   \n",
       "2364  Finfish and Shellfish Products           Fish, milkfish, raw   \n",
       "2437  Finfish and Shellfish Products   Fish, turbot, european, raw   \n",
       "\n",
       "      Protein (g)  Carbohydrates (g)  Fat (g)  \n",
       "2312        20.35                0.0     4.84  \n",
       "2364        20.53                0.0     6.73  \n",
       "2437        16.05                0.0     2.95  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poss_diet[poss_diet[\"Food Group\"] == \"Finfish and Shellfish Products\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finfish and Shellfish Products is not really represented by our prices database, so we drop the category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "poss_diet = poss_diet[~poss_diet[\"Food Group\"].isin([\"Finfish and Shellfish Products\"])].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now adjust the units. The grams of proteins in 100g serve **[grams of proteins/100g]** times the kcalalories in 1 gram of proteins **[kcal/g of proteins]** will result in the kilocalories obtained from proteins for 100g serve **[kcal/100g]**. Same argument che be extended to carbohydrates and fat.  <br /> According to the [National Agriculture Library](https://www.nal.usda.gov/fnic/how-many-calories-are-one-gram-fat-carbohydrate-or-protein):\n",
    " - 1 gram of protein corresponds to **4 kcal**\n",
    " - 1 gram of carbohydrates corresponds to **4 kcal**\n",
    " - 1 gram of fat corresponds to **9 kcal**\n",
    "\n",
    "According to what has been said above, we will multiply the values and obtain a new dataframe `diet_kcal` in which values have units of **[kcal/100g]**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "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>Food Group</th>\n",
       "      <th>Food Name</th>\n",
       "      <th>Protein (kcal/100g)</th>\n",
       "      <th>Carbohydrates (kcal/100g)</th>\n",
       "      <th>Fat (kcal/100g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, meat and skin and...</td>\n",
       "      <td>73.32</td>\n",
       "      <td>0.52</td>\n",
       "      <td>133.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, meat and skin, raw</td>\n",
       "      <td>74.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>135.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, meat only, raw</td>\n",
       "      <td>85.56</td>\n",
       "      <td>0.00</td>\n",
       "      <td>27.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, skin only, raw</td>\n",
       "      <td>53.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>291.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, giblets, raw</td>\n",
       "      <td>71.52</td>\n",
       "      <td>7.20</td>\n",
       "      <td>40.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Food Group                                          Food Name  \\\n",
       "0  Poultry Products  Chicken, broilers or fryers, meat and skin and...   \n",
       "1  Poultry Products    Chicken, broilers or fryers, meat and skin, raw   \n",
       "2  Poultry Products        Chicken, broilers or fryers, meat only, raw   \n",
       "3  Poultry Products        Chicken, broilers or fryers, skin only, raw   \n",
       "4  Poultry Products          Chicken, broilers or fryers, giblets, raw   \n",
       "\n",
       "   Protein (kcal/100g)  Carbohydrates (kcal/100g)  Fat (kcal/100g)  \n",
       "0                73.32                       0.52           133.47  \n",
       "1                74.40                       0.00           135.54  \n",
       "2                85.56                       0.00            27.72  \n",
       "3                53.32                       0.00           291.15  \n",
       "4                71.52                       7.20            40.23  "
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#multiply values with vector of kcal/g, merging to keep Food \n",
    "kcal_g = np.array([4,4,9])\n",
    "diet_kcal = poss_diet[[\"Food Group\", \"Food Name\"]].merge(poss_diet[poss_diet.columns[2:]].multiply(kcal_g),left_index=True, right_index=True)\n",
    "diet_kcal.rename(columns={'Protein (g)':'Protein (kcal/100g)',\n",
    "                          'Carbohydrates (g)':'Carbohydrates (kcal/100g)',\n",
    "                          'Fat (g)':'Fat (kcal/100g)'}, \n",
    "                 inplace=True)\n",
    "diet_kcal.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next point we are going to do now is trying to give a rank to each product taking into account the necessity that each person, in their diet, should have: \n",
    "* 55% from proteins\n",
    "* 25% from carbohydrates \n",
    "* 20% from fats.\n",
    "(Data adapted from [here](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1479724/))  \n",
    "In order to do so, we apply a greedy ranking based on how well a particular food is is representing these shares."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rank_food(food):\n",
    "    prot = food['Protein (kcal/100g)']\n",
    "    carb = food['Carbohydrates (kcal/100g)']\n",
    "    fat = food['Fat (kcal/100g)']\n",
    "    \n",
    "    if (prot == 0 and carb == 0 and fat == 0):\n",
    "        return -1\n",
    "    \n",
    "    tot = prot + carb + fat\n",
    "    \n",
    "    err_prot = abs(tot*0.55/4 - prot) / 100\n",
    "    err_carb = abs(tot*0.25/4 - carb) / 100\n",
    "    err_fat = abs(tot*0.20/9 - fat) / 100\n",
    "    \n",
    "    avg_err = (err_prot + err_carb + err_fat)/3\n",
    "    \n",
    "    return avg_err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "diet_kcal['rank'] = diet_kcal.apply(rank_food, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next step we take the best **3** out of each group (because there may be inconsistencies in the results due to the filtering process).  \n",
    "From these, we then choose our **representative product for each group**. We will use this to compute the final diet in the next and final step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "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>Food Group</th>\n",
       "      <th>Food Name</th>\n",
       "      <th>Protein (kcal/100g)</th>\n",
       "      <th>Carbohydrates (kcal/100g)</th>\n",
       "      <th>Fat (kcal/100g)</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beef Products</td>\n",
       "      <td>Beef, New Zealand, imported, variety meats and...</td>\n",
       "      <td>59.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17.82</td>\n",
       "      <td>0.232495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beef Products</td>\n",
       "      <td>Beef, variety meats and by-products, tripe, raw</td>\n",
       "      <td>48.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>33.21</td>\n",
       "      <td>0.245225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beef Products</td>\n",
       "      <td>Beef, New Zealand, imported, variety meats and...</td>\n",
       "      <td>62.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.76</td>\n",
       "      <td>0.260241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cereal Grains and Pasta</td>\n",
       "      <td>Wild rice, raw</td>\n",
       "      <td>58.92</td>\n",
       "      <td>299.60</td>\n",
       "      <td>9.72</td>\n",
       "      <td>0.954696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cereal Grains and Pasta</td>\n",
       "      <td>Barley, pearled, raw</td>\n",
       "      <td>39.64</td>\n",
       "      <td>310.88</td>\n",
       "      <td>10.44</td>\n",
       "      <td>1.002436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cereal Grains and Pasta</td>\n",
       "      <td>Oat bran, raw</td>\n",
       "      <td>69.20</td>\n",
       "      <td>264.88</td>\n",
       "      <td>63.27</td>\n",
       "      <td>1.030167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Fruits and Fruit Juices</td>\n",
       "      <td>Rose-apples, raw</td>\n",
       "      <td>2.40</td>\n",
       "      <td>22.80</td>\n",
       "      <td>2.70</td>\n",
       "      <td>0.081908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fruits and Fruit Juices</td>\n",
       "      <td>Pitanga, (surinam-cherry), raw</td>\n",
       "      <td>3.20</td>\n",
       "      <td>29.96</td>\n",
       "      <td>3.60</td>\n",
       "      <td>0.107667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fruits and Fruit Juices</td>\n",
       "      <td>Strawberries, raw</td>\n",
       "      <td>2.68</td>\n",
       "      <td>30.72</td>\n",
       "      <td>2.70</td>\n",
       "      <td>0.108818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lamb, Veal, and Game Products</td>\n",
       "      <td>Lamb, New Zealand, imported, sweetbread, raw</td>\n",
       "      <td>44.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28.44</td>\n",
       "      <td>0.217991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Lamb, Veal, and Game Products</td>\n",
       "      <td>Goat, raw</td>\n",
       "      <td>82.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20.79</td>\n",
       "      <td>0.310525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lamb, Veal, and Game Products</td>\n",
       "      <td>Game meat, beefalo, composite of cuts, raw</td>\n",
       "      <td>93.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>43.20</td>\n",
       "      <td>0.410463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Legumes and Legume Products</td>\n",
       "      <td>SILK Banana-Strawberry soy yogurt</td>\n",
       "      <td>9.40</td>\n",
       "      <td>68.24</td>\n",
       "      <td>10.62</td>\n",
       "      <td>0.247061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Legumes and Legume Products</td>\n",
       "      <td>SILK Strawberry soy yogurt</td>\n",
       "      <td>9.40</td>\n",
       "      <td>72.96</td>\n",
       "      <td>10.62</td>\n",
       "      <td>0.263624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Legumes and Legume Products</td>\n",
       "      <td>Beans, kidney, all types, mature seeds, raw</td>\n",
       "      <td>94.32</td>\n",
       "      <td>240.04</td>\n",
       "      <td>7.47</td>\n",
       "      <td>0.887067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, gizzard, all classes, raw</td>\n",
       "      <td>70.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.54</td>\n",
       "      <td>0.268366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, giblets, raw</td>\n",
       "      <td>71.52</td>\n",
       "      <td>7.20</td>\n",
       "      <td>40.23</td>\n",
       "      <td>0.309951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, roasting, light meat, meat only, raw</td>\n",
       "      <td>88.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.67</td>\n",
       "      <td>0.311368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Cucumber, peeled, raw</td>\n",
       "      <td>2.36</td>\n",
       "      <td>8.64</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.032252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Lettuce, butterhead (includes boston and bibb ...</td>\n",
       "      <td>5.40</td>\n",
       "      <td>8.92</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.042259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Lettuce, red leaf, raw</td>\n",
       "      <td>5.32</td>\n",
       "      <td>9.04</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.042363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Food Group  \\\n",
       "0                       Beef Products   \n",
       "1                       Beef Products   \n",
       "2                       Beef Products   \n",
       "3             Cereal Grains and Pasta   \n",
       "4             Cereal Grains and Pasta   \n",
       "5             Cereal Grains and Pasta   \n",
       "6             Fruits and Fruit Juices   \n",
       "7             Fruits and Fruit Juices   \n",
       "8             Fruits and Fruit Juices   \n",
       "9       Lamb, Veal, and Game Products   \n",
       "10      Lamb, Veal, and Game Products   \n",
       "11      Lamb, Veal, and Game Products   \n",
       "12        Legumes and Legume Products   \n",
       "13        Legumes and Legume Products   \n",
       "14        Legumes and Legume Products   \n",
       "15                   Poultry Products   \n",
       "16                   Poultry Products   \n",
       "17                   Poultry Products   \n",
       "18  Vegetables and Vegetable Products   \n",
       "19  Vegetables and Vegetable Products   \n",
       "20  Vegetables and Vegetable Products   \n",
       "\n",
       "                                            Food Name  Protein (kcal/100g)  \\\n",
       "0   Beef, New Zealand, imported, variety meats and...                59.44   \n",
       "1     Beef, variety meats and by-products, tripe, raw                48.28   \n",
       "2   Beef, New Zealand, imported, variety meats and...                62.72   \n",
       "3                                      Wild rice, raw                58.92   \n",
       "4                                Barley, pearled, raw                39.64   \n",
       "5                                       Oat bran, raw                69.20   \n",
       "6                                    Rose-apples, raw                 2.40   \n",
       "7                      Pitanga, (surinam-cherry), raw                 3.20   \n",
       "8                                   Strawberries, raw                 2.68   \n",
       "9        Lamb, New Zealand, imported, sweetbread, raw                44.00   \n",
       "10                                          Goat, raw                82.40   \n",
       "11         Game meat, beefalo, composite of cuts, raw                93.20   \n",
       "12                  SILK Banana-Strawberry soy yogurt                 9.40   \n",
       "13                         SILK Strawberry soy yogurt                 9.40   \n",
       "14        Beans, kidney, all types, mature seeds, raw                94.32   \n",
       "15                 Chicken, gizzard, all classes, raw                70.64   \n",
       "16          Chicken, broilers or fryers, giblets, raw                71.52   \n",
       "17      Chicken, roasting, light meat, meat only, raw                88.80   \n",
       "18                              Cucumber, peeled, raw                 2.36   \n",
       "19  Lettuce, butterhead (includes boston and bibb ...                 5.40   \n",
       "20                             Lettuce, red leaf, raw                 5.32   \n",
       "\n",
       "    Carbohydrates (kcal/100g)  Fat (kcal/100g)      rank  \n",
       "0                        0.00            17.82  0.232495  \n",
       "1                        0.00            33.21  0.245225  \n",
       "2                        0.00            23.76  0.260241  \n",
       "3                      299.60             9.72  0.954696  \n",
       "4                      310.88            10.44  1.002436  \n",
       "5                      264.88            63.27  1.030167  \n",
       "6                       22.80             2.70  0.081908  \n",
       "7                       29.96             3.60  0.107667  \n",
       "8                       30.72             2.70  0.108818  \n",
       "9                        0.00            28.44  0.217991  \n",
       "10                       0.00            20.79  0.310525  \n",
       "11                       0.00            43.20  0.410463  \n",
       "12                      68.24            10.62  0.247061  \n",
       "13                      72.96            10.62  0.263624  \n",
       "14                     240.04             7.47  0.887067  \n",
       "15                       0.00            18.54  0.268366  \n",
       "16                       7.20            40.23  0.309951  \n",
       "17                       0.00            14.67  0.311368  \n",
       "18                       8.64             1.44  0.032252  \n",
       "19                       8.92             1.98  0.042259  \n",
       "20                       9.04             1.98  0.042363  "
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diet_kcal.groupby([\"Food Group\"]).apply(lambda x: x.sort_values(['rank'])\\\n",
    "                                 .reset_index(drop=True).groupby(\"Food Group\").head(3)).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's further analyze this results.  \n",
    "We can start from the category that is less represented (2 results instead of 3): **Lamb, Veal, and Game Products**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "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",
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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>Food Group</th>\n",
       "      <th>Food Name</th>\n",
       "      <th>Protein (kcal/100g)</th>\n",
       "      <th>Carbohydrates (kcal/100g)</th>\n",
       "      <th>Fat (kcal/100g)</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>Lamb, Veal, and Game Products</td>\n",
       "      <td>Game meat, beefalo, composite of cuts, raw</td>\n",
       "      <td>93.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.20</td>\n",
       "      <td>0.410463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Lamb, Veal, and Game Products</td>\n",
       "      <td>Goat, raw</td>\n",
       "      <td>82.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.79</td>\n",
       "      <td>0.310525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>Lamb, Veal, and Game Products</td>\n",
       "      <td>Lamb, New Zealand, imported, sweetbread, raw</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.44</td>\n",
       "      <td>0.217991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Food Group  \\\n",
       "298  Lamb, Veal, and Game Products   \n",
       "299  Lamb, Veal, and Game Products   \n",
       "300  Lamb, Veal, and Game Products   \n",
       "\n",
       "                                        Food Name  Protein (kcal/100g)  \\\n",
       "298    Game meat, beefalo, composite of cuts, raw                 93.2   \n",
       "299                                     Goat, raw                 82.4   \n",
       "300  Lamb, New Zealand, imported, sweetbread, raw                 44.0   \n",
       "\n",
       "     Carbohydrates (kcal/100g)  Fat (kcal/100g)      rank  \n",
       "298                        0.0            43.20  0.410463  \n",
       "299                        0.0            20.79  0.310525  \n",
       "300                        0.0            28.44  0.217991  "
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diet_kcal[diet_kcal[\"Food Group\"] == \"Lamb, Veal, and Game Products\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, this category doesn't really have a representation. The residue results are here just as consequence of the filtering process (using `str.contains` caused this).\n",
    "**For this reason, we drop it**, than we increase the range of possible products from 3 to **5** and reprint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "diet_kcal = diet_kcal[~diet_kcal[\"Food Group\"].isin([\"Lamb, Veal, and Game Products\"])].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "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>Food Group</th>\n",
       "      <th>Food Name</th>\n",
       "      <th>Protein (kcal/100g)</th>\n",
       "      <th>Carbohydrates (kcal/100g)</th>\n",
       "      <th>Fat (kcal/100g)</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beef Products</td>\n",
       "      <td>Beef, New Zealand, imported, variety meats and...</td>\n",
       "      <td>59.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17.82</td>\n",
       "      <td>0.232495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beef Products</td>\n",
       "      <td>Beef, variety meats and by-products, tripe, raw</td>\n",
       "      <td>48.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>33.21</td>\n",
       "      <td>0.245225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beef Products</td>\n",
       "      <td>Beef, New Zealand, imported, variety meats and...</td>\n",
       "      <td>62.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.76</td>\n",
       "      <td>0.260241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Beef Products</td>\n",
       "      <td>Beef, variety meats and by-products, lungs, raw</td>\n",
       "      <td>64.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22.50</td>\n",
       "      <td>0.262708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Beef Products</td>\n",
       "      <td>Beef, variety meats and by-products, kidneys, raw</td>\n",
       "      <td>69.60</td>\n",
       "      <td>1.16</td>\n",
       "      <td>27.81</td>\n",
       "      <td>0.288889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cereal Grains and Pasta</td>\n",
       "      <td>Wild rice, raw</td>\n",
       "      <td>58.92</td>\n",
       "      <td>299.60</td>\n",
       "      <td>9.72</td>\n",
       "      <td>0.954696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cereal Grains and Pasta</td>\n",
       "      <td>Barley, pearled, raw</td>\n",
       "      <td>39.64</td>\n",
       "      <td>310.88</td>\n",
       "      <td>10.44</td>\n",
       "      <td>1.002436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cereal Grains and Pasta</td>\n",
       "      <td>Oat bran, raw</td>\n",
       "      <td>69.20</td>\n",
       "      <td>264.88</td>\n",
       "      <td>63.27</td>\n",
       "      <td>1.030167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cereal Grains and Pasta</td>\n",
       "      <td>Rice, brown, medium-grain, raw</td>\n",
       "      <td>30.00</td>\n",
       "      <td>304.68</td>\n",
       "      <td>24.12</td>\n",
       "      <td>1.059122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Cereal Grains and Pasta</td>\n",
       "      <td>Rice, white, medium-grain, raw, enriched</td>\n",
       "      <td>26.44</td>\n",
       "      <td>317.36</td>\n",
       "      <td>5.22</td>\n",
       "      <td>1.065442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Fruits and Fruit Juices</td>\n",
       "      <td>Rose-apples, raw</td>\n",
       "      <td>2.40</td>\n",
       "      <td>22.80</td>\n",
       "      <td>2.70</td>\n",
       "      <td>0.081908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Fruits and Fruit Juices</td>\n",
       "      <td>Pitanga, (surinam-cherry), raw</td>\n",
       "      <td>3.20</td>\n",
       "      <td>29.96</td>\n",
       "      <td>3.60</td>\n",
       "      <td>0.107667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Fruits and Fruit Juices</td>\n",
       "      <td>Strawberries, raw</td>\n",
       "      <td>2.68</td>\n",
       "      <td>30.72</td>\n",
       "      <td>2.70</td>\n",
       "      <td>0.108818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Fruits and Fruit Juices</td>\n",
       "      <td>Acerola, (west indian cherry), raw</td>\n",
       "      <td>1.60</td>\n",
       "      <td>30.76</td>\n",
       "      <td>2.70</td>\n",
       "      <td>0.112368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Fruits and Fruit Juices</td>\n",
       "      <td>Strawberries, frozen, unsweetened</td>\n",
       "      <td>1.72</td>\n",
       "      <td>36.52</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.126202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Legumes and Legume Products</td>\n",
       "      <td>SILK Banana-Strawberry soy yogurt</td>\n",
       "      <td>9.40</td>\n",
       "      <td>68.24</td>\n",
       "      <td>10.62</td>\n",
       "      <td>0.247061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Legumes and Legume Products</td>\n",
       "      <td>SILK Strawberry soy yogurt</td>\n",
       "      <td>9.40</td>\n",
       "      <td>72.96</td>\n",
       "      <td>10.62</td>\n",
       "      <td>0.263624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Legumes and Legume Products</td>\n",
       "      <td>Beans, kidney, all types, mature seeds, raw</td>\n",
       "      <td>94.32</td>\n",
       "      <td>240.04</td>\n",
       "      <td>7.47</td>\n",
       "      <td>0.887067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Legumes and Legume Products</td>\n",
       "      <td>Lima beans, thin seeded (baby), mature seeds, raw</td>\n",
       "      <td>82.48</td>\n",
       "      <td>251.32</td>\n",
       "      <td>8.37</td>\n",
       "      <td>0.887107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Legumes and Legume Products</td>\n",
       "      <td>Beans, white, mature seeds, raw</td>\n",
       "      <td>93.44</td>\n",
       "      <td>241.08</td>\n",
       "      <td>7.65</td>\n",
       "      <td>0.887107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, gizzard, all classes, raw</td>\n",
       "      <td>70.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.54</td>\n",
       "      <td>0.268366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, broilers or fryers, giblets, raw</td>\n",
       "      <td>71.52</td>\n",
       "      <td>7.20</td>\n",
       "      <td>40.23</td>\n",
       "      <td>0.309951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, roasting, light meat, meat only, raw</td>\n",
       "      <td>88.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.67</td>\n",
       "      <td>0.311368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, roasting, meat only, raw</td>\n",
       "      <td>81.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>24.30</td>\n",
       "      <td>0.317838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Poultry Products</td>\n",
       "      <td>Chicken, dark meat, drumstick, meat only, with...</td>\n",
       "      <td>76.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.34</td>\n",
       "      <td>0.319282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Cucumber, peeled, raw</td>\n",
       "      <td>2.36</td>\n",
       "      <td>8.64</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.032252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Lettuce, butterhead (includes boston and bibb ...</td>\n",
       "      <td>5.40</td>\n",
       "      <td>8.92</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.042259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Lettuce, red leaf, raw</td>\n",
       "      <td>5.32</td>\n",
       "      <td>9.04</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.042363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Lettuce, iceberg (includes crisphead types), raw</td>\n",
       "      <td>3.60</td>\n",
       "      <td>11.88</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.043400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Cucumber, with peel, raw</td>\n",
       "      <td>2.60</td>\n",
       "      <td>14.52</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.046952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Food Group  \\\n",
       "0                       Beef Products   \n",
       "1                       Beef Products   \n",
       "2                       Beef Products   \n",
       "3                       Beef Products   \n",
       "4                       Beef Products   \n",
       "5             Cereal Grains and Pasta   \n",
       "6             Cereal Grains and Pasta   \n",
       "7             Cereal Grains and Pasta   \n",
       "8             Cereal Grains and Pasta   \n",
       "9             Cereal Grains and Pasta   \n",
       "10            Fruits and Fruit Juices   \n",
       "11            Fruits and Fruit Juices   \n",
       "12            Fruits and Fruit Juices   \n",
       "13            Fruits and Fruit Juices   \n",
       "14            Fruits and Fruit Juices   \n",
       "15        Legumes and Legume Products   \n",
       "16        Legumes and Legume Products   \n",
       "17        Legumes and Legume Products   \n",
       "18        Legumes and Legume Products   \n",
       "19        Legumes and Legume Products   \n",
       "20                   Poultry Products   \n",
       "21                   Poultry Products   \n",
       "22                   Poultry Products   \n",
       "23                   Poultry Products   \n",
       "24                   Poultry Products   \n",
       "25  Vegetables and Vegetable Products   \n",
       "26  Vegetables and Vegetable Products   \n",
       "27  Vegetables and Vegetable Products   \n",
       "28  Vegetables and Vegetable Products   \n",
       "29  Vegetables and Vegetable Products   \n",
       "\n",
       "                                            Food Name  Protein (kcal/100g)  \\\n",
       "0   Beef, New Zealand, imported, variety meats and...                59.44   \n",
       "1     Beef, variety meats and by-products, tripe, raw                48.28   \n",
       "2   Beef, New Zealand, imported, variety meats and...                62.72   \n",
       "3     Beef, variety meats and by-products, lungs, raw                64.80   \n",
       "4   Beef, variety meats and by-products, kidneys, raw                69.60   \n",
       "5                                      Wild rice, raw                58.92   \n",
       "6                                Barley, pearled, raw                39.64   \n",
       "7                                       Oat bran, raw                69.20   \n",
       "8                      Rice, brown, medium-grain, raw                30.00   \n",
       "9            Rice, white, medium-grain, raw, enriched                26.44   \n",
       "10                                   Rose-apples, raw                 2.40   \n",
       "11                     Pitanga, (surinam-cherry), raw                 3.20   \n",
       "12                                  Strawberries, raw                 2.68   \n",
       "13                 Acerola, (west indian cherry), raw                 1.60   \n",
       "14                  Strawberries, frozen, unsweetened                 1.72   \n",
       "15                  SILK Banana-Strawberry soy yogurt                 9.40   \n",
       "16                         SILK Strawberry soy yogurt                 9.40   \n",
       "17        Beans, kidney, all types, mature seeds, raw                94.32   \n",
       "18  Lima beans, thin seeded (baby), mature seeds, raw                82.48   \n",
       "19                    Beans, white, mature seeds, raw                93.44   \n",
       "20                 Chicken, gizzard, all classes, raw                70.64   \n",
       "21          Chicken, broilers or fryers, giblets, raw                71.52   \n",
       "22      Chicken, roasting, light meat, meat only, raw                88.80   \n",
       "23                  Chicken, roasting, meat only, raw                81.32   \n",
       "24  Chicken, dark meat, drumstick, meat only, with...                76.76   \n",
       "25                              Cucumber, peeled, raw                 2.36   \n",
       "26  Lettuce, butterhead (includes boston and bibb ...                 5.40   \n",
       "27                             Lettuce, red leaf, raw                 5.32   \n",
       "28   Lettuce, iceberg (includes crisphead types), raw                 3.60   \n",
       "29                           Cucumber, with peel, raw                 2.60   \n",
       "\n",
       "    Carbohydrates (kcal/100g)  Fat (kcal/100g)      rank  \n",
       "0                        0.00            17.82  0.232495  \n",
       "1                        0.00            33.21  0.245225  \n",
       "2                        0.00            23.76  0.260241  \n",
       "3                        0.00            22.50  0.262708  \n",
       "4                        1.16            27.81  0.288889  \n",
       "5                      299.60             9.72  0.954696  \n",
       "6                      310.88            10.44  1.002436  \n",
       "7                      264.88            63.27  1.030167  \n",
       "8                      304.68            24.12  1.059122  \n",
       "9                      317.36             5.22  1.065442  \n",
       "10                      22.80             2.70  0.081908  \n",
       "11                      29.96             3.60  0.107667  \n",
       "12                      30.72             2.70  0.108818  \n",
       "13                      30.76             2.70  0.112368  \n",
       "14                      36.52             0.99  0.126202  \n",
       "15                      68.24            10.62  0.247061  \n",
       "16                      72.96            10.62  0.263624  \n",
       "17                     240.04             7.47  0.887067  \n",
       "18                     251.32             8.37  0.887107  \n",
       "19                     241.08             7.65  0.887107  \n",
       "20                       0.00            18.54  0.268366  \n",
       "21                       7.20            40.23  0.309951  \n",
       "22                       0.00            14.67  0.311368  \n",
       "23                       0.00            24.30  0.317838  \n",
       "24                       0.00            29.34  0.319282  \n",
       "25                       8.64             1.44  0.032252  \n",
       "26                       8.92             1.98  0.042259  \n",
       "27                       9.04             1.98  0.042363  \n",
       "28                      11.88             1.26  0.043400  \n",
       "29                      14.52             0.99  0.046952  "
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diet_kcal.groupby([\"Food Group\"]).apply(lambda x: x.sort_values(['rank'])\\\n",
    "                                 .reset_index(drop=True).groupby(\"Food Group\").head(5)).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the shape of our final diet is **way more clear**:\n",
    "- From the _**Beef Products**_ category we have a great variety of meats. We can take **the median over the first 10 rows** to have our representative beef nutrients.\n",
    "- From the _**Cereal Grains and Pasta**_ group, a good product would be one of **rice, bearley** or **oat**. We keep all of them for now. Further analysis will be in the next section.\n",
    "- From the _**Fruits and Fruit Juices**_ category, we take the **Strawberries** and **Apples** (not rose-apples as shown here but apples, as there are some in the dataframe). The other products are probably shown due to some inconsistence with the food items presented before.\n",
    "- From **_Poultry Products_**, we take **Chicken, roasting, light meat, meat only, raw** as it's the most simple one.\n",
    "- For the _**Legumes and Legume Products**_ and _**Vegetables and Vegetable Products**_, we need further analysis (because these two categories can be very useful for our analysis):\n",
    "    - From _**Legumes and Legume Products**_ we go for sure for **Beans**, but the yogurt there presented is an inconsistency. Therefore, we need further explorations.\n",
    "    - From _**Vegetables and Vegetable Products**_ we could take **Lettuce, green leaf, raw** and **cucumber**. We should analyze if there's the possibility to take other products.\n",
    "\n",
    "We can now build the **final dataframe** with nutrient for each product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "prod_diet_final = pd.DataFrame(columns=[\"Product\", \"Protein (kcal/100g)\", \"Carbohydrates (kcal/100g)\",\"Fat (kcal/100g)\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Beef Products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "# taking an average over the first 5 beef product\n",
    "best_beef = diet_kcal[diet_kcal[\"Food Group\"]==\"Beef Products\"].sort_values(by=\"rank\").head(10)\n",
    "beef_repr = [best_beef[\"Protein (kcal/100g)\"].median(),\\\n",
    "             best_beef[\"Carbohydrates (kcal/100g)\"].median(),\\\n",
    "             best_beef[\"Fat (kcal/100g)\"].median()]\n",
    "beef_list = [\"Beef Meat\"] + beef_repr\n",
    "beef = pd.DataFrame(np.array([beef_list]), columns=[\"Product\", \"Protein (kcal/100g)\", \"Carbohydrates (kcal/100g)\",\"Fat (kcal/100g)\"])\n",
    "prod_diet_final = prod_diet_final.append(beef)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cereal Grains and Pasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "# taking the top 3 in cereals (rice, bearley, oat)\n",
    "best_cereal = diet_kcal[diet_kcal[\"Food Group\"]==\"Cereal Grains and Pasta\"].sort_values(by=\"rank\").head(3)\n",
    "best_cereal = best_cereal.drop(columns=[\"Food Group\", \"rank\"]).rename(columns={\"Food Name\":\"Product\"})\n",
    "best_cereal = best_cereal.replace({\"Product\":{\"Wild rice, raw\": \"Rice\", \"Barley, pearled, raw\": \"Barley\", \"Oat bran, raw\": \"Oats\"}})\n",
    "prod_diet_final = prod_diet_final.append(best_cereal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fruits and Fruit Juices\n",
    "By further analysis, we discovered that it's possible to add also **cherries** to the possible diet. So we keep those too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "# taking apples, cherries and strawberries \n",
    "fruits = diet_kcal[(diet_kcal[\"Food Name\"]==\"Apples, raw, with skin\") | (diet_kcal[\"Food Name\"]==\"Strawberries, raw\") | (diet_kcal[\"Food Name\"]==\"Cherries, sour, red, raw\")]\n",
    "fruits = fruits.drop(columns=[\"Food Group\", \"rank\"]).rename(columns={\"Food Name\":\"Product\"})\n",
    "fruits = fruits.replace({\"Product\":{\"Apples, raw, with skin\": \"Apples\", \"Cherries, sour, red, raw\": \"Cherries\", \"Strawberries, raw\": \"Strawberries\"}})\n",
    "prod_diet_final = prod_diet_final.append(fruits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Poultry Products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "# just take Chicken, roasting, light meat, meat only, raw\n",
    "poultry = diet_kcal[diet_kcal[\"Food Name\"]==\"Chicken, roasting, light meat, meat only, raw\"]\n",
    "poultry = poultry.drop(columns=[\"Food Group\", \"rank\"]).rename(columns={\"Food Name\":\"Product\"})\n",
    "poultry = poultry.replace({\"Product\":{\"Chicken, roasting, light meat, meat only, raw\": \"Chicken\"}})\n",
    "prod_diet_final = prod_diet_final.append(poultry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Legumes and Legume Products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Food Group</th>\n",
       "      <th>Food Name</th>\n",
       "      <th>Protein (kcal/100g)</th>\n",
       "      <th>Carbohydrates (kcal/100g)</th>\n",
       "      <th>Fat (kcal/100g)</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>Legumes and Legume Products</td>\n",
       "      <td>Beans, adzuki, mature seeds, raw</td>\n",
       "      <td>79.48</td>\n",
       "      <td>251.60</td>\n",
       "      <td>4.77</td>\n",
       "      <td>0.888678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Legumes and Legume Products</td>\n",
       "      <td>Beans, black, mature seeds, raw</td>\n",
       "      <td>86.40</td>\n",
       "      <td>249.44</td>\n",
       "      <td>12.78</td>\n",
       "      <td>0.903830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>Legumes and Legume Products</td>\n",
       "      <td>Beans, black turtle, mature seeds, raw</td>\n",
       "      <td>85.00</td>\n",
       "      <td>253.00</td>\n",
       "      <td>8.10</td>\n",
       "      <td>0.897296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>Legumes and Legume Products</td>\n",
       "      <td>Beans, cranberry (roman), mature seeds, raw</td>\n",
       "      <td>92.12</td>\n",
       "      <td>240.20</td>\n",
       "      <td>11.07</td>\n",
       "      <td>0.890270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>Legumes and Legume Products</td>\n",
       "      <td>Beans, french, mature seeds, raw</td>\n",
       "      <td>75.24</td>\n",
       "      <td>256.44</td>\n",
       "      <td>18.18</td>\n",
       "      <td>0.907044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Food Group                                    Food Name  \\\n",
       "261  Legumes and Legume Products             Beans, adzuki, mature seeds, raw   \n",
       "262  Legumes and Legume Products              Beans, black, mature seeds, raw   \n",
       "263  Legumes and Legume Products       Beans, black turtle, mature seeds, raw   \n",
       "264  Legumes and Legume Products  Beans, cranberry (roman), mature seeds, raw   \n",
       "265  Legumes and Legume Products             Beans, french, mature seeds, raw   \n",
       "\n",
       "     Protein (kcal/100g)  Carbohydrates (kcal/100g)  Fat (kcal/100g)      rank  \n",
       "261                79.48                     251.60             4.77  0.888678  \n",
       "262                86.40                     249.44            12.78  0.903830  \n",
       "263                85.00                     253.00             8.10  0.897296  \n",
       "264                92.12                     240.20            11.07  0.890270  \n",
       "265                75.24                     256.44            18.18  0.907044  "
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_leg = diet_kcal[diet_kcal[\"Food Group\"]==\"Legumes and Legume Products\"]\n",
    "check_leg[check_leg[\"Food Name\"].str.contains(\"bean\", case=False)].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Food Group</th>\n",
       "      <th>Food Name</th>\n",
       "      <th>Protein (kcal/100g)</th>\n",
       "      <th>Carbohydrates (kcal/100g)</th>\n",
       "      <th>Fat (kcal/100g)</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>Legumes and Legume Products</td>\n",
       "      <td>Chickpeas (garbanzo beans, bengal gram), matur...</td>\n",
       "      <td>81.88</td>\n",
       "      <td>251.80</td>\n",
       "      <td>54.36</td>\n",
       "      <td>1.006030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>Legumes and Legume Products</td>\n",
       "      <td>Cowpeas, catjang, mature seeds, raw</td>\n",
       "      <td>95.40</td>\n",
       "      <td>238.56</td>\n",
       "      <td>18.63</td>\n",
       "      <td>0.914122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>Legumes and Legume Products</td>\n",
       "      <td>Cowpeas, common (blackeyes, crowder, southern)...</td>\n",
       "      <td>94.08</td>\n",
       "      <td>240.12</td>\n",
       "      <td>11.34</td>\n",
       "      <td>0.895844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Legumes and Legume Products</td>\n",
       "      <td>Peas, green, split, mature seeds, raw</td>\n",
       "      <td>95.28</td>\n",
       "      <td>254.96</td>\n",
       "      <td>10.44</td>\n",
       "      <td>0.935096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Legumes and Legume Products</td>\n",
       "      <td>Peanuts, all types, raw</td>\n",
       "      <td>103.20</td>\n",
       "      <td>64.52</td>\n",
       "      <td>443.16</td>\n",
       "      <td>1.583763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Food Group  \\\n",
       "278  Legumes and Legume Products   \n",
       "279  Legumes and Legume Products   \n",
       "280  Legumes and Legume Products   \n",
       "287  Legumes and Legume Products   \n",
       "288  Legumes and Legume Products   \n",
       "\n",
       "                                             Food Name  Protein (kcal/100g)  \\\n",
       "278  Chickpeas (garbanzo beans, bengal gram), matur...                81.88   \n",
       "279                Cowpeas, catjang, mature seeds, raw                95.40   \n",
       "280  Cowpeas, common (blackeyes, crowder, southern)...                94.08   \n",
       "287              Peas, green, split, mature seeds, raw                95.28   \n",
       "288                            Peanuts, all types, raw               103.20   \n",
       "\n",
       "     Carbohydrates (kcal/100g)  Fat (kcal/100g)      rank  \n",
       "278                     251.80            54.36  1.006030  \n",
       "279                     238.56            18.63  0.914122  \n",
       "280                     240.12            11.34  0.895844  \n",
       "287                     254.96            10.44  0.935096  \n",
       "288                      64.52           443.16  1.583763  "
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_leg[check_leg[\"Food Name\"].str.contains(\"pea\", case=False)].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, we have both the product we mentioned before. As **only 2 types of legumes** were chosen, because their **nutriments provision is usually high**, we decide to keep both of them for our next analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Taking one for beans and one for peas\n",
    "beans = diet_kcal[diet_kcal[\"Food Name\"]==\"Beans, adzuki, mature seeds, raw\"]\n",
    "beans = beans.drop(columns=[\"Food Group\", \"rank\"]).rename(columns={\"Food Name\":\"Product\"})\n",
    "beans = beans.replace({\"Product\":{\"Beans, adzuki, mature seeds, raw\": \"Beans\"}})\n",
    "\n",
    "peas = diet_kcal[diet_kcal[\"Food Name\"]==\"Peas, green, split, mature seeds, raw\"]\n",
    "peas = peas.drop(columns=[\"Food Group\", \"rank\"]).rename(columns={\"Food Name\":\"Product\"})\n",
    "peas = peas.replace({\"Product\":{\"Peas, green, split, mature seeds, raw\": \"Peas\"}})\n",
    "\n",
    "prod_diet_final = prod_diet_final.append(beans)\n",
    "prod_diet_final = prod_diet_final.append(peas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vegetables and Vegetable Products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Food Name</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Cucumber, peeled, raw</td>\n",
       "      <td>2.36</td>\n",
       "      <td>8.64</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.032252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Lettuce, butterhead (includes boston and bibb ...</td>\n",
       "      <td>5.40</td>\n",
       "      <td>8.92</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.042259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Lettuce, red leaf, raw</td>\n",
       "      <td>5.32</td>\n",
       "      <td>9.04</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.042363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Lettuce, iceberg (includes crisphead types), raw</td>\n",
       "      <td>3.60</td>\n",
       "      <td>11.88</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.043400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Cucumber, with peel, raw</td>\n",
       "      <td>2.60</td>\n",
       "      <td>14.52</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.046952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Tomatoes, yellow, raw</td>\n",
       "      <td>3.92</td>\n",
       "      <td>11.92</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.047133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Lettuce, green leaf, raw</td>\n",
       "      <td>5.44</td>\n",
       "      <td>11.48</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.047367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Tomatoes, orange, raw</td>\n",
       "      <td>4.64</td>\n",
       "      <td>12.72</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0.049441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Balsam-pear (bitter gourd), pods, raw</td>\n",
       "      <td>4.00</td>\n",
       "      <td>14.80</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.052707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Lettuce, cos or romaine, raw</td>\n",
       "      <td>4.92</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.70</td>\n",
       "      <td>0.053874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Food Group  \\\n",
       "104  Vegetables and Vegetable Products   \n",
       "107  Vegetables and Vegetable Products   \n",
       "111  Vegetables and Vegetable Products   \n",
       "109  Vegetables and Vegetable Products   \n",
       "103  Vegetables and Vegetable Products   \n",
       "133  Vegetables and Vegetable Products   \n",
       "110  Vegetables and Vegetable Products   \n",
       "132  Vegetables and Vegetable Products   \n",
       "90   Vegetables and Vegetable Products   \n",
       "108  Vegetables and Vegetable Products   \n",
       "\n",
       "                                             Food Name  Protein (kcal/100g)  \\\n",
       "104                              Cucumber, peeled, raw                 2.36   \n",
       "107  Lettuce, butterhead (includes boston and bibb ...                 5.40   \n",
       "111                             Lettuce, red leaf, raw                 5.32   \n",
       "109   Lettuce, iceberg (includes crisphead types), raw                 3.60   \n",
       "103                           Cucumber, with peel, raw                 2.60   \n",
       "133                              Tomatoes, yellow, raw                 3.92   \n",
       "110                           Lettuce, green leaf, raw                 5.44   \n",
       "132                              Tomatoes, orange, raw                 4.64   \n",
       "90               Balsam-pear (bitter gourd), pods, raw                 4.00   \n",
       "108                       Lettuce, cos or romaine, raw                 4.92   \n",
       "\n",
       "     Carbohydrates (kcal/100g)  Fat (kcal/100g)      rank  \n",
       "104                       8.64             1.44  0.032252  \n",
       "107                       8.92             1.98  0.042259  \n",
       "111                       9.04             1.98  0.042363  \n",
       "109                      11.88             1.26  0.043400  \n",
       "103                      14.52             0.99  0.046952  \n",
       "133                      11.92             2.34  0.047133  \n",
       "110                      11.48             1.35  0.047367  \n",
       "132                      12.72             1.71  0.049441  \n",
       "90                       14.80             1.53  0.052707  \n",
       "108                      13.16             2.70  0.053874  "
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diet_kcal[diet_kcal[\"Food Group\"]==\"Vegetables and Vegetable Products\"].sort_values(by=\"rank\").head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the list of most viable **10 vegetables** includes **Lettuce, cucumber and tomatoes** (different kinds for the latter).  \n",
    "We decide to take the three (we search for the red one in the case of tomatoes, as it is the most common)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "# take Lettuce, green leaf, raw and Cucumber\n",
    "lettuce = diet_kcal[diet_kcal[\"Food Name\"]==\"Lettuce, green leaf, raw\"]\n",
    "lettuce = lettuce.drop(columns=[\"Food Group\", \"rank\"]).rename(columns={\"Food Name\":\"Product\"})\n",
    "lettuce = lettuce.replace({\"Product\":{\"Lettuce, green leaf, raw\": \"Lettuces\"}})\n",
    "\n",
    "cucumber = diet_kcal[diet_kcal[\"Food Name\"]==\"Cucumber, peeled, raw\"]\n",
    "cucumber = cucumber.drop(columns=[\"Food Group\", \"rank\"]).rename(columns={\"Food Name\":\"Product\"})\n",
    "cucumber = cucumber.replace({\"Product\":{\"Cucumber, peeled, raw\": \"Cucumbers\"}})\n",
    "\n",
    "prod_diet_final = prod_diet_final.append(lettuce)\n",
    "prod_diet_final = prod_diet_final.append(cucumber)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "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>Food Group</th>\n",
       "      <th>Food Name</th>\n",
       "      <th>Protein (kcal/100g)</th>\n",
       "      <th>Carbohydrates (kcal/100g)</th>\n",
       "      <th>Fat (kcal/100g)</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Tomatoes, green, raw</td>\n",
       "      <td>4.80</td>\n",
       "      <td>20.40</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Tomatoes, red, ripe, raw, year round average</td>\n",
       "      <td>3.52</td>\n",
       "      <td>15.56</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.054133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Tomatoes, orange, raw</td>\n",
       "      <td>4.64</td>\n",
       "      <td>12.72</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0.049441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>Tomatoes, yellow, raw</td>\n",
       "      <td>3.92</td>\n",
       "      <td>11.92</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.047133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Food Group  \\\n",
       "125  Vegetables and Vegetable Products   \n",
       "126  Vegetables and Vegetable Products   \n",
       "132  Vegetables and Vegetable Products   \n",
       "133  Vegetables and Vegetable Products   \n",
       "\n",
       "                                        Food Name  Protein (kcal/100g)  \\\n",
       "125                          Tomatoes, green, raw                 4.80   \n",
       "126  Tomatoes, red, ripe, raw, year round average                 3.52   \n",
       "132                         Tomatoes, orange, raw                 4.64   \n",
       "133                         Tomatoes, yellow, raw                 3.92   \n",
       "\n",
       "     Carbohydrates (kcal/100g)  Fat (kcal/100g)      rank  \n",
       "125                      20.40             1.80  0.070000  \n",
       "126                      15.56             1.80  0.054133  \n",
       "132                      12.72             1.71  0.049441  \n",
       "133                      11.92             2.34  0.047133  "
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#average over tomatoes\n",
    "tomatoes = diet_kcal[diet_kcal[\"Food Name\"].str.contains(\"tomatoe\", case=False)]\n",
    "tomatoes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Found the name for the red tomatoe. Add it to the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "tomatoes = diet_kcal[diet_kcal[\"Food Name\"]==\"Tomatoes, red, ripe, raw, year round average\"]\n",
    "tomatoes = tomatoes.drop(columns=[\"Food Group\", \"rank\"]).rename(columns={\"Food Name\":\"Product\"})\n",
    "tomatoes = tomatoes.replace({\"Product\":{\"Tomatoes, red, ripe, raw, year round average\": \"Tomatoes\"}})\n",
    "\n",
    "prod_diet_final = prod_diet_final.append(tomatoes).reset_index(drop=True).set_index(\"Product\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can finally look at **our final diet dataframe** `prod_diet_final`. This dataframe will be used in the next section to build an optimal diet **based on the production prices**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "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>Protein (kcal/100g)</th>\n",
       "      <th>Carbohydrates (kcal/100g)</th>\n",
       "      <th>Fat (kcal/100g)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Beef Meat</th>\n",
       "      <td>70.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rice</th>\n",
       "      <td>58.92</td>\n",
       "      <td>299.6</td>\n",
       "      <td>9.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barley</th>\n",
       "      <td>39.64</td>\n",
       "      <td>310.88</td>\n",
       "      <td>10.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oats</th>\n",
       "      <td>69.2</td>\n",
       "      <td>264.88</td>\n",
       "      <td>63.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apples</th>\n",
       "      <td>1.04</td>\n",
       "      <td>55.24</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cherries</th>\n",
       "      <td>4</td>\n",
       "      <td>48.72</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strawberries</th>\n",
       "      <td>2.68</td>\n",
       "      <td>30.72</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicken</th>\n",
       "      <td>88.8</td>\n",
       "      <td>0</td>\n",
       "      <td>14.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beans</th>\n",
       "      <td>79.48</td>\n",
       "      <td>251.6</td>\n",
       "      <td>4.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peas</th>\n",
       "      <td>95.28</td>\n",
       "      <td>254.96</td>\n",
       "      <td>10.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lettuces</th>\n",
       "      <td>5.44</td>\n",
       "      <td>11.48</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cucumbers</th>\n",
       "      <td>2.36</td>\n",
       "      <td>8.64</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomatoes</th>\n",
       "      <td>3.52</td>\n",
       "      <td>15.56</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Protein (kcal/100g) Carbohydrates (kcal/100g) Fat (kcal/100g)\n",
       "Product                                                                   \n",
       "Beef Meat                  70.24                       0.0           28.98\n",
       "Rice                       58.92                     299.6            9.72\n",
       "Barley                     39.64                    310.88           10.44\n",
       "Oats                        69.2                    264.88           63.27\n",
       "Apples                      1.04                     55.24            1.53\n",
       "Cherries                       4                     48.72             2.7\n",
       "Strawberries                2.68                     30.72             2.7\n",
       "Chicken                     88.8                         0           14.67\n",
       "Beans                      79.48                     251.6            4.77\n",
       "Peas                       95.28                    254.96           10.44\n",
       "Lettuces                    5.44                     11.48            1.35\n",
       "Cucumbers                   2.36                      8.64            1.44\n",
       "Tomatoes                    3.52                     15.56             1.8"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_diet_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making sure all the values are float\n",
    "prod_diet_final[\"Protein (kcal/100g)\"] = prod_diet_final[\"Protein (kcal/100g)\"].astype(float)\n",
    "prod_diet_final[\"Carbohydrates (kcal/100g)\"] = prod_diet_final[\"Carbohydrates (kcal/100g)\"].astype(float)\n",
    "prod_diet_final[\"Fat (kcal/100g)\"] = prod_diet_final[\"Fat (kcal/100g)\"].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving the result into a pickle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "prod_diet_final.to_pickle(\"data/processed/prod_diet_final.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "prod_diet_final = pd.read_pickle(\"data/processed/prod_diet_final.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "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>Macronutrient</th>\n",
       "      <th>Product</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Protein (kcal/100g)</td>\n",
       "      <td>Beef Meat</td>\n",
       "      <td>70.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Protein (kcal/100g)</td>\n",
       "      <td>Rice</td>\n",
       "      <td>58.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Protein (kcal/100g)</td>\n",
       "      <td>Barley</td>\n",
       "      <td>39.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Protein (kcal/100g)</td>\n",
       "      <td>Oats</td>\n",
       "      <td>69.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Protein (kcal/100g)</td>\n",
       "      <td>Apples</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Macronutrient    Product  Value\n",
       "0  Protein (kcal/100g)  Beef Meat  70.24\n",
       "1  Protein (kcal/100g)       Rice  58.92\n",
       "2  Protein (kcal/100g)     Barley  39.64\n",
       "3  Protein (kcal/100g)       Oats  69.20\n",
       "4  Protein (kcal/100g)     Apples   1.04"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating an exploded version of prod_diet_final \n",
    "plot_diet_exploxed = prod_diet_final.copy()\n",
    "#Resetting index\n",
    "plot_diet_exploxed.reset_index()\n",
    "#Unstack result, converting to dataframe and reset index\n",
    "plot_diet_exploxed = plot_diet_exploxed.unstack().to_frame().reset_index(level=['Product',0])\n",
    "#Casting values column to float\n",
    "#plot_diet_exploxed[0]= plot_diet_exploxed[0].astype(float)\n",
    "#renaming columns\n",
    "plot_diet_exploxed = plot_diet_exploxed.rename(columns={\"level_0\":\"Macronutrient\",0:\"Value\"})\n",
    "#showing results\n",
    "plot_diet_exploxed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Initialize bar plot \n",
    "bp = sns.barplot(x='Product', y=\"Value\", hue='Macronutrient', data=plot_diet_exploxed)\n",
    "#Set log axis in order to show more values since with have both big numbers and very small numbers\n",
    "bp.set(yscale=\"log\") \n",
    "plt.ylabel('Log of caloric density [kcal/100g]')\n",
    "plt.xlabel('Food items')\n",
    "plt.title('Caloric density of different food items with respect to their share of macronutrients');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prod_diet_final.plot(kind='bar',stacked=True)\n",
    "plt.ylabel('Caloric density [kcal/100g]')\n",
    "plt.xlabel('Food items')\n",
    "plt.title('Stacked barplot of caloric density of different food items with respect to their share of macronutrients');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code which generates the 3D plot used for the macronutrients represation is as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.linspace(0, 20, 13)\n",
    "\n",
    "fig = go.Figure(data=[go.Scatter3d(\n",
    "    name=\"\",\n",
    "    text=prod_diet_final.index.values,\n",
    "    x=prod_diet_final[\"Protein (kcal/100g)\"],\n",
    "    y=prod_diet_final[\"Carbohydrates (kcal/100g)\"],\n",
    "    z=prod_diet_final[\"Fat (kcal/100g)\"],\n",
    "    hovertemplate=\"Item: %{text}<br />Protein: %{x:.2f} kcal/100g<br />Carbohydrates: %{y:.2f} kcal/100g<br />Fat: %{z:.2f} kcal/100g\",\n",
    "    mode=\"markers\",\n",
    "    marker=dict(\n",
    "        size=8,\n",
    "        color=t,                # set color to an array/list of desired values\n",
    "        colorscale='Viridis',   # choose a colorscale\n",
    "        opacity=0.8\n",
    "    )\n",
    ")])\n",
    "\n",
    "\n",
    "camera = dict(\n",
    "    eye=dict(x=-1.4, y=-1.4, z=0.05),\n",
    "    center=dict(x=0,y=0,z=-0.2)\n",
    ")\n",
    "\n",
    "name = 'Shares of macronutrients in each food item\"'\n",
    "\n",
    "\n",
    "\n",
    "# tight layout\n",
    "fig.update_layout(\n",
    "    margin=dict(l=0, r=0, b=0, t=0),\n",
    "    title=name,\n",
    "    scene = dict(\n",
    "        camera=camera,\n",
    "        xaxis = dict(title='Protein [kcal/100g]'), \n",
    "        yaxis = dict(title='Carbohydrates [kcal/100g]'),\n",
    "        zaxis = dict(title='Fat [kcal/100g]'),\n",
    "        dragmode = 'turntable'),\n",
    "    paper_bgcolor = \"rgba(0,0,0,0)\"\n",
    "    )\n",
    "\n",
    "py.plot(fig, filename='docs/_includes/3d_macros.html', include_plotlyjs=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://manuleo.github.io/mADAm_files/3d_macros.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x15b7f9c10>"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(src=\"https://manuleo.github.io/mADAm_files/3d_macros.html\", width=800, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the 3D spatial representation of the shares of macronutrients, **chicken** and **meat beef** occupy the region in which **proteins** and **fat** density is high. On the other hand, we also observe that **oats** has very high carbs caloric density, while it doesn't really have a stong protein and fat footprint. In addition, we also observe that the most of **vegetables** and **fruits** have quite low caloric density. The products listed will be the **basis of the perfect diet** that we will compute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4) Final filter on the prices to retain only the needed ones\n",
    "We need now to prepare the prices matrix for the next section. In order to do so, we filter the product we have in the `prices` dataframe accordingly with what we have delined in the last part.  \n",
    "Theoretically, we should have **65 elements** in the final dataframe (5 countries, 13 products)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a conversion in float for all the prices just to be sure\n",
    "prices.Price = prices.Price.apply(lambda x: float(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_final = prices[prices.Product.isin(prod_diet_final.index.values)]\n",
    "prices_final.index.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We miss **2** products. We must investigate over those:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set of missing product in prices: {'Barley', 'Beef Meat'}\n"
     ]
    }
   ],
   "source": [
    "print(\"Set of missing product in prices:\", set(prod_diet_final.index.values) - set(prices_final.Product.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Probably these products have another name in the previous dataframe (as example, _\"Beef Meat\"_ was set as _\"Meat\"_ by us).  \n",
    "We look into these problems and manually solve them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "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>Country</th>\n",
       "      <th>Product</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>France</td>\n",
       "      <td>Meat</td>\n",
       "      <td>4549.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Meat</td>\n",
       "      <td>4033.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Meat</td>\n",
       "      <td>3762.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Meat</td>\n",
       "      <td>5328.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Meat</td>\n",
       "      <td>2774.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Meat, pig</td>\n",
       "      <td>1350.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Meat, pig</td>\n",
       "      <td>1685.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Meat, pig</td>\n",
       "      <td>2844.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Meat, pig</td>\n",
       "      <td>1983.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>France</td>\n",
       "      <td>Meat, pig</td>\n",
       "      <td>1469.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country    Product   Price\n",
       "60          France       Meat  4549.5\n",
       "61         Germany       Meat  4033.1\n",
       "62           Italy       Meat  3762.0\n",
       "63  United Kingdom       Meat  5328.2\n",
       "64           Spain       Meat  2774.8\n",
       "65           Spain  Meat, pig  1350.8\n",
       "66         Germany  Meat, pig  1685.6\n",
       "67           Italy  Meat, pig  2844.8\n",
       "68  United Kingdom  Meat, pig  1983.0\n",
       "69          France  Meat, pig  1469.0"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for Meat\n",
    "prices[prices.Product.str.contains(\"Meat\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, we have _\"Meat\"_ instead of _\"Beef Meat\"_. Fixing it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = prices.replace({\"Product\":{\"Meat\": \"Beef Meat\"}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "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>Country</th>\n",
       "      <th>Product</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Feed Barley</td>\n",
       "      <td>172.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Feed Barley</td>\n",
       "      <td>156.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Feed Barley</td>\n",
       "      <td>155.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Feed Barley</td>\n",
       "      <td>175.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>France</td>\n",
       "      <td>Feed Barley</td>\n",
       "      <td>171.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>France</td>\n",
       "      <td>Malting Barley</td>\n",
       "      <td>195.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Malting Barley</td>\n",
       "      <td>209.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Malting Barley</td>\n",
       "      <td>180.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Malting Barley</td>\n",
       "      <td>207.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Malting Barley</td>\n",
       "      <td>177.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country         Product   Price\n",
       "40           Italy     Feed Barley  172.92\n",
       "41         Germany     Feed Barley  156.54\n",
       "42  United Kingdom     Feed Barley  155.11\n",
       "43           Spain     Feed Barley  175.67\n",
       "44          France     Feed Barley  171.22\n",
       "55          France  Malting Barley  195.27\n",
       "56         Germany  Malting Barley  209.30\n",
       "57           Spain  Malting Barley  180.00\n",
       "58           Italy  Malting Barley  207.90\n",
       "59  United Kingdom  Malting Barley  177.60"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for Barley\n",
    "prices[prices.Product.str.contains(\"Barley\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this case, we have _\"Feed Barley\"_ and _\"Malting Barley\"_ from which choose. As we don't know from which kind of barley is derived the one we are considering in the `USDA_food` we decide to take an **average** between the two prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "barleys = prices[prices.Product.str.contains(\"Barley\")]\n",
    "barleys = barleys.groupby([\"Country\"]).mean().reset_index()\n",
    "barleys[\"Product\"] = \"Barley\"\n",
    "barleys = barleys[[\"Country\", \"Product\", \"Price\"]]\n",
    "prices = prices.append(barleys)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the final fix, we can now print the **`prices_final` dataframe** for the prices for our interesting product for all the countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "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>Country</th>\n",
       "      <th>Product</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Apples</td>\n",
       "      <td>1343.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Apples</td>\n",
       "      <td>597.443000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Apples</td>\n",
       "      <td>898.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>France</td>\n",
       "      <td>Apples</td>\n",
       "      <td>1029.792500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Apples</td>\n",
       "      <td>713.742857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country Product        Price\n",
       "0  United Kingdom  Apples  1343.400000\n",
       "1         Germany  Apples   597.443000\n",
       "2           Spain  Apples   898.040000\n",
       "3          France  Apples  1029.792500\n",
       "4           Italy  Apples   713.742857"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_final = prices[prices.Product.isin(prod_diet_final.index.values)]\n",
    "prices_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to elaborate on this dataframe. For our subsequent section, we need a **matrix with countries as index and product as columns**. We therefore work on the last dataframe a bit and produce a final one named `final_prices`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "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>Apples</th>\n",
       "      <th>Barley</th>\n",
       "      <th>Beans</th>\n",
       "      <th>Beef Meat</th>\n",
       "      <th>Cherries</th>\n",
       "      <th>Chicken</th>\n",
       "      <th>Cucumbers</th>\n",
       "      <th>Lettuces</th>\n",
       "      <th>Oats</th>\n",
       "      <th>Peas</th>\n",
       "      <th>Rice</th>\n",
       "      <th>Strawberries</th>\n",
       "      <th>Tomatoes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1029.792500</td>\n",
       "      <td>183.245</td>\n",
       "      <td>262.90</td>\n",
       "      <td>4549.5</td>\n",
       "      <td>4450.60</td>\n",
       "      <td>2530.00</td>\n",
       "      <td>951.7</td>\n",
       "      <td>1269.30</td>\n",
       "      <td>145.7</td>\n",
       "      <td>356.80</td>\n",
       "      <td>372.0</td>\n",
       "      <td>3438.60</td>\n",
       "      <td>779.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>597.443000</td>\n",
       "      <td>182.920</td>\n",
       "      <td>2347.80</td>\n",
       "      <td>4033.1</td>\n",
       "      <td>3648.37</td>\n",
       "      <td>3174.38</td>\n",
       "      <td>753.1</td>\n",
       "      <td>473.22</td>\n",
       "      <td>182.7</td>\n",
       "      <td>2848.30</td>\n",
       "      <td>345.9</td>\n",
       "      <td>3404.50</td>\n",
       "      <td>1945.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>713.742857</td>\n",
       "      <td>190.410</td>\n",
       "      <td>1931.16</td>\n",
       "      <td>3762.0</td>\n",
       "      <td>2134.00</td>\n",
       "      <td>2244.77</td>\n",
       "      <td>748.0</td>\n",
       "      <td>1159.29</td>\n",
       "      <td>272.3</td>\n",
       "      <td>1323.63</td>\n",
       "      <td>403.7</td>\n",
       "      <td>1952.83</td>\n",
       "      <td>1003.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>898.040000</td>\n",
       "      <td>177.835</td>\n",
       "      <td>1921.40</td>\n",
       "      <td>2774.8</td>\n",
       "      <td>1815.00</td>\n",
       "      <td>1671.23</td>\n",
       "      <td>644.8</td>\n",
       "      <td>906.62</td>\n",
       "      <td>171.3</td>\n",
       "      <td>506.40</td>\n",
       "      <td>344.6</td>\n",
       "      <td>1259.06</td>\n",
       "      <td>372.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>1343.400000</td>\n",
       "      <td>166.355</td>\n",
       "      <td>2907.00</td>\n",
       "      <td>5328.2</td>\n",
       "      <td>4574.90</td>\n",
       "      <td>1845.69</td>\n",
       "      <td>1218.0</td>\n",
       "      <td>893.75</td>\n",
       "      <td>193.4</td>\n",
       "      <td>2861.60</td>\n",
       "      <td>380.7</td>\n",
       "      <td>2949.54</td>\n",
       "      <td>1320.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Apples   Barley    Beans  Beef Meat  Cherries  Chicken  \\\n",
       "Country                                                                       \n",
       "France          1029.792500  183.245   262.90     4549.5   4450.60  2530.00   \n",
       "Germany          597.443000  182.920  2347.80     4033.1   3648.37  3174.38   \n",
       "Italy            713.742857  190.410  1931.16     3762.0   2134.00  2244.77   \n",
       "Spain            898.040000  177.835  1921.40     2774.8   1815.00  1671.23   \n",
       "United Kingdom  1343.400000  166.355  2907.00     5328.2   4574.90  1845.69   \n",
       "\n",
       "                Cucumbers  Lettuces   Oats     Peas   Rice  Strawberries  \\\n",
       "Country                                                                    \n",
       "France              951.7   1269.30  145.7   356.80  372.0       3438.60   \n",
       "Germany             753.1    473.22  182.7  2848.30  345.9       3404.50   \n",
       "Italy               748.0   1159.29  272.3  1323.63  403.7       1952.83   \n",
       "Spain               644.8    906.62  171.3   506.40  344.6       1259.06   \n",
       "United Kingdom     1218.0    893.75  193.4  2861.60  380.7       2949.54   \n",
       "\n",
       "                Tomatoes  \n",
       "Country                   \n",
       "France             779.0  \n",
       "Germany           1945.3  \n",
       "Italy             1003.4  \n",
       "Spain              372.4  \n",
       "United Kingdom    1320.7  "
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reshaping with pivot\n",
    "final_prices = pd.pivot_table(prices_final, index=\"Country\", columns=\"Product\", aggfunc=\"first\")\n",
    "\n",
    "#cleaning\n",
    "final_prices.index = final_prices.index.to_flat_index()\n",
    "final_prices.columns = final_prices.columns.get_level_values(1)\n",
    "final_prices.columns.name = None\n",
    "\n",
    "# final print\n",
    "final_prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Final footnote*: the values here reported are **USD/tonnes**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the result into a pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_prices.to_pickle('data/processed/final_prices.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_prices = pd.read_pickle('data/processed/final_prices.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Final footnote*: the values here reported are **USD/tonnes**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Composing the optimal DIET for every helper country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The composition of the diet is one of the **key steps** of our analysis. We decide to compute the amount of product by minimizing the acutal costs of shipments that every European country has to meet. In order to model the problem we need to define an objective function which in this case will be the **non-negative weighted sum of products' cost**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem we want to model is a **Linear Program**. The library we will use to solve is **PICOS** and the solver will be **GUROBI**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In particular:\n",
    "- $Y$ is a matrix in $R^{mxn}$ in which $m$ is the number of products in our diet and $n$ is the number of African countries. Each entry $y_{ij}$ of the matrix $Y\\in{R^{mxn}}$ is the amount [tonne] of food product $i\\in{1,...,m}$ sent by the country $j\\in{1,...,n}$\n",
    "- The weights are the unit cost of each product [USD/tonne]. We will name the costs $c\\in{R^{mx1}}$.\n",
    "- The objective function is $\\sum_{i=1}^{m}\\sum_{j=1}^{n}{c_jy_{ij}}$\n",
    "- The constraints can be considered a restriction on the value that our decision variable will assume. By restricting the feasibile set we will impose the following limits: \n",
    "  - non-negativity\n",
    "  - supply and demand must be met according to the correct shares of proteins,carbs and fats \n",
    "  - even distribution of resources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The strategy is to take advantage of the results we obtained from the previous optimization problem (`food_opt_distribution_df`) and use them a starting point for the computation of the **ideal diet** for every country.\n",
    "- The term **ideal** is used because the output of the problem will be the cheapest solution to meet African food demand considering the limitations of our model and the approximation used so far.\n",
    "- The term **diet** is used to indicate the set of products that will be sent from every European country to the African ones based on the optimal distribution computed before. Since retrieving the prices for every product in different country, the list of food items available to choose is restricted (`final_prices`). \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A last note is that the problem will be solved 5 times, one for each of `best_countries`. We will have 5 outputs to analyse. Let's do it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "italy_giveup_val = food_opt_distribution_df.loc[\"Italy\"].values\n",
    "italy_giveup_index = food_opt_distribution_df.loc[\"Italy\"].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Apples</th>\n",
       "      <th>Barley</th>\n",
       "      <th>Beans</th>\n",
       "      <th>Beef Meat</th>\n",
       "      <th>Cherries</th>\n",
       "      <th>Chicken</th>\n",
       "      <th>Cucumbers</th>\n",
       "      <th>Lettuces</th>\n",
       "      <th>Oats</th>\n",
       "      <th>Peas</th>\n",
       "      <th>Rice</th>\n",
       "      <th>Strawberries</th>\n",
       "      <th>Tomatoes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1029.792500</td>\n",
       "      <td>183.245</td>\n",
       "      <td>262.90</td>\n",
       "      <td>4549.5</td>\n",
       "      <td>4450.60</td>\n",
       "      <td>2530.00</td>\n",
       "      <td>951.7</td>\n",
       "      <td>1269.30</td>\n",
       "      <td>145.7</td>\n",
       "      <td>356.80</td>\n",
       "      <td>372.0</td>\n",
       "      <td>3438.60</td>\n",
       "      <td>779.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>597.443000</td>\n",
       "      <td>182.920</td>\n",
       "      <td>2347.80</td>\n",
       "      <td>4033.1</td>\n",
       "      <td>3648.37</td>\n",
       "      <td>3174.38</td>\n",
       "      <td>753.1</td>\n",
       "      <td>473.22</td>\n",
       "      <td>182.7</td>\n",
       "      <td>2848.30</td>\n",
       "      <td>345.9</td>\n",
       "      <td>3404.50</td>\n",
       "      <td>1945.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>713.742857</td>\n",
       "      <td>190.410</td>\n",
       "      <td>1931.16</td>\n",
       "      <td>3762.0</td>\n",
       "      <td>2134.00</td>\n",
       "      <td>2244.77</td>\n",
       "      <td>748.0</td>\n",
       "      <td>1159.29</td>\n",
       "      <td>272.3</td>\n",
       "      <td>1323.63</td>\n",
       "      <td>403.7</td>\n",
       "      <td>1952.83</td>\n",
       "      <td>1003.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>898.040000</td>\n",
       "      <td>177.835</td>\n",
       "      <td>1921.40</td>\n",
       "      <td>2774.8</td>\n",
       "      <td>1815.00</td>\n",
       "      <td>1671.23</td>\n",
       "      <td>644.8</td>\n",
       "      <td>906.62</td>\n",
       "      <td>171.3</td>\n",
       "      <td>506.40</td>\n",
       "      <td>344.6</td>\n",
       "      <td>1259.06</td>\n",
       "      <td>372.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>1343.400000</td>\n",
       "      <td>166.355</td>\n",
       "      <td>2907.00</td>\n",
       "      <td>5328.2</td>\n",
       "      <td>4574.90</td>\n",
       "      <td>1845.69</td>\n",
       "      <td>1218.0</td>\n",
       "      <td>893.75</td>\n",
       "      <td>193.4</td>\n",
       "      <td>2861.60</td>\n",
       "      <td>380.7</td>\n",
       "      <td>2949.54</td>\n",
       "      <td>1320.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Apples   Barley    Beans  Beef Meat  Cherries  Chicken  \\\n",
       "Country                                                                       \n",
       "France          1029.792500  183.245   262.90     4549.5   4450.60  2530.00   \n",
       "Germany          597.443000  182.920  2347.80     4033.1   3648.37  3174.38   \n",
       "Italy            713.742857  190.410  1931.16     3762.0   2134.00  2244.77   \n",
       "Spain            898.040000  177.835  1921.40     2774.8   1815.00  1671.23   \n",
       "United Kingdom  1343.400000  166.355  2907.00     5328.2   4574.90  1845.69   \n",
       "\n",
       "                Cucumbers  Lettuces   Oats     Peas   Rice  Strawberries  \\\n",
       "Country                                                                    \n",
       "France              951.7   1269.30  145.7   356.80  372.0       3438.60   \n",
       "Germany             753.1    473.22  182.7  2848.30  345.9       3404.50   \n",
       "Italy               748.0   1159.29  272.3  1323.63  403.7       1952.83   \n",
       "Spain               644.8    906.62  171.3   506.40  344.6       1259.06   \n",
       "United Kingdom     1218.0    893.75  193.4  2861.60  380.7       2949.54   \n",
       "\n",
       "                Tomatoes  \n",
       "Country                   \n",
       "France             779.0  \n",
       "Germany           1945.3  \n",
       "Italy             1003.4  \n",
       "Spain              372.4  \n",
       "United Kingdom    1320.7  "
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "italy_prices_val = final_prices.loc[\"Italy\"].values.reshape(-1,1)\n",
    "italy_prices_index = final_prices.loc[\"Italy\"].index\n",
    "final_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "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>Protein (kcal/100g)</th>\n",
       "      <th>Carbohydrates (kcal/100g)</th>\n",
       "      <th>Fat (kcal/100g)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apples</th>\n",
       "      <td>1.04</td>\n",
       "      <td>55.24</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barley</th>\n",
       "      <td>39.64</td>\n",
       "      <td>310.88</td>\n",
       "      <td>10.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beans</th>\n",
       "      <td>79.48</td>\n",
       "      <td>251.60</td>\n",
       "      <td>4.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beef Meat</th>\n",
       "      <td>70.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cherries</th>\n",
       "      <td>4.00</td>\n",
       "      <td>48.72</td>\n",
       "      <td>2.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicken</th>\n",
       "      <td>88.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cucumbers</th>\n",
       "      <td>2.36</td>\n",
       "      <td>8.64</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lettuces</th>\n",
       "      <td>5.44</td>\n",
       "      <td>11.48</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oats</th>\n",
       "      <td>69.20</td>\n",
       "      <td>264.88</td>\n",
       "      <td>63.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peas</th>\n",
       "      <td>95.28</td>\n",
       "      <td>254.96</td>\n",
       "      <td>10.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rice</th>\n",
       "      <td>58.92</td>\n",
       "      <td>299.60</td>\n",
       "      <td>9.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strawberries</th>\n",
       "      <td>2.68</td>\n",
       "      <td>30.72</td>\n",
       "      <td>2.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomatoes</th>\n",
       "      <td>3.52</td>\n",
       "      <td>15.56</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Protein (kcal/100g)  Carbohydrates (kcal/100g)  Fat (kcal/100g)\n",
       "Product                                                                      \n",
       "Apples                       1.04                      55.24             1.53\n",
       "Barley                      39.64                     310.88            10.44\n",
       "Beans                       79.48                     251.60             4.77\n",
       "Beef Meat                   70.24                       0.00            28.98\n",
       "Cherries                     4.00                      48.72             2.70\n",
       "Chicken                     88.80                       0.00            14.67\n",
       "Cucumbers                    2.36                       8.64             1.44\n",
       "Lettuces                     5.44                      11.48             1.35\n",
       "Oats                        69.20                     264.88            63.27\n",
       "Peas                        95.28                     254.96            10.44\n",
       "Rice                        58.92                     299.60             9.72\n",
       "Strawberries                 2.68                      30.72             2.70\n",
       "Tomatoes                     3.52                      15.56             1.80"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_diet_final = prod_diet_final.sort_values(by=\"Product\")\n",
    "prod_diet_index = prod_diet_final.index\n",
    "prod_diet_val = prod_diet_final.to_numpy()/10**-6\n",
    "prod_diet_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First off, we initialize the problem `prob`. Secondly, we add the decision variable $Y\\in{R^{mxn}}$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob = pic.Problem() #initalize convex problem\n",
    "Y = prob.add_variable('Y', (italy_prices_val.size,italy_giveup_val.size)) #definition of decision matrix of variables nxm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The go on setting the objective function `obj` and the nature of the problem `minimization`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = pic.sum(italy_prices_val.T * Y) #define obj function \n",
    "prob.set_objective(\"min\", obj) #set objective function "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With regards to the constrains:\n",
    "- Non-negativity is required because **phisically-speaking it doesn't make sense** to ship negative quantity of food. In addition, by a mathematical point of view we will have to set a lower bound for the objective function, otherwise the optimization will push the optimal value to $-\\infty$.\n",
    "- The second constraint is modeled to make sure that **every African country receives the right shares of its demand in terms of macronutrients**.\n",
    "- The third constraint will ensure that the problem's resolution yields attendible results. To guarantee this, one important assumption has to be taken: **For every country, the amount of each product should cover a share between 0.01% to 35.5% of kilocalories demand**. In this way, we will have an even distribution of products for every single country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initialize constraints\n",
    "constraints = []\n",
    "\n",
    "#Define non-negativity constraint\n",
    "constraints.append(prob.add_constraint(Y>=0))\n",
    "\n",
    "#Define constraints proteins,carbs and fats\n",
    "#Define shares of proteins,carbs and fat as an absolute variable (not subject to optimization)\n",
    "shares = np.array([0.55,0.25,0.2])\n",
    "for i in range(0,italy_giveup_val.size):\n",
    "     for j in range(0,shares.size):\n",
    "        constraints.append(prob.add_constraint(Y[:,i].T*prod_diet_val[:,j].reshape(-1,1)==shares[j]*italy_giveup_val[i])) \n",
    "#Define constraints to provide an upper bound (every product has to be sent at most to cover the 35,5% of the final demand) \n",
    "for i in range(0,italy_giveup_val.size):\n",
    "    for j in range(0,italy_prices_val.size):\n",
    "        constraints.append(prob.add_constraint(pic.sum(Y[j,i]*prod_diet_val[j,:].reshape(1,-1))<=0.355*italy_giveup_val[i])) \n",
    "        constraints.append(prob.add_constraint(pic.sum(Y[j,i]*prod_diet_val[j,:].reshape(1,-1))>=0.0001*italy_giveup_val[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using license file /Users/riccardovasapollo/gurobi.lic\n",
      "Academic license - for non-commercial use only\n",
      "Reset all parameters\n",
      "The solution of the problem is:\n",
      "[ 5.73e+00  2.38e+00  1.95e+00  1.77e+00  1.37e+00  1.13e+00  1.04e+00 ... ]\n",
      "[ 8.86e+01  3.69e+01  3.02e+01  2.75e+01  2.12e+01  1.74e+01  1.60e+01 ... ]\n",
      "[ 9.86e-01  4.10e-01  3.36e-01  3.05e-01  2.36e-01  1.94e-01  1.78e-01 ... ]\n",
      "[ 1.18e+04  4.93e+03  4.04e+03  3.67e+03  2.83e+03  2.33e+03  2.14e+03 ... ]\n",
      "[ 5.97e+00  2.49e+00  2.04e+00  1.85e+00  1.43e+00  1.17e+00  1.08e+00 ... ]\n",
      "[ 8.74e+03  3.64e+03  2.98e+03  2.71e+03  2.09e+03  1.72e+03  1.58e+03 ... ]\n",
      "[ 1.68e+03  7.01e+02  5.74e+02  5.22e+02  4.03e+02  3.31e+02  3.05e+02 ... ]\n",
      "[ 1.81e+01  7.54e+00  6.18e+00  5.61e+00  4.34e+00  3.56e+00  3.28e+00 ... ]\n",
      "[ 2.96e+03  1.23e+03  1.01e+03  9.16e+02  7.08e+02  5.81e+02  5.35e+02 ... ]\n",
      "[ 9.18e-01  3.82e-01  3.13e-01  2.84e-01  2.20e-01  1.80e-01  1.66e-01 ... ]\n",
      "[ 8.99e-01  3.74e-01  3.06e-01  2.79e-01  2.15e-01  1.77e-01  1.63e-01 ... ]\n",
      "[ 9.17e+00  3.82e+00  3.13e+00  2.84e+00  2.19e+00  1.80e+00  1.66e+00 ... ]\n",
      "[ 1.59e+01  6.60e+00  5.40e+00  4.91e+00  3.79e+00  3.12e+00  2.87e+00 ... ]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Solving problem with gurobi solver (License available for free academic use)\n",
    "solution = prob.solve(verbose=0, solver = 'gurobi')\n",
    "Y_opt_ITdiet = Y.value\n",
    "print('The solution of the problem is:')\n",
    "print(Y_opt_ITdiet);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting the solution into a more agile dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ethiopia</th>\n",
       "      <th>Kenya</th>\n",
       "      <th>United Republic of Tanzania</th>\n",
       "      <th>Madagascar</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Sudan</th>\n",
       "      <th>Uganda</th>\n",
       "      <th>Chad</th>\n",
       "      <th>Mozambique</th>\n",
       "      <th>Senegal</th>\n",
       "      <th>...</th>\n",
       "      <th>Congo</th>\n",
       "      <th>Central African Republic</th>\n",
       "      <th>Liberia</th>\n",
       "      <th>Namibia</th>\n",
       "      <th>Togo</th>\n",
       "      <th>Botswana</th>\n",
       "      <th>Guinea-Bissau</th>\n",
       "      <th>Eswatini</th>\n",
       "      <th>Lesotho</th>\n",
       "      <th>Djibouti</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apples</th>\n",
       "      <td>5.725581</td>\n",
       "      <td>2.383284</td>\n",
       "      <td>1.951548</td>\n",
       "      <td>1.774081</td>\n",
       "      <td>1.370546</td>\n",
       "      <td>1.125086</td>\n",
       "      <td>1.035212</td>\n",
       "      <td>0.913324</td>\n",
       "      <td>0.749409</td>\n",
       "      <td>0.664980</td>\n",
       "      <td>...</td>\n",
       "      <td>0.263661</td>\n",
       "      <td>0.232392</td>\n",
       "      <td>0.130483</td>\n",
       "      <td>0.113529</td>\n",
       "      <td>0.110133</td>\n",
       "      <td>0.063273</td>\n",
       "      <td>0.051926</td>\n",
       "      <td>0.039848</td>\n",
       "      <td>0.015356</td>\n",
       "      <td>0.014821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barley</th>\n",
       "      <td>88.599557</td>\n",
       "      <td>36.879736</td>\n",
       "      <td>30.198907</td>\n",
       "      <td>27.452727</td>\n",
       "      <td>21.208282</td>\n",
       "      <td>17.409951</td>\n",
       "      <td>16.019216</td>\n",
       "      <td>14.133077</td>\n",
       "      <td>11.596601</td>\n",
       "      <td>10.290117</td>\n",
       "      <td>...</td>\n",
       "      <td>4.079979</td>\n",
       "      <td>3.596116</td>\n",
       "      <td>2.019140</td>\n",
       "      <td>1.756779</td>\n",
       "      <td>1.704242</td>\n",
       "      <td>0.979106</td>\n",
       "      <td>0.803521</td>\n",
       "      <td>0.616618</td>\n",
       "      <td>0.237626</td>\n",
       "      <td>0.229343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beans</th>\n",
       "      <td>0.985547</td>\n",
       "      <td>0.410236</td>\n",
       "      <td>0.335921</td>\n",
       "      <td>0.305373</td>\n",
       "      <td>0.235913</td>\n",
       "      <td>0.193661</td>\n",
       "      <td>0.178191</td>\n",
       "      <td>0.157211</td>\n",
       "      <td>0.128996</td>\n",
       "      <td>0.114463</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045384</td>\n",
       "      <td>0.040002</td>\n",
       "      <td>0.022460</td>\n",
       "      <td>0.019542</td>\n",
       "      <td>0.018957</td>\n",
       "      <td>0.010891</td>\n",
       "      <td>0.008938</td>\n",
       "      <td>0.006859</td>\n",
       "      <td>0.002643</td>\n",
       "      <td>0.002551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beef Meat</th>\n",
       "      <td>11842.726527</td>\n",
       "      <td>4929.557696</td>\n",
       "      <td>4036.559600</td>\n",
       "      <td>3669.489481</td>\n",
       "      <td>2834.821002</td>\n",
       "      <td>2327.114171</td>\n",
       "      <td>2141.220573</td>\n",
       "      <td>1889.108393</td>\n",
       "      <td>1550.068388</td>\n",
       "      <td>1375.436180</td>\n",
       "      <td>...</td>\n",
       "      <td>545.353426</td>\n",
       "      <td>480.677541</td>\n",
       "      <td>269.889804</td>\n",
       "      <td>234.821184</td>\n",
       "      <td>227.798787</td>\n",
       "      <td>130.872874</td>\n",
       "      <td>107.403290</td>\n",
       "      <td>82.420742</td>\n",
       "      <td>31.762410</td>\n",
       "      <td>30.655362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cherries</th>\n",
       "      <td>5.972498</td>\n",
       "      <td>2.486064</td>\n",
       "      <td>2.035709</td>\n",
       "      <td>1.850589</td>\n",
       "      <td>1.429651</td>\n",
       "      <td>1.173605</td>\n",
       "      <td>1.079856</td>\n",
       "      <td>0.952711</td>\n",
       "      <td>0.781727</td>\n",
       "      <td>0.693657</td>\n",
       "      <td>...</td>\n",
       "      <td>0.275031</td>\n",
       "      <td>0.242414</td>\n",
       "      <td>0.136110</td>\n",
       "      <td>0.118425</td>\n",
       "      <td>0.114883</td>\n",
       "      <td>0.066002</td>\n",
       "      <td>0.054165</td>\n",
       "      <td>0.041566</td>\n",
       "      <td>0.016018</td>\n",
       "      <td>0.015460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicken</th>\n",
       "      <td>8739.780757</td>\n",
       "      <td>3637.950550</td>\n",
       "      <td>2978.929374</td>\n",
       "      <td>2708.036319</td>\n",
       "      <td>2092.061654</td>\n",
       "      <td>1717.380504</td>\n",
       "      <td>1580.193406</td>\n",
       "      <td>1394.137840</td>\n",
       "      <td>1143.930651</td>\n",
       "      <td>1015.054314</td>\n",
       "      <td>...</td>\n",
       "      <td>402.463856</td>\n",
       "      <td>354.733879</td>\n",
       "      <td>199.175224</td>\n",
       "      <td>173.295031</td>\n",
       "      <td>168.112592</td>\n",
       "      <td>96.582507</td>\n",
       "      <td>79.262255</td>\n",
       "      <td>60.825454</td>\n",
       "      <td>23.440253</td>\n",
       "      <td>22.623265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cucumbers</th>\n",
       "      <td>1684.407936</td>\n",
       "      <td>701.138043</td>\n",
       "      <td>574.125647</td>\n",
       "      <td>521.916739</td>\n",
       "      <td>403.200647</td>\n",
       "      <td>330.988778</td>\n",
       "      <td>304.548865</td>\n",
       "      <td>268.690589</td>\n",
       "      <td>220.468444</td>\n",
       "      <td>195.630256</td>\n",
       "      <td>...</td>\n",
       "      <td>77.566398</td>\n",
       "      <td>68.367454</td>\n",
       "      <td>38.386813</td>\n",
       "      <td>33.398953</td>\n",
       "      <td>32.400147</td>\n",
       "      <td>18.614236</td>\n",
       "      <td>15.276124</td>\n",
       "      <td>11.722820</td>\n",
       "      <td>4.517613</td>\n",
       "      <td>4.360156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lettuces</th>\n",
       "      <td>18.116906</td>\n",
       "      <td>7.541197</td>\n",
       "      <td>6.175096</td>\n",
       "      <td>5.613555</td>\n",
       "      <td>4.336686</td>\n",
       "      <td>3.560000</td>\n",
       "      <td>3.275622</td>\n",
       "      <td>2.889942</td>\n",
       "      <td>2.371282</td>\n",
       "      <td>2.104131</td>\n",
       "      <td>...</td>\n",
       "      <td>0.834277</td>\n",
       "      <td>0.735337</td>\n",
       "      <td>0.412875</td>\n",
       "      <td>0.359228</td>\n",
       "      <td>0.348485</td>\n",
       "      <td>0.200208</td>\n",
       "      <td>0.164305</td>\n",
       "      <td>0.126087</td>\n",
       "      <td>0.048590</td>\n",
       "      <td>0.046896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oats</th>\n",
       "      <td>2957.179630</td>\n",
       "      <td>1230.931709</td>\n",
       "      <td>1007.946253</td>\n",
       "      <td>916.287269</td>\n",
       "      <td>707.866968</td>\n",
       "      <td>581.090394</td>\n",
       "      <td>534.671965</td>\n",
       "      <td>471.718472</td>\n",
       "      <td>387.058728</td>\n",
       "      <td>343.452316</td>\n",
       "      <td>...</td>\n",
       "      <td>136.177091</td>\n",
       "      <td>120.027245</td>\n",
       "      <td>67.392642</td>\n",
       "      <td>58.635857</td>\n",
       "      <td>56.882334</td>\n",
       "      <td>32.679518</td>\n",
       "      <td>26.819062</td>\n",
       "      <td>20.580813</td>\n",
       "      <td>7.931210</td>\n",
       "      <td>7.654775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peas</th>\n",
       "      <td>0.917700</td>\n",
       "      <td>0.381994</td>\n",
       "      <td>0.312795</td>\n",
       "      <td>0.284351</td>\n",
       "      <td>0.219672</td>\n",
       "      <td>0.180329</td>\n",
       "      <td>0.165924</td>\n",
       "      <td>0.146388</td>\n",
       "      <td>0.120116</td>\n",
       "      <td>0.106583</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042260</td>\n",
       "      <td>0.037248</td>\n",
       "      <td>0.020914</td>\n",
       "      <td>0.018196</td>\n",
       "      <td>0.017652</td>\n",
       "      <td>0.010141</td>\n",
       "      <td>0.008323</td>\n",
       "      <td>0.006387</td>\n",
       "      <td>0.002461</td>\n",
       "      <td>0.002376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rice</th>\n",
       "      <td>0.898859</td>\n",
       "      <td>0.374152</td>\n",
       "      <td>0.306374</td>\n",
       "      <td>0.278513</td>\n",
       "      <td>0.215162</td>\n",
       "      <td>0.176627</td>\n",
       "      <td>0.162518</td>\n",
       "      <td>0.143383</td>\n",
       "      <td>0.117650</td>\n",
       "      <td>0.104395</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041392</td>\n",
       "      <td>0.036483</td>\n",
       "      <td>0.020485</td>\n",
       "      <td>0.017823</td>\n",
       "      <td>0.017290</td>\n",
       "      <td>0.009933</td>\n",
       "      <td>0.008152</td>\n",
       "      <td>0.006256</td>\n",
       "      <td>0.002411</td>\n",
       "      <td>0.002327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strawberries</th>\n",
       "      <td>9.168861</td>\n",
       "      <td>3.816556</td>\n",
       "      <td>3.125180</td>\n",
       "      <td>2.840987</td>\n",
       "      <td>2.194771</td>\n",
       "      <td>1.801695</td>\n",
       "      <td>1.657773</td>\n",
       "      <td>1.462583</td>\n",
       "      <td>1.200092</td>\n",
       "      <td>1.064888</td>\n",
       "      <td>...</td>\n",
       "      <td>0.422223</td>\n",
       "      <td>0.372150</td>\n",
       "      <td>0.208954</td>\n",
       "      <td>0.181803</td>\n",
       "      <td>0.176366</td>\n",
       "      <td>0.101324</td>\n",
       "      <td>0.083154</td>\n",
       "      <td>0.063812</td>\n",
       "      <td>0.024591</td>\n",
       "      <td>0.023734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomatoes</th>\n",
       "      <td>15.852292</td>\n",
       "      <td>6.598547</td>\n",
       "      <td>5.403209</td>\n",
       "      <td>4.911860</td>\n",
       "      <td>3.794600</td>\n",
       "      <td>3.115000</td>\n",
       "      <td>2.866169</td>\n",
       "      <td>2.528700</td>\n",
       "      <td>2.074872</td>\n",
       "      <td>1.841115</td>\n",
       "      <td>...</td>\n",
       "      <td>0.729993</td>\n",
       "      <td>0.643419</td>\n",
       "      <td>0.361266</td>\n",
       "      <td>0.314324</td>\n",
       "      <td>0.304924</td>\n",
       "      <td>0.175182</td>\n",
       "      <td>0.143767</td>\n",
       "      <td>0.110326</td>\n",
       "      <td>0.042516</td>\n",
       "      <td>0.041034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Ethiopia        Kenya  United Republic of Tanzania  \\\n",
       "Product                                                                \n",
       "Apples            5.725581     2.383284                     1.951548   \n",
       "Barley           88.599557    36.879736                    30.198907   \n",
       "Beans             0.985547     0.410236                     0.335921   \n",
       "Beef Meat     11842.726527  4929.557696                  4036.559600   \n",
       "Cherries          5.972498     2.486064                     2.035709   \n",
       "Chicken        8739.780757  3637.950550                  2978.929374   \n",
       "Cucumbers      1684.407936   701.138043                   574.125647   \n",
       "Lettuces         18.116906     7.541197                     6.175096   \n",
       "Oats           2957.179630  1230.931709                  1007.946253   \n",
       "Peas              0.917700     0.381994                     0.312795   \n",
       "Rice              0.898859     0.374152                     0.306374   \n",
       "Strawberries      9.168861     3.816556                     3.125180   \n",
       "Tomatoes         15.852292     6.598547                     5.403209   \n",
       "\n",
       "               Madagascar       Zambia        Sudan       Uganda         Chad  \\\n",
       "Product                                                                         \n",
       "Apples           1.774081     1.370546     1.125086     1.035212     0.913324   \n",
       "Barley          27.452727    21.208282    17.409951    16.019216    14.133077   \n",
       "Beans            0.305373     0.235913     0.193661     0.178191     0.157211   \n",
       "Beef Meat     3669.489481  2834.821002  2327.114171  2141.220573  1889.108393   \n",
       "Cherries         1.850589     1.429651     1.173605     1.079856     0.952711   \n",
       "Chicken       2708.036319  2092.061654  1717.380504  1580.193406  1394.137840   \n",
       "Cucumbers      521.916739   403.200647   330.988778   304.548865   268.690589   \n",
       "Lettuces         5.613555     4.336686     3.560000     3.275622     2.889942   \n",
       "Oats           916.287269   707.866968   581.090394   534.671965   471.718472   \n",
       "Peas             0.284351     0.219672     0.180329     0.165924     0.146388   \n",
       "Rice             0.278513     0.215162     0.176627     0.162518     0.143383   \n",
       "Strawberries     2.840987     2.194771     1.801695     1.657773     1.462583   \n",
       "Tomatoes         4.911860     3.794600     3.115000     2.866169     2.528700   \n",
       "\n",
       "               Mozambique      Senegal  ...       Congo  \\\n",
       "Product                                 ...               \n",
       "Apples           0.749409     0.664980  ...    0.263661   \n",
       "Barley          11.596601    10.290117  ...    4.079979   \n",
       "Beans            0.128996     0.114463  ...    0.045384   \n",
       "Beef Meat     1550.068388  1375.436180  ...  545.353426   \n",
       "Cherries         0.781727     0.693657  ...    0.275031   \n",
       "Chicken       1143.930651  1015.054314  ...  402.463856   \n",
       "Cucumbers      220.468444   195.630256  ...   77.566398   \n",
       "Lettuces         2.371282     2.104131  ...    0.834277   \n",
       "Oats           387.058728   343.452316  ...  136.177091   \n",
       "Peas             0.120116     0.106583  ...    0.042260   \n",
       "Rice             0.117650     0.104395  ...    0.041392   \n",
       "Strawberries     1.200092     1.064888  ...    0.422223   \n",
       "Tomatoes         2.074872     1.841115  ...    0.729993   \n",
       "\n",
       "              Central African Republic     Liberia     Namibia        Togo  \\\n",
       "Product                                                                      \n",
       "Apples                        0.232392    0.130483    0.113529    0.110133   \n",
       "Barley                        3.596116    2.019140    1.756779    1.704242   \n",
       "Beans                         0.040002    0.022460    0.019542    0.018957   \n",
       "Beef Meat                   480.677541  269.889804  234.821184  227.798787   \n",
       "Cherries                      0.242414    0.136110    0.118425    0.114883   \n",
       "Chicken                     354.733879  199.175224  173.295031  168.112592   \n",
       "Cucumbers                    68.367454   38.386813   33.398953   32.400147   \n",
       "Lettuces                      0.735337    0.412875    0.359228    0.348485   \n",
       "Oats                        120.027245   67.392642   58.635857   56.882334   \n",
       "Peas                          0.037248    0.020914    0.018196    0.017652   \n",
       "Rice                          0.036483    0.020485    0.017823    0.017290   \n",
       "Strawberries                  0.372150    0.208954    0.181803    0.176366   \n",
       "Tomatoes                      0.643419    0.361266    0.314324    0.304924   \n",
       "\n",
       "                Botswana  Guinea-Bissau   Eswatini    Lesotho   Djibouti  \n",
       "Product                                                                   \n",
       "Apples          0.063273       0.051926   0.039848   0.015356   0.014821  \n",
       "Barley          0.979106       0.803521   0.616618   0.237626   0.229343  \n",
       "Beans           0.010891       0.008938   0.006859   0.002643   0.002551  \n",
       "Beef Meat     130.872874     107.403290  82.420742  31.762410  30.655362  \n",
       "Cherries        0.066002       0.054165   0.041566   0.016018   0.015460  \n",
       "Chicken        96.582507      79.262255  60.825454  23.440253  22.623265  \n",
       "Cucumbers      18.614236      15.276124  11.722820   4.517613   4.360156  \n",
       "Lettuces        0.200208       0.164305   0.126087   0.048590   0.046896  \n",
       "Oats           32.679518      26.819062  20.580813   7.931210   7.654775  \n",
       "Peas            0.010141       0.008323   0.006387   0.002461   0.002376  \n",
       "Rice            0.009933       0.008152   0.006256   0.002411   0.002327  \n",
       "Strawberries    0.101324       0.083154   0.063812   0.024591   0.023734  \n",
       "Tomatoes        0.175182       0.143767   0.110326   0.042516   0.041034  \n",
       "\n",
       "[13 rows x 25 columns]"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_ITdiet = np.array(Y_opt_ITdiet)\n",
    "result_ITdiet_df= pd.DataFrame(data=result_ITdiet, index = prod_diet_index)\n",
    "result_ITdiet_df.columns = italy_giveup_index\n",
    "result_ITdiet_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now have a look at the results yielded by the optmization considering that Italy is the donor country. The products that are being most used are:\n",
    "- **Beef Meat** with a price of **3762\\$/ton** and a caloric protein density of **70.24 kcal/100g** and caloric fat density **28.98 kcal/100g**\n",
    "- **Chicken** with a price of **2244.77\\$/ton** and a caloric protein density of **88.80 kcal/100g** and **14.67 kcal/100g** caloric fat density\n",
    "- **Cucumbers** with a price of **748.0\\$/ton** and a caloric carbs density of **8.64 kcal/100g** caloric proteins density **2.36 kcal/100g**\n",
    "- **Oats** with a price of **272.3\\$/ton** and a caloric carbs density of **264.88 kcal/100g**\n",
    "- **Tomatoes** with a price of **1372.4\\$/ton** and a caloric carbs density of **15.56 kcal/100g** \n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We derive from this values that the optimization is actually choosing the products that **costs the least** while **contributing the most to the kilocalories demand**. Moreover, the constraint of shares of macronutrients is respected so our diets are close to what in reality one would need biologically. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In conclusion, **the results we obtained are quite surprising**. We have obtained a tangible number of tons of food items that every European country should send in order to meet the demand in Africa. <br>\n",
    "If the reader want to have an overview on:\n",
    "- **the food item**, he can look at the rows of `result_ITdiet_df` and obtain **the distribution of for every food items** over the 25 African countries in respect to the donor country (In this example Italy).\n",
    "- **the diet**, the reader can take the columns to see a **full diet composed by different food itmes with the respective quantitites**. This full diet can be provided by each European countries for which we solved the problem and in this context it will shipped from Italy with respect to what the prices and availability in Italy are."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem shown above was an example on how we will solve the problem for just one country. For the sake of simplicity, a function `LPSolverDietEurope` will be created and run on the 5 countries. The function will output 5 dataframes with the respective solutions. For more details, have a look at *LPSolverDietEurope.py* in the repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reset all parameters\n",
      "Reset all parameters\n",
      "Reset all parameters\n",
      "Reset all parameters\n",
      "Reset all parameters\n"
     ]
    }
   ],
   "source": [
    "import LPSolverDietEurope as lp\n",
    "diet_dict_eu_countries = {name: lp.LPSolverDietEurope(name) for name in best_countries}\n",
    "for countries in best_countries:\n",
    "    diet_dict_eu_countries[countries].to_pickle(\"data/processed/\" + countries + \"_opt_diet.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading pickle of solutions\n",
    "import pandas as pd\n",
    "Final_Diet_It = pd.read_pickle(\"data/processed/Italy_opt_diet.pkl\")\n",
    "Final_Diet_Ge = pd.read_pickle(\"data/processed/Germany_opt_diet.pkl\")\n",
    "Final_Diet_Fr = pd.read_pickle(\"data/processed/France_opt_diet.pkl\")\n",
    "Final_Diet_Sp = pd.read_pickle(\"data/processed/Spain_opt_diet.pkl\")\n",
    "Final_Diet_UK = pd.read_pickle(\"data/processed/United Kingdom_opt_diet.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have our five final dataframes containing the tons of products for every country that can be considered as **OUR FINAL DIETS**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Ethiopia</th>\n",
       "      <th>Kenya</th>\n",
       "      <th>United Republic of Tanzania</th>\n",
       "      <th>Madagascar</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Sudan</th>\n",
       "      <th>Uganda</th>\n",
       "      <th>Chad</th>\n",
       "      <th>Mozambique</th>\n",
       "      <th>Senegal</th>\n",
       "      <th>...</th>\n",
       "      <th>Congo</th>\n",
       "      <th>Central African Republic</th>\n",
       "      <th>Liberia</th>\n",
       "      <th>Namibia</th>\n",
       "      <th>Togo</th>\n",
       "      <th>Botswana</th>\n",
       "      <th>Guinea-Bissau</th>\n",
       "      <th>Eswatini</th>\n",
       "      <th>Lesotho</th>\n",
       "      <th>Djibouti</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apples</th>\n",
       "      <td>5.725581</td>\n",
       "      <td>2.383284</td>\n",
       "      <td>1.951548</td>\n",
       "      <td>1.774081</td>\n",
       "      <td>1.370546</td>\n",
       "      <td>1.125086</td>\n",
       "      <td>1.035212</td>\n",
       "      <td>0.913324</td>\n",
       "      <td>0.749409</td>\n",
       "      <td>0.664980</td>\n",
       "      <td>...</td>\n",
       "      <td>0.263661</td>\n",
       "      <td>0.232392</td>\n",
       "      <td>0.130483</td>\n",
       "      <td>0.113529</td>\n",
       "      <td>0.110133</td>\n",
       "      <td>0.063273</td>\n",
       "      <td>0.051926</td>\n",
       "      <td>0.039848</td>\n",
       "      <td>0.015356</td>\n",
       "      <td>0.014821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barley</th>\n",
       "      <td>88.599557</td>\n",
       "      <td>36.879736</td>\n",
       "      <td>30.198907</td>\n",
       "      <td>27.452727</td>\n",
       "      <td>21.208282</td>\n",
       "      <td>17.409951</td>\n",
       "      <td>16.019216</td>\n",
       "      <td>14.133077</td>\n",
       "      <td>11.596601</td>\n",
       "      <td>10.290117</td>\n",
       "      <td>...</td>\n",
       "      <td>4.079979</td>\n",
       "      <td>3.596116</td>\n",
       "      <td>2.019140</td>\n",
       "      <td>1.756779</td>\n",
       "      <td>1.704242</td>\n",
       "      <td>0.979106</td>\n",
       "      <td>0.803521</td>\n",
       "      <td>0.616618</td>\n",
       "      <td>0.237626</td>\n",
       "      <td>0.229343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beans</th>\n",
       "      <td>0.985547</td>\n",
       "      <td>0.410236</td>\n",
       "      <td>0.335921</td>\n",
       "      <td>0.305373</td>\n",
       "      <td>0.235913</td>\n",
       "      <td>0.193661</td>\n",
       "      <td>0.178191</td>\n",
       "      <td>0.157211</td>\n",
       "      <td>0.128996</td>\n",
       "      <td>0.114463</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045384</td>\n",
       "      <td>0.040002</td>\n",
       "      <td>0.022460</td>\n",
       "      <td>0.019542</td>\n",
       "      <td>0.018957</td>\n",
       "      <td>0.010891</td>\n",
       "      <td>0.008938</td>\n",
       "      <td>0.006859</td>\n",
       "      <td>0.002643</td>\n",
       "      <td>0.002551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beef Meat</th>\n",
       "      <td>11842.726527</td>\n",
       "      <td>4929.557696</td>\n",
       "      <td>4036.559600</td>\n",
       "      <td>3669.489481</td>\n",
       "      <td>2834.821002</td>\n",
       "      <td>2327.114171</td>\n",
       "      <td>2141.220573</td>\n",
       "      <td>1889.108393</td>\n",
       "      <td>1550.068388</td>\n",
       "      <td>1375.436180</td>\n",
       "      <td>...</td>\n",
       "      <td>545.353426</td>\n",
       "      <td>480.677541</td>\n",
       "      <td>269.889804</td>\n",
       "      <td>234.821184</td>\n",
       "      <td>227.798787</td>\n",
       "      <td>130.872874</td>\n",
       "      <td>107.403290</td>\n",
       "      <td>82.420742</td>\n",
       "      <td>31.762410</td>\n",
       "      <td>30.655362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cherries</th>\n",
       "      <td>5.972498</td>\n",
       "      <td>2.486064</td>\n",
       "      <td>2.035709</td>\n",
       "      <td>1.850589</td>\n",
       "      <td>1.429651</td>\n",
       "      <td>1.173605</td>\n",
       "      <td>1.079856</td>\n",
       "      <td>0.952711</td>\n",
       "      <td>0.781727</td>\n",
       "      <td>0.693657</td>\n",
       "      <td>...</td>\n",
       "      <td>0.275031</td>\n",
       "      <td>0.242414</td>\n",
       "      <td>0.136110</td>\n",
       "      <td>0.118425</td>\n",
       "      <td>0.114883</td>\n",
       "      <td>0.066002</td>\n",
       "      <td>0.054165</td>\n",
       "      <td>0.041566</td>\n",
       "      <td>0.016018</td>\n",
       "      <td>0.015460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Ethiopia        Kenya  United Republic of Tanzania  \\\n",
       "Product                                                             \n",
       "Apples         5.725581     2.383284                     1.951548   \n",
       "Barley        88.599557    36.879736                    30.198907   \n",
       "Beans          0.985547     0.410236                     0.335921   \n",
       "Beef Meat  11842.726527  4929.557696                  4036.559600   \n",
       "Cherries       5.972498     2.486064                     2.035709   \n",
       "\n",
       "            Madagascar       Zambia        Sudan       Uganda         Chad  \\\n",
       "Product                                                                      \n",
       "Apples        1.774081     1.370546     1.125086     1.035212     0.913324   \n",
       "Barley       27.452727    21.208282    17.409951    16.019216    14.133077   \n",
       "Beans         0.305373     0.235913     0.193661     0.178191     0.157211   \n",
       "Beef Meat  3669.489481  2834.821002  2327.114171  2141.220573  1889.108393   \n",
       "Cherries      1.850589     1.429651     1.173605     1.079856     0.952711   \n",
       "\n",
       "            Mozambique      Senegal  ...       Congo  \\\n",
       "Product                              ...               \n",
       "Apples        0.749409     0.664980  ...    0.263661   \n",
       "Barley       11.596601    10.290117  ...    4.079979   \n",
       "Beans         0.128996     0.114463  ...    0.045384   \n",
       "Beef Meat  1550.068388  1375.436180  ...  545.353426   \n",
       "Cherries      0.781727     0.693657  ...    0.275031   \n",
       "\n",
       "           Central African Republic     Liberia     Namibia        Togo  \\\n",
       "Product                                                                   \n",
       "Apples                     0.232392    0.130483    0.113529    0.110133   \n",
       "Barley                     3.596116    2.019140    1.756779    1.704242   \n",
       "Beans                      0.040002    0.022460    0.019542    0.018957   \n",
       "Beef Meat                480.677541  269.889804  234.821184  227.798787   \n",
       "Cherries                   0.242414    0.136110    0.118425    0.114883   \n",
       "\n",
       "             Botswana  Guinea-Bissau   Eswatini    Lesotho   Djibouti  \n",
       "Product                                                                \n",
       "Apples       0.063273       0.051926   0.039848   0.015356   0.014821  \n",
       "Barley       0.979106       0.803521   0.616618   0.237626   0.229343  \n",
       "Beans        0.010891       0.008938   0.006859   0.002643   0.002551  \n",
       "Beef Meat  130.872874     107.403290  82.420742  31.762410  30.655362  \n",
       "Cherries     0.066002       0.054165   0.041566   0.016018   0.015460  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final_Diet_It.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Ethiopia</th>\n",
       "      <th>Kenya</th>\n",
       "      <th>United Republic of Tanzania</th>\n",
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       "      <th>Guinea-Bissau</th>\n",
       "      <th>Eswatini</th>\n",
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       "      <th>Apples</th>\n",
       "      <td>6.958864</td>\n",
       "      <td>2.897727</td>\n",
       "      <td>2.373135</td>\n",
       "      <td>2.157500</td>\n",
       "      <td>1.667174</td>\n",
       "      <td>1.368922</td>\n",
       "      <td>1.259719</td>\n",
       "      <td>1.111616</td>\n",
       "      <td>0.912447</td>\n",
       "      <td>0.809860</td>\n",
       "      <td>...</td>\n",
       "      <td>0.322228</td>\n",
       "      <td>0.284234</td>\n",
       "      <td>0.160407</td>\n",
       "      <td>0.139806</td>\n",
       "      <td>0.135680</td>\n",
       "      <td>0.078741</td>\n",
       "      <td>0.064954</td>\n",
       "      <td>0.050278</td>\n",
       "      <td>0.020519</td>\n",
       "      <td>0.019869</td>\n",
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       "    <tr>\n",
       "      <th>Barley</th>\n",
       "      <td>107.683788</td>\n",
       "      <td>44.840387</td>\n",
       "      <td>36.722686</td>\n",
       "      <td>33.385876</td>\n",
       "      <td>25.798414</td>\n",
       "      <td>21.183160</td>\n",
       "      <td>19.493315</td>\n",
       "      <td>17.201517</td>\n",
       "      <td>14.119511</td>\n",
       "      <td>12.532036</td>\n",
       "      <td>...</td>\n",
       "      <td>4.986260</td>\n",
       "      <td>4.398331</td>\n",
       "      <td>2.482189</td>\n",
       "      <td>2.163401</td>\n",
       "      <td>2.099565</td>\n",
       "      <td>1.218470</td>\n",
       "      <td>1.005123</td>\n",
       "      <td>0.778022</td>\n",
       "      <td>0.317518</td>\n",
       "      <td>0.307454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beans</th>\n",
       "      <td>1.197832</td>\n",
       "      <td>0.498787</td>\n",
       "      <td>0.408489</td>\n",
       "      <td>0.371371</td>\n",
       "      <td>0.286971</td>\n",
       "      <td>0.235633</td>\n",
       "      <td>0.216836</td>\n",
       "      <td>0.191343</td>\n",
       "      <td>0.157060</td>\n",
       "      <td>0.139401</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055465</td>\n",
       "      <td>0.048925</td>\n",
       "      <td>0.027611</td>\n",
       "      <td>0.024065</td>\n",
       "      <td>0.023355</td>\n",
       "      <td>0.013554</td>\n",
       "      <td>0.011181</td>\n",
       "      <td>0.008654</td>\n",
       "      <td>0.003532</td>\n",
       "      <td>0.003420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beef Meat</th>\n",
       "      <td>14393.634574</td>\n",
       "      <td>5993.624089</td>\n",
       "      <td>4908.565479</td>\n",
       "      <td>4462.548186</td>\n",
       "      <td>3448.364366</td>\n",
       "      <td>2831.463085</td>\n",
       "      <td>2605.588641</td>\n",
       "      <td>2299.253730</td>\n",
       "      <td>1887.295094</td>\n",
       "      <td>1675.104069</td>\n",
       "      <td>...</td>\n",
       "      <td>666.492244</td>\n",
       "      <td>587.906269</td>\n",
       "      <td>331.783607</td>\n",
       "      <td>289.172645</td>\n",
       "      <td>280.639914</td>\n",
       "      <td>162.867774</td>\n",
       "      <td>134.350497</td>\n",
       "      <td>103.994858</td>\n",
       "      <td>42.441245</td>\n",
       "      <td>41.096099</td>\n",
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       "    <tr>\n",
       "      <th>Cherries</th>\n",
       "      <td>7.258967</td>\n",
       "      <td>3.022692</td>\n",
       "      <td>2.475477</td>\n",
       "      <td>2.250543</td>\n",
       "      <td>1.739072</td>\n",
       "      <td>1.427957</td>\n",
       "      <td>1.314045</td>\n",
       "      <td>1.159555</td>\n",
       "      <td>0.951797</td>\n",
       "      <td>0.844785</td>\n",
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       "      <td>0.336124</td>\n",
       "      <td>0.296492</td>\n",
       "      <td>0.167324</td>\n",
       "      <td>0.145835</td>\n",
       "      <td>0.141532</td>\n",
       "      <td>0.082137</td>\n",
       "      <td>0.067755</td>\n",
       "      <td>0.052446</td>\n",
       "      <td>0.021404</td>\n",
       "      <td>0.020725</td>\n",
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      "text/plain": [
       "               Ethiopia        Kenya  United Republic of Tanzania  \\\n",
       "Product                                                             \n",
       "Apples         6.958864     2.897727                     2.373135   \n",
       "Barley       107.683788    44.840387                    36.722686   \n",
       "Beans          1.197832     0.498787                     0.408489   \n",
       "Beef Meat  14393.634574  5993.624089                  4908.565479   \n",
       "Cherries       7.258967     3.022692                     2.475477   \n",
       "\n",
       "            Madagascar       Zambia        Sudan       Uganda         Chad  \\\n",
       "Product                                                                      \n",
       "Apples        2.157500     1.667174     1.368922     1.259719     1.111616   \n",
       "Barley       33.385876    25.798414    21.183160    19.493315    17.201517   \n",
       "Beans         0.371371     0.286971     0.235633     0.216836     0.191343   \n",
       "Beef Meat  4462.548186  3448.364366  2831.463085  2605.588641  2299.253730   \n",
       "Cherries      2.250543     1.739072     1.427957     1.314045     1.159555   \n",
       "\n",
       "            Mozambique      Senegal  ...       Congo  \\\n",
       "Product                              ...               \n",
       "Apples        0.912447     0.809860  ...    0.322228   \n",
       "Barley       14.119511    12.532036  ...    4.986260   \n",
       "Beans         0.157060     0.139401  ...    0.055465   \n",
       "Beef Meat  1887.295094  1675.104069  ...  666.492244   \n",
       "Cherries      0.951797     0.844785  ...    0.336124   \n",
       "\n",
       "           Central African Republic     Liberia     Namibia        Togo  \\\n",
       "Product                                                                   \n",
       "Apples                     0.284234    0.160407    0.139806    0.135680   \n",
       "Barley                     4.398331    2.482189    2.163401    2.099565   \n",
       "Beans                      0.048925    0.027611    0.024065    0.023355   \n",
       "Beef Meat                587.906269  331.783607  289.172645  280.639914   \n",
       "Cherries                   0.296492    0.167324    0.145835    0.141532   \n",
       "\n",
       "             Botswana  Guinea-Bissau    Eswatini    Lesotho   Djibouti  \n",
       "Product                                                                 \n",
       "Apples       0.078741       0.064954    0.050278   0.020519   0.019869  \n",
       "Barley       1.218470       1.005123    0.778022   0.317518   0.307454  \n",
       "Beans        0.013554       0.011181    0.008654   0.003532   0.003420  \n",
       "Beef Meat  162.867774     134.350497  103.994858  42.441245  41.096099  \n",
       "Cherries     0.082137       0.067755    0.052446   0.021404   0.020725  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final_Diet_Ge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ethiopia</th>\n",
       "      <th>Kenya</th>\n",
       "      <th>United Republic of Tanzania</th>\n",
       "      <th>Madagascar</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Sudan</th>\n",
       "      <th>Uganda</th>\n",
       "      <th>Chad</th>\n",
       "      <th>Mozambique</th>\n",
       "      <th>Senegal</th>\n",
       "      <th>...</th>\n",
       "      <th>Congo</th>\n",
       "      <th>Central African Republic</th>\n",
       "      <th>Liberia</th>\n",
       "      <th>Namibia</th>\n",
       "      <th>Togo</th>\n",
       "      <th>Botswana</th>\n",
       "      <th>Guinea-Bissau</th>\n",
       "      <th>Eswatini</th>\n",
       "      <th>Lesotho</th>\n",
       "      <th>Djibouti</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apples</th>\n",
       "      <td>6.434336</td>\n",
       "      <td>2.678929</td>\n",
       "      <td>2.193830</td>\n",
       "      <td>1.994428</td>\n",
       "      <td>1.541015</td>\n",
       "      <td>1.265216</td>\n",
       "      <td>1.164234</td>\n",
       "      <td>1.027280</td>\n",
       "      <td>0.843105</td>\n",
       "      <td>0.748241</td>\n",
       "      <td>...</td>\n",
       "      <td>0.297319</td>\n",
       "      <td>0.262185</td>\n",
       "      <td>0.147680</td>\n",
       "      <td>0.128630</td>\n",
       "      <td>0.124815</td>\n",
       "      <td>0.072162</td>\n",
       "      <td>0.059413</td>\n",
       "      <td>0.045842</td>\n",
       "      <td>0.018323</td>\n",
       "      <td>0.017722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barley</th>\n",
       "      <td>65.543362</td>\n",
       "      <td>27.288907</td>\n",
       "      <td>22.347446</td>\n",
       "      <td>20.316240</td>\n",
       "      <td>15.697550</td>\n",
       "      <td>12.888122</td>\n",
       "      <td>11.859468</td>\n",
       "      <td>10.464390</td>\n",
       "      <td>8.588291</td>\n",
       "      <td>7.621952</td>\n",
       "      <td>...</td>\n",
       "      <td>3.028637</td>\n",
       "      <td>2.670749</td>\n",
       "      <td>1.504342</td>\n",
       "      <td>1.310287</td>\n",
       "      <td>1.271429</td>\n",
       "      <td>0.735083</td>\n",
       "      <td>0.605212</td>\n",
       "      <td>0.466970</td>\n",
       "      <td>0.186649</td>\n",
       "      <td>0.180523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beans</th>\n",
       "      <td>1.107545</td>\n",
       "      <td>0.461125</td>\n",
       "      <td>0.377625</td>\n",
       "      <td>0.343302</td>\n",
       "      <td>0.265256</td>\n",
       "      <td>0.217782</td>\n",
       "      <td>0.200400</td>\n",
       "      <td>0.176826</td>\n",
       "      <td>0.145124</td>\n",
       "      <td>0.128795</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051178</td>\n",
       "      <td>0.045130</td>\n",
       "      <td>0.025420</td>\n",
       "      <td>0.022141</td>\n",
       "      <td>0.021484</td>\n",
       "      <td>0.012421</td>\n",
       "      <td>0.010227</td>\n",
       "      <td>0.007891</td>\n",
       "      <td>0.003154</td>\n",
       "      <td>0.003050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beef Meat</th>\n",
       "      <td>13308.706472</td>\n",
       "      <td>5541.065427</td>\n",
       "      <td>4537.692180</td>\n",
       "      <td>4125.251953</td>\n",
       "      <td>3187.417901</td>\n",
       "      <td>2616.958171</td>\n",
       "      <td>2408.088003</td>\n",
       "      <td>2124.814591</td>\n",
       "      <td>1743.869019</td>\n",
       "      <td>1547.652154</td>\n",
       "      <td>...</td>\n",
       "      <td>614.970627</td>\n",
       "      <td>542.300760</td>\n",
       "      <td>305.459520</td>\n",
       "      <td>266.056395</td>\n",
       "      <td>258.166025</td>\n",
       "      <td>149.260006</td>\n",
       "      <td>122.889566</td>\n",
       "      <td>94.819159</td>\n",
       "      <td>37.899424</td>\n",
       "      <td>36.655543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cherries</th>\n",
       "      <td>6.711818</td>\n",
       "      <td>2.794458</td>\n",
       "      <td>2.288439</td>\n",
       "      <td>2.080438</td>\n",
       "      <td>1.607472</td>\n",
       "      <td>1.319779</td>\n",
       "      <td>1.214442</td>\n",
       "      <td>1.071582</td>\n",
       "      <td>0.879464</td>\n",
       "      <td>0.780509</td>\n",
       "      <td>...</td>\n",
       "      <td>0.310141</td>\n",
       "      <td>0.273492</td>\n",
       "      <td>0.154049</td>\n",
       "      <td>0.134177</td>\n",
       "      <td>0.130198</td>\n",
       "      <td>0.075274</td>\n",
       "      <td>0.061975</td>\n",
       "      <td>0.047819</td>\n",
       "      <td>0.019113</td>\n",
       "      <td>0.018486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Ethiopia        Kenya  United Republic of Tanzania  \\\n",
       "Product                                                             \n",
       "Apples         6.434336     2.678929                     2.193830   \n",
       "Barley        65.543362    27.288907                    22.347446   \n",
       "Beans          1.107545     0.461125                     0.377625   \n",
       "Beef Meat  13308.706472  5541.065427                  4537.692180   \n",
       "Cherries       6.711818     2.794458                     2.288439   \n",
       "\n",
       "            Madagascar       Zambia        Sudan       Uganda         Chad  \\\n",
       "Product                                                                      \n",
       "Apples        1.994428     1.541015     1.265216     1.164234     1.027280   \n",
       "Barley       20.316240    15.697550    12.888122    11.859468    10.464390   \n",
       "Beans         0.343302     0.265256     0.217782     0.200400     0.176826   \n",
       "Beef Meat  4125.251953  3187.417901  2616.958171  2408.088003  2124.814591   \n",
       "Cherries      2.080438     1.607472     1.319779     1.214442     1.071582   \n",
       "\n",
       "            Mozambique      Senegal  ...       Congo  \\\n",
       "Product                              ...               \n",
       "Apples        0.843105     0.748241  ...    0.297319   \n",
       "Barley        8.588291     7.621952  ...    3.028637   \n",
       "Beans         0.145124     0.128795  ...    0.051178   \n",
       "Beef Meat  1743.869019  1547.652154  ...  614.970627   \n",
       "Cherries      0.879464     0.780509  ...    0.310141   \n",
       "\n",
       "           Central African Republic     Liberia     Namibia        Togo  \\\n",
       "Product                                                                   \n",
       "Apples                     0.262185    0.147680    0.128630    0.124815   \n",
       "Barley                     2.670749    1.504342    1.310287    1.271429   \n",
       "Beans                      0.045130    0.025420    0.022141    0.021484   \n",
       "Beef Meat                542.300760  305.459520  266.056395  258.166025   \n",
       "Cherries                   0.273492    0.154049    0.134177    0.130198   \n",
       "\n",
       "             Botswana  Guinea-Bissau   Eswatini    Lesotho   Djibouti  \n",
       "Product                                                                \n",
       "Apples       0.072162       0.059413   0.045842   0.018323   0.017722  \n",
       "Barley       0.735083       0.605212   0.466970   0.186649   0.180523  \n",
       "Beans        0.012421       0.010227   0.007891   0.003154   0.003050  \n",
       "Beef Meat  149.260006     122.889566  94.819159  37.899424  36.655543  \n",
       "Cherries     0.075274       0.061975   0.047819   0.019113   0.018486  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final_Diet_Fr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Ethiopia</th>\n",
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       "      <td>1.840723</td>\n",
       "      <td>1.673290</td>\n",
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       "      <td>0.626894</td>\n",
       "      <td>...</td>\n",
       "      <td>0.248265</td>\n",
       "      <td>0.218764</td>\n",
       "      <td>0.122617</td>\n",
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       "      <td>0.103418</td>\n",
       "      <td>0.059207</td>\n",
       "      <td>0.048501</td>\n",
       "      <td>0.037106</td>\n",
       "      <td>0.013999</td>\n",
       "      <td>0.013494</td>\n",
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       "    <tr>\n",
       "      <th>Barley</th>\n",
       "      <td>55.021166</td>\n",
       "      <td>22.899758</td>\n",
       "      <td>18.750524</td>\n",
       "      <td>17.044967</td>\n",
       "      <td>13.166756</td>\n",
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       "      <td>7.197270</td>\n",
       "      <td>6.385857</td>\n",
       "      <td>...</td>\n",
       "      <td>2.528954</td>\n",
       "      <td>2.228443</td>\n",
       "      <td>1.249037</td>\n",
       "      <td>1.086094</td>\n",
       "      <td>1.053465</td>\n",
       "      <td>0.603108</td>\n",
       "      <td>0.494058</td>\n",
       "      <td>0.377979</td>\n",
       "      <td>0.142600</td>\n",
       "      <td>0.137456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beans</th>\n",
       "      <td>0.929742</td>\n",
       "      <td>0.386958</td>\n",
       "      <td>0.316844</td>\n",
       "      <td>0.288024</td>\n",
       "      <td>0.222490</td>\n",
       "      <td>0.182628</td>\n",
       "      <td>0.168033</td>\n",
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       "      <td>0.121619</td>\n",
       "      <td>0.107908</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042734</td>\n",
       "      <td>0.037656</td>\n",
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       "      <td>0.018353</td>\n",
       "      <td>0.017801</td>\n",
       "      <td>0.010191</td>\n",
       "      <td>0.008349</td>\n",
       "      <td>0.006387</td>\n",
       "      <td>0.002410</td>\n",
       "      <td>0.002323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beef Meat</th>\n",
       "      <td>11172.154280</td>\n",
       "      <td>4649.840286</td>\n",
       "      <td>3807.330271</td>\n",
       "      <td>3461.013461</td>\n",
       "      <td>2673.535239</td>\n",
       "      <td>2194.532996</td>\n",
       "      <td>2019.149400</td>\n",
       "      <td>1781.291069</td>\n",
       "      <td>1461.419614</td>\n",
       "      <td>1296.660714</td>\n",
       "      <td>...</td>\n",
       "      <td>513.508951</td>\n",
       "      <td>452.489693</td>\n",
       "      <td>253.619414</td>\n",
       "      <td>220.533495</td>\n",
       "      <td>213.908128</td>\n",
       "      <td>122.462185</td>\n",
       "      <td>100.319518</td>\n",
       "      <td>76.749427</td>\n",
       "      <td>28.955202</td>\n",
       "      <td>27.910743</td>\n",
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       "    <tr>\n",
       "      <th>Cherries</th>\n",
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       "      <td>2.344997</td>\n",
       "      <td>1.920105</td>\n",
       "      <td>1.745451</td>\n",
       "      <td>1.348312</td>\n",
       "      <td>1.106742</td>\n",
       "      <td>1.018293</td>\n",
       "      <td>0.898337</td>\n",
       "      <td>0.737020</td>\n",
       "      <td>0.653929</td>\n",
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       "      <td>0.228199</td>\n",
       "      <td>0.127905</td>\n",
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       "      <td>0.061760</td>\n",
       "      <td>0.050593</td>\n",
       "      <td>0.038706</td>\n",
       "      <td>0.014603</td>\n",
       "      <td>0.014076</td>\n",
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      "text/plain": [
       "               Ethiopia        Kenya  United Republic of Tanzania  \\\n",
       "Product                                                             \n",
       "Apples         5.401381     2.248050                     1.840723   \n",
       "Barley        55.021166    22.899758                    18.750524   \n",
       "Beans          0.929742     0.386958                     0.316844   \n",
       "Beef Meat  11172.154280  4649.840286                  3807.330271   \n",
       "Cherries       5.634317     2.344997                     1.920105   \n",
       "\n",
       "            Madagascar       Zambia        Sudan       Uganda         Chad  \\\n",
       "Product                                                                      \n",
       "Apples        1.673290     1.292569     1.060987     0.976194     0.861198   \n",
       "Barley       17.044967    13.166756    10.807742     9.944005     8.772588   \n",
       "Beans         0.288024     0.222490     0.182628     0.168033     0.148238   \n",
       "Beef Meat  3461.013461  2673.535239  2194.532996  2019.149400  1781.291069   \n",
       "Cherries      1.745451     1.348312     1.106742     1.018293     0.898337   \n",
       "\n",
       "            Mozambique      Senegal  ...       Congo  \\\n",
       "Product                              ...               \n",
       "Apples        0.706550     0.626894  ...    0.248265   \n",
       "Barley        7.197270     6.385857  ...    2.528954   \n",
       "Beans         0.121619     0.107908  ...    0.042734   \n",
       "Beef Meat  1461.419614  1296.660714  ...  513.508951   \n",
       "Cherries      0.737020     0.653929  ...    0.258972   \n",
       "\n",
       "           Central African Republic     Liberia     Namibia        Togo  \\\n",
       "Product                                                                   \n",
       "Apples                     0.218764    0.122617    0.106621    0.103418   \n",
       "Barley                     2.228443    1.249037    1.086094    1.053465   \n",
       "Beans                      0.037656    0.021106    0.018353    0.017801   \n",
       "Beef Meat                452.489693  253.619414  220.533495  213.908128   \n",
       "Cherries                   0.228199    0.127905    0.111219    0.107878   \n",
       "\n",
       "             Botswana  Guinea-Bissau   Eswatini    Lesotho   Djibouti  \n",
       "Product                                                                \n",
       "Apples       0.059207       0.048501   0.037106   0.013999   0.013494  \n",
       "Barley       0.603108       0.494058   0.377979   0.142600   0.137456  \n",
       "Beans        0.010191       0.008349   0.006387   0.002410   0.002323  \n",
       "Beef Meat  122.462185     100.319518  76.749427  28.955202  27.910743  \n",
       "Cherries     0.061760       0.050593   0.038706   0.014603   0.014076  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final_Diet_Sp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
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       "    .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>Ethiopia</th>\n",
       "      <th>Kenya</th>\n",
       "      <th>United Republic of Tanzania</th>\n",
       "      <th>Madagascar</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Sudan</th>\n",
       "      <th>Uganda</th>\n",
       "      <th>Chad</th>\n",
       "      <th>Mozambique</th>\n",
       "      <th>Senegal</th>\n",
       "      <th>...</th>\n",
       "      <th>Congo</th>\n",
       "      <th>Central African Republic</th>\n",
       "      <th>Liberia</th>\n",
       "      <th>Namibia</th>\n",
       "      <th>Togo</th>\n",
       "      <th>Botswana</th>\n",
       "      <th>Guinea-Bissau</th>\n",
       "      <th>Eswatini</th>\n",
       "      <th>Lesotho</th>\n",
       "      <th>Djibouti</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apples</th>\n",
       "      <td>6.349053</td>\n",
       "      <td>2.643354</td>\n",
       "      <td>2.164676</td>\n",
       "      <td>1.967914</td>\n",
       "      <td>1.520503</td>\n",
       "      <td>1.248354</td>\n",
       "      <td>1.148709</td>\n",
       "      <td>1.013568</td>\n",
       "      <td>0.831831</td>\n",
       "      <td>0.738222</td>\n",
       "      <td>...</td>\n",
       "      <td>0.293269</td>\n",
       "      <td>0.258600</td>\n",
       "      <td>0.145611</td>\n",
       "      <td>0.126813</td>\n",
       "      <td>0.123048</td>\n",
       "      <td>0.071093</td>\n",
       "      <td>0.058512</td>\n",
       "      <td>0.045121</td>\n",
       "      <td>0.017966</td>\n",
       "      <td>0.017373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barley</th>\n",
       "      <td>64.674623</td>\n",
       "      <td>26.926528</td>\n",
       "      <td>22.050475</td>\n",
       "      <td>20.046156</td>\n",
       "      <td>15.488601</td>\n",
       "      <td>12.716361</td>\n",
       "      <td>11.701323</td>\n",
       "      <td>10.324710</td>\n",
       "      <td>8.473444</td>\n",
       "      <td>7.519897</td>\n",
       "      <td>...</td>\n",
       "      <td>2.987382</td>\n",
       "      <td>2.634231</td>\n",
       "      <td>1.483263</td>\n",
       "      <td>1.291777</td>\n",
       "      <td>1.253433</td>\n",
       "      <td>0.724187</td>\n",
       "      <td>0.596035</td>\n",
       "      <td>0.459623</td>\n",
       "      <td>0.183012</td>\n",
       "      <td>0.176967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beans</th>\n",
       "      <td>1.092865</td>\n",
       "      <td>0.455002</td>\n",
       "      <td>0.372607</td>\n",
       "      <td>0.338738</td>\n",
       "      <td>0.261725</td>\n",
       "      <td>0.214880</td>\n",
       "      <td>0.197728</td>\n",
       "      <td>0.174466</td>\n",
       "      <td>0.143183</td>\n",
       "      <td>0.127070</td>\n",
       "      <td>...</td>\n",
       "      <td>0.050480</td>\n",
       "      <td>0.044513</td>\n",
       "      <td>0.025064</td>\n",
       "      <td>0.021828</td>\n",
       "      <td>0.021180</td>\n",
       "      <td>0.012237</td>\n",
       "      <td>0.010072</td>\n",
       "      <td>0.007767</td>\n",
       "      <td>0.003093</td>\n",
       "      <td>0.002990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beef Meat</th>\n",
       "      <td>13132.307414</td>\n",
       "      <td>5467.483665</td>\n",
       "      <td>4477.391686</td>\n",
       "      <td>4070.410773</td>\n",
       "      <td>3144.990472</td>\n",
       "      <td>2582.081699</td>\n",
       "      <td>2375.976265</td>\n",
       "      <td>2096.452436</td>\n",
       "      <td>1720.549294</td>\n",
       "      <td>1526.929677</td>\n",
       "      <td>...</td>\n",
       "      <td>606.593699</td>\n",
       "      <td>534.885735</td>\n",
       "      <td>301.179472</td>\n",
       "      <td>262.297911</td>\n",
       "      <td>254.511983</td>\n",
       "      <td>147.047511</td>\n",
       "      <td>121.026126</td>\n",
       "      <td>93.327277</td>\n",
       "      <td>37.160966</td>\n",
       "      <td>35.933551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cherries</th>\n",
       "      <td>6.622857</td>\n",
       "      <td>2.757350</td>\n",
       "      <td>2.258029</td>\n",
       "      <td>2.052781</td>\n",
       "      <td>1.586075</td>\n",
       "      <td>1.302190</td>\n",
       "      <td>1.198247</td>\n",
       "      <td>1.057278</td>\n",
       "      <td>0.867704</td>\n",
       "      <td>0.770058</td>\n",
       "      <td>...</td>\n",
       "      <td>0.305916</td>\n",
       "      <td>0.269752</td>\n",
       "      <td>0.151890</td>\n",
       "      <td>0.132282</td>\n",
       "      <td>0.128355</td>\n",
       "      <td>0.074159</td>\n",
       "      <td>0.061036</td>\n",
       "      <td>0.047067</td>\n",
       "      <td>0.018741</td>\n",
       "      <td>0.018122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Ethiopia        Kenya  United Republic of Tanzania  \\\n",
       "Product                                                             \n",
       "Apples         6.349053     2.643354                     2.164676   \n",
       "Barley        64.674623    26.926528                    22.050475   \n",
       "Beans          1.092865     0.455002                     0.372607   \n",
       "Beef Meat  13132.307414  5467.483665                  4477.391686   \n",
       "Cherries       6.622857     2.757350                     2.258029   \n",
       "\n",
       "            Madagascar       Zambia        Sudan       Uganda         Chad  \\\n",
       "Product                                                                      \n",
       "Apples        1.967914     1.520503     1.248354     1.148709     1.013568   \n",
       "Barley       20.046156    15.488601    12.716361    11.701323    10.324710   \n",
       "Beans         0.338738     0.261725     0.214880     0.197728     0.174466   \n",
       "Beef Meat  4070.410773  3144.990472  2582.081699  2375.976265  2096.452436   \n",
       "Cherries      2.052781     1.586075     1.302190     1.198247     1.057278   \n",
       "\n",
       "            Mozambique      Senegal  ...       Congo  \\\n",
       "Product                              ...               \n",
       "Apples        0.831831     0.738222  ...    0.293269   \n",
       "Barley        8.473444     7.519897  ...    2.987382   \n",
       "Beans         0.143183     0.127070  ...    0.050480   \n",
       "Beef Meat  1720.549294  1526.929677  ...  606.593699   \n",
       "Cherries      0.867704     0.770058  ...    0.305916   \n",
       "\n",
       "           Central African Republic     Liberia     Namibia        Togo  \\\n",
       "Product                                                                   \n",
       "Apples                     0.258600    0.145611    0.126813    0.123048   \n",
       "Barley                     2.634231    1.483263    1.291777    1.253433   \n",
       "Beans                      0.044513    0.025064    0.021828    0.021180   \n",
       "Beef Meat                534.885735  301.179472  262.297911  254.511983   \n",
       "Cherries                   0.269752    0.151890    0.132282    0.128355   \n",
       "\n",
       "             Botswana  Guinea-Bissau   Eswatini    Lesotho   Djibouti  \n",
       "Product                                                                \n",
       "Apples       0.071093       0.058512   0.045121   0.017966   0.017373  \n",
       "Barley       0.724187       0.596035   0.459623   0.183012   0.176967  \n",
       "Beans        0.012237       0.010072   0.007767   0.003093   0.002990  \n",
       "Beef Meat  147.047511     121.026126  93.327277  37.160966  35.933551  \n",
       "Cherries     0.074159       0.061036   0.047067   0.018741   0.018122  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final_Diet_UK.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code for the chord plot is present in [this notebook](https://nbviewer.jupyter.org/github/manuleo/mADAm-2019/blob/master/Chord.ipynb). (Chord.ipynb from our repository). We didn't put it here for scalability reasons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below cells contain the code for the sunburst plot generation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_hierarchical_dataframe(df, levels, value_column, color_columns=None, prod=None):\n",
    "    \"\"\"\n",
    "    Build a hierarchy of levels for Sunburst or Treemap charts.\n",
    "\n",
    "    Levels are given starting from the bottom to the top of the hierarchy, \n",
    "    ie the last level corresponds to the root.\n",
    "    \"\"\"\n",
    "    df_all_trees = pd.DataFrame(columns=['id', 'label', 'parent', 'value', 'log_val','color'])\n",
    "    for i, level in enumerate(levels):\n",
    "        df_tree = pd.DataFrame(columns=['id', 'parent', 'value','log_val', 'color'])\n",
    "        dfg = df.groupby(levels[i:]).sum(numerical_only=True)\n",
    "        dfg = dfg.reset_index()\n",
    "        df_tree['id'] = dfg[level].copy()\n",
    "        if(i==0):\n",
    "            df_tree['label'] = dfg['Product'].copy()\n",
    "        else:\n",
    "            df_tree['label'] = dfg[level].copy()\n",
    "        if i < len(levels) - 1:\n",
    "            df_tree['parent'] = dfg[levels[i+1]].copy()\n",
    "        else:\n",
    "            df_tree['parent'] = 'Total'\n",
    "        df_tree['log_val'] = np.log(dfg[value_column]+2)\n",
    "        df_tree['value'] = dfg[value_column]\n",
    "        df_tree['color'] = dfg[color_columns[0]]\n",
    "        df_all_trees = df_all_trees.append(df_tree, ignore_index=True)\n",
    "    total = pd.Series(dict(id='Total', parent='', \n",
    "                              value=df_tree['value'].sum(),\n",
    "                              color=df_tree['color'].sum(), \n",
    "                              label=\"Total\",\n",
    "#                               log_val=np.log1p(df[value_column].sum())))\n",
    "                              log_val=sum(df_all_trees[df_all_trees.id.isin(prod)]['log_val'])))\n",
    "    df_all_trees = df_all_trees.append(total, ignore_index=True)\n",
    "    \n",
    "    return df_all_trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_sunburst(dataset, path, EU=False):\n",
    "    df_val = dataset.copy()\n",
    "    prod = dataset.index.values\n",
    "    df_val = df_val.apply(pd.to_numeric)\n",
    "    df_val = df_val.stack().to_frame().reset_index()\n",
    "    df_val = df_val.rename(columns = {\"level_1\": \"af_countries\", 0:\"val\"})\n",
    "    levels = ['id', 'af_countries'] # levels used for the hierarchical chart\n",
    "    color_columns = ['val']\n",
    "    value_column = 'val'\n",
    "    id_n = df_val.index.values\n",
    "    df_val['id'] = df_val.Product + id_n.astype(str)\n",
    "    df_val = df_val[['Product', 'id', 'af_countries', 'val']]\n",
    "    df_all_trees = build_hierarchical_dataframe(df_val, levels, value_column, color_columns, prod)\n",
    "    df_all_trees = df_all_trees.replace({'United Republic of Tanzania':'Tanzania', 'Central African Republic':'Centr. Afr. Rep.'})\n",
    "    \n",
    "    if (EU):\n",
    "        color_scale = \"YlGnBu\"\n",
    "    else:\n",
    "        color_scale = \"RdBu\"\n",
    "    fig =go.Figure(go.Sunburst(\n",
    "        ids=df_all_trees.id,\n",
    "        labels=df_all_trees.label,\n",
    "        parents=df_all_trees.parent,\n",
    "        maxdepth=2,\n",
    "        values=df_all_trees.log_val,\n",
    "        customdata = df_all_trees.value,\n",
    "        hovertemplate='<b>%{label}</b><br><br>Need: %{customdata:.3f} tons',\n",
    "        name='',\n",
    "        marker = dict(colorscale=color_scale, line=dict(color=\"#777\", width=0.5)),\n",
    "#         branchvalues='total'\n",
    "    ))\n",
    "    fig.update_layout(margin = dict(t=0, l=0, r=0, b=20), autosize = False, height=500, width=500, paper_bgcolor = \"rgba(0,0,0,0)\", plot_bgcolor=\"rgba(0,0,0,0)\")\n",
    "    py.plot(fig, filename=path, include_plotlyjs=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The corresponding Chord Plot and SunBurst Plot are available at the following links:\n",
    "- **Italy**: \n",
    "    - [Chord](https://manuleo.github.io/mADAm_files/chord_it.html)\n",
    "    - [SunBurst](https://manuleo.github.io/mADAm_files/sunburst_it.html)\n",
    "- **Germany**: \n",
    "    - [Chord](https://manuleo.github.io/mADAm_files/chord_ge.html)\n",
    "    - [SunBurst](https://manuleo.github.io/mADAm_files/sunburst_ge.html)\n",
    "- **France**: \n",
    "    - [Chord](https://manuleo.github.io/mADAm_files/chord_fr.html)\n",
    "    - [SunBurst](https://manuleo.github.io/mADAm_files/sunburst_fr.html)\n",
    "- **Spain**: \n",
    "    - [Chord](https://manuleo.github.io/mADAm_files/chord_es.html)\n",
    "    - [SunBurst](https://manuleo.github.io/mADAm_files/sunburst_sp.html)\n",
    "- **United Kingdom**: \n",
    "    - [Chord](https://manuleo.github.io/mADAm_files/chord_uk.html)\n",
    "    - [SunBurst](https://manuleo.github.io/mADAm_files/sunburst_uk.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final important step of our analysis is to **compute a more general diet** that takes into account of all the countries contributing to the cause. The first optmization problem gave us the kilocalories that every European contry has to give up to save every African country. Considering this, the second optimization problem could have been carried out considering just the total calories given jointly by all Eruope but **increasing the granularity enabled us to determine appropriate  individual diets**. In this final part we will have a look at the **final global diet** that comprises the effort of all European countries. The result is simply obtained by summing up the 5 dataframes generated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Ethiopia</th>\n",
       "      <th>Kenya</th>\n",
       "      <th>United Republic of Tanzania</th>\n",
       "      <th>Madagascar</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Sudan</th>\n",
       "      <th>Uganda</th>\n",
       "      <th>Chad</th>\n",
       "      <th>Mozambique</th>\n",
       "      <th>Senegal</th>\n",
       "      <th>...</th>\n",
       "      <th>Congo</th>\n",
       "      <th>Central African Republic</th>\n",
       "      <th>Liberia</th>\n",
       "      <th>Namibia</th>\n",
       "      <th>Togo</th>\n",
       "      <th>Botswana</th>\n",
       "      <th>Guinea-Bissau</th>\n",
       "      <th>Eswatini</th>\n",
       "      <th>Lesotho</th>\n",
       "      <th>Djibouti</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apples</th>\n",
       "      <td>30.869216</td>\n",
       "      <td>12.851344</td>\n",
       "      <td>10.523913</td>\n",
       "      <td>9.567214</td>\n",
       "      <td>7.391807</td>\n",
       "      <td>6.068565</td>\n",
       "      <td>5.584069</td>\n",
       "      <td>4.926986</td>\n",
       "      <td>4.043342</td>\n",
       "      <td>3.588196</td>\n",
       "      <td>...</td>\n",
       "      <td>1.424742</td>\n",
       "      <td>1.256176</td>\n",
       "      <td>0.706798</td>\n",
       "      <td>0.615398</td>\n",
       "      <td>0.597095</td>\n",
       "      <td>0.344476</td>\n",
       "      <td>0.283307</td>\n",
       "      <td>0.218195</td>\n",
       "      <td>0.086163</td>\n",
       "      <td>0.083278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barley</th>\n",
       "      <td>381.522496</td>\n",
       "      <td>158.835317</td>\n",
       "      <td>130.070038</td>\n",
       "      <td>118.245966</td>\n",
       "      <td>91.359603</td>\n",
       "      <td>75.005337</td>\n",
       "      <td>69.017327</td>\n",
       "      <td>60.896283</td>\n",
       "      <td>49.975117</td>\n",
       "      <td>44.349860</td>\n",
       "      <td>...</td>\n",
       "      <td>17.611212</td>\n",
       "      <td>15.527871</td>\n",
       "      <td>8.737970</td>\n",
       "      <td>7.608339</td>\n",
       "      <td>7.382133</td>\n",
       "      <td>4.259953</td>\n",
       "      <td>3.503950</td>\n",
       "      <td>2.699212</td>\n",
       "      <td>1.067404</td>\n",
       "      <td>1.031744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beans</th>\n",
       "      <td>5.313531</td>\n",
       "      <td>2.212107</td>\n",
       "      <td>1.811485</td>\n",
       "      <td>1.646808</td>\n",
       "      <td>1.272355</td>\n",
       "      <td>1.044585</td>\n",
       "      <td>0.961188</td>\n",
       "      <td>0.848084</td>\n",
       "      <td>0.695982</td>\n",
       "      <td>0.617638</td>\n",
       "      <td>...</td>\n",
       "      <td>0.245241</td>\n",
       "      <td>0.216226</td>\n",
       "      <td>0.121661</td>\n",
       "      <td>0.105929</td>\n",
       "      <td>0.102778</td>\n",
       "      <td>0.059295</td>\n",
       "      <td>0.048766</td>\n",
       "      <td>0.037558</td>\n",
       "      <td>0.014831</td>\n",
       "      <td>0.014335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beef Meat</th>\n",
       "      <td>63849.529268</td>\n",
       "      <td>26581.571162</td>\n",
       "      <td>21767.539216</td>\n",
       "      <td>19788.713854</td>\n",
       "      <td>15289.128980</td>\n",
       "      <td>12552.150121</td>\n",
       "      <td>11550.022882</td>\n",
       "      <td>10190.920219</td>\n",
       "      <td>8363.201409</td>\n",
       "      <td>7421.782794</td>\n",
       "      <td>...</td>\n",
       "      <td>2946.918947</td>\n",
       "      <td>2598.259997</td>\n",
       "      <td>1461.931816</td>\n",
       "      <td>1272.881630</td>\n",
       "      <td>1235.024837</td>\n",
       "      <td>712.510350</td>\n",
       "      <td>585.988997</td>\n",
       "      <td>451.311463</td>\n",
       "      <td>178.219247</td>\n",
       "      <td>172.251297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cherries</th>\n",
       "      <td>32.200458</td>\n",
       "      <td>13.405561</td>\n",
       "      <td>10.977759</td>\n",
       "      <td>9.979802</td>\n",
       "      <td>7.710581</td>\n",
       "      <td>6.330273</td>\n",
       "      <td>5.824883</td>\n",
       "      <td>5.139463</td>\n",
       "      <td>4.217712</td>\n",
       "      <td>3.742938</td>\n",
       "      <td>...</td>\n",
       "      <td>1.486184</td>\n",
       "      <td>1.310349</td>\n",
       "      <td>0.737278</td>\n",
       "      <td>0.641937</td>\n",
       "      <td>0.622845</td>\n",
       "      <td>0.359332</td>\n",
       "      <td>0.295525</td>\n",
       "      <td>0.227604</td>\n",
       "      <td>0.089879</td>\n",
       "      <td>0.086869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Ethiopia         Kenya  United Republic of Tanzania  \\\n",
       "Product                                                              \n",
       "Apples        30.869216     12.851344                    10.523913   \n",
       "Barley       381.522496    158.835317                   130.070038   \n",
       "Beans          5.313531      2.212107                     1.811485   \n",
       "Beef Meat  63849.529268  26581.571162                 21767.539216   \n",
       "Cherries      32.200458     13.405561                    10.977759   \n",
       "\n",
       "             Madagascar        Zambia         Sudan        Uganda  \\\n",
       "Product                                                             \n",
       "Apples         9.567214      7.391807      6.068565      5.584069   \n",
       "Barley       118.245966     91.359603     75.005337     69.017327   \n",
       "Beans          1.646808      1.272355      1.044585      0.961188   \n",
       "Beef Meat  19788.713854  15289.128980  12552.150121  11550.022882   \n",
       "Cherries       9.979802      7.710581      6.330273      5.824883   \n",
       "\n",
       "                   Chad   Mozambique      Senegal  ...        Congo  \\\n",
       "Product                                            ...                \n",
       "Apples         4.926986     4.043342     3.588196  ...     1.424742   \n",
       "Barley        60.896283    49.975117    44.349860  ...    17.611212   \n",
       "Beans          0.848084     0.695982     0.617638  ...     0.245241   \n",
       "Beef Meat  10190.920219  8363.201409  7421.782794  ...  2946.918947   \n",
       "Cherries       5.139463     4.217712     3.742938  ...     1.486184   \n",
       "\n",
       "           Central African Republic      Liberia      Namibia         Togo  \\\n",
       "Product                                                                      \n",
       "Apples                     1.256176     0.706798     0.615398     0.597095   \n",
       "Barley                    15.527871     8.737970     7.608339     7.382133   \n",
       "Beans                      0.216226     0.121661     0.105929     0.102778   \n",
       "Beef Meat               2598.259997  1461.931816  1272.881630  1235.024837   \n",
       "Cherries                   1.310349     0.737278     0.641937     0.622845   \n",
       "\n",
       "             Botswana  Guinea-Bissau    Eswatini     Lesotho    Djibouti  \n",
       "Product                                                                   \n",
       "Apples       0.344476       0.283307    0.218195    0.086163    0.083278  \n",
       "Barley       4.259953       3.503950    2.699212    1.067404    1.031744  \n",
       "Beans        0.059295       0.048766    0.037558    0.014831    0.014335  \n",
       "Beef Meat  712.510350     585.988997  451.311463  178.219247  172.251297  \n",
       "Cherries     0.359332       0.295525    0.227604    0.089879    0.086869  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FINAL_Diet_df = Final_Diet_It + Final_Diet_UK + Final_Diet_Ge + Final_Diet_Sp + Final_Diet_Fr\n",
    "FINAL_Diet_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at them and then visualize the data with an interactive **Chord plot** and a **SunBurst plot**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://manuleo.github.io/mADAm_files/chord.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x15907f510>"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(src=\"https://manuleo.github.io/mADAm_files/chord.html\", width=800, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This chord plot provides a general overview about how food could be redistributed with minimal expenditures.Germany and France were found to contribute the most, with each providing slightly more than 100,000 tons. This is mainly due to their higher GDP. On the other end,  Ethiopia claim the highest share of food aid of all examined countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://manuleo.github.io/mADAm_files/sunburst_EU.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x15ba39b10>"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(src=\"https://manuleo.github.io/mADAm_files/sunburst_EU.html\", width=800, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SunBurst plot shows basically the final result of our whole analysis. It is used to wrap up the global final diet that the analysis propose to every African country. It is important to understand that when we talk about a diet we mean a list of food items in which we indicate the amount of food [tons] that has to be shipped. \n",
    "<br>More specifically, we can analyse Ethiopia case in which the diet is composed by:\n",
    "- **meat beef** (\\~10000 tons)\n",
    "- **chicken** (\\~8000 tons)\n",
    "- **oats**(\\~2700 tons) \n",
    "- **tomatoes** (\\~1400 tons)\n",
    "\n",
    "<br>The other products are being considered while in a neglegible extent as they have either high prices low caloric density. The toal extent to which **barley, cucumber, lattuces, strawberries, cherries, apples,beans,peas and rice** contribute jointly for \\~150 tons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the diet will be proportional with respect to the food deficit of each African country. This is important because we can apply the previous analysis to all the other African countries and take it as a guide to understand the results in a different context."
   ]
  }
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