{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7c5d059b-ed8a-4e2e-9420-25890f648895",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20879/1378035245.py:1: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n",
      "/home/agobbi/miniconda3/envs/pid/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import psycopg2 as pg\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import confusion_matrix,matthews_corrcoef,accuracy_score\n",
    "import optuna\n",
    "import pickle\n",
    "from sklearn.feature_selection import SequentialFeatureSelector\n",
    "reload_data = False\n",
    "def norm(x):\n",
    "    if len(x)==1 and x[0]=='':\n",
    "        return []\n",
    "    else:\n",
    "        return x\n",
    "if reload_data:\n",
    "    engine = pg.connect(\"dbname='safeidx' user='fbk_mpba' host='172.104.247.67' port='5432' password='fbk2024$'\")\n",
    "    df = pd.read_sql('select * from fbk_export_20240212', con=engine) \n",
    "    with open('../src/data.pkl','wb') as f:\n",
    "        pickle.dump(df,f)\n",
    "else:\n",
    "    with open('../src/data.pkl','rb') as f:\n",
    "        df = pickle.load(f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8343038e-6c6c-44ed-ab0a-dc10b8124822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.270580319372186"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100-100*df[pd.isnull(df.india)].shape[0]/df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "15672159-0f8b-4fd6-a1b9-ea9ade8cd22d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18419"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[~pd.isnull(df.india)].shape[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e84f07bf-0c95-4b13-915b-2d603422f6f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150107, 22)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b7fb69bc-5308-4c06-a184-6fa5d79534da",
   "metadata": {},
   "outputs": [],
   "source": [
    "## these columns can lead to overfit!\n",
    "df.drop(columns=['dateandtime','skiarea_id','day_of_year','minute_of_day','year'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8100ef4a-cfb9-4ce3-966e-577b6d51096e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>skiarea_name</th>\n",
       "      <th>season</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>cause</th>\n",
       "      <th>town</th>\n",
       "      <th>province</th>\n",
       "      <th>gender</th>\n",
       "      <th>equipment</th>\n",
       "      <th>helmet</th>\n",
       "      <th>destination</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>india</th>\n",
       "      <th>age</th>\n",
       "      <th>country</th>\n",
       "      <th>injury_side</th>\n",
       "      <th>injury_general_location</th>\n",
       "      <th>evacuation_vehicles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pampeago</td>\n",
       "      <td>2009</td>\n",
       "      <td>novice</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>SIKLOS</td>\n",
       "      <td></td>\n",
       "      <td>F</td>\n",
       "      <td>ski</td>\n",
       "      <td>None</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>distortion</td>\n",
       "      <td>None</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Ungheria</td>\n",
       "      <td>L</td>\n",
       "      <td>lower_limbs</td>\n",
       "      <td>[akja]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pampeago</td>\n",
       "      <td>2009</td>\n",
       "      <td>advanced</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>MALMO</td>\n",
       "      <td></td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>None</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>bruise</td>\n",
       "      <td>None</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Svezia</td>\n",
       "      <td>R</td>\n",
       "      <td>skull_or_face</td>\n",
       "      <td>[akja]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pampeago</td>\n",
       "      <td>2009</td>\n",
       "      <td>advanced</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>CALDARO</td>\n",
       "      <td>BZ</td>\n",
       "      <td>F</td>\n",
       "      <td>ski</td>\n",
       "      <td>None</td>\n",
       "      <td>domicile</td>\n",
       "      <td>other</td>\n",
       "      <td>None</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Italia</td>\n",
       "      <td>R</td>\n",
       "      <td>None</td>\n",
       "      <td>[snowmobile]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pampeago</td>\n",
       "      <td>2009</td>\n",
       "      <td>advanced</td>\n",
       "      <td>collision_person</td>\n",
       "      <td>LINZ</td>\n",
       "      <td></td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>None</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>bruise</td>\n",
       "      <td>None</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Austria</td>\n",
       "      <td>R</td>\n",
       "      <td>lower_limbs</td>\n",
       "      <td>[snowmobile]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pampeago</td>\n",
       "      <td>2009</td>\n",
       "      <td>advanced</td>\n",
       "      <td>collision_person</td>\n",
       "      <td>RUSAVA</td>\n",
       "      <td></td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>None</td>\n",
       "      <td>other</td>\n",
       "      <td>bruise</td>\n",
       "      <td>None</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Repubblica Ceca</td>\n",
       "      <td>L</td>\n",
       "      <td>lower_limbs</td>\n",
       "      <td>[other]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150102</th>\n",
       "      <td>Speikboden</td>\n",
       "      <td>2024</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>Dettmannsdorf OT Dettmannsdorf - Kölzow</td>\n",
       "      <td>None</td>\n",
       "      <td>F</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>other</td>\n",
       "      <td>i2</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Germania</td>\n",
       "      <td>L</td>\n",
       "      <td>lower_limbs</td>\n",
       "      <td>[snowmobile_sled, helicopter]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150103</th>\n",
       "      <td>Bardonecchia</td>\n",
       "      <td>2024</td>\n",
       "      <td>easy</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>Milano</td>\n",
       "      <td>Milano</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>wound</td>\n",
       "      <td>None</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Italia</td>\n",
       "      <td>L</td>\n",
       "      <td>upper_limbs</td>\n",
       "      <td>[indipendently]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150104</th>\n",
       "      <td>Klausberg</td>\n",
       "      <td>2024</td>\n",
       "      <td>easy</td>\n",
       "      <td>collision_person</td>\n",
       "      <td>Düsseldorf</td>\n",
       "      <td>None</td>\n",
       "      <td>F</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>traumacenter</td>\n",
       "      <td>other</td>\n",
       "      <td>i2</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Germania</td>\n",
       "      <td>L</td>\n",
       "      <td>upper_limbs</td>\n",
       "      <td>[car, ski_lift, snowmobile]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150105</th>\n",
       "      <td>Moena Lusia</td>\n",
       "      <td>2024</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>Rimini</td>\n",
       "      <td>Rimini</td>\n",
       "      <td>F</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>domicile</td>\n",
       "      <td>bruise</td>\n",
       "      <td>None</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Italia</td>\n",
       "      <td>R</td>\n",
       "      <td>lower_limbs</td>\n",
       "      <td>[ski_lift, snowmobile]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150106</th>\n",
       "      <td>Porta Vescovo - Arabba</td>\n",
       "      <td>2024</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>Busto Garolfo</td>\n",
       "      <td>Milano</td>\n",
       "      <td>F</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>domicile</td>\n",
       "      <td>distortion</td>\n",
       "      <td>None</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Italia</td>\n",
       "      <td>L</td>\n",
       "      <td>lower_limbs</td>\n",
       "      <td>[akja]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150107 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  skiarea_name  season    difficulty             cause  \\\n",
       "0                     Pampeago    2009        novice        fall_alone   \n",
       "1                     Pampeago    2009      advanced        fall_alone   \n",
       "2                     Pampeago    2009      advanced        fall_alone   \n",
       "3                     Pampeago    2009      advanced  collision_person   \n",
       "4                     Pampeago    2009      advanced  collision_person   \n",
       "...                        ...     ...           ...               ...   \n",
       "150102              Speikboden    2024  intermediate        fall_alone   \n",
       "150103            Bardonecchia    2024          easy        fall_alone   \n",
       "150104               Klausberg    2024          easy  collision_person   \n",
       "150105             Moena Lusia    2024  intermediate        fall_alone   \n",
       "150106  Porta Vescovo - Arabba    2024  intermediate        fall_alone   \n",
       "\n",
       "                                           town province gender equipment  \\\n",
       "0                                        SIKLOS               F       ski   \n",
       "1                                         MALMO               M       ski   \n",
       "2                                       CALDARO       BZ      F       ski   \n",
       "3                                          LINZ               M       ski   \n",
       "4                                        RUSAVA               M       ski   \n",
       "...                                         ...      ...    ...       ...   \n",
       "150102  Dettmannsdorf OT Dettmannsdorf - Kölzow     None      F       ski   \n",
       "150103                                   Milano   Milano      M       ski   \n",
       "150104                               Düsseldorf     None      F       ski   \n",
       "150105                                   Rimini   Rimini      F       ski   \n",
       "150106                            Busto Garolfo   Milano      F       ski   \n",
       "\n",
       "       helmet              destination   diagnosis india   age  \\\n",
       "0        None  hospital_emergency_room  distortion  None  32.0   \n",
       "1        None  hospital_emergency_room      bruise  None  32.0   \n",
       "2        None                 domicile       other  None  12.0   \n",
       "3        None  hospital_emergency_room      bruise  None  58.0   \n",
       "4        None                    other      bruise  None  25.0   \n",
       "...       ...                      ...         ...   ...   ...   \n",
       "150102   True  hospital_emergency_room       other    i2  37.0   \n",
       "150103   True                     None       wound  None  33.0   \n",
       "150104   True             traumacenter       other    i2  66.0   \n",
       "150105   True                 domicile      bruise  None  14.0   \n",
       "150106   True                 domicile  distortion  None  58.0   \n",
       "\n",
       "                country injury_side injury_general_location  \\\n",
       "0              Ungheria           L             lower_limbs   \n",
       "1                Svezia           R           skull_or_face   \n",
       "2                Italia           R                    None   \n",
       "3               Austria           R             lower_limbs   \n",
       "4       Repubblica Ceca           L             lower_limbs   \n",
       "...                 ...         ...                     ...   \n",
       "150102         Germania           L             lower_limbs   \n",
       "150103           Italia           L             upper_limbs   \n",
       "150104         Germania           L             upper_limbs   \n",
       "150105           Italia           R             lower_limbs   \n",
       "150106           Italia           L             lower_limbs   \n",
       "\n",
       "                  evacuation_vehicles  \n",
       "0                              [akja]  \n",
       "1                              [akja]  \n",
       "2                        [snowmobile]  \n",
       "3                        [snowmobile]  \n",
       "4                             [other]  \n",
       "...                               ...  \n",
       "150102  [snowmobile_sled, helicopter]  \n",
       "150103                [indipendently]  \n",
       "150104    [car, ski_lift, snowmobile]  \n",
       "150105         [ski_lift, snowmobile]  \n",
       "150106                         [akja]  \n",
       "\n",
       "[150107 rows x 17 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "03aa2a04-93fa-469e-a678-685cacdebd6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "##evacuation_vehicles must be explicitated\n",
    "ev = set({})\n",
    "for i,row in df.iterrows():\n",
    "    ev = ev.union(set(row.evacuation_vehicles))\n",
    "for c in ev:\n",
    "    df[c] = False\n",
    "for i,row in df.iterrows():\n",
    "    for c in row.evacuation_vehicles:\n",
    "        df.loc[i,c] = True\n",
    "df.drop(columns=['town','province','evacuation_vehicles'],inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8831be31-0ba5-459f-916a-e81f18e9e1b2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['skiarea_name', 'season', 'difficulty', 'cause', 'gender', 'equipment',\n",
       "       'helmet', 'destination', 'diagnosis', 'india', 'age', 'country',\n",
       "       'injury_side', 'injury_general_location', 'snowmobile', 'other', 'akja',\n",
       "       'offroad_vehicle', 'ski_lift', 'skiarea_ambulance', 'quad',\n",
       "       'privat_helicopter', 'ambulance', 'car', 'snowmobile_sled',\n",
       "       'indipendently', 'helicopter'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c5ca842-be34-434f-b7b0-c30c2769260e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7965bba1-b376-4de1-8f41-cb3c034db75c",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>india</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>i0</td>\n",
       "      <td>850</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>i1</td>\n",
       "      <td>13899</td>\n",
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       "      <th>2</th>\n",
       "      <td>i2</td>\n",
       "      <td>3416</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>i3</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>i4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  india  count\n",
       "0    i0    850\n",
       "1    i1  13899\n",
       "2    i2   3416\n",
       "3    i3    154\n",
       "4    i4     11"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "66148399-693d-4d63-9be0-5716dcc9f334",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aa = labeled.groupby('india').age.count().reset_index().rename(columns={'age':'Nsamples'})\n",
    "import matplotlib\n",
    "matplotlib.rcParams.update({'font.size': 18})\n",
    "\n",
    "plt.bar(aa.india, aa.Nsamples,log=True)\n",
    "plt.xlabel('India')\n",
    "plt.ylabel('Counts (log)')\n",
    "plt.tight_layout()\n",
    "plt.savefig('counts.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "33617e77-7c2b-41a3-96c0-8930aa5ac869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a4sCBA7F3795ijwIAjKPMxAkAUBrECQCQFHECACRFnAAASclMnLhaBwBKQ2bixNU6AFAaMhMnAEBpECcAQFLECQCQFHECACRFnAAASREnAEBSMhMn7nMCAKUhM3HiPicAUBoyEycAQGkQJwBAUsQJAJAUcQIAJEWcAABJEScAQFIyEyfucwIApSEzceI+JwBQGjITJwBAaRAnAEBSxAkAkBRxAgAkRZwAAEkRJwBAUsQJAJAUcQIAJEWcAABJyUycuH09AJSGzMSJ29cDQGnITJwAAKVBnAAASREnAEBSxAkAkBRxAgAkRZwAAEkRJwBAUsQJAJAUcQIAJEWcAABJEScAQFIyEye++A8ASkNm4sQX/wFAachMnAAApUGcAABJEScAQFLECQCQFHECACRFnAAASREnAEBSxAkAkBRxAgAkRZwAAEkRJwBAUsQJAJAUcQIAJEWcAABJEScAQFLECQCQFHECACQlM3GSy+Wivr4+mpqaij0KADCOMhMnbW1tceDAgdi7d2+xRwEAxlFm4gQAKA3iBABIijgBAJIiTgCApIgTACAp4gQASIo4AQCSIk4AgKSIEwAgKeIEAEiKOAEAkiJOAICkiBMAICniBABIijgBAJIiTgCApIgTACAp4gQASIo4AQCSIk4AgKSIEwAgKeIEAEiKOAEAkiJOAICkiBMAIClFiZPbbrstPvnJT8btt99ejN0DAAmbUoyd3nffffGtb30rnn322WLsHgAmRN3aHcUe4ZK8/djNRd1/Ud45Wbp0acyYMaMYuwYAEldwnOzatStWrFgRNTU1UVZWFtu3bz9rnVwuF3V1dTFt2rRYvHhx7NmzZyxmBQBKQMFxMjg4GA0NDZHL5T729W3btkV7e3t0dHREV1dXNDQ0xPLly+PIkSOXPSwAcOUr+JyTlpaWaGlpOefrGzdujNWrV8eqVasiImLz5s2xY8eO2LJlS6xdu7bgAYeGhmJoaGj0+cDAQMHbAACyY0zPOTl58mTs27cvmpub/7eDSZOiubk5du/efUnb3LBhQ1RUVIw+amtrx2pcACBBYxonx44di+Hh4aiqqjpjeVVVVfT29o4+b25ujjvuuCN+97vfxbx5884bLuvWrYv+/v7RR09Pz1iODAAkpiiXEr/00ksXvW55eXmUl5eP4zQAQErG9J2TysrKmDx5cvT19Z2xvK+vL6qrq8dyVwDAFWpM42Tq1KmxYMGC6OzsHF02MjISnZ2dsWTJkrHcFQBwhSr4Y50TJ07EwYMHR593d3fH/v37Y/bs2TF//vxob2+P1tbWWLhwYSxatCg2bdoUg4ODo1fvXKpcLhe5XC6Gh4cvazsAQNoKjpPXXnstli1bNvq8vb09IiJaW1tj69atcdddd8XRo0dj/fr10dvbG42NjbFz586zTpItVFtbW7S1tcXAwEBUVFRc1rYAgHQVHCdLly6NfD5/3nXWrFkTa9asueShAIDSVZTv1gEAOBdxAgAkJTNxksvlor6+Ppqamoo9CgAwjopyE7ZLceqE2P7+/pg1a9a4fcfOyNAH47Ld8ZTF7xvyc+Zc/L8xMfycJ0YWf84R4/OzPrXNC523GhFRlr+YtRLyzjvv+H4dAMionp6emDdv3nnXyVycjIyMxOHDh2PGjBlRVlY2ptseGBiI2tra6OnpiZkzZ47pthl/jl/2OYbZ5xhm33gdw3w+H++//37U1NTEpEnnP6skMx/rnDJp0qQLFtflmjlzpl+qDHP8ss8xzD7HMPvG4xhe7H3KMnNCLABQGsQJAJAUcXKa8vLy6OjoiPLy8mKPwiVw/LLPMcw+xzD7UjiGmTshFgC4snnnBABIijgBAJIiTgCApJR0nCxdujTuv//+Yo/BJXL8ss8xzD7HMNuSPX75Evbvf/87PzAwkM/n8/lf/epX+S9/+cv52bNn5yMi/9e//rW4w3FBpx+/kZGR/MMPP5yvrq7OT5s2LX/TTTfl//nPfxZ5Qi7k1DE8efJk/qGHHspff/31+enTp+fnzp2bX7lyZf7dd98t9ohcwOm/hx0dHfnrrrsuP3369PysWbPyN910U/6VV14p8oScz+nH73Tf/va38xGR//GPfzzxQ+Xz+ZJ+52T27NkxY8aMiIgYHByMG2+8MR5//PEiT8XFOv34/fCHP4wnn3wyNm/eHK+++mpcddVVsXz58vjwww+LPCXnc+oYfvDBB9HV1RUPP/xwdHV1xa9//et4880345Zbbin2iFzA6b+H1157bTz11FPx+uuvx5/+9Keoq6uLr3zlK3H06NEiT8m5nH78TvnNb34Tr7zyStTU1BRpqijtd06++MUv5u+7774zlnV3d3vnJCNOHb+RkZF8dXV1/oknnhh97fjx4/ny8vL8L37xiyJOyIV83O/gKXv27MlHRP7QoUMTOxQFOd8x7O/vz0dE/qWXXprYobho///4vfPOO/lPfepT+X/84x/5q6++2jsncKm6u7ujt7c3mpubR5dVVFTE4sWLY/fu3UWcjMvR398fZWVlMWvWrGKPwiU4efJkPP3001FRURENDQ3FHoeLMDIyEitXrowHH3wwPvvZzxZ1lsx98R/8f729vRERUVVVdcbyqqqq0dfIlg8//DC+//3vxze+8Q1fHpcxL7zwQtx9993xwQcfxNy5c+PFF1+MysrKYo/FRXj88cdjypQp8d3vfrfYo5T21TpAej766KO48847I5/Px09/+tNij0OBli1bFvv374+//OUv8dWvfjXuvPPOOHLkSLHH4gL27dsXP/nJT2Lr1q1RVlZW7HHECdlXXV0dERF9fX1nLO/r6xt9jWw4FSaHDh2KF1980bsmGXTVVVfFpz/96bjhhhvimWeeiSlTpsQzzzxT7LG4gJdffjmOHDkS8+fPjylTpsSUKVPi0KFD8b3vfS/q6uomfB5xQuZdc801UV1dHZ2dnaPLBgYG4tVXX40lS5YUcTIKcSpM3nrrrXjppZdizpw5xR6JMTAyMhJDQ0PFHoMLWLlyZfz973+P/fv3jz5qamriwQcfjN///vcTPo9zTv7rvffei3/9619x+PDhiIh48803I+I//yr3r++0lZWVxf333x+PPPJIfOYzn4lrrrkmHn744aipqYlbb7212ONxET766KO4/fbbo6urK1544YUYHh4ePV9o9uzZMXXq1CJPyIUMDg7Go48+GrfcckvMnTs3jh07FrlcLt5999244447ij0eFzBnzpyz/kHwiU98Iqqrq+O6666b8HnEyX89//zzsWrVqtHnd999d0REdHR0xA9+8IMiTcXFeuihh2JwcDDuvffeOH78eNx4442xc+fOmDZtWrFH4yK8++678fzzz0dERGNj4xmv/eEPf4ilS5dO/FAUZPLkyfHGG2/Es88+G8eOHYs5c+ZEU1NTvPzyy0W/8oPsKcvn8/liDwEAcIpzTgCApIgTACAp4gQASIo4AQCSIk4AgKSIEwAgKeIEAEiKOAEAkiJOAICkiBMAICniBABIijgBAJLyf+ZIelfgFbnwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot india distribution\n",
    "labeled = df[~pd.isna(df.india)].reset_index().drop(columns='index')\n",
    "labeled['age'] =  labeled['age'].astype(np.float32).fillna(np.nan)\n",
    "plt.hist(labeled.india,log=True);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c1d79124-481c-49c6-a8f8-d9ac64fbc5d9",
   "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>skiarea_name</th>\n",
       "      <th>season</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>cause</th>\n",
       "      <th>gender</th>\n",
       "      <th>equipment</th>\n",
       "      <th>helmet</th>\n",
       "      <th>destination</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>india</th>\n",
       "      <th>...</th>\n",
       "      <th>ambulance</th>\n",
       "      <th>privat_helicopter</th>\n",
       "      <th>skiarea_ambulance</th>\n",
       "      <th>snowmobile</th>\n",
       "      <th>quad</th>\n",
       "      <th>helicopter</th>\n",
       "      <th>akja</th>\n",
       "      <th>offroad_vehicle</th>\n",
       "      <th>other</th>\n",
       "      <th>indipendently</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2018</td>\n",
       "      <td>None</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>F</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>distortion</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2018</td>\n",
       "      <td>easy</td>\n",
       "      <td>illness</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>domicile</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2018</td>\n",
       "      <td>advanced</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>wound</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2018</td>\n",
       "      <td>advanced</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>fracture</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2018</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  skiarea_name  season    difficulty       cause gender equipment helmet  \\\n",
       "0    Kronplatz    2018          None  fall_alone      F       ski   True   \n",
       "1    Kronplatz    2018          easy     illness      M       ski   True   \n",
       "2    Kronplatz    2018      advanced  fall_alone      M       ski   True   \n",
       "3    Kronplatz    2018      advanced  fall_alone      M       ski   True   \n",
       "4    Kronplatz    2018  intermediate  fall_alone      M       ski   True   \n",
       "\n",
       "               destination   diagnosis  india  ...  ambulance  \\\n",
       "0  hospital_emergency_room  distortion      1  ...      False   \n",
       "1                 domicile       other      0  ...      False   \n",
       "2  hospital_emergency_room       wound      1  ...      False   \n",
       "3  hospital_emergency_room    fracture      1  ...      False   \n",
       "4  hospital_emergency_room       other      1  ...      False   \n",
       "\n",
       "  privat_helicopter skiarea_ambulance snowmobile   quad helicopter   akja  \\\n",
       "0             False             False       True  False      False  False   \n",
       "1             False             False      False  False      False  False   \n",
       "2             False             False      False  False      False   True   \n",
       "3             False             False      False  False      False   True   \n",
       "4             False             False      False  False      False   True   \n",
       "\n",
       "  offroad_vehicle  other indipendently  \n",
       "0           False  False         False  \n",
       "1           False  False         False  \n",
       "2           False  False         False  \n",
       "3           False  False         False  \n",
       "4           False  False         False  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labeled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d7b5244e-4cf0-4ee6-96ba-dda6d4be4a49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labeled.groupby(c)[c].count().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1118a12c-9793-45b2-b65c-267de35cebc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difficulty 5\n",
      "cause 10\n",
      "gender 3\n",
      "equipment 12\n",
      "helmet 3\n",
      "destination 9\n",
      "diagnosis 32\n",
      "country 75\n",
      "injury_side 3\n",
      "injury_general_location 5\n",
      "snowmobile 2\n",
      "other 2\n",
      "akja 2\n",
      "offroad_vehicle 2\n",
      "ski_lift 2\n",
      "skiarea_ambulance 2\n",
      "quad 2\n",
      "privat_helicopter 2\n",
      "ambulance 2\n",
      "car 2\n",
      "snowmobile_sled 2\n",
      "indipendently 2\n",
      "helicopter 2\n"
     ]
    }
   ],
   "source": [
    "for c in labeled.columns:\n",
    "    if c not in ['india','age','season','skiarea_name']:\n",
    "        labeled[c] = labeled[c].astype('str')  \n",
    "        print(c,labeled.groupby(c)[c].count().shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b5342102-3219-4362-a6ef-0095a8144e37",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_69977/944685202.py:8: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n",
      "  plt.figure()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AwECNHj1aa9askSSFh4crPz9fGRkZ5nrr169XWVmZwsLCzJrU1FSVlJSYNSkpKWrZsqXq1q1r1qxbt85h/ykpKQoPD6/MQwIAAFWU26WuUFhYqG+//dZ8fejQIe3atUv16tVTo0aNVL9+fYf6GjVqKCAgQC1btpQkBQcHq3fv3hoyZIjmzZunkpISxcfHa8CAAebHzR9++GG98MILio2N1dixY7V3717NmjVLr7/+urnd4cOHq3v37poxY4b69u2rjz/+WDt27HD4mDkAAKi+LvlMzo4dO9SuXTu1a9dOkjRy5Ei1a9dOkyZNuuhtLFq0SK1atVLPnj3Vp08fde3a1SGc+Pj4aO3atTp06JA6dOigUaNGadKkSQ7P0uncubM+/PBDvfXWW2rTpo2WLl2qpKQk3XLLLZd6SAAAwIKu6Dk5VR3PyUF1wXNyAFiJ056TAwAAcC0g5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEu65JCTmpqqe+65R4GBgbLZbEpKSjLnSkpKNHbsWIWGhqpWrVoKDAzUwIEDdeLECYdt5OXlKSYmRt7e3vL19VVsbKwKCwsdanbv3q2IiAh5enoqKChICQkJFXpZsmSJWrVqJU9PT4WGhmr16tWXejgAAMCiLjnkFBUVqU2bNpozZ06FuZ9//lk7d+7U888/r507d2rZsmXKysrSX/7yF4e6mJgY7du3TykpKVq5cqVSU1M1dOhQc95ut6tXr15q3LixMjIy9Oqrr2ry5Ml66623zJqtW7fqoYceUmxsrL766itFR0crOjpae/fuvdRDAgAAFmQzDMO47JVtNi1fvlzR0dEXrNm+fbtuu+02HTlyRI0aNVJmZqZCQkK0fft2dezYUZKUnJysPn366IcfflBgYKDmzp2rCRMmKDs7W+7u7pKkcePGKSkpSQcOHJAk9e/fX0VFRVq5cqW5r06dOqlt27aaN2/eRfVvt9vl4+OjgoICeXt7X+a7UDU1GbfK2S3gT3R4Wl9ntwAAleZif39f9XtyCgoKZLPZ5OvrK0lKS0uTr6+vGXAkKTIyUi4uLkpPTzdrunXrZgYcSYqKilJWVpZOnTpl1kRGRjrsKyoqSmlpaRfspbi4WHa73WEBAADWdFVDzpkzZzR27Fg99NBDZtLKzs6Wn5+fQ52bm5vq1aun7Oxss8bf39+hpvz1H9WUz5/P1KlT5ePjYy5BQUFXdoAAAOCaddVCTklJiR588EEZhqG5c+derd1ckvHjx6ugoMBcjh075uyWAADAVeJ2NTZaHnCOHDmi9evXO1wvCwgIUG5urkN9aWmp8vLyFBAQYNbk5OQ41JS//qOa8vnz8fDwkIeHx+UfGAAAqDIq/UxOecD55ptv9Pnnn6t+/foO8+Hh4crPz1dGRoY5tn79epWVlSksLMysSU1NVUlJiVmTkpKili1bqm7dumbNunXrHLadkpKi8PDwyj4kAABQBV1yyCksLNSuXbu0a9cuSdKhQ4e0a9cuHT16VCUlJbr//vu1Y8cOLVq0SOfOnVN2drays7N19uxZSVJwcLB69+6tIUOGaNu2bdqyZYvi4+M1YMAABQYGSpIefvhhubu7KzY2Vvv27dPixYs1a9YsjRw50uxj+PDhSk5O1owZM3TgwAFNnjxZO3bsUHx8fCW8LQAAoKq75I+Qb9iwQbfffnuF8UGDBmny5Mlq2rTpedf74osv1KNHD0m/PAwwPj5en332mVxcXNSvXz/Nnj1btWvXNut3796tuLg4bd++XQ0aNNAzzzyjsWPHOmxzyZIlmjhxog4fPqzmzZsrISFBffr0uehj4SPkqC74CDkAK7nY399X9Jycqo6Qg+qCkAPASq6Z5+QAAAA4AyEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABYEiEHAABY0iWHnNTUVN1zzz0KDAyUzWZTUlKSw7xhGJo0aZIaNmwoLy8vRUZG6ptvvnGoycvLU0xMjLy9veXr66vY2FgVFhY61OzevVsRERHy9PRUUFCQEhISKvSyZMkStWrVSp6engoNDdXq1asv9XAAAIBFXXLIKSoqUps2bTRnzpzzzickJGj27NmaN2+e0tPTVatWLUVFRenMmTNmTUxMjPbt26eUlBStXLlSqampGjp0qDlvt9vVq1cvNW7cWBkZGXr11Vc1efJkvfXWW2bN1q1b9dBDDyk2NlZfffWVoqOjFR0drb17917qIQEAAAuyGYZhXPbKNpuWL1+u6OhoSb+cxQkMDNSoUaP03HPPSZIKCgrk7++vxMREDRgwQJmZmQoJCdH27dvVsWNHSVJycrL69OmjH374QYGBgZo7d64mTJig7Oxsubu7S5LGjRunpKQkHThwQJLUv39/FRUVaeXKlWY/nTp1Utu2bTVv3ryL6t9ut8vHx0cFBQXy9va+3LehSmoybpWzW8Cf6PC0vs5uAQAqzcX+/q7Ue3IOHTqk7OxsRUZGmmM+Pj4KCwtTWlqaJCktLU2+vr5mwJGkyMhIubi4KD093azp1q2bGXAkKSoqSllZWTp16pRZ8+v9lNeU7wcAAFRvbpW5sezsbEmSv7+/w7i/v785l52dLT8/P8cm3NxUr149h5qmTZtW2Eb5XN26dZWdnf27+zmf4uJiFRcXm6/tdvulHB4AAKhCqtWnq6ZOnSofHx9zCQoKcnZLAADgKqnUkBMQECBJysnJcRjPyckx5wICApSbm+swX1paqry8PIea823j1/u4UE35/PmMHz9eBQUF5nLs2LFLPUQAAFBFVGrIadq0qQICArRu3TpzzG63Kz09XeHh4ZKk8PBw5efnKyMjw6xZv369ysrKFBYWZtakpqaqpKTErElJSVHLli1Vt25ds+bX+ymvKd/P+Xh4eMjb29thAQAA1nTJIaewsFC7du3Srl27JP1ys/GuXbt09OhR2Ww2jRgxQlOmTNGKFSu0Z88eDRw4UIGBgeYnsIKDg9W7d28NGTJE27Zt05YtWxQfH68BAwYoMDBQkvTwww/L3d1dsbGx2rdvnxYvXqxZs2Zp5MiRZh/Dhw9XcnKyZsyYoQMHDmjy5MnasWOH4uPjr/xdAQAAVd4l33i8Y8cO3X777ebr8uAxaNAgJSYmasyYMSoqKtLQoUOVn5+vrl27Kjk5WZ6enuY6ixYtUnx8vHr27CkXFxf169dPs2fPNud9fHy0du1axcXFqUOHDmrQoIEmTZrk8Cydzp0768MPP9TEiRP1f//3f2revLmSkpJ0yy23XNYbAQAArOWKnpNT1fGcHFQXPCcHgJU45Tk5AAAA1wpCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsCRCDgAAsKRKDznnzp3T888/r6ZNm8rLy0s33XSTXnrpJRmGYdYYhqFJkyapYcOG8vLyUmRkpL755huH7eTl5SkmJkbe3t7y9fVVbGysCgsLHWp2796tiIgIeXp6KigoSAkJCZV9OAAAoIqq9JAzffp0zZ07V2+++aYyMzM1ffp0JSQk6I033jBrEhISNHv2bM2bN0/p6emqVauWoqKidObMGbMmJiZG+/btU0pKilauXKnU1FQNHTrUnLfb7erVq5caN26sjIwMvfrqq5o8ebLeeuutyj4kAABQBdmMX59iqQR33323/P399e6775pj/fr1k5eXlz744AMZhqHAwECNGjVKzz33nCSpoKBA/v7+SkxM1IABA5SZmamQkBBt375dHTt2lCQlJyerT58++uGHHxQYGKi5c+dqwoQJys7Olru7uyRp3LhxSkpK0oEDBy6qV7vdLh8fHxUUFMjb27sy34ZrXpNxq5zdAv5Eh6f1dXYLAFBpLvb3d6WfyencubPWrVungwcPSpK+/vprbd68WXfddZck6dChQ8rOzlZkZKS5jo+Pj8LCwpSWliZJSktLk6+vrxlwJCkyMlIuLi5KT083a7p162YGHEmKiopSVlaWTp06dd7eiouLZbfbHRYAAGBNbpW9wXHjxslut6tVq1ZydXXVuXPn9PLLLysmJkaSlJ2dLUny9/d3WM/f39+cy87Olp+fn2Ojbm6qV6+eQ03Tpk0rbKN8rm7duhV6mzp1ql544YVKOEoAAHCtq/QzOZ988okWLVqkDz/8UDt37tTChQv12muvaeHChZW9q0s2fvx4FRQUmMuxY8ec3RIAALhKKv1MzujRozVu3DgNGDBAkhQaGqojR45o6tSpGjRokAICAiRJOTk5atiwobleTk6O2rZtK0kKCAhQbm6uw3ZLS0uVl5dnrh8QEKCcnByHmvLX5TW/5eHhIQ8Pjys/SAAAcM2r9DM5P//8s1xcHDfr6uqqsrIySVLTpk0VEBCgdevWmfN2u13p6ekKDw+XJIWHhys/P18ZGRlmzfr161VWVqawsDCzJjU1VSUlJWZNSkqKWrZsed5LVQAAoHqp9JBzzz336OWXX9aqVat0+PBhLV++XP/4xz/017/+VZJks9k0YsQITZkyRStWrNCePXs0cOBABQYGKjo6WpIUHBys3r17a8iQIdq2bZu2bNmi+Ph4DRgwQIGBgZKkhx9+WO7u7oqNjdW+ffu0ePFizZo1SyNHjqzsQwIAAFVQpV+ueuONN/T888/r6aefVm5urgIDA/XEE09o0qRJZs2YMWNUVFSkoUOHKj8/X127dlVycrI8PT3NmkWLFik+Pl49e/aUi4uL+vXrp9mzZ5vzPj4+Wrt2reLi4tShQwc1aNBAkyZNcniWDgAAqL4q/Tk5VQnPyUF1wXNyAFiJ056TAwAAcC0g5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEsi5AAAAEu6KiHn+PHjeuSRR1S/fn15eXkpNDRUO3bsMOcNw9CkSZPUsGFDeXl5KTIyUt98843DNvLy8hQTEyNvb2/5+voqNjZWhYWFDjW7d+9WRESEPD09FRQUpISEhKtxOAAAoAqq9JBz6tQpdenSRTVq1NB//vMf7d+/XzNmzFDdunXNmoSEBM2ePVvz5s1Tenq6atWqpaioKJ05c8asiYmJ0b59+5SSkqKVK1cqNTVVQ4cONeftdrt69eqlxo0bKyMjQ6+++qomT56st956q7IPCQAAVEE2wzCMytzguHHjtGXLFm3atOm884ZhKDAwUKNGjdJzzz0nSSooKJC/v78SExM1YMAAZWZmKiQkRNu3b1fHjh0lScnJyerTp49++OEHBQYGau7cuZowYYKys7Pl7u5u7jspKUkHDhy4qF7tdrt8fHxUUFAgb2/vSjj6qqPJuFXObgF/osPT+jq7BQCoNBf7+7vSz+SsWLFCHTt21AMPPCA/Pz+1a9dOb7/9tjl/6NAhZWdnKzIy0hzz8fFRWFiY0tLSJElpaWny9fU1A44kRUZGysXFRenp6WZNt27dzIAjSVFRUcrKytKpU6fO21txcbHsdrvDAgAArKnSQ87333+vuXPnqnnz5lqzZo2eeuopDRs2TAsXLpQkZWdnS5L8/f0d1vP39zfnsrOz5efn5zDv5uamevXqOdScbxu/3sdvTZ06VT4+PuYSFBR0hUcLAACuVZUecsrKytS+fXu98sorateunYYOHaohQ4Zo3rx5lb2rSzZ+/HgVFBSYy7Fjx5zdEgAAuEoqPeQ0bNhQISEhDmPBwcE6evSoJCkgIECSlJOT41CTk5NjzgUEBCg3N9dhvrS0VHl5eQ4159vGr/fxWx4eHvL29nZYAACANVV6yOnSpYuysrIcxg4ePKjGjRtLkpo2baqAgACtW7fOnLfb7UpPT1d4eLgkKTw8XPn5+crIyDBr1q9fr7KyMoWFhZk1qampKikpMWtSUlLUsmVLh09yAQCA6qnSQ86zzz6rL7/8Uq+88oq+/fZbffjhh3rrrbcUFxcnSbLZbBoxYoSmTJmiFStWaM+ePRo4cKACAwMVHR0t6ZczP71799aQIUO0bds2bdmyRfHx8RowYIACAwMlSQ8//LDc3d0VGxurffv2afHixZo1a5ZGjhxZ2YcEAACqILfK3uCtt96q5cuXa/z48XrxxRfVtGlTzZw5UzExMWbNmDFjVFRUpKFDhyo/P19du3ZVcnKyPD09zZpFixYpPj5ePXv2lIuLi/r166fZs2eb8z4+Plq7dq3i4uLUoUMHNWjQQJMmTXJ4lg4AAKi+Kv05OVUJz8lBdcFzcgBYidOekwMAAHAtIOQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLIuQAAABLuuohZ9q0abLZbBoxYoQ5dubMGcXFxal+/fqqXbu2+vXrp5ycHIf1jh49qr59+6pmzZry8/PT6NGjVVpa6lCzYcMGtW/fXh4eHmrWrJkSExOv9uEAAIAq4qqGnO3bt2v+/Plq3bq1w/izzz6rzz77TEuWLNHGjRt14sQJ3Xfffeb8uXPn1LdvX509e1Zbt27VwoULlZiYqEmTJpk1hw4dUt++fXX77bdr165dGjFihB5//HGtWbPmah4SAACoIq5ayCksLFRMTIzefvtt1a1b1xwvKCjQu+++q3/84x+644471KFDBy1YsEBbt27Vl19+KUlau3at9u/frw8++EBt27bVXXfdpZdeeklz5szR2bNnJUnz5s1T06ZNNWPGDAUHBys+Pl7333+/Xn/99at1SAAAoAq5aiEnLi5Offv2VWRkpMN4RkaGSkpKHMZbtWqlRo0aKS0tTZKUlpam0NBQ+fv7mzVRUVGy2+3at2+fWfPbbUdFRZnbOJ/i4mLZ7XaHBQAAWJPb1djoxx9/rJ07d2r79u0V5rKzs+Xu7i5fX1+HcX9/f2VnZ5s1vw445fPlc79XY7fbdfr0aXl5eVXY99SpU/XCCy9c9nEBAICqo9LP5Bw7dkzDhw/XokWL5OnpWdmbvyLjx49XQUGBuRw7dszZLQEAgKuk0kNORkaGcnNz1b59e7m5ucnNzU0bN27U7Nmz5ebmJn9/f509e1b5+fkO6+Xk5CggIECSFBAQUOHTVuWv/6jG29v7vGdxJMnDw0Pe3t4OCwAAsKZKDzk9e/bUnj17tGvXLnPp2LGjYmJizD/XqFFD69atM9fJysrS0aNHFR4eLkkKDw/Xnj17lJuba9akpKTI29tbISEhZs2vt1FeU74NAABQvVX6PTl16tTRLbfc4jBWq1Yt1a9f3xyPjY3VyJEjVa9ePXl7e+uZZ55ReHi4OnXqJEnq1auXQkJC9OijjyohIUHZ2dmaOHGi4uLi5OHhIUl68skn9eabb2rMmDH629/+pvXr1+uTTz7RqlWrKvuQAABAFXRVbjz+I6+//rpcXFzUr18/FRcXKyoqSv/85z/NeVdXV61cuVJPPfWUwsPDVatWLQ0aNEgvvviiWdO0aVOtWrVKzz77rGbNmqUbbrhB77zzjqKiopxxSAAA4BpjMwzDcHYTzmK32+Xj46OCgoJqd39Ok3Gc8apODk/r6+wWAKDSXOzvb767CgAAWBIhBwAAWBIhBwAAWBIhBwAAWBIhBwAAWBIhBwAAWBIhBwAAWBIhBwAAWBIhBwAAWJJTvtYBAHD18ETz6oUnml8YZ3IAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlEXIAAIAlVXrImTp1qm699VbVqVNHfn5+io6OVlZWlkPNmTNnFBcXp/r166t27drq16+fcnJyHGqOHj2qvn37qmbNmvLz89Po0aNVWlrqULNhwwa1b99eHh4eatasmRITEyv7cAAAQBVV6SFn48aNiouL05dffqmUlBSVlJSoV69eKioqMmueffZZffbZZ1qyZIk2btyoEydO6L777jPnz507p759++rs2bPaunWrFi5cqMTERE2aNMmsOXTokPr27avbb79du3bt0ogRI/T4449rzZo1lX1IAACgCrIZhmFczR2cPHlSfn5+2rhxo7p166aCggJdd911+vDDD3X//fdLkg4cOKDg4GClpaWpU6dO+s9//qO7775bJ06ckL+/vyRp3rx5Gjt2rE6ePCl3d3eNHTtWq1at0t69e819DRgwQPn5+UpOTr6o3ux2u3x8fFRQUCBvb+/KP/hrWJNxq5zdAv5Eh6f1dXYL+BPx8129VMef74v9/X3V78kpKCiQJNWrV0+SlJGRoZKSEkVGRpo1rVq1UqNGjZSWliZJSktLU2hoqBlwJCkqKkp2u1379u0za369jfKa8m2cT3Fxsex2u8MCAACs6aqGnLKyMo0YMUJdunTRLbfcIknKzs6Wu7u7fH19HWr9/f2VnZ1t1vw64JTPl8/9Xo3dbtfp06fP28/UqVPl4+NjLkFBQVd8jAAA4Np0VUNOXFyc9u7dq48//vhq7uaijR8/XgUFBeZy7NgxZ7cEAACuErerteH4+HitXLlSqampuuGGG8zxgIAAnT17Vvn5+Q5nc3JychQQEGDWbNu2zWF75Z+++nXNbz+RlZOTI29vb3l5eZ23Jw8PD3l4eFzxsQEAgGtfpZ/JMQxD8fHxWr58udavX6+mTZs6zHfo0EE1atTQunXrzLGsrCwdPXpU4eHhkqTw8HDt2bNHubm5Zk1KSoq8vb0VEhJi1vx6G+U15dsAAADVW6WfyYmLi9OHH36oTz/9VHXq1DHvofHx8ZGXl5d8fHwUGxurkSNHql69evL29tYzzzyj8PBwderUSZLUq1cvhYSE6NFHH1VCQoKys7M1ceJExcXFmWdinnzySb355psaM2aM/va3v2n9+vX65JNPtGoVnyoAAABX4UzO3LlzVVBQoB49eqhhw4bmsnjxYrPm9ddf1913361+/fqpW7duCggI0LJly8x5V1dXrVy5Uq6urgoPD9cjjzyigQMH6sUXXzRrmjZtqlWrViklJUVt2rTRjBkz9M477ygqKqqyDwkAAFRBV/05OdcynpOD6qI6PkejOuPnu3qpjj/f18xzcgAAAJyBkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACyJkAMAACypyoecOXPmqEmTJvL09FRYWJi2bdvm7JYAAMA1oEqHnMWLF2vkyJH6+9//rp07d6pNmzaKiopSbm6us1sDAABOVqVDzj/+8Q8NGTJEjz32mEJCQjRv3jzVrFlT//rXv5zdGgAAcLIqG3LOnj2rjIwMRUZGmmMuLi6KjIxUWlqaEzsDAADXAjdnN3C5/vvf/+rcuXPy9/d3GPf399eBAwfOu05xcbGKi4vN1wUFBZIku91+9Rq9RpUV/+zsFvAnqo7/j1dn/HxXL9Xx57v8mA3D+N26KhtyLsfUqVP1wgsvVBgPCgpyQjfAn8dnprM7AHC1VOef759++kk+Pj4XnK+yIadBgwZydXVVTk6Ow3hOTo4CAgLOu8748eM1cuRI83VZWZny8vJUv3592Wy2q9ovnM9utysoKEjHjh2Tt7e3s9sBUIn4+a5eDMPQTz/9pMDAwN+tq7Ihx93dXR06dNC6desUHR0t6ZfQsm7dOsXHx593HQ8PD3l4eDiM+fr6XuVOca3x9vbmL0HAovj5rj5+7wxOuSobciRp5MiRGjRokDp27KjbbrtNM2fOVFFRkR577DFntwYAAJysSoec/v376+TJk5o0aZKys7PVtm1bJScnV7gZGQAAVD9VOuRIUnx8/AUvTwG/5uHhob///e8VLlkCqPr4+cb52Iw/+vwVAABAFVRlHwYIAADwewg5AADAkgg5AADAkgg5AADAkgg5AIAqadOmTXrkkUcUHh6u48ePS5Lef/99bd682cmd4VpByAEAVDn//ve/FRUVJS8vL3311Vfmly8XFBTolVdecXJ3uFYQclAtnD17VllZWSotLXV2KwAqwZQpUzRv3jy9/fbbqlGjhjnepUsX7dy504md4VpCyIGl/fzzz4qNjVXNmjV188036+jRo5KkZ555RtOmTXNydwAuV1ZWlrp161Zh3MfHR/n5+X9+Q7gmEXJgaePHj9fXX3+tDRs2yNPT0xyPjIzU4sWLndgZgCsREBCgb7/9tsL45s2bdeONNzqhI1yLCDmwtKSkJL355pvq2rWrbDabOX7zzTfru+++c2JnAK7EkCFDNHz4cKWnp8tms+nEiRNatGiRnnvuOT311FPObg/XiCr/3VXA7zl58qT8/PwqjBcVFTmEHgBVy7hx41RWVqaePXvq559/Vrdu3eTh4aHnnntOzzzzjLPbwzWCMzmwtI4dO2rVqlXm6/Jg88477yg8PNxZbQG4QjabTRMmTFBeXp727t2rL7/8UidPntRLL73k7NZwDeFMDiztlVde0V133aX9+/ertLRUs2bN0v79+7V161Zt3LjR2e0BuELu7u4KCQlxdhu4RvEt5LC87777TtOmTdPXX3+twsJCtW/fXmPHjlVoaKizWwNwmW6//fbfveS8fv36P7EbXKs4kwPLu+mmm/T22287uw0Alaht27YOr0tKSrRr1y7t3btXgwYNck5TuOYQcmBpO3fuVI0aNcyzNp9++qkWLFigkJAQTZ48We7u7k7uEMDleP311887PnnyZBUWFv7J3eBaxY3HsLQnnnhCBw8elCR9//336t+/v2rWrKklS5ZozJgxTu4OQGV75JFH9K9//cvZbeAaQciBpR08eNA8rb1kyRJ1795dH374oRITE/Xvf//buc0BqHRpaWkOD/5E9cblKliaYRgqKyuTJH3++ee6++67JUlBQUH673//68zWAFyB++67z+G1YRj68ccftWPHDj3//PNO6grXGkIOLK1jx46aMmWKIiMjtXHjRs2dO1eSdOjQIfn7+zu5OwCXy8fHx+G1i4uLWrZsqRdffFG9evVyUle41hByYGkzZ85UTEyMkpKSNGHCBDVr1kyStHTpUnXu3NnJ3QG4HOfOndNjjz2m0NBQ1a1b19nt4BrGc3JQLZ05c0aurq6qUaOGs1sBcBk8PT2VmZmppk2bOrsVXMO48RjVkqenJwEHqMJuueUWff/9985uA9c4zuTAcurWrXvRX76Zl5d3lbsBcDUkJydr/Pjxeumll9ShQwfVqlXLYd7b29tJneFaQsiB5SxcuPCia3kyKlC1vPjiixo1apTq1Kljjv36HzWGYchms+ncuXPOaA/XGEIOAKDKcHV11Y8//qjMzMzfrevevfuf1BGuZYQcVBtnzpzR2bNnHcY4pQ1ULS4uLsrOzpafn5+zW0EVwI3HsLSioiLFx8fLz89PtWrVUt26dR0WAFXPxd5zB/CcHFjamDFj9MUXX2ju3Ll69NFHNWfOHB0/flzz58/XtGnTnN0egMvQokWLPww6fKgAEperYHGNGjXSe++9px49esjb21s7d+5Us2bN9P777+ujjz7S6tWrnd0igEvg4uKimTNnVnji8W/xoQJInMmBxeXl5enGG2+U9Mv9N+X/uuvataueeuopZ7YG4DINGDCAe3JwUbgnB5Z244036tChQ5KkVq1a6ZNPPpEkffbZZ/L19XViZwAuB/fj4FIQcmBJ33//vcrKyvTYY4/p66+/liSNGzdOc+bMkaenp5599lmNHj3ayV0CuFTcYYFLwT05sKTyZ2mUn9Lu37+/Zs+erTNnzigjI0PNmjVT69atndwlAOBqIuTAkn77LI06dero66+/Nu/PAQBYH5erAACAJRFyYEk2m63CDYrcsAgA1QsfIYclGYahwYMHy8PDQ9IvX+nw5JNPVvim4mXLljmjPQDAn4CQA0v67YPAHnnkESd1AgBwFm48BgAAlsQ9OQAAwJIIOQAAwJIIOQAAwJIIOQAsyWazKSkp6YLzhw8fls1m065duyRJGzZskM1mU35+/lXvrUePHhoxYsQVbSMxMZHvXwP+AJ+uAlAtBQUF6ccff1SDBg2c3QqAq4SQA6BacnV1VUBAgLPbAHAVcbkKqIaWLl2q0NBQeXl5qX79+oqMjFRRUZEGDx6s6Ohovfbaa2rYsKHq16+vuLg4lZSUmOueOnVKAwcOVN26dVWzZk3ddddd+uabbyT98hDG6667TkuXLjXr27Ztq4YNG5qvN2/eLA8PD/3888+SfrmsNH/+fN19992qWbOmgoODlZaWpm+//VY9evRQrVq11LlzZ3333XcOxzB37lzddNNNcnd3V8uWLfX+++9XOM4ff/xRd911l7y8vHTjjTc69PXby1Xns3nzZkVERMjLy0tBQUEaNmyYioqKLuo9/uc//6nmzZvL09NT/v7+uv/++y9YW1xcrOeee07XX3+9atWqpbCwMG3YsMGhJjExUY0aNVLNmjX117/+Vf/73/8uqg+gOiPkANXMjz/+qIceekh/+9vflJmZqQ0bNui+++5T+SOzvvjiC3333Xf64osvtHDhQiUmJioxMdFcf/DgwdqxY4dWrFihtLQ0GYahPn36qKSkRDabTd26dTN/QZ86dUqZmZk6ffq0Dhw4IEnauHGjbr31VtWsWdPc5ksvvaSBAwdq165datWqlR5++GE98cQTGj9+vHbs2CHDMBQfH2/WL1++XMOHD9eoUaO0d+9ePfHEE3rsscf0xRdfOBzr888/r379+unrr79WTEyMBgwYoMzMzIt6n7777jv17t1b/fr10+7du7V48WJt3rzZoY8L2bFjh4YNG6YXX3xRWVlZSk5OVrdu3S5YHx8fr7S0NH388cfavXu3HnjgAfXu3dsMj+np6YqNjVV8fLx27dql22+/XVOmTLmo4wCqNQNAtZKRkWFIMg4fPlxhbtCgQUbjxo2N0tJSc+yBBx4w+vfvbxiGYRw8eNCQZGzZssWc/+9//2t4eXkZn3zyiWEYhjF79mzj5ptvNgzDMJKSkoywsDDj3nvvNebOnWsYhmFERkYa//d//2euL8mYOHGi+TotLc2QZLz77rvm2EcffWR4enqarzt37mwMGTLEofcHHnjA6NOnj8N2n3zySYeasLAw46mnnjIMwzAOHTpkSDK++uorwzAM44svvjAkGadOnTIMwzBiY2ONoUOHOqy/adMmw8XFxTh9+nSF9+7X/v3vfxve3t6G3W4/73z37t2N4cOHG4ZhGEeOHDFcXV2N48ePO9T07NnTGD9+vGEYhvHQQw85HJthGEb//v0NHx+f3+0DqO44kwNUM23atFHPnj0VGhqqBx54QG+//bZOnTplzt98881ydXU1Xzds2FC5ubmSpMzMTLm5uSksLMycr1+/vlq2bGmeIenevbv279+vkydPauPGjerRo4d69OihDRs2qKSkRFu3blWPHj0cemrdurX5Z39/f0lSaGiow9iZM2dkt9vNPrp06eKwjS5dulQ4SxMeHl7h9cWeyfn666+VmJio2rVrm0tUVJTKysp06NCh3133zjvvVOPGjXXjjTfq0Ucf1aJFi8zLc7+1Z88enTt3Ti1atHDY18aNG81LdJmZmQ7v+fmODUBF3HgMVDOurq5KSUnR1q1btXbtWr3xxhuaMGGC0tPTJUk1atRwqLfZbCorK7vo7YeGhqpevXrauHGjNm7cqJdfflkBAQGaPn26tm/frpKSEnXu3NlhnV/vs/zb4s83dil9XKnCwkI98cQTGjZsWIW5Ro0a/e66derU0c6dO7VhwwatXbtWkyZN0uTJk7V9+/YKH/suLCyUq6urMjIyHMKlJNWuXfuKjwOozjiTA1RDNptNXbp00QsvvKCvvvpK7u7uWr58+R+uFxwcrNLSUjMQSdL//vc/ZWVlKSQkxNx2RESEPv30U+3bt09du3ZV69atVVxcrPnz56tjx44Vvg3+UgUHB2vLli0OY1u2bDF7KPfll19WeB0cHHxR+2jfvr3279+vZs2aVVjc3d3/cH03NzdFRkYqISFBu3fv1uHDh7V+/foKde3atdO5c+eUm5tbYT/ln/4KDg52eM/Pd2wAKuJMDlDNpKena926derVq5f8/PyUnp6ukydPKjg4WLt37/7ddZs3b657771XQ4YM0fz581WnTh2NGzdO119/ve69916zrkePHho1apQ6duxono3o1q2bFi1apNGjR1/xMYwePVoPPvig2rVrp8jISH322WdatmyZPv/8c4e6JUuWqGPHjuratasWLVqkbdu26d13372ofYwdO1adOnVSfHy8Hn/8cdWqVUv79+9XSkqK3nzzzd9dd+XKlfr+++/VrVs31a1bV6tXr1ZZWZlatmxZobZFixaKiYnRwIEDNWPGDLVr104nT57UunXr1Lp1a/Xt21fDhg1Tly5d9Nprr+nee+/VmjVrlJycfPFvGFBNcSYHqGa8vb2VmpqqPn36qEWLFpo4caJmzJihu+6666LWX7BggTp06KC7775b4eHhMgxDq1evdri81L17d507d87h3psePXpUGLtc0dHRmjVrll577TXdfPPNmj9/vhYsWFBh2y+88II+/vhjtW7dWu+9954++uijCmd7LqR169bauHGjDh48qIiICLVr106TJk1SYGDgH67r6+urZcuW6Y477lBwcLDmzZunjz76SDfffPN56xcsWKCBAwdq1KhRatmypaKjo7V9+3bzslinTp309ttva9asWWrTpo3Wrl2riRMnXtRxANWZzTD+v8+NAgAAWAhncgAAgCURcgDgEm3atMnh496/XQBcG7hcBQCX6PTp0zp+/PgF55s1a/YndgPgQgg5AADAkrhcBQAALImQAwAALImQAwAALImQAwAALImQAwAALImQAwAALImQAwAALImQAwAALOn/Acm9J8grWTEkAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot of categorical variables, there are some under-represented categories, \n",
    "## maybe it is possible to obtain a more stable model removing such classes\n",
    "to_remove = {}\n",
    "TH = 0.5\n",
    "for c in labeled.columns:\n",
    "    if c not in ['india','age','season','skiarea_name']:\n",
    "        labeled[c] = labeled[c].astype('str')  \n",
    "        plt.figure()\n",
    "        labeled.groupby(c)[c].count().plot(kind='bar')\n",
    "        plt.title(c)\n",
    "        tmp = labeled.groupby(c)[c].count()\n",
    "        tmp = 100*tmp/tmp.max()\n",
    "        tmp = tmp[tmp<TH]\n",
    "        if len(tmp)>0:\n",
    "            for k in tmp.index:\n",
    "                if c not in to_remove.keys():\n",
    "                    to_remove[c]= []\n",
    "                to_remove[c].append(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "456c45af-c0e7-4c32-aafe-7e5f4473ed1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "labeled_small = labeled.copy()\n",
    "for c in to_remove.keys():\n",
    "    for k in to_remove[c]:\n",
    "        labeled_small.loc[labeled_small[c]==k,c] = 'other'\n",
    "for c in labeled_small.columns:\n",
    "    if c not in ['age','season','skiarea_name']:\n",
    "        labeled_small[c] =  labeled_small[c].fillna('None').astype('category')  \n",
    "        labeled[c] =  labeled[c].fillna('None').astype('category')\n",
    "labeled.dropna(inplace=True)\n",
    "labeled_small.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4b2cfcf0-7fb7-459d-8829-c35f5575fe1b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:3: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n",
      "  plt.figure()\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_69977/134691014.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALAkSg4AALCkPC85WVlZGjJkiMLDw+Xj46OyZcvq7bfflmEY9n0Mw9DQoUNVvHhx+fj4KCoqSgkJCQ7Pk5ycrPbt28vf31+BgYHq2rWr0tLSHPbZvXu36tWrJ29vb5UoUUJjx47N67cDAABcVJ6XnHfffVfTp0/XtGnTFB8fr3fffVdjx47V1KlT7fuMHTtWU6ZM0YwZM7Rlyxb5+vqqSZMmunz5sn2f9u3b69dff9WKFSu0ePFirVu3Tj169LBvT01NVePGjVWqVClt375d48aN07BhwzRz5sy8fksAAMAF2Yw/H2LJA0899ZRCQkL00Ucf2cdat24tHx8f/e9//5NhGAoLC1O/fv3Uv39/SVJKSopCQkI0e/ZstW3bVvHx8apcubK2bdumyMhISdLSpUvVrFkzHT9+XGFhYZo+fboGDx6spKQkeXp6SpIGDhyohQsXau/evbeUNTU1VQEBAUpJSZG/v39efgzATZUeuMTsCHaHxzQ3OwIA3LJb/fmd50dy6tSpo1WrVmn//v2SpF27dmn9+vV68sknJUmJiYlKSkpSVFSU/XsCAgL0yCOPaNOmTZKkTZs2KTAw0F5wJCkqKkpubm7asmWLfZ/69evbC44kNWnSRPv27dO5c+dyzZaRkaHU1FSHBwAAsCaPvH7CgQMHKjU1VRUrVpS7u7uysrL0zjvvqH379pKkpKQkSVJISIjD94WEhNi3JSUlKTg42DGoh4cKFy7ssE94eHiO57i+LSgoKEe20aNHa/jw4XnwLgEAgLPL8yM58+fP1+eff665c+dqx44dmjNnjt577z3NmTMnr1/qtg0aNEgpKSn2x7Fjx8yOBAAA7pI8P5IzYMAADRw4UG3btpUkRURE6MiRIxo9erQ6deqk0NBQSdKpU6dUvHhx+/edOnVK1atXlySFhobq9OnTDs+bmZmp5ORk+/eHhobq1KlTDvtc//r6Pn/l5eUlLy+vO3+TAADA6eX5kZyLFy/Kzc3xad3d3ZWdnS1JCg8PV2hoqFatWmXfnpqaqi1btqh27dqSpNq1a+v8+fPavn27fZ/Vq1crOztbjzzyiH2fdevW6erVq/Z9VqxYoQoVKuR6qgoAAOQveV5yWrRooXfeeUdLlizR4cOH9e2332rChAl6+umnJUk2m019+/bVyJEj9d133ykuLk4dO3ZUWFiYWrVqJUmqVKmSmjZtqu7du2vr1q3asGGDevbsqbZt2yosLEyS1K5dO3l6eqpr16769ddf9eWXX2ry5MmKiYnJ67cEAABcUJ6frpo6daqGDBmiV199VadPn1ZYWJj+85//aOjQofZ9Xn/9daWnp6tHjx46f/68HnvsMS1dulTe3t72fT7//HP17NlTjRo1kpubm1q3bq0pU6bYtwcEBGj58uWKjo5WzZo1VbRoUQ0dOtRhLR0AAJB/5fk6Oa6EdXJgJtbJAYB/xrR1cgAAAJwBJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFjSXSk5J06c0IsvvqgiRYrIx8dHERER+uWXX+zbDcPQ0KFDVbx4cfn4+CgqKkoJCQkOz5GcnKz27dvL399fgYGB6tq1q9LS0hz22b17t+rVqydvb2+VKFFCY8eOvRtvBwAAuKA8Lznnzp1T3bp1VaBAAf3444/67bffNH78eAUFBdn3GTt2rKZMmaIZM2Zoy5Yt8vX1VZMmTXT58mX7Pu3bt9evv/6qFStWaPHixVq3bp169Ohh356amqrGjRurVKlS2r59u8aNG6dhw4Zp5syZef2WAACAC7IZhmHk5RMOHDhQGzZs0M8//5zrdsMwFBYWpn79+ql///6SpJSUFIWEhGj27Nlq27at4uPjVblyZW3btk2RkZGSpKVLl6pZs2Y6fvy4wsLCNH36dA0ePFhJSUny9PS0v/bChQu1d+/eW8qampqqgIAApaSkyN/fPw/ePXDrSg9cYnYEu8NjmpsdAQBu2a3+/M7zIznfffedIiMj9dxzzyk4OFg1atTQrFmz7NsTExOVlJSkqKgo+1hAQIAeeeQRbdq0SZK0adMmBQYG2guOJEVFRcnNzU1btmyx71O/fn17wZGkJk2aaN++fTp37lxevy0AAOBi8rzkHDp0SNOnT1f58uW1bNkyvfLKK+rdu7fmzJkjSUpKSpIkhYSEOHxfSEiIfVtSUpKCg4Mdtnt4eKhw4cIO++T2HH9+jb/KyMhQamqqwwMAAFiTR14/YXZ2tiIjIzVq1ChJUo0aNbRnzx7NmDFDnTp1yuuXuy2jR4/W8OHDTc0AAADujTw/klO8eHFVrlzZYaxSpUo6evSoJCk0NFSSdOrUKYd9Tp06Zd8WGhqq06dPO2zPzMxUcnKywz65PcefX+OvBg0apJSUFPvj2LFj/+QtAgAAF5DnJadu3brat2+fw9j+/ftVqlQpSVJ4eLhCQ0O1atUq+/bU1FRt2bJFtWvXliTVrl1b58+f1/bt2+37rF69WtnZ2XrkkUfs+6xbt05Xr16177NixQpVqFDB4UquP/Py8pK/v7/DAwAAWFOel5zXXntNmzdv1qhRo3TgwAHNnTtXM2fOVHR0tCTJZrOpb9++GjlypL777jvFxcWpY8eOCgsLU6tWrSRdO/LTtGlTde/eXVu3btWGDRvUs2dPtW3bVmFhYZKkdu3aydPTU127dtWvv/6qL7/8UpMnT1ZMTExevyUAAOCC8nxOzsMPP6xvv/1WgwYN0ogRIxQeHq5Jkyapffv29n1ef/11paenq0ePHjp//rwee+wxLV26VN7e3vZ9Pv/8c/Xs2VONGjWSm5ubWrdurSlTpti3BwQEaPny5YqOjlbNmjVVtGhRDR061GEtHQAAkH/l+To5roR1cmAm1skBgH/GtHVyAAAAnAElBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWNJdLzljxoyRzWZT37597WOXL19WdHS0ihQpokKFCql169Y6deqUw/cdPXpUzZs3V8GCBRUcHKwBAwYoMzPTYZ+ffvpJDz30kLy8vFSuXDnNnj37br8dAADgIu5qydm2bZs++OADVa1a1WH8tdde0/fff68FCxZo7dq1OnnypJ555hn79qysLDVv3lxXrlzRxo0bNWfOHM2ePVtDhw6175OYmKjmzZurQYMGio2NVd++fdWtWzctW7bsbr4lAADgIu5ayUlLS1P79u01a9YsBQUF2cdTUlL00UcfacKECWrYsKFq1qypTz75RBs3btTmzZslScuXL9dvv/2m//3vf6pevbqefPJJvf3223r//fd15coVSdKMGTMUHh6u8ePHq1KlSurZs6eeffZZTZw48W69JQAA4ELuWsmJjo5W8+bNFRUV5TC+fft2Xb161WG8YsWKKlmypDZt2iRJ2rRpkyIiIhQSEmLfp0mTJkpNTdWvv/5q3+evz92kSRP7c+QmIyNDqampDg8AAGBNHnfjSb/44gvt2LFD27Zty7EtKSlJnp6eCgwMdBgPCQlRUlKSfZ8/F5zr269v+7t9UlNTdenSJfn4+OR47dGjR2v48OH/+H0BAADXkedHco4dO6Y+ffro888/l7e3d14//R0ZNGiQUlJS7I9jx46ZHQkAANwleV5ytm/frtOnT+uhhx6Sh4eHPDw8tHbtWk2ZMkUeHh4KCQnRlStXdP78eYfvO3XqlEJDQyVJoaGhOa62uv71zfbx9/fP9SiOJHl5ecnf39/hAQAArCnPS06jRo0UFxen2NhY+yMyMlLt27e3/7lAgQJatWqV/Xv27duno0ePqnbt2pKk2rVrKy4uTqdPn7bvs2LFCvn7+6ty5cr2ff78HNf3uf4cAAAgf8vzOTl+fn568MEHHcZ8fX1VpEgR+3jXrl0VExOjwoULy9/fX7169VLt2rX16KOPSpIaN26sypUrq0OHDho7dqySkpL05ptvKjo6Wl5eXpKkl19+WdOmTdPrr7+uLl26aPXq1Zo/f76WLFmS128JAAC4oLsy8fhmJk6cKDc3N7Vu3VoZGRlq0qSJ/u///s++3d3dXYsXL9Yrr7yi2rVry9fXV506ddKIESPs+4SHh2vJkiV67bXXNHnyZN1///368MMP1aRJEzPeEgAAcDI2wzAMs0OYJTU1VQEBAUpJSWF+Du650gOd56jj4THNzY4AALfsVn9+c+8qAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSZQcAABgSR5mBwAAAHdP6YFLzI7g4PCY5vfstTiSAwAALImSAwAALInTVbir8vNhUgCAuTiSAwAALImSAwAALImSAwAALImSAwAALImSAwAALCnPS87o0aP18MMPy8/PT8HBwWrVqpX27dvnsM/ly5cVHR2tIkWKqFChQmrdurVOnTrlsM/Ro0fVvHlzFSxYUMHBwRowYIAyMzMd9vnpp5/00EMPycvLS+XKldPs2bPz+u0AAAAXleclZ+3atYqOjtbmzZu1YsUKXb16VY0bN1Z6erp9n9dee03ff/+9FixYoLVr1+rkyZN65pln7NuzsrLUvHlzXblyRRs3btScOXM0e/ZsDR061L5PYmKimjdvrgYNGig2NlZ9+/ZVt27dtGzZsrx+SwAAwAXl+To5S5cudfh69uzZCg4O1vbt21W/fn2lpKToo48+0ty5c9WwYUNJ0ieffKJKlSpp8+bNevTRR7V8+XL99ttvWrlypUJCQlS9enW9/fbbeuONNzRs2DB5enpqxowZCg8P1/jx4yVJlSpV0vr16zVx4kQ1adIkr98WAABwMXd9Tk5KSookqXDhwpKk7du36+rVq4qKirLvU7FiRZUsWVKbNm2SJG3atEkREREKCQmx79OkSROlpqbq119/te/z5+e4vs/158hNRkaGUlNTHR4AAMCa7mrJyc7OVt++fVW3bl09+OCDkqSkpCR5enoqMDDQYd+QkBAlJSXZ9/lzwbm+/fq2v9snNTVVly5dyjXP6NGjFRAQYH+UKFHijt8jAABwTne15ERHR2vPnj364osv7ubL3LJBgwYpJSXF/jh27JjZkQAAwF1y1+5d1bNnTy1evFjr1q3T/fffbx8PDQ3VlStXdP78eYejOadOnVJoaKh9n61btzo83/Wrr/68z1+vyDp16pT8/f3l4+OTayYvLy95eXnd8XsDAADOL8+P5BiGoZ49e+rbb7/V6tWrFR4e7rC9Zs2aKlCggFatWmUf27dvn44eParatWtLkmrXrq24uDidPn3avs+KFSvk7++vypUr2/f583Nc3+f6cwAAgPwtz4/kREdHa+7cuVq0aJH8/Pzsc2gCAgLk4+OjgIAAde3aVTExMSpcuLD8/f3Vq1cv1a5dW48++qgkqXHjxqpcubI6dOigsWPHKikpSW+++aaio6PtR2JefvllTZs2Ta+//rq6dOmi1atXa/78+VqyxLnueg0AAMyR50dypk+frpSUFD3xxBMqXry4/fHll1/a95k4caKeeuoptW7dWvXr11doaKi++eYb+3Z3d3ctXrxY7u7uql27tl588UV17NhRI0aMsO8THh6uJUuWaMWKFapWrZrGjx+vDz/8kMvHAQCApLtwJMcwjJvu4+3trffff1/vv//+DfcpVaqUfvjhh799nieeeEI7d+687YwAAMD6uHcVAACwpLt2dRUA/FOlBzrP3LrDY5qbHQHAP8SRHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEkeZgewgtIDl5gdwcHhMc3NjgAAgOk4kgMAACyJkgMAACyJkgMAACyJkgMAACyJiccA4EKc6UIHLnKAs+NIDgAAsCRKDgAAsCRKDgAAsCRKDgAAsCSXn3j8/vvva9y4cUpKSlK1atU0depU1apVy+xYAIB7jEnZ+CuXPpLz5ZdfKiYmRm+99ZZ27NihatWqqUmTJjp9+rTZ0QAAgMlcuuRMmDBB3bt310svvaTKlStrxowZKliwoD7++GOzowEAAJO5bMm5cuWKtm/frqioKPuYm5uboqKitGnTJhOTAQAAZ+Cyc3LOnDmjrKwshYSEOIyHhIRo7969uX5PRkaGMjIy7F+npKRIklJTU+8oS3bGxTv6/rx2p+8nL/HZ3JgzfTbO9LlIfDZ/h8/mxvhscudMn4uUN5/N9ecwDONv93PZkvNPjB49WsOHD88xXqJECRPS3D0Bk8xO4Lz4bHLH53JjfDY3xmdzY3w2N5aXn82FCxcUEBBww+0uW3KKFi0qd3d3nTp1ymH81KlTCg0NzfV7Bg0apJiYGPvX2dnZSk5OVpEiRWSz2e5q3ptJTU1ViRIldOzYMfn7+5uaxdnw2dwYn82N8dnkjs/lxvhsbszZPhvDMHThwgWFhYX97X4uW3I8PT1Vs2ZNrVq1Sq1atZJ0rbSsWrVKPXv2zPV7vLy85OXl5TAWGBh4l5PeHn9/f6f4C+SM+GxujM/mxvhscsfncmN8NjfmTJ/N3x3Buc5lS44kxcTEqFOnToqMjFStWrU0adIkpaen66WXXjI7GgAAMJlLl5w2bdrojz/+0NChQ5WUlKTq1atr6dKlOSYjAwCA/MelS44k9ezZ84anp1yJl5eX3nrrrRyn08Bn83f4bG6MzyZ3fC43xmdzY6762diMm11/BQAA4IJcdjFAAACAv0PJAQAAlkTJAQAAlkTJAQAAlkTJAQAADjIzM/Xpp5/muKuAq6HkwGl99tlnqlu3rsLCwnTkyBFJ0qRJk7Ro0SKTk8HZXL16VR4eHtqzZ4/ZUQBL8PDw0Msvv6zLly+bHeWOUHJMlpmZqZUrV+qDDz7QhQsXJEknT55UWlqaycnMNX36dMXExKhZs2Y6f/68srKyJF27DcekSZPMDQenU6BAAZUsWdL+9wQ5paSkKDk5Ocd4cnKyU90x2wyffPKJFixYkGN8wYIFmjNnjgmJnEOtWrUUGxtrdow7Qskx0ZEjRxQREaF///vfio6O1h9//CFJevfdd9W/f3+T05lr6tSpmjVrlgYPHix3d3f7eGRkpOLi4kxMZr6zZ88qOjpalStXVtGiRVW4cGGHR341ePBg/fe//831Bzmktm3b6osvvsgxPn/+fLVt29aERM5j9OjRKlq0aI7x4OBgjRo1yoREzuHVV19VTEyMpk2bpk2bNmn37t0OD1fAYoAmatWqlfz8/PTRRx+pSJEi2rVrl8qUKaOffvpJ3bt3V0JCgtkRTePj46O9e/eqVKlS8vPzs382CQkJqlq1qi5dumR2RNM0a9ZMBw4cUNeuXRUSEiKbzeawvVOnTiYlM1eNGjV04MABXb16VaVKlZKvr6/D9h07dpiUzDkULlxYGzZsUKVKlRzG9+7dq7p16+rs2bMmJTOft7e39u7dq9KlSzuMHz58WJUqVcq3/964ueU8DmKz2WQYhmw2m0scOXX52zq4sp9//lkbN26Up6enw3jp0qV14sQJk1I5h/DwcMXGxqpUqVIO40uXLs3xj3R+8/PPP2v9+vWqVq2a2VGcSqtWrcyO4NQyMjKUmZmZY/zq1av59of4dcHBwdq9e3eOkrNr1y4VKVLEnFBOIDEx0ewId4ySY6Ls7Oxcm/Dx48fl5+dnQiLnERMTo+joaF2+fFmGYWjr1q2aN2+eRo8erQ8//NDseKaqWLFivv+hlJu33nrL7AhOrVatWpo5c6amTp3qMD5jxgzVrFnTpFTO4YUXXlDv3r3l5+en+vXrS5LWrl2rPn365OtTeX/9JdMlGTDN888/b3Tv3t0wDMMoVKiQcejQIePChQtGw4YNjc6dO5ucznz/+9//jHLlyhk2m82w2WzGfffdZ3z44YdmxzLd1q1bjYYNGxo//fSTcebMGSMlJcXhkZ+dO3fOmDVrljFw4EDj7NmzhmEYxvbt243jx4+bnMx869evN7y9vY169eoZw4YNM4YNG2bUq1fP8Pb2NtatW2d2PFNlZGQYzz//vGGz2YwCBQoYBQoUMNzd3Y2XXnrJyMjIMDueqT799FOjTp06RvHixY3Dhw8bhmEYEydONBYuXGhyslvDnBwTHT9+XE2aNJFhGEpISFBkZKQSEhJUtGhRrVu3TsHBwWZHdAoXL15UWloan8f/k5CQoHbt2uWYY2K40Hnyu2H37t2KiopSQECADh8+rH379qlMmTJ68803dfToUX366admRzRdbGysxo0bp9jYWPn4+Khq1aoaNGiQypcvb3Y0p7B//37t2rVLPj4+ioiIsMaRjDswffp0DR06VH379tU777yjPXv2qEyZMpo9e7bmzJmjNWvWmB3xpig5JsvMzNQXX3yh3bt3Ky0tTQ899JDat28vHx8fs6PBSdWqVUseHh7q06dPrhOPH3/8cZOSmSsqKkoPPfSQxo4d6zBZfePGjWrXrp0OHz5sdkTApVSuXFmjRo2yXyRz/f+pPXv26IknntCZM2fMjnhTzMkxmYeHh1588UWzYziFGjVq5PiBfSP5+UqZPXv2aOfOnapQoYLZUZzKtm3b9MEHH+QYv++++5SUlGRCIvOlpqbK39/f/ue/c32//CImJkZvv/22fH19FRMT87f7Tpgw4R6lci6JiYmqUaNGjnEvLy+lp6ebkOj2UXJMlpCQoDVr1uj06dPKzs522DZ06FCTUpmDq2NuTWRkpI4dO0bJ+QsvL69cf5Dv379fxYoVMyGR+YKCgvT7778rODhYgYGBuf4SkV9Pc+7cuVNXr161//lGbvUXLyuywlWulBwTzZo1S6+88oqKFi2q0NBQh/+ZbDZbvis5XB1za3r16qU+ffpowIABioiIUIECBRy2V61a1aRk5mrZsqVGjBih+fPnS7r2/9DRo0f1xhtvqHXr1ianM8fq1avtC0S6wvyJe+nPnwefTe6scJUrc3JMVKpUKb366qt64403zI4CF2KFBbruhpSUFD377LP65ZdfdOHCBYWFhSkpKUm1a9fWDz/8kGNxQAA39/nnn2vYsGE6ePCgJCksLEzDhw9X165dTU52ayg5JvL391dsbKzKlCljdhSnULhwYe3fv19FixZVUFDQ3x4mzs9L91+/WemN5PcrQtavX+8wkT8qKsrsSE7j/Pnz2rp1a66nxzt27GhSKvOlp6drzJgxWrVqVa6fzaFDh0xK5jxc9SpXSo6Junbtqocfflgvv/yy2VGcwpw5c9S2bVt5eXlp9uzZf1ty8uutC4B/6vvvv1f79u2VlpYmf3//HKfH8/MvDi+88ILWrl2rDh06qHjx4jn+7enTp49JyczVsGFDffPNNwoMDHQYT01NVatWrbR69Wpzgt0GSo6JRo8erQkTJqh58+a5zq3o3bu3Scng7A4ePKhJkyYpPj5e0rVLPfv06aOyZcuanMxcq1atuuFv4x9//LFJqZzDAw88oGbNmmnUqFEqWLCg2XGcSmBgoJYsWaK6deuaHcWpuLm5KSkpKcfRm9OnT+u+++6zT9x2Zkw8NtHMmTNVqFAhrV27VmvXrnXYZrPZ8nXJ+eGHH+Tu7q4mTZo4jC9fvlxZWVl68sknTUpmvmXLlqlly5aqXr26/R/lDRs2qEqVKvr+++/1r3/9y+SE5hg+fLhGjBihyMjIXH8bz+9OnDih3r17U3ByERQUZJ+gDTncYfy3335zWIIhKytLS5cu1X333WdGtNt3z9dYBm5BRESEsWTJkhzjP/74o1G1alUTEjmP6tWrG2+88UaO8TfeeMOoUaOGCYmcQ2hoqPHpp5+aHcNpPf3008aXX35pdgyn9NlnnxnPPvuskZ6ebnYUp2Cz2Qw3NzfDzc3NfludPz8KFixofPTRR2bHvCWcrnIS1/8z8NvnNT4+PoqPj89xV+DDhw+rSpUqLrMQ1d3g7e2tuLi4HEvx79+/X1WrVtXly5dNSmauIkWKaOvWrfn+lN2NfPTRRxoxYoReeumlXE+Pt2zZ0qRk5qtRo4YOHjwowzBUunTpHJ9Nflt89MiRIzIMQ2XKlNHWrVsd1pny9PRUcHCw3N3dTUx46zhdZbJPP/1U48aNU0JCgqRr580HDBigDh06mJzMXAEBATp06FCOknPgwIF8fylwsWLFFBsbm6PkxMbGutyVD3mpW7dumjt3roYMGWJ2FKfUvXt3SdKIESNybMvPSw9ILET6V9ev0PzrvDZXRMkx0YQJEzRkyBD17NnTPrdi/fr1evnll3XmzBm99tprJic0z7///W/17dtX3377rf038wMHDqhfv375+jdO6doPqx49eujQoUOqU6eOpGtzct59992bLk9vNX9+v9nZ2Zo5c6ZWrlypqlWr5vhtPL8uzX+dFX5g3S0sRHpjrr4qP6erTBQeHq7hw4fnWJ9izpw5GjZsmBITE01KZr6UlBQ1bdpUv/zyi+6//35J1+7aXq9evVwvacxPDMPQpEmTNH78eJ08eVLStQW6BgwYoN69e+erU54NGjS45X1Z1Ra4PTdbld8VTuNRckzk7e2tPXv2qFy5cg7jCQkJioiIyLdzK64zDEMrVqzQrl275OPjo6pVq6p+/fpmx3IqFy5ckCT5+fmZnATOaMqUKerRo4e8vb01ZcqUv903v13NyeKjN2eFVfkpOSZ68MEH1a5dO/33v/91GB85cqS+/PJLxcXFmZQMruCPP/7Qvn37JEkVK1ZU0aJFTU5kri5dumjy5Mk5Cl96erp69eqVL9fJCQ8P1y+//KIiRYooPDz8hvvZbLZ8t6rvnxcfnTNnzt/um18XH7XCqvyUHBN9/fXXatOmjaKiohzWO1m1apXmz5+vp59+2uSE9xa/dd6a6z+0P/30U/s5cnd3d3Xs2FFTp07Nt+uguLu72++4/WdnzpxRaGioMjMzTUoGuCYrrMpPyTHZ9u3bNWHCBO3du1eSVKlSJfXr1081atQwOdm9x2+dt+Y///mPVq5cqWnTpjlMWO/du7f+9a9/afr06SYnvLdSU1NlGIaCgoKUkJDgcLlrVlaWvv/+ew0cONA+fwmQrv29uVX+/v53MYnzssKq/JQcwMUULVpUX331lZ544gmH8TVr1uj555/XH3/8YU4wk7i5uf3tfAqbzabhw4dr8ODB9zCVc7idq+3y29VnN/t7I12bF5ifL6+3wi+bXEJuglv5n8tms3F4Hbm6ePGiQkJCcowHBwfr4sWLJiQy15o1a2QYhho2bKivv/7aYXl+T09PlSpVSmFhYSYmNM/OnTtvab/8dEXedVxtd3NWuMKXIzkmWLRo0Q23bdq0SVOmTFF2dna+vrrKMAx99dVXN1yf4ZtvvjEpmfkaNWqkIkWK6NNPP5W3t7ck6dKlS+rUqZOSk5O1cuVKkxOa48iRI/L399fHH39sv3FplSpV1KVLFwUEBJicDnBdV65cUWJiosqWLSsPD9c6NkLJcRL79u3TwIED9f3336t9+/YaMWKEfdXJ/KhPnz764IMP1KBBA4WEhOT4TfOTTz4xKZn54uLi1LRpU2VkZKhatWqSpF27dsnb21vLli1TlSpVTE5ojl9++UVNmzaVt7e3atWqJUnatm2bLl26pOXLl+uhhx4yOaG5UlJSlJWVleNGlMnJyfLw8Mi3806ka/+eFCpUSM8995zD+IIFC3Tx4sV8e3XVxYsX1atXL/vVZ/v371eZMmXUq1cv3XfffRo4cKDJCW/BPbpHFm7gxIkTRrdu3YwCBQoYTz31lBEXF2d2JKcQFBSU6w06cU16eroxc+ZMIyYmxoiJiTFmzZplXLx40exYpnrssceMzp07G1evXrWPXb161ejUqZNRr149E5M5h6ZNmxrvv/9+jvHp06cbTz75pAmJnEf58uWN1atX5xj/6aefjAceeMCERM6hd+/eRs2aNY2ff/7Z8PX1NQ4ePGgYhmEsXLjQqF69usnpbg0lxyTnz583Xn/9dcPHx8eoXbu2sW7dOrMjOZXSpUsb8fHxZsdwOleuXDHKlClj/Pbbb2ZHcTre3t65/p359ddfDR8fHxMSOZegoKBc/97Ex8cbhQsXNiGR8/Dy8jISExNzjCcmJhre3t73PpCTKFmypLFp0ybDMAyjUKFC9pKTkJBg+Pn5mRntlrmZfSQpPxo7dqzKlCmjxYsXa968edq4caPq1atndiynMmzYMA0fPlyXLl0yO4pTKVCgQL6eq/V3/P39dfTo0Rzjx44dY0VoSRkZGblezHD16tV8//9ZcHCwdu/enWN8165dKlKkiAmJnMMff/yR601/09PTXWayumvNILKIgQMHysfHR+XKldOcOXNuuNpmfp5c+/zzz2vevHkKDg5W6dKlc6zP4Ar3TLlboqOj9e677+rDDz90uUmAd1ObNm3UtWtXvffeew43Lh0wYIBeeOEFk9OZr1atWpo5c6amTp3qMD5jxgzVrFnTpFTO4YUXXlDv3r3l5+dnv3XM2rVr1adPH7Vt29bkdOaJjIzUkiVL1KtXL0n//1V4H374oWrXrm1mtFvGv5Am6Nixo8u0YLN06tRJ27dv14svvpjrxOP8bNu2bVq1apWWL1+uiIgI+fr6OmzPr+X4vffek81mU8eOHe1HLAoUKKBXXnlFY8aMMTmd+UaOHKmoqCjt2rVLjRo1kiStWrVK27Zt0/Lly01OZ663335bhw8fVqNGjey/OGRnZ6tjx44aNWqUyenMM2rUKD355JP67bfflJmZqcmTJ+u3337Txo0btXbtWrPj3RKuroJT8vX11bJly/TYY4+ZHcXpvPTSS3+7PT9feSZduyLk4MGDkqSyZcvm29tc5CY2Nlbjxo1TbGys/aa3gwYNUvny5c2O5hQSEhLsn01ERES+vsL1uoMHD2rMmDHatWuX0tLS9NBDD+mNN95QRESE2dFuCSUHTqlixYqaP3++qlatanYUl7VhwwZFRkbKy8vL7ChwIWPGjNHLL7+swMBAs6M4HSvcsDK/YeIxnNL48eP1+uuv6/Dhw2ZHcVlPPvmkTpw4YXYMuJhRo0YpOTnZ7BhOKb8dE/jhhx+0bNmyHOPLli3Tjz/+aEKi20fJgVN68cUXtWbNGpUtW1Z+fn4qXLiwwwM3l9/+QUbe4O8Nrhs4cGCu9+0yDMM1FgIUE4/hpCZNmmR2BADI1xISElS5cuUc4xUrVtSBAwdMSHT7KDlwSvl1GXUAcBYBAQE6dOiQSpcu7TB+4MCBHFd1OitKDpxWVlaWFi5c6HCzxZYtW8rd3d3kZADyo/y2lMW///1v9e3bV99++63Kli0r6VrB6devn1q2bGlyultDyYFTOnDggJo1a6YTJ06oQoUKkqTRo0erRIkSWrJkif1/ONxYfvsHGbjb8tt8pbFjx6pp06aqWLGi7r//fknS8ePHVa9ePb333nsmp7s1lBw4pd69e6ts2bLavHmzfaLx2bNn9eKLL6p3795asmSJyQmdX377Bxl5o169evLx8TE7hlP68ccfdd9995kd454JCAjQxo0btWLFCu3atcu+ttL1VaFdAevkwCn5+vpq8+bNORac2rVrl+rWrau0tDSTkpnvrbfeUpcuXVioDLft4MGD+uSTT3Tw4EFNnjxZwcHB+vHHH1WyZElVqVLF7Himad26tWrVqqU33njDYXzs2LHatm2bFixYYFIy3CkuIYdT8vLy0oULF3KMp6WlydPT04REzmPRokUqW7asGjVqpLlz5yojI8PsSHABa9euVUREhLZs2aJvvvnG/ovCrl279NZbb5mczlzr1q1Ts2bNcow/+eSTWrdunQmJnEd6erp++OEHzZgxQ1OmTHF4uARzbn4O/L0OHToYVapUMTZv3mxkZ2cb2dnZxqZNm4wHH3zQ6NSpk9nxTLdjxw6jV69eRtGiRY3AwEDj5ZdfNrZu3Wp2LDixRx991Bg/frxhGIZRqFAh4+DBg4ZhGMaWLVuM++67z8xopvP29jb27t2bYzw+Pt7w9vY2IZFz2LFjhxEaGmr4+/sb7u7uRrFixQybzWb4+voa4eHhZse7JRzJgVOaMmWKypYtq9q1a8vb21ve3t6qU6eOypUrxxo6kmrUqKEpU6bo5MmT+uijj3T8+HHVrVtXVatW1eTJk5WSkmJ2RDiZuLg4Pf300znGg4ODdebMGRMSOY+IiAh9+eWXOca/+OKLXNeJyS9ee+01tWjRQufOnZOPj482b96sI0eOqGbNmkw8Bu5EYGCgFi1apAMHDtgvIa9UqZLKlStncjLnYhiGrl69qitXrsgwDAUFBWnatGkaMmSIZs2apTZt2pgdEU4iMDBQv//+u8LDwx3Gd+7cma8m0+ZmyJAheuaZZ3Tw4EE1bNhQ0rU7tM+bNy9fz8eJjY3VBx98IDc3N7m7uysjI0NlypTR2LFj1alTJz3zzDNmR7wpSg6cRkxMzN9uX7Nmjf3PEyZMuNtxnNr27dv1ySefaN68efLy8lLHjh31/vvv20vg1KlT1bt3b0oO7Nq2bas33nhDCxYskM1mU3Z2tjZs2KD+/furY8eOZsczVYsWLbRw4UKNGjVKX331lf0qopUrV+rxxx83O55pChQoIDe3ayd8goODdfToUVWqVEkBAQE6duyYyeluDVdXwWk0aNDA4esdO3YoMzPTvk7O/v375e7urpo1a2r16tVmRHQKERER2rt3rxo3bqzu3burRYsWORZIPHPmjIKDg5WdnW1SSjibK1euKDo6WrNnz1ZWVpY8PDyUlZWldu3aafbs2SyyiRwaN26szp07q127durevbt2796t3r1767PPPtO5c+e0ZcsWsyPeFCUHTmnChAn66aefNGfOHAUFBUmSzp07p5deekn16tVTv379TE5onrfffltdunTJ96cY8M8cO3ZMcXFxSktLU40aNVS+fHmzI8FJ/fLLL7pw4YIaNGig06dPq2PHjtq4caPKly+vjz/+WNWqVTM74k1RcuCU7rvvPi1fvjzH2h179uxR48aNdfLkSZOSAa5pxIgR6t+/vwoWLOgwfunSJY0bN05Dhw41KZk5ChcurP3796to0aIKCgr62xXCk5OT72Ey5CVKDpySn5+fvv/+ez3xxBMO42vWrFHLli1zXUMnv2DhMvwT7u7u+v333xUcHOwwfvbsWQUHBysrK8ukZOaYM2eO2rZtKy8vL82ZM+dv9+WGwa6LkgOn1LFjR/38888aP368atWqJUnasmWLBgwYoHr16t30HyUrK1asmFavXp1jNei4uDhFRUXp1KlTJiWDM3Nzc9OpU6dUrFgxh/HVq1erTZs2+uOPP0xKBmd19uxZDR06VGvWrNHp06dzzPFzhSNcXF0FpzRjxgz1799f7dq109WrVyVJHh4e6tq1q8aNG2dyOnPdaNXnAgUKKDU11YREcGbXT8XYbDY98MADDqdlsrKylJaWppdfftnEhOa4nf9X/P3972IS59WhQwcdOHBAXbt2VUhIiEve9JcjOXBq6enpOnjwoCSpbNmy8vX1NTmR+WrVqqWnnnoqxxyKYcOG6fvvv9f27dtNSgZnNGfOHBmGoS5dumjSpEkKCAiwb/P09FTp0qVVu3ZtExOaw83N7aY/tA3DkM1my3en8q7z8/PT+vXrXWKC8Y1wJAdOzdfXV1WrVjU7hlNh4TLcjuvzScLDw1WnTh0VKFDA5ETO4c/rbiF3FStW1KVLl8yOcUc4kgO4oCVLlmjUqFGKjY21L1z21ltv5euFy3DrLl++rCtXrjiM5ddTMrixbdu2aeDAgRo6dKgefPDBHAXZFf7OUHIAIB+4ePGiXn/9dc2fP19nz57NsT2/npK57ty5c/roo4/st5GpXLmyXnrpJRUuXNjkZOZJSEhQu3bttGPHDodxVzqNR8kBXNSVK1dyveKhZMmSJiWCM4uOjtaaNWv09ttvq0OHDnr//fd14sQJffDBBxozZozat29vdkTTrFu3Ti1atFBAQIAiIyMlXbt1yvnz5/X999+rfv36Jic0R61ateTh4aE+ffrkOvHYFY4cU3IAF5OQkKAuXbpo48aNDuOu9NsV7r2SJUvq008/1RNPPCF/f3/t2LFD5cqV02effaZ58+bphx9+MDuiaSIiIlS7dm1Nnz7dfnuLrKwsvfrqq9q4caPi4uJMTmiOggULaufOnfZb67giJh4DLqZz587y8PDQ4sWLVbx4cZe8rBP3XnJyssqUKSPp2lyK62ucPPbYY3rllVfMjGa6AwcO6KuvvnK4f5e7u7tiYmL06aefmpjMXJGRkTp27BglB8C9Exsbq+3bt6tixYpmR4ELKVOmjBITE1WyZElVrFhR8+fPV61atfT9998rMDDQ7HimeuihhxQfH5/jh3l8fLxLXz59p3r16qU+ffpowIABioiIyDHx2BWufKXkAC6mcuXKOnPmjNkx4GJeeukl7dq1S48//rgGDhyoFi1aaNq0abp69aomTJhgdrx7bvfu3fY/9+7dW3369NGBAwf06KOPSpI2b96s999/X2PGjDErounatGkjSerSpYt9zGazudSpcebkAC5m9erVevPNNzVq1Khcf7tyhcs6Yb4jR45o+/btKleunEv8Rp7Xri8GeLMfga7yw/xuOHLkyN9uL1Wq1D1K8s9RcgAX4+bmJkk55uK40m9XuPeOHTumEiVKmB3DadzsB/ifucIPc+SO01WAi2GlVvwTpUuX1mOPPaYXX3xRzz77rIKCgsyOZCqKy83dbNJ1x44d71GSf44jOQCQD+zcuVNz587VF198oT/++ENNmzbViy++qBYtWsjLy8vseKaywg/zu+GvRfjq1au6ePGiPD09VbBgQZe4CzklB3BBP//8sz744AMdOnRICxYs0H333afPPvtM4eHheuyxx8yOBydmGIZ++uknzZ07V19//bWys7P1zDPP6OOPPzY7mmms8MP8XklISNArr7yiAQMGqEmTJmbHuSk3swMAuD1ff/21mjRpIh8fH+3YsUMZGRmSpJSUFI0aNcrkdHB2NptNDRo00KxZs7Ry5UqFh4drzpw5Zscy1blz5xweaWlp2rdvnx577DHNmzfP7HhOpXz58hozZoz69OljdpRbQskBXMzIkSM1Y8YMzZo1y+HKqrp16+a4xwzwV8ePH9fYsWNVvXp11apVS4UKFdL7779vdiyn42o/zO8lDw8PnTx50uwYt4SJx4CL2bdvX6730gkICND58+fvfSC4hA8++EBz587Vhg0bVLFiRbVv316LFi1iAu7fcKUf5nfDd9995/C1YRj6/fffNW3aNNWtW9ekVLeHkgO4mNDQUB04cEClS5d2GF+/fr192X7gr0aOHKkXXnhBU6ZMyder+ObGCj/M74ZWrVo5fG2z2VSsWDE1bNhQ48ePNyfUbaLkAC6me/fu6tOnjz7++GPZbDadPHlSmzZtUv/+/TVkyBCz48FJHT16lPuc3YAVfpjfDdnZ2WZHuGOUHMDFDBw4UNnZ2WrUqJEuXryo+vXry8vLS/3791evXr3Mjgcnsnv3bj344INyc3O76Z208+Oqx9f9+Yf59T9fX3QTro1LyAEXdeXKFR04cEBpaWmqXLmyChUqZHYkOBk3NzclJSUpODg419sYuNp9iO6mjz76SBMnTlRCQoKkaxOP+/btq27dupmczFzHjx/Xd999p6NHj+rKlSsO21zhnmccyQFclKenpypXrmx2DDixxMREFStWzP5n5G7o0KGaMGGCevXqpdq1a0uSNm3apNdee01Hjx7ViBEjTE5ojlWrVqlly5YqU6aM9u7dqwcffFCHDx+WYRh66KGHzI53SziSA7iYp59+Ote5FTabTd7e3ipXrpzatWunChUqmJAOzujq1av6z3/+oyFDhig8PNzsOE6nWLFimjJlil544QWH8Xnz5qlXr146c+aMScnMVatWLT355JMaPny4/Pz8tGvXLgUHB6t9+/Zq2rSpXnnlFbMj3hQnHQEXExAQoNWrV2vHjh2y2Wyy2WzauXOnVq9erczMTH355ZeqVq2aNmzYYHZUOIkCBQro66+/NjuG07p69aoiIyNzjNesWVOZmZkmJHIO8fHx9ltaeHh46NKlSypUqJBGjBihd9991+R0t4aSA7iY0NBQtWvXTocOHdLXX3+tr7/+WgcPHtSLL76osmXLKj4+Xp06ddIbb7xhdlQ4kVatWmnhwoVmx3BKHTp00PTp03OMz5w5U+3btzchkXPw9fW1z8MpXry4Dh48aN/mKke3OF0FuJhixYppw4YNeuCBBxzG9+/frzp16ujMmTOKi4tTvXr1WBwQdiNHjtT48ePVqFEj1axZU76+vg7be/fubVIy8/Xq1UuffvqpSpQooUcffVSStGXLFh09elQdO3Z0WFncFSbb5pVWrVqpefPm6t69u/r3769Fixapc+fO+uabbxQUFKSVK1eaHfGmKDmAiwkKCtKcOXPUsmVLh/HvvvtOnTp10rlz55SQkKBatWrp3LlzJqWEs/m7uTg2m02HDh26h2mcS4MGDW5pP5vNptWrV9/lNM7j0KFDSktLU9WqVZWenq5+/fpp48aNKl++vCZMmOASq2VzdRXgYjp06KCuXbvqv//9rx5++GFJ0rZt2zRq1Cj7+fO1a9eqSpUqZsaEk/nz1VXXf7dlccBr1qxZY3YEp5OVlaXjx4/b10/y9fXVjBkzTE51+ziSA7iYrKwsjRkzRtOmTdOpU6ckSSEhIerVq5feeOMNubu76+jRo3Jzc9P9999vclo4E9aCwe3w9vZWfHy8S1+Rx5EcwIVkZmZq7ty56tatmwYPHqzU1FRJkr+/v8N+JUuWNCMenBhrweB2Pfjggzp06JBLlxyO5AAupmDBgoqPj3eJ8+FwHqwFg9u1dOlSDRo0SG+//Xauk9X/+suVM+JIDuBiatWqpZ07d1JycFtYCwa3q1mzZpKkli1bOszfcqVbgVByABfz6quvql+/fjp+/Hiuv13l5xst4saurwXz10ug8/taMLixTz75RCVKlJC7u7vDeHZ2to4ePWpSqtvD6SrAxeR2d2RutIibYS0Y3C53d3f9/vvvCg4Odhg/e/asgoODXeLfGo7kAC6GGy3in9izZ4/9porXV64tWrSoihYtqj179tj347JyXHf9F6e/SktLk7e3twmJbh8lB3AxzMXBP8FaMLhVMTExkq4V3iFDhqhgwYL2bVlZWdqyZYuqV69uUrrbQ8kBXNBnn32mGTNmKDExUZs2bVKpUqU0adIkhYeH69///rfZ8QC4sJ07d0q6diQnLi5Onp6e9m2enp6qVq2a+vfvb1a820LJAVzM9OnTNXToUPXt21fvvPOO/bx4YGCgJk2aRMkBcEeuH/V76aWXNHnyZJe4VPxGmHgMuJjKlStr1KhRatWqlfz8/LRr1y6VKVNGe/bs0RNPPMF6JwDw/+S8TAOAU0tMTFSNGjVyjHt5eSk9Pd2ERADgnCg5gIsJDw9XbGxsjvGlS5eqUqVK9z4QADgp5uQALiYmJkbR0dG6fPmyDMPQ1q1bNW/ePI0ePVoffvih2fEAwGkwJwdwQZ9//rmGDRtmX+8kLCxMw4cPV9euXU1OBgDOg5IDuLCLFy8qLS0tx4qkAABKDgAAsCjm5AAu5uzZsxo6dKjWrFmj06dPKzs722F7cnKySckAwLlQcgAX06FDBx04cEBdu3ZVSEgI9xoCgBvgdBXgYvz8/LR+/XpVq1bN7CgA4NRYJwdwMRUrVtSlS5fMjgEATo8jOYCL2bZtmwYOHKihQ4fqwQcfVIECBRy2u/J9ZgAgLzEnB3AxgYGBSk1NVcOGDR3GDcOQzWaz37ATAPI7Sg7gYtq3b68CBQpo7ty5TDwGgL/B6SrAxRQsWFA7d+5UhQoVzI4CAE6NiceAi4mMjNSxY8fMjgEATo8jOYCLWbBggYYNG6YBAwYoIiIix8TjqlWrmpQMAJwLJQdwMW5uOQ/A2mw2Jh4DwF8w8RhwMYmJiWZHAACXwJEcAABgSUw8BlzQZ599prp16yosLExHjhyRJE2aNEmLFi0yORkAOA9KDuBipk+frpiYGDVr1kznz5+3z8EJDAzUpEmTzA0HAE6EkgO4mKlTp2rWrFkaPHiw3N3d7eORkZGKi4szMRkAOBdKDuBiEhMTVaNGjRzjXl5eSk9PNyERADgnSg7gYsLDwxUbG5tjfOnSpapUqdK9DwQATopLyAEXExMTo+joaF2+fFmGYWjr1q2aN2+eRo8erQ8//NDseADgNLiEHHBBn3/+uYYNG6aDBw9KksLCwjR8+HB17drV5GQA4DwoOYALu3jxotLS0hQcHJxj24YNGxQZGSkvLy8TkgGA+Sg5gEX5+/srNjZWZcqUMTsKAJiCiceARfH7C4D8jpIDAAAsiZIDAAAsiZIDAAAsiZIDWJTNZjM7AgCYipIDWBQTjwHkd1xCDgAALInbOgAuoEaNGrd8+mnHjh13OQ0AuAZKDuACWrVqZXYEAHA5nK4CAACWxMRjAABgSZyuAlxMVlaWJk6cqPnz5+vo0aO6cuWKw/bk5GSTkgGAc+FIDuBihg8frgkTJqhNmzZKSUlRTEyMnnnmGbm5uWnYsGFmxwMAp8GcHMDFlC1bVlOmTFHz5s3l5+en2NhY+9jmzZs1d+5csyMCgFPgSA7gYpKSkhQRESFJKlSokFJSUiRJTz31lJYsWWJmNABwKpQcwMXcf//9+v333yVdO6qzfPlySdK2bdvk5eVlZjQAcCqUHMDFPP3001q1apUkqVevXhoyZIjKly+vjh07qkuXLianAwDnwZwcwMVt3rxZGzduVPny5dWiRQuz4wCA06DkAC5m3bp1qlOnjjw8HFeAyMzM1MaNG1W/fn2TkgGAc6HkAC7G3d1dv//+u4KDgx3Gz549q+DgYGVlZZmUDACcC3NyABdjGEauN+s8e/asfH19TUgEAM6JFY8BF/HMM89Ikmw2mzp37uxwJVVWVpZ2796tOnXqmBUPAJwOJQdwEQEBAZKuHcnx8/OTj4+PfZunp6ceffRRde/e3ax4AOB0mJMDuJjhw4erf//+nJoCgJug5AAAAEvidBXgAh566CGtWrVKQUFBqlGjRq4Tj6/bsWPHPUwGAM6LkgO4gH//+9/2icatWrUyNwwAuAhOVwEAAEviSA7gon755RfFx8dLkipXrqyaNWuanAgAnAslB3Axx48f1wsvvKANGzYoMDBQknT+/HnVqVNHX3zxhe6//35zAwKAk2DFY8DFdOvWTVevXlV8fLySk5OVnJys+Ph4ZWdnq1u3bmbHAwCnwZwcwMX4+Pho48aNqlGjhsP49u3bVa9ePV28eNGkZADgXDiSA7iYEiVK6OrVqznGs7KyFBYWZkIiAHBOlBzAxYwbN069evXSL7/8Yh/75Zdf1KdPH7333nsmJgMA58LpKsDFBAUF6eLFi8rMzJSHx7VrB67/+a+3ekhOTjYjIgA4Ba6uAlzMpEmTzI4AAC6BIzkAAMCSmJMDuJgdO3YoLi7O/vWiRYvUqlUr/fe//9WVK1dMTAYAzoWSA7iY//znP9q/f78k6dChQ2rTpo0KFiyoBQsW6PXXXzc5HQA4D0oO4GL279+v6tWrS5IWLFigxx9/XHPnztXs2bP19ddfmxsOAJwIJQdwMYZhKDs7W5K0cuVKNWvWTNK19XPOnDljZjQAcCqUHMDFREZGauTIkfrss8+0du1aNW/eXJKUmJiokJAQk9MBgPOg5AAuZtKkSdqxY4d69uypwYMHq1y5cpKkr776SnXq1DE5HQA4Dy4hByzi8uXLcnd3V4ECBcyOAgBOgcUAARe1fft2xcfHS5IqV66shx56yOREAOBcKDmAizl9+rTatGmjtWvXKjAwUJJ0/vx5NWjQQF988YWKFStmbkAAcBLMyQFcTK9evZSWlqZff/1VycnJSk5O1p49e5SamqrevXubHQ8AnAZzcgAXExAQoJUrV+rhhx92GN+6dasaN26s8+fPmxMMAJwMR3IAF5OdnZ3r5OICBQrY188BAFByAJfTsGFD9enTRydPnrSPnThxQq+99poaNWpkYjIAcC6crgJczLFjx9SyZUv9+uuvKlGihCTp6NGjioiI0Hfffaf777/f5IQA4BwoOYALMgxDq1atsl9CXqlSJUVFRZmcCgCcCyUHcEGrVq3SqlWrdPr06RzzcD7++GOTUgGAc2GdHMDFDB8+XCNGjFBkZKSKFy8um81mdiQAcEocyQFcTPHixTV27Fh16NDB7CgA4NS4ugpwMVeuXOFGnABwCyg5gIvp1q2b5s6da3YMAHB6nK4CXEBMTIz9z9nZ2ZozZ46qVq2qqlWr5lgYcMKECfc6HgA4JUoO4AIaNGhwS/vZbDatXr36LqcBANdAyQEAAJbEnBwAAGBJlBwAAGBJlBwAAGBJlBwAd8UTTzyhvn37uuzz/5nNZtPChQvvyWsByDvc1gGAU/vpp5/UoEEDnTt3ToGBgfbxb775Jsfl83dq2LBhWrhwoWJjYx3Gf//9dwUFBeXpawG4+yg5AFxS4cKF79lrhYaG3rPXApB3OF0F4I6lp6erY8eOKlSokIoXL67x48c7bM/IyFD//v113333ydfXV4888oh++ukn+/YjR46oRYsWCgoKkq+vr6pUqaIffvhBhw8ftq8RFBQUJJvNps6dO0vKebqqdOnSGjVqlLp06SI/Pz+VLFlSM2fOdMjxxhtv6IEHHlDBggVVpkwZDRkyRFevXpUkzZ49W8OHD9euXbtks9lks9k0e/ZsSTlPV8XFxalhw4by8fFRkSJF1KNHD6Wlpdm3d+7cWa1atdJ7772n4sWLq0iRIoqOjra/FoB7g5ID4I4NGDBAa9eu1aJFi7R8+XL99NNP2rFjh317z549tWnTJn3xxRfavXu3nnvuOTVt2lQJCQmSpOjoaGVkZGjdunWKi4vTu+++q0KFCqlEiRL6+uuvJUn79u3T77//rsmTJ98wx/jx4xUZGamdO3fq1Vdf1SuvvKJ9+/bZt/v5+Wn27Nn67bffNHnyZM2aNUsTJ06UJLVp00b9+vVTlSpV9Pvvv+v3339XmzZtcrxGenq6mjRpoqCgIG3btk0LFizQypUr1bNnT4f91qxZo4MHD2rNmjWaM2eOZs+ebS9NAO4RAwDuwIULFwxPT09j/vz59rGzZ88aPj4+Rp8+fYwjR44Y7u7uxokTJxy+r1GjRsagQYMMwzCMiIgIY9iwYbk+/5o1awxJxrlz5xzGH3/8caNPnz72r0uVKmW8+OKL9q+zs7ON4OBgY/r06TfMPm7cOKNmzZr2r9966y2jWrVqOfaTZHz77beGYRjGzJkzjaCgICMtLc2+fcmSJYabm5uRlJRkGIZhdOrUyShVqpSRmZlp3+e5554z2rRpc8MsAPIec3IA3JGDBw/qypUreuSRR+xjhQsXVoUKFSRdO7WTlZWlBx54wOH7MjIyVKRIEUlS79699corr2j58uWKiopS69atVbVq1dvO8ufvsdlsCg0N1enTp+1jX375paZMmaKDBw8qLS1NmZmZ8vf3v63XiI+PV7Vq1eTr62sfq1u3rrKzs7Vv3z6FhIRIkqpUqSJ3d3f7PsWLF1dcXNxtvycA/xynqwDcVWlpaXJ3d9f27dsVGxtrf8THx9tPPXXr1k2HDh1Shw4dFBcXp8jISE2dOvW2X+uvV1vZbDZlZ2dLkjZt2qT27durWbNmWrx4sXbu3KnBgwfrypUrd/4mbzMLgHuDkgPgjpQtW1YFChTQli1b7GPnzp3T/v37JUk1atRQVlaWTp8+rXLlyjk8/nzVUokSJfTyyy/rm2++Ub9+/TRr1ixJkqenpyQpKyvrjnJu3LhRpUqV0uDBgxUZGany5cvryJEjDvt4enre9HUqVaqkXbt2KT093T62YcMGubm52Y9eAXAOlBwAd6RQoULq2rWrBgwYoNWrV2vPnj3q3Lmz3Nyu/fPywAMPqH379urYsaO++eYbJSYmauvWrRo9erSWLFkiSerbt6+WLVumxMRE7dixQ2vWrFGlSpUkSaVKlZLNZtPixYv1xx9/OFzFdDvKly+vo0eP6osvvtDBgwc1ZcoUffvttw77lC5dWomJiYqNjdWZM2eUkZGR43nat28vb29vderUSXv27NGaNWvUq1cvdejQwX6qCoBzoOQAuGPjxo1TvXr11KJFC0VFRemxxx5TzZo17ds/+eQTdezYUf369VOFChXUqlUrbdu2TSVLlpR07ShNdHS0KlWqpKZNm+qBBx7Q//3f/0mS7rvvPg0fPlwDBw5USEhIjquYblXLli312muvqWfPnqpevbo2btyoIUOGOOzTunVrNW3aVA0aNFCxYsU0b968HM9TsGBBLVu2TMnJyXr44Yf17LPPqlGjRpo2bdo/ygXg7rEZhmGYHQIAACCvcSQHAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABY0v8HdNKgn2JiwiAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BSzy9BPyJDo5q4+klFFicyQEAAEYicgAAgJGIHAAAYKR8j5wLFy7o1VdfVVhYmPz9/XXXXXdp+PDhsizLnmNZlhISElSxYkX5+/srOjpa+/btc9vPiRMn1KVLFwUEBKhMmTKKjY3VqVOn3OZs27ZNUVFR8vPzU2hoqMaMGZPfhwMAAAqpfI+c0aNHa8qUKZo8ebJSU1M1evRojRkzRpMmTbLnjBkzRhMnTtTUqVOVnJyskiVLKiYmRmfPnrXndOnSRTt37tTy5cu1ePFirV69Wr169bK3u1wutWzZUpUrV1ZKSorefvttDRs2TO+//35+HxIAACiE8v3dVevXr1e7du3Ups3Fq72rVKmiTz/9VBs2bJB08SzO+PHjNXToULVr106SNGvWLAUFBWnhwoXq3LmzUlNTtXTpUm3cuFENGzaUJE2aNEmtW7fW2LFjFRISotmzZ+vcuXP6+OOP5ePjo9q1a2vLli1655133GIIAAAUTfl+JqdRo0ZasWKF9u7dK0naunWr1q5dq1atWkmSDhw4oLS0NEVHR9uPcTqdioiIUFJSkiQpKSlJZcqUsQNHkqKjo+Xl5aXk5GR7TtOmTeXj42PPiYmJ0Z49e3Ty5Mn8PiwAAFDI5PuZnMGDB8vlcqlGjRry9vbWhQsX9Oabb6pLly6SpLS0NElSUFCQ2+OCgoLsbWlpaQoMDHRfaLFiKleunNucsLCwPPvI3Va2bNk8a8vKylJWVpZ93+Vy3cyhAgCAAizfz+R8/vnnmj17tubMmaPNmzdr5syZGjt2rGbOnJnfT3XdRo4cKafTad9CQ0M9vSQAAHCL5HvkDBgwQIMHD1bnzp0VHh6up556Sv369dPIkSMlScHBwZKk9PR0t8elp6fb24KDg3Xs2DG37dnZ2Tpx4oTbnMvt49Ln+KMhQ4YoMzPTvh0+fPgmjxYAABRU+R45v//+u7y83Hfr7e2tnJwcSVJYWJiCg4O1YsUKe7vL5VJycrIiIyMlSZGRkcrIyFBKSoo9Z+XKlcrJyVFERIQ9Z/Xq1Tp//rw9Z/ny5apevfplX6qSJF9fXwUEBLjdAACAmfI9cv7yl7/ozTff1JIlS3Tw4EEtWLBA77zzjv76179KkhwOh/r27asRI0Zo0aJF2r59u7p166aQkBC1b99eklSzZk098sgj6tmzpzZs2KB169YpPj5enTt3VkhIiCTpySeflI+Pj2JjY7Vz507NnTtXEyZMUP/+/fP7kAAAQCGU7xceT5o0Sa+++qqef/55HTt2TCEhIerdu7cSEhLsOQMHDtTp06fVq1cvZWRkqEmTJlq6dKn8/PzsObNnz1Z8fLxatGghLy8vdezYURMnTrS3O51OLVu2THFxcWrQoIEqVKighIQE3j4OAAAkSQ7r0o8iLmJcLpecTqcyMzOL3EtXfEtx0cK3FBct/H4XLUXx9/ta//3mu6sAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAY6ZZEzi+//KKuXbuqfPny8vf3V3h4uDZt2mRvtyxLCQkJqlixovz9/RUdHa19+/a57ePEiRPq0qWLAgICVKZMGcXGxurUqVNuc7Zt26aoqCj5+fkpNDRUY8aMuRWHAwAACqF8j5yTJ0+qcePGKl68uL755hvt2rVL48aNU9myZe05Y8aM0cSJEzV16lQlJyerZMmSiomJ0dmzZ+05Xbp00c6dO7V8+XItXrxYq1evVq9eveztLpdLLVu2VOXKlZWSkqK3335bw4YN0/vvv5/fhwQAAAqhYvm9w9GjRys0NFTTp0+3x8LCwuw/W5al8ePHa+jQoWrXrp0kadasWQoKCtLChQvVuXNnpaamaunSpdq4caMaNmwoSZo0aZJat26tsWPHKiQkRLNnz9a5c+f08ccfy8fHR7Vr19aWLVv0zjvvuMUQAAAomvL9TM6iRYvUsGFDPfbYYwoMDNS9996rDz74wN5+4MABpaWlKTo62h5zOp2KiIhQUlKSJCkpKUllypSxA0eSoqOj5eXlpeTkZHtO06ZN5ePjY8+JiYnRnj17dPLkycuuLSsrSy6Xy+0GAADMlO+Rs3//fk2ZMkXVqlXTt99+qz59+ujFF1/UzJkzJUlpaWmSpKCgILfHBQUF2dvS0tIUGBjotr1YsWIqV66c25zL7ePS5/ijkSNHyul02rfQ0NCbPFoAAFBQ5Xvk5OTkqH79+nrrrbd07733qlevXurZs6emTp2a30913YYMGaLMzEz7dvjwYU8vCQAA3CL5HjkVK1ZUrVq13MZq1qypQ4cOSZKCg4MlSenp6W5z0tPT7W3BwcE6duyY2/bs7GydOHHCbc7l9nHpc/yRr6+vAgIC3G4AAMBM+R45jRs31p49e9zG9u7dq8qVK0u6eBFycHCwVqxYYW93uVxKTk5WZGSkJCkyMlIZGRlKSUmx56xcuVI5OTmKiIiw56xevVrnz5+35yxfvlzVq1d3eycXAAAomvI9cvr166cffvhBb731ln766SfNmTNH77//vuLi4iRJDodDffv21YgRI7Ro0SJt375d3bp1U0hIiNq3by/p4pmfRx55RD179tSGDRu0bt06xcfHq3PnzgoJCZEkPfnkk/Lx8VFsbKx27typuXPnasKECerfv39+HxIAACiE8v0t5Pfdd58WLFigIUOG6I033lBYWJjGjx+vLl262HMGDhyo06dPq1evXsrIyFCTJk20dOlS+fn52XNmz56t+Ph4tWjRQl5eXurYsaMmTpxob3c6nVq2bJni4uLUoEEDVahQQQkJCbx9HAAASJIclmVZnl6Ep7hcLjmdTmVmZha563OqDF7i6SXgT3RwVBtPLwF/In6/i5ai+Pt9rf9+891VAADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjHTLI2fUqFFyOBzq27evPXb27FnFxcWpfPnyKlWqlDp27Kj09HS3xx06dEht2rRRiRIlFBgYqAEDBig7O9ttzqpVq1S/fn35+vqqatWqmjFjxq0+HAAAUEjc0sjZuHGjpk2bprp167qN9+vXT1999ZXmzZunxMREHTlyRB06dLC3X7hwQW3atNG5c+e0fv16zZw5UzNmzFBCQoI958CBA2rTpo0efPBBbdmyRX379tWzzz6rb7/99lYeEgAAKCRuWeScOnVKXbp00QcffKCyZcva45mZmfroo4/0zjvv6KGHHlKDBg00ffp0rV+/Xj/88IMkadmyZdq1a5f++c9/6p577lGrVq00fPhwvffeezp37pwkaerUqQoLC9O4ceNUs2ZNxcfH69FHH9W77757qw4JAAAUIrcscuLi4tSmTRtFR0e7jaekpOj8+fNu4zVq1NAdd9yhpKQkSVJSUpLCw8MVFBRkz4mJiZHL5dLOnTvtOX/cd0xMjL2Py8nKypLL5XK7AQAAMxW7FTv97LPPtHnzZm3cuDHPtrS0NPn4+KhMmTJu40FBQUpLS7PnXBo4udtzt11tjsvl0pkzZ+Tv75/nuUeOHKnXX3/9ho8LAAAUHvl+Jufw4cN66aWXNHv2bPn5+eX37m/KkCFDlJmZad8OHz7s6SUBAIBbJN8jJyUlRceOHVP9+vVVrFgxFStWTImJiZo4caKKFSumoKAgnTt3ThkZGW6PS09PV3BwsCQpODg4z7utcu//rzkBAQGXPYsjSb6+vgoICHC7AQAAM+V75LRo0ULbt2/Xli1b7FvDhg3VpUsX+8/FixfXihUr7Mfs2bNHhw4dUmRkpCQpMjJS27dv17Fjx+w5y5cvV0BAgGrVqmXPuXQfuXNy9wEAAIq2fL8mp3Tp0qpTp47bWMmSJVW+fHl7PDY2Vv3791e5cuUUEBCgF154QZGRkXrggQckSS1btlStWrX01FNPacyYMUpLS9PQoUMVFxcnX19fSdJzzz2nyZMna+DAgXrmmWe0cuVKff7551qyZEl+HxIAACiEbsmFx//Lu+++Ky8vL3Xs2FFZWVmKiYnRP/7xD3u7t7e3Fi9erD59+igyMlIlS5ZU9+7d9cYbb9hzwsLCtGTJEvXr108TJkxQpUqV9OGHHyomJsYThwQAAAoYh2VZlqcX4Skul0tOp1OZmZlF7vqcKoM541WUHBzVxtNLwJ+I3++ipSj+fl/rv998dxUAADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAj5XvkjBw5Uvfdd59Kly6twMBAtW/fXnv27HGbc/bsWcXFxal8+fIqVaqUOnbsqPT0dLc5hw4dUps2bVSiRAkFBgZqwIABys7OdpuzatUq1a9fX76+vqpatapmzJiR34cDAAAKqXyPnMTERMXFxemHH37Q8uXLdf78ebVs2VKnT5+25/Tr109fffWV5s2bp8TERB05ckQdOnSwt1+4cEFt2rTRuXPntH79es2cOVMzZsxQQkKCPefAgQNq06aNHnzwQW3ZskV9+/bVs88+q2+//Ta/DwkAABRCDsuyrFv5BMePH1dgYKASExPVtGlTZWZm6rbbbtOcOXP06KOPSpJ2796tmjVrKikpSQ888IC++eYbtW3bVkeOHFFQUJAkaerUqRo0aJCOHz8uHx8fDRo0SEuWLNGOHTvs5+rcubMyMjK0dOnSa1qby+WS0+lUZmamAgIC8v/gC7Aqg5d4egn4Ex0c1cbTS8CfiN/voqUo/n5f67/ft/yanMzMTElSuXLlJEkpKSk6f/68oqOj7Tk1atTQHXfcoaSkJElSUlKSwsPD7cCRpJiYGLlcLu3cudOec+k+cufk7uNysrKy5HK53G4AAMBMtzRycnJy1LdvXzVu3Fh16tSRJKWlpcnHx0dlypRxmxsUFKS0tDR7zqWBk7s9d9vV5rhcLp05c+ay6xk5cqScTqd9Cw0NveljBAAABdMtjZy4uDjt2LFDn3322a18mms2ZMgQZWZm2rfDhw97ekkAAOAWKXardhwfH6/Fixdr9erVqlSpkj0eHBysc+fOKSMjw+1sTnp6uoKDg+05GzZscNtf7ruvLp3zx3dkpaenKyAgQP7+/pddk6+vr3x9fW/62AAAQMGX72dyLMtSfHy8FixYoJUrVyosLMxte4MGDVS8eHGtWLHCHtuzZ48OHTqkyMhISVJkZKS2b9+uY8eO2XOWL1+ugIAA1apVy55z6T5y5+TuAwAAFG35fiYnLi5Oc+bM0ZdffqnSpUvb19A4nU75+/vL6XQqNjZW/fv3V7ly5RQQEKAXXnhBkZGReuCBByRJLVu2VK1atfTUU09pzJgxSktL09ChQxUXF2efiXnuuec0efJkDRw4UM8884xWrlypzz//XEuW8K4CAABwC87kTJkyRZmZmWrevLkqVqxo3+bOnWvPeffdd9W2bVt17NhRTZs2VXBwsL744gt7u7e3txYvXixvb29FRkaqa9eu6tatm9544w17TlhYmJYsWaLly5erXr16GjdunD788EPFxMTk9yEBAIBC6JZ/Tk5BxufkoKgoip+jUZTx+120FMXf7wLzOTkAAACeQOQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxX6yHnvvfdUpUoV+fn5KSIiQhs2bPD0kgAAQAFQqCNn7ty56t+/v1577TVt3rxZ9erVU0xMjI4dO+bppQEAAA8r1JHzzjvvqGfPnnr66adVq1YtTZ06VSVKlNDHH3/s6aUBAAAPK7SRc+7cOaWkpCg6Otoe8/LyUnR0tJKSkjy4MgAAUBAU8/QCbtSvv/6qCxcuKCgoyG08KChIu3fvvuxjsrKylJWVZd/PzMyUJLlcrlu30AIqJ+t3Ty8Bf6Ki+P/xoozf76KlKP5+5x6zZVlXnVdoI+dGjBw5Uq+//nqe8dDQUA+sBvjzOMd7egUAbpWi/Pv922+/yel0XnF7oY2cChUqyNvbW+np6W7j6enpCg4OvuxjhgwZov79+9v3c3JydOLECZUvX14Oh+OWrhee53K5FBoaqsOHDysgIMDTywGQj/j9Llosy9Jvv/2mkJCQq84rtJHj4+OjBg0aaMWKFWrfvr2ki9GyYsUKxcfHX/Yxvr6+8vX1dRsrU6bMLV4pCpqAgAD+EgQMxe930XG1Mzi5Cm3kSFL//v3VvXt3NWzYUPfff7/Gjx+v06dP6+mnn/b00gAAgIcV6sjp1KmTjh8/roSEBKWlpemee+7R0qVL81yMDAAAip5CHTmSFB8ff8WXp4BL+fr66rXXXsvzkiWAwo/fb1yOw/pf778CAAAohArthwECAABcDZEDAACMROQAAAAjETkAAMBIRA4AoFBas2aNunbtqsjISP3yyy+SpE8++URr16718MpQUBA5AIBC51//+pdiYmLk7++vH3/80f7y5czMTL311lseXh0KCiIHRcK5c+e0Z88eZWdne3opAPLBiBEjNHXqVH3wwQcqXry4Pd64cWNt3rzZgytDQULkwGi///67YmNjVaJECdWuXVuHDh2SJL3wwgsaNWqUh1cH4Ebt2bNHTZs2zTPudDqVkZHx5y8IBRKRA6MNGTJEW7du1apVq+Tn52ePR0dHa+7cuR5cGYCbERwcrJ9++inP+Nq1a3XnnXd6YEUoiIgcGG3hwoWaPHmymjRpIofDYY/Xrl1bP//8swdXBuBm9OzZUy+99JKSk5PlcDh05MgRzZ49W6+88or69Onj6eWhgCj0310FXM3x48cVGBiYZ/z06dNu0QOgcBk8eLBycnLUokUL/f7772ratKl8fX31yiuv6IUXXvD08lBAcCYHRmvYsKGWLFli388Nmw8//FCRkZGeWhaAm+RwOPT3v/9dJ06c0I4dO/TDDz/o+PHjGj58uKeXhgKEMzkw2ltvvaVWrVpp165dys7O1oQJE7Rr1y6tX79eiYmJnl4egJvk4+OjWrVqeXoZKKD4FnIY7+eff9aoUaO0detWnTp1SvXr19egQYMUHh7u6aUBuEEPPvjgVV9yXrly5Z+4GhRUnMmB8e666y598MEHnl4GgHx0zz33uN0/f/68tmzZoh07dqh79+6eWRQKHCIHRtu8ebOKFy9un7X58ssvNX36dNWqVUvDhg2Tj4+Ph1cI4Ea8++67lx0fNmyYTp069SevBgUVFx7DaL1799bevXslSfv371enTp1UokQJzZs3TwMHDvTw6gDkt65du+rjjz/29DJQQBA5MNrevXvt09rz5s1Ts2bNNGfOHM2YMUP/+te/PLs4APkuKSnJ7YM/UbTxchWMZlmWcnJyJEnfffed2rZtK0kKDQ3Vr7/+6smlAbgJHTp0cLtvWZaOHj2qTZs26dVXX/XQqlDQEDkwWsOGDTVixAhFR0crMTFRU6ZMkSQdOHBAQUFBHl4dgBvldDrd7nt5eal69ep644031LJlSw+tCgUNkQOjjR8/Xl26dNHChQv197//XVWrVpUkzZ8/X40aNfLw6gDciAsXLujpp59WeHi4ypYt6+nloADjc3JQJJ09e1be3t4qXry4p5cC4Ab4+fkpNTVVYWFhnl4KCjAuPEaR5OfnR+AAhVidOnW0f/9+Ty8DBRxncmCcsmXLXvOXb544ceIWrwbArbB06VINGTJEw4cPV4MGDVSyZEm37QEBAR5aGQoSIgfGmTlz5jXP5ZNRgcLljTfe0Msvv6zSpUvbY5f+R41lWXI4HLpw4YInlocChsgBABQa3t7eOnr0qFJTU686r1mzZn/SilCQETkoMs6ePatz5865jXFKGyhcvLy8lJaWpsDAQE8vBYUAFx7DaKdPn1Z8fLwCAwNVsmRJlS1b1u0GoPC51mvuAD4nB0YbOHCgvv/+e02ZMkVPPfWU3nvvPf3yyy+aNm2aRo0a5enlAbgBd9999/8MHd5UAImXq2C4O+64Q7NmzVLz5s0VEBCgzZs3q2rVqvrkk0/06aef6uuvv/b0EgFcBy8vL40fPz7PJx7/EW8qgMSZHBjuxIkTuvPOOyVdvP4m97/umjRpoj59+nhyaQBuUOfOnbkmB9eEa3JgtDvvvFMHDhyQJNWoUUOff/65JOmrr75SmTJlPLgyADeC63FwPYgcGGn//v3KycnR008/ra1bt0qSBg8erPfee09+fn7q16+fBgwY4OFVArheXGGB68E1OTBS7mdp5J7S7tSpkyZOnKizZ88qJSVFVatWVd26dT28SgDArUTkwEh//CyN0qVLa+vWrfb1OQAA8/FyFQAAMBKRAyM5HI48FyhywSIAFC28hRxGsixLPXr0kK+vr6SLX+nw3HPP5fmm4i+++MITywMA/AmIHBjpjx8E1rVrVw+tBADgKVx4DAAAjMQ1OQAAwEhEDgAAMBKRAwAAjETkAMBVOBwOLVy48IrbDx48KIfDoS1btkiSVq1aJYfDoYyMjD9lfQCujHdXAcBNCA0N1dGjR1WhQgVPLwXAHxA5AHATvL29FRwc7OllALgMXq4CcMPmz5+v8PBw+fv7q3z58oqOjtbp06fVo0cPtW/fXmPHjlXFihVVvnx5xcXF6fz58/ZjT548qW7duqls2bIqUaKEWrVqpX379km6+GGOt912m+bPn2/Pv+eee1SxYkX7/tq1a+Xr66vff/9d0sWXlaZNm6a2bduqRIkSqlmzppKSkvTTTz+pefPmKlmypBo1aqSff/7Z7RimTJmiu+66Sz4+Pqpevbo++eSTPMd59OhRtWrVSv7+/rrzzjvd1vXHl6suZ+3atYqKipK/v79CQ0P14osv6vTp09f3wwZw3YgcADfk6NGjeuKJJ/TMM88oNTVVq1atUocOHZT70Vvff/+9fv75Z33//feaOXOmZsyYoRkzZtiP79GjhzZt2qRFixYpKSlJlmWpdevWOn/+vBwOh5o2bapVq1ZJuhhEqampOnPmjHbv3i1JSkxM1H333acSJUrY+xw+fLi6deumLVu2qEaNGnryySfVu3dvDRkyRJs2bZJlWYqPj7fnL1iwQC+99JJefvll7dixQ71799bTTz+t77//3u1YX331VXXs2FFbt25Vly5d1LlzZ6Wmpl7Tz+nnn3/WI488oo4dO2rbtm2aO3eu1q5d67YOALeIBQA3ICUlxZJkHTx4MM+27t27W5UrV7ays7Ptsccee8zq1KmTZVmWtXfvXkuStW7dOnv7r7/+avn7+1uff/65ZVmWNXHiRKt27dqWZVnWwoULrYiICKtdu3bWlClTLMuyrOjoaOtvf/ub/XhJ1tChQ+37SUlJliTro48+ssc+/fRTy8/Pz77fqFEjq2fPnm5rf+yxx6zWrVu77fe5555zmxMREWH16dPHsizLOnDggCXJ+vHHHy3Lsqzvv//ekmSdPHnSsizLio2NtXr16uX2+DVr1lheXl7WmTNn8vzsAOQfzuQAuCH16tVTixYtFB4erscee0wffPCBTp48aW+vXbu2vL297fsVK1bUsWPHJEmpqakqVqyYIiIi7O3ly5dX9erV7TMkzZo1065du3T8+HElJiaqefPmat68uVatWqXz589r/fr1at68udua6tata/85KChIkhQeHu42dvbsWblcLnsdjRs3dttH48aN85yliYyMzHP/Ws/kbN26VTNmzFCpUqXsW0xMjHJycnTgwIFr2geAG0PkALgh3t7eWr58ub755hvVqlVLkyZNUvXq1e1/uIsXL+423+FwKCcn55r3Hx4ernLlyikxMdEtchITE7Vx40adP39ejRo1cnvMpc+Z+63zlxu7nnXcrFOnTql3797asmWLfdu6dav27dunu+66609bB1AUETkAbpjD4VDjxo31+uuv68cff5SPj48WLFjwPx9Xs2ZNZWdnKzk52R7773//qz179qhWrVr2vqOiovTll19q586datKkierWrausrCxNmzZNDRs2zPOt8terZs2aWrdundvYunXr7DXk+uGHH/Lcr1mz5jU9R/369bVr1y5VrVo1z83Hx+em1g/g6ngLOYAbkpycrBUrVqhly5YKDAxUcnKyjh8/rpo1a2rbtm1XfWy1atXUrl079ezZU9OmTVPp0qU1ePBg3X777WrXrp09r3nz5nr55ZfVsGFDlSpVSpLUtGlTzZ49WwMGDLjpYxgwYIAef/xx3XvvvYqOjtZXX32lL774Qt99953bvHnz5qlhw4Zq0qSJZs+erQ0bNuijjz66pucYNGiQHnjgAcXHx+vZZ59VyZIltWvXLi1fvlyTJ0++6WMAcGWcyQFwQwICArR69Wq1bt1ad999t4YOHapx48apVatW1/T46dOnq0GDBmrbtq0iIyNlWZa+/vprt5eXmjVrpgsXLrhde9O8efM8Yzeqffv2mjBhgsaOHavatWtr2rRpmj59ep59v/766/rss89Ut25dzZo1S59++mmesz1XUrduXSUmJmrv3r2KiorSvffeq4SEBIWEhNz0+gFcncOy/r/3ewIAABiEMzkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAj/T8t7CVeXXwCUAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c in labeled_small.columns:\n",
    "    if c not in ['india','age','season','skiarea_name']:\n",
    "        plt.figure()\n",
    "        labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
    "        plt.title(c)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b188f19-faf3-468c-b283-5cbc5c3cdcdc",
   "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>skiarea_name</th>\n",
       "      <th>season</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>cause</th>\n",
       "      <th>gender</th>\n",
       "      <th>equipment</th>\n",
       "      <th>helmet</th>\n",
       "      <th>destination</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>india</th>\n",
       "      <th>...</th>\n",
       "      <th>ambulance</th>\n",
       "      <th>privat_helicopter</th>\n",
       "      <th>skiarea_ambulance</th>\n",
       "      <th>snowmobile</th>\n",
       "      <th>quad</th>\n",
       "      <th>helicopter</th>\n",
       "      <th>akja</th>\n",
       "      <th>offroad_vehicle</th>\n",
       "      <th>other</th>\n",
       "      <th>indipendently</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2018</td>\n",
       "      <td>None</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>F</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>distortion</td>\n",
       "      <td>i1</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2018</td>\n",
       "      <td>easy</td>\n",
       "      <td>illness</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>domicile</td>\n",
       "      <td>other</td>\n",
       "      <td>i0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2018</td>\n",
       "      <td>advanced</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>wound</td>\n",
       "      <td>i1</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2018</td>\n",
       "      <td>advanced</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>fracture</td>\n",
       "      <td>i1</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kronplatz</td>\n",
       "      <td>2018</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>True</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>other</td>\n",
       "      <td>i1</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  skiarea_name  season    difficulty       cause gender equipment helmet  \\\n",
       "0    Kronplatz    2018          None  fall_alone      F       ski   True   \n",
       "1    Kronplatz    2018          easy     illness      M       ski   True   \n",
       "2    Kronplatz    2018      advanced  fall_alone      M       ski   True   \n",
       "3    Kronplatz    2018      advanced  fall_alone      M       ski   True   \n",
       "4    Kronplatz    2018  intermediate  fall_alone      M       ski   True   \n",
       "\n",
       "               destination   diagnosis india  ...  ambulance  \\\n",
       "0  hospital_emergency_room  distortion    i1  ...      False   \n",
       "1                 domicile       other    i0  ...      False   \n",
       "2  hospital_emergency_room       wound    i1  ...      False   \n",
       "3  hospital_emergency_room    fracture    i1  ...      False   \n",
       "4  hospital_emergency_room       other    i1  ...      False   \n",
       "\n",
       "  privat_helicopter skiarea_ambulance snowmobile   quad helicopter   akja  \\\n",
       "0             False             False       True  False      False  False   \n",
       "1             False             False      False  False      False  False   \n",
       "2             False             False      False  False      False   True   \n",
       "3             False             False      False  False      False   True   \n",
       "4             False             False      False  False      False   True   \n",
       "\n",
       "  offroad_vehicle  other indipendently  \n",
       "0           False  False         False  \n",
       "1           False  False         False  \n",
       "2           False  False         False  \n",
       "3           False  False         False  \n",
       "4           False  False         False  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labeled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f15dddca-d861-4921-9654-44c197352c70",
   "metadata": {},
   "outputs": [],
   "source": [
    "labeled.india = labeled.india.apply(lambda x: x.replace('i','')).astype(int)\n",
    "labeled_small.india = labeled_small.india.apply(lambda x: x.replace('i','')).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b075edc2-900b-4c18-b7fe-5862dfb085d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2034, 27)\n",
      "(2183, 27)\n"
     ]
    }
   ],
   "source": [
    "##TEST \n",
    "SKI_AREA_TEST = 'Klausberg'\n",
    "SEASON_TEST_SKIAREA = 'Kronplatz'\n",
    "SEASON_TEST_YEAR = 2023 ##2023-2024\n",
    "test_area = labeled[labeled.skiarea_name==SKI_AREA_TEST]\n",
    "test_area_small = labeled_small[labeled_small.skiarea_name==SKI_AREA_TEST]\n",
    "print(test_area_small.shape)\n",
    "test_area_season = labeled[(labeled.skiarea_name==SEASON_TEST_SKIAREA)&(labeled.season>=SEASON_TEST_YEAR)]\n",
    "test_area_season_small = labeled_small[(labeled_small.skiarea_name==SEASON_TEST_SKIAREA)&(labeled_small.season>=SEASON_TEST_YEAR)]\n",
    "print(test_area_season_small.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59c4293e-a728-4f20-bd56-b1032f679ee1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fabf354e-f39e-4cde-af84-c65a277d309a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "labeled_tmp = labeled[labeled.skiarea_name!=SKI_AREA_TEST]\n",
    "labeled_tmp = labeled_tmp[(labeled_tmp.skiarea_name!=SEASON_TEST_SKIAREA)|(labeled_tmp.season<SEASON_TEST_YEAR) ]\n",
    "X_train, X_valid, y_train, y_valid = train_test_split( labeled_tmp.drop(columns=['india','season','skiarea_name']),\n",
    "                                                    labeled_tmp.india, test_size=0.33, random_state=0,stratify=labeled_tmp.india)\n",
    "\n",
    "labeled_small_tmp = labeled_small[labeled_small.skiarea_name!=SKI_AREA_TEST]\n",
    "labeled_small_tmp = labeled_small_tmp[(labeled_small_tmp.skiarea_name!=SEASON_TEST_SKIAREA)|(labeled_small_tmp.season<SEASON_TEST_YEAR) ]\n",
    "X_train_small, X_valid_small, y_train_small, y_valid_small = train_test_split( labeled_small_tmp.drop(columns=['india','season','skiarea_name']),\n",
    "                                                    labeled_small_tmp.india, test_size=0.33, random_state=0,stratify=labeled_small_tmp.india)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9f3772e4-8142-4f0f-934a-40406253cde2",
   "metadata": {},
   "outputs": [],
   "source": [
    "use_smote = False\n",
    "if use_smote:\n",
    "    from imblearn.over_sampling import RandomOverSampler\n",
    "    \n",
    "    sm = RandomOverSampler()\n",
    "    X_train_smote,y_train_smote = sm.fit_resample(X_train,y_train)\n",
    "    X_train_smote_small,y_train_smote_small = sm.fit_resample(X_train_small,y_train_small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8a26ce2b-d197-4356-abce-af6001fbe64a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   class          p\n",
      "0      0   0.203424\n",
      "1      1   0.013596\n",
      "2      2   0.056109\n",
      "3      3   1.130661\n",
      "4      4  16.206137\n"
     ]
    }
   ],
   "source": [
    "w = pd.DataFrame(np.unique(y_train,return_counts=True)).T\n",
    "w.columns = ['class','p']\n",
    "w.p = np.sqrt(w.p.sum())/w.p\n",
    "print(w)\n",
    "weight_train = pd.merge(pd.DataFrame({'class':y_train}),w).p.values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ee98e56a-1527-4d78-b80a-185527c7c5e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "##THIS IS BETTER\n",
    "#w = pd.DataFrame(np.unique(y_train,return_counts=True)).T\n",
    "#w.columns = ['class','p']\n",
    "#w.p = w.p.sum()/w.p/w.shape[0]\n",
    "#print(w)\n",
    "#weight_train = pd.merge(pd.DataFrame({'class':y_train}),w).p.values\n",
    "#weight_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3a5ec39b-0f0b-44d5-8e09-a2d65b39727a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01359575, 0.05610896, 0.01359575, ..., 0.20342432, 0.05610896,\n",
       "       0.05610896])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9d687e5e-43d6-41a2-8c66-e92e743ad10b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-02-19 10:13:34,770] A new study created in memory with name: no-name-eb45f3a5-a8c6-45fe-a5a1-a9a50593fb27\n",
      "[I 2024-02-19 10:13:35,613] Trial 0 finished with value: 0.1989111911567868 and parameters: {'learning_rate': 0.069550503828446, 'max_depth': 14, 'min_child_weight': 4, 'gamma': 6.840625787856297, 'subsample': 0.6425250659956084, 'colsample_bytree': 0.8479444601860839, 'alpha': 7.62883839298031, 'lambda': 4.463721336424616}. Best is trial 0 with value: 0.1989111911567868.\n",
      "[I 2024-02-19 10:13:36,882] Trial 1 finished with value: 0.3173094420130114 and parameters: {'learning_rate': 0.03760597869350516, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 4.912637931042977, 'subsample': 0.4614006609002679, 'colsample_bytree': 0.6144190767209258, 'alpha': 1.3722886308582516, 'lambda': 4.119799280632812}. Best is trial 1 with value: 0.3173094420130114.\n",
      "[I 2024-02-19 10:13:38,133] Trial 2 finished with value: 0.36354566542920486 and parameters: {'learning_rate': 0.029446413231195095, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 2.5116270178516666, 'subsample': 0.6028998025025123, 'colsample_bytree': 0.4858612684354104, 'alpha': 0.34718217874583845, 'lambda': 9.860165840433806}. Best is trial 2 with value: 0.36354566542920486.\n",
      "[I 2024-02-19 10:13:38,984] Trial 3 finished with value: 0.3469922576483624 and parameters: {'learning_rate': 0.0931440873208689, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 5.398315788862382, 'subsample': 0.7374388452465628, 'colsample_bytree': 0.6099362325539116, 'alpha': 1.573690896526272, 'lambda': 6.743192670912046}. Best is trial 2 with value: 0.36354566542920486.\n",
      "[I 2024-02-19 10:13:40,126] Trial 4 finished with value: 0.3553579929950666 and parameters: {'learning_rate': 0.10094953721240169, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 6.771659154396598, 'subsample': 0.8307108084973739, 'colsample_bytree': 0.8405436108695855, 'alpha': 2.394788740804512, 'lambda': 0.459689474247279}. Best is trial 2 with value: 0.36354566542920486.\n",
      "[I 2024-02-19 10:13:40,694] Trial 5 finished with value: 0.06749159519010905 and parameters: {'learning_rate': 0.18326819665760533, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 3.4390811741434213, 'subsample': 0.1250067874138027, 'colsample_bytree': 0.6078486142313761, 'alpha': 2.128222676485192, 'lambda': 6.894295009378579}. Best is trial 2 with value: 0.36354566542920486.\n",
      "[I 2024-02-19 10:13:41,977] Trial 6 finished with value: 0.2796601470387234 and parameters: {'learning_rate': 0.14434840651917577, 'max_depth': 13, 'min_child_weight': 7, 'gamma': 3.2592579714506664, 'subsample': 0.6323857794453664, 'colsample_bytree': 0.2822278209426363, 'alpha': 7.906296889490516, 'lambda': 8.057488811454071}. Best is trial 2 with value: 0.36354566542920486.\n",
      "[I 2024-02-19 10:13:44,631] Trial 7 finished with value: 0.21177098284323326 and parameters: {'learning_rate': 0.10010721013138292, 'max_depth': 13, 'min_child_weight': 5, 'gamma': 3.073250654257352, 'subsample': 0.47203300990891284, 'colsample_bytree': 0.22244504883898525, 'alpha': 5.408988151064258, 'lambda': 8.001282868539162}. Best is trial 2 with value: 0.36354566542920486.\n",
      "[I 2024-02-19 10:13:47,064] Trial 8 finished with value: 0.30667656366595547 and parameters: {'learning_rate': 0.14477429246102166, 'max_depth': 15, 'min_child_weight': 5, 'gamma': 5.899630739279243, 'subsample': 0.8740551059888441, 'colsample_bytree': 0.2415491856674957, 'alpha': 5.664496275130779, 'lambda': 6.693223223679813}. Best is trial 2 with value: 0.36354566542920486.\n",
      "[I 2024-02-19 10:13:49,885] Trial 9 finished with value: 0.2119615781834734 and parameters: {'learning_rate': 0.0422426309896827, 'max_depth': 7, 'min_child_weight': 6, 'gamma': 4.526235544521565, 'subsample': 0.5760526647430548, 'colsample_bytree': 0.7807267418752107, 'alpha': 7.131576033845945, 'lambda': 6.513330886086037}. Best is trial 2 with value: 0.36354566542920486.\n",
      "[I 2024-02-19 10:13:52,174] Trial 10 finished with value: 0.06953989904244982 and parameters: {'learning_rate': 0.010701520291106499, 'max_depth': 10, 'min_child_weight': 8, 'gamma': 0.06479159219237607, 'subsample': 0.2623430461732232, 'colsample_bytree': 0.04664751604958017, 'alpha': 9.703557274131079, 'lambda': 9.960507825545388}. Best is trial 2 with value: 0.36354566542920486.\n",
      "[I 2024-02-19 10:13:54,373] Trial 11 finished with value: 0.296018021915827 and parameters: {'learning_rate': 0.13484465403211848, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 9.785779840140961, 'subsample': 0.9958388818283195, 'colsample_bytree': 0.9948753637421689, 'alpha': 0.08940044102630251, 'lambda': 0.26393724259605644}. Best is trial 2 with value: 0.36354566542920486.\n",
      "[I 2024-02-19 10:13:57,895] Trial 12 finished with value: 0.3578177261876427 and parameters: {'learning_rate': 0.05943796484157278, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.6235501524201794, 'subsample': 0.8301335017256678, 'colsample_bytree': 0.36492426044031184, 'alpha': 3.5209163840645976, 'lambda': 0.0049537822908228835}. Best is trial 2 with value: 0.36354566542920486.\n",
      "[I 2024-02-19 10:14:07,644] Trial 13 finished with value: 0.3601897108601743 and parameters: {'learning_rate': 0.05600671271774382, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.3916831126594196, 'subsample': 0.36839729743851035, 'colsample_bytree': 0.4190250244017122, 'alpha': 3.9183929421630177, 'lambda': 2.3818210108731956}. Best is trial 2 with value: 0.36354566542920486.\n",
      "[I 2024-02-19 10:14:09,649] Trial 14 finished with value: 0.2910789010379856 and parameters: {'learning_rate': 0.01384113819556998, 'max_depth': 11, 'min_child_weight': 6, 'gamma': 1.6656701057711567, 'subsample': 0.3652087074918706, 'colsample_bytree': 0.43459587658231974, 'alpha': 3.879721916418248, 'lambda': 2.4559277286907975}. Best is trial 2 with value: 0.36354566542920486.\n",
      "[I 2024-02-19 10:14:09,943] Trial 15 finished with value: 0.0 and parameters: {'learning_rate': 0.07156381580665228, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 1.8385966026353417, 'subsample': 0.015758021260530053, 'colsample_bytree': 0.5228550162565715, 'alpha': 0.09016096975018562, 'lambda': 2.705110074238757}. Best is trial 2 with value: 0.36354566542920486.\n",
      "[I 2024-02-19 10:14:10,982] Trial 16 finished with value: 0.28312350940882697 and parameters: {'learning_rate': 0.04125684281186955, 'max_depth': 11, 'min_child_weight': 4, 'gamma': 1.6157977162022974, 'subsample': 0.336073768131864, 'colsample_bytree': 0.11800169806585414, 'alpha': 3.9880713938745433, 'lambda': 2.7342083360470086}. Best is trial 2 with value: 0.36354566542920486.\n",
      "[I 2024-02-19 10:14:11,944] Trial 17 finished with value: 0.16113039821522662 and parameters: {'learning_rate': 0.02813417814680368, 'max_depth': 8, 'min_child_weight': 7, 'gamma': 0.9966392106676694, 'subsample': 0.1927056202002801, 'colsample_bytree': 0.4070951664196225, 'alpha': 6.386830946360401, 'lambda': 9.942244707539652}. Best is trial 2 with value: 0.36354566542920486.\n",
      "[I 2024-02-19 10:14:13,353] Trial 18 finished with value: 0.3690994046311561 and parameters: {'learning_rate': 0.056995542065032434, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 2.5306348896612794, 'subsample': 0.5451656344243945, 'colsample_bytree': 0.6882195416817001, 'alpha': 3.0135042715100377, 'lambda': 1.7436739390330613}. Best is trial 18 with value: 0.3690994046311561.\n",
      "[I 2024-02-19 10:14:13,848] Trial 19 finished with value: 0.38021154229260695 and parameters: {'learning_rate': 0.07867489721170512, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 2.586644655622084, 'subsample': 0.5392761769021751, 'colsample_bytree': 0.7108401916037683, 'alpha': 0.9416845707223416, 'lambda': 1.3769470861159108}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:14,398] Trial 20 finished with value: 0.3054230933913223 and parameters: {'learning_rate': 0.12484103766078836, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 9.327984882142594, 'subsample': 0.7119677778364462, 'colsample_bytree': 0.7229354327286066, 'alpha': 2.9111122451317426, 'lambda': 1.3801988872474444}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:15,033] Trial 21 finished with value: 0.37407683293138505 and parameters: {'learning_rate': 0.08103432159089231, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 2.59681889622373, 'subsample': 0.5258528415487354, 'colsample_bytree': 0.6873998628844763, 'alpha': 0.8353635954365758, 'lambda': 3.6936968540042288}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:15,742] Trial 22 finished with value: 0.3749480953109007 and parameters: {'learning_rate': 0.07920865891907926, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 3.993986954550577, 'subsample': 0.5257495324815407, 'colsample_bytree': 0.7006358226315926, 'alpha': 1.1898068896528393, 'lambda': 1.374410273990853}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:17,201] Trial 23 finished with value: 0.36816577053770094 and parameters: {'learning_rate': 0.08396740683062687, 'max_depth': 11, 'min_child_weight': 1, 'gamma': 3.969592950846927, 'subsample': 0.48659502808780225, 'colsample_bytree': 0.934858161632823, 'alpha': 1.2216921313188327, 'lambda': 3.7031843840071828}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:18,011] Trial 24 finished with value: 0.3624387193384365 and parameters: {'learning_rate': 0.11138709186488453, 'max_depth': 15, 'min_child_weight': 2, 'gamma': 3.9395169891905493, 'subsample': 0.44747738239893475, 'colsample_bytree': 0.6745473667375015, 'alpha': 1.158130933781993, 'lambda': 1.1765468454483599}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:18,783] Trial 25 finished with value: 0.36835947894914156 and parameters: {'learning_rate': 0.08506405204459774, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 2.5519615543299317, 'subsample': 0.725028372531701, 'colsample_bytree': 0.7889861193783336, 'alpha': 0.7644133937240639, 'lambda': 5.238161849657985}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:19,507] Trial 26 finished with value: 0.3166301996968598 and parameters: {'learning_rate': 0.11761287160799525, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 4.26099053985612, 'subsample': 0.4151952536423443, 'colsample_bytree': 0.5463164672549061, 'alpha': 2.277274726814209, 'lambda': 3.333706286292495}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:20,687] Trial 27 finished with value: 0.2081857032628807 and parameters: {'learning_rate': 0.072014196764922, 'max_depth': 11, 'min_child_weight': 4, 'gamma': 8.205674218907095, 'subsample': 0.5385517782888344, 'colsample_bytree': 0.8935633393024219, 'alpha': 4.788270466248106, 'lambda': 5.607192520821766}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:21,214] Trial 28 finished with value: 0.365308901262515 and parameters: {'learning_rate': 0.08513509643164022, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 5.825277543374127, 'subsample': 0.67184892288243, 'colsample_bytree': 0.7616437660262038, 'alpha': 1.7579313128397873, 'lambda': 0.9764905958532231}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:22,049] Trial 29 finished with value: 0.3530335834365412 and parameters: {'learning_rate': 0.16872969499730872, 'max_depth': 13, 'min_child_weight': 4, 'gamma': 1.2045645907922289, 'subsample': 0.2579371320149449, 'colsample_bytree': 0.8757406620085857, 'alpha': 0.8995407426102728, 'lambda': 4.7403676301459}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:22,630] Trial 30 finished with value: 0.30570300455612204 and parameters: {'learning_rate': 0.07022879093472606, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 6.546118747763682, 'subsample': 0.639302580300093, 'colsample_bytree': 0.6435425831756715, 'alpha': 2.895903686108459, 'lambda': 3.400145681450892}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:23,816] Trial 31 finished with value: 0.36160148919313434 and parameters: {'learning_rate': 0.05489855207490591, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 2.583296314820343, 'subsample': 0.5209458580202515, 'colsample_bytree': 0.6589436736945957, 'alpha': 2.7333694157126986, 'lambda': 1.258190038989869}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:24,648] Trial 32 finished with value: 0.3663470426057162 and parameters: {'learning_rate': 0.06238154303138824, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 2.2197428643962467, 'subsample': 0.5550628106870165, 'colsample_bytree': 0.7273453565118077, 'alpha': 1.8444988006387577, 'lambda': 2.001033483906445}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:25,447] Trial 33 finished with value: 0.37456090172546697 and parameters: {'learning_rate': 0.08025091365342168, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 3.5093012174188214, 'subsample': 0.4123548607690278, 'colsample_bytree': 0.7039618757853195, 'alpha': 0.7095102164925087, 'lambda': 1.9072319471167178}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:26,032] Trial 34 finished with value: 0.34684068871647633 and parameters: {'learning_rate': 0.07974068041033128, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 3.5359660176448076, 'subsample': 0.30164911557757834, 'colsample_bytree': 0.5718836729256032, 'alpha': 0.8102237376011983, 'lambda': 4.404868040871975}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:27,160] Trial 35 finished with value: 0.3766772168732836 and parameters: {'learning_rate': 0.09517806302919067, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 4.591261428158235, 'subsample': 0.40638491721598724, 'colsample_bytree': 0.8241784947710293, 'alpha': 0.6300543189313546, 'lambda': 1.8866986022566348}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:27,894] Trial 36 finished with value: 0.35637806459415705 and parameters: {'learning_rate': 0.09852972059111027, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 5.0318215268852455, 'subsample': 0.39308960175464164, 'colsample_bytree': 0.8246132117699616, 'alpha': 1.6312486113292788, 'lambda': 0.6730343391263622}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:29,210] Trial 37 finished with value: 0.3506978371506154 and parameters: {'learning_rate': 0.0921599625020239, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 4.8969126037568085, 'subsample': 0.44297783017777337, 'colsample_bytree': 0.9548031635333143, 'alpha': 0.44472091924190327, 'lambda': 2.108345175137151}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:29,643] Trial 38 finished with value: 0.2949390706160349 and parameters: {'learning_rate': 0.111341997114174, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 7.560947205392322, 'subsample': 0.21250203611393248, 'colsample_bytree': 0.6008897249196847, 'alpha': 0.3793940567097855, 'lambda': 1.7488555740868268}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:30,479] Trial 39 finished with value: 0.3729230649578527 and parameters: {'learning_rate': 0.10928515166924727, 'max_depth': 13, 'min_child_weight': 3, 'gamma': 3.146629366079835, 'subsample': 0.5986728393722172, 'colsample_bytree': 0.8321037877403964, 'alpha': 1.4275137139530005, 'lambda': 3.0459860797459632}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:31,287] Trial 40 finished with value: 0.3008069849327868 and parameters: {'learning_rate': 0.09735469153450842, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 5.57390515407664, 'subsample': 0.48533036853975403, 'colsample_bytree': 0.48700431875470324, 'alpha': 2.3240075546897874, 'lambda': 0.6870434721711307}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:32,141] Trial 41 finished with value: 0.36274459144261195 and parameters: {'learning_rate': 0.07755015168553643, 'max_depth': 11, 'min_child_weight': 2, 'gamma': 3.9014834138743693, 'subsample': 0.42654436952583613, 'colsample_bytree': 0.7116243205742013, 'alpha': 0.6622606955597372, 'lambda': 3.8902597186019805}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:32,895] Trial 42 finished with value: 0.36237387273814387 and parameters: {'learning_rate': 0.09071257760670333, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 4.779588562252487, 'subsample': 0.6038423272533361, 'colsample_bytree': 0.7602779546978584, 'alpha': 0.025133905179141935, 'lambda': 1.5646705695468301}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:33,715] Trial 43 finished with value: 0.3660806593516918 and parameters: {'learning_rate': 0.11982048608196116, 'max_depth': 13, 'min_child_weight': 2, 'gamma': 3.0437163161625276, 'subsample': 0.6714278944947499, 'colsample_bytree': 0.6286409219442748, 'alpha': 1.2043194987606491, 'lambda': 2.1053603617283256}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:34,548] Trial 44 finished with value: 0.3770469456266217 and parameters: {'learning_rate': 0.047920876695168764, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 3.435146317245488, 'subsample': 0.49330478773749975, 'colsample_bytree': 0.8216517011969833, 'alpha': 1.8814035492824353, 'lambda': 2.9361390560022613}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:35,300] Trial 45 finished with value: 0.31053049354524054 and parameters: {'learning_rate': 0.048342290331597454, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 3.653640489228429, 'subsample': 0.3208211486881428, 'colsample_bytree': 0.8901598340114755, 'alpha': 1.986351173112259, 'lambda': 0.8726763596736058}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:36,499] Trial 46 finished with value: 0.36652702847673846 and parameters: {'learning_rate': 0.026642528328479163, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 4.397029335067222, 'subsample': 0.48580660303601997, 'colsample_bytree': 0.8124092434705529, 'alpha': 1.61717691642616, 'lambda': 2.9524656344092426}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:37,238] Trial 47 finished with value: 0.0952766793600364 and parameters: {'learning_rate': 0.06492161658904921, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 6.242468228287279, 'subsample': 0.3876984119196679, 'colsample_bytree': 0.8626450538129173, 'alpha': 8.758544051770897, 'lambda': 0.05287063429000183}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:38,437] Trial 48 finished with value: 0.29456000976544083 and parameters: {'learning_rate': 0.04955667270282674, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 3.0586126263359343, 'subsample': 0.5857626643554134, 'colsample_bytree': 0.7595451161282062, 'alpha': 4.785509653147296, 'lambda': 2.225577221046623}. Best is trial 19 with value: 0.38021154229260695.\n",
      "[I 2024-02-19 10:14:39,298] Trial 49 finished with value: 0.3830283561616933 and parameters: {'learning_rate': 0.10275570687879744, 'max_depth': 11, 'min_child_weight': 5, 'gamma': 1.9526036468526011, 'subsample': 0.7988763745777528, 'colsample_bytree': 0.9276002374269038, 'alpha': 3.3521369114743047, 'lambda': 0.46438865430030163}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:14:41,155] Trial 50 finished with value: 0.3805155011712817 and parameters: {'learning_rate': 0.13004191964554607, 'max_depth': 10, 'min_child_weight': 5, 'gamma': 2.228928110672676, 'subsample': 0.8034208450733273, 'colsample_bytree': 0.9999293988841261, 'alpha': 2.549388467834418, 'lambda': 0.4372238797644581}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:14:48,401] Trial 51 finished with value: 0.377903892963383 and parameters: {'learning_rate': 0.15039192971879642, 'max_depth': 9, 'min_child_weight': 5, 'gamma': 2.021261640235484, 'subsample': 0.8169831467289955, 'colsample_bytree': 0.999607265170071, 'alpha': 3.4196581002580695, 'lambda': 0.44118118920310556}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:14:54,983] Trial 52 finished with value: 0.3823866974781913 and parameters: {'learning_rate': 0.1665519646150275, 'max_depth': 9, 'min_child_weight': 5, 'gamma': 2.0238166489010876, 'subsample': 0.8016463314466447, 'colsample_bytree': 0.9879435771306513, 'alpha': 3.372480317820817, 'lambda': 0.3176155632435872}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:00,790] Trial 53 finished with value: 0.3827601221215673 and parameters: {'learning_rate': 0.1601441302990082, 'max_depth': 9, 'min_child_weight': 5, 'gamma': 2.1126459193803586, 'subsample': 0.8220472887609624, 'colsample_bytree': 0.9977308246984505, 'alpha': 3.341028884523573, 'lambda': 0.4787222439399762}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:07,587] Trial 54 finished with value: 0.3788880328157674 and parameters: {'learning_rate': 0.16049816411363915, 'max_depth': 9, 'min_child_weight': 5, 'gamma': 1.0362415983441422, 'subsample': 0.8087552723075739, 'colsample_bytree': 0.9824942062931402, 'alpha': 3.3941379933427176, 'lambda': 0.31874667081061325}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:08,469] Trial 55 finished with value: 0.3797769955419876 and parameters: {'learning_rate': 0.19455157876573187, 'max_depth': 8, 'min_child_weight': 6, 'gamma': 0.9605139665070547, 'subsample': 0.9223191096678139, 'colsample_bytree': 0.9489603587498643, 'alpha': 4.297958036540576, 'lambda': 0.3774023229957386}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:08,943] Trial 56 finished with value: 0.35987632846176315 and parameters: {'learning_rate': 0.19146543437849375, 'max_depth': 8, 'min_child_weight': 6, 'gamma': 1.291576579488271, 'subsample': 0.948288053253493, 'colsample_bytree': 0.9337897173311835, 'alpha': 5.351294292843528, 'lambda': 0.4966205553038779}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:09,744] Trial 57 finished with value: 0.3760151105937106 and parameters: {'learning_rate': 0.1772570342546199, 'max_depth': 7, 'min_child_weight': 6, 'gamma': 0.40701264845135565, 'subsample': 0.8874944523387844, 'colsample_bytree': 0.9221449545394239, 'alpha': 4.5290986548216985, 'lambda': 9.111906872764033}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:10,438] Trial 58 finished with value: 0.3539944200752658 and parameters: {'learning_rate': 0.1954032449007967, 'max_depth': 10, 'min_child_weight': 5, 'gamma': 1.5303960895029936, 'subsample': 0.775652966807317, 'colsample_bytree': 0.9628445485788133, 'alpha': 3.882456638015224, 'lambda': 0.9317463802667227}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:11,598] Trial 59 finished with value: 0.3709613884880622 and parameters: {'learning_rate': 0.13033553043438456, 'max_depth': 7, 'min_child_weight': 7, 'gamma': 2.2184549596214214, 'subsample': 0.8879468225756181, 'colsample_bytree': 0.9055388946146998, 'alpha': 4.2582956302321655, 'lambda': 0.01752749450291058}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:12,311] Trial 60 finished with value: 0.3701511808172418 and parameters: {'learning_rate': 0.1447489811164233, 'max_depth': 10, 'min_child_weight': 5, 'gamma': 0.5459150753535456, 'subsample': 0.9685508809217889, 'colsample_bytree': 0.9598290064681299, 'alpha': 6.261330471140818, 'lambda': 1.0672139840305706}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:13,204] Trial 61 finished with value: 0.37732143342190017 and parameters: {'learning_rate': 0.15952883619696032, 'max_depth': 9, 'min_child_weight': 6, 'gamma': 0.0534040153437122, 'subsample': 0.7700067807945093, 'colsample_bytree': 0.9935277612352792, 'alpha': 3.392141666823103, 'lambda': 0.3550271576018659}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:14,131] Trial 62 finished with value: 0.37869430400283083 and parameters: {'learning_rate': 0.1652781012256434, 'max_depth': 8, 'min_child_weight': 5, 'gamma': 0.9677669586338875, 'subsample': 0.8544090290711488, 'colsample_bytree': 0.9738734562945465, 'alpha': 3.2489760014670477, 'lambda': 0.2989247358479585}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:15,096] Trial 63 finished with value: 0.3768287277036443 and parameters: {'learning_rate': 0.1770646338506441, 'max_depth': 9, 'min_child_weight': 5, 'gamma': 0.9283398175623354, 'subsample': 0.9329497540738492, 'colsample_bytree': 0.9180584792607623, 'alpha': 2.67026541717743, 'lambda': 0.6901835440353972}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:16,072] Trial 64 finished with value: 0.3580343386522813 and parameters: {'learning_rate': 0.15235997531306422, 'max_depth': 8, 'min_child_weight': 6, 'gamma': 1.7944579070312905, 'subsample': 0.7954825683883351, 'colsample_bytree': 0.8679718443136369, 'alpha': 4.232753352489371, 'lambda': 1.38811631189186}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:17,101] Trial 65 finished with value: 0.3778984867539911 and parameters: {'learning_rate': 0.13867864769686067, 'max_depth': 10, 'min_child_weight': 4, 'gamma': 2.1634476171538894, 'subsample': 0.915748546856819, 'colsample_bytree': 0.944870549680314, 'alpha': 3.578166766109644, 'lambda': 0.0795033100638296}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:17,780] Trial 66 finished with value: 0.37732134904933745 and parameters: {'learning_rate': 0.18505675533810118, 'max_depth': 9, 'min_child_weight': 5, 'gamma': 1.429783608677621, 'subsample': 0.7403174282738699, 'colsample_bytree': 0.9919129577137339, 'alpha': 2.5024102007971067, 'lambda': 0.6788582889721422}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:18,812] Trial 67 finished with value: 0.3728055361324293 and parameters: {'learning_rate': 0.15695369879286375, 'max_depth': 10, 'min_child_weight': 4, 'gamma': 0.7199867363585983, 'subsample': 0.8542638694775468, 'colsample_bytree': 0.9052931471219782, 'alpha': 3.1335499943158958, 'lambda': 1.1388077685198539}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:19,948] Trial 68 finished with value: 0.3460391359910944 and parameters: {'learning_rate': 0.19966532323441946, 'max_depth': 6, 'min_child_weight': 7, 'gamma': 2.803186302101794, 'subsample': 0.705970132270134, 'colsample_bytree': 0.9607322776714077, 'alpha': 3.7187956339627872, 'lambda': 1.532221941472221}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:20,429] Trial 69 finished with value: 0.35854877104657085 and parameters: {'learning_rate': 0.172251442158733, 'max_depth': 11, 'min_child_weight': 5, 'gamma': 1.8859614996708265, 'subsample': 0.9980609298621623, 'colsample_bytree': 0.9304976816619058, 'alpha': 5.229963568423263, 'lambda': 6.139091212974293}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:21,133] Trial 70 finished with value: 0.3651297134984216 and parameters: {'learning_rate': 0.18540818803700201, 'max_depth': 8, 'min_child_weight': 4, 'gamma': 1.204426900103079, 'subsample': 0.8127296608097367, 'colsample_bytree': 0.8511558396347576, 'alpha': 4.561996698337515, 'lambda': 0.3755900910762332}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:22,179] Trial 71 finished with value: 0.373940186622436 and parameters: {'learning_rate': 0.16123657104949835, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 1.121188842796447, 'subsample': 0.8653812025407227, 'colsample_bytree': 0.9801272030412925, 'alpha': 3.2038887793445827, 'lambda': 0.2178888067911786}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:23,220] Trial 72 finished with value: 0.3747533098415062 and parameters: {'learning_rate': 0.135122558318431, 'max_depth': 8, 'min_child_weight': 5, 'gamma': 0.7207802483727501, 'subsample': 0.7632639302930635, 'colsample_bytree': 0.9736327060057066, 'alpha': 4.253230491223987, 'lambda': 0.8392668704466445}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:24,119] Trial 73 finished with value: 0.3808042535123061 and parameters: {'learning_rate': 0.1657278951832768, 'max_depth': 9, 'min_child_weight': 5, 'gamma': 1.6933753482283749, 'subsample': 0.8494367395658661, 'colsample_bytree': 0.8928944209016594, 'alpha': 3.0607468759498895, 'lambda': 0.41135272218497154}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:25,070] Trial 74 finished with value: 0.37940603930042804 and parameters: {'learning_rate': 0.15161132087778778, 'max_depth': 9, 'min_child_weight': 6, 'gamma': 2.3396913216252875, 'subsample': 0.9106287366030408, 'colsample_bytree': 0.8857422511716315, 'alpha': 2.779174862557353, 'lambda': 0.5741233166096347}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:25,902] Trial 75 finished with value: 0.37152978020709565 and parameters: {'learning_rate': 0.1739141814443562, 'max_depth': 9, 'min_child_weight': 6, 'gamma': 2.355523170105843, 'subsample': 0.9015325732936557, 'colsample_bytree': 0.881698821038415, 'alpha': 2.739445589326466, 'lambda': 7.809598103692307}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:26,654] Trial 76 finished with value: 0.3676073861561685 and parameters: {'learning_rate': 0.1514986454974577, 'max_depth': 11, 'min_child_weight': 6, 'gamma': 2.7228480548848735, 'subsample': 0.8411388600984953, 'colsample_bytree': 0.2914125078099186, 'alpha': 2.1374741824340346, 'lambda': 1.0983179880637828}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:27,491] Trial 77 finished with value: 0.374570004290516 and parameters: {'learning_rate': 0.13990044596469786, 'max_depth': 10, 'min_child_weight': 6, 'gamma': 1.656592745050867, 'subsample': 0.678092339702622, 'colsample_bytree': 0.7997268458352731, 'alpha': 2.480316233234247, 'lambda': 0.6816143509311475}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:28,693] Trial 78 finished with value: 0.3802027210278118 and parameters: {'learning_rate': 0.12779127890522035, 'max_depth': 9, 'min_child_weight': 7, 'gamma': 2.7797120236717934, 'subsample': 0.9467332511187958, 'colsample_bytree': 0.9072368982357, 'alpha': 2.986671548519693, 'lambda': 1.5977552728547997}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:29,744] Trial 79 finished with value: 0.3585801236583085 and parameters: {'learning_rate': 0.10529687714342653, 'max_depth': 10, 'min_child_weight': 8, 'gamma': 2.835022277574841, 'subsample': 0.9629859798748553, 'colsample_bytree': 0.8507212071159805, 'alpha': 3.6983452528990814, 'lambda': 1.5242925871485027}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:30,805] Trial 80 finished with value: 0.3604244388853136 and parameters: {'learning_rate': 0.1265008454936514, 'max_depth': 9, 'min_child_weight': 7, 'gamma': 1.9479925193736354, 'subsample': 0.7443121453158968, 'colsample_bytree': 0.9338345281427091, 'alpha': 3.084864422007334, 'lambda': 1.213986916430751}. Best is trial 49 with value: 0.3830283561616933.\n",
      "[I 2024-02-19 10:15:32,073] Trial 81 finished with value: 0.388032929708448 and parameters: {'learning_rate': 0.11898271220695222, 'max_depth': 9, 'min_child_weight': 7, 'gamma': 2.4488291452774615, 'subsample': 0.9132867919988988, 'colsample_bytree': 0.8911765642979349, 'alpha': 2.8510121414202585, 'lambda': 2.4924677667035873}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:15:33,098] Trial 82 finished with value: 0.37627615612381893 and parameters: {'learning_rate': 0.11507324732113369, 'max_depth': 8, 'min_child_weight': 8, 'gamma': 1.4886947822314105, 'subsample': 0.9320318989810381, 'colsample_bytree': 0.9114203379488531, 'alpha': 4.005957652493822, 'lambda': 1.7852180571191847}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:15:34,003] Trial 83 finished with value: 0.38108790908340956 and parameters: {'learning_rate': 0.12313986448530076, 'max_depth': 11, 'min_child_weight': 7, 'gamma': 2.4288359211365242, 'subsample': 0.9707150555783779, 'colsample_bytree': 0.9493954015994968, 'alpha': 3.0181898659896635, 'lambda': 2.503510244209597}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:15:35,033] Trial 84 finished with value: 0.37637691787501665 and parameters: {'learning_rate': 0.12240250966168677, 'max_depth': 11, 'min_child_weight': 7, 'gamma': 2.4048467859599447, 'subsample': 0.9638265603567272, 'colsample_bytree': 0.8498733017055893, 'alpha': 2.098711228987648, 'lambda': 2.68012097760608}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:15:36,244] Trial 85 finished with value: 0.3583127391716854 and parameters: {'learning_rate': 0.12762059948668375, 'max_depth': 10, 'min_child_weight': 7, 'gamma': 3.2567660327979944, 'subsample': 0.8750038706612354, 'colsample_bytree': 0.8942221087425957, 'alpha': 3.001753821814117, 'lambda': 2.2779185086675153}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:15:38,747] Trial 86 finished with value: 0.3687770855116418 and parameters: {'learning_rate': 0.10542142616757182, 'max_depth': 11, 'min_child_weight': 4, 'gamma': 2.9031529654557815, 'subsample': 0.780039871500276, 'colsample_bytree': 0.7800391174755281, 'alpha': 2.5177280043232204, 'lambda': 2.495468860003161}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:15:44,455] Trial 87 finished with value: 0.3817027384438883 and parameters: {'learning_rate': 0.133452215576525, 'max_depth': 10, 'min_child_weight': 7, 'gamma': 2.086829094056687, 'subsample': 0.8383622054370741, 'colsample_bytree': 0.9463747126955687, 'alpha': 2.8789807055645826, 'lambda': 0.9188153253626168}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:15:51,807] Trial 88 finished with value: 0.3674282057993548 and parameters: {'learning_rate': 0.11812529543986973, 'max_depth': 10, 'min_child_weight': 8, 'gamma': 2.0120602696104815, 'subsample': 0.8140554221326352, 'colsample_bytree': 0.9998867009971433, 'alpha': 2.2321061652375755, 'lambda': 1.0089028944812457}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:15:58,719] Trial 89 finished with value: 0.37826669454624506 and parameters: {'learning_rate': 0.13464506149390154, 'max_depth': 11, 'min_child_weight': 4, 'gamma': 1.7962887781888046, 'subsample': 0.8320961076379655, 'colsample_bytree': 0.9486945260979227, 'alpha': 3.6147770438170985, 'lambda': 0.8056064911535643}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:06,598] Trial 90 finished with value: 0.36753816741441464 and parameters: {'learning_rate': 0.10220967033563502, 'max_depth': 12, 'min_child_weight': 7, 'gamma': 2.5158546457038984, 'subsample': 0.844552022707763, 'colsample_bytree': 0.14054131368232742, 'alpha': 1.435030112372492, 'lambda': 1.3857243543978734}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:07,490] Trial 91 finished with value: 0.018888798775548128 and parameters: {'learning_rate': 0.11529346011774853, 'max_depth': 9, 'min_child_weight': 7, 'gamma': 2.6617288765425378, 'subsample': 0.07146547609713272, 'colsample_bytree': 0.9212399031033807, 'alpha': 3.293013857859467, 'lambda': 1.737361112504511}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:08,416] Trial 92 finished with value: 0.3851035655433326 and parameters: {'learning_rate': 0.13121111375552375, 'max_depth': 10, 'min_child_weight': 7, 'gamma': 2.142588520738553, 'subsample': 0.883758225433067, 'colsample_bytree': 0.8680781163785168, 'alpha': 2.848009259059878, 'lambda': 0.16958175268311493}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:09,075] Trial 93 finished with value: 0.36998380619377774 and parameters: {'learning_rate': 0.14134663644602904, 'max_depth': 10, 'min_child_weight': 8, 'gamma': 2.0604107055942817, 'subsample': 0.8837576269081139, 'colsample_bytree': 0.8670609269987726, 'alpha': 2.6264177388887227, 'lambda': 0.16787017490621764}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:10,139] Trial 94 finished with value: 0.3679659101195837 and parameters: {'learning_rate': 0.13454321413220607, 'max_depth': 10, 'min_child_weight': 5, 'gamma': 1.351388828941272, 'subsample': 0.7916978656968164, 'colsample_bytree': 0.9621434105656904, 'alpha': 2.3352756348283066, 'lambda': 3.3587802311367967}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:10,658] Trial 95 finished with value: 0.3614532612271381 and parameters: {'learning_rate': 0.16539849509410587, 'max_depth': 11, 'min_child_weight': 7, 'gamma': 1.6417197515367274, 'subsample': 0.7160476409615391, 'colsample_bytree': 0.7421886283081045, 'alpha': 3.967057855341963, 'lambda': 0.4828889198401117}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:11,408] Trial 96 finished with value: 0.3854087325976572 and parameters: {'learning_rate': 0.12244258052281823, 'max_depth': 10, 'min_child_weight': 5, 'gamma': 2.195832849682197, 'subsample': 0.7584018247825708, 'colsample_bytree': 0.93669663553482, 'alpha': 2.909794092171679, 'lambda': 0.005211145729359434}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:12,496] Trial 97 finished with value: 0.378119661855301 and parameters: {'learning_rate': 0.12245198311878536, 'max_depth': 10, 'min_child_weight': 5, 'gamma': 2.230314129446208, 'subsample': 0.9841906167351379, 'colsample_bytree': 0.9389675440382911, 'alpha': 2.855291170288533, 'lambda': 0.04775634908151255}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:13,433] Trial 98 finished with value: 0.3819813007517522 and parameters: {'learning_rate': 0.13190918081509256, 'max_depth': 10, 'min_child_weight': 5, 'gamma': 1.7361124120750344, 'subsample': 0.755827896562223, 'colsample_bytree': 0.9783575647611493, 'alpha': 3.4985332932980016, 'lambda': 0.0005080408353798638}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:14,512] Trial 99 finished with value: 0.3807766549740972 and parameters: {'learning_rate': 0.14691365385360217, 'max_depth': 10, 'min_child_weight': 5, 'gamma': 1.7423908076043724, 'subsample': 0.763172472696379, 'colsample_bytree': 0.8391485300167415, 'alpha': 3.5320616868959016, 'lambda': 0.00021662579505903664}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:16,046] Trial 100 finished with value: 0.3814383268193462 and parameters: {'learning_rate': 0.13191229413519961, 'max_depth': 9, 'min_child_weight': 6, 'gamma': 1.989559902400011, 'subsample': 0.90207593425487, 'colsample_bytree': 0.9754905219126925, 'alpha': 3.1312686723385514, 'lambda': 4.970523631444983}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:17,140] Trial 101 finished with value: 0.37985593809682855 and parameters: {'learning_rate': 0.11007887514595946, 'max_depth': 9, 'min_child_weight': 6, 'gamma': 1.9761153274491092, 'subsample': 0.8674118294273128, 'colsample_bytree': 0.9753048565410082, 'alpha': 3.7800157220226995, 'lambda': 4.140376533589302}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:18,364] Trial 102 finished with value: 0.3768900784191279 and parameters: {'learning_rate': 0.13122285459002028, 'max_depth': 9, 'min_child_weight': 5, 'gamma': 2.438028402360909, 'subsample': 0.8931568220752997, 'colsample_bytree': 0.8859608358318286, 'alpha': 3.1592322148442973, 'lambda': 7.407550283376809}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:19,290] Trial 103 finished with value: 0.37872220626351 and parameters: {'learning_rate': 0.12291082058735552, 'max_depth': 9, 'min_child_weight': 7, 'gamma': 1.5989624572035563, 'subsample': 0.8328723394226939, 'colsample_bytree': 0.9498547224928937, 'alpha': 2.9093725450035466, 'lambda': 5.003659533295498}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:20,354] Trial 104 finished with value: 0.36844294570374087 and parameters: {'learning_rate': 0.1140681605873625, 'max_depth': 10, 'min_child_weight': 6, 'gamma': 1.465152658447882, 'subsample': 0.6904317144816156, 'colsample_bytree': 0.9226011060135041, 'alpha': 3.420191925009162, 'lambda': 5.823851893580767}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:21,351] Trial 105 finished with value: 0.37594908678632366 and parameters: {'learning_rate': 0.1563755787123815, 'max_depth': 11, 'min_child_weight': 5, 'gamma': 3.0180845466957793, 'subsample': 0.7461479022174675, 'colsample_bytree': 0.9724935822509249, 'alpha': 2.8389585021515837, 'lambda': 9.010700763650775}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:22,492] Trial 106 finished with value: 0.3761286223050802 and parameters: {'learning_rate': 0.14727445606890127, 'max_depth': 9, 'min_child_weight': 5, 'gamma': 2.2083595982137028, 'subsample': 0.9355396111287724, 'colsample_bytree': 0.9031613904688667, 'alpha': 4.084732875432029, 'lambda': 0.28696400333000116}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:23,563] Trial 107 finished with value: 0.3793295440381579 and parameters: {'learning_rate': 0.12044649643929219, 'max_depth': 11, 'min_child_weight': 6, 'gamma': 3.277391649760741, 'subsample': 0.8603124293046069, 'colsample_bytree': 0.9386760431252696, 'alpha': 3.1838826798592286, 'lambda': 0.8153108425637563}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:24,388] Trial 108 finished with value: 0.38259585860954487 and parameters: {'learning_rate': 0.13701269146198852, 'max_depth': 10, 'min_child_weight': 7, 'gamma': 1.2906504958547538, 'subsample': 0.9062151778328217, 'colsample_bytree': 0.8773114404110869, 'alpha': 1.9300241107897176, 'lambda': 0.5313679809627874}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:25,316] Trial 109 finished with value: 0.38116903767013416 and parameters: {'learning_rate': 0.14228925248087002, 'max_depth': 10, 'min_child_weight': 7, 'gamma': 1.2780628231637654, 'subsample': 0.9079709416979059, 'colsample_bytree': 0.8679888255936382, 'alpha': 1.7671260425015252, 'lambda': 0.5929530334055365}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:26,101] Trial 110 finished with value: 0.3875235100188877 and parameters: {'learning_rate': 0.13660269397715857, 'max_depth': 10, 'min_child_weight': 7, 'gamma': 0.3605387661181858, 'subsample': 0.9071365403476342, 'colsample_bytree': 0.8007117534042274, 'alpha': 1.7976271556189145, 'lambda': 0.5891331946362586}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:27,007] Trial 111 finished with value: 0.37985126562997823 and parameters: {'learning_rate': 0.13799351627280843, 'max_depth': 10, 'min_child_weight': 7, 'gamma': 0.593481516340969, 'subsample': 0.9070063691492631, 'colsample_bytree': 0.8606610234312064, 'alpha': 1.7282226796427722, 'lambda': 0.5867706413460869}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:28,151] Trial 112 finished with value: 0.38513508553250136 and parameters: {'learning_rate': 0.14247326048489473, 'max_depth': 10, 'min_child_weight': 7, 'gamma': 0.2571968628941701, 'subsample': 0.8242532874061053, 'colsample_bytree': 0.8026315186245557, 'alpha': 1.9325471316617229, 'lambda': 0.940005396297794}. Best is trial 81 with value: 0.388032929708448.\n",
      "[I 2024-02-19 10:16:29,143] Trial 113 finished with value: 0.38830840718913345 and parameters: {'learning_rate': 0.13350701217353259, 'max_depth': 10, 'min_child_weight': 7, 'gamma': 0.1017791057343658, 'subsample': 0.7893295111554546, 'colsample_bytree': 0.8279781370182546, 'alpha': 1.9886530009825236, 'lambda': 0.9276145274959222}. Best is trial 113 with value: 0.38830840718913345.\n",
      "[I 2024-02-19 10:16:30,341] Trial 114 finished with value: 0.3808192210729238 and parameters: {'learning_rate': 0.13773626863403293, 'max_depth': 10, 'min_child_weight': 7, 'gamma': 0.27886880539401604, 'subsample': 0.7905028360255243, 'colsample_bytree': 0.802794952122905, 'alpha': 1.9171326870085508, 'lambda': 0.9153526291735614}. Best is trial 113 with value: 0.38830840718913345.\n",
      "[I 2024-02-19 10:16:31,188] Trial 115 finished with value: 0.3845367421602576 and parameters: {'learning_rate': 0.14670479216886761, 'max_depth': 10, 'min_child_weight': 7, 'gamma': 0.18976808278416246, 'subsample': 0.8239566155803937, 'colsample_bytree': 0.8254097321549714, 'alpha': 2.1888925294601465, 'lambda': 0.20566723883861626}. Best is trial 113 with value: 0.38830840718913345.\n",
      "[I 2024-02-19 10:16:32,230] Trial 116 finished with value: 0.39090659840503833 and parameters: {'learning_rate': 0.142767860890253, 'max_depth': 10, 'min_child_weight': 8, 'gamma': 0.2734992913384042, 'subsample': 0.8211725238637654, 'colsample_bytree': 0.7665321647421648, 'alpha': 1.082697862000078, 'lambda': 0.21703221328095165}. Best is trial 116 with value: 0.39090659840503833.\n",
      "[I 2024-02-19 10:16:33,447] Trial 117 finished with value: 0.3893051768137494 and parameters: {'learning_rate': 0.1475515743317614, 'max_depth': 10, 'min_child_weight': 8, 'gamma': 0.3329883677387461, 'subsample': 0.8274283462617753, 'colsample_bytree': 0.7900165430393001, 'alpha': 1.4179147826083582, 'lambda': 0.22456759795020909}. Best is trial 116 with value: 0.39090659840503833.\n",
      "[I 2024-02-19 10:16:34,309] Trial 118 finished with value: 0.3946467630967234 and parameters: {'learning_rate': 0.14421608169554098, 'max_depth': 10, 'min_child_weight': 8, 'gamma': 0.18520445437475105, 'subsample': 0.8244606248358445, 'colsample_bytree': 0.7834991483011353, 'alpha': 0.9681227514257491, 'lambda': 0.19697112455332907}. Best is trial 118 with value: 0.3946467630967234.\n",
      "[I 2024-02-19 10:16:35,413] Trial 119 finished with value: 0.39995890576155413 and parameters: {'learning_rate': 0.1465586227207264, 'max_depth': 10, 'min_child_weight': 8, 'gamma': 0.1404392187977397, 'subsample': 0.8304547979663782, 'colsample_bytree': 0.7793959814675587, 'alpha': 1.115718567917165, 'lambda': 0.21976177696376034}. Best is trial 119 with value: 0.39995890576155413.\n",
      "[I 2024-02-19 10:16:36,418] Trial 120 finished with value: 0.39187375481691983 and parameters: {'learning_rate': 0.14298918203804836, 'max_depth': 10, 'min_child_weight': 8, 'gamma': 0.21626210514906954, 'subsample': 0.8254074225725518, 'colsample_bytree': 0.767994547454798, 'alpha': 1.0309494836766466, 'lambda': 0.19938723419177173}. Best is trial 119 with value: 0.39995890576155413.\n",
      "[I 2024-02-19 10:16:37,628] Trial 121 finished with value: 0.3939646988708827 and parameters: {'learning_rate': 0.146569868975694, 'max_depth': 10, 'min_child_weight': 8, 'gamma': 0.20120209022537416, 'subsample': 0.7853430928394, 'colsample_bytree': 0.7796226414348254, 'alpha': 1.0188967995223193, 'lambda': 0.19098321408021807}. Best is trial 119 with value: 0.39995890576155413.\n",
      "[I 2024-02-19 10:16:38,530] Trial 122 finished with value: 0.39139886459718937 and parameters: {'learning_rate': 0.14801797514814452, 'max_depth': 10, 'min_child_weight': 8, 'gamma': 0.20735254404364914, 'subsample': 0.8196307956625022, 'colsample_bytree': 0.7371528142576212, 'alpha': 1.1601174686865061, 'lambda': 0.21580091611102356}. Best is trial 119 with value: 0.39995890576155413.\n",
      "[I 2024-02-19 10:16:39,836] Trial 123 finished with value: 0.4118351367950381 and parameters: {'learning_rate': 0.14253226979236008, 'max_depth': 10, 'min_child_weight': 8, 'gamma': 0.03384532741082347, 'subsample': 0.7796779954892915, 'colsample_bytree': 0.7768307501217127, 'alpha': 1.0231011030639612, 'lambda': 0.19108468307111012}. Best is trial 123 with value: 0.4118351367950381.\n",
      "[I 2024-02-19 10:16:41,962] Trial 124 finished with value: 0.4237036800758482 and parameters: {'learning_rate': 0.1553615772055884, 'max_depth': 10, 'min_child_weight': 8, 'gamma': 0.010742932196796395, 'subsample': 0.7813276097718636, 'colsample_bytree': 0.7779369756520177, 'alpha': 1.0451436379219656, 'lambda': 0.24690595697403814}. Best is trial 124 with value: 0.4237036800758482.\n",
      "[I 2024-02-19 10:16:47,011] Trial 125 finished with value: 0.4244115754043933 and parameters: {'learning_rate': 0.1555308517255899, 'max_depth': 10, 'min_child_weight': 8, 'gamma': 0.043353658201691875, 'subsample': 0.7790229599683651, 'colsample_bytree': 0.7440217189048252, 'alpha': 0.44685491406970923, 'lambda': 0.21549913351114147}. Best is trial 125 with value: 0.4244115754043933.\n",
      "[I 2024-02-19 10:16:56,139] Trial 126 finished with value: 0.4021847343539056 and parameters: {'learning_rate': 0.15592807553678567, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.08458594020174828, 'subsample': 0.7136071985514321, 'colsample_bytree': 0.7743815655262752, 'alpha': 1.0773011890667548, 'lambda': 0.24072645200307316}. Best is trial 125 with value: 0.4244115754043933.\n",
      "[I 2024-02-19 10:17:10,589] Trial 127 finished with value: 0.41312838365554355 and parameters: {'learning_rate': 0.1553432630893849, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.021990984155059956, 'subsample': 0.735708973017605, 'colsample_bytree': 0.7386119438115714, 'alpha': 1.0264228069249186, 'lambda': 0.24019766490992348}. Best is trial 125 with value: 0.4244115754043933.\n",
      "[I 2024-02-19 10:17:14,218] Trial 128 finished with value: 0.4205267767625451 and parameters: {'learning_rate': 0.154077080626478, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.00023755329589383867, 'subsample': 0.712557868699309, 'colsample_bytree': 0.7741463495514608, 'alpha': 1.0447490510162336, 'lambda': 0.24221340030728808}. Best is trial 125 with value: 0.4244115754043933.\n",
      "[I 2024-02-19 10:17:15,862] Trial 129 finished with value: 0.4154946403440966 and parameters: {'learning_rate': 0.15531229912688096, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.006096799100529239, 'subsample': 0.6455731544704826, 'colsample_bytree': 0.7380002210331652, 'alpha': 1.0260846823037646, 'lambda': 0.18985622966421978}. Best is trial 125 with value: 0.4244115754043933.\n",
      "[I 2024-02-19 10:17:17,525] Trial 130 finished with value: 0.4167596277113508 and parameters: {'learning_rate': 0.15504417769594048, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.015240046626642377, 'subsample': 0.6278638696584153, 'colsample_bytree': 0.737900294651464, 'alpha': 0.9341271815441237, 'lambda': 0.30069929656672734}. Best is trial 125 with value: 0.4244115754043933.\n",
      "[I 2024-02-19 10:17:18,839] Trial 131 finished with value: 0.4030774541014472 and parameters: {'learning_rate': 0.15367266674753244, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.05197290080222645, 'subsample': 0.6214389825757335, 'colsample_bytree': 0.7482804397920083, 'alpha': 1.0788066915579833, 'lambda': 0.24841304689906601}. Best is trial 125 with value: 0.4244115754043933.\n",
      "[I 2024-02-19 10:17:20,760] Trial 132 finished with value: 0.4216047916230389 and parameters: {'learning_rate': 0.15550068789608146, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.015510062089910202, 'subsample': 0.6528876955433285, 'colsample_bytree': 0.7255929105508566, 'alpha': 0.21850112679433054, 'lambda': 0.7021920394531083}. Best is trial 125 with value: 0.4244115754043933.\n",
      "[I 2024-02-19 10:17:22,279] Trial 133 finished with value: 0.4119660184777726 and parameters: {'learning_rate': 0.1552752920416746, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.05160112354241586, 'subsample': 0.6388671044655422, 'colsample_bytree': 0.6914293147083045, 'alpha': 0.5029321464691316, 'lambda': 0.6683542952395458}. Best is trial 125 with value: 0.4244115754043933.\n",
      "[I 2024-02-19 10:17:23,988] Trial 134 finished with value: 0.42725824900165327 and parameters: {'learning_rate': 0.15588639879332453, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.026940794226355258, 'subsample': 0.6285996123923545, 'colsample_bytree': 0.6760914422138812, 'alpha': 0.23436581176528692, 'lambda': 0.7327412263913031}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:25,605] Trial 135 finished with value: 0.42015328015421255 and parameters: {'learning_rate': 0.15428425857983938, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.026220716628310978, 'subsample': 0.6510700969777392, 'colsample_bytree': 0.7020665587618634, 'alpha': 0.20384424192370798, 'lambda': 0.7358963028177921}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:26,471] Trial 136 finished with value: 0.38874750633595684 and parameters: {'learning_rate': 0.15555099901511843, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.5448379447557972, 'subsample': 0.6254705274123783, 'colsample_bytree': 0.6769030098545028, 'alpha': 0.2908040674857252, 'lambda': 0.6830161873730733}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:28,232] Trial 137 finished with value: 0.425438946907734 and parameters: {'learning_rate': 0.15543037135653343, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.0018307056988076942, 'subsample': 0.6619979186658651, 'colsample_bytree': 0.6962779224051571, 'alpha': 0.4606309335751003, 'lambda': 1.1587656890664886}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:29,713] Trial 138 finished with value: 0.4189101154818119 and parameters: {'learning_rate': 0.16270420113075923, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.02089693885157101, 'subsample': 0.6623044036710576, 'colsample_bytree': 0.7006283039513054, 'alpha': 0.5106537995731342, 'lambda': 1.0995457943409717}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:30,607] Trial 139 finished with value: 0.37766431827444885 and parameters: {'learning_rate': 0.16238954385191867, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.7801434254772995, 'subsample': 0.6502775504851785, 'colsample_bytree': 0.6497585619526822, 'alpha': 0.45536463284025164, 'lambda': 0.7856448214163216}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:31,914] Trial 140 finished with value: 0.3827340610565793 and parameters: {'learning_rate': 0.15286684751932222, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.4771430823795734, 'subsample': 0.6513909801079633, 'colsample_bytree': 0.7016589167682958, 'alpha': 0.5726782738816938, 'lambda': 1.2387592616763743}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:32,838] Trial 141 finished with value: 0.377041416318225 and parameters: {'learning_rate': 0.1714181704772277, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.818706291460487, 'subsample': 0.6219487351702928, 'colsample_bytree': 0.7162925441367333, 'alpha': 0.2935343133897833, 'lambda': 1.1807144371218863}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:33,956] Trial 142 finished with value: 0.41200929423563076 and parameters: {'learning_rate': 0.15816137830710375, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.08255619415078183, 'subsample': 0.5743947317697473, 'colsample_bytree': 0.7417201133944337, 'alpha': 0.009753819971572042, 'lambda': 0.44045194013143324}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:35,306] Trial 143 finished with value: 0.41405447107940296 and parameters: {'learning_rate': 0.1619465303699826, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.003590859219748402, 'subsample': 0.5681863833262405, 'colsample_bytree': 0.7491977566886494, 'alpha': 0.08637913684555037, 'lambda': 0.48430079673801407}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:36,698] Trial 144 finished with value: 0.41872034317335743 and parameters: {'learning_rate': 0.1627037336052602, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.004528605803618511, 'subsample': 0.5579882268187975, 'colsample_bytree': 0.6849491917586531, 'alpha': 0.0011072974944564828, 'lambda': 0.48306980598625665}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:37,820] Trial 145 finished with value: 0.3881866374542763 and parameters: {'learning_rate': 0.15969852805704005, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.5470401480944845, 'subsample': 0.5630282656596701, 'colsample_bytree': 0.6239167878879313, 'alpha': 0.06938609129233769, 'lambda': 1.0693600467000755}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:39,470] Trial 146 finished with value: 0.4221533905185588 and parameters: {'learning_rate': 0.16928939640937826, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.002207742386870512, 'subsample': 0.5760573746482279, 'colsample_bytree': 0.6867205894304976, 'alpha': 0.1971017937880587, 'lambda': 0.7136575705353596}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:40,599] Trial 147 finished with value: 0.3865784749484887 and parameters: {'learning_rate': 0.169419994279495, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.43350415929891106, 'subsample': 0.5994627270028388, 'colsample_bytree': 0.5926583925444168, 'alpha': 0.22347512224812152, 'lambda': 0.4426679733105524}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:42,103] Trial 148 finished with value: 0.42091312543157516 and parameters: {'learning_rate': 0.17959893486446096, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.0009963436378529408, 'subsample': 0.5800844211964273, 'colsample_bytree': 0.6654922249147005, 'alpha': 0.6904207529341362, 'lambda': 1.3446398930059829}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:43,003] Trial 149 finished with value: 0.3747885694491799 and parameters: {'learning_rate': 0.18200452641148507, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.6169172422753559, 'subsample': 0.6710944071647029, 'colsample_bytree': 0.6629021496390506, 'alpha': 0.76286008354432, 'lambda': 0.7813286634890093}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:44,616] Trial 150 finished with value: 0.4173261052832694 and parameters: {'learning_rate': 0.1638051920170503, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.0052603531006448516, 'subsample': 0.5253489106912553, 'colsample_bytree': 0.7180053719503017, 'alpha': 0.6979259826420361, 'lambda': 1.3255053094625309}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:45,422] Trial 151 finished with value: 0.38618039342317373 and parameters: {'learning_rate': 0.16334934653739272, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.3892368714855843, 'subsample': 0.5202294181100559, 'colsample_bytree': 0.7208994013446984, 'alpha': 0.7524385638262733, 'lambda': 1.305346566103018}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:47,458] Trial 152 finished with value: 0.4268158353493928 and parameters: {'learning_rate': 0.17516916651221515, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.0009839130105606681, 'subsample': 0.6900860366546593, 'colsample_bytree': 0.6792780606857217, 'alpha': 0.20776454554568885, 'lambda': 1.4481881194962682}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:50,038] Trial 153 finished with value: 0.4206676777309613 and parameters: {'learning_rate': 0.16817063403855104, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.0017457212920102202, 'subsample': 0.5831423541071624, 'colsample_bytree': 0.684780864231983, 'alpha': 0.21184098011473415, 'lambda': 1.5335107575183475}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:51,481] Trial 154 finished with value: 0.37650748329231054 and parameters: {'learning_rate': 0.17597264434274748, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.7641540583672112, 'subsample': 0.5986571484772003, 'colsample_bytree': 0.674318212219859, 'alpha': 0.6196207013968091, 'lambda': 1.9982910511064285}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:17:54,109] Trial 155 finished with value: 0.3899705876389868 and parameters: {'learning_rate': 0.16807877749083128, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.44392755519472943, 'subsample': 0.5414241294131724, 'colsample_bytree': 0.6405097772020253, 'alpha': 0.32674894713610564, 'lambda': 1.419305412616424}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:18:01,042] Trial 156 finished with value: 0.3883333896415601 and parameters: {'learning_rate': 0.1795722233590387, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.3482913684132807, 'subsample': 0.693330360045366, 'colsample_bytree': 0.7057258857362083, 'alpha': 0.4543102968147945, 'lambda': 1.655799152323644}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:18:09,638] Trial 157 finished with value: 0.382200112133248 and parameters: {'learning_rate': 0.1873872380582076, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.6191843594499898, 'subsample': 0.6585289191212724, 'colsample_bytree': 0.6685173591203342, 'alpha': 0.2462377897251009, 'lambda': 1.1678004903211185}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:18:17,863] Trial 158 finished with value: 0.3715080805052733 and parameters: {'learning_rate': 0.1720519092645726, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.923424157636409, 'subsample': 0.588540679101699, 'colsample_bytree': 0.6923478368571825, 'alpha': 0.6581912811522732, 'lambda': 1.5585711169542646}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:18:19,361] Trial 159 finished with value: 0.38448544564900233 and parameters: {'learning_rate': 0.16768225757498098, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.42355365499528763, 'subsample': 0.6135480163557393, 'colsample_bytree': 0.7199083645020783, 'alpha': 0.8517042882326421, 'lambda': 1.0909055067649849}. Best is trial 134 with value: 0.42725824900165327.\n",
      "[I 2024-02-19 10:18:20,931] Trial 160 finished with value: 0.42812063637551845 and parameters: {'learning_rate': 0.1632594479939538, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.003957335500352216, 'subsample': 0.6869821967213818, 'colsample_bytree': 0.604243441973652, 'alpha': 0.21257898878531437, 'lambda': 1.8950767900542842}. Best is trial 160 with value: 0.42812063637551845.\n",
      "[I 2024-02-19 10:18:21,891] Trial 161 finished with value: 0.39142405799080476 and parameters: {'learning_rate': 0.1742738833693449, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.313474326901409, 'subsample': 0.6862123831412814, 'colsample_bytree': 0.610029390579616, 'alpha': 0.18745489815030783, 'lambda': 0.9855620551623763}. Best is trial 160 with value: 0.42812063637551845.\n",
      "[I 2024-02-19 10:18:22,884] Trial 162 finished with value: 0.3764649557589591 and parameters: {'learning_rate': 0.16340724030311993, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.6407740811114867, 'subsample': 0.6612265389679064, 'colsample_bytree': 0.6504047067195147, 'alpha': 0.4588606234898918, 'lambda': 1.3498843575554855}. Best is trial 160 with value: 0.42812063637551845.\n",
      "[I 2024-02-19 10:18:24,422] Trial 163 finished with value: 0.4234476356912909 and parameters: {'learning_rate': 0.1596921254789786, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.03937734152980793, 'subsample': 0.635503913123713, 'colsample_bytree': 0.6867273750357492, 'alpha': 0.00881164925769981, 'lambda': 1.7120590051669073}. Best is trial 160 with value: 0.42812063637551845.\n",
      "[I 2024-02-19 10:18:26,256] Trial 164 finished with value: 0.42334769821217105 and parameters: {'learning_rate': 0.16603504871913807, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.0006929089007030228, 'subsample': 0.5514687577727964, 'colsample_bytree': 0.5376048415508529, 'alpha': 0.014576382030750068, 'lambda': 1.5026778908598637}. Best is trial 160 with value: 0.42812063637551845.\n",
      "[I 2024-02-19 10:18:27,356] Trial 165 finished with value: 0.3987987036924373 and parameters: {'learning_rate': 0.16896321561202124, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.30301571445048137, 'subsample': 0.5349679004918584, 'colsample_bytree': 0.5511548232552628, 'alpha': 0.039298584322560434, 'lambda': 1.897167113678369}. Best is trial 160 with value: 0.42812063637551845.\n",
      "[I 2024-02-19 10:18:27,848] Trial 166 finished with value: 0.3493823587761819 and parameters: {'learning_rate': 0.18053367141485785, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 7.315357529100203, 'subsample': 0.5604172763779958, 'colsample_bytree': 0.6334478750886257, 'alpha': 0.35981777937437653, 'lambda': 1.6275847272949142}. Best is trial 160 with value: 0.42812063637551845.\n",
      "[I 2024-02-19 10:18:28,690] Trial 167 finished with value: 0.3803659443693816 and parameters: {'learning_rate': 0.1638268629860689, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.41592400428397486, 'subsample': 0.6913742047420375, 'colsample_bytree': 0.4964600726911597, 'alpha': 0.577435667905408, 'lambda': 1.7766753805185156}. Best is trial 160 with value: 0.42812063637551845.\n",
      "[I 2024-02-19 10:18:29,428] Trial 168 finished with value: 0.2759074106099543 and parameters: {'learning_rate': 0.15869994085307706, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 8.503106218065296, 'subsample': 0.45958317072554333, 'colsample_bytree': 0.5712047626816548, 'alpha': 0.16904950298050433, 'lambda': 2.0920737209414284}. Best is trial 160 with value: 0.42812063637551845.\n",
      "[I 2024-02-19 10:18:30,814] Trial 169 finished with value: 0.4089576864481811 and parameters: {'learning_rate': 0.17736051186081442, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.2251625856888692, 'subsample': 0.5080901705842129, 'colsample_bytree': 0.6808779243519144, 'alpha': 0.034775742833646414, 'lambda': 1.411627969438464}. Best is trial 160 with value: 0.42812063637551845.\n",
      "[I 2024-02-19 10:18:32,477] Trial 170 finished with value: 0.42662395049216667 and parameters: {'learning_rate': 0.15078293412421367, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.011585426301606794, 'subsample': 0.5793183349378045, 'colsample_bytree': 0.7032201700897809, 'alpha': 0.3611177408954336, 'lambda': 1.2556732130410497}. Best is trial 160 with value: 0.42812063637551845.\n",
      "[I 2024-02-19 10:18:33,496] Trial 171 finished with value: 0.3931897382880652 and parameters: {'learning_rate': 0.1504653443387001, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.23371931025785075, 'subsample': 0.5795818847235191, 'colsample_bytree': 0.6960357495436851, 'alpha': 0.4224558357154651, 'lambda': 1.5302780421654663}. Best is trial 160 with value: 0.42812063637551845.\n",
      "[I 2024-02-19 10:18:35,092] Trial 172 finished with value: 0.41419969428608294 and parameters: {'learning_rate': 0.16595182079461637, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.00029981121790351867, 'subsample': 0.5517451640220306, 'colsample_bytree': 0.47090283371669484, 'alpha': 0.6569891031619056, 'lambda': 1.2040796862130339}. Best is trial 160 with value: 0.42812063637551845.\n",
      "[I 2024-02-19 10:18:36,087] Trial 173 finished with value: 0.3867050948704734 and parameters: {'learning_rate': 0.16242674429834028, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.5046940221286059, 'subsample': 0.5891305881212787, 'colsample_bytree': 0.6575657196384666, 'alpha': 0.286335221002027, 'lambda': 1.8810895154220653}. Best is trial 160 with value: 0.42812063637551845.\n",
      "[I 2024-02-19 10:18:36,793] Trial 174 finished with value: 0.3439163623206332 and parameters: {'learning_rate': 0.15967879850820815, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.29413158695397923, 'subsample': 0.6094408172770901, 'colsample_bytree': 0.7114097217344708, 'alpha': 7.7666948900276, 'lambda': 1.3745606335943124}. Best is trial 160 with value: 0.42812063637551845.\n",
      "[I 2024-02-19 10:18:38,618] Trial 175 finished with value: 0.4329889983106493 and parameters: {'learning_rate': 0.1704801044643151, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.010972399233225304, 'subsample': 0.7236216577074104, 'colsample_bytree': 0.6827246978961221, 'alpha': 0.016123887824285865, 'lambda': 0.8527127107292134}. Best is trial 175 with value: 0.4329889983106493.\n",
      "[I 2024-02-19 10:18:39,829] Trial 176 finished with value: 0.38221339703284996 and parameters: {'learning_rate': 0.1709697415959729, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.6975139347993676, 'subsample': 0.7197795294643322, 'colsample_bytree': 0.5897922786817325, 'alpha': 0.18457521883131633, 'lambda': 0.8597891124755885}. Best is trial 175 with value: 0.4329889983106493.\n",
      "[I 2024-02-19 10:18:41,177] Trial 177 finished with value: 0.4046941849038161 and parameters: {'learning_rate': 0.15108152437333572, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.22217606408199508, 'subsample': 0.7050707148033715, 'colsample_bytree': 0.6800621248197436, 'alpha': 0.42148026149333656, 'lambda': 0.9998329286394868}. Best is trial 175 with value: 0.4329889983106493.\n",
      "[I 2024-02-19 10:18:42,217] Trial 178 finished with value: 0.38919198981398734 and parameters: {'learning_rate': 0.17450855179479766, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.5115590874254858, 'subsample': 0.6742158463394379, 'colsample_bytree': 0.6304349750774615, 'alpha': 0.03729449506555786, 'lambda': 0.6477111658536037}. Best is trial 175 with value: 0.4329889983106493.\n",
      "[I 2024-02-19 10:18:43,847] Trial 179 finished with value: 0.4022863165838248 and parameters: {'learning_rate': 0.1684969466441448, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.20593688780147376, 'subsample': 0.640104845143588, 'colsample_bytree': 0.5229012375625783, 'alpha': 0.27534109772047255, 'lambda': 2.210456291063974}. Best is trial 175 with value: 0.4329889983106493.\n",
      "[I 2024-02-19 10:18:45,649] Trial 180 finished with value: 0.4311034431806223 and parameters: {'learning_rate': 0.15902914626056064, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.019970367335104308, 'subsample': 0.7300511507966604, 'colsample_bytree': 0.6622790703380835, 'alpha': 0.011807362615853002, 'lambda': 0.7697756415002283}. Best is trial 175 with value: 0.4329889983106493.\n",
      "[I 2024-02-19 10:18:47,300] Trial 181 finished with value: 0.4335513382890096 and parameters: {'learning_rate': 0.1600009457173964, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.028158256235382508, 'subsample': 0.732400648109167, 'colsample_bytree': 0.6583992536831179, 'alpha': 0.01140232170294414, 'lambda': 0.7604876116646705}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:18:48,848] Trial 182 finished with value: 0.4318765137057829 and parameters: {'learning_rate': 0.159710099693, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.0034515316295380544, 'subsample': 0.7247748089622521, 'colsample_bytree': 0.6627660102863305, 'alpha': 0.5190922047267217, 'lambda': 1.0871851339548657}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:18:49,887] Trial 183 finished with value: 0.3943216040772993 and parameters: {'learning_rate': 0.1512032483952509, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.39392347355036933, 'subsample': 0.719495761550976, 'colsample_bytree': 0.6534471272463227, 'alpha': 0.3837348641111029, 'lambda': 0.7930335559525198}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:18:51,342] Trial 184 finished with value: 0.40378930586660067 and parameters: {'learning_rate': 0.15763610244047405, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.21584954671593315, 'subsample': 0.7309452699400238, 'colsample_bytree': 0.6653823864384611, 'alpha': 0.25517715325678236, 'lambda': 1.698917612281024}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:18:52,535] Trial 185 finished with value: 0.34904548973217314 and parameters: {'learning_rate': 0.15799323673003882, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.5035249002184381, 'subsample': 0.7057390082792367, 'colsample_bytree': 0.6277614904679775, 'alpha': 8.248321040819011, 'lambda': 1.011380204551228}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:18:54,338] Trial 186 finished with value: 0.3902240886615254 and parameters: {'learning_rate': 0.16687434756631425, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.2143767966166022, 'subsample': 0.6780545460702192, 'colsample_bytree': 0.6664108181958333, 'alpha': 0.7445331864572456, 'lambda': 0.7719335641938695}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:18:57,405] Trial 187 finished with value: 0.3875346507582805 and parameters: {'learning_rate': 0.18905622628600952, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.7735836226960027, 'subsample': 0.7369911447533912, 'colsample_bytree': 0.6186264711173303, 'alpha': 0.001375262632929997, 'lambda': 1.1754758948748663}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:03,832] Trial 188 finished with value: 0.3922683224027636 and parameters: {'learning_rate': 0.17264055829459787, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.35440212226770773, 'subsample': 0.7502746684711779, 'colsample_bytree': 0.6449082378050405, 'alpha': 0.4966027784094932, 'lambda': 1.4929051241108195}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:18,975] Trial 189 finished with value: 0.4246309116357871 and parameters: {'learning_rate': 0.15857209541135642, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.021547645125060133, 'subsample': 0.6977061981967219, 'colsample_bytree': 0.6935864551893489, 'alpha': 0.22424936537629486, 'lambda': 0.6967834434936285}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:21,213] Trial 190 finished with value: 0.3876706730910314 and parameters: {'learning_rate': 0.1772818410001507, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.19982551721316177, 'subsample': 0.7008205444445487, 'colsample_bytree': 0.6030971038852698, 'alpha': 0.5860858162949846, 'lambda': 0.9966484481187194}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:22,778] Trial 191 finished with value: 0.43229919876022976 and parameters: {'learning_rate': 0.15956373319649775, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.03324134760625895, 'subsample': 0.7317212006936282, 'colsample_bytree': 0.6928778953724977, 'alpha': 0.2124689514809517, 'lambda': 0.692475988326585}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:23,743] Trial 192 finished with value: 0.4093844732381694 and parameters: {'learning_rate': 0.1592982399556232, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.18487457936289492, 'subsample': 0.7250191116067066, 'colsample_bytree': 0.684460561552667, 'alpha': 0.18982603336661796, 'lambda': 0.6926938796634483}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:25,369] Trial 193 finished with value: 0.4250413151872736 and parameters: {'learning_rate': 0.16572510739624063, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.010253718559070268, 'subsample': 0.7579216455906658, 'colsample_bytree': 0.7250174710437082, 'alpha': 0.8134137182649458, 'lambda': 1.2650739400409448}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:26,259] Trial 194 finished with value: 0.3991248438932652 and parameters: {'learning_rate': 0.16702736917716282, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.42021151437821974, 'subsample': 0.7454966559100376, 'colsample_bytree': 0.7273293614995764, 'alpha': 0.38701523416574585, 'lambda': 1.2596259462144155}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:27,256] Trial 195 finished with value: 0.3829519952255024 and parameters: {'learning_rate': 0.17111751086965493, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.5536109323941646, 'subsample': 0.7679661372738058, 'colsample_bytree': 0.6954246858281826, 'alpha': 0.8080932156753508, 'lambda': 1.4967015068714449}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:28,225] Trial 196 finished with value: 0.41765903258196213 and parameters: {'learning_rate': 0.16574113143287414, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.21855654319682816, 'subsample': 0.7619331287519242, 'colsample_bytree': 0.6700650489947604, 'alpha': 0.015755267619522312, 'lambda': 0.9045352498710035}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:29,408] Trial 197 finished with value: 0.36445900460033526 and parameters: {'learning_rate': 0.1612293770116051, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.006845687066669069, 'subsample': 0.6883685022650106, 'colsample_bytree': 0.719556955117198, 'alpha': 6.910197107647162, 'lambda': 1.1869070257663865}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:31,808] Trial 198 finished with value: 0.4246856039631874 and parameters: {'learning_rate': 0.18376527857991615, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.010301628706604813, 'subsample': 0.6303482383982644, 'colsample_bytree': 0.6473827864382622, 'alpha': 0.28632900794795474, 'lambda': 1.7142095623826181}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:32,876] Trial 199 finished with value: 0.3935988119216641 and parameters: {'learning_rate': 0.18382244332176603, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.23292918260984335, 'subsample': 0.6374780938326692, 'colsample_bytree': 0.6433203547284403, 'alpha': 0.5634600231508048, 'lambda': 1.8509283963627923}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:33,676] Trial 200 finished with value: 0.3846357381682381 and parameters: {'learning_rate': 0.17938410428540574, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.6320805504990032, 'subsample': 0.7302152243345987, 'colsample_bytree': 0.6579833370525598, 'alpha': 0.33643778102156335, 'lambda': 0.5662039781183175}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:34,996] Trial 201 finished with value: 0.4066486111270624 and parameters: {'learning_rate': 0.15962802932859535, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.177987248480942, 'subsample': 0.6086691576299804, 'colsample_bytree': 0.6795106522713577, 'alpha': 0.21462476015135462, 'lambda': 1.5681616211655138}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:36,140] Trial 202 finished with value: 0.39303367803742745 and parameters: {'learning_rate': 0.15074388189876936, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.391322209778008, 'subsample': 0.5817999484474605, 'colsample_bytree': 0.6958404244895315, 'alpha': 0.20724343513624208, 'lambda': 1.7334466237491588}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:37,805] Trial 203 finished with value: 0.430434381999685 and parameters: {'learning_rate': 0.17002611788850258, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.02190002104903371, 'subsample': 0.6653836051893919, 'colsample_bytree': 0.7173062781120398, 'alpha': 0.0006022375891087776, 'lambda': 1.384398396538039}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:38,237] Trial 204 finished with value: 0.27082622679180185 and parameters: {'learning_rate': 0.1740556115243551, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 9.950378596825153, 'subsample': 0.6703290524327571, 'colsample_bytree': 0.7549014046587477, 'alpha': 0.47680154641769323, 'lambda': 1.3136410902169398}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:39,088] Trial 205 finished with value: 0.38876111686232556 and parameters: {'learning_rate': 0.1580225995080395, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.35613843407094303, 'subsample': 0.6354118842722409, 'colsample_bytree': 0.7183086896842148, 'alpha': 0.6380844716428489, 'lambda': 0.9004917722843818}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:40,072] Trial 206 finished with value: 0.41009006676392146 and parameters: {'learning_rate': 0.1925045115756419, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.1651724392438195, 'subsample': 0.6714936420254592, 'colsample_bytree': 0.5723940650654659, 'alpha': 0.014259609415772189, 'lambda': 1.0984379271386822}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:41,770] Trial 207 finished with value: 0.4323468145534489 and parameters: {'learning_rate': 0.16511893335370842, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.009372091177001043, 'subsample': 0.7071966972242667, 'colsample_bytree': 0.7090384358106155, 'alpha': 0.37249318200769366, 'lambda': 1.2883723249641963}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:43,413] Trial 208 finished with value: 0.38976130115849683 and parameters: {'learning_rate': 0.16480316602204656, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.384011048764641, 'subsample': 0.7066013785675328, 'colsample_bytree': 0.7273907989970192, 'alpha': 0.35207959309638537, 'lambda': 0.7889592978260811}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:45,099] Trial 209 finished with value: 0.42966685118398323 and parameters: {'learning_rate': 0.17026870354378232, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.01387577135309603, 'subsample': 0.6932550539960392, 'colsample_bytree': 0.7110131651081905, 'alpha': 0.1533076796788354, 'lambda': 1.0618877581640955}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:46,218] Trial 210 finished with value: 0.4101427756183553 and parameters: {'learning_rate': 0.17006731185882998, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.18898404465647362, 'subsample': 0.6893054259346921, 'colsample_bytree': 0.700130918244127, 'alpha': 0.025337612478201932, 'lambda': 2.0542394736129945}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:47,789] Trial 211 finished with value: 0.4260324638848795 and parameters: {'learning_rate': 0.1617885170217669, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.022880945273640704, 'subsample': 0.7004151570470715, 'colsample_bytree': 0.7499876982964808, 'alpha': 0.37883432421998875, 'lambda': 1.102694780083172}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:49,410] Trial 212 finished with value: 0.428842673196051 and parameters: {'learning_rate': 0.16193916639360634, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.006235295722440726, 'subsample': 0.7254106328749064, 'colsample_bytree': 0.7594973367488831, 'alpha': 0.4023248926903661, 'lambda': 1.0797275511987963}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:50,708] Trial 213 finished with value: 0.40286551722920294 and parameters: {'learning_rate': 0.1606045457855014, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.18558065242594105, 'subsample': 0.7309622678601881, 'colsample_bytree': 0.7508850740961308, 'alpha': 0.4485565067909166, 'lambda': 1.197832045076114}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:52,101] Trial 214 finished with value: 0.37422151341857834 and parameters: {'learning_rate': 0.1646689295207137, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.004014787150945269, 'subsample': 0.7507130935965519, 'colsample_bytree': 0.7570003441600966, 'alpha': 5.6875786916063165, 'lambda': 0.9907013421083728}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:53,831] Trial 215 finished with value: 0.3882184882016951 and parameters: {'learning_rate': 0.1605698217974199, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.3765371505132214, 'subsample': 0.713654978609547, 'colsample_bytree': 0.7116069475957415, 'alpha': 0.38725847649932427, 'lambda': 1.3352529223813767}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:54,990] Trial 216 finished with value: 0.38028305008061963 and parameters: {'learning_rate': 0.15332695838895175, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.5511740913192753, 'subsample': 0.7024619823747356, 'colsample_bytree': 0.7372490949371505, 'alpha': 0.7632321863167754, 'lambda': 1.0495143199087873}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:56,570] Trial 217 finished with value: 0.4081725745839792 and parameters: {'learning_rate': 0.16436502480193527, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.22005016444026274, 'subsample': 0.6883638558163857, 'colsample_bytree': 0.7049767008215071, 'alpha': 0.01003506151869149, 'lambda': 1.6774017540519108}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:19:58,866] Trial 218 finished with value: 0.39023717448875994 and parameters: {'learning_rate': 0.15725285330958927, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.3363620064111018, 'subsample': 0.7407263674715595, 'colsample_bytree': 0.4659286780211061, 'alpha': 0.5362907164580577, 'lambda': 1.4656660397979386}. Best is trial 181 with value: 0.4335513382890096.\n",
      "[I 2024-02-19 10:20:07,082] Trial 219 finished with value: 0.43479963783102427 and parameters: {'learning_rate': 0.1615893056069889, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.015187272473460899, 'subsample': 0.7721573769779451, 'colsample_bytree': 0.6520589148930115, 'alpha': 0.22461617291307484, 'lambda': 1.1824216658635331}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:12,859] Trial 220 finished with value: 0.34668118302529727 and parameters: {'learning_rate': 0.16159319527570776, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.1822334630948282, 'subsample': 0.7727215098865008, 'colsample_bytree': 0.6386608479746801, 'alpha': 9.299038266788733, 'lambda': 1.209716023303512}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:24,795] Trial 221 finished with value: 0.4315188711927779 and parameters: {'learning_rate': 0.16552491458466115, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.005558727549628426, 'subsample': 0.7184055815449184, 'colsample_bytree': 0.6613025813214953, 'alpha': 0.2396470060492256, 'lambda': 1.074662007065963}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:26,020] Trial 222 finished with value: 0.40949126519155127 and parameters: {'learning_rate': 0.1571687425468224, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.17436773410623188, 'subsample': 0.7238294147967109, 'colsample_bytree': 0.6600197561058354, 'alpha': 0.3101403647046509, 'lambda': 0.9534970018331215}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:27,494] Trial 223 finished with value: 0.4291192584998662 and parameters: {'learning_rate': 0.17279476485520567, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.031836810739902424, 'subsample': 0.7496148910805746, 'colsample_bytree': 0.6674564465061288, 'alpha': 0.22036323374515163, 'lambda': 1.1240505971816497}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:28,288] Trial 224 finished with value: 0.36633653485274587 and parameters: {'learning_rate': 0.17354779111353427, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 5.314860154750928, 'subsample': 0.7580193166116089, 'colsample_bytree': 0.6164047433179864, 'alpha': 0.8073007268661472, 'lambda': 1.1138360109407528}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:29,848] Trial 225 finished with value: 0.43027004971627897 and parameters: {'learning_rate': 0.16882970326127236, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.0044312049733754464, 'subsample': 0.7770038145791067, 'colsample_bytree': 0.6705486704353737, 'alpha': 0.5483583187921048, 'lambda': 0.8906303394488968}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:30,582] Trial 226 finished with value: 0.3922366305630186 and parameters: {'learning_rate': 0.17017177524916036, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.3680401663839694, 'subsample': 0.7415397909077748, 'colsample_bytree': 0.64930878171233, 'alpha': 0.47175629376488926, 'lambda': 0.8666529382932341}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:32,326] Trial 227 finished with value: 0.4228903275258025 and parameters: {'learning_rate': 0.17674304870077578, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.009215525776720513, 'subsample': 0.7216893571874433, 'colsample_bytree': 0.6710966907074715, 'alpha': 0.22373809331884772, 'lambda': 1.1830684379110652}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:33,324] Trial 228 finished with value: 0.3819587519089197 and parameters: {'learning_rate': 0.16856567493974609, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.5071609191631496, 'subsample': 0.6990353269648337, 'colsample_bytree': 0.634996422306864, 'alpha': 0.5606773109640193, 'lambda': 0.9123464776427428}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:34,276] Trial 229 finished with value: 0.4009927258432482 and parameters: {'learning_rate': 0.17220079087715065, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.23040054147605982, 'subsample': 0.7705386977375156, 'colsample_bytree': 0.7098471369762595, 'alpha': 0.3488480352051517, 'lambda': 1.3517475157263117}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:35,108] Trial 230 finished with value: 0.40168582923554014 and parameters: {'learning_rate': 0.16588118891253492, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.32981286534121895, 'subsample': 0.7507348659596298, 'colsample_bytree': 0.6803701439664344, 'alpha': 0.2052691800016274, 'lambda': 1.0504981149811434}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:36,811] Trial 231 finished with value: 0.42878430021308556 and parameters: {'learning_rate': 0.16317341732262153, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.009074156721127208, 'subsample': 0.7954467259399186, 'colsample_bytree': 0.6641324288097771, 'alpha': 0.5953418111287772, 'lambda': 0.5822647875431808}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:38,489] Trial 232 finished with value: 0.4287636381453081 and parameters: {'learning_rate': 0.16287524621549632, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.006239237885027367, 'subsample': 0.7942921523952469, 'colsample_bytree': 0.6600168235222182, 'alpha': 0.5785659541110655, 'lambda': 0.6378311379512777}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:39,523] Trial 233 finished with value: 0.3955860289884112 and parameters: {'learning_rate': 0.16460247266663453, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.18552802441245556, 'subsample': 0.6728116947888368, 'colsample_bytree': 0.654000016614598, 'alpha': 0.6705729825918638, 'lambda': 0.7086602600519268}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:41,232] Trial 234 finished with value: 0.43072279656019435 and parameters: {'learning_rate': 0.16176067355403786, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.016429686250828208, 'subsample': 0.7127140494060115, 'colsample_bytree': 0.6780091264753573, 'alpha': 0.21857931111258644, 'lambda': 0.5550389722499321}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:42,424] Trial 235 finished with value: 0.4004601254885632 and parameters: {'learning_rate': 0.16859820395746694, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.21621048234172044, 'subsample': 0.7259723068889496, 'colsample_bytree': 0.6734098803631529, 'alpha': 0.5569999065289514, 'lambda': 0.5326061102780225}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:43,721] Trial 236 finished with value: 0.39295701937204014 and parameters: {'learning_rate': 0.1632706616710006, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.343333609073516, 'subsample': 0.7874978985219071, 'colsample_bytree': 0.6241949442679842, 'alpha': 0.36149896036581775, 'lambda': 0.9992147919529489}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:45,045] Trial 237 finished with value: 0.39305099018761724 and parameters: {'learning_rate': 0.17398171295894974, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.18396561831132385, 'subsample': 0.7945749216568683, 'colsample_bytree': 0.6542547989268449, 'alpha': 0.7959621403880364, 'lambda': 0.8336527165114767}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:46,840] Trial 238 finished with value: 0.4253506161216402 and parameters: {'learning_rate': 0.16702402659103088, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.00732187452107971, 'subsample': 0.7527846456997792, 'colsample_bytree': 0.6683713057640186, 'alpha': 0.18423824781668952, 'lambda': 1.2138153727145424}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:48,657] Trial 239 finished with value: 0.43330512207297156 and parameters: {'learning_rate': 0.17021002916995157, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.0007263039520023374, 'subsample': 0.7623894778959626, 'colsample_bytree': 0.6723914986097024, 'alpha': 0.182265334421122, 'lambda': 1.2091617661492147}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:49,502] Trial 240 finished with value: 0.3911794622175129 and parameters: {'learning_rate': 0.17077775653125632, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.5282594571207606, 'subsample': 0.7346597818199051, 'colsample_bytree': 0.6700066641491604, 'alpha': 0.158171741336961, 'lambda': 1.0515910941750297}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:51,227] Trial 241 finished with value: 0.4311068159205295 and parameters: {'learning_rate': 0.1670283243197143, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.014614546867578182, 'subsample': 0.7473651609258144, 'colsample_bytree': 0.6915577569060158, 'alpha': 0.42506435737440923, 'lambda': 1.1705442883945834}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:52,968] Trial 242 finished with value: 0.43079820404453883 and parameters: {'learning_rate': 0.16270986532373993, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.00625098598661425, 'subsample': 0.7147589993201783, 'colsample_bytree': 0.6888925566801342, 'alpha': 0.17635429715959922, 'lambda': 1.1908271396555177}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:54,187] Trial 243 finished with value: 0.3956918847525964 and parameters: {'learning_rate': 0.1620759762614687, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.2542690539706993, 'subsample': 0.7071295685461608, 'colsample_bytree': 0.6942398657610908, 'alpha': 0.4322364767775143, 'lambda': 0.894758159283654}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:55,493] Trial 244 finished with value: 0.412594330749868 and parameters: {'learning_rate': 0.1617870025601971, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.19475549623642738, 'subsample': 0.7353367763964388, 'colsample_bytree': 0.6886299709858551, 'alpha': 0.1848913792641923, 'lambda': 0.5890687170019944}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:57,241] Trial 245 finished with value: 0.4315680646928221 and parameters: {'learning_rate': 0.17010431704335882, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.009403909853602517, 'subsample': 0.7196342257692444, 'colsample_bytree': 0.7040983677213837, 'alpha': 0.5556683730171355, 'lambda': 1.064696732417743}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:20:58,722] Trial 246 finished with value: 0.3908974713627652 and parameters: {'learning_rate': 0.17153554359078135, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.3694684117469907, 'subsample': 0.714012172118065, 'colsample_bytree': 0.7073566513872431, 'alpha': 0.3826401903243454, 'lambda': 0.8409873901718773}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:21:00,948] Trial 247 finished with value: 0.4034133808860592 and parameters: {'learning_rate': 0.17497373989129725, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.21297529421487996, 'subsample': 0.7637410999156634, 'colsample_bytree': 0.3718357664327212, 'alpha': 0.15749355504656154, 'lambda': 1.042664875570004}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:21:04,302] Trial 248 finished with value: 0.39381108378855456 and parameters: {'learning_rate': 0.16808981463399122, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.015112374944933015, 'subsample': 0.7197822345075279, 'colsample_bytree': 0.0425593409430462, 'alpha': 0.615829992537938, 'lambda': 0.5585121924964622}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:21:10,050] Trial 249 finished with value: 0.38449244319104814 and parameters: {'learning_rate': 0.16563817376558662, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.43898021954364935, 'subsample': 0.6849644303041196, 'colsample_bytree': 0.6744856169163851, 'alpha': 0.3625819546886778, 'lambda': 1.3782542832641944}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:21:20,836] Trial 250 finished with value: 0.39718105904309503 and parameters: {'learning_rate': 0.17030665492414343, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.20245980346005937, 'subsample': 0.7969891415977304, 'colsample_bytree': 0.6452796882451428, 'alpha': 0.5912813605490413, 'lambda': 0.8180166085776488}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:21:26,970] Trial 251 finished with value: 0.3929684183677454 and parameters: {'learning_rate': 0.17648972637674612, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.6252833053849276, 'subsample': 0.7344994936799829, 'colsample_bytree': 0.682347754579714, 'alpha': 0.1613480457106696, 'lambda': 1.2470487259237772}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:21:28,952] Trial 252 finished with value: 0.3527511669707235 and parameters: {'learning_rate': 0.16204698433056797, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 6.251807224015902, 'subsample': 0.7030856784358205, 'colsample_bytree': 0.7048908706290005, 'alpha': 0.0403889615749002, 'lambda': 6.732029810399883}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:21:30,019] Trial 253 finished with value: 0.4177972944031759 and parameters: {'learning_rate': 0.16639907456396572, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.15570006236371225, 'subsample': 0.7702814679717517, 'colsample_bytree': 0.6640837847065266, 'alpha': 0.005218043845230086, 'lambda': 1.0798412685069358}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:21:30,945] Trial 254 finished with value: 0.3897976986893131 and parameters: {'learning_rate': 0.1611738436842875, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.3873817999949312, 'subsample': 0.7497857625510157, 'colsample_bytree': 0.610502262445614, 'alpha': 0.37291214924928395, 'lambda': 0.6736616028988127}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:21:31,963] Trial 255 finished with value: 0.4002605119472172 and parameters: {'learning_rate': 0.17153116684739933, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.16705861499189587, 'subsample': 0.7207973003944924, 'colsample_bytree': 0.6429055091321259, 'alpha': 0.5831485553038194, 'lambda': 1.4344023069968734}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:21:32,784] Trial 256 finished with value: 0.4003597359197617 and parameters: {'learning_rate': 0.16390424864994102, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.3691477861494735, 'subsample': 0.8043599343211799, 'colsample_bytree': 0.7309252202011607, 'alpha': 0.29488658626985725, 'lambda': 0.9180567114017923}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:21:34,326] Trial 257 finished with value: 0.4220555353669981 and parameters: {'learning_rate': 0.16984553393430118, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.005748722016130411, 'subsample': 0.6869566034353093, 'colsample_bytree': 0.6820469498147812, 'alpha': 0.8233219833545835, 'lambda': 0.45305707476932977}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:21:36,237] Trial 258 finished with value: 0.42707646548355654 and parameters: {'learning_rate': 0.1593475208687258, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.010957769586953676, 'subsample': 0.7090276977727212, 'colsample_bytree': 0.7142984835384537, 'alpha': 0.0025959673433634534, 'lambda': 1.2502065201892323}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:21:37,955] Trial 259 finished with value: 0.3977121869610014 and parameters: {'learning_rate': 0.014685171489185564, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.2042438014268214, 'subsample': 0.7470151753011036, 'colsample_bytree': 0.7137753997915836, 'alpha': 0.14824769598083573, 'lambda': 1.4355531578753744}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:21:38,788] Trial 260 finished with value: 0.3892007645732855 and parameters: {'learning_rate': 0.1585214236750836, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.5324170607811022, 'subsample': 0.7204492879403663, 'colsample_bytree': 0.6598374880365732, 'alpha': 0.14887474604990725, 'lambda': 0.7488851104121803}. Best is trial 219 with value: 0.43479963783102427.\n",
      "[I 2024-02-19 10:21:40,781] Trial 261 finished with value: 0.43602994508448456 and parameters: {'learning_rate': 0.17483632268295193, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.007963997883769032, 'subsample': 0.7763177472920988, 'colsample_bytree': 0.6288995921498627, 'alpha': 0.031444689192538365, 'lambda': 1.3116386145385912}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:21:41,677] Trial 262 finished with value: 0.3891477771984983 and parameters: {'learning_rate': 0.17849571260472852, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.7320672060637142, 'subsample': 0.7783650734412414, 'colsample_bytree': 0.627108572351693, 'alpha': 0.01925810368322691, 'lambda': 0.9540367448016367}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:21:42,605] Trial 263 finished with value: 0.39194175857193797 and parameters: {'learning_rate': 0.17413408293994168, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.3543250867394524, 'subsample': 0.7752375818214527, 'colsample_bytree': 0.595328205440669, 'alpha': 0.18712068206270133, 'lambda': 1.2887136707998779}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:21:43,590] Trial 264 finished with value: 0.4151501080636888 and parameters: {'learning_rate': 0.17507031906873607, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.19175792367072667, 'subsample': 0.7439743828848842, 'colsample_bytree': 0.630446463443984, 'alpha': 0.022214072153504478, 'lambda': 0.5499594724590278}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:21:45,378] Trial 265 finished with value: 0.4308810450383265 and parameters: {'learning_rate': 0.16711042596255823, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.005486846033434377, 'subsample': 0.8085385929995936, 'colsample_bytree': 0.6493947525741791, 'alpha': 0.5343124094986504, 'lambda': 1.1548663320122283}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:21:46,529] Trial 266 finished with value: 0.3893369701088546 and parameters: {'learning_rate': 0.16787464736443716, 'max_depth': 5, 'min_child_weight': 8, 'gamma': 0.33106696083886705, 'subsample': 0.7954426004653538, 'colsample_bytree': 0.6513071201779119, 'alpha': 0.5676026687282081, 'lambda': 1.0858895415475953}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:21:48,106] Trial 267 finished with value: 0.4236544424724703 and parameters: {'learning_rate': 0.16564758015475978, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.012261947707822725, 'subsample': 0.8088067060658263, 'colsample_bytree': 0.6088280934138369, 'alpha': 0.534428652874102, 'lambda': 0.780573662281844}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:21:48,964] Trial 268 finished with value: 0.39810666269189293 and parameters: {'learning_rate': 0.1688728591502029, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.5248039368237016, 'subsample': 0.7689643869004739, 'colsample_bytree': 0.6353057108974683, 'alpha': 0.004020193371535171, 'lambda': 0.8646964380218368}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:21:49,760] Trial 269 finished with value: 0.4042238257094801 and parameters: {'learning_rate': 0.16332493565813772, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.241865636824619, 'subsample': 0.7407344888376397, 'colsample_bytree': 0.659233310378477, 'alpha': 0.3325580801200012, 'lambda': 0.5135206008875459}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:21:50,769] Trial 270 finished with value: 0.0883269113464883 and parameters: {'learning_rate': 0.1592186150599597, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 9.37235187782655, 'subsample': 0.15439799071250881, 'colsample_bytree': 0.08627239796551289, 'alpha': 0.8611732519683823, 'lambda': 1.153873994405905}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:21:52,553] Trial 271 finished with value: 0.4285082252227358 and parameters: {'learning_rate': 0.17139534829749958, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.0026426564701152655, 'subsample': 0.7607031891461895, 'colsample_bytree': 0.6874878886642323, 'alpha': 0.6238918839837937, 'lambda': 4.504355256938413}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:21:54,382] Trial 272 finished with value: 0.43506538960452745 and parameters: {'learning_rate': 0.17170357085748272, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.00045758363691903933, 'subsample': 0.7599369205896715, 'colsample_bytree': 0.6696060998734962, 'alpha': 0.666822168688091, 'lambda': 0.6688467701542068}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:21:55,386] Trial 273 finished with value: 0.3852963423298989 and parameters: {'learning_rate': 0.1724834410148284, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.26303242553751194, 'subsample': 0.7788566128584067, 'colsample_bytree': 0.6571337442371348, 'alpha': 1.2849102479401173, 'lambda': 7.581843326899367}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:21:57,177] Trial 274 finished with value: 0.4228394710411135 and parameters: {'learning_rate': 0.18050335527226577, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.003915544615835745, 'subsample': 0.7600539329804542, 'colsample_bytree': 0.6891202412344781, 'alpha': 0.7452489923943201, 'lambda': 6.395762691529729}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:21:58,126] Trial 275 finished with value: 0.3909704933172391 and parameters: {'learning_rate': 0.17021641048315164, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.4641646244748952, 'subsample': 0.8030247332901821, 'colsample_bytree': 0.6336697711605115, 'alpha': 0.6334767267136461, 'lambda': 2.9911167695840053}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:21:59,010] Trial 276 finished with value: 0.3871691155051307 and parameters: {'learning_rate': 0.1771188036476453, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.2524783231836277, 'subsample': 0.7576061842384706, 'colsample_bytree': 0.6694397815519747, 'alpha': 0.8689142119564055, 'lambda': 5.5774692423121595}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:00,228] Trial 277 finished with value: 0.39984763493791015 and parameters: {'learning_rate': 0.17237465651561534, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.1886315030301437, 'subsample': 0.7870021370944447, 'colsample_bytree': 0.6902410610451086, 'alpha': 0.4957206077061901, 'lambda': 4.1082197178873745}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:01,168] Trial 278 finished with value: 0.3840424156472624 and parameters: {'learning_rate': 0.16805148529455385, 'max_depth': 13, 'min_child_weight': 3, 'gamma': 0.6720837010234033, 'subsample': 0.7332718718075067, 'colsample_bytree': 0.64902886238439, 'alpha': 0.45750278107757547, 'lambda': 0.6510580804212989}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:03,221] Trial 279 finished with value: 0.4194512929565349 and parameters: {'learning_rate': 0.16637985129239485, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.0047485047945990105, 'subsample': 0.764728110755429, 'colsample_bytree': 0.6179816152028492, 'alpha': 0.7075039042871508, 'lambda': 0.9885061502427464}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:04,682] Trial 280 finished with value: 0.38604803643894814 and parameters: {'learning_rate': 0.17335216481413207, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.42236830254372487, 'subsample': 0.8098293582371039, 'colsample_bytree': 0.6718652119665075, 'alpha': 0.3441095472633515, 'lambda': 8.186043292400889}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:08,246] Trial 281 finished with value: 0.3959900986618694 and parameters: {'learning_rate': 0.1655996117631773, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.18953442005635102, 'subsample': 0.7371351444420933, 'colsample_bytree': 0.6949858665570087, 'alpha': 0.5781921403816767, 'lambda': 0.9168937570673221}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:10,497] Trial 282 finished with value: 0.38637450778550064 and parameters: {'learning_rate': 0.18129263422380595, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.9209620425378565, 'subsample': 0.7821301454896038, 'colsample_bytree': 0.6407208749099884, 'alpha': 0.31587658774308436, 'lambda': 0.5042270975305239}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:20,585] Trial 283 finished with value: 0.38782182979501506 and parameters: {'learning_rate': 0.1704284443189848, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.18331139751198675, 'subsample': 0.7511180349314625, 'colsample_bytree': 0.6638463510239334, 'alpha': 0.9305785998220744, 'lambda': 3.4778340173487523}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:28,895] Trial 284 finished with value: 0.37497141966556746 and parameters: {'learning_rate': 0.16371834515711736, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.3766494210549768, 'subsample': 0.7211306579832126, 'colsample_bytree': 0.7235342418363154, 'alpha': 0.6934404227824564, 'lambda': 4.662285917872376}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:32,212] Trial 285 finished with value: 0.40378857113407335 and parameters: {'learning_rate': 0.17770273950363738, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.17071768347119465, 'subsample': 0.7538250950602317, 'colsample_bytree': 0.6982449244445195, 'alpha': 0.45543477678174105, 'lambda': 1.5398972936180215}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:33,203] Trial 286 finished with value: 0.38985029600119153 and parameters: {'learning_rate': 0.16838837088291558, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.5678618513098846, 'subsample': 0.7912715897674149, 'colsample_bytree': 0.6766532274072878, 'alpha': 0.21649070592782466, 'lambda': 0.6986089824841722}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:34,334] Trial 287 finished with value: 0.393678495173728 and parameters: {'learning_rate': 0.17456105193725202, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.18380510479634526, 'subsample': 0.72727163293082, 'colsample_bytree': 0.5862427259888942, 'alpha': 0.23160847841725107, 'lambda': 9.700260602033255}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:35,850] Trial 288 finished with value: 0.42737965717255144 and parameters: {'learning_rate': 0.16267765635109466, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 0.012154400419212622, 'subsample': 0.7700419259410757, 'colsample_bytree': 0.6456413666715952, 'alpha': 0.47293662630865174, 'lambda': 1.0770318433908284}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:36,569] Trial 289 finished with value: 0.3660736800936647 and parameters: {'learning_rate': 0.17131559735208093, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 4.2702687744102015, 'subsample': 0.704808478841391, 'colsample_bytree': 0.6100246313917407, 'alpha': 0.22101520785251977, 'lambda': 7.136650361395905}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:38,172] Trial 290 finished with value: 0.4287970773871532 and parameters: {'learning_rate': 0.16676244824504927, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.0064343676508509355, 'subsample': 0.7384152499180939, 'colsample_bytree': 0.681794816405907, 'alpha': 0.6903990600579342, 'lambda': 1.3007643077880886}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:38,922] Trial 291 finished with value: 0.3861576491547788 and parameters: {'learning_rate': 0.16680162214118993, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.3603490051002467, 'subsample': 0.7362764546946962, 'colsample_bytree': 0.7076008987131296, 'alpha': 0.888549650184494, 'lambda': 1.3491264378207861}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:40,615] Trial 292 finished with value: 0.43279497097559455 and parameters: {'learning_rate': 0.1694069315045364, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.003745350545504543, 'subsample': 0.7612786488599843, 'colsample_bytree': 0.6797297931465569, 'alpha': 0.6118322115494986, 'lambda': 0.42255921403179725}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:42,037] Trial 293 finished with value: 0.3798111742532082 and parameters: {'learning_rate': 0.16734160310023372, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.37449495557943135, 'subsample': 0.7981176924778547, 'colsample_bytree': 0.6653938928399491, 'alpha': 1.2541958357117502, 'lambda': 0.39783222969067367}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:42,347] Trial 294 finished with value: 0.0 and parameters: {'learning_rate': 0.1601305185421161, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.1998557426785875, 'subsample': 0.014365219458495782, 'colsample_bytree': 0.7350268387309655, 'alpha': 0.7766613393946268, 'lambda': 0.46009153648689133}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:43,377] Trial 295 finished with value: 0.37952946679368754 and parameters: {'learning_rate': 0.1765859620333518, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.5574992783213227, 'subsample': 0.7189441316275726, 'colsample_bytree': 0.6798678095990078, 'alpha': 0.46923150519909596, 'lambda': 0.8060544360575528}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:45,076] Trial 296 finished with value: 0.4017864019523878 and parameters: {'learning_rate': 0.16572380743899573, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.17633313437095208, 'subsample': 0.7448493269050622, 'colsample_bytree': 0.7095487380265563, 'alpha': 0.632609315589184, 'lambda': 1.1386221523384343}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:46,082] Trial 297 finished with value: 0.39443849066680514 and parameters: {'learning_rate': 0.16963804955861506, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.382056954166879, 'subsample': 0.7760165656425783, 'colsample_bytree': 0.6560902484435142, 'alpha': 0.370969454647503, 'lambda': 0.7276726701111023}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:47,846] Trial 298 finished with value: 0.4339491271846134 and parameters: {'learning_rate': 0.1634571907035122, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.004425817412437031, 'subsample': 0.7372422775692921, 'colsample_bytree': 0.6863078424450072, 'alpha': 0.00518822245019801, 'lambda': 0.9205842917581708}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:49,099] Trial 299 finished with value: 0.42392320516109394 and parameters: {'learning_rate': 0.17308828910136678, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.19430367310164237, 'subsample': 0.7293292670567673, 'colsample_bytree': 0.6931442086000723, 'alpha': 0.011792120070583223, 'lambda': 1.312720092512308}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:51,306] Trial 300 finished with value: 0.4291592837709085 and parameters: {'learning_rate': 0.1646304553642101, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.00589076939871194, 'subsample': 0.7088867330913416, 'colsample_bytree': 0.726538331867834, 'alpha': 0.14528453638613048, 'lambda': 0.975236676686291}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:52,194] Trial 301 finished with value: 0.3910442402407247 and parameters: {'learning_rate': 0.16820265203754192, 'max_depth': 14, 'min_child_weight': 8, 'gamma': 0.7092928681675237, 'subsample': 0.7292111448984525, 'colsample_bytree': 0.7269269693265156, 'alpha': 9.950586524909433e-05, 'lambda': 1.001265727891485}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:53,146] Trial 302 finished with value: 0.3453993912980317 and parameters: {'learning_rate': 0.15833779220957508, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 8.159096237851434, 'subsample': 0.7101109919440505, 'colsample_bytree': 0.7576819552671467, 'alpha': 0.18299846482757298, 'lambda': 1.5528533178574186}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:54,082] Trial 303 finished with value: 0.39338305225567044 and parameters: {'learning_rate': 0.17353921670185193, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.36083964333259483, 'subsample': 0.7015075667635066, 'colsample_bytree': 0.7252257676156304, 'alpha': 0.21578355685050624, 'lambda': 1.1831523537954742}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:55,098] Trial 304 finished with value: 0.41115663546820697 and parameters: {'learning_rate': 0.16593857029052053, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.18435876237046037, 'subsample': 0.749542595754416, 'colsample_bytree': 0.7081592481089187, 'alpha': 0.3349544493928856, 'lambda': 0.9009489942118405}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:56,037] Trial 305 finished with value: 0.38788894547084496 and parameters: {'learning_rate': 0.18006624146788666, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.49891102042888, 'subsample': 0.7217619064611672, 'colsample_bytree': 0.2673339291120699, 'alpha': 0.17059270113086866, 'lambda': 1.330850721480233}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:56,532] Trial 306 finished with value: 0.36934265122381393 and parameters: {'learning_rate': 0.16984188129712194, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 3.6972720598680464, 'subsample': 0.6780717146423504, 'colsample_bytree': 0.7424198318393861, 'alpha': 0.3379518206531426, 'lambda': 1.0266144158199726}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:58,273] Trial 307 finished with value: 0.43109187545561456 and parameters: {'learning_rate': 0.15444099540816592, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.009231197074412646, 'subsample': 0.7526544860363074, 'colsample_bytree': 0.6954533034461896, 'alpha': 0.010915908706325276, 'lambda': 1.1486035825756762}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:22:59,165] Trial 308 finished with value: 0.40824063879079686 and parameters: {'learning_rate': 0.15412989191705867, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.3102291630560387, 'subsample': 0.7575300245603508, 'colsample_bytree': 0.7074202706227748, 'alpha': 0.027749649778765206, 'lambda': 0.9031340271335175}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:23:00,146] Trial 309 finished with value: 0.4114234006462039 and parameters: {'learning_rate': 0.15811692242889067, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.18912371247461007, 'subsample': 0.7048470809339025, 'colsample_bytree': 0.726569172963673, 'alpha': 0.15397427011975226, 'lambda': 1.0913198523237133}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:23:01,121] Trial 310 finished with value: 0.3939038035409304 and parameters: {'learning_rate': 0.0747405884595487, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.4840252486315071, 'subsample': 0.7650718943839586, 'colsample_bytree': 0.6912251856295423, 'alpha': 0.007613437385275007, 'lambda': 0.8044628215411361}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:23:02,163] Trial 311 finished with value: 0.4003300358865505 and parameters: {'learning_rate': 0.1621484077791577, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.1623910872450003, 'subsample': 0.7191321506419064, 'colsample_bytree': 0.7084295162126308, 'alpha': 0.39605591437349413, 'lambda': 1.1250779123244103}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:23:03,573] Trial 312 finished with value: 0.39940063047182767 and parameters: {'learning_rate': 0.03535780055934862, 'max_depth': 15, 'min_child_weight': 7, 'gamma': 0.34596461915487786, 'subsample': 0.7443383886078084, 'colsample_bytree': 0.6830365136489039, 'alpha': 0.22912284030382116, 'lambda': 1.4981336716773845}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:23:04,569] Trial 313 finished with value: 0.3783064650065498 and parameters: {'learning_rate': 0.15602041538289957, 'max_depth': 6, 'min_child_weight': 8, 'gamma': 0.6601344632709802, 'subsample': 0.6900535423141665, 'colsample_bytree': 0.7389296343506249, 'alpha': 0.014460083148158165, 'lambda': 0.9198332847027921}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:23:06,100] Trial 314 finished with value: 0.41295184877807706 and parameters: {'learning_rate': 0.1607180016276391, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.1623916614770291, 'subsample': 0.7375055411984762, 'colsample_bytree': 0.6316891159293655, 'alpha': 0.32798817707838845, 'lambda': 1.2109330159006684}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:23:09,937] Trial 315 finished with value: 0.4298252539663478 and parameters: {'learning_rate': 0.176425841830689, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.031514532119921154, 'subsample': 0.7722575415203041, 'colsample_bytree': 0.6967888874617142, 'alpha': 0.18693176378957305, 'lambda': 0.6733118578922246}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:23:12,791] Trial 316 finished with value: 0.40260914143596827 and parameters: {'learning_rate': 0.1765834643479931, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.33312404602270856, 'subsample': 0.7711787088922147, 'colsample_bytree': 0.6962086253814327, 'alpha': 0.18699934883716746, 'lambda': 0.48119310936320125}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:23:18,749] Trial 317 finished with value: 0.36964705932781705 and parameters: {'learning_rate': 0.18443294026915633, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.007454101193150899, 'subsample': 0.24396590724032718, 'colsample_bytree': 0.6734866072054652, 'alpha': 0.15515616767167978, 'lambda': 0.6591247778917353}. Best is trial 261 with value: 0.43602994508448456.\n",
      "[I 2024-02-19 10:23:34,411] Trial 318 finished with value: 0.4365396728643933 and parameters: {'learning_rate': 0.17857618570939005, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 0.00531501207113909, 'subsample': 0.7567843424373748, 'colsample_bytree': 0.7195284642518693, 'alpha': 0.0046773684369526836, 'lambda': 0.8142680023817291}. Best is trial 318 with value: 0.4365396728643933.\n",
      "[I 2024-02-19 10:23:36,915] Trial 319 finished with value: 0.3687099328686601 and parameters: {'learning_rate': 0.1824592635610342, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 4.649498662123512, 'subsample': 0.7815180662637349, 'colsample_bytree': 0.7238243073236101, 'alpha': 8.823204638686022e-05, 'lambda': 0.3880413658091159}. Best is trial 318 with value: 0.4365396728643933.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def objective(trial,X_train,y_train,X_valid,y_valid,weight_train):\n",
    "    params = dict(\n",
    "                learning_rate = trial.suggest_float(\"learning_rate\", 0.01, 0.2),\n",
    "                max_depth= trial.suggest_int(\"max_depth\",5, 15),\n",
    "                min_child_weight = trial.suggest_int(\"min_child_weight\", 1, 8),\n",
    "                gamma = trial.suggest_float(\"gamma\", 0, 10),\n",
    "                subsample = trial.suggest_float(\"subsample\", 0.01,1),\n",
    "                colsample_bytree = trial.suggest_float(\"colsample_bytree\", 0.01,1),\n",
    "                alpha = trial.suggest_float(\"alpha\", 0, 10),\n",
    "                objective= 'multi:softprob', \n",
    "                nthread=4, \n",
    "                 num_class= 5,\n",
    "                seed=27)\n",
    "    params['lambda'] = trial.suggest_float(\"lambda\", 0, 10)\n",
    "\n",
    "    \n",
    "    dtrain = xgb.DMatrix(X_train,y_train, \n",
    "                         enable_categorical=True,\n",
    "                         weight=weight_train)\n",
    "    dvalid = xgb.DMatrix(X_valid,y_valid,\n",
    "                         enable_categorical=True,\n",
    "                         )\n",
    "\n",
    "\n",
    "    bst = xgb.train(params, dtrain,verbose_eval=False, num_boost_round=600,\n",
    "                    evals = [(dtrain, \"train\"), (dvalid, \"valid\")],\n",
    "                    early_stopping_rounds=100)\n",
    "\n",
    "    preds = bst.predict(dvalid)\n",
    "    ##MCC is more solid\n",
    "    mcc = matthews_corrcoef(y_valid,preds.argmax(1))    \n",
    "    \n",
    "    return mcc\n",
    "\n",
    "\n",
    "\n",
    "study = optuna.create_study(direction=\"maximize\")\n",
    "study.optimize(lambda trial: objective(trial,X_train,y_train,X_valid,y_valid,weight_train), n_trials=1000, timeout=600,)\n",
    "\n",
    "params_final = dict(\n",
    "            objective= 'multi:softprob', \n",
    "            nthread=4, \n",
    "             num_class= 5,\n",
    "            seed=27)\n",
    "params_final.update(study.best_params)\n",
    "dtrain = xgb.DMatrix(X_train,y_train, \n",
    "                     enable_categorical=True,\n",
    "                     weight=weight_train)\n",
    "dvalid = xgb.DMatrix(X_valid,y_valid,\n",
    "                     enable_categorical=True,\n",
    "                     )   \n",
    "bst = xgb.train(params_final, dtrain,verbose_eval=False, num_boost_round=600,\n",
    "                evals = [(dtrain, \"train\"), (dvalid, \"valid\")],\n",
    "                early_stopping_rounds=100,)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d47e795b-7f13-473c-bf1c-90e5964b67ed",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-02-12 16:16:47,769] A new study created in memory with name: no-name-49cc3e7a-a081-45be-88c6-7bf32f094399\n",
      "[I 2024-02-12 16:16:47,978] Trial 0 finished with value: 0.0 and parameters: {'learning_rate': 0.05491468823977778, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 7.0040114746112145, 'subsample': 0.04250317969539257, 'colsample_bytree': 0.5947297992544279, 'alpha': 2.5510124618789054, 'lambda': 5.6437883532023765}. Best is trial 0 with value: 0.0.\n",
      "[I 2024-02-12 16:16:48,793] Trial 1 finished with value: 0.31426941781461487 and parameters: {'learning_rate': 0.1461147252441012, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 9.744462681704128, 'subsample': 0.708376383454642, 'colsample_bytree': 0.771251938342384, 'alpha': 5.398629268029964, 'lambda': 5.948122996822612}. Best is trial 1 with value: 0.31426941781461487.\n",
      "[I 2024-02-12 16:16:49,607] Trial 2 finished with value: 0.2651178596474042 and parameters: {'learning_rate': 0.021135907717799468, 'max_depth': 7, 'min_child_weight': 6, 'gamma': 9.116347808329234, 'subsample': 0.7430734884473474, 'colsample_bytree': 0.8328227650604986, 'alpha': 7.391787614074625, 'lambda': 5.505806857283488}. Best is trial 1 with value: 0.31426941781461487.\n",
      "[I 2024-02-12 16:16:49,879] Trial 3 finished with value: 0.26437496737308513 and parameters: {'learning_rate': 0.1704264262047213, 'max_depth': 5, 'min_child_weight': 8, 'gamma': 8.531007196392617, 'subsample': 0.6195823673679227, 'colsample_bytree': 0.6267674349805429, 'alpha': 2.926430827585167, 'lambda': 7.113967663145817}. Best is trial 1 with value: 0.31426941781461487.\n",
      "[I 2024-02-12 16:16:50,544] Trial 4 finished with value: 0.330082727516718 and parameters: {'learning_rate': 0.18761689495304196, 'max_depth': 8, 'min_child_weight': 6, 'gamma': 6.25588560543839, 'subsample': 0.9555498645657602, 'colsample_bytree': 0.056030838378330027, 'alpha': 4.4471592639165305, 'lambda': 6.33572172209983}. Best is trial 4 with value: 0.330082727516718.\n",
      "[I 2024-02-12 16:16:51,237] Trial 5 finished with value: 0.10364465586439843 and parameters: {'learning_rate': 0.08656194885377075, 'max_depth': 15, 'min_child_weight': 2, 'gamma': 4.206733217217745, 'subsample': 0.3702706611101172, 'colsample_bytree': 0.5173804575284041, 'alpha': 9.769464023728728, 'lambda': 1.2217967775863836}. Best is trial 4 with value: 0.330082727516718.\n",
      "[I 2024-02-12 16:16:51,878] Trial 6 finished with value: 0.24957588007271345 and parameters: {'learning_rate': 0.03605727653895495, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 2.147495560853152, 'subsample': 0.17138351502157503, 'colsample_bytree': 0.025945207728118626, 'alpha': 2.5625948842755673, 'lambda': 4.706143035793961}. Best is trial 4 with value: 0.330082727516718.\n",
      "[I 2024-02-12 16:16:52,332] Trial 7 finished with value: 0.07995984396360116 and parameters: {'learning_rate': 0.12989152123434763, 'max_depth': 11, 'min_child_weight': 7, 'gamma': 5.510392945188215, 'subsample': 0.21977285021525467, 'colsample_bytree': 0.8331858987340801, 'alpha': 5.844553540987083, 'lambda': 6.077833972639671}. Best is trial 4 with value: 0.330082727516718.\n",
      "[I 2024-02-12 16:16:52,587] Trial 8 finished with value: 0.0 and parameters: {'learning_rate': 0.14753275425171336, 'max_depth': 11, 'min_child_weight': 6, 'gamma': 9.588501388719523, 'subsample': 0.04466263202923004, 'colsample_bytree': 0.10028907081629487, 'alpha': 6.552817328917455, 'lambda': 6.210505566064523}. Best is trial 4 with value: 0.330082727516718.\n",
      "[I 2024-02-12 16:16:53,456] Trial 9 finished with value: 0.3848728933928666 and parameters: {'learning_rate': 0.12492609679471187, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.02624559511297697, 'subsample': 0.4205263767322105, 'colsample_bytree': 0.6797666326685518, 'alpha': 3.241625702270011, 'lambda': 3.9742929061843455}. Best is trial 9 with value: 0.3848728933928666.\n",
      "[I 2024-02-12 16:16:54,502] Trial 10 finished with value: 0.3955957467431076 and parameters: {'learning_rate': 0.09387562951731308, 'max_depth': 5, 'min_child_weight': 4, 'gamma': 0.2160496836640984, 'subsample': 0.4605414974422147, 'colsample_bytree': 0.3178466826719262, 'alpha': 0.021769450820666947, 'lambda': 9.322839237856002}. Best is trial 10 with value: 0.3955957467431076.\n",
      "[I 2024-02-12 16:16:55,754] Trial 11 finished with value: 0.40947640831183274 and parameters: {'learning_rate': 0.09512025139976174, 'max_depth': 5, 'min_child_weight': 4, 'gamma': 0.008656670575627343, 'subsample': 0.44354417354287173, 'colsample_bytree': 0.31586343816886, 'alpha': 0.2641145792822929, 'lambda': 9.418556182166906}. Best is trial 11 with value: 0.40947640831183274.\n",
      "[I 2024-02-12 16:16:56,426] Trial 12 finished with value: 0.37453929647972206 and parameters: {'learning_rate': 0.08413787888118301, 'max_depth': 5, 'min_child_weight': 4, 'gamma': 0.5700030131345719, 'subsample': 0.4905820408676766, 'colsample_bytree': 0.3313312294240558, 'alpha': 0.49080017338413656, 'lambda': 9.620523094233715}. Best is trial 11 with value: 0.40947640831183274.\n",
      "[I 2024-02-12 16:16:57,335] Trial 13 finished with value: 0.3533584462582549 and parameters: {'learning_rate': 0.09489136875034261, 'max_depth': 5, 'min_child_weight': 4, 'gamma': 2.499650860353785, 'subsample': 0.27048139011982075, 'colsample_bytree': 0.3094930670147986, 'alpha': 0.24867288039661845, 'lambda': 9.895400744016086}. Best is trial 11 with value: 0.40947640831183274.\n",
      "[I 2024-02-12 16:16:58,354] Trial 14 finished with value: 0.3715131225035217 and parameters: {'learning_rate': 0.05993229829925746, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 1.7463203817984243, 'subsample': 0.557578426690309, 'colsample_bytree': 0.29286271974972833, 'alpha': 1.351004185811311, 'lambda': 8.228336125254598}. Best is trial 11 with value: 0.40947640831183274.\n",
      "[I 2024-02-12 16:16:58,916] Trial 15 finished with value: 0.32996739450775153 and parameters: {'learning_rate': 0.11220933609931687, 'max_depth': 15, 'min_child_weight': 5, 'gamma': 4.023754594522787, 'subsample': 0.36443281130122374, 'colsample_bytree': 0.42544189055845955, 'alpha': 1.2386201954733411, 'lambda': 8.90276821816338}. Best is trial 11 with value: 0.40947640831183274.\n",
      "[I 2024-02-12 16:16:59,744] Trial 16 finished with value: 0.3684492559919588 and parameters: {'learning_rate': 0.06467857724099514, 'max_depth': 6, 'min_child_weight': 3, 'gamma': 1.0772828434551593, 'subsample': 0.8134932475051078, 'colsample_bytree': 0.20395199912549, 'alpha': 0.05034039422675417, 'lambda': 7.764394985266916}. Best is trial 11 with value: 0.40947640831183274.\n",
      "[I 2024-02-12 16:17:00,539] Trial 17 finished with value: 0.3409809951954197 and parameters: {'learning_rate': 0.10735252957802138, 'max_depth': 13, 'min_child_weight': 5, 'gamma': 2.89526781670647, 'subsample': 0.5076168171234539, 'colsample_bytree': 0.4171814178022194, 'alpha': 4.235319207408312, 'lambda': 2.0455416015545556}. Best is trial 11 with value: 0.40947640831183274.\n",
      "[I 2024-02-12 16:17:01,275] Trial 18 finished with value: 0.35995067575892636 and parameters: {'learning_rate': 0.07556920691032631, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 3.3215489161445104, 'subsample': 0.6420884132848483, 'colsample_bytree': 0.14043673557403716, 'alpha': 1.5931414154751273, 'lambda': 3.5128419263141604}. Best is trial 11 with value: 0.40947640831183274.\n",
      "[I 2024-02-12 16:17:01,893] Trial 19 finished with value: 0.10828439646383313 and parameters: {'learning_rate': 0.15152855762375478, 'max_depth': 10, 'min_child_weight': 4, 'gamma': 1.2350368012252186, 'subsample': 0.3076541051993388, 'colsample_bytree': 0.2441196291570909, 'alpha': 8.038014045730344, 'lambda': 8.525714301578237}. Best is trial 11 with value: 0.40947640831183274.\n",
      "[I 2024-02-12 16:17:02,806] Trial 20 finished with value: 0.3665084959189171 and parameters: {'learning_rate': 0.04333948154468013, 'max_depth': 6, 'min_child_weight': 5, 'gamma': 0.708363604325788, 'subsample': 0.4485847669040674, 'colsample_bytree': 0.49057433342832385, 'alpha': 1.7749013935880145, 'lambda': 7.138239207908493}. Best is trial 11 with value: 0.40947640831183274.\n",
      "[I 2024-02-12 16:17:03,448] Trial 21 finished with value: 0.37149285921894415 and parameters: {'learning_rate': 0.12248921911531217, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 0.10292484103797815, 'subsample': 0.4136586475326477, 'colsample_bytree': 0.9930712456312765, 'alpha': 3.602691635521951, 'lambda': 3.819624627705272}. Best is trial 11 with value: 0.40947640831183274.\n",
      "[I 2024-02-12 16:17:04,429] Trial 22 finished with value: 0.4162196662095675 and parameters: {'learning_rate': 0.1311447851338093, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.06583155993435952, 'subsample': 0.5454953094220072, 'colsample_bytree': 0.6397819599717101, 'alpha': 0.9667806106844579, 'lambda': 3.2616987344906363}. Best is trial 22 with value: 0.4162196662095675.\n",
      "[I 2024-02-12 16:17:05,002] Trial 23 finished with value: 0.37704751803306 and parameters: {'learning_rate': 0.10131510468692781, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 1.4051681873209998, 'subsample': 0.5566811656668686, 'colsample_bytree': 0.41096394138301556, 'alpha': 0.9005921295577367, 'lambda': 2.566254817025775}. Best is trial 22 with value: 0.4162196662095675.\n",
      "[I 2024-02-12 16:17:05,948] Trial 24 finished with value: 0.3699505939840709 and parameters: {'learning_rate': 0.13158206049424728, 'max_depth': 7, 'min_child_weight': 4, 'gamma': 0.11020236496456359, 'subsample': 0.603950234736229, 'colsample_bytree': 0.1982477269799486, 'alpha': 1.9597285675972038, 'lambda': 9.13403560181324}. Best is trial 22 with value: 0.4162196662095675.\n",
      "[I 2024-02-12 16:17:06,294] Trial 25 finished with value: 0.37079622127524037 and parameters: {'learning_rate': 0.16560929559541546, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 1.9956276144928538, 'subsample': 0.7070654046190014, 'colsample_bytree': 0.5301510163666454, 'alpha': 0.04994553958345571, 'lambda': 0.2659995471444505}. Best is trial 22 with value: 0.4162196662095675.\n",
      "[I 2024-02-12 16:17:06,964] Trial 26 finished with value: 0.37096109091922724 and parameters: {'learning_rate': 0.07618354387978864, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 3.325014235344767, 'subsample': 0.9031842896069551, 'colsample_bytree': 0.7109556737604257, 'alpha': 0.9225631227683471, 'lambda': 2.9419197194054405}. Best is trial 22 with value: 0.4162196662095675.\n",
      "[I 2024-02-12 16:17:07,527] Trial 27 finished with value: 0.2646427913384273 and parameters: {'learning_rate': 0.11530396466921713, 'max_depth': 9, 'min_child_weight': 4, 'gamma': 0.935981440439387, 'subsample': 0.14403067893817012, 'colsample_bytree': 0.39709423017218026, 'alpha': 2.2522834517996055, 'lambda': 4.940647583418129}. Best is trial 22 with value: 0.4162196662095675.\n",
      "[I 2024-02-12 16:17:07,908] Trial 28 finished with value: 0.3407034893154764 and parameters: {'learning_rate': 0.09695126519991482, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 4.441880383704177, 'subsample': 0.31253500136366263, 'colsample_bytree': 0.590156740035056, 'alpha': 0.8020817837824505, 'lambda': 7.477551483650797}. Best is trial 22 with value: 0.4162196662095675.\n",
      "[I 2024-02-12 16:17:08,232] Trial 29 finished with value: 0.21883152671029973 and parameters: {'learning_rate': 0.14099255543125974, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 7.947050900205572, 'subsample': 0.5305362307232009, 'colsample_bytree': 0.33215518638392894, 'alpha': 3.930808832126739, 'lambda': 9.305225035577044}. Best is trial 22 with value: 0.4162196662095675.\n",
      "[I 2024-02-12 16:17:08,695] Trial 30 finished with value: 0.3545294846158613 and parameters: {'learning_rate': 0.19663415030340536, 'max_depth': 8, 'min_child_weight': 4, 'gamma': 5.519855789157528, 'subsample': 0.8069750748353486, 'colsample_bytree': 0.5895023904992998, 'alpha': 2.519664105979575, 'lambda': 1.6722660276225447}. Best is trial 22 with value: 0.4162196662095675.\n",
      "[I 2024-02-12 16:17:09,437] Trial 31 finished with value: 0.3827737257446576 and parameters: {'learning_rate': 0.12265613808314997, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.09611779329885824, 'subsample': 0.43904643403092636, 'colsample_bytree': 0.702423618357831, 'alpha': 2.941312097734704, 'lambda': 4.04349800383807}. Best is trial 22 with value: 0.4162196662095675.\n",
      "[I 2024-02-12 16:17:10,423] Trial 32 finished with value: 0.4332211957331512 and parameters: {'learning_rate': 0.134561823334836, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.02599906811648156, 'subsample': 0.3820681510273423, 'colsample_bytree': 0.6958805517777873, 'alpha': 0.6761233564820119, 'lambda': 4.198175240253944}. Best is trial 32 with value: 0.4332211957331512.\n",
      "[I 2024-02-12 16:17:11,006] Trial 33 finished with value: 0.3773613041153083 and parameters: {'learning_rate': 0.16568385494301657, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 1.5242939714017623, 'subsample': 0.3503048798283337, 'colsample_bytree': 0.9479217956334393, 'alpha': 0.6109097010766686, 'lambda': 4.605633993901287}. Best is trial 32 with value: 0.4332211957331512.\n",
      "[I 2024-02-12 16:17:11,635] Trial 34 finished with value: 0.3856148240830008 and parameters: {'learning_rate': 0.1395984959077608, 'max_depth': 7, 'min_child_weight': 2, 'gamma': 0.6666856430195309, 'subsample': 0.6590112309784539, 'colsample_bytree': 0.8334877119629402, 'alpha': 1.4335478437651064, 'lambda': 3.162148539846126}. Best is trial 32 with value: 0.4332211957331512.\n",
      "[I 2024-02-12 16:17:12,567] Trial 35 finished with value: 0.24189510940898235 and parameters: {'learning_rate': 0.15980331060854852, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 6.985841141590097, 'subsample': 0.4776065249468049, 'colsample_bytree': 0.750923118378235, 'alpha': 2.0194021884173914, 'lambda': 8.060458796578146}. Best is trial 32 with value: 0.4332211957331512.\n",
      "[I 2024-02-12 16:17:13,944] Trial 36 finished with value: 0.36173013193312065 and parameters: {'learning_rate': 0.09022984927670337, 'max_depth': 6, 'min_child_weight': 6, 'gamma': 0.6484753454372649, 'subsample': 0.2569243529983963, 'colsample_bytree': 0.4587003397392044, 'alpha': 0.048597808047988966, 'lambda': 5.209095715316717}. Best is trial 32 with value: 0.4332211957331512.\n",
      "[I 2024-02-12 16:17:20,448] Trial 37 finished with value: 0.33858356155943925 and parameters: {'learning_rate': 0.10663827594785191, 'max_depth': 8, 'min_child_weight': 7, 'gamma': 2.5223037912269883, 'subsample': 0.5956277114574631, 'colsample_bytree': 0.613196232951687, 'alpha': 4.8699682144075345, 'lambda': 5.618671171276212}. Best is trial 32 with value: 0.4332211957331512.\n",
      "[I 2024-02-12 16:17:26,029] Trial 38 finished with value: 0.372285252886509 and parameters: {'learning_rate': 0.18169585844867614, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 1.7036673105340538, 'subsample': 0.38395788178780166, 'colsample_bytree': 0.5453402192408798, 'alpha': 1.1338788632318548, 'lambda': 2.432215203192567}. Best is trial 32 with value: 0.4332211957331512.\n",
      "[I 2024-02-12 16:17:35,467] Trial 39 finished with value: -0.00739462858688373 and parameters: {'learning_rate': 0.01691115802007781, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.5102792786228774, 'subsample': 0.15314848570278916, 'colsample_bytree': 0.7892611570770502, 'alpha': 9.906269657689514, 'lambda': 0.9426294797180068}. Best is trial 32 with value: 0.4332211957331512.\n",
      "[I 2024-02-12 16:17:42,401] Trial 40 finished with value: 0.23744406672559162 and parameters: {'learning_rate': 0.06964246274834249, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 1.253707252198399, 'subsample': 0.46240951392702556, 'colsample_bytree': 0.6541428432324393, 'alpha': 9.030194179361892, 'lambda': 4.298708419049978}. Best is trial 32 with value: 0.4332211957331512.\n",
      "[I 2024-02-12 16:17:42,955] Trial 41 finished with value: 0.3876254202624337 and parameters: {'learning_rate': 0.13918799360801737, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.498702273137845, 'subsample': 0.684905044818107, 'colsample_bytree': 0.8927266203803091, 'alpha': 1.4670752423879585, 'lambda': 3.2190106534494882}. Best is trial 32 with value: 0.4332211957331512.\n",
      "[I 2024-02-12 16:17:44,133] Trial 42 finished with value: 0.4515447585649826 and parameters: {'learning_rate': 0.1508694604985379, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.021569451189764617, 'subsample': 0.7272986833630608, 'colsample_bytree': 0.9005595263136777, 'alpha': 0.5719716341680463, 'lambda': 6.54709056005899}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:17:44,785] Trial 43 finished with value: 0.413860149122176 and parameters: {'learning_rate': 0.13304739066552373, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2465835233306503, 'subsample': 0.7645045906222752, 'colsample_bytree': 0.8920710897240133, 'alpha': 0.5915000927052421, 'lambda': 6.746882705174356}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:17:46,163] Trial 44 finished with value: 0.4467602480343248 and parameters: {'learning_rate': 0.15501431044466, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.004787547329890642, 'subsample': 0.7597689976981858, 'colsample_bytree': 0.8655768499009906, 'alpha': 0.5547704813807603, 'lambda': 6.748385366259196}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:17:46,921] Trial 45 finished with value: 0.3780388431150961 and parameters: {'learning_rate': 0.15596765097724025, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 1.0301553578539588, 'subsample': 0.788345012087309, 'colsample_bytree': 0.8765752007535913, 'alpha': 0.7668444642116163, 'lambda': 6.633605131946472}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:17:47,596] Trial 46 finished with value: 0.38696155279588584 and parameters: {'learning_rate': 0.17743481628509114, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 2.144335010132884, 'subsample': 0.7651008534160448, 'colsample_bytree': 0.9180327468050141, 'alpha': 2.5553845817529437, 'lambda': 6.461981684842406}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:17:48,609] Trial 47 finished with value: 0.37016222212865163 and parameters: {'learning_rate': 0.13295445216825463, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.4864357204530082, 'subsample': 0.8599321024831587, 'colsample_bytree': 0.7669171891842911, 'alpha': 5.638874163706746, 'lambda': 5.718999593073118}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:17:49,455] Trial 48 finished with value: 0.3833563143108062 and parameters: {'learning_rate': 0.14681884106585458, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.9739294832764049, 'subsample': 0.9694283694852237, 'colsample_bytree': 0.9884257104230483, 'alpha': 0.4661600997405364, 'lambda': 6.771872843349014}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:17:50,092] Trial 49 finished with value: 0.3760304227791432 and parameters: {'learning_rate': 0.1733303972995202, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 1.8134642416947013, 'subsample': 0.8598885119094078, 'colsample_bytree': 0.8306712606320035, 'alpha': 3.356396814788399, 'lambda': 6.055032804006862}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:17:50,483] Trial 50 finished with value: 0.3406362338632443 and parameters: {'learning_rate': 0.15759368160152543, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 2.4724929184312665, 'subsample': 0.7355002055951648, 'colsample_bytree': 0.8010491983971106, 'alpha': 6.671388246862097, 'lambda': 7.088037715237575}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:17:51,808] Trial 51 finished with value: 0.4349734744696996 and parameters: {'learning_rate': 0.11472151072554845, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.05003422740200894, 'subsample': 0.7434502626451576, 'colsample_bytree': 0.8723494492380427, 'alpha': 0.5417459919379478, 'lambda': 5.101156041716502}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:17:53,187] Trial 52 finished with value: 0.43585975479381844 and parameters: {'learning_rate': 0.11452928475423774, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.04071802765336889, 'subsample': 0.7255945833670169, 'colsample_bytree': 0.9398019579989045, 'alpha': 0.5122587583254299, 'lambda': 5.2184011896171}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:17:53,928] Trial 53 finished with value: 0.3702281684529345 and parameters: {'learning_rate': 0.11676655820861714, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.8913475608441421, 'subsample': 0.8530632671824165, 'colsample_bytree': 0.8610140544479056, 'alpha': 1.228906569415676, 'lambda': 5.179829612744489}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:17:54,656] Trial 54 finished with value: 0.3783485082940645 and parameters: {'learning_rate': 0.12454038143956725, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 0.4729860041541201, 'subsample': 0.7277859603686777, 'colsample_bytree': 0.944852509880787, 'alpha': 1.7657644041750735, 'lambda': 4.716982460967618}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:17:55,761] Trial 55 finished with value: 0.43342927311967494 and parameters: {'learning_rate': 0.1407059853660295, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.06671390385403236, 'subsample': 0.9369221746305691, 'colsample_bytree': 0.7190170115118194, 'alpha': 0.4109263278433538, 'lambda': 4.330836404989922}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:17:56,215] Trial 56 finished with value: 0.37152646649672716 and parameters: {'learning_rate': 0.1497898423386569, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 1.3393797131230634, 'subsample': 0.8988540927125445, 'colsample_bytree': 0.7339374104821883, 'alpha': 0.36688576376224763, 'lambda': 4.33102016775106}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:17:56,950] Trial 57 finished with value: 0.3845579749544949 and parameters: {'learning_rate': 0.14175317603807477, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.3447432363570958, 'subsample': 0.9136687136511619, 'colsample_bytree': 0.9543338178362842, 'alpha': 2.2064408530134614, 'lambda': 5.752606005906085}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:17:58,418] Trial 58 finished with value: 0.44372029217270226 and parameters: {'learning_rate': 0.11452837379107102, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.010974453153894916, 'subsample': 0.9439030002783286, 'colsample_bytree': 0.8173899068601472, 'alpha': 0.4635774865826685, 'lambda': 5.40384468178611}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:17:58,994] Trial 59 finished with value: 0.33457868648504885 and parameters: {'learning_rate': 0.1142860702072018, 'max_depth': 11, 'min_child_weight': 2, 'gamma': 8.66625323245174, 'subsample': 0.9902355420100406, 'colsample_bytree': 0.8447752089173584, 'alpha': 0.3414232269616541, 'lambda': 5.335788159306608}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:17:59,814] Trial 60 finished with value: 0.3722245406520392 and parameters: {'learning_rate': 0.10449582339433916, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 0.7803072061065098, 'subsample': 0.9343477907112083, 'colsample_bytree': 0.8009296261788308, 'alpha': 1.1147096651855506, 'lambda': 6.261944717016428}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:00,414] Trial 61 finished with value: 0.4097173127316371 and parameters: {'learning_rate': 0.12019179723523025, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.27607431912015856, 'subsample': 0.6767681709718281, 'colsample_bytree': 0.9183043539947126, 'alpha': 0.6490808452402868, 'lambda': 3.7380379720005017}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:01,849] Trial 62 finished with value: 0.4451721931427528 and parameters: {'learning_rate': 0.11202734982385336, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0784234907653313, 'subsample': 0.8264905831076416, 'colsample_bytree': 0.9188429137638919, 'alpha': 0.03338075551602626, 'lambda': 4.534776178786762}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:02,491] Trial 63 finished with value: 0.38046398709164214 and parameters: {'learning_rate': 0.1002064508614301, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 1.151511707096185, 'subsample': 0.8492571348287431, 'colsample_bytree': 0.9098437700700812, 'alpha': 0.08300726615193937, 'lambda': 4.9131211960029715}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:03,176] Trial 64 finished with value: 0.37172563168719164 and parameters: {'learning_rate': 0.12507859629616427, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.7341517771800687, 'subsample': 0.8177509459062169, 'colsample_bytree': 0.9693694250939646, 'alpha': 1.7013511835773272, 'lambda': 5.891179780892122}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:04,308] Trial 65 finished with value: 0.4273311582274753 and parameters: {'learning_rate': 0.08573728611335509, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 0.011950537840268956, 'subsample': 0.9586142086460326, 'colsample_bytree': 0.8162872702350269, 'alpha': 1.0335297676793949, 'lambda': 5.4116987873706375}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:04,857] Trial 66 finished with value: 0.3723218299665664 and parameters: {'learning_rate': 0.10728337879403617, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 1.5279881094582282, 'subsample': 0.7616742988786473, 'colsample_bytree': 0.8541784159689687, 'alpha': 0.34498942052759446, 'lambda': 4.5677780548721545}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:05,663] Trial 67 finished with value: 0.38344007195070706 and parameters: {'learning_rate': 0.1125766432536245, 'max_depth': 6, 'min_child_weight': 8, 'gamma': 0.30361134840747317, 'subsample': 0.8896425615957054, 'colsample_bytree': 0.9987407591599207, 'alpha': 1.4427205284348905, 'lambda': 7.061707529590482}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:06,268] Trial 68 finished with value: 0.3759259500228235 and parameters: {'learning_rate': 0.16727527490337116, 'max_depth': 12, 'min_child_weight': 3, 'gamma': 1.0134430772054546, 'subsample': 0.7081942781543448, 'colsample_bytree': 0.9395697940154752, 'alpha': 0.005750541162478584, 'lambda': 7.431962780871684}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:07,513] Trial 69 finished with value: 0.44104757382256193 and parameters: {'learning_rate': 0.15502834688994302, 'max_depth': 7, 'min_child_weight': 2, 'gamma': 0.012549894562059394, 'subsample': 0.6324459282844281, 'colsample_bytree': 0.7772960231706607, 'alpha': 0.8402009606326095, 'lambda': 4.980997477169992}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:08,057] Trial 70 finished with value: 0.3516455943287364 and parameters: {'learning_rate': 0.18717911936442388, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 5.759440943329443, 'subsample': 0.6401163069278759, 'colsample_bytree': 0.8759222508689638, 'alpha': 0.8915955781620745, 'lambda': 4.949907875982044}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:08,758] Trial 71 finished with value: 0.40126618050328855 and parameters: {'learning_rate': 0.12740418862821393, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.4361968603584465, 'subsample': 0.7985827944820696, 'colsample_bytree': 0.7630898352721327, 'alpha': 0.3900272202152598, 'lambda': 3.7197718004485525}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:09,481] Trial 72 finished with value: 0.3721624687518289 and parameters: {'learning_rate': 0.1630016077711665, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 0.7667484128389881, 'subsample': 0.8259445369307231, 'colsample_bytree': 0.9155465354357197, 'alpha': 1.303184293302784, 'lambda': 5.532209011243554}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:10,712] Trial 73 finished with value: 0.4403220509642132 and parameters: {'learning_rate': 0.15334173903455867, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.01416016030812535, 'subsample': 0.6172680367492984, 'colsample_bytree': 0.7311972721101838, 'alpha': 0.6825292950192035, 'lambda': 4.585725290746401}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:11,829] Trial 74 finished with value: 0.39149113068739694 and parameters: {'learning_rate': 0.15507064088106565, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.4457411732819955, 'subsample': 0.6055639209000849, 'colsample_bytree': 0.7902428445783598, 'alpha': 0.8202531135123803, 'lambda': 6.087436735739998}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:12,413] Trial 75 finished with value: 0.39775181630896816 and parameters: {'learning_rate': 0.17250650172143298, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 0.29006585142347874, 'subsample': 0.6682320615227844, 'colsample_bytree': 0.8646614062633937, 'alpha': 1.1753151944098672, 'lambda': 5.1430260004115}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:13,086] Trial 76 finished with value: 0.3700819650450247 and parameters: {'learning_rate': 0.15246779217146403, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 1.2850823331648855, 'subsample': 0.5784822028305252, 'colsample_bytree': 0.8954137629915228, 'alpha': 1.9266643745884293, 'lambda': 4.610771828944339}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:14,440] Trial 77 finished with value: 0.42145753441236866 and parameters: {'learning_rate': 0.14475752707980014, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.010723781909454916, 'subsample': 0.6948010830220607, 'colsample_bytree': 0.6654547643748777, 'alpha': 1.619248580112715, 'lambda': 3.9746351234879924}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:15,367] Trial 78 finished with value: 0.3682958143771901 and parameters: {'learning_rate': 0.0791330211171273, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 4.664811251538329, 'subsample': 0.6261987859568716, 'colsample_bytree': 0.8189437374909522, 'alpha': 0.6602832117142344, 'lambda': 6.486652304747719}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:16,785] Trial 79 finished with value: 0.38419631685405836 and parameters: {'learning_rate': 0.16121328208074562, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.7469454744037871, 'subsample': 0.7395623977484085, 'colsample_bytree': 0.731657134362316, 'alpha': 0.17713557476960717, 'lambda': 5.471795182450996}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:23,883] Trial 80 finished with value: 0.34365940069933015 and parameters: {'learning_rate': 0.09194552154182817, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 9.947416460929038, 'subsample': 0.7860294691494316, 'colsample_bytree': 0.9307461962527707, 'alpha': 0.8366808785589728, 'lambda': 4.800942199666804}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:32,490] Trial 81 finished with value: 0.41546383542849186 and parameters: {'learning_rate': 0.13650832492602913, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.19697553645667562, 'subsample': 0.8830717904318643, 'colsample_bytree': 0.7451941304601102, 'alpha': 0.45930448428100357, 'lambda': 4.42628910503734}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:44,110] Trial 82 finished with value: 0.4492084728735465 and parameters: {'learning_rate': 0.14527523929128705, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.01802478574758925, 'subsample': 0.9226714327022453, 'colsample_bytree': 0.7849182350733195, 'alpha': 0.21015753109102692, 'lambda': 5.868649104928193}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:44,998] Trial 83 finished with value: 0.36749740805800607 and parameters: {'learning_rate': 0.11930112341813501, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 3.7810412531436115, 'subsample': 0.8294128084164436, 'colsample_bytree': 0.774935083493695, 'alpha': 0.2425580748137513, 'lambda': 5.903874805122443}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:45,831] Trial 84 finished with value: 0.3914886781415486 and parameters: {'learning_rate': 0.15077391455740652, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.544276982139745, 'subsample': 0.7157583258164757, 'colsample_bytree': 0.9725088682956077, 'alpha': 1.0291657295428016, 'lambda': 6.266643645552321}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:46,330] Trial 85 finished with value: 0.3656749265553925 and parameters: {'learning_rate': 0.11187830386872147, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 6.932434567925563, 'subsample': 0.5226289948675993, 'colsample_bytree': 0.8397570646441004, 'alpha': 0.6415965230099616, 'lambda': 5.027611500306812}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:47,338] Trial 86 finished with value: 0.37353364317893867 and parameters: {'learning_rate': 0.046845080235881256, 'max_depth': 5, 'min_child_weight': 7, 'gamma': 0.2947349827356053, 'subsample': 0.6448841013433888, 'colsample_bytree': 0.8880604507338706, 'alpha': 1.3934054146733703, 'lambda': 6.9033181104813055}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:47,974] Trial 87 finished with value: 0.3772037718967737 and parameters: {'learning_rate': 0.1691190016141131, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.9120465116445848, 'subsample': 0.7635622646371308, 'colsample_bytree': 0.6843107869816983, 'alpha': 0.19138406750435605, 'lambda': 7.702127975433898}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:48,964] Trial 88 finished with value: 0.38798143124700096 and parameters: {'learning_rate': 0.026929892875599035, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.6091600670963976, 'subsample': 0.5704011506061996, 'colsample_bytree': 0.8117466251737285, 'alpha': 0.5451169490376474, 'lambda': 5.691602670334067}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:49,661] Trial 89 finished with value: 0.3769724159049134 and parameters: {'learning_rate': 0.12941420979333026, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.012627597067474662, 'subsample': 0.9242421433663912, 'colsample_bytree': 0.8614756367068794, 'alpha': 7.60264357904557, 'lambda': 5.290495823881754}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:50,223] Trial 90 finished with value: 0.3534323292528728 and parameters: {'learning_rate': 0.14622221982514663, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 1.5585748258226286, 'subsample': 0.8769289993910192, 'colsample_bytree': 0.7715505948993143, 'alpha': 4.958714369762612, 'lambda': 4.158139550537384}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:51,058] Trial 91 finished with value: 0.4018556788185963 and parameters: {'learning_rate': 0.1416547827181466, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.2195778334159637, 'subsample': 0.9984773097258999, 'colsample_bytree': 0.83976308891066, 'alpha': 0.9561513913423859, 'lambda': 4.454144982579733}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:51,543] Trial 92 finished with value: 0.3673987673831634 and parameters: {'learning_rate': 0.1374367584812345, 'max_depth': 6, 'min_child_weight': 3, 'gamma': 1.1076807877037804, 'subsample': 0.9468303954680228, 'colsample_bytree': 0.7173137692881971, 'alpha': 0.47426506798389867, 'lambda': 3.38889209593712}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:52,234] Trial 93 finished with value: 0.38437503845292137 and parameters: {'learning_rate': 0.1566300006635171, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.5874302022676515, 'subsample': 0.9684161246406333, 'colsample_bytree': 0.6266980092032384, 'alpha': 0.21893436903540492, 'lambda': 4.7420582377484015}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:53,109] Trial 94 finished with value: 0.40370498130037935 and parameters: {'learning_rate': 0.10986109485801938, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.21152414521246662, 'subsample': 0.8390503747631465, 'colsample_bytree': 0.7944905536015704, 'alpha': 0.7325302783188052, 'lambda': 6.4377456093353675}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:54,233] Trial 95 finished with value: 0.43173346908410065 and parameters: {'learning_rate': 0.1028979377910885, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 0.018761529431481495, 'subsample': 0.7929019071564658, 'colsample_bytree': 0.9593466340565316, 'alpha': 1.1387120394406383, 'lambda': 5.924921315879154}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:54,380] Trial 96 finished with value: 0.0 and parameters: {'learning_rate': 0.16164191984730875, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.8411359775534322, 'subsample': 0.011849226301553495, 'colsample_bytree': 0.7170319876961775, 'alpha': 0.34463268428822885, 'lambda': 5.096340676338756}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:55,142] Trial 97 finished with value: 0.4077676582972751 and parameters: {'learning_rate': 0.14614908217186215, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.3803771708530995, 'subsample': 0.9338630254972522, 'colsample_bytree': 0.8943461773489778, 'alpha': 0.09502352363393951, 'lambda': 5.549746659495815}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:55,899] Trial 98 finished with value: 0.3778005706376732 and parameters: {'learning_rate': 0.15320789194065845, 'max_depth': 6, 'min_child_weight': 6, 'gamma': 0.6340065872323377, 'subsample': 0.6921984923305887, 'colsample_bytree': 0.9343673198407182, 'alpha': 1.5362424918577349, 'lambda': 3.8709531964186916}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:56,780] Trial 99 finished with value: 0.38926972814508476 and parameters: {'learning_rate': 0.17722100653217154, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.24097629287042585, 'subsample': 0.7430559354995507, 'colsample_bytree': 0.8206436642938058, 'alpha': 2.3105377367811224, 'lambda': 4.145978112863053}. Best is trial 42 with value: 0.4515447585649826.\n",
      "[I 2024-02-12 16:18:58,366] Trial 100 finished with value: 0.45251333315961784 and parameters: {'learning_rate': 0.11742675323329901, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.009653432197245581, 'subsample': 0.7759207972645427, 'colsample_bytree': 0.8701098385258211, 'alpha': 0.030392456229342862, 'lambda': 4.921074216288191}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:18:59,041] Trial 101 finished with value: 0.3953894558580467 and parameters: {'learning_rate': 0.11805618677960733, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.48377313760207713, 'subsample': 0.7753428892036522, 'colsample_bytree': 0.8758808607558992, 'alpha': 0.023008951679117096, 'lambda': 4.847160422388723}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:18:59,997] Trial 102 finished with value: 0.43466353118414797 and parameters: {'learning_rate': 0.1255914872067487, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.04713106409073059, 'subsample': 0.8728772399729108, 'colsample_bytree': 0.909845538125826, 'alpha': 0.5154238897355653, 'lambda': 5.30296632448296}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:01,271] Trial 103 finished with value: 0.4361921368813337 and parameters: {'learning_rate': 0.0966416517691451, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.01702981599483061, 'subsample': 0.8718963314647336, 'colsample_bytree': 0.9109616245859313, 'alpha': 0.6436294513967752, 'lambda': 5.336480955998116}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:02,699] Trial 104 finished with value: 0.3982854480575716 and parameters: {'learning_rate': 0.0982810222047259, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 0.302454610505598, 'subsample': 0.7175840033757551, 'colsample_bytree': 0.9824725200772388, 'alpha': 0.8381954400541749, 'lambda': 6.0980650097214975}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:03,581] Trial 105 finished with value: 0.37047203843025195 and parameters: {'learning_rate': 0.110746656608194, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.8914481266219887, 'subsample': 0.9073208037741192, 'colsample_bytree': 0.8551341310971952, 'alpha': 1.2721671280618687, 'lambda': 5.821738331697933}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:04,265] Trial 106 finished with value: 0.3612928805341775 and parameters: {'learning_rate': 0.12216293095807214, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.6606657951005614, 'subsample': 0.764343580861134, 'colsample_bytree': 0.9263320902761685, 'alpha': 6.104586823253092, 'lambda': 5.135206144914417}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:05,255] Trial 107 finished with value: 0.37602963448095034 and parameters: {'learning_rate': 0.10396112925476698, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 1.1683126691786074, 'subsample': 0.8062117910448912, 'colsample_bytree': 0.9545173013718814, 'alpha': 4.659084785377676, 'lambda': 7.39096576046277}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:05,896] Trial 108 finished with value: 0.3931013608968786 and parameters: {'learning_rate': 0.09576967122924206, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.42168446058357556, 'subsample': 0.6591205352238678, 'colsample_bytree': 0.9030257491051191, 'alpha': 0.6813912703186955, 'lambda': 5.420464917598196}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:06,742] Trial 109 finished with value: 0.3846849439968579 and parameters: {'learning_rate': 0.08222456039759993, 'max_depth': 5, 'min_child_weight': 4, 'gamma': 0.14093703347678482, 'subsample': 0.841586093616325, 'colsample_bytree': 0.022403106899887226, 'alpha': 1.0037524437068375, 'lambda': 6.645626414133772}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:07,424] Trial 110 finished with value: 0.3794726730394604 and parameters: {'learning_rate': 0.0911472411861287, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.9959988675928251, 'subsample': 0.8139099676895354, 'colsample_bytree': 0.8688765604033853, 'alpha': 0.25639014829560247, 'lambda': 4.590626278836759}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:08,387] Trial 111 finished with value: 0.4300365241884243 and parameters: {'learning_rate': 0.11580279725888963, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.05857054566104005, 'subsample': 0.8603580420682505, 'colsample_bytree': 0.9084910549388105, 'alpha': 0.5315840123049695, 'lambda': 5.2253681039195845}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:09,173] Trial 112 finished with value: 0.4008895338211171 and parameters: {'learning_rate': 0.13026004063573515, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.39159746791340266, 'subsample': 0.7291881700435753, 'colsample_bytree': 0.8366316385539674, 'alpha': 0.012820165158726415, 'lambda': 5.62100313992729}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:10,213] Trial 113 finished with value: 0.40837739416341284 and parameters: {'learning_rate': 0.12263527980708232, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.20831287502716106, 'subsample': 0.8748624578354635, 'colsample_bytree': 0.8878597796853679, 'alpha': 0.6051548892035806, 'lambda': 4.9983312746046895}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:10,820] Trial 114 finished with value: 0.38257400472907166 and parameters: {'learning_rate': 0.11000163483980735, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 0.6537593359676641, 'subsample': 0.7516481373204911, 'colsample_bytree': 0.5681994401263468, 'alpha': 0.3418683782992262, 'lambda': 5.330982987737886}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:12,322] Trial 115 finished with value: 0.4437353683792194 and parameters: {'learning_rate': 0.12775116807363845, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0024660388780445765, 'subsample': 0.786203466571078, 'colsample_bytree': 0.7806695928587837, 'alpha': 0.8246004383041599, 'lambda': 4.691393007744233}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:13,709] Trial 116 finished with value: 0.3871902206178975 and parameters: {'learning_rate': 0.11706509755609437, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.4231301972781011, 'subsample': 0.7865342617190758, 'colsample_bytree': 0.7481619169156064, 'alpha': 0.8672275371354452, 'lambda': 4.737808499554575}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:21,164] Trial 117 finished with value: 0.36600936929394984 and parameters: {'learning_rate': 0.13502340738649674, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0013345552991560414, 'subsample': 0.6777933786081561, 'colsample_bytree': 0.782399523065637, 'alpha': 8.911955587462007, 'lambda': 3.57879492423025}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:26,952] Trial 118 finished with value: 0.3687704320947291 and parameters: {'learning_rate': 0.09973901598725142, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 7.5786419871859785, 'subsample': 0.7056950052568559, 'colsample_bytree': 0.8079386539159779, 'alpha': 1.1937624197204655, 'lambda': 4.394270134036376}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:35,785] Trial 119 finished with value: 0.4066391119239125 and parameters: {'learning_rate': 0.10761455043565869, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 0.23680288892111562, 'subsample': 0.7490603165311555, 'colsample_bytree': 0.8490663442701272, 'alpha': 0.7595273936318617, 'lambda': 4.873724512235513}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:41,832] Trial 120 finished with value: 0.3864124208509433 and parameters: {'learning_rate': 0.15830041916796234, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.7952489116795535, 'subsample': 0.6258440359703926, 'colsample_bytree': 0.7591386325856289, 'alpha': 0.18660485938219884, 'lambda': 5.723338118603923}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:43,151] Trial 121 finished with value: 0.4426804876649642 and parameters: {'learning_rate': 0.12766839900999347, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.01848552931056965, 'subsample': 0.8238526458608599, 'colsample_bytree': 0.9397577566335202, 'alpha': 0.503168820717003, 'lambda': 5.425298896172128}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:44,156] Trial 122 finished with value: 0.38116081988625666 and parameters: {'learning_rate': 0.14995275867368787, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.5000406116894596, 'subsample': 0.8243393310493339, 'colsample_bytree': 0.9348905871127656, 'alpha': 1.029609850531254, 'lambda': 4.535694230712583}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:45,318] Trial 123 finished with value: 0.3911859305027743 and parameters: {'learning_rate': 0.16445627754436418, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.19045553165042217, 'subsample': 0.8074545700035665, 'colsample_bytree': 0.05996562082797674, 'alpha': 0.4242509532522447, 'lambda': 6.2647051340241715}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:46,267] Trial 124 finished with value: 0.4079438950631707 and parameters: {'learning_rate': 0.12758346103012072, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.009361304844211896, 'subsample': 0.7793120147258671, 'colsample_bytree': 0.9667230905073132, 'alpha': 2.8764853643314785, 'lambda': 6.055264807814673}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:47,071] Trial 125 finished with value: 0.39758470397025647 and parameters: {'learning_rate': 0.1195612611082701, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.32699548712717336, 'subsample': 0.8416721571906546, 'colsample_bytree': 0.8790669390627972, 'alpha': 0.5982305451180217, 'lambda': 5.5172464895475}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:47,317] Trial 126 finished with value: 0.35737803690932907 and parameters: {'learning_rate': 0.14258947257664176, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 9.31427779055454, 'subsample': 0.9001708794011143, 'colsample_bytree': 0.9996165179412887, 'alpha': 0.18036111604113103, 'lambda': 4.990031058653418}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:47,938] Trial 127 finished with value: 0.3842692740322305 and parameters: {'learning_rate': 0.1322364138803055, 'max_depth': 6, 'min_child_weight': 3, 'gamma': 0.6146840252936181, 'subsample': 0.7314136198745107, 'colsample_bytree': 0.8310451269837024, 'alpha': 0.8058821276384359, 'lambda': 6.850139817141606}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:48,876] Trial 128 finished with value: 0.40069374108800937 and parameters: {'learning_rate': 0.11282091821901008, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.4123093937892462, 'subsample': 0.6987425192357369, 'colsample_bytree': 0.8019115592711724, 'alpha': 0.015376319025213303, 'lambda': 4.055045130301629}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:50,114] Trial 129 finished with value: 0.4516066468043706 and parameters: {'learning_rate': 0.14861554421845993, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.001469412913168884, 'subsample': 0.9841861650337138, 'colsample_bytree': 0.9489312597343401, 'alpha': 0.34861172333222656, 'lambda': 5.194275684685863}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:50,702] Trial 130 finished with value: 0.3913370996377839 and parameters: {'learning_rate': 0.14884822286756524, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.7894487617755962, 'subsample': 0.9588895841542829, 'colsample_bytree': 0.9539289121352074, 'alpha': 0.33847747931612454, 'lambda': 4.753893970191985}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:51,522] Trial 131 finished with value: 0.4144849585045321 and parameters: {'learning_rate': 0.15355503522596467, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.18466782232403356, 'subsample': 0.9853322535631543, 'colsample_bytree': 0.9164636574579212, 'alpha': 0.5185767586361214, 'lambda': 5.19819028121445}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:52,167] Trial 132 finished with value: 0.37796203294909175 and parameters: {'learning_rate': 0.13809033315782338, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0.20075355237662398, 'subsample': 0.7948215044903951, 'colsample_bytree': 0.9449552010243069, 'alpha': 3.9982827740140436, 'lambda': 4.283115320354905}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:53,100] Trial 133 finished with value: 0.42846041520203576 and parameters: {'learning_rate': 0.15832535948502507, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.016358960450506364, 'subsample': 0.9249854157340724, 'colsample_bytree': 0.8604247052824638, 'alpha': 0.9935752136711483, 'lambda': 5.449416221243323}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:53,578] Trial 134 finished with value: 0.3851776481855626 and parameters: {'learning_rate': 0.14341013641306335, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.5132623262161294, 'subsample': 0.8892562747350911, 'colsample_bytree': 0.9707676655552335, 'alpha': 1.2937325244735272, 'lambda': 5.879425687563785}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:54,381] Trial 135 finished with value: 0.3951758378189122 and parameters: {'learning_rate': 0.11501056567309431, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.406796742150651, 'subsample': 0.9761603680038942, 'colsample_bytree': 0.8999747504430021, 'alpha': 0.6995046280467065, 'lambda': 4.8808601409344705}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:55,344] Trial 136 finished with value: 0.41659356726006375 and parameters: {'learning_rate': 0.105447020389827, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.24657081188848956, 'subsample': 0.9127959020318868, 'colsample_bytree': 0.9289797442877524, 'alpha': 0.25448348181714875, 'lambda': 5.123918343143497}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:56,161] Trial 137 finished with value: 0.38868508016584097 and parameters: {'learning_rate': 0.12122845978821066, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.6486825924156356, 'subsample': 0.8278612672799428, 'colsample_bytree': 0.8218966562702891, 'alpha': 0.42474391294774516, 'lambda': 4.583052365456239}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:56,905] Trial 138 finished with value: 0.36721479212674096 and parameters: {'learning_rate': 0.1265867136914992, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 3.251438592889062, 'subsample': 0.8593829553894506, 'colsample_bytree': 0.7876578595532259, 'alpha': 0.8499210501357912, 'lambda': 6.539155142448672}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:57,855] Trial 139 finished with value: 0.3942013884838457 and parameters: {'learning_rate': 0.1487946292532355, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.1546362169273803, 'subsample': 0.9543353393326963, 'colsample_bytree': 0.4867452468285012, 'alpha': 1.5086049371380055, 'lambda': 5.652079852079456}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:58,375] Trial 140 finished with value: 0.36183684944314026 and parameters: {'learning_rate': 0.13391060856413617, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.9503584196622494, 'subsample': 0.09454464761226256, 'colsample_bytree': 0.8739620497258397, 'alpha': 0.0022840657987586166, 'lambda': 5.338661838598218}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:19:59,523] Trial 141 finished with value: 0.43006404109866986 and parameters: {'learning_rate': 0.1300279980301146, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.05504532715371687, 'subsample': 0.8668414194725338, 'colsample_bytree': 0.9113375167258433, 'alpha': 0.5238190635680976, 'lambda': 5.11252363370676}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:00,724] Trial 142 finished with value: 0.446653357268853 and parameters: {'learning_rate': 0.1257463770510666, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.015400151556454966, 'subsample': 0.7494458386797861, 'colsample_bytree': 0.8882712376836325, 'alpha': 0.5704292836552176, 'lambda': 5.367609818044535}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:01,550] Trial 143 finished with value: 0.3825811800429844 and parameters: {'learning_rate': 0.1536974370716586, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.002772643849543538, 'subsample': 0.7239876611893858, 'colsample_bytree': 0.8474569704648724, 'alpha': 5.2847467709846025, 'lambda': 4.950035178444193}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:02,151] Trial 144 finished with value: 0.396233794618483 and parameters: {'learning_rate': 0.12294499037896746, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.3696816579659205, 'subsample': 0.7680619432529605, 'colsample_bytree': 0.8808052606666196, 'alpha': 0.2702484679659291, 'lambda': 7.266336346832569}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:02,746] Trial 145 finished with value: 0.3853927536974045 and parameters: {'learning_rate': 0.10867977442253658, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 0.5087008357345487, 'subsample': 0.7478941091835368, 'colsample_bytree': 0.931348339291409, 'alpha': 0.7101921223215292, 'lambda': 5.5302724325170045}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:03,432] Trial 146 finished with value: 0.39775008928900524 and parameters: {'learning_rate': 0.11470889050448797, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2887144483847279, 'subsample': 0.7766686851989767, 'colsample_bytree': 0.8913416049971745, 'alpha': 1.0835750261713057, 'lambda': 4.710234435169136}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:04,317] Trial 147 finished with value: 0.3887709898792875 and parameters: {'learning_rate': 0.14487353345147558, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.7334576242232616, 'subsample': 0.5885377546544216, 'colsample_bytree': 0.9744001260151234, 'alpha': 0.43289575929522417, 'lambda': 5.808347638778356}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:05,128] Trial 148 finished with value: 0.4001102795504165 and parameters: {'learning_rate': 0.1182704606299469, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 0.24434694120255354, 'subsample': 0.6496266048411061, 'colsample_bytree': 0.8269187990308773, 'alpha': 0.9142355640046957, 'lambda': 6.084594515050835}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:05,953] Trial 149 finished with value: 0.4013218929141049 and parameters: {'learning_rate': 0.13846820621207, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.5267189742407009, 'subsample': 0.8048183415646541, 'colsample_bytree': 0.8597284228177735, 'alpha': 0.2057036699325227, 'lambda': 5.394065551892052}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:06,856] Trial 150 finished with value: 0.40965797399615694 and parameters: {'learning_rate': 0.10157657541580221, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.1816052738403119, 'subsample': 0.7489690578017787, 'colsample_bytree': 0.9441560450671849, 'alpha': 0.6369479592265809, 'lambda': 6.33567511905436}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:08,414] Trial 151 finished with value: 0.4381208925870068 and parameters: {'learning_rate': 0.12787811632497192, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.004422439455407097, 'subsample': 0.546694264920744, 'colsample_bytree': 0.9087464989046407, 'alpha': 0.5430122705864027, 'lambda': 5.264191237252971}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:10,119] Trial 152 finished with value: 0.4384435693932488 and parameters: {'learning_rate': 0.12552165500010792, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.015222624692392248, 'subsample': 0.6099518117364018, 'colsample_bytree': 0.9153161292888072, 'alpha': 0.3782211213360377, 'lambda': 5.013482143559591}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:12,913] Trial 153 finished with value: 0.4440780811017584 and parameters: {'learning_rate': 0.12592300760011688, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.02089723579823786, 'subsample': 0.5722967368459055, 'colsample_bytree': 0.9208200795387119, 'alpha': 0.27085670789318894, 'lambda': 5.268286658197622}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:22,652] Trial 154 finished with value: 0.3951194600555715 and parameters: {'learning_rate': 0.13057645017416947, 'max_depth': 5, 'min_child_weight': 4, 'gamma': 0.39764072653626553, 'subsample': 0.5556536620094645, 'colsample_bytree': 0.9169258013549181, 'alpha': 0.19253764213030788, 'lambda': 4.42379512278513}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:36,567] Trial 155 finished with value: 0.445059466158506 and parameters: {'learning_rate': 0.12602363087039448, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.03834554500848231, 'subsample': 0.5320716322687021, 'colsample_bytree': 0.8947383828691096, 'alpha': 0.357850670929821, 'lambda': 4.9149696523630775}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:40,242] Trial 156 finished with value: 0.39826857855690717 and parameters: {'learning_rate': 0.1269638696849971, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.28582118554094266, 'subsample': 0.496086199160575, 'colsample_bytree': 0.3516006999772813, 'alpha': 0.26021178030580366, 'lambda': 4.863213625440908}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:41,706] Trial 157 finished with value: 0.44201506559183784 and parameters: {'learning_rate': 0.12402556711849327, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.015315751392839673, 'subsample': 0.5314695300245996, 'colsample_bytree': 0.8878894868384946, 'alpha': 0.0042954600220616035, 'lambda': 4.640812107352123}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:42,369] Trial 158 finished with value: 0.3964231228027781 and parameters: {'learning_rate': 0.13506617979839655, 'max_depth': 6, 'min_child_weight': 3, 'gamma': 0.6472946007319998, 'subsample': 0.6127730418518909, 'colsample_bytree': 0.8906901004518012, 'alpha': 0.04087767856924998, 'lambda': 4.238977855755296}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:42,934] Trial 159 finished with value: 0.392495573398283 and parameters: {'learning_rate': 0.1227099685009934, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.3562243063239523, 'subsample': 0.47966192635609267, 'colsample_bytree': 0.7381907261046277, 'alpha': 0.3594486291677571, 'lambda': 4.665917465739496}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:43,913] Trial 160 finished with value: 0.4208392120458221 and parameters: {'learning_rate': 0.16051780106416386, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.21922141097500392, 'subsample': 0.5854037409819217, 'colsample_bytree': 0.7699458629422696, 'alpha': 0.0009669015546306392, 'lambda': 4.493608282049374}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:45,254] Trial 161 finished with value: 0.4366985629859296 and parameters: {'learning_rate': 0.1253024182045804, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.020789027627253445, 'subsample': 0.5673947491623886, 'colsample_bytree': 0.8985671132720537, 'alpha': 0.4019498693450481, 'lambda': 5.01693669878227}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:46,367] Trial 162 finished with value: 0.43104149798365227 and parameters: {'learning_rate': 0.12876977626563385, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.03517016457534278, 'subsample': 0.5318064214571719, 'colsample_bytree': 0.8667157050089596, 'alpha': 0.7899332985175288, 'lambda': 4.828124460694813}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:48,017] Trial 163 finished with value: 0.44138695893050817 and parameters: {'learning_rate': 0.11850718769158151, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.011115044086438323, 'subsample': 0.5353960733099357, 'colsample_bytree': 0.9222304098292978, 'alpha': 0.5070255018955987, 'lambda': 5.131094980062904}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:48,604] Trial 164 finished with value: 0.4020602735799442 and parameters: {'learning_rate': 0.12465334643608413, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.4694378631918681, 'subsample': 0.5161955019201081, 'colsample_bytree': 0.9532746847537132, 'alpha': 0.205662901548204, 'lambda': 5.074612175417023}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:49,588] Trial 165 finished with value: 0.4062537185429516 and parameters: {'learning_rate': 0.11934109725663003, 'max_depth': 9, 'min_child_weight': 4, 'gamma': 0.20440355216756606, 'subsample': 0.5382312652812242, 'colsample_bytree': 0.9266918730781628, 'alpha': 0.38326255511191226, 'lambda': 4.646193573505204}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:50,138] Trial 166 finished with value: 0.36861325506494325 and parameters: {'learning_rate': 0.13342891914318739, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 6.257192152190401, 'subsample': 0.6011508769565346, 'colsample_bytree': 0.8443212140314463, 'alpha': 0.7313924511012474, 'lambda': 5.650959078475581}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:51,161] Trial 167 finished with value: 0.37956272352634485 and parameters: {'learning_rate': 0.12020340518963668, 'max_depth': 5, 'min_child_weight': 8, 'gamma': 0.3534937718371065, 'subsample': 0.5138184597266696, 'colsample_bytree': 0.9850431552124181, 'alpha': 0.9799768382274485, 'lambda': 4.914238801365426}. Best is trial 100 with value: 0.45251333315961784.\n",
      "[I 2024-02-12 16:20:52,873] Trial 168 finished with value: 0.46674363150962034 and parameters: {'learning_rate': 0.15604392347278626, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0030378534372456105, 'subsample': 0.5662660735389222, 'colsample_bytree': 0.8075017882863788, 'alpha': 0.17478248383533299, 'lambda': 0.11632378320619896}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:20:53,406] Trial 169 finished with value: 0.40015208780954287 and parameters: {'learning_rate': 0.15603642517388397, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.5393722220536825, 'subsample': 0.5655877264809177, 'colsample_bytree': 0.7870994821117622, 'alpha': 0.19558799392541254, 'lambda': 1.8679205736810376}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:20:54,210] Trial 170 finished with value: 0.4192308111534595 and parameters: {'learning_rate': 0.16671891771232022, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.20095771011619534, 'subsample': 0.49821020725553294, 'colsample_bytree': 0.811243657391573, 'alpha': 0.01910754826718608, 'lambda': 9.90568645324806}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:20:55,850] Trial 171 finished with value: 0.45204452082821994 and parameters: {'learning_rate': 0.15023475856149265, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0037355410551188177, 'subsample': 0.6152515054608061, 'colsample_bytree': 0.8843660074330598, 'alpha': 0.4985464685577627, 'lambda': 1.4159624172165861}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:20:57,370] Trial 172 finished with value: 0.44963998631085395 and parameters: {'learning_rate': 0.15024439067293918, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.008096100329355756, 'subsample': 0.636287361366773, 'colsample_bytree': 0.8819727377551185, 'alpha': 0.5630685260834528, 'lambda': 0.7000216216307013}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:20:58,242] Trial 173 finished with value: 0.42864156109417406 and parameters: {'learning_rate': 0.14655375914002347, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.22041344573178245, 'subsample': 0.635461207893921, 'colsample_bytree': 0.8777284353786844, 'alpha': 0.5509194997052976, 'lambda': 0.13664308473070103}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:20:59,079] Trial 174 finished with value: 0.42634370208388733 and parameters: {'learning_rate': 0.15062365871326727, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.36445515270332535, 'subsample': 0.5458688881672318, 'colsample_bytree': 0.8471437414406066, 'alpha': 0.2453482390366015, 'lambda': 0.5413551606627396}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:00,893] Trial 175 finished with value: 0.45084167981335993 and parameters: {'learning_rate': 0.1403700267216889, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0010063853148695213, 'subsample': 0.5710801853449359, 'colsample_bytree': 0.8921841315351544, 'alpha': 0.5145770392213456, 'lambda': 1.0676336389360999}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:02,584] Trial 176 finished with value: 0.4485331739016619 and parameters: {'learning_rate': 0.14246658995240158, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.002172491308682637, 'subsample': 0.5782042486458647, 'colsample_bytree': 0.8805940177500143, 'alpha': 0.49229697933037864, 'lambda': 1.2588170977075142}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:03,349] Trial 177 finished with value: 0.40375626309801405 and parameters: {'learning_rate': 0.13986924268559064, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.614322161756105, 'subsample': 0.5894188597769083, 'colsample_bytree': 0.8902365755240668, 'alpha': 0.1942591939432065, 'lambda': 1.2213890177490383}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:04,450] Trial 178 finished with value: 0.44227648537018877 and parameters: {'learning_rate': 0.14319116970850562, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2379898282929741, 'subsample': 0.5675545504890881, 'colsample_bytree': 0.8614350389858745, 'alpha': 0.004286151374819844, 'lambda': 1.1565702815636105}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:05,136] Trial 179 finished with value: 0.408903313577555 and parameters: {'learning_rate': 0.14257924095663077, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.344828775011662, 'subsample': 0.5719275264127839, 'colsample_bytree': 0.8643330298276656, 'alpha': 0.6044774490848993, 'lambda': 0.7532808310365431}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:06,082] Trial 180 finished with value: 0.38785669311591087 and parameters: {'learning_rate': 0.14541904801715647, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.7770234166358843, 'subsample': 0.5923826441194519, 'colsample_bytree': 0.8247161445444677, 'alpha': 0.3612804828877049, 'lambda': 1.4024964104976}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:06,889] Trial 181 finished with value: 0.43149179207310423 and parameters: {'learning_rate': 0.1489634752406268, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.20649411990367061, 'subsample': 0.5711178381531792, 'colsample_bytree': 0.8821704159550198, 'alpha': 0.11617424004630908, 'lambda': 1.1831160833035321}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:08,739] Trial 182 finished with value: 0.46066194440741176 and parameters: {'learning_rate': 0.1404758509540483, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0056670778525371125, 'subsample': 0.5208387520541018, 'colsample_bytree': 0.852667228601008, 'alpha': 0.041347052473993985, 'lambda': 0.782366062160004}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:09,846] Trial 183 finished with value: 0.4363649927525919 and parameters: {'learning_rate': 0.13852546715484287, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2073157553431535, 'subsample': 0.46084112255910104, 'colsample_bytree': 0.8487348511482128, 'alpha': 0.406944715186124, 'lambda': 0.4271793248076669}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:10,494] Trial 184 finished with value: 0.4018490507409263 and parameters: {'learning_rate': 0.15153363342511547, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.4692538977350869, 'subsample': 0.5550924980646662, 'colsample_bytree': 0.835257690909246, 'alpha': 0.6808512632557249, 'lambda': 0.8253186520221414}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:10,886] Trial 185 finished with value: 0.3601832549861469 and parameters: {'learning_rate': 0.14171032622428262, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 4.988314500919422, 'subsample': 0.5069972565748476, 'colsample_bytree': 0.8621168969624864, 'alpha': 0.23213433134736602, 'lambda': 0.9331169673260926}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:12,600] Trial 186 finished with value: 0.42400426006421466 and parameters: {'learning_rate': 0.13623479568019337, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2068092896194634, 'subsample': 0.5986010878199659, 'colsample_bytree': 0.8970799670676557, 'alpha': 0.47690944580485295, 'lambda': 1.4460172524128296}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:23,803] Trial 187 finished with value: 0.42243673066823206 and parameters: {'learning_rate': 0.14755169299691315, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.40710260677310667, 'subsample': 0.9961862534306265, 'colsample_bytree': 0.8139590506653954, 'alpha': 0.0003036342874196407, 'lambda': 1.0974497012963151}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:35,164] Trial 188 finished with value: 0.4181262146713389 and parameters: {'learning_rate': 0.14411432084949743, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.19460071655979685, 'subsample': 0.6700559327470808, 'colsample_bytree': 0.9401354652906391, 'alpha': 0.7771582449823534, 'lambda': 0.4859048006307849}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:39,044] Trial 189 finished with value: 0.4018687903371201 and parameters: {'learning_rate': 0.15544392378652286, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.5941232702932173, 'subsample': 0.4352198724372537, 'colsample_bytree': 0.8689873047315076, 'alpha': 0.3182027699215566, 'lambda': 1.4116500184930116}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:39,885] Trial 190 finished with value: 0.4219283114371551 and parameters: {'learning_rate': 0.13912521460329016, 'max_depth': 11, 'min_child_weight': 1, 'gamma': 0.1770918154038889, 'subsample': 0.5504535479990744, 'colsample_bytree': 0.8333330648839837, 'alpha': 0.9500921027403133, 'lambda': 0.02581111726345897}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:41,288] Trial 191 finished with value: 0.456010047124081 and parameters: {'learning_rate': 0.15096040158588617, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0322372279964416, 'subsample': 0.580331926436765, 'colsample_bytree': 0.8945460771323682, 'alpha': 0.10468985513558776, 'lambda': 1.6292727900655635}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:43,108] Trial 192 finished with value: 0.45683041076621655 and parameters: {'learning_rate': 0.15072651973369036, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0039854619879041995, 'subsample': 0.6282807432206285, 'colsample_bytree': 0.8989645744914648, 'alpha': 0.17960011968147954, 'lambda': 0.677575004859279}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:44,499] Trial 193 finished with value: 0.4478548097272651 and parameters: {'learning_rate': 0.15180038619700334, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.006654674959547665, 'subsample': 0.6193277611540104, 'colsample_bytree': 0.9069585063293815, 'alpha': 0.5310835183173503, 'lambda': 0.6683222082475795}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:45,670] Trial 194 finished with value: 0.45222763117450243 and parameters: {'learning_rate': 0.15998156378480644, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.02070937681708528, 'subsample': 0.6201535086492453, 'colsample_bytree': 0.903836748462037, 'alpha': 0.23349462016328826, 'lambda': 0.7881950752445144}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:47,283] Trial 195 finished with value: 0.45261797889807165 and parameters: {'learning_rate': 0.15992600714415675, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0032466879560072307, 'subsample': 0.6562902389155096, 'colsample_bytree': 0.9018103395169242, 'alpha': 0.22448932855768083, 'lambda': 0.7053909002871128}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:49,173] Trial 196 finished with value: 0.451929416292252 and parameters: {'learning_rate': 0.16271304989325705, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0017656290325936798, 'subsample': 0.625969417585664, 'colsample_bytree': 0.9037530753891342, 'alpha': 0.17759826209065943, 'lambda': 2.25976412659904}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:50,155] Trial 197 finished with value: 0.4223545221001665 and parameters: {'learning_rate': 0.1629344438069676, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.39462391407294367, 'subsample': 0.6250629044520011, 'colsample_bytree': 0.9020499314603294, 'alpha': 0.1882183723588739, 'lambda': 0.6750954619588337}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:50,963] Trial 198 finished with value: 0.4099639563779689 and parameters: {'learning_rate': 0.15817263324716432, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.37676457092702775, 'subsample': 0.6604379996030496, 'colsample_bytree': 0.9015841453645246, 'alpha': 0.5594806124662097, 'lambda': 1.0124596941029962}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:51,724] Trial 199 finished with value: 0.43177987438412485 and parameters: {'learning_rate': 0.17186805329067567, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.1953734993211754, 'subsample': 0.6416555497254702, 'colsample_bytree': 0.8780838956805266, 'alpha': 0.19146460899278628, 'lambda': 0.26512252432257083}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:52,937] Trial 200 finished with value: 0.4534246225948962 and parameters: {'learning_rate': 0.16281305228904908, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.015440049658679879, 'subsample': 0.6244038543465054, 'colsample_bytree': 0.9044056117710921, 'alpha': 0.3830464745655008, 'lambda': 1.7634800608037118}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:53,866] Trial 201 finished with value: 0.43111246430969735 and parameters: {'learning_rate': 0.16566027780116854, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.19156133253149932, 'subsample': 0.6229624790813341, 'colsample_bytree': 0.9031516873608992, 'alpha': 0.37219597706089264, 'lambda': 1.7520313091461739}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:55,704] Trial 202 finished with value: 0.45035858228986286 and parameters: {'learning_rate': 0.16137543429979856, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0050326106416356264, 'subsample': 0.6114868446261269, 'colsample_bytree': 0.8861059899792173, 'alpha': 0.1595889503883396, 'lambda': 2.2458214671439487}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:56,371] Trial 203 finished with value: 0.421939897672936 and parameters: {'learning_rate': 0.15892461259034693, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.3961244953500628, 'subsample': 0.650827730934778, 'colsample_bytree': 0.8817191989827685, 'alpha': 0.004137583407842982, 'lambda': 2.2757829314097275}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:56,981] Trial 204 finished with value: 0.3753030040666255 and parameters: {'learning_rate': 0.16110123127935566, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0035036425886331504, 'subsample': 0.6146392761023682, 'colsample_bytree': 0.924150939551919, 'alpha': 6.809145299876748, 'lambda': 1.6055153775379691}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:21:58,615] Trial 205 finished with value: 0.4578151395286442 and parameters: {'learning_rate': 0.16846005581628443, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.007930769161481919, 'subsample': 0.6785663231665036, 'colsample_bytree': 0.9609563281266423, 'alpha': 0.19325074866139147, 'lambda': 0.5993449882631106}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:00,122] Trial 206 finished with value: 0.4458100042525239 and parameters: {'learning_rate': 0.16931669284552897, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0015625062886962969, 'subsample': 0.6834939469517327, 'colsample_bytree': 0.9528253055915709, 'alpha': 0.6372823089908832, 'lambda': 0.6127904182952959}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:00,742] Trial 207 finished with value: 0.4200863360538092 and parameters: {'learning_rate': 0.15366667033602555, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2410906451609104, 'subsample': 0.6358062841202388, 'colsample_bytree': 0.8757633910503456, 'alpha': 0.26560825546363226, 'lambda': 2.038300213099416}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:01,542] Trial 208 finished with value: 0.39938334970146316 and parameters: {'learning_rate': 0.16430574070355794, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.5639426148595408, 'subsample': 0.6601807303512809, 'colsample_bytree': 0.9354248365176947, 'alpha': 0.4926012051286098, 'lambda': 0.8656846679370972}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:02,160] Trial 209 finished with value: 0.42569854062279605 and parameters: {'learning_rate': 0.17661039493262332, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.33916334575578566, 'subsample': 0.6054197091676672, 'colsample_bytree': 0.9627061112585615, 'alpha': 0.1808737728447643, 'lambda': 0.33591974322384893}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:02,892] Trial 210 finished with value: 0.4183124874034238 and parameters: {'learning_rate': 0.15308049747045613, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.20479734270760838, 'subsample': 0.673180173831202, 'colsample_bytree': 0.9070957413779612, 'alpha': 0.4117951077087064, 'lambda': 2.9983608579080645}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:04,104] Trial 211 finished with value: 0.4469657355193101 and parameters: {'learning_rate': 0.16870195146282507, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.009367889904226823, 'subsample': 0.6980151862426389, 'colsample_bytree': 0.9499972678478571, 'alpha': 0.6611150054880119, 'lambda': 0.6204724991807117}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:05,724] Trial 212 finished with value: 0.4461138139421787 and parameters: {'learning_rate': 0.16149344764419507, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.007428208640244889, 'subsample': 0.6265113278482596, 'colsample_bytree': 0.9275745647829838, 'alpha': 0.6866482103984042, 'lambda': 0.7116404535598105}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:07,422] Trial 213 finished with value: 0.44509318820917054 and parameters: {'learning_rate': 0.1676765612215441, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0046832903271384445, 'subsample': 0.6504601519122664, 'colsample_bytree': 0.2652404197556465, 'alpha': 0.18921238452331218, 'lambda': 2.66060715207202}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:08,436] Trial 214 finished with value: 0.4200273203872346 and parameters: {'learning_rate': 0.15027746247218654, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2201257003628429, 'subsample': 0.7018963206697736, 'colsample_bytree': 0.8876490751070742, 'alpha': 0.5316378532689463, 'lambda': 1.5694013350375353}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:14,201] Trial 215 finished with value: 0.41113499960228295 and parameters: {'learning_rate': 0.1557288028726043, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.4222821185264268, 'subsample': 0.6060408490029138, 'colsample_bytree': 0.8585757440120282, 'alpha': 0.36353145781732127, 'lambda': 0.9824087099607622}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:23,775] Trial 216 finished with value: 0.42163836614194056 and parameters: {'learning_rate': 0.15838419189618202, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.20578664163176588, 'subsample': 0.6868864067400022, 'colsample_bytree': 0.9657991902478305, 'alpha': 0.7332339655770811, 'lambda': 0.25974703966048285}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:31,700] Trial 217 finished with value: 0.41194853982985524 and parameters: {'learning_rate': 0.1749787321984163, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.18897805899905956, 'subsample': 0.6373480090394987, 'colsample_bytree': 0.17681105534450997, 'alpha': 0.17487082355321565, 'lambda': 0.5081304353509453}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:37,883] Trial 218 finished with value: 0.4512320544223753 and parameters: {'learning_rate': 0.16874995020465977, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.002448155728845743, 'subsample': 0.5904568131880955, 'colsample_bytree': 0.9124805657043555, 'alpha': 0.4981855338780726, 'lambda': 1.3274961065773463}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:38,614] Trial 219 finished with value: 0.4100931948261467 and parameters: {'learning_rate': 0.17008755881183985, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.47525677049041204, 'subsample': 0.6000988236050021, 'colsample_bytree': 0.944053619623367, 'alpha': 0.3565133940918655, 'lambda': 1.2866495841536092}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:39,203] Trial 220 finished with value: 0.4180790103324725 and parameters: {'learning_rate': 0.17960669760382358, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.34702084089377744, 'subsample': 0.5869497897327843, 'colsample_bytree': 0.9159012980042055, 'alpha': 0.15668844376459728, 'lambda': 2.1755170578679257}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:39,943] Trial 221 finished with value: 0.4297737360350523 and parameters: {'learning_rate': 0.16465009382308132, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.16367932035855967, 'subsample': 0.6181574299950174, 'colsample_bytree': 0.901287465063526, 'alpha': 0.5561624092648757, 'lambda': 1.7792063391993653}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:41,205] Trial 222 finished with value: 0.4534732927237251 and parameters: {'learning_rate': 0.16150666239336228, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.021967633905755235, 'subsample': 0.6594031601246939, 'colsample_bytree': 0.8781073482235557, 'alpha': 0.45094812482171276, 'lambda': 0.807842329575356}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:42,833] Trial 223 finished with value: 0.44635220367317285 and parameters: {'learning_rate': 0.1625396851465866, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0005749879852892268, 'subsample': 0.6569223745142695, 'colsample_bytree': 0.8698792501840029, 'alpha': 0.37161545799254037, 'lambda': 0.8180899695056212}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:43,470] Trial 224 finished with value: 0.3716577526718594 and parameters: {'learning_rate': 0.18348030049718683, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 4.080345102997913, 'subsample': 0.6799509065068445, 'colsample_bytree': 0.9198757692365346, 'alpha': 0.009250595850285676, 'lambda': 0.7123555835924825}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:44,084] Trial 225 finished with value: 0.4208885781732984 and parameters: {'learning_rate': 0.17188705036690965, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.18441532215049503, 'subsample': 0.5858684937515721, 'colsample_bytree': 0.8511163776555056, 'alpha': 0.8386258841051858, 'lambda': 0.9858211748657255}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:45,383] Trial 226 finished with value: 0.44328786118466323 and parameters: {'learning_rate': 0.1576584375577509, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.019730393928099266, 'subsample': 0.6328946914519872, 'colsample_bytree': 0.8811208113966166, 'alpha': 0.3158729733768011, 'lambda': 1.3802668553392328}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:46,022] Trial 227 finished with value: 0.4195608440010936 and parameters: {'learning_rate': 0.15137679664829737, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2967092847066283, 'subsample': 0.6156279676143587, 'colsample_bytree': 0.9378802993327991, 'alpha': 0.5644164582263896, 'lambda': 0.49964060145182154}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:46,522] Trial 228 finished with value: 0.4103857218580296 and parameters: {'learning_rate': 0.16764436912990646, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.5495092751791053, 'subsample': 0.7113981918886746, 'colsample_bytree': 0.9032658136632152, 'alpha': 0.16168257561316357, 'lambda': 1.1402251302231134}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:48,227] Trial 229 finished with value: 0.4524881451803992 and parameters: {'learning_rate': 0.14803806008789525, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0002814225885009961, 'subsample': 0.6510659307190135, 'colsample_bytree': 0.9186512747205877, 'alpha': 0.44961180904829373, 'lambda': 2.5867671572654936}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:48,932] Trial 230 finished with value: 0.4164059350682048 and parameters: {'learning_rate': 0.14739500566729508, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.21307711448723735, 'subsample': 0.6485389304817334, 'colsample_bytree': 0.9537056325610731, 'alpha': 0.41184739581713453, 'lambda': 2.022766215259112}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:49,873] Trial 231 finished with value: 0.42456985972923666 and parameters: {'learning_rate': 0.15537414869814495, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.16459387703833653, 'subsample': 0.6315922401099501, 'colsample_bytree': 0.9093326872323221, 'alpha': 0.6619260855882555, 'lambda': 1.5486850540051242}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:51,857] Trial 232 finished with value: 0.4592387329545535 and parameters: {'learning_rate': 0.15923289112822328, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.004822844491653387, 'subsample': 0.6693580449661487, 'colsample_bytree': 0.8880258505223803, 'alpha': 0.0014873980272593068, 'lambda': 2.7049066627615406}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:53,799] Trial 233 finished with value: 0.4529611911105493 and parameters: {'learning_rate': 0.16221458194129862, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.029157318271883448, 'subsample': 0.6653636314706907, 'colsample_bytree': 0.9287440982394984, 'alpha': 0.012601972114871962, 'lambda': 2.849038498083337}. Best is trial 168 with value: 0.46674363150962034.\n",
      "[I 2024-02-12 16:22:55,631] Trial 234 finished with value: 0.4671584355563095 and parameters: {'learning_rate': 0.16150105435370582, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0028722394612759293, 'subsample': 0.6649731394784183, 'colsample_bytree': 0.8936784795480582, 'alpha': 0.009799489107733456, 'lambda': 2.6045574364658113}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:22:56,274] Trial 235 finished with value: 0.4190076507663797 and parameters: {'learning_rate': 0.16175735340472805, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.3682553455591341, 'subsample': 0.6678107853616414, 'colsample_bytree': 0.8819663052808773, 'alpha': 0.006689741794698362, 'lambda': 2.662175013205627}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:22:57,116] Trial 236 finished with value: 0.43874265690156394 and parameters: {'learning_rate': 0.16056301508570936, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2027446625831075, 'subsample': 0.664151362721815, 'colsample_bytree': 0.9238191125424183, 'alpha': 0.013653156855318205, 'lambda': 2.5245329348217256}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:22:58,900] Trial 237 finished with value: 0.455486373648378 and parameters: {'learning_rate': 0.15899593928476427, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.00088279645757961, 'subsample': 0.6530819224887366, 'colsample_bytree': 0.8918907183557809, 'alpha': 0.19266580630747607, 'lambda': 2.3256771746613127}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:22:59,556] Trial 238 finished with value: 0.4130230436808313 and parameters: {'learning_rate': 0.1636315759588893, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.3610214719458057, 'subsample': 0.6498361565821396, 'colsample_bytree': 0.9009682588385928, 'alpha': 0.1601955578340397, 'lambda': 2.289006170330677}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:00,161] Trial 239 finished with value: 0.3552432754910148 and parameters: {'learning_rate': 0.15839519420046513, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 8.42750402127673, 'subsample': 0.6690287642834705, 'colsample_bytree': 0.8585230278966937, 'alpha': 0.19088395748835812, 'lambda': 2.595352494057029}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:00,901] Trial 240 finished with value: 0.43258578831202893 and parameters: {'learning_rate': 0.16532810574378015, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.1869347341319225, 'subsample': 0.690369881029358, 'colsample_bytree': 0.9257153641163592, 'alpha': 0.011027833739089998, 'lambda': 3.0852074565212497}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:02,227] Trial 241 finished with value: 0.44553546871262195 and parameters: {'learning_rate': 0.14832484484700967, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.024709481741685958, 'subsample': 0.6449189982511949, 'colsample_bytree': 0.8876069665638016, 'alpha': 0.2784038240819149, 'lambda': 2.494168868040529}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:03,484] Trial 242 finished with value: 0.4532548254555076 and parameters: {'learning_rate': 0.15558395510636913, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.016647976268046705, 'subsample': 0.5997684918495707, 'colsample_bytree': 0.8748003598471302, 'alpha': 0.24856931860649137, 'lambda': 1.8793831931391016}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:05,417] Trial 243 finished with value: 0.45926176190465 and parameters: {'learning_rate': 0.15617784989519837, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.001419103571376809, 'subsample': 0.6318965314693087, 'colsample_bytree': 0.8934399294318048, 'alpha': 0.2044049424840343, 'lambda': 2.7721614923695053}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:06,305] Trial 244 finished with value: 0.42910225425195403 and parameters: {'learning_rate': 0.15665246334247854, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.20083865169104423, 'subsample': 0.6021958731944858, 'colsample_bytree': 0.8686027255014418, 'alpha': 0.2793867036272614, 'lambda': 2.9160068891543918}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:07,489] Trial 245 finished with value: 0.41842682160891914 and parameters: {'learning_rate': 0.15980169106929018, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.35818723487546555, 'subsample': 0.6385389939397513, 'colsample_bytree': 0.8952166151455077, 'alpha': 0.16709183365044292, 'lambda': 2.83676175914128}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:24,731] Trial 246 finished with value: 0.45442305796973165 and parameters: {'learning_rate': 0.15441567249684668, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.005978061091925148, 'subsample': 0.6178812688365216, 'colsample_bytree': 0.9170546617774503, 'alpha': 0.0011726454936623054, 'lambda': 1.9409247316851563}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:32,217] Trial 247 finished with value: 0.42972109303465644 and parameters: {'learning_rate': 0.1552958923911964, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.20886086504672585, 'subsample': 0.6070134408746723, 'colsample_bytree': 0.9274503039250763, 'alpha': 0.13399628787583517, 'lambda': 1.863220133992022}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:36,204] Trial 248 finished with value: 0.4216858422249511 and parameters: {'learning_rate': 0.16098889015682014, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.3830819047476965, 'subsample': 0.6198009864022971, 'colsample_bytree': 0.9147575864556957, 'alpha': 0.005406728452413467, 'lambda': 2.347997311890274}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:38,375] Trial 249 finished with value: 0.4515778979895566 and parameters: {'learning_rate': 0.1540554313879508, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.002790223716180817, 'subsample': 0.6795727399528579, 'colsample_bytree': 0.8998369308994513, 'alpha': 0.3138000823035129, 'lambda': 1.986332349286574}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:39,291] Trial 250 finished with value: 0.42192017706060253 and parameters: {'learning_rate': 0.1546001137759396, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.1995851054458593, 'subsample': 0.6735952807732601, 'colsample_bytree': 0.9790143754799185, 'alpha': 0.33373355215231504, 'lambda': 2.052081711739125}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:39,771] Trial 251 finished with value: 0.40039365090920936 and parameters: {'learning_rate': 0.15196990292684465, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.5199463233450226, 'subsample': 0.6845261138255996, 'colsample_bytree': 0.936758266414753, 'alpha': 0.32653011852194175, 'lambda': 2.8272806900178598}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:41,585] Trial 252 finished with value: 0.46081759035022346 and parameters: {'learning_rate': 0.1659300035515759, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0020966651619024404, 'subsample': 0.6651072457619535, 'colsample_bytree': 0.9119199358845846, 'alpha': 0.01918724312158543, 'lambda': 1.869721467491614}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:42,217] Trial 253 finished with value: 0.36659750458565143 and parameters: {'learning_rate': 0.16519186310012618, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 2.3834338541465385, 'subsample': 0.6526819949726173, 'colsample_bytree': 0.9085953973379381, 'alpha': 0.019958441640021918, 'lambda': 1.8955236034661285}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:43,175] Trial 254 finished with value: 0.43317497369634217 and parameters: {'learning_rate': 0.15812642482545328, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.22142163705865456, 'subsample': 0.6626511834452876, 'colsample_bytree': 0.9350493368104644, 'alpha': 0.011868980069107336, 'lambda': 2.0373766074497004}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:44,844] Trial 255 finished with value: 0.45959219255392214 and parameters: {'learning_rate': 0.16975137760134268, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.001623871060379539, 'subsample': 0.7034681556858031, 'colsample_bytree': 0.9082773477267571, 'alpha': 0.22978681543229562, 'lambda': 2.37081679470199}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:45,540] Trial 256 finished with value: 0.4134290154769227 and parameters: {'learning_rate': 0.1646340327662744, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.3579154498285241, 'subsample': 0.7070021581566173, 'colsample_bytree': 0.9002032595059445, 'alpha': 0.21444679266360375, 'lambda': 2.463263520852158}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:47,253] Trial 257 finished with value: 0.4564381384399513 and parameters: {'learning_rate': 0.17285392307695868, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0007435641171083078, 'subsample': 0.6760613947075552, 'colsample_bytree': 0.9552646288419346, 'alpha': 0.25137016933762, 'lambda': 8.836418809720888}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:48,989] Trial 258 finished with value: 0.4566082077389051 and parameters: {'learning_rate': 0.17536023928638958, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0007675145137743637, 'subsample': 0.6941530750178236, 'colsample_bytree': 0.9609630894684024, 'alpha': 0.22342963943735886, 'lambda': 8.419742452760223}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:49,940] Trial 259 finished with value: 0.41545851611978657 and parameters: {'learning_rate': 0.17365818053094093, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.2012469906476968, 'subsample': 0.6605812229333377, 'colsample_bytree': 0.9795569691474305, 'alpha': 0.18562438876875292, 'lambda': 8.933193287341982}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:50,350] Trial 260 finished with value: 0.2690479471565098 and parameters: {'learning_rate': 0.17399411277823124, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.5793125611284877, 'subsample': 0.6969066976706777, 'colsample_bytree': 0.9664106228423555, 'alpha': 9.565181608699445, 'lambda': 8.66940394892395}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:50,942] Trial 261 finished with value: 0.4093540430635931 and parameters: {'learning_rate': 0.18186022747594033, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.34270857836079943, 'subsample': 0.6722636928125231, 'colsample_bytree': 0.955941543494577, 'alpha': 0.1538446771132717, 'lambda': 9.251291008623257}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:51,940] Trial 262 finished with value: 0.41598003544410145 and parameters: {'learning_rate': 0.16827417214623497, 'max_depth': 5, 'min_child_weight': 7, 'gamma': 0.21543774758079404, 'subsample': 0.6393723561404211, 'colsample_bytree': 0.9453324967828372, 'alpha': 0.023791137239300195, 'lambda': 2.186152461363189}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:53,567] Trial 263 finished with value: 0.45478515455350993 and parameters: {'learning_rate': 0.17685725873163333, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.001775031609136591, 'subsample': 0.6899547092750632, 'colsample_bytree': 0.9786129184228466, 'alpha': 0.3438059179637347, 'lambda': 9.499606722597088}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:54,197] Trial 264 finished with value: 0.4053705851530027 and parameters: {'learning_rate': 0.17679403860925655, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.42567290000780605, 'subsample': 0.7185753256151941, 'colsample_bytree': 0.9738226521385891, 'alpha': 0.2234451118666257, 'lambda': 3.2524724197227717}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:55,995] Trial 265 finished with value: 0.4584971164857198 and parameters: {'learning_rate': 0.17103533242474706, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0002865041624520951, 'subsample': 0.6751144517782177, 'colsample_bytree': 0.9316171214488214, 'alpha': 0.006272469735121744, 'lambda': 1.6904057168786983}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:56,779] Trial 266 finished with value: 0.44094127476698136 and parameters: {'learning_rate': 0.171331500415212, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.16190129994630262, 'subsample': 0.6919557106904264, 'colsample_bytree': 0.984717516784059, 'alpha': 0.011387691067034793, 'lambda': 1.7072236258744873}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:57,319] Trial 267 finished with value: 0.37165858850675043 and parameters: {'learning_rate': 0.17361173491541995, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 3.52099488828146, 'subsample': 0.6809382660509904, 'colsample_bytree': 0.987526784540752, 'alpha': 0.35323831830455465, 'lambda': 2.7674436555932305}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:57,842] Trial 268 finished with value: 0.3727238190950401 and parameters: {'learning_rate': 0.16873377425688668, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.6159481832951702, 'subsample': 0.7116217553438229, 'colsample_bytree': 0.9598733892669038, 'alpha': 3.7144955353868943, 'lambda': 1.682230961448512}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:58,732] Trial 269 finished with value: 0.41195289827357945 and parameters: {'learning_rate': 0.1926985602619816, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.1941413460350923, 'subsample': 0.6542355252230255, 'colsample_bytree': 0.9326427534128825, 'alpha': 0.3678738041808511, 'lambda': 9.696502837211758}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:23:59,646] Trial 270 finished with value: 0.40383916874146325 and parameters: {'learning_rate': 0.17724691056765907, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.3978778027536338, 'subsample': 0.6944296027203339, 'colsample_bytree': 0.9268185728908838, 'alpha': 0.23296965378644308, 'lambda': 8.561199429994591}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:00,305] Trial 271 finished with value: 0.36219599573075206 and parameters: {'learning_rate': 0.18680915219135746, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.17985285833332854, 'subsample': 0.6674864278628398, 'colsample_bytree': 0.9482675299624179, 'alpha': 8.19373638779432, 'lambda': 9.001598468069233}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:02,768] Trial 272 finished with value: 0.4457517388393203 and parameters: {'learning_rate': 0.1712310779602096, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.007809549437097894, 'subsample': 0.6469544988863984, 'colsample_bytree': 0.9991707997032964, 'alpha': 0.3750792854911199, 'lambda': 9.55042667794747}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:08,785] Trial 273 finished with value: 0.4169491753475373 and parameters: {'learning_rate': 0.16575943961601872, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.34094243037898625, 'subsample': 0.6830164452337328, 'colsample_bytree': 0.8634597989736302, 'alpha': 0.15256686193219493, 'lambda': 1.8462640390698497}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:16,023] Trial 274 finished with value: 0.4448595585854667 and parameters: {'learning_rate': 0.17865963113561115, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.18258853109585202, 'subsample': 0.7238008968080404, 'colsample_bytree': 0.9651304185811779, 'alpha': 0.04451016736759707, 'lambda': 0.010278468475759348}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:21,969] Trial 275 finished with value: 0.4125410471557202 and parameters: {'learning_rate': 0.15764906787812957, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.5051011843700489, 'subsample': 0.632925496474844, 'colsample_bytree': 0.9216316208795947, 'alpha': 0.017481975442607992, 'lambda': 2.4365380541678436}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:28,563] Trial 276 finished with value: 0.44896331130613154 and parameters: {'learning_rate': 0.1611331274926848, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.006443254047482171, 'subsample': 0.660611390077985, 'colsample_bytree': 0.8734302575130898, 'alpha': 0.38378758121474366, 'lambda': 2.725064096257014}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:29,201] Trial 277 finished with value: 0.3852691409578112 and parameters: {'learning_rate': 0.16706146664708696, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.7578228954076001, 'subsample': 0.7039773724670283, 'colsample_bytree': 0.8397938520174109, 'alpha': 0.2581600222214867, 'lambda': 8.062469378974553}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:29,545] Trial 278 finished with value: 0.3855102083441178 and parameters: {'learning_rate': 0.17188975908407625, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.31424592064187706, 'subsample': 0.6463481087808728, 'colsample_bytree': 0.9365334120910925, 'alpha': 3.080635480083538, 'lambda': 8.250875011800703}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:30,237] Trial 279 finished with value: 0.43866074716662806 and parameters: {'learning_rate': 0.1587679715739466, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.18705497682967184, 'subsample': 0.6813174265396478, 'colsample_bytree': 0.915954559318816, 'alpha': 0.021134511665003568, 'lambda': 0.3052767648807649}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:30,904] Trial 280 finished with value: 0.40187824717033815 and parameters: {'learning_rate': 0.18236072569821918, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.510346074125952, 'subsample': 0.6238078859123775, 'colsample_bytree': 0.8536695609733205, 'alpha': 0.44484976726728187, 'lambda': 1.5688129425438302}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:31,510] Trial 281 finished with value: 0.365891625127684 and parameters: {'learning_rate': 0.16323458418270165, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 6.474771594785656, 'subsample': 0.6637706227380712, 'colsample_bytree': 0.8855007642741477, 'alpha': 0.25504175165992293, 'lambda': 2.390093352007225}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:32,388] Trial 282 finished with value: 0.4221822520371261 and parameters: {'learning_rate': 0.1550827276314325, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.18565812204909973, 'subsample': 0.6376472493471496, 'colsample_bytree': 0.9524660264726016, 'alpha': 0.4257947037921417, 'lambda': 2.185401632578263}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:34,070] Trial 283 finished with value: 0.461236152773168 and parameters: {'learning_rate': 0.1662144177421425, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0031377821846424563, 'subsample': 0.6111644410772273, 'colsample_bytree': 0.9203599799680793, 'alpha': 0.0008618346439484559, 'lambda': 3.031175538777775}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:35,462] Trial 284 finished with value: 0.44863024695436216 and parameters: {'learning_rate': 0.17435416175553714, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.010464616568645325, 'subsample': 0.6944416972036574, 'colsample_bytree': 0.43926549296790107, 'alpha': 0.1557352480066178, 'lambda': 2.940920105402923}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:36,110] Trial 285 finished with value: 0.42035235776051066 and parameters: {'learning_rate': 0.16631079832393547, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.3451570196180372, 'subsample': 0.5929290699212627, 'colsample_bytree': 0.9210353890290768, 'alpha': 0.01404206636430192, 'lambda': 3.098265836936973}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:37,994] Trial 286 finished with value: 0.4260337222782416 and parameters: {'learning_rate': 0.011583907054456707, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 0.18006428631643073, 'subsample': 0.6572232534017778, 'colsample_bytree': 0.946191284861316, 'alpha': 0.014363322278586332, 'lambda': 2.5557340462620197}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:40,075] Trial 287 finished with value: 0.44685438885005885 and parameters: {'learning_rate': 0.16978805058780408, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0014005823829229532, 'subsample': 0.6725403423185022, 'colsample_bytree': 0.963821610734066, 'alpha': 0.23484944679553973, 'lambda': 3.4456089512534}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:40,847] Trial 288 finished with value: 0.41694109665635337 and parameters: {'learning_rate': 0.16364580913364876, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.3754901885244301, 'subsample': 0.6276992015357021, 'colsample_bytree': 0.9150175725222012, 'alpha': 0.2217560421710514, 'lambda': 2.666896383627944}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:41,566] Trial 289 finished with value: 0.36462538859000404 and parameters: {'learning_rate': 0.16024449152279108, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 4.468462412890895, 'subsample': 0.7213151891081871, 'colsample_bytree': 0.9300317346290294, 'alpha': 0.009822991769888566, 'lambda': 2.102595946513925}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:42,343] Trial 290 finished with value: 0.38925138797678155 and parameters: {'learning_rate': 0.16739426561840906, 'max_depth': 5, 'min_child_weight': 6, 'gamma': 0.6603355533077834, 'subsample': 0.6049977067826723, 'colsample_bytree': 0.8977559973529265, 'alpha': 0.3957398569784859, 'lambda': 2.4213943763724166}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:43,111] Trial 291 finished with value: 0.37211595497981836 and parameters: {'learning_rate': 0.17868789113909567, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 2.8748422630950823, 'subsample': 0.6482604740876998, 'colsample_bytree': 0.9733116044210505, 'alpha': 0.004421206735935304, 'lambda': 9.351811924397827}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:44,085] Trial 292 finished with value: 0.4270474446523257 and parameters: {'learning_rate': 0.17062239063901813, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.18894748885368487, 'subsample': 0.6785425077100475, 'colsample_bytree': 0.8699097662648454, 'alpha': 0.2778928737828033, 'lambda': 2.807620873886992}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:45,246] Trial 293 finished with value: 0.439431934649064 and parameters: {'learning_rate': 0.15914836183597583, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.0027422201641681063, 'subsample': 0.2114691784921498, 'colsample_bytree': 0.9367616224561724, 'alpha': 0.455647505380375, 'lambda': 1.9062580722302145}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:46,202] Trial 294 finished with value: 0.41995554722077466 and parameters: {'learning_rate': 0.16368237955415635, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.3621273499411871, 'subsample': 0.6993221353835122, 'colsample_bytree': 0.9994268791915822, 'alpha': 0.2825452338159533, 'lambda': 0.3632573128582078}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:46,762] Trial 295 finished with value: 0.3658322654964628 and parameters: {'learning_rate': 0.15672340009108726, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 5.525633361391278, 'subsample': 0.6227115321280143, 'colsample_bytree': 0.9085349420006076, 'alpha': 0.1767641953909227, 'lambda': 0.928333268631538}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:47,575] Trial 296 finished with value: 0.4023234880910283 and parameters: {'learning_rate': 0.15269255375358856, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.18998767996956295, 'subsample': 0.6643825826385316, 'colsample_bytree': 0.8927394409707554, 'alpha': 0.5795022362892156, 'lambda': 1.7277289259070343}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:48,120] Trial 297 finished with value: 0.4038755239619149 and parameters: {'learning_rate': 0.16204902020370268, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.525016683730434, 'subsample': 0.5837978648143973, 'colsample_bytree': 0.9178760452265378, 'alpha': 0.22494816939885065, 'lambda': 3.1792695837582645}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:49,056] Trial 298 finished with value: 0.41461693685821577 and parameters: {'learning_rate': 0.16722043504705403, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.18630219747869603, 'subsample': 0.6460093021030913, 'colsample_bytree': 0.9488680717078559, 'alpha': 0.4279151100281869, 'lambda': 7.693599852415668}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:49,690] Trial 299 finished with value: 0.4173644017083104 and parameters: {'learning_rate': 0.17447048114501895, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.4040427235959809, 'subsample': 0.602932290208238, 'colsample_bytree': 0.8508502056178129, 'alpha': 0.0011862987260569963, 'lambda': 2.639768300234552}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:50,722] Trial 300 finished with value: 0.3995613459579798 and parameters: {'learning_rate': 0.1555715186195507, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.17216964503338902, 'subsample': 0.6822214696215153, 'colsample_bytree': 0.5264215254202746, 'alpha': 0.7282902364914131, 'lambda': 9.674235082165412}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:51,442] Trial 301 finished with value: 0.4122861953759259 and parameters: {'learning_rate': 0.16000465422125962, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.3211537238901055, 'subsample': 0.6380042602341709, 'colsample_bytree': 0.3642208175550007, 'alpha': 0.22933511650993507, 'lambda': 0.19619985512657623}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:52,921] Trial 302 finished with value: 0.44131413401988184 and parameters: {'learning_rate': 0.17116120502839122, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.002175485122561809, 'subsample': 0.7307767678115316, 'colsample_bytree': 0.8741306443794673, 'alpha': 0.4436549789427935, 'lambda': 2.284045446269171}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:53,690] Trial 303 finished with value: 0.39075574595519186 and parameters: {'learning_rate': 0.18461888094251516, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.6389931116670864, 'subsample': 0.6154659693700959, 'colsample_bytree': 0.9380555417976485, 'alpha': 0.1714996706828528, 'lambda': 8.355008065951274}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:54,869] Trial 304 finished with value: 0.44835591191954316 and parameters: {'learning_rate': 0.16493935485729647, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.01216862989472975, 'subsample': 0.7120472423369175, 'colsample_bytree': 0.8964059553890182, 'alpha': 0.5512480681349458, 'lambda': 0.4996754731323224}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:55,977] Trial 305 finished with value: 0.3893964905779144 and parameters: {'learning_rate': 0.17925580286126316, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.002475482059627971, 'subsample': 0.6582559134803326, 'colsample_bytree': 0.9643805428514035, 'alpha': 4.2839344048744925, 'lambda': 2.9826685174518923}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:24:57,079] Trial 306 finished with value: 0.41768756581546673 and parameters: {'learning_rate': 0.15199459992831119, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.3233463014061909, 'subsample': 0.689769645494732, 'colsample_bytree': 0.921690922056659, 'alpha': 0.35180095853227855, 'lambda': 0.835428540107674}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:03,168] Trial 307 finished with value: 0.43308276812494456 and parameters: {'learning_rate': 0.15794039669740728, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.21194931489260557, 'subsample': 0.6322282433025361, 'colsample_bytree': 0.8777591354431045, 'alpha': 0.0058977436234602925, 'lambda': 2.0481860072968345}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:08,939] Trial 308 finished with value: 0.3560510429644299 and parameters: {'learning_rate': 0.16259950186582775, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 7.445966837617849, 'subsample': 0.5837707979765925, 'colsample_bytree': 0.903079945499641, 'alpha': 0.003407928802940441, 'lambda': 1.6287436526289785}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:14,948] Trial 309 finished with value: 0.3997362937399343 and parameters: {'learning_rate': 0.17554203642644062, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.5022632037888445, 'subsample': 0.5572427489629794, 'colsample_bytree': 0.9810054864683727, 'alpha': 0.21752864056173557, 'lambda': 8.783871823609537}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:18,702] Trial 310 finished with value: 0.3642703952471719 and parameters: {'learning_rate': 0.16845636981926934, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0.20409747141242252, 'subsample': 0.6745864559108887, 'colsample_bytree': 0.8445009967904348, 'alpha': 6.091776742364288, 'lambda': 1.8799020557380255}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:26,434] Trial 311 finished with value: 0.4441570782609944 and parameters: {'learning_rate': 0.15604219888063747, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.002175873296557107, 'subsample': 0.6479939547700745, 'colsample_bytree': 0.9352353161528404, 'alpha': 0.6640296179943627, 'lambda': 2.4226438915914943}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:27,092] Trial 312 finished with value: 0.40041516078378697 and parameters: {'learning_rate': 0.16474390079646026, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.36979664179270544, 'subsample': 0.6198488823437297, 'colsample_bytree': 0.8768941572966736, 'alpha': 0.38834254602615, 'lambda': 9.076586850162808}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:27,790] Trial 313 finished with value: 0.3939786556883105 and parameters: {'learning_rate': 0.1600943105509703, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.7964167146733914, 'subsample': 0.6007133481646706, 'colsample_bytree': 0.9057744442838394, 'alpha': 0.20031314229819686, 'lambda': 2.6508520787255945}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:29,107] Trial 314 finished with value: 0.44008833479369597 and parameters: {'learning_rate': 0.05030976980654483, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.18346173402471547, 'subsample': 0.7005834199854193, 'colsample_bytree': 0.9547248390862814, 'alpha': 0.0014862934991889884, 'lambda': 0.5674393109348883}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:29,411] Trial 315 finished with value: 0.37430091487339 and parameters: {'learning_rate': 0.17243147193374633, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 1.8712623320962887, 'subsample': 0.6671155183819781, 'colsample_bytree': 0.8611822435186041, 'alpha': 0.4781970579782856, 'lambda': 2.834708751455137}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:29,989] Trial 316 finished with value: 0.40522648336453043 and parameters: {'learning_rate': 0.14874143312625027, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.48322397992027044, 'subsample': 0.6392993915747067, 'colsample_bytree': 0.9190339619176516, 'alpha': 0.19585383726908892, 'lambda': 2.2256921224043893}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:30,850] Trial 317 finished with value: 0.41782549538313446 and parameters: {'learning_rate': 0.1531488000170069, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.20863834377518817, 'subsample': 0.658714258006903, 'colsample_bytree': 0.8884488977746686, 'alpha': 0.6824636762554563, 'lambda': 0.8161821999184116}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:31,684] Trial 318 finished with value: 0.43265698878734316 and parameters: {'learning_rate': 0.16723594643821163, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.14947727643933967, 'subsample': 0.6866885282930859, 'colsample_bytree': 0.9167701532937547, 'alpha': 0.3666734002812029, 'lambda': 1.1238954693141783}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:33,365] Trial 319 finished with value: 0.4551890958602543 and parameters: {'learning_rate': 0.15805144917894393, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.01032406317881485, 'subsample': 0.6205286591563914, 'colsample_bytree': 0.9453617971584735, 'alpha': 0.1856277812771286, 'lambda': 1.4506861046931065}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:34,026] Trial 320 finished with value: 0.41887506660678153 and parameters: {'learning_rate': 0.15592111567466327, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.34213855501284374, 'subsample': 0.7104120846277387, 'colsample_bytree': 0.9760619660074192, 'alpha': 0.1902883208547827, 'lambda': 1.788502792259273}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:35,381] Trial 321 finished with value: 0.4447025711875531 and parameters: {'learning_rate': 0.15201488555949283, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 0.012622704294024449, 'subsample': 0.5661604674576691, 'colsample_bytree': 0.9486106705631917, 'alpha': 0.48522373526822826, 'lambda': 1.4623268586416955}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:35,987] Trial 322 finished with value: 0.4113933488861229 and parameters: {'learning_rate': 0.146743510148974, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.525068525650247, 'subsample': 0.6407305738841468, 'colsample_bytree': 0.9359914256054452, 'alpha': 0.010116201832524624, 'lambda': 1.5702199723575827}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:36,670] Trial 323 finished with value: 0.427580392650907 and parameters: {'learning_rate': 0.16391556602727558, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.1986604993278858, 'subsample': 0.6030677915173931, 'colsample_bytree': 0.9637432065270982, 'alpha': 0.3628991921065018, 'lambda': 1.8936102836105186}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:37,710] Trial 324 finished with value: 0.4198170144034004 and parameters: {'learning_rate': 0.17498865657436424, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.31271725214833407, 'subsample': 0.6649146767303793, 'colsample_bytree': 0.984262023330609, 'alpha': 0.1919915098278144, 'lambda': 2.128228619173366}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:39,113] Trial 325 finished with value: 0.44357000752272036 and parameters: {'learning_rate': 0.17119364675514234, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.006623056175198589, 'subsample': 0.5824741362663842, 'colsample_bytree': 0.9300172634726699, 'alpha': 0.8564011704312653, 'lambda': 2.5317386595592337}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:40,004] Trial 326 finished with value: 0.42668109685453154 and parameters: {'learning_rate': 0.15767634648167766, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.18317180619000614, 'subsample': 0.6247916867491544, 'colsample_bytree': 0.8952526851798488, 'alpha': 0.5606969252334784, 'lambda': 1.2881920831992115}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:40,926] Trial 327 finished with value: 0.40030382548941007 and parameters: {'learning_rate': 0.16153086073050224, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.62320502902034, 'subsample': 0.6822046944840671, 'colsample_bytree': 0.8616173573037409, 'alpha': 0.17342148432284293, 'lambda': 1.7095350068637478}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:41,527] Trial 328 finished with value: 0.3984019554300773 and parameters: {'learning_rate': 0.1672065649832628, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.387055350434044, 'subsample': 0.735710559008605, 'colsample_bytree': 0.8324708960674719, 'alpha': 0.37740657426509766, 'lambda': 9.918830144309105}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:42,357] Trial 329 finished with value: 0.4346119295013032 and parameters: {'learning_rate': 0.15346284135076346, 'max_depth': 11, 'min_child_weight': 1, 'gamma': 0.17202778344251357, 'subsample': 0.6526352120661993, 'colsample_bytree': 0.9503328071955814, 'alpha': 0.014982393258387988, 'lambda': 2.3379228650128034}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:43,281] Trial 330 finished with value: 0.3874908001600618 and parameters: {'learning_rate': 0.1480096799993335, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.34294225593933797, 'subsample': 0.7005126897941103, 'colsample_bytree': 0.8823131263185188, 'alpha': 2.715807006457968, 'lambda': 2.005041528318565}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:44,113] Trial 331 finished with value: 0.41179876952731637 and parameters: {'learning_rate': 0.17959566577253186, 'max_depth': 5, 'min_child_weight': 4, 'gamma': 0.15768875313402778, 'subsample': 0.6101163576000179, 'colsample_bytree': 0.9294303451378165, 'alpha': 0.6722266050500718, 'lambda': 0.2002347802830844}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:45,507] Trial 332 finished with value: 0.4493177884963518 and parameters: {'learning_rate': 0.15913872324337922, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.01777375096113514, 'subsample': 0.6714959827417933, 'colsample_bytree': 0.9136075535637874, 'alpha': 0.3262256794090669, 'lambda': 3.2166071036301433}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:46,076] Trial 333 finished with value: 0.41000284559637246 and parameters: {'learning_rate': 0.16937150110837598, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.510111171501265, 'subsample': 0.6307373228453302, 'colsample_bytree': 0.9652086193991061, 'alpha': 0.16151716358796914, 'lambda': 2.9232678374885643}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:47,282] Trial 334 finished with value: 0.3890108042517874 and parameters: {'learning_rate': 0.16344827105073007, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.002923075736201325, 'subsample': 0.6408979150451392, 'colsample_bytree': 0.9005692704546112, 'alpha': 4.676003694483297, 'lambda': 9.523946467744931}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:48,820] Trial 335 finished with value: 0.4521050576895236 and parameters: {'learning_rate': 0.15524929531550885, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.006471087999052582, 'subsample': 0.4803049021497271, 'colsample_bytree': 0.8661950909517908, 'alpha': 0.5346314315238321, 'lambda': 1.4474808570649376}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:49,591] Trial 336 finished with value: 0.4144990146626942 and parameters: {'learning_rate': 0.15008491434120533, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.3212686182787443, 'subsample': 0.55080213517054, 'colsample_bytree': 0.9501997274223174, 'alpha': 0.3158460545755242, 'lambda': 2.671711110079035}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:50,277] Trial 337 finished with value: 0.39979847846948113 and parameters: {'learning_rate': 0.16556618137216741, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.7556192602365089, 'subsample': 0.5903059044489527, 'colsample_bytree': 0.9180280919945768, 'alpha': 0.17332687383686518, 'lambda': 2.168854687123025}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:51,104] Trial 338 finished with value: 0.4381467573235178 and parameters: {'learning_rate': 0.1741496780639372, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.1949901771340507, 'subsample': 0.6542623455852452, 'colsample_bytree': 0.8848090925186758, 'alpha': 0.012473807205331342, 'lambda': 2.495182527296431}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:51,909] Trial 339 finished with value: 0.39706706407322556 and parameters: {'learning_rate': 0.16063420951126023, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.4729021208714538, 'subsample': 0.6913373387323508, 'colsample_bytree': 0.6608311136179736, 'alpha': 0.4825205833816283, 'lambda': 0.40052926729094096}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:52,727] Trial 340 finished with value: 0.36389424803500103 and parameters: {'learning_rate': 0.15699799659774252, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.008274417498744246, 'subsample': 0.6755038229839128, 'colsample_bytree': 0.09889569248782965, 'alpha': 3.42799346568044, 'lambda': 3.5572435062670933}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:53,331] Trial 341 finished with value: 0.36663767716139317 and parameters: {'learning_rate': 0.1458682482519257, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.28864833671705814, 'subsample': 0.6136085125632816, 'colsample_bytree': 0.8538368005403543, 'alpha': 7.000204383297966, 'lambda': 1.1276656024868528}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:54,312] Trial 342 finished with value: 0.44169024319138916 and parameters: {'learning_rate': 0.17049967448306078, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.18856799609492078, 'subsample': 0.7186723477285316, 'colsample_bytree': 0.9325250322851821, 'alpha': 0.0031480054088572906, 'lambda': 1.733882776518257}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:54,987] Trial 343 finished with value: 0.3894904993409135 and parameters: {'learning_rate': 0.1621532567535823, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.4847394091169113, 'subsample': 0.6537863314457036, 'colsample_bytree': 0.5875960276450167, 'alpha': 0.7096063116284652, 'lambda': 9.243247888805376}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:25:57,447] Trial 344 finished with value: 0.4541143758471386 and parameters: {'learning_rate': 0.17748784611160018, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.0024053293166655023, 'subsample': 0.5149544132657411, 'colsample_bytree': 0.8925582762719256, 'alpha': 0.3257866320906204, 'lambda': 1.9734940508964742}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:01,033] Trial 345 finished with value: 0.4302512082557251 and parameters: {'learning_rate': 0.18981826843240168, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.20866812274687754, 'subsample': 0.5067321497096706, 'colsample_bytree': 0.9912752927967885, 'alpha': 0.2226458249222486, 'lambda': 2.042722230966537}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:07,184] Trial 346 finished with value: 0.3938661630595849 and parameters: {'learning_rate': 0.1822057932501048, 'max_depth': 14, 'min_child_weight': 5, 'gamma': 0.34928212835082084, 'subsample': 0.518548998309692, 'colsample_bytree': 0.8869145325047633, 'alpha': 0.33005429421945887, 'lambda': 1.858171071397736}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:22,710] Trial 347 finished with value: 0.4563666925678516 and parameters: {'learning_rate': 0.17630481627385441, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.004704476899912665, 'subsample': 0.5320795237007775, 'colsample_bytree': 0.8413313152853689, 'alpha': 0.0007089361276739758, 'lambda': 0.012692204957041375}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:25,163] Trial 348 finished with value: 0.44821830986540506 and parameters: {'learning_rate': 0.1807413085075597, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.004977257229431369, 'subsample': 0.5259360608379766, 'colsample_bytree': 0.8362398219901491, 'alpha': 0.18098227059298244, 'lambda': 0.08919456140551385}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:25,763] Trial 349 finished with value: 0.4158295580453698 and parameters: {'learning_rate': 0.17467163600103464, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.6336085836643007, 'subsample': 0.5328637800153294, 'colsample_bytree': 0.8459748752022229, 'alpha': 0.0050332090085450365, 'lambda': 0.03073262777405726}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:26,133] Trial 350 finished with value: 0.36164958783909934 and parameters: {'learning_rate': 0.17574060526420582, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.1889905316878595, 'subsample': 0.569450895466885, 'colsample_bytree': 0.48839372187804914, 'alpha': 5.565582315359436, 'lambda': 0.4765674046696405}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:27,006] Trial 351 finished with value: 0.39668608906763086 and parameters: {'learning_rate': 0.17824921235374538, 'max_depth': 5, 'min_child_weight': 7, 'gamma': 0.37562670263145737, 'subsample': 0.4885367457232911, 'colsample_bytree': 0.8675606919607011, 'alpha': 0.34864840943118214, 'lambda': 0.348690777845661}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:28,437] Trial 352 finished with value: 0.44888444102537894 and parameters: {'learning_rate': 0.19864269960413142, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.001901852016876373, 'subsample': 0.5495439503714042, 'colsample_bytree': 0.8255054391150918, 'alpha': 0.5398939932796969, 'lambda': 1.4152904880764647}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:29,138] Trial 353 finished with value: 0.44267138131397343 and parameters: {'learning_rate': 0.17133115347132274, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.19983959469035825, 'subsample': 0.5036544844543964, 'colsample_bytree': 0.8986255160231297, 'alpha': 0.19657186768751092, 'lambda': 0.16841017052638915}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:29,775] Trial 354 finished with value: 0.4279749111944849 and parameters: {'learning_rate': 0.1768483076620702, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.3576456603573678, 'subsample': 0.5481735677354568, 'colsample_bytree': 0.9662872726676954, 'alpha': 0.004376253774550559, 'lambda': 1.6582809257847744}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:30,915] Trial 355 finished with value: 0.4377513530701889 and parameters: {'learning_rate': 0.1677965545009121, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.0005884301509192852, 'subsample': 0.3296109333614334, 'colsample_bytree': 0.942233013460149, 'alpha': 0.35279240595608985, 'lambda': 0.614320073053705}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:31,494] Trial 356 finished with value: 0.39548562954455496 and parameters: {'learning_rate': 0.07249888801311263, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 0.609032787181526, 'subsample': 0.5626813955228629, 'colsample_bytree': 0.9054232662246229, 'alpha': 0.8362820430586033, 'lambda': 0.9461878545745354}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:31,986] Trial 357 finished with value: 0.39145697747273955 and parameters: {'learning_rate': 0.1855850349438634, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.9835551289236583, 'subsample': 0.5839337705488936, 'colsample_bytree': 0.9995521623664319, 'alpha': 0.1824371127128009, 'lambda': 1.887482972029052}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:32,764] Trial 358 finished with value: 0.41124892045794176 and parameters: {'learning_rate': 0.17068972525780252, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.23366076554386103, 'subsample': 0.5419215702663359, 'colsample_bytree': 0.8101015611386424, 'alpha': 0.5769163003627702, 'lambda': 2.27906442758732}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:33,831] Trial 359 finished with value: 0.42949109652469125 and parameters: {'learning_rate': 0.06072741627151893, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.20467256148034232, 'subsample': 0.47195278045026984, 'colsample_bytree': 0.879707866087956, 'alpha': 0.35652003602811927, 'lambda': 1.5403493902790115}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:34,572] Trial 360 finished with value: 0.41048078150718675 and parameters: {'learning_rate': 0.16636279358368006, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.4473701980843454, 'subsample': 0.5167776271966552, 'colsample_bytree': 0.8426220652164101, 'alpha': 0.1734664291289739, 'lambda': 2.039063183286217}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:36,298] Trial 361 finished with value: 0.4554276799753951 and parameters: {'learning_rate': 0.18228838984690807, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.00048351778181892024, 'subsample': 0.7054122120512489, 'colsample_bytree': 0.937110274036428, 'alpha': 0.623262062790036, 'lambda': 0.2941973014996172}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:37,015] Trial 362 finished with value: 0.4232750128467548 and parameters: {'learning_rate': 0.18191621017970996, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.1851719070611961, 'subsample': 0.7132603792576118, 'colsample_bytree': 0.963584095013888, 'alpha': 0.715737402993471, 'lambda': 0.1617330951259464}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:37,847] Trial 363 finished with value: 0.41487754388513887 and parameters: {'learning_rate': 0.1857957466295923, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.34577760532559154, 'subsample': 0.7346729042678005, 'colsample_bytree': 0.9475306535693855, 'alpha': 0.4431004251316965, 'lambda': 0.00836169502517875}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:38,543] Trial 364 finished with value: 0.44613634592052986 and parameters: {'learning_rate': 0.18096002689352247, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.18192466390755407, 'subsample': 0.6999849798522579, 'colsample_bytree': 0.9288284256218421, 'alpha': 0.004481198777957107, 'lambda': 0.37635216702331614}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:38,937] Trial 365 finished with value: 0.3804579748931984 and parameters: {'learning_rate': 0.19165559203938637, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.7650262936989495, 'subsample': 0.6877270546397166, 'colsample_bytree': 0.9407049432225333, 'alpha': 2.0916291920273062, 'lambda': 0.294826162192878}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:39,548] Trial 366 finished with value: 0.40341034732404535 and parameters: {'learning_rate': 0.1778437452852749, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.4874906761316441, 'subsample': 0.5967033980201101, 'colsample_bytree': 0.9807952973763953, 'alpha': 0.569236895518794, 'lambda': 2.363780480972525}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:41,634] Trial 367 finished with value: 0.43480522549844963 and parameters: {'learning_rate': 0.034072321578229786, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.002124847001548639, 'subsample': 0.7135506641521694, 'colsample_bytree': 0.9240714820190938, 'alpha': 0.8408952490323449, 'lambda': 1.71712290548785}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:42,362] Trial 368 finished with value: 0.4161719220304036 and parameters: {'learning_rate': 0.17388391648632034, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.17888457942758607, 'subsample': 0.5257793235582792, 'colsample_bytree': 0.8630092968749727, 'alpha': 0.38444625580980324, 'lambda': 8.029894798553398}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:44,016] Trial 369 finished with value: 0.45278068954614636 and parameters: {'learning_rate': 0.17744998262475742, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.006343095081646526, 'subsample': 0.6758554729952699, 'colsample_bytree': 0.9608577232713845, 'alpha': 0.19854093607652293, 'lambda': 8.7224760284448}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:44,929] Trial 370 finished with value: 0.4027696156824234 and parameters: {'learning_rate': 0.18870362541960894, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.3614635289770114, 'subsample': 0.5777654793891662, 'colsample_bytree': 0.9083790006160216, 'alpha': 0.33868348709458684, 'lambda': 8.442246549505107}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:45,587] Trial 371 finished with value: 0.439149887886655 and parameters: {'learning_rate': 0.1836488439497262, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.17862300608495757, 'subsample': 0.6279248549384291, 'colsample_bytree': 0.88624393842491, 'alpha': 0.005508879430223143, 'lambda': 2.189791672815119}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:46,269] Trial 372 finished with value: 0.3989581338405285 and parameters: {'learning_rate': 0.17273845713591593, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.5514992273240178, 'subsample': 0.44869653702752665, 'colsample_bytree': 0.9169789288197151, 'alpha': 0.5206353084992874, 'lambda': 1.9444955140066513}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:46,892] Trial 373 finished with value: 0.42682339655013884 and parameters: {'learning_rate': 0.16595638365939647, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.32605565424180133, 'subsample': 0.6888479178789544, 'colsample_bytree': 0.9443241518719832, 'alpha': 0.22099267472926787, 'lambda': 0.5537784638299093}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:47,190] Trial 374 finished with value: 0.29244014109054867 and parameters: {'learning_rate': 0.16945528119936165, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.18520345679070568, 'subsample': 0.4981270501321867, 'colsample_bytree': 0.977849539032507, 'alpha': 7.981714637465398, 'lambda': 2.7775913691880003}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:47,652] Trial 375 finished with value: 0.368317183335887 and parameters: {'learning_rate': 0.1634226501509948, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 9.525828009087192, 'subsample': 0.7269187582482418, 'colsample_bytree': 0.8942337622640474, 'alpha': 0.629366777346332, 'lambda': 3.3472621754035234}. Best is trial 234 with value: 0.4671584355563095.\n",
      "[I 2024-02-12 16:26:49,287] Trial 376 finished with value: 0.46260352793795456 and parameters: {'learning_rate': 0.15732262800725139, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.011029921140892305, 'subsample': 0.7029740967477707, 'colsample_bytree': 0.8700937300159991, 'alpha': 0.17847200631111168, 'lambda': 0.28100152426151526}. Best is trial 234 with value: 0.4671584355563095.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def objective_small(trial):#,X_train,y_train,X_valid,y_valid,weight_train):\n",
    "    params = dict(\n",
    "                #booster = trial.suggest_categorical(\"booster\",['gbtree', 'gblinear', 'dart']),\n",
    "                learning_rate = trial.suggest_float(\"learning_rate\", 0.01, 0.2),\n",
    "                max_depth= trial.suggest_int(\"max_depth\",5, 15),\n",
    "                min_child_weight = trial.suggest_int(\"min_child_weight\", 1, 8),\n",
    "                gamma = trial.suggest_float(\"gamma\", 0, 10),\n",
    "                subsample = trial.suggest_float(\"subsample\", 0.01,1),\n",
    "                colsample_bytree = trial.suggest_float(\"colsample_bytree\", 0.01,1),\n",
    "                alpha = trial.suggest_float(\"alpha\", 0, 10),\n",
    "                objective= 'multi:softprob', \n",
    "                nthread=4, \n",
    "                 num_class= 5,\n",
    "                seed=27)\n",
    "    params['lambda'] = trial.suggest_float(\"lambda\", 0, 10)\n",
    "\n",
    "    \n",
    "    dtrain = xgb.DMatrix(X_train_small,y_train_small, \n",
    "                         enable_categorical=True,\n",
    "                         weight=weight_train)\n",
    "    dvalid = xgb.DMatrix(X_valid_small,y_valid_small,\n",
    "                         enable_categorical=True,\n",
    "                         )\n",
    "\n",
    "\n",
    "    bst = xgb.train(params, dtrain,verbose_eval=False, num_boost_round=600,\n",
    "                    evals = [(dtrain, \"train\"), (dvalid, \"valid\")],\n",
    "                    early_stopping_rounds=100)\n",
    "\n",
    "    preds = bst.predict(dvalid)\n",
    "    ##MCC is more solid\n",
    "    mcc = matthews_corrcoef(y_valid_small,preds.argmax(1))    \n",
    "    \n",
    "    return mcc\n",
    "\n",
    "\n",
    "\n",
    "study_small = optuna.create_study(direction=\"maximize\")\n",
    "study_small.optimize(objective_small, n_trials=1000, timeout=600)\n",
    "params_final = dict(\n",
    "            objective= 'multi:softprob', \n",
    "            nthread=4, \n",
    "             num_class= 5,\n",
    "            seed=27)\n",
    "params_final.update(study_small.best_params)\n",
    "dtrain_small = xgb.DMatrix(X_train_small,y_train_small, \n",
    "                     enable_categorical=True,\n",
    "                     weight=weight_train)\n",
    "dvalid_small = xgb.DMatrix(X_valid_small,y_valid_small,\n",
    "                     enable_categorical=True,\n",
    "                     )   \n",
    "bst_small = xgb.train(params_final, dtrain_small,verbose_eval=False, num_boost_round=600,\n",
    "                evals = [(dtrain_small, \"train\"), (dvalid_small, \"valid\")],\n",
    "                early_stopping_rounds=100,)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "12b079e4-85c5-40f8-bb70-ac35008903eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_smote:\n",
    "    def objective_small_smote(trial):#,X_train,y_train,X_valid,y_valid,weight_train):\n",
    "        params = dict(\n",
    "                    #booster = trial.suggest_categorical(\"booster\",['gbtree', 'gblinear', 'dart']),\n",
    "                    learning_rate = trial.suggest_float(\"learning_rate\", 0.01, 0.2),\n",
    "                    max_depth= trial.suggest_int(\"max_depth\",5, 15),\n",
    "                    min_child_weight = trial.suggest_int(\"min_child_weight\", 1, 8),\n",
    "                    gamma = trial.suggest_float(\"gamma\", 0, 10),\n",
    "                    subsample = trial.suggest_float(\"subsample\", 0.01,1),\n",
    "                    colsample_bytree = trial.suggest_float(\"colsample_bytree\", 0.01,1),\n",
    "                    alpha = trial.suggest_float(\"alpha\", 0, 10),\n",
    "                    objective= 'multi:softprob', \n",
    "                    nthread=4, \n",
    "                     num_class= 5,\n",
    "                    seed=27)\n",
    "        params['lambda'] = trial.suggest_float(\"lambda\", 0, 10)\n",
    "    \n",
    "        \n",
    "        dtrain = xgb.DMatrix(X_train_smote_small,y_train_smote_small, \n",
    "                             enable_categorical=True,)\n",
    "        dvalid = xgb.DMatrix(X_valid_small,y_valid_small,\n",
    "                             enable_categorical=True,\n",
    "                             )\n",
    "    \n",
    "    \n",
    "        bst = xgb.train(params, dtrain,verbose_eval=False, num_boost_round=400,\n",
    "                        evals = [(dtrain, \"train\"), (dvalid, \"valid\")],\n",
    "                        early_stopping_rounds=100)\n",
    "    \n",
    "        preds = bst.predict(dvalid)\n",
    "        ##MCC is more solid\n",
    "        mcc = matthews_corrcoef(y_valid_small,preds.argmax(1))    \n",
    "        \n",
    "        return mcc\n",
    "    \n",
    "    \n",
    "    \n",
    "    study_small = optuna.create_study(direction=\"maximize\")\n",
    "    study_small.optimize(objective_small, n_trials=200, timeout=600)\n",
    "    params_final = dict(\n",
    "                objective= 'multi:softprob', \n",
    "                nthread=4, \n",
    "                 num_class= 5,\n",
    "                seed=27)\n",
    "    params_final.update(study_small.best_params)\n",
    "    dtrain_small = xgb.DMatrix(X_train_smote_small,y_train_smote_small, \n",
    "                         enable_categorical=True,\n",
    "                      )\n",
    "    dvalid_small = xgb.DMatrix(X_valid_small,y_valid_small,\n",
    "                         enable_categorical=True,\n",
    "                         )   \n",
    "    bst_small = xgb.train(params_final, dtrain_small,verbose_eval=False, num_boost_round=400,\n",
    "                    evals = [(dtrain_small, \"train\"), (dvalid_small, \"valid\")],\n",
    "                    early_stopping_rounds=100,)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5521a853-15cc-4868-b72a-f497e274ab93",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b794f14-4b51-40ad-9d18-8ad72138922f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2eafaf84-04d1-429a-b630-a785fa70e7f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Feature Importance Score')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_imp = pd.Series(bst_small.get_fscore()).sort_values(ascending=False)\n",
    "feat_imp.plot(kind='bar', title='Feature Importances')\n",
    "plt.ylabel('Feature Importance Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d6e04c2-37bb-4d9c-8ab6-63e93ec29fb1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "06b5f27d-2f28-48f0-b584-5a1729631cdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 206   29    1    0    0]\n",
      " [ 224 2572  717   11    0]\n",
      " [  10  192  595   55    1]\n",
      " [   2    2   22   16    0]\n",
      " [   2    0    0    0    1]]\n",
      "0.4669354220226053\n",
      "0.7277801631601546\n",
      "########################################\n",
      "[[ 461   14    3    0    0]\n",
      " [ 433 5275 1408   36    0]\n",
      " [  10  251 1392   80    0]\n",
      " [   0    0    0   86    0]\n",
      " [   0    0    0    0    6]]\n",
      "0.5578338794431741\n",
      "0.7636171337916446\n"
     ]
    }
   ],
   "source": [
    "preds_class_valid = bst.predict(dvalid)\n",
    "preds_class_train= bst.predict(dtrain)\n",
    "print(confusion_matrix(y_valid,preds_class_valid.argmax(1)))\n",
    "print(matthews_corrcoef(y_valid,preds_class_valid.argmax(1)))\n",
    "print(accuracy_score(y_valid,preds_class_valid.argmax(1)))\n",
    "print('########################################')\n",
    "print(confusion_matrix(y_train,preds_class_train.argmax(1)))\n",
    "print(matthews_corrcoef(y_train,preds_class_train.argmax(1)))\n",
    "print(accuracy_score(y_train,preds_class_train.argmax(1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4437941d-9b76-4950-bdb6-cfab5793ca03",
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_smote:\n",
    "    preds_class_valid = bst_small.predict(dvalid_small)\n",
    "    preds_class_train= bst_small.predict(dtrain_small)\n",
    "    print(confusion_matrix(y_valid_small,preds_class_valid.argmax(1)))\n",
    "    print(matthews_corrcoef(y_valid_small,preds_class_valid.argmax(1)))\n",
    "    print(accuracy_score(y_valid_small,preds_class_valid.argmax(1)))\n",
    "    print('########################################')\n",
    "    print(confusion_matrix(y_train_smote_small,preds_class_train.argmax(1)))\n",
    "    print(matthews_corrcoef(y_train_smote_small,preds_class_train.argmax(1)))\n",
    "    print(accuracy_score(y_train_smote_small,preds_class_train.argmax(1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b2f861da-5fa1-4e1b-8e6d-b1080dab1c24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 191   42    2    0    1]\n",
      " [ 161 2702  641   12    8]\n",
      " [   7  226  571   47    2]\n",
      " [   2    2   29    9    0]\n",
      " [   2    1    0    0    0]]\n",
      "0.4671584355563095\n",
      "0.7455989695148132\n",
      "########################################\n",
      "[[ 474    2    1    0    1]\n",
      " [ 261 5744 1115   28    4]\n",
      " [   3  188 1504   37    1]\n",
      " [   0    0    0   86    0]\n",
      " [   0    0    0    0    6]]\n",
      "0.6559067720290881\n",
      "0.8264410364886303\n"
     ]
    }
   ],
   "source": [
    "\n",
    "preds_class_valid = bst_small.predict(dvalid_small)\n",
    "preds_class_train= bst_small.predict(dtrain_small)\n",
    "print(confusion_matrix(y_valid_small,preds_class_valid.argmax(1)))\n",
    "print(matthews_corrcoef(y_valid_small,preds_class_valid.argmax(1)))\n",
    "print(accuracy_score(y_valid_small,preds_class_valid.argmax(1)))\n",
    "print('########################################')\n",
    "print(confusion_matrix(y_train_small,preds_class_train.argmax(1)))\n",
    "print(matthews_corrcoef(y_train_small,preds_class_train.argmax(1)))\n",
    "print(accuracy_score(y_train_small,preds_class_train.argmax(1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d7c1f2f6-e484-4b57-adc8-7307bbb2022f",
   "metadata": {},
   "outputs": [
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r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(u+t,s)}}},84046:function(t,e,r){\"use strict\";var n=r(71828),i=r(89298),a=r(85501),o=r(25625),s=r(50215);function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a(\"visible\"),u=a(\"clicktoshow\");if(l||u){o(t,e,r,a);for(var c=e.showarrow,f=[\"x\",\"y\"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var v=f[d],g=i.coerceRef(t,e,p,v,\"\",\"paper\");if(\"paper\"!==g&&i.getFromId(p,g)._annIndices.push(e._index),i.coercePosition(e,p,a,g,v,.5),c){var y=\"a\"+v,m=i.coerceRef(t,e,p,y,\"pixel\",[\"pixel\",\"paper\"]);\"pixel\"!==m&&m!==g&&(m=e[y]=\"pixel\");var x=\"pixel\"===m?h[d]:.4;i.coercePosition(e,p,a,m,y,x)}a(v+\"anchor\"),a(v+\"shift\")}if(n.noneOrAll(t,e,[\"x\",\"y\"]),c&&n.noneOrAll(t,e,[\"ax\",\"ay\"]),u){var b=a(\"xclick\"),_=a(\"yclick\");e._xclick=void 0===b?e.x:i.cleanPosition(b,p,e.xref),e._yclick=void 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F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append(\"rect\").attr(\"class\",\"bg\").style(\"stroke-width\",F+\"px\").call(u.stroke,e.bordercolor).call(u.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll(\"#\"+L).data(U?[0]:[]);V.enter().append(\"clipPath\").classed(\"annclip\",!0).attr(\"id\",L).append(\"rect\"),V.exit().remove();var H=e.font,q=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append(\"text\").classed(\"annotation-text\",!0).text(q);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(Y).on(\"edit\",(function(r){e.text=r,this.call(Y),S(\"text\",r),y&&y.autorange&&M(y._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0),i.call(\"_guiRelayout\",t,E())})):G.call(Y)}else n.selectAll(\"#\"+L).remove();function Z(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function Y(r){return r.call(c.font,H).attr({\"text-anchor\":{left:\"start\",right:\"end\"}[e.align]||\"middle\"}),h.convertToTspans(r,t,W),r}function W(){var r=G.selectAll(\"a\");1===r.size()&&r.text()===G.text()&&R.insert(\"a\",\":first-child\").attr({\"xlink:xlink:href\":r.attr(\"xlink:href\"),\"xlink:xlink:show\":r.attr(\"xlink:show\")}).style({cursor:\"pointer\"}).node().appendChild(j.node());var n=R.select(\".annotation-text-math-group\"),f=!n.empty(),v=c.bBox((f?n:G).node()),b=v.width,_=v.height,A=e.width||b,z=e.height||_,B=Math.round(A+2*N),H=Math.round(z+2*N);function q(t,e){return\"auto\"===e&&(e=t<1/3?\"left\":t>2/3?\"right\":\"center\"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var Y=!1,W=[\"x\",\"y\"],X=0;X<W.length;X++){var J,K,$,Q,tt,et=W[X],rt=e[et+\"ref\"]||et,nt=e[\"a\"+et+\"ref\"],it={x:y,y:x}[et],at=(P+(\"x\"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=H*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ut=e[et+\"anchor\"],ct=e[et+\"shift\"]*(\"x\"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&\"domain\"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e[\"a\"+et]))<0||pt>1)&&(Y=!0):Y=!0),J=it._offset+it.r2p(e[et]),Q=.5}else{var dt=\"domain\"===ht;\"x\"===et?($=e[et],J=dt?it._offset+it._length*$:J=T.l+T.w*$):($=1-e[et],J=dt?it._offset+it._length*$:J=T.t+T.h*$),Q=e.showarrow?.5:$}if(e.showarrow){ft.head=J;var vt=e[\"a\"+et];if(tt=ot*q(.5,e.xanchor)-st*q(.5,e.yanchor),nt===rt){var gt=l.getRefType(nt);\"domain\"===gt?(\"y\"===et&&(vt=1-vt),ft.tail=it._offset+it._length*vt):\"paper\"===gt?\"y\"===et?(vt=1-vt,ft.tail=T.t+T.h*vt):ft.tail=T.l+T.w*vt:ft.tail=it._offset+it.r2p(vt),K=tt}else ft.tail=J+vt,K=tt+vt;ft.text=ft.tail+tt;var yt=w[\"x\"===et?\"width\":\"height\"];if(\"paper\"===rt&&(ft.head=o.constrain(ft.head,1,yt-1)),\"pixel\"===nt){var mt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-yt;mt>0?(ft.tail+=mt,ft.text+=mt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ct,ft.head+=ct}else K=tt=lt*q(Q,ut),ft.text=J+tt;ft.text+=ct,tt+=ct,K+=ct,e[\"_\"+et+\"padplus\"]=lt/2+K,e[\"_\"+et+\"padminus\"]=lt/2-K,e[\"_\"+et+\"size\"]=lt,e[\"_\"+et+\"shift\"]=tt}if(Y)R.remove();else{var bt=0,_t=0;if(\"left\"!==e.align&&(bt=(A-b)*(\"center\"===e.align?.5:1)),\"top\"!==e.valign&&(_t=(z-_)*(\"middle\"===e.valign?.5:1)),f)n.select(\"svg\").attr({x:N+bt-1,y:N+_t}).call(c.setClipUrl,U?L:null,t);else{var wt=N+_t-v.top,Tt=N+bt-v.left;G.call(h.positionText,Tt,wt).call(c.setClipUrl,U?L:null,t)}V.select(\"rect\").call(c.setRect,N,N,A,z),j.call(c.setRect,F/2,F/2,B-F,H-F),R.call(c.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-H/2)),I.attr({transform:\"rotate(\"+P+\",\"+C.x.text+\",\"+C.y.text+\")\"});var kt,At=function(r,n){O.selectAll(\".annotation-arrow-g\").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,v=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,v,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr(\"width\"),D=+j.attr(\"height\"),z=v-.5*L,F=z+L,B=b-.5*D,N=B+D,U=[[z,B,z,N],[z,N,F,N],[F,N,F,B],[F,B,z,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,H=e.arrowcolor,q=e.arrowside,G=O.append(\"g\").style({opacity:u.opacity(H)}).classed(\"annotation-arrow-g\",!0),Z=G.append(\"path\").attr(\"d\",\"M\"+h+\",\"+p+\"L\"+l+\",\"+f).style(\"stroke-width\",V+\"px\").call(u.stroke,u.rgb(H));if(g(Z,q,e),k.annotationPosition&&Z.node().parentNode&&!a){var Y=l,W=f;if(e.standoff){var X=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));Y+=e.standoff*(h-l)/X,W+=e.standoff*(p-f)/X}var J,K,$=G.append(\"path\").classed(\"annotation-arrow\",!0).classed(\"anndrag\",!0).classed(\"cursor-move\",!0).attr({d:\"M3,3H-3V-3H3ZM0,0L\"+(h-Y)+\",\"+(p-W),transform:s(Y,W)}).style(\"stroke-width\",V+6+\"px\").call(u.stroke,\"rgba(0,0,0,0)\").call(u.fill,\"rgba(0,0,0,0)\");d.init({element:$.node(),gd:t,prepFn:function(){var t=c.getTranslate(R);J=t.x,K=t.y,y&&y.autorange&&M(y._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0)},moveFn:function(t,r){var n=w(J,K),i=n[0]+t,a=n[1]+r;R.call(c.setTranslate,i,a),S(\"x\",m(y,t,\"x\",T,e)),S(\"y\",m(x,r,\"y\",T,e)),e.axref===e.xref&&S(\"ax\",m(y,t,\"ax\",T,e)),e.ayref===e.yref&&S(\"ay\",m(x,r,\"ay\",T,e)),G.attr(\"transform\",s(t,r)),I.attr({transform:\"rotate(\"+P+\",\"+i+\",\"+a+\")\"})},doneFn:function(){i.call(\"_guiRelayout\",t,E());var 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e=t._fullLayout;e._infolayer.selectAll(\".annotation\").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&y(t,r);return a.previousPromises(t)},drawOne:y,drawRaw:x}},13011:function(t,e,r){\"use strict\";var n=r(39898),i=r(7901),a=r(82884),o=r(71828),s=o.strScale,l=o.strRotate,u=o.strTranslate;t.exports=function(t,e,r){var o,c,f,h,p=t.node(),d=a[r.arrowhead||0],v=a[r.startarrowhead||0],g=(r.arrowwidth||1)*(r.arrowsize||1),y=(r.arrowwidth||1)*(r.startarrowsize||1),m=e.indexOf(\"start\")>=0,x=e.indexOf(\"end\")>=0,b=d.backoff*g+r.standoff,_=v.backoff*y+r.startstandoff;if(\"line\"===p.nodeName){o={x:+t.attr(\"x1\"),y:+t.attr(\"y1\")},c={x:+t.attr(\"x2\"),y:+t.attr(\"y2\")};var w=o.x-c.x,T=o.y-c.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void D();if(b){if(b*b>w*w+T*T)return void D();var k=b*Math.cos(f),A=b*Math.sin(f);c.x+=k,c.y+=A,t.attr({x2:c.x,y2:c.y})}if(_){if(_*_>w*w+T*T)return void D();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if(\"path\"===p.nodeName){var E=p.getTotalLength(),L=\"\";if(E<b+_)return void D();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L=\"0px,\"+_+\"px,\";var O=p.getPointAtLength(E),I=p.getPointAtLength(E-.1);h=Math.atan2(O.y-I.y,O.x-I.x),c=p.getPointAtLength(Math.max(0,E-b)),L+=E-(L?_+b:b)+\"px,\"+E+\"px\",t.style(\"stroke-dasharray\",L)}function D(){t.style(\"stroke-dasharray\",\"0px,100px\")}function z(e,a,o,c){e.path&&(e.noRotate&&(o=0),n.select(p.parentNode).append(\"path\").attr({class:t.attr(\"class\"),d:e.path,transform:u(a.x,a.y)+l(180*o/Math.PI)+s(c)}).style({fill:i.rgb(r.arrowcolor),\"stroke-width\":0}))}m&&z(v,o,f,y),x&&z(d,c,h,g)}},32745:function(t,e,r){\"use strict\";var n=r(92605),i=r(44317);t.exports={moduleType:\"component\",name:\"annotations\",layoutAttributes:r(50215),supplyLayoutDefaults:r(84046),includeBasePlot:r(76325)(\"annotations\"),calcAutorange:r(3749),draw:n.draw,drawOne:n.drawOne,drawRaw:n.drawRaw,hasClickToShow:i.hasClickToShow,onClick:i.onClick,convertCoords:r(94128)}},26997:function(t,e,r){\"use strict\";var n=r(50215),i=r(30962).overrideAll,a=r(44467).templatedArray;t.exports=i(a(\"annotation\",{visible:n.visible,x:{valType:\"any\"},y:{valType:\"any\"},z:{valType:\"any\"},ax:{valType:\"number\"},ay:{valType:\"number\"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),\"calc\",\"from-root\")},5485:function(t,e,r){\"use strict\";var n=r(71828),i=r(89298);function a(t,e){var r=e.fullSceneLayout.domain,a=e.fullLayout._size,o={pdata:null,type:\"linear\",autorange:!1,range:[-1/0,1/0]};t._xa={},n.extendFlat(t._xa,o),i.setConvert(t._xa),t._xa._offset=a.l+r.x[0]*a.w,t._xa.l2p=function(){return.5*(1+t._pdata[0]/t._pdata[3])*a.w*(r.x[1]-r.x[0])},t._ya={},n.extendFlat(t._ya,o),i.setConvert(t._ya),t._ya._offset=a.t+(1-r.y[1])*a.h,t._ya.l2p=function(){return.5*(1-t._pdata[1]/t._pdata[3])*a.h*(r.y[1]-r.y[0])}}t.exports=function(t){for(var e=t.fullSceneLayout.annotations,r=0;r<e.length;r++)a(e[r],t);t.fullLayout._infolayer.selectAll(\".annotation-\"+t.id).remove()}},20226:function(t,e,r){\"use strict\";var n=r(71828),i=r(89298),a=r(85501),o=r(25625),s=r(26997);function l(t,e,r,a){function l(r,i){return n.coerce(t,e,s,r,i)}function u(t){var n=t+\"axis\",a={_fullLayout:{}};return 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n=r(39898),i=r(92770),a=r(84267),o=r(71828),s=o.strTranslate,l=o.strRotate,u=r(11086),c=r(63893),f=r(39918),h=r(91424),p=r(7901),d=r(28569),v=r(89298),g=r(73972),y=r(23469),m=r(26675),x=r(99017),b=r(43969),_=m.YANGLE,w=Math.PI*_/180,T=1/Math.sin(w),k=Math.cos(w),A=Math.sin(w),M=m.HOVERARROWSIZE,S=m.HOVERTEXTPAD,E={box:!0,ohlc:!0,violin:!0,candlestick:!0},L={scatter:!0,scattergl:!0,splom:!0};function C(t){return[t.trace.index,t.index,t.x0,t.y0,t.name,t.attr,t.xa?t.xa._id:\"\",t.ya?t.ya._id:\"\"].join(\",\")}e.hover=function(t,e,r,a){t=o.getGraphDiv(t);var s=e.target;o.throttle(t._fullLayout._uid+m.HOVERID,m.HOVERMINTIME,(function(){!function(t,e,r,a,s){r||(r=\"xy\");var l=Array.isArray(r)?r:[r],c=t._fullLayout,h=c._plots||[],v=h[r],m=c._has(\"cartesian\");if(v){var x=v.overlays.map((function(t){return t.id}));l=l.concat(x)}for(var b=l.length,_=new Array(b),w=new Array(b),k=!1,A=0;A<b;A++){var S=l[A];if(h[S])k=!0,_[A]=h[S].xaxis,w[A]=h[S].yaxis;else{if(!c[S]||!c[S]._subplot)return void 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ct=o.apply3DTransform(c._invTransform)(st,lt);if(st=ct[0],lt=ct[1],st<0||st>_[0]._length||lt<0||lt>w[0]._length)return d.unhoverRaw(t,e)}else st=\"xpx\"in e?e.xpx:_[0]._length/2,lt=\"ypx\"in e?e.ypx:w[0]._length/2;if(e.pointerX=st+_[0]._offset,e.pointerY=lt+w[0]._offset,q=\"xval\"in e?y.flat(l,e.xval):y.p2c(_,st),G=\"yval\"in e?y.flat(l,e.yval):y.p2c(w,lt),!i(q[0])||!i(G[0]))return o.warn(\"Fx.hover failed\",e,t),d.unhoverRaw(t,e)}var ft=1/0;function ht(t,r){for(Y=0;Y<it.length;Y++)if((W=it[Y])&&W[0]&&W[0].trace&&!0===(X=W[0].trace).visible&&0!==X._length&&-1===[\"carpet\",\"contourcarpet\"].indexOf(X._module.name)){if(\"splom\"===X.type?J=l[K=0]:(J=y.getSubplot(X),K=l.indexOf(J)),$=I,y.isUnifiedHover($)&&($=$.charAt(0)),et={cd:W,trace:X,xa:_[K],ya:w[K],maxHoverDistance:N,maxSpikeDistance:H,index:!1,distance:Math.min(ft,N),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:p.defaultLine,name:X.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 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data\"!==t.xa.spikesnap}));if(d.length){var v=d[0];i(v.x0)&&i(v.y0)&&(h=dt(v),(!at.vLinePoint||at.vLinePoint.spikeDistance>h.spikeDistance)&&(at.vLinePoint=h))}var g=f.filter((function(t){return t.ya.showspikes&&\"hovered data\"!==t.ya.spikesnap}));if(g.length){var x=g[0];i(x.x0)&&i(x.y0)&&(h=dt(x),(!at.hLinePoint||at.hLinePoint.spikeDistance>h.spikeDistance)&&(at.hLinePoint=h))}}}}}function pt(t,e,r){for(var n,i=null,a=1/0,o=0;o<t.length;o++)n=t[o].spikeDistance,r&&0===o&&(n=-1/0),n<=a&&n<=e&&(i=t[o],a=n);return i}function dt(t){return t?{xa:t.xa,ya:t.ya,x:void 0!==t.xSpike?t.xSpike:(t.x0+t.x1)/2,y:void 0!==t.ySpike?t.ySpike:(t.y0+t.y1)/2,distance:t.distance,spikeDistance:t.spikeDistance,curveNumber:t.trace.index,color:t.color,pointNumber:t.index}:null}ht();var vt={fullLayout:c,container:c._hoverlayer,event:e},gt=t._spikepoints,yt={vLinePoint:at.vLinePoint,hLinePoint:at.hLinePoint};t._spikepoints=yt;var mt=function(){nt.sort((function(t,e){return t.distance-e.distance})),nt=function(t,e){for(var r=e.charAt(0),n=[],i=[],a=[],o=0;o<t.length;o++){var s=t[o];g.traceIs(s.trace,\"bar-like\")||g.traceIs(s.trace,\"box-violin\")?a.push(s):s.trace[r+\"period\"]?i.push(s):n.push(s)}return n.concat(i).concat(a)}(nt,I)};mt();var xt=I.charAt(0),bt=(\"x\"===xt||\"y\"===xt)&&nt[0]&&L[nt[0].trace.type];if(m&&0!==H&&0!==nt.length){var _t=pt(nt.filter((function(t){return t.ya.showspikes})),H,bt);at.hLinePoint=dt(_t);var wt=pt(nt.filter((function(t){return t.xa.showspikes})),H,bt);at.vLinePoint=dt(wt)}if(0===nt.length){var Tt=d.unhoverRaw(t,e);return!m||null===at.hLinePoint&&null===at.vLinePoint||B(gt)&&F(t,at,vt),Tt}if(m&&B(gt)&&F(t,at,vt),y.isXYhover($)&&0!==nt[0].length&&\"splom\"!==nt[0].trace.type){var kt=nt[0],At=(nt=E[kt.trace.type]?nt.filter((function(t){return t.trace.index===kt.trace.index})):[kt]).length;ht(j(\"x\",kt,c),j(\"y\",kt,c));var Mt,St=[],Et={},Lt=0,Ct=function(t){var e=E[t.trace.type]?C(t):t.trace.index;if(Et[e]){var r=Et[e]-1,n=St[r];r>0&&Math.abs(t.distance)<Math.abs(n.distance)&&(St[r]=t)}else Lt++,Et[e]=Lt,St.push(t)};for(Mt=0;Mt<At;Mt++)Ct(nt[Mt]);for(Mt=nt.length-1;Mt>At-1;Mt--)Ct(nt[Mt]);nt=St,mt()}var Pt=t._hoverdata,Ot=[],It=U(t),Dt=V(t);for(Z=0;Z<nt.length;Z++){var zt=nt[Z],Rt=y.makeEventData(zt,zt.trace,zt.cd);if(!1!==zt.hovertemplate){var Ft=!1;zt.cd[zt.index]&&zt.cd[zt.index].ht&&(Ft=zt.cd[zt.index].ht),zt.hovertemplate=Ft||zt.trace.hovertemplate||!1}if(zt.xa&&zt.ya){var Bt=zt.x0+zt.xa._offset,Nt=zt.x1+zt.xa._offset,jt=zt.y0+zt.ya._offset,Ut=zt.y1+zt.ya._offset,Vt=Math.min(Bt,Nt),Ht=Math.max(Bt,Nt),qt=Math.min(jt,Ut),Gt=Math.max(jt,Ut);Rt.bbox={x0:Vt+Dt,x1:Ht+Dt,y0:qt+It,y1:Gt+It}}zt.eventData=[Rt],Ot.push(Rt)}t._hoverdata=Ot;var Zt=\"y\"===I&&(it.length>1||nt.length>1)||\"closest\"===I&&ot&&nt.length>1,Yt=p.combine(c.plot_bgcolor||p.background,c.paper_bgcolor),Wt=O(nt,{gd:t,hovermode:I,rotateLabels:Zt,bgColor:Yt,container:c._hoverlayer,outerContainer:c._paper.node(),commonLabelOpts:c.hoverlabel,hoverdistance:c.hoverdistance}),Xt=Wt.hoverLabels;if(y.isUnifiedHover(I)||(function(t,e,r,n){var i,a,o,s,l,u,c,f=e?\"xa\":\"ya\",h=e?\"ya\":\"xa\",p=0,d=1,v=t.size(),g=new Array(v),y=0,m=n.minX,x=n.maxX,b=n.minY,_=n.maxY,w=function(t){return t*r._invScaleX},k=function(t){return t*r._invScaleY};function A(t){var e=t[0],r=t[t.length-1];if(a=e.pmin-e.pos-e.dp+e.size,o=r.pos+r.dp+r.size-e.pmax,a>.01){for(l=t.length-1;l>=0;l--)t[l].dp+=a;i=!1}if(!(o<.01)){if(a<-.01){for(l=t.length-1;l>=0;l--)t[l].dp-=o;i=!1}if(i){var n=0;for(s=0;s<t.length;s++)(u=t[s]).pos+u.dp+u.size>e.pmax&&n++;for(s=t.length-1;s>=0&&!(n<=0);s--)(u=t[s]).pos>e.pmax-1&&(u.del=!0,n--);for(s=0;s<t.length&&!(n<=0);s++)if((u=t[s]).pos<e.pmin+1)for(u.del=!0,n--,o=2*u.size,l=t.length-1;l>=0;l--)t[l].dp-=o;for(s=t.length-1;s>=0&&!(n<=0);s--)(u=t[s]).pos+u.dp+u.size>e.pmax&&(u.del=!0,n--)}}}for(t.each((function(t){var n=t[f],i=t[h],a=\"x\"===n._id.charAt(0),o=n.range;0===y&&o&&o[0]>o[1]!==a&&(d=-1);var s=0,l=a?r.width:r.height;if(\"x\"===r.hovermode||\"y\"===r.hovermode){var u,c,p=D(t,e),v=t.anchor,A=\"end\"===v?-1:1;if(\"middle\"===v)c=(u=t.crossPos+(a?k(p.y-t.by/2):w(t.bx/2+t.tx2width/2)))+(a?k(t.by):w(t.bx));else if(a)c=(u=t.crossPos+k(M+p.y)-k(t.by/2-M))+k(t.by);else{var S=w(A*M+p.x),E=S+w(A*t.bx);u=t.crossPos+Math.min(S,E),c=t.crossPos+Math.max(S,E)}a?void 0!==b&&void 0!==_&&Math.min(c,_)-Math.max(u,b)>1&&(\"left\"===i.side?(s=i._mainLinePosition,l=r.width):l=i._mainLinePosition):void 0!==m&&void 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Jt=g.getComponentMethod(\"annotations\",\"hasClickToShow\")(t,Ot);f(n.select(s),Jt?\"pointer\":\"\")}s&&!a&&function(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var i=r[n],a=t._hoverdata[n];if(i.curveNumber!==a.curveNumber||String(i.pointNumber)!==String(a.pointNumber)||String(i.pointNumbers)!==String(a.pointNumbers))return!0}return!1}(t,0,Pt)&&(Pt&&t.emit(\"plotly_unhover\",{event:e,points:Pt}),t.emit(\"plotly_hover\",{event:e,points:t._hoverdata,xaxes:_,yaxes:w,xvals:q,yvals:G}))}(t,e,r,a,s)}))},e.loneHover=function(t,e){var r=!0;Array.isArray(t)||(r=!1,t=[t]);var i=e.gd,a=U(i),o=V(i),s=O(t.map((function(t){var r=t._x0||t.x0||t.x||0,n=t._x1||t.x1||t.x||0,s=t._y0||t.y0||t.y||0,l=t._y1||t.y1||t.y||0,u=t.eventData;if(u){var c=Math.min(r,n),f=Math.max(r,n),h=Math.min(s,l),d=Math.max(s,l),v=t.trace;if(g.traceIs(v,\"gl3d\")){var y=i._fullLayout[v.scene]._scene.container,m=y.offsetLeft,x=y.offsetTop;c+=m,f+=m,h+=x,d+=x}u.bbox={x0:c+o,x1:f+o,y0:h+a,y1:d+a},e.inOut_bbox&&e.inOut_bbox.push(u.bbox)}else u=!1;return{color:t.color||p.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,nameLength:t.nameLength,textAlign:t.textAlign,trace:t.trace||{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:t.hovertemplate||!1,hovertemplateLabels:t.hovertemplateLabels||!1,eventData:u}})),{gd:i,hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor||p.background,container:n.select(e.container),outerContainer:e.outerContainer||e.container}).hoverLabels,l=0,u=0;return s.sort((function(t,e){return t.y0-e.y0})).each((function(t,r){var n=t.y0-t.by/2;t.offset=n-5<l?l-n+5:0,l=n+t.by+t.offset,r===e.anchorIndex&&(u=t.offset)})).each((function(t){t.offset-=u})),z(s,!1,i._fullLayout._invScaleX,i._fullLayout._invScaleY),r?s:s.node()};var P=/<extra>([\\s\\S]*)<\\/extra>/;function O(t,e){var r=e.gd,i=r._fullLayout,a=e.hovermode,u=e.rotateLabels,f=e.bgColor,d=e.container,v=e.outerContainer,w=e.commonLabelOpts||{};if(0===t.length)return[[]];var T=e.fontFamily||m.HOVERFONT,k=e.fontSize||m.HOVERFONTSIZE,A=t[0],E=A.xa,L=A.ya,P=a.charAt(0),O=P+\"Label\",D=A[O];if(void 0===D&&\"multicategory\"===E.type)for(var z=0;z<t.length&&void 0===(D=t[z][O]);z++);var R=H(r,v),F=R.top,B=R.width,N=R.height,j=void 0!==D&&A.distance<=e.hoverdistance&&(\"x\"===a||\"y\"===a);if(j){var U,V,q=!0;for(U=0;U<t.length;U++)if(q&&void 0===t[U].zLabel&&(q=!1),V=t[U].hoverinfo||t[U].trace.hoverinfo){var G=Array.isArray(V)?V:V.split(\"+\");if(-1===G.indexOf(\"all\")&&-1===G.indexOf(a)){j=!1;break}}q&&(j=!1)}var 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n,i,o=r.container,s=r.fullLayout,l=s._size,u=r.event,c=!!e.hLinePoint,f=!!e.vLinePoint;if(o.selectAll(\".spikeline\").remove(),f||c){var d=p.combine(s.plot_bgcolor,s.paper_bgcolor);if(c){var g,y,m=e.hLinePoint;n=m&&m.xa,\"cursor\"===(i=m&&m.ya).spikesnap?(g=u.pointerX,y=u.pointerY):(g=n._offset+m.x,y=i._offset+m.y);var x,b,_=a.readability(m.color,d)<1.5?p.contrast(d):m.color,w=i.spikemode,T=i.spikethickness,k=i.spikecolor||_,A=v.getPxPosition(t,i);if(-1!==w.indexOf(\"toaxis\")||-1!==w.indexOf(\"across\")){if(-1!==w.indexOf(\"toaxis\")&&(x=A,b=g),-1!==w.indexOf(\"across\")){var 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n=r(41940),i=r(22399),a=(0,r(44467).templatedArray)(\"button\",{visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},step:{valType:\"enumerated\",values:[\"month\",\"year\",\"day\",\"hour\",\"minute\",\"second\",\"all\"],dflt:\"month\",editType:\"plot\"},stepmode:{valType:\"enumerated\",values:[\"backward\",\"todate\"],dflt:\"backward\",editType:\"plot\"},count:{valType:\"number\",min:0,dflt:1,editType:\"plot\"},label:{valType:\"string\",editType:\"plot\"},editType:\"plot\"});t.exports={visible:{valType:\"boolean\",editType:\"plot\"},buttons:a,x:{valType:\"number\",min:-2,max:3,editType:\"plot\"},xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\",editType:\"plot\"},y:{valType:\"number\",min:-2,max:3,editType:\"plot\"},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"bottom\",editType:\"plot\"},font:n({editType:\"plot\"}),bgcolor:{valType:\"color\",dflt:i.lightLine,editType:\"plot\"},activecolor:{valType:\"color\",editType:\"plot\"},bordercolor:{valType:\"color\",dflt:i.defaultLine,editType:\"plot\"},borderwidth:{valType:\"number\",min:0,dflt:0,editType:\"plot\"},editType:\"plot\"}},89573:function(t){\"use 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i,a;l.ensureSingle(t,\"text\",\"selector-text\",(function(t){t.attr(\"text-anchor\",\"middle\")})).call(s.font,e.font).text((i=r,a=n._fullLayout._meta,i.label?a?l.templateString(i.label,a):i.label:\"all\"===i.step?\"all\":i.count+i.step.charAt(0))).call((function(t){c.convertToTspans(t,n)}))}t.exports=function(t){var e=t._fullLayout._infolayer.selectAll(\".rangeselector\").data(function(t){for(var e=f.list(t,\"x\",!0),r=[],n=0;n<e.length;n++){var i=e[n];i.rangeselector&&i.rangeselector.visible&&r.push(i)}return r}(t),m);e.enter().append(\"g\").classed(\"rangeselector\",!0),e.exit().remove(),e.style({cursor:\"pointer\",\"pointer-events\":\"all\"}),e.each((function(e){var r=n.select(this),o=e,f=o.rangeselector,h=r.selectAll(\"g.button\").data(l.filterVisible(f.buttons));h.enter().append(\"g\").classed(\"button\",!0),h.exit().remove(),h.each((function(e){var r=n.select(this),a=y(o,e);e._isActive=function(t,e,r){if(\"all\"===e.step)return!0===t.autorange;var n=Object.keys(r);return t.range[0]===r[n[0]]&&t.range[1]===r[n[1]]}(o,e,a),r.call(x,f,e),r.call(b,f,e,t),r.on(\"click\",(function(){t._dragged||i.call(\"_guiRelayout\",t,a)})),r.on(\"mouseover\",(function(){e._isHovered=!0,r.call(x,f,e)})),r.on(\"mouseout\",(function(){e._isHovered=!1,r.call(x,f,e)}))})),function(t,e,r,i,o){var f=0,h=0,y=r.borderwidth;e.each((function(){var t=n.select(this).select(\".selector-text\"),e=r.font.size*p,i=Math.max(e*c.lineCount(t),16)+3;h=Math.max(h,i)})),e.each((function(){var t=n.select(this),e=t.select(\".selector-rect\"),i=t.select(\".selector-text\"),a=i.node()&&s.bBox(i.node()).width,o=r.font.size*p,l=c.lineCount(i),d=Math.max(a+10,g.minButtonWidth);t.attr(\"transform\",u(y+f,y)),e.attr({x:0,y:0,width:d,height:h}),c.positionText(i,d/2,h/2-(l-1)*o/2+3),f+=d+5}));var m=t._fullLayout._size,x=m.l+m.w*r.x,b=m.t+m.h*(1-r.y),_=\"left\";l.isRightAnchor(r)&&(x-=f,_=\"right\"),l.isCenterAnchor(r)&&(x-=f/2,_=\"center\");var w=\"top\";l.isBottomAnchor(r)&&(b-=h,w=\"bottom\"),l.isMiddleAnchor(r)&&(b-=h/2,w=\"middle\"),f=Math.ceil(f),h=Math.ceil(h),x=Math.round(x),b=Math.round(b),a.autoMargin(t,i+\"-range-selector\",{x:r.x,y:r.y,l:f*d[_],r:f*v[_],b:h*v[w],t:h*d[w]}),o.attr(\"transform\",u(x,b))}(t,h,f,o._name,r)}))}},70565:function(t,e,r){\"use strict\";var n=r(81041),i=r(71828).titleCase;t.exports=function(t,e){var r=t._name,a={};if(\"all\"===e.step)a[r+\".autorange\"]=!0;else{var o=function(t,e){var r,a=t.range,o=new Date(t.r2l(a[1])),s=e.step,l=n[\"utc\"+i(s)],u=e.count;switch(e.stepmode){case\"backward\":r=t.l2r(+l.offset(o,-u));break;case\"todate\":var c=l.offset(o,-u);r=t.l2r(+l.ceil(c))}return[r,a[1]]}(t,e);a[r+\".range[0]\"]=o[0],a[r+\".range[1]\"]=o[1]}return a}},97218:function(t,e,r){\"use strict\";t.exports={moduleType:\"component\",name:\"rangeselector\",schema:{subplots:{xaxis:{rangeselector:r(37113)}}},layoutAttributes:r(37113),handleDefaults:r(28674),draw:r(21598)}},75148:function(t,e,r){\"use 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n=r(71828),i=r(44467),a=r(41675),o=r(75148),s=r(47850);t.exports=function(t,e,r){var l=t[r],u=e[r];if(l.rangeslider||e._requestRangeslider[u._id]){n.isPlainObject(l.rangeslider)||(l.rangeslider={});var c,f,h=l.rangeslider,p=i.newContainer(u,\"rangeslider\");if(_(\"visible\")){_(\"bgcolor\",e.plot_bgcolor),_(\"bordercolor\"),_(\"borderwidth\"),_(\"thickness\"),_(\"autorange\",!u.isValidRange(h.range)),_(\"range\");var d=e._subplots;if(d)for(var v=d.cartesian.filter((function(t){return t.substr(0,t.indexOf(\"y\"))===a.name2id(r)})).map((function(t){return t.substr(t.indexOf(\"y\"),t.length)})),g=n.simpleMap(v,a.id2name),y=0;y<g.length;y++){var m=g[y];c=h[m]||{},f=i.newContainer(p,m,\"yaxis\");var x,b=e[m];c.range&&b.isValidRange(c.range)&&(x=\"fixed\"),\"match\"!==w(\"rangemode\",x)&&w(\"range\",b.range.slice())}p._input=h}}function _(t,e){return n.coerce(h,p,o,t,e)}function w(t,e){return n.coerce(c,f,s,t,e)}}},72413:function(t,e,r){\"use strict\";var 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x(t,e,r,n,i){o.ensureSingle(t,\"rect\",v.maskMinClassName,(function(t){t.attr({x:0,y:0,\"shape-rendering\":\"crispEdges\"})})).attr(\"height\",n._height).call(u.fill,v.maskColor),o.ensureSingle(t,\"rect\",v.maskMaxClassName,(function(t){t.attr({y:0,\"shape-rendering\":\"crispEdges\"})})).attr(\"height\",n._height).call(u.fill,v.maskColor),\"match\"!==i.rangemode&&(o.ensureSingle(t,\"rect\",v.maskMinOppAxisClassName,(function(t){t.attr({y:0,\"shape-rendering\":\"crispEdges\"})})).attr(\"width\",n._width).call(u.fill,v.maskOppAxisColor),o.ensureSingle(t,\"rect\",v.maskMaxOppAxisClassName,(function(t){t.attr({y:0,\"shape-rendering\":\"crispEdges\"})})).attr(\"width\",n._width).style(\"border-top\",v.maskOppBorder).call(u.fill,v.maskOppAxisColor))}function b(t,e,r,n){e._context.staticPlot||o.ensureSingle(t,\"rect\",v.slideBoxClassName,(function(t){t.attr({y:0,cursor:v.slideBoxCursor,\"shape-rendering\":\"crispEdges\"})})).attr({height:n._height,fill:v.slideBoxFill})}function _(t,e,r,n){var i=o.ensureSingle(t,\"g\",v.grabberMinClassName),a=o.ensureSingle(t,\"g\",v.grabberMaxClassName),s={x:0,width:v.handleWidth,rx:v.handleRadius,fill:u.background,stroke:u.defaultLine,\"stroke-width\":v.handleStrokeWidth,\"shape-rendering\":\"crispEdges\"},l={y:Math.round(n._height/4),height:Math.round(n._height/2)};o.ensureSingle(i,\"rect\",v.handleMinClassName,(function(t){t.attr(s)})).attr(l),o.ensureSingle(a,\"rect\",v.handleMaxClassName,(function(t){t.attr(s)})).attr(l);var c={width:v.grabAreaWidth,x:0,y:0,fill:v.grabAreaFill,cursor:e._context.staticPlot?void 0:v.grabAreaCursor};o.ensureSingle(i,\"rect\",v.grabAreaMinClassName,(function(t){t.attr(c)})).attr(\"height\",n._height),o.ensureSingle(a,\"rect\",v.grabAreaMaxClassName,(function(t){t.attr(c)})).attr(\"height\",n._height)}t.exports=function(t){for(var e=t._fullLayout,r=e._rangeSliderData,a=0;a<r.length;a++){var l=r[a][v.name];l._clipId=l._id+\"-\"+e._uid}var u=e._infolayer.selectAll(\"g.\"+v.containerClassName).data(r,(function(t){return t._name}));u.exit().each((function(t){var r=t[v.name];e._topdefs.select(\"#\"+r._clipId).remove()})).remove(),0!==r.length&&(u.enter().append(\"g\").classed(v.containerClassName,!0).attr(\"pointer-events\",\"all\"),u.each((function(r){var a=n.select(this),l=r[v.name],u=e[h.id2name(r.anchor)],f=l[h.id2name(r.anchor)];if(l.range){var w,T=o.simpleMap(l.range,r.r2l),k=o.simpleMap(r.range,r.r2l);w=k[0]<k[1]?[Math.min(T[0],k[0]),Math.max(T[1],k[1])]:[Math.max(T[0],k[0]),Math.min(T[1],k[1])],l.range=l._input.range=o.simpleMap(w,r.l2r)}r.cleanRange(\"rangeslider.range\");var A=e._size,M=r.domain;l._width=A.w*(M[1]-M[0]);var S=Math.round(A.l+A.w*M[0]),E=Math.round(A.t+A.h*(1-r._counterDomainMin)+(\"bottom\"===r.side?r._depth:0)+l._offsetShift+v.extraPad);a.attr(\"transform\",s(S,E)),l._rl=o.simpleMap(l.range,r.r2l);var L=l._rl[0],C=l._rl[1],P=C-L;if(l.p2d=function(t){return t/l._width*P+L},l.d2p=function(t){return(t-L)/P*l._width},r.rangebreaks){var O=r.locateBreaks(L,C);if(O.length){var I,D,z=0;for(I=0;I<O.length;I++)z+=(D=O[I]).max-D.min;var R=l._width/(C-L-z),F=[-R*L];for(I=0;I<O.length;I++)D=O[I],F.push(F[F.length-1]-R*(D.max-D.min));for(l.d2p=function(t){for(var e=F[0],r=0;r<O.length;r++){var n=O[r];if(t>=n.max)e=F[r+1];else if(t<n.min)break}return e+R*t},I=0;I<O.length;I++)(D=O[I]).pmin=l.d2p(D.min),D.pmax=l.d2p(D.max);l.p2d=function(t){for(var e=F[0],r=0;r<O.length;r++){var n=O[r];if(t>=n.pmax)e=F[r+1];else if(t<n.pmin)break}return(t-e)/R}}}if(\"match\"!==f.rangemode){var B=u.r2l(f.range[0]),N=u.r2l(f.range[1])-B;l.d2pOppAxis=function(t){return(t-B)/N*l._height}}a.call(g,t,r,l).call(y,t,r,l).call(m,t,r,l).call(x,t,r,l,f).call(b,t,r,l).call(_,t,r,l),function(t,e,r,a){if(!e._context.staticPlot){var s=t.select(\"rect.\"+v.slideBoxClassName).node(),l=t.select(\"rect.\"+v.grabAreaMinClassName).node(),u=t.select(\"rect.\"+v.grabAreaMaxClassName).node();t.on(\"mousedown\",c),t.on(\"touchstart\",c)}function c(){var c=n.event,f=c.target,h=c.clientX||c.touches[0].clientX,v=h-t.node().getBoundingClientRect().left,g=a.d2p(r._rl[0]),y=a.d2p(r._rl[1]),m=p.coverSlip();function x(t){var c,p,x,b=+(t.clientX||t.touches[0].clientX)-h;switch(f){case s:if(x=\"ew-resize\",g+b>r._length||y+b<0)return;c=g+b,p=y+b;break;case l:if(x=\"col-resize\",g+b>r._length)return;c=g+b,p=y;break;case u:if(x=\"col-resize\",y+b<0)return;c=g,p=y+b;break;default:x=\"ew-resize\",c=v,p=v+b}if(p<c){var _=p;p=c,c=_}a._pixelMin=c,a._pixelMax=p,d(n.select(m),x),function(t,e,r,n){function a(t){return r.l2r(o.constrain(t,n._rl[0],n._rl[1]))}var s=a(n.p2d(n._pixelMin)),l=a(n.p2d(n._pixelMax));window.requestAnimationFrame((function(){i.call(\"_guiRelayout\",e,r._name+\".range\",[s,l])}))}(0,e,r,a)}function b(){m.removeEventListener(\"mousemove\",x),m.removeEventListener(\"mouseup\",b),this.removeEventListener(\"touchmove\",x),this.removeEventListener(\"touchend\",b),o.removeElement(m)}this.addEventListener(\"touchmove\",x),this.addEventListener(\"touchend\",b),m.addEventListener(\"mousemove\",x),m.addEventListener(\"mouseup\",b)}}(a,t,r,l),function(t,e,r,n,i,a){var l=v.handleWidth/2;function u(t){return o.constrain(t,0,n._width)}function c(t){return o.constrain(t,0,n._height)}function f(t){return o.constrain(t,-l,n._width+l)}var h=u(n.d2p(r._rl[0])),p=u(n.d2p(r._rl[1]));if(t.select(\"rect.\"+v.slideBoxClassName).attr(\"x\",h).attr(\"width\",p-h),t.select(\"rect.\"+v.maskMinClassName).attr(\"width\",h),t.select(\"rect.\"+v.maskMaxClassName).attr(\"x\",p).attr(\"width\",n._width-p),\"match\"!==a.rangemode){var d=n._height-c(n.d2pOppAxis(i._rl[1])),g=n._height-c(n.d2pOppAxis(i._rl[0]));t.select(\"rect.\"+v.maskMinOppAxisClassName).attr(\"x\",h).attr(\"height\",d).attr(\"width\",p-h),t.select(\"rect.\"+v.maskMaxOppAxisClassName).attr(\"x\",h).attr(\"y\",g).attr(\"height\",n._height-g).attr(\"width\",p-h),t.select(\"rect.\"+v.slideBoxClassName).attr(\"y\",d).attr(\"height\",g-d)}var y=.5,m=Math.round(f(h-l))-y,x=Math.round(f(p-l))+y;t.select(\"g.\"+v.grabberMinClassName).attr(\"transform\",s(m,y)),t.select(\"g.\"+v.grabberMaxClassName).attr(\"transform\",s(x,y))}(a,0,r,l,u,f),\"bottom\"===r.side&&c.draw(t,r._id+\"title\",{propContainer:r,propName:r._name+\".title\",placeholder:e._dfltTitle.x,attributes:{x:r._offset+r._length/2,y:E+l._height+l._offsetShift+10+1.5*r.title.font.size,\"text-anchor\":\"middle\"}})})))}},549:function(t,e,r){\"use strict\";var n=r(41675),i=r(63893),a=r(73251),o=r(18783).LINE_SPACING,s=a.name;function l(t){var e=t&&t[s];return e&&e.visible}e.isVisible=l,e.makeData=function(t){var e=n.list({_fullLayout:t},\"x\",!0),r=t.margin,i=[];if(!t._has(\"gl2d\"))for(var a=0;a<e.length;a++){var o=e[a];if(l(o)){i.push(o);var u=o[s];u._id=s+o._id,u._height=(t.height-r.b-r.t)*u.thickness,u._offsetShift=Math.floor(u.borderwidth/2)}}t._rangeSliderData=i},e.autoMarginOpts=function(t,e){var r=t._fullLayout,n=e[s],l=e._id.charAt(0),u=0,c=0;return\"bottom\"===e.side&&(u=e._depth,e.title.text!==r._dfltTitle[l]&&(c=1.5*e.title.font.size+10+n._offsetShift,c+=(e.title.text.match(i.BR_TAG_ALL)||[]).length*e.title.font.size*o)),{x:0,y:e._counterDomainMin,l:0,r:0,t:0,b:n._height+u+Math.max(r.margin.b,c),pad:a.extraPad+2*n._offsetShift}}},13137:function(t,e,r){\"use strict\";var n=r(71828),i=r(75148),a=r(47850),o=r(549);t.exports={moduleType:\"component\",name:\"rangeslider\",schema:{subplots:{xaxis:{rangeslider:n.extendFlat({},i,{yaxis:a})}}},layoutAttributes:r(75148),handleDefaults:r(26377),calcAutorange:r(88443),draw:r(72413),isVisible:o.isVisible,makeData:o.makeData,autoMarginOpts:o.autoMarginOpts}},47850:function(t){\"use strict\";t.exports={_isSubplotObj:!0,rangemode:{valType:\"enumerated\",values:[\"auto\",\"fixed\",\"match\"],dflt:\"match\",editType:\"calc\"},range:{valType:\"info_array\",items:[{valType:\"any\",editType:\"plot\"},{valType:\"any\",editType:\"plot\"}],editType:\"plot\"},editType:\"calc\"}},8389:function(t,e,r){\"use strict\";var n=r(50215),i=r(82196).line,a=r(79952).P,o=r(1426).extendFlat,s=r(30962).overrideAll,l=r(44467).templatedArray;r(24695),t.exports=s(l(\"selection\",{type:{valType:\"enumerated\",values:[\"rect\",\"path\"]},xref:o({},n.xref,{}),yref:o({},n.yref,{}),x0:{valType:\"any\"},x1:{valType:\"any\"},y0:{valType:\"any\"},y1:{valType:\"any\"},path:{valType:\"string\",editType:\"arraydraw\"},opacity:{valType:\"number\",min:0,max:1,dflt:.7,editType:\"arraydraw\"},line:{color:i.color,width:o({},i.width,{min:1,dflt:1}),dash:o({},a,{dflt:\"dot\"})}}),\"arraydraw\",\"from-root\")},34122:function(t){\"use strict\";t.exports={BENDPX:1.5,MINSELECT:12,SELECTDELAY:100,SELECTID:\"-select\"}},59402:function(t,e,r){\"use strict\";var n=r(71828),i=r(89298),a=r(85501),o=r(8389),s=r(30477);function l(t,e,r){function a(r,i){return n.coerce(t,e,o,r,i)}var l=a(\"path\"),u=\"path\"!==a(\"type\",l?\"path\":\"rect\");u&&delete e.path,a(\"opacity\"),a(\"line.color\"),a(\"line.width\"),a(\"line.dash\");for(var c=[\"x\",\"y\"],f=0;f<2;f++){var h,p,d,v=c[f],g={_fullLayout:r},y=i.coerceRef(t,e,g,v);if((h=i.getFromId(g,y))._selectionIndices.push(e._index),d=s.rangeToShapePosition(h),p=s.shapePositionToRange(h),u){var m=v+\"0\",x=v+\"1\",b=t[m],_=t[x];t[m]=p(t[m],!0),t[x]=p(t[x],!0),i.coercePosition(e,g,a,y,m),i.coercePosition(e,g,a,y,x);var w=e[m],T=e[x];void 0!==w&&void 0!==T&&(e[m]=d(w),e[x]=d(T),t[m]=b,t[x]=_)}}u&&n.noneOrAll(t,e,[\"x0\",\"x1\",\"y0\",\"y1\"])}t.exports=function(t,e){a(t,e,{name:\"selections\",handleItemDefaults:l});for(var r=e.selections,n=0;n<r.length;n++){var i=r[n];i&&void 0===i.path&&(void 0!==i.x0&&void 0!==i.x1&&void 0!==i.y0&&void 0!==i.y1||(e.selections[n]=null))}}},32485:function(t,e,r){\"use strict\";var n=r(60165).readPaths,i=r(42359),a=r(51873).clearOutlineControllers,o=r(7901),s=r(91424),l=r(44467).arrayEditor,u=r(30477),c=u.getPathString;function f(t){var e=t._fullLayout;for(var r in a(t),e._selectionLayer.selectAll(\"path\").remove(),e._plots){var n=e._plots[r].selectionLayer;n&&n.selectAll(\"path\").remove()}for(var i=0;i<e.selections.length;i++)p(t,i)}function h(t){return t._context.editSelection}function p(t,e){t._fullLayout._paperdiv.selectAll('.selectionlayer [data-index=\"'+e+'\"]').remove();var r=u.makeSelectionsOptionsAndPlotinfo(t,e),a=r.options,p=r.plotinfo;a._input&&function(r){var u=c(t,a),g={\"data-index\":e,\"fill-rule\":\"evenodd\",d:u},y=a.opacity,m=\"rgba(0,0,0,0)\",x=a.line.color||o.contrast(t._fullLayout.plot_bgcolor),b=a.line.width,_=a.line.dash;b||(b=5,_=\"solid\");var w=h(t)&&t._fullLayout._activeSelectionIndex===e;w&&(m=t._fullLayout.activeselection.fillcolor,y=t._fullLayout.activeselection.opacity);for(var T=[],k=1;k>=0;k--){var A=r.append(\"path\").attr(g).style(\"opacity\",k?.1:y).call(o.stroke,x).call(o.fill,m).call(s.dashLine,k?\"solid\":_,k?4+b:b);if(d(A,t,a),w){var M=l(t.layout,\"selections\",a);A.style({cursor:\"move\"});var S={element:A.node(),plotinfo:p,gd:t,editHelpers:M,isActiveSelection:!0},E=n(u,t);i(E,A,S)}else A.style(\"pointer-events\",k?\"all\":\"none\");T[k]=A}var L=T[0];T[1].node().addEventListener(\"click\",(function(){return function(t,e){if(h(t)){var r=+e.node().getAttribute(\"data-index\");if(r>=0){if(r===t._fullLayout._activeSelectionIndex)return void v(t);t._fullLayout._activeSelectionIndex=r,t._fullLayout._deactivateSelection=v,f(t)}}}(t,L)}))}(t._fullLayout._selectionLayer)}function d(t,e,r){var n=r.xref+r.yref;s.setClipUrl(t,\"clip\"+e._fullLayout._uid+n,e)}function v(t){h(t)&&t._fullLayout._activeSelectionIndex>=0&&(a(t),delete t._fullLayout._activeSelectionIndex,f(t))}t.exports={draw:f,drawOne:p,activateLastSelection:function(t){if(h(t)){var e=t._fullLayout.selections.length-1;t._fullLayout._activeSelectionIndex=e,t._fullLayout._deactivateSelection=v,f(t)}}}},53777:function(t,e,r){\"use strict\";var n=r(79952).P,i=r(1426).extendFlat;t.exports={newselection:{mode:{valType:\"enumerated\",values:[\"immediate\",\"gradual\"],dflt:\"immediate\",editType:\"none\"},line:{color:{valType:\"color\",editType:\"none\"},width:{valType:\"number\",min:1,dflt:1,editType:\"none\"},dash:i({},n,{dflt:\"dot\",editType:\"none\"}),editType:\"none\"},editType:\"none\"},activeselection:{fillcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"none\"},opacity:{valType:\"number\",min:0,max:1,dflt:.5,editType:\"none\"},editType:\"none\"}}},90849:function(t){\"use strict\";t.exports=function(t,e,r){r(\"newselection.mode\"),r(\"newselection.line.width\")&&(r(\"newselection.line.color\"),r(\"newselection.line.dash\")),r(\"activeselection.fillcolor\"),r(\"activeselection.opacity\")}},35855:function(t,e,r){\"use strict\";var n=r(64505).selectMode,i=r(51873).clearOutline,a=r(60165),o=a.readPaths,s=a.writePaths,l=a.fixDatesForPaths;t.exports=function(t,e){if(t.length){var r=t[0][0];if(r){var a=r.getAttribute(\"d\"),u=e.gd,c=u._fullLayout.newselection,f=e.plotinfo,h=f.xaxis,p=f.yaxis,d=e.isActiveSelection,v=e.dragmode,g=(u.layout||{}).selections||[];if(!n(v)&&void 0!==d){var y=u._fullLayout._activeSelectionIndex;if(y<g.length)switch(u._fullLayout.selections[y].type){case\"rect\":v=\"select\";break;case\"path\":v=\"lasso\"}}var m,x=o(a,u,f,d),b={xref:h._id,yref:p._id,opacity:c.opacity,line:{color:c.line.color,width:c.line.width,dash:c.line.dash}};1===x.length&&(m=x[0]),m&&5===m.length&&\"select\"===v?(b.type=\"rect\",b.x0=m[0][1],b.y0=m[0][2],b.x1=m[2][1],b.y1=m[2][2]):(b.type=\"path\",h&&p&&l(x,h,p),b.path=s(x),m=null),i(u);for(var _=e.editHelpers,w=(_||{}).modifyItem,T=[],k=0;k<g.length;k++){var A=u._fullLayout.selections[k];if(A){if(T[k]=A._input,void 0!==d&&k===u._fullLayout._activeSelectionIndex){var M=b;switch(A.type){case\"rect\":w(\"x0\",M.x0),w(\"x1\",M.x1),w(\"y0\",M.y0),w(\"y1\",M.y1);break;case\"path\":w(\"path\",M.path)}}}else T[k]=A}return void 0===d?(T.push(b),T):_?_.getUpdateObj():{}}}}},75549:function(t,e,r){\"use strict\";var n=r(71828).strTranslate;function i(t,e){switch(t.type){case\"log\":return t.p2d(e);case\"date\":return t.p2r(e,0,t.calendar);default:return t.p2r(e)}}t.exports={p2r:i,r2p:function(t,e){switch(t.type){case\"log\":return t.d2p(e);case\"date\":return t.r2p(e,0,t.calendar);default:return t.r2p(e)}},axValue:function(t){var e=\"y\"===t._id.charAt(0)?1:0;return function(r){return i(t,r[e])}},getTransform:function(t){return n(t.xaxis._offset,t.yaxis._offset)}}},47322:function(t,e,r){\"use strict\";var n=r(32485),i=r(3937);t.exports={moduleType:\"component\",name:\"selections\",layoutAttributes:r(8389),supplyLayoutDefaults:r(59402),supplyDrawNewSelectionDefaults:r(90849),includeBasePlot:r(76325)(\"selections\"),draw:n.draw,drawOne:n.drawOne,reselect:i.reselect,prepSelect:i.prepSelect,clearOutline:i.clearOutline,clearSelectionsCache:i.clearSelectionsCache,selectOnClick:i.selectOnClick}},3937:function(t,e,r){\"use strict\";var n=r(52142),i=r(38258),a=r(73972),o=r(91424).dashStyle,s=r(7901),l=r(30211),u=r(23469).makeEventData,c=r(64505),f=c.freeMode,h=c.rectMode,p=c.drawMode,d=c.openMode,v=c.selectMode,g=r(30477),y=r(21459),m=r(42359),x=r(51873).clearOutline,b=r(60165),_=b.handleEllipse,w=b.readPaths,T=r(90551).newShapes,k=r(35855),A=r(32485).activateLastSelection,M=r(71828),S=M.sorterAsc,E=r(61082),L=r(79990),C=r(41675).getFromId,P=r(33306),O=r(61549).redrawReglTraces,I=r(34122),D=I.MINSELECT,z=E.filter,R=E.tester,F=r(75549),B=F.p2r,N=F.axValue,j=F.getTransform;function U(t){return void 0!==t.subplot}function V(t,e,r,n,i,a,o){var s,l,u,c,f,h,p,v,g,y=e._hoverdata,x=e._fullLayout.clickmode.indexOf(\"event\")>-1,b=[];if(function(t){return t&&Array.isArray(t)&&!0!==t[0].hoverOnBox}(y)){Z(t,e,a);var _=function(t,e){var r,n,i=t[0],a=-1,o=[];for(n=0;n<e.length;n++)if(r=e[n],i.fullData._expandedIndex===r.cd[0].trace._expandedIndex){if(!0===i.hoverOnBox)break;void 0!==i.pointNumber?a=i.pointNumber:void 0!==i.binNumber&&(a=i.binNumber,o=i.pointNumbers);break}return{pointNumber:a,pointNumbers:o,searchInfo:r}}(y,s=X(e,r,n,i));if(_.pointNumbers.length>0?function(t,e){var r,n,i,a=[];for(i=0;i<t.length;i++)(r=t[i]).cd[0].trace.selectedpoints&&r.cd[0].trace.selectedpoints.length>0&&a.push(r);if(1===a.length&&a[0]===e.searchInfo&&(n=e.searchInfo.cd[0].trace).selectedpoints.length===e.pointNumbers.length){for(i=0;i<e.pointNumbers.length;i++)if(n.selectedpoints.indexOf(e.pointNumbers[i])<0)return!1;return!0}return!1}(s,_):function(t){var e,r,n=0;for(r=0;r<t.length;r++)if((e=t[r].cd[0].trace).selectedpoints){if(e.selectedpoints.length>1)return!1;if((n+=e.selectedpoints.length)>1)return!1}return 1===n}(s)&&(h=K(_))){for(o&&o.remove(),g=0;g<s.length;g++)(l=s[g])._module.selectPoints(l,!1);$(e,s),Y(a),x&&ht(e)}else{for(p=t.shiftKey&&(void 0!==h?h:K(_)),u=function(t,e,r){return{pointNumber:t,searchInfo:e,subtract:!!r}}(_.pointNumber,_.searchInfo,p),c=G(a.selectionDefs.concat([u])),g=0;g<s.length;g++)if(f=tt(s[g]._module.selectPoints(s[g],c),s[g]),b.length)for(var w=0;w<f.length;w++)b.push(f[w]);else b=f;if($(e,s,v={points:b}),u&&a&&a.selectionDefs.push(u),o){var T=a.mergedPolygons,k=d(a.dragmode);m(et(T,k),o,a)}x&&ft(e,v)}}}function H(t){return\"pointNumber\"in t&&\"searchInfo\"in t}function q(t){return{xmin:0,xmax:0,ymin:0,ymax:0,pts:[],contains:function(e,r,n,i){var a=t.searchInfo.cd[0].trace._expandedIndex;return i.cd[0].trace._expandedIndex===a&&n===t.pointNumber},isRect:!1,degenerate:!1,subtract:!!t.subtract}}function G(t){if(t.length){for(var e=[],r=H(t[0])?0:t[0][0][0],n=r,i=H(t[0])?0:t[0][0][1],a=i,o=0;o<t.length;o++)if(H(t[o]))e.push(q(t[o]));else{var s=R(t[o]);s.subtract=!!t[o].subtract,e.push(s),r=Math.min(r,s.xmin),n=Math.max(n,s.xmax),i=Math.min(i,s.ymin),a=Math.max(a,s.ymax)}return{xmin:r,xmax:n,ymin:i,ymax:a,pts:[],contains:function(t,r,n,i){for(var a=!1,o=0;o<e.length;o++)e[o].contains(t,r,n,i)&&(a=!e[o].subtract);return a},isRect:!1,degenerate:!1}}}function Z(t,e,r){var n=e._fullLayout,i=r.plotinfo,a=r.dragmode,o=n._lastSelectedSubplot&&n._lastSelectedSubplot===i.id,s=(t.shiftKey||t.altKey)&&!(p(a)&&d(a));o&&s&&i.selection&&i.selection.selectionDefs&&!r.selectionDefs?(r.selectionDefs=i.selection.selectionDefs,r.mergedPolygons=i.selection.mergedPolygons):s&&i.selection||Y(r),o||(x(e),n._lastSelectedSubplot=i.id)}function Y(t,e){var r=t.dragmode,n=t.plotinfo,i=t.gd;(function(t){return t._fullLayout._activeShapeIndex>=0})(i)&&i._fullLayout._deactivateShape(i),function(t){return t._fullLayout._activeSelectionIndex>=0}(i)&&i._fullLayout._deactivateSelection(i);var o=i._fullLayout._zoomlayer,s=p(r),l=v(r);if(s||l){var u,c,f=o.selectAll(\".select-outline-\"+n.id);f&&i._fullLayout._outlining&&(s&&(u=T(f,t)),u&&a.call(\"_guiRelayout\",i,{shapes:u}),l&&!U(t)&&(c=k(f,t)),c&&(i._fullLayout._noEmitSelectedAtStart=!0,a.call(\"_guiRelayout\",i,{selections:c}).then((function(){e&&A(i)}))),i._fullLayout._outlining=!1)}n.selection={},n.selection.selectionDefs=t.selectionDefs=[],n.selection.mergedPolygons=t.mergedPolygons=[]}function W(t){return t._id}function X(t,e,r,n){if(!t.calcdata)return[];var i,a,o,s=[],l=e.map(W),u=r.map(W);for(o=0;o<t.calcdata.length;o++)if(!0===(a=(i=t.calcdata[o])[0].trace).visible&&a._module&&a._module.selectPoints)if(!U({subplot:n})||a.subplot!==n&&a.geo!==n)if(\"splom\"===a.type){if(a._xaxes[l[0]]&&a._yaxes[u[0]]){var c=J(a._module,i,e[0],r[0]);c.scene=t._fullLayout._splomScenes[a.uid],s.push(c)}}else if(\"sankey\"===a.type){var f=J(a._module,i,e[0],r[0]);s.push(f)}else{if(-1===l.indexOf(a.xaxis))continue;if(-1===u.indexOf(a.yaxis))continue;s.push(J(a._module,i,C(t,a.xaxis),C(t,a.yaxis)))}else s.push(J(a._module,i,e[0],r[0]));return s}function J(t,e,r,n){return{_module:t,cd:e,xaxis:r,yaxis:n}}function K(t){var e=t.searchInfo.cd[0].trace,r=t.pointNumber,n=t.pointNumbers,i=n.length>0?n[0]:r;return!!e.selectedpoints&&e.selectedpoints.indexOf(i)>-1}function $(t,e,r){var n,i;for(n=0;n<e.length;n++){var o=e[n].cd[0].trace._fullInput,s=t._fullLayout._tracePreGUI[o.uid]||{};void 0===s.selectedpoints&&(s.selectedpoints=o._input.selectedpoints||null)}if(r){var l=r.points||[];for(n=0;n<e.length;n++)(i=e[n].cd[0].trace)._input.selectedpoints=i._fullInput.selectedpoints=[],i._fullInput!==i&&(i.selectedpoints=[]);for(var u=0;u<l.length;u++){var c=l[u],f=c.data,h=c.fullData,p=c.pointIndex,d=c.pointIndices;d?([].push.apply(f.selectedpoints,d),i._fullInput!==i&&[].push.apply(h.selectedpoints,d)):(f.selectedpoints.push(p),i._fullInput!==i&&h.selectedpoints.push(p))}}else for(n=0;n<e.length;n++)delete(i=e[n].cd[0].trace).selectedpoints,delete i._input.selectedpoints,i._fullInput!==i&&delete i._fullInput.selectedpoints;!function(t,e){for(var r=!1,n=0;n<e.length;n++){var i=e[n],o=i.cd;a.traceIs(o[0].trace,\"regl\")&&(r=!0);var s=i._module,l=s.styleOnSelect||s.style;l&&(l(t,o,o[0].node3),o[0].nodeRangePlot3&&l(t,o,o[0].nodeRangePlot3))}r&&(P(t),O(t))}(t,e)}function Q(t,e,r){for(var i=(r?n.difference:n.union)({regions:t},{regions:[e]}).regions.reverse(),a=0;a<i.length;a++){var o=i[a];o.subtract=st(o,i.slice(0,a))}return i}function tt(t,e){if(Array.isArray(t))for(var r=e.cd,n=e.cd[0].trace,i=0;i<t.length;i++)t[i]=u(t[i],n,r);return t}function et(t,e){for(var r=[],n=0;n<t.length;n++){r[n]=[];for(var 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n=e[r][0].trace,i=t._fullLayout._splomScenes;if(i){var a=i[n.uid];a&&(a.selectBatch=[])}}}(t);for(var O={},I=0;I<P.length;I++){var D=P[I],z=D.indexOf(\"y\"),R=D.slice(0,z),F=D.slice(z),B=o&&s?r:void 0;if(B=at(f,R,F,B)){var N=n;if(!l){var j=C(t,R,\"x\"),U=C(t,F,\"y\");N=X(t,[j],[U],D);for(var V=0;V<N.length;V++){var H=N[V],q=H.cd[0],G=q.trace;if(\"scattergl\"===H._module.name&&!q.t.xpx){var Z=G.x,Y=G.y,W=G._length;q.t.xpx=[],q.t.ypx=[];for(var J=0;J<W;J++)q.t.xpx[J]=j.c2p(Z[J]),q.t.ypx[J]=U.c2p(Y[J])}\"splom\"===H._module.name&&(O[G.uid]||(O[G.uid]=!0))}}var K=rt(B,N);u=u.concat(K),c=c.concat(N)}}var Q={points:u};$(t,c,Q);var tt=h.clickmode.indexOf(\"event\")>-1&&e;if(!a&&e){var et=ot(t,!0);if(et.length){var nt=et[0].xref,pt=et[0].yref;if(nt&&pt){var dt=ut(et);ct([C(t,nt,\"x\"),C(t,pt,\"y\")])(Q,dt)}}t._fullLayout._noEmitSelectedAtStart?t._fullLayout._noEmitSelectedAtStart=!1:tt&&ft(t,Q),h._reselect=!1}if(!a&&h._deselect){var vt=h._deselect;(function(t,e,r){for(var n=0;n<r.length;n++){var i=r[n];if(i.xaxis&&i.xaxis._id===t&&i.yaxis&&i.yaxis._id===e)return!0}return!1})(o=vt.xref,s=vt.yref,c)||it(t,o,s,n),tt&&(Q.points.length?ft(t,Q):ht(t)),h._deselect=!1}return{eventData:Q,selectionTesters:r}}function it(t,e,r,n){n=X(t,[C(t,e,\"x\")],[C(t,r,\"y\")],e+r);for(var i=0;i<n.length;i++){var a=n[i];a._module.selectPoints(a,!1)}$(t,n)}function at(t,e,r,n){for(var i,a=0;a<t.length;a++){var o=t[a];e===o.xref&&r===o.yref&&(i?n=G(i=Q(i,o,!!o.subtract)):(i=[o],n=R(o)))}return n}function ot(t,e){for(var r=[],n=t._fullLayout,i=n.selections,a=i.length,o=0;o<a;o++)if(!e||o===n._activeSelectionIndex){var s=i[o];if(s){var l,u,c,f,h,p=s.xref,d=s.yref,v=C(t,p,\"x\"),m=C(t,d,\"y\");if(\"rect\"===s.type){h=[];var x=lt(v,s.x0),b=lt(v,s.x1),_=lt(m,s.y0),w=lt(m,s.y1);h=[[x,_],[x,w],[b,w],[b,_]],l=Math.min(x,b),u=Math.max(x,b),c=Math.min(_,w),f=Math.max(_,w),h.xmin=l,h.xmax=u,h.ymin=c,h.ymax=f,h.xref=p,h.yref=d,h.subtract=!1,h.isRect=!0,r.push(h)}else if(\"path\"===s.type)for(var T=s.path.split(\"Z\"),k=[],A=0;A<T.length;A++){var M=T[A];if(M){M+=\"Z\";var S=g.extractPathCoords(M,y.paramIsX,\"raw\"),E=g.extractPathCoords(M,y.paramIsY,\"raw\");l=1/0,u=-1/0,c=1/0,f=-1/0,h=[];for(var L=0;L<S.length;L++){var P=lt(v,S[L]),O=lt(m,E[L]);h.push([P,O]),l=Math.min(P,l),u=Math.max(P,u),c=Math.min(O,c),f=Math.max(O,f)}h.xmin=l,h.xmax=u,h.ymin=c,h.ymax=f,h.xref=p,h.yref=d,h.subtract=st(h,k),k.push(h),r.push(h)}}}}return r}function st(t,e){for(var r=!1,n=0;n<e.length;n++)for(var a=e[n],o=0;o<t.length;o++)if(i(t[o],a)){r=!r;break}return r}function lt(t,e){return\"date\"===t.type&&(e=e.replace(\"_\",\" \")),\"log\"===t.type?t.c2p(e):t.r2p(e,null,t.calendar)}function ut(t){for(var e=t.length,r=[],n=0;n<e;n++){var i=t[n];r=(r=r.concat(i)).concat([i[0]])}return(a=r).isRect=5===a.length&&a[0][0]===a[4][0]&&a[0][1]===a[4][1]&&a[0][0]===a[1][0]&&a[2][0]===a[3][0]&&a[0][1]===a[3][1]&&a[1][1]===a[2][1]||a[0][1]===a[1][1]&&a[2][1]===a[3][1]&&a[0][0]===a[3][0]&&a[1][0]===a[2][0],a.isRect&&(a.xmin=Math.min(a[0][0],a[2][0]),a.xmax=Math.max(a[0][0],a[2][0]),a.ymin=Math.min(a[0][1],a[2][1]),a.ymax=Math.max(a[0][1],a[2][1])),a;var a}function ct(t){return function(e,r){for(var n,i,a=0;a<t.length;a++){var o=t[a],s=o._id,l=s.charAt(0);if(r.isRect){n||(n={});var u=r[l+\"min\"],c=r[l+\"max\"];void 0!==u&&void 0!==c&&(n[s]=[B(o,u),B(o,c)].sort(S))}else i||(i={}),i[s]=r.map(N(o))}n&&(e.range=n),i&&(e.lassoPoints=i)}}function ft(t,e){e&&(e.selections=(t.layout||{}).selections||[]),t.emit(\"plotly_selected\",e)}function ht(t){t.emit(\"plotly_deselect\",null)}t.exports={reselect:nt,prepSelect:function(t,e,r,n,i){var u=!U(n),c=f(i),g=h(i),y=d(i),x=p(i),b=v(i),w=\"drawcircle\"===i,T=\"drawline\"===i||w,k=n.gd,A=k._fullLayout,S=b&&\"immediate\"===A.newselection.mode&&u,E=A._zoomlayer,C=n.element.getBoundingClientRect(),P=n.plotinfo,O=j(P),F=e-C.left,B=r-C.top;A._calcInverseTransform(k);var N=M.apply3DTransform(A._invTransform)(F,B);F=N[0],B=N[1];var H,q,W,J,K,tt,at,ot=A._invScaleX,st=A._invScaleY,lt=F,pt=B,dt=\"M\"+F+\",\"+B,vt=n.xaxes[0],gt=n.yaxes[0],yt=vt._length,mt=gt._length,xt=t.altKey&&!(p(i)&&y);Z(t,k,n),c&&(H=z([[F,B]],I.BENDPX));var bt=E.selectAll(\"path.select-outline-\"+P.id).data([1]),_t=x?A.newshape:A.newselection;x&&(n.hasText=_t.label.text||_t.label.texttemplate);var wt=x&&!y?_t.fillcolor:\"rgba(0,0,0,0)\",Tt=_t.line.color||(u?s.contrast(k._fullLayout.plot_bgcolor):\"#7f7f7f\");bt.enter().append(\"path\").attr(\"class\",\"select-outline 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v=\"pixel\"===h.xsizemode?h.xanchor:h.x0,g=\"pixel\"===h.xsizemode?h.xanchor:h.x1;(f=c(u=i.getFromId(t,h.xref),v,g,h.path,a.paramIsX))&&(h._extremes[u._id]=i.findExtremes(u,f,s(h)))}if(\"paper\"!==h.yref&&\"domain\"!==d){var y=\"pixel\"===h.ysizemode?h.yanchor:h.y0,m=\"pixel\"===h.ysizemode?h.yanchor:h.y1;(f=c(u=i.getFromId(t,h.yref),y,m,h.path,a.paramIsY))&&(h._extremes[u._id]=i.findExtremes(u,f,l(h)))}}}},21459:function(t){\"use strict\";t.exports={segmentRE:/[MLHVQCTSZ][^MLHVQCTSZ]*/g,paramRE:/[^\\s,]+/g,paramIsX:{M:{0:!0,drawn:0},L:{0:!0,drawn:0},H:{0:!0,drawn:0},V:{},Q:{0:!0,2:!0,drawn:2},C:{0:!0,2:!0,4:!0,drawn:4},T:{0:!0,drawn:0},S:{0:!0,2:!0,drawn:2},Z:{}},paramIsY:{M:{1:!0,drawn:1},L:{1:!0,drawn:1},H:{},V:{0:!0,drawn:0},Q:{1:!0,3:!0,drawn:3},C:{1:!0,3:!0,5:!0,drawn:5},T:{1:!0,drawn:1},S:{1:!0,3:!0,drawn:5},Z:{}},numParams:{M:2,L:2,H:1,V:1,Q:4,C:6,T:2,S:4,Z:0}}},84726:function(t,e,r){\"use strict\";var n=r(71828),i=r(89298),a=r(85501),o=r(89827),s=r(30477);function 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p=2*h*Math.PI/a;f.push([s+u*Math.cos(p),l+c*Math.sin(p)])}return f},e.ellipseOver=function(t){var e=t.x0,r=t.y0,n=t.x1,i=t.y1,a=n-e,s=i-r,l=((e-=a)+n)/2,u=((r-=s)+i)/2;return{x0:l-(a*=o),y0:u-(s*=o),x1:l+a,y1:u+s}},e.fixDatesForPaths=function(t,e,r){var n=\"date\"===e.type,i=\"date\"===r.type;if(!n&&!i)return t;for(var a=0;a<t.length;a++)for(var o=0;o<t[a].length;o++)for(var s=0;s+2<t[a][o].length;s+=2)n&&(t[a][o][s+1]=t[a][o][s+1].replace(\" \",\"_\")),i&&(t[a][o][s+2]=t[a][o][s+2].replace(\" \",\"_\"));return t}},90551:function(t,e,r){\"use strict\";var n=r(64505),i=n.drawMode,a=n.openMode,o=r(89995),s=o.i000,l=o.i090,u=o.i180,c=o.i270,f=o.cos45,h=o.sin45,p=r(75549),d=p.p2r,v=p.r2p,g=r(51873).clearOutline,y=r(60165),m=y.readPaths,x=y.writePaths,b=y.ellipseOver,_=y.fixDatesForPaths;function w(t,e,r){var n,i=t[0][0],o=e.gd,p=i.getAttribute(\"d\"),g=o._fullLayout.newshape,y=e.plotinfo,w=e.isActiveShape,T=y.xaxis,k=y.yaxis,A=!!y.domain||!y.xaxis,M=!!y.domain||!y.yaxis,S=a(r),E=m(p,o,y,w),L={editable:!0,visible:g.visible,name:g.name,showlegend:g.showlegend,legend:g.legend,legendwidth:g.legendwidth,legendgroup:g.legendgroup,legendgrouptitle:{text:g.legendgrouptitle.text,font:g.legendgrouptitle.font},legendrank:g.legendrank,label:g.label,xref:A?\"paper\":T._id,yref:M?\"paper\":k._id,layer:g.layer,opacity:g.opacity,line:{color:g.line.color,width:g.line.width,dash:g.line.dash}};if(S||(L.fillcolor=g.fillcolor,L.fillrule=g.fillrule),1===E.length&&(n=E[0]),n&&5===n.length&&\"drawrect\"===r)L.type=\"rect\",L.x0=n[0][1],L.y0=n[0][2],L.x1=n[2][1],L.y1=n[2][2];else if(n&&\"drawline\"===r)L.type=\"line\",L.x0=n[0][1],L.y0=n[0][2],L.x1=n[1][1],L.y1=n[1][2];else if(n&&\"drawcircle\"===r){L.type=\"circle\";var C=n[s][1],P=n[l][1],O=n[u][1],I=n[c][1],D=n[s][2],z=n[l][2],R=n[u][2],F=n[c][2],B=y.xaxis&&(\"date\"===y.xaxis.type||\"log\"===y.xaxis.type),N=y.yaxis&&(\"date\"===y.yaxis.type||\"log\"===y.yaxis.type);B&&(C=v(y.xaxis,C),P=v(y.xaxis,P),O=v(y.xaxis,O),I=v(y.xaxis,I)),N&&(D=v(y.yaxis,D),z=v(y.yaxis,z),R=v(y.yaxis,R),F=v(y.yaxis,F));var j=(P+I)/2,U=(D+R)/2,V=b({x0:j,y0:U,x1:j+(I-P+O-C)/2*f,y1:U+(F-z+R-D)/2*h});B&&(V.x0=d(y.xaxis,V.x0),V.x1=d(y.xaxis,V.x1)),N&&(V.y0=d(y.yaxis,V.y0),V.y1=d(y.yaxis,V.y1)),L.x0=V.x0,L.y0=V.y0,L.x1=V.x1,L.y1=V.y1}else L.type=\"path\",T&&k&&_(E,T,k),L.path=x(E),n=null;return L}t.exports={newShapes:function(t,e){if(t.length&&t[0][0]){var r=e.gd,n=e.isActiveShape,a=e.dragmode,o=(r.layout||{}).shapes||[];if(!i(a)&&void 0!==n){var s=r._fullLayout._activeShapeIndex;if(s<o.length)switch(r._fullLayout.shapes[s].type){case\"rect\":a=\"drawrect\";break;case\"circle\":a=\"drawcircle\";break;case\"line\":a=\"drawline\";break;case\"path\":var l=o[s].path||\"\";a=\"Z\"===l[l.length-1]?\"drawclosedpath\":\"drawopenpath\"}}var u=w(t,e,a);g(r);for(var c=e.editHelpers,f=(c||{}).modifyItem,h=[],p=0;p<o.length;p++){var d=r._fullLayout.shapes[p];if(h[p]=d._input,void 0!==n&&p===r._fullLayout._activeShapeIndex){var v=u;switch(d.type){case\"line\":case\"rect\":case\"circle\":f(\"x0\",v.x0),f(\"x1\",v.x1),f(\"y0\",v.y0),f(\"y1\",v.y1);break;case\"path\":f(\"path\",v.path)}}}return void 0===n?(h.push(u),h):c?c.getUpdateObj():{}}},createShapeObj:w}},51873:function(t){\"use strict\";t.exports={clearOutlineControllers:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(\".outline-controllers\").remove()},clearOutline:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(\".select-outline\").remove(),t._fullLayout._outlining=!1}}},30477:function(t,e,r){\"use strict\";var n=r(21459),i=r(71828),a=r(89298);e.rangeToShapePosition=function(t){return\"log\"===t.type?t.r2d:function(t){return t}},e.shapePositionToRange=function(t){return\"log\"===t.type?t.d2r:function(t){return t}},e.decodeDate=function(t){return function(e){return e.replace&&(e=e.replace(\"_\",\" \")),t(e)}},e.encodeDate=function(t){return function(e){return t(e).replace(\" \",\"_\")}},e.extractPathCoords=function(t,e,r){var a=[];return t.match(n.segmentRE).forEach((function(t){var o=e[t.charAt(0)].drawn;if(void 0!==o){var s=t.substr(1).match(n.paramRE);if(s&&!(s.length<o)){var l=s[o],u=r?l:i.cleanNumber(l);a.push(u)}}})),a},e.getDataToPixel=function(t,r,n,i){var a,o=t._fullLayout._size;if(r)if(\"domain\"===i)a=function(t){return r._length*(n?1-t:t)+r._offset};else{var s=e.shapePositionToRange(r);a=function(t){return r._offset+r.r2p(s(t,!0))},\"date\"===r.type&&(a=e.decodeDate(a))}else a=n?function(t){return o.t+o.h*(1-t)}:function(t){return o.l+o.w*t};return a},e.getPixelToData=function(t,r,n,i){var a,o=t._fullLayout._size;if(r)if(\"domain\"===i)a=function(t){var e=(t-r._offset)/r._length;return n?1-e:e};else{var s=e.rangeToShapePosition(r);a=function(t){return s(r.p2r(t-r._offset))}}else a=n?function(t){return 1-(t-o.t)/o.h}:function(t){return(t-o.l)/o.w};return a},e.roundPositionForSharpStrokeRendering=function(t,e){var r=1===Math.round(e%2),n=Math.round(t);return r?n+.5:n},e.makeShapesOptionsAndPlotinfo=function(t,e){var r=t._fullLayout.shapes[e]||{},n=t._fullLayout._plots[r.xref+r.yref];return n?n._hadPlotinfo=!0:(n={},r.xref&&\"paper\"!==r.xref&&(n.xaxis=t._fullLayout[r.xref+\"axis\"]),r.yref&&\"paper\"!==r.yref&&(n.yaxis=t._fullLayout[r.yref+\"axis\"])),n.xsizemode=r.xsizemode,n.ysizemode=r.ysizemode,n.xanchor=r.xanchor,n.yanchor=r.yanchor,{options:r,plotinfo:n}},e.makeSelectionsOptionsAndPlotinfo=function(t,e){var r=t._fullLayout.selections[e]||{},n=t._fullLayout._plots[r.xref+r.yref];return n?n._hadPlotinfo=!0:(n={},r.xref&&(n.xaxis=t._fullLayout[r.xref+\"axis\"]),r.yref&&(n.yaxis=t._fullLayout[r.yref+\"axis\"])),{options:r,plotinfo:n}},e.getPathString=function(t,r){var o,s,l,u,c,f,h,p,d=r.type,v=a.getRefType(r.xref),g=a.getRefType(r.yref),y=a.getFromId(t,r.xref),m=a.getFromId(t,r.yref),x=t._fullLayout._size;if(y?\"domain\"===v?s=function(t){return y._offset+y._length*t}:(o=e.shapePositionToRange(y),s=function(t){return y._offset+y.r2p(o(t,!0))}):s=function(t){return x.l+x.w*t},m?\"domain\"===g?u=function(t){return m._offset+m._length*(1-t)}:(l=e.shapePositionToRange(m),u=function(t){return m._offset+m.r2p(l(t,!0))}):u=function(t){return x.t+x.h*(1-t)},\"path\"===d)return y&&\"date\"===y.type&&(s=e.decodeDate(s)),m&&\"date\"===m.type&&(u=e.decodeDate(u)),function(t,e,r){var a=t.path,o=t.xsizemode,s=t.ysizemode,l=t.xanchor,u=t.yanchor;return a.replace(n.segmentRE,(function(t){var a=0,c=t.charAt(0),f=n.paramIsX[c],h=n.paramIsY[c],p=n.numParams[c],d=t.substr(1).replace(n.paramRE,(function(t){return f[a]?t=\"pixel\"===o?e(l)+Number(t):e(t):h[a]&&(t=\"pixel\"===s?r(u)-Number(t):r(t)),++a>p&&(t=\"X\"),t}));return a>p&&(d=d.replace(/[\\s,]*X.*/,\"\"),i.log(\"Ignoring extra params in segment \"+t)),c+d}))}(r,s,u);if(\"pixel\"===r.xsizemode){var b=s(r.xanchor);c=b+r.x0,f=b+r.x1}else c=s(r.x0),f=s(r.x1);if(\"pixel\"===r.ysizemode){var _=u(r.yanchor);h=_-r.y0,p=_-r.y1}else h=u(r.y0),p=u(r.y1);if(\"line\"===d)return\"M\"+c+\",\"+h+\"L\"+f+\",\"+p;if(\"rect\"===d)return\"M\"+c+\",\"+h+\"H\"+f+\"V\"+p+\"H\"+c+\"Z\";var w=(c+f)/2,T=(h+p)/2,k=Math.abs(w-c),A=Math.abs(T-h),M=\"A\"+k+\",\"+A,S=w+k+\",\"+T;return\"M\"+S+M+\" 0 1,1 \"+w+\",\"+(T-A)+M+\" 0 0,1 \"+S+\"Z\"}},89853:function(t,e,r){\"use strict\";var n=r(34031);t.exports={moduleType:\"component\",name:\"shapes\",layoutAttributes:r(89827),supplyLayoutDefaults:r(84726),supplyDrawNewShapeDefaults:r(45547),includeBasePlot:r(76325)(\"shapes\"),calcAutorange:r(5627),draw:n.draw,drawOne:n.drawOne}},37281:function(t){\"use strict\";function e(t,e){return e?e.d2l(t):t}function r(t,e){return e?e.l2d(t):t}function n(t,r){return e(t.x1,r)-e(t.x0,r)}function i(t,r,n){return e(t.y1,n)-e(t.y0,n)}t.exports={x0:function(t){return t.x0},x1:function(t){return t.x1},y0:function(t){return t.y0},y1:function(t){return t.y1},slope:function(t,e,r){return\"line\"!==t.type?void 0:i(t,0,r)/n(t,e)},dx:n,dy:i,width:function(t,e){return Math.abs(n(t,e))},height:function(t,e,r){return Math.abs(i(t,0,r))},length:function(t,e,r){return\"line\"!==t.type?void 0:Math.sqrt(Math.pow(n(t,e),2)+Math.pow(i(t,0,r),2))},xcenter:function(t,n){return r((e(t.x1,n)+e(t.x0,n))/2,n)},ycenter:function(t,n,i){return r((e(t.y1,i)+e(t.y0,i))/2,i)}}},75067:function(t,e,r){\"use 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n=r(41940),i=r(35025),a=r(1426).extendDeepAll,o=r(30962).overrideAll,s=r(85594),l=r(44467).templatedArray,u=r(98292),c=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}});t.exports=o(l(\"slider\",{visible:{valType:\"boolean\",dflt:!0},active:{valType:\"number\",min:0,dflt:0},steps:c,lenmode:{valType:\"enumerated\",values:[\"fraction\",\"pixels\"],dflt:\"fraction\"},len:{valType:\"number\",min:0,dflt:1},x:{valType:\"number\",min:-2,max:3,dflt:0},pad:a(i({editType:\"arraydraw\"}),{},{t:{dflt:20}}),xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\"},y:{valType:\"number\",min:-2,max:3,dflt:0},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},transition:{duration:{valType:\"number\",min:0,dflt:150},easing:{valType:\"enumerated\",values:s.transition.easing.values,dflt:\"cubic-in-out\"}},currentvalue:{visible:{valType:\"boolean\",dflt:!0},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\"},offset:{valType:\"number\",dflt:10},prefix:{valType:\"string\"},suffix:{valType:\"string\"},font:n({})},font:n({}),activebgcolor:{valType:\"color\",dflt:u.gripBgActiveColor},bgcolor:{valType:\"color\",dflt:u.railBgColor},bordercolor:{valType:\"color\",dflt:u.railBorderColor},borderwidth:{valType:\"number\",min:0,dflt:u.railBorderWidth},ticklen:{valType:\"number\",min:0,dflt:u.tickLength},tickcolor:{valType:\"color\",dflt:u.tickColor},tickwidth:{valType:\"number\",min:0,dflt:1},minorticklen:{valType:\"number\",min:0,dflt:u.minorTickLength}}),\"arraydraw\",\"from-root\")},98292:function(t){\"use 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n.coerce(t,e,s,r,i)}if(\"skip\"===t.method||Array.isArray(t.args)?r(\"visible\"):e.visible=!1){r(\"method\"),r(\"args\");var i=r(\"label\",\"step-\"+e._index);r(\"value\",i),r(\"execute\")}}t.exports=function(t,e){i(t,e,{name:o,handleItemDefaults:l})}},44504:function(t,e,r){\"use strict\";var n=r(39898),i=r(74875),a=r(7901),o=r(91424),s=r(71828),l=s.strTranslate,u=r(63893),c=r(44467).arrayEditor,f=r(98292),h=r(18783),p=h.LINE_SPACING,d=h.FROM_TL,v=h.FROM_BR;function g(t){return f.autoMarginIdRoot+t._index}function y(t){return t._index}function m(t,e){var r=o.tester.selectAll(\"g.\"+f.labelGroupClass).data(e._visibleSteps);r.enter().append(\"g\").classed(f.labelGroupClass,!0);var a=0,l=0;r.each((function(t){var r=_(n.select(this),{step:t},e).node();if(r){var i=o.bBox(r);l=Math.max(l,i.height),a=Math.max(a,i.width)}})),r.remove();var c=e._dims={};c.inputAreaWidth=Math.max(f.railWidth,f.gripHeight);var h=t._fullLayout._size;c.lx=h.l+h.w*e.x,c.ly=h.t+h.h*(1-e.y),\"fraction\"===e.lenmode?c.outerLength=Math.round(h.w*e.len):c.outerLength=e.len,c.inputAreaStart=0,c.inputAreaLength=Math.round(c.outerLength-e.pad.l-e.pad.r);var p=(c.inputAreaLength-2*f.stepInset)/(e._stepCount-1),y=a+f.labelPadding;if(c.labelStride=Math.max(1,Math.ceil(y/p)),c.labelHeight=l,c.currentValueMaxWidth=0,c.currentValueHeight=0,c.currentValueTotalHeight=0,c.currentValueMaxLines=1,e.currentvalue.visible){var m=o.tester.append(\"g\");r.each((function(t){var r=x(m,e,t.label),n=r.node()&&o.bBox(r.node())||{width:0,height:0},i=u.lineCount(r);c.currentValueMaxWidth=Math.max(c.currentValueMaxWidth,Math.ceil(n.width)),c.currentValueHeight=Math.max(c.currentValueHeight,Math.ceil(n.height)),c.currentValueMaxLines=Math.max(c.currentValueMaxLines,i)})),c.currentValueTotalHeight=c.currentValueHeight+e.currentvalue.offset,m.remove()}c.height=c.currentValueTotalHeight+f.tickOffset+e.ticklen+f.labelOffset+c.labelHeight+e.pad.t+e.pad.b;var b=\"left\";s.isRightAnchor(e)&&(c.lx-=c.outerLength,b=\"right\"),s.isCenterAnchor(e)&&(c.lx-=c.outerLength/2,b=\"center\");var w=\"top\";s.isBottomAnchor(e)&&(c.ly-=c.height,w=\"bottom\"),s.isMiddleAnchor(e)&&(c.ly-=c.height/2,w=\"middle\"),c.outerLength=Math.ceil(c.outerLength),c.height=Math.ceil(c.height),c.lx=Math.round(c.lx),c.ly=Math.round(c.ly);var T={y:e.y,b:c.height*v[w],t:c.height*d[w]};\"fraction\"===e.lenmode?(T.l=0,T.xl=e.x-e.len*d[b],T.r=0,T.xr=e.x+e.len*v[b]):(T.x=e.x,T.l=c.outerLength*d[b],T.r=c.outerLength*v[b]),i.autoMargin(t,g(e),T)}function x(t,e,r){if(e.currentvalue.visible){var n,i,a=e._dims;switch(e.currentvalue.xanchor){case\"right\":n=a.inputAreaLength-f.currentValueInset-a.currentValueMaxWidth,i=\"left\";break;case\"center\":n=.5*a.inputAreaLength,i=\"middle\";break;default:n=f.currentValueInset,i=\"left\"}var l=s.ensureSingle(t,\"text\",f.labelClass,(function(t){t.attr({\"text-anchor\":i,\"data-notex\":1})})),c=e.currentvalue.prefix?e.currentvalue.prefix:\"\";if(\"string\"==typeof r)c+=r;else{var h=e.steps[e.active].label,d=e._gd._fullLayout._meta;d&&(h=s.templateString(h,d)),c+=h}e.currentvalue.suffix&&(c+=e.currentvalue.suffix),l.call(o.font,e.currentvalue.font).text(c).call(u.convertToTspans,e._gd);var v=u.lineCount(l),g=(a.currentValueMaxLines+1-v)*e.currentvalue.font.size*p;return 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a=Math.round(n*(r._stepCount-1)),o=r._visibleSteps[a]._index;o!==r.active&&k(t,e,r,o,!0,i)}function k(t,e,r,n,a,o){var s=r.active;r.active=n,c(t.layout,f.name,r).applyUpdate(\"active\",n);var l=r.steps[r.active];e.call(S,r,o),e.call(x,r),t.emit(\"plotly_sliderchange\",{slider:r,step:r.steps[r.active],interaction:a,previousActive:s}),l&&l.method&&a&&(e._nextMethod?(e._nextMethod.step=l,e._nextMethod.doCallback=a,e._nextMethod.doTransition=o):(e._nextMethod={step:l,doCallback:a,doTransition:o},e._nextMethodRaf=window.requestAnimationFrame((function(){var r=e._nextMethod.step;r.method&&(r.execute&&i.executeAPICommand(t,r.method,r.args),e._nextMethod=null,e._nextMethodRaf=null)}))))}function A(t,e,r){if(!e._context.staticPlot){var i=r.node(),o=n.select(e);t.on(\"mousedown\",l),t.on(\"touchstart\",l)}function s(){return r.data()[0]}function l(){var t=s();e.emit(\"plotly_sliderstart\",{slider:t});var l=r.select(\".\"+f.gripRectClass);n.event.stopPropagation(),n.event.preventDefault(),l.call(a.fill,t.activebgcolor);var u=L(t,n.mouse(i)[0]);function c(){var t=s(),a=L(t,n.mouse(i)[0]);T(e,r,t,a,!1)}function h(){var t=s();t._dragging=!1,l.call(a.fill,t.bgcolor),o.on(\"mouseup\",null),o.on(\"mousemove\",null),o.on(\"touchend\",null),o.on(\"touchmove\",null),e.emit(\"plotly_sliderend\",{slider:t,step:t.steps[t.active]})}T(e,r,t,u,!0),t._dragging=!0,o.on(\"mousemove\",c),o.on(\"touchmove\",c),o.on(\"mouseup\",h),o.on(\"touchend\",h)}}function M(t,e){var r=t.selectAll(\"rect.\"+f.tickRectClass).data(e._visibleSteps),i=e._dims;r.enter().append(\"rect\").classed(f.tickRectClass,!0),r.exit().remove(),r.attr({width:e.tickwidth+\"px\",\"shape-rendering\":\"crispEdges\"}),r.each((function(t,r){var s=r%i.labelStride==0,l=n.select(this);l.attr({height:s?e.ticklen:e.minorticklen}).call(a.fill,e.tickcolor),o.setTranslate(l,E(e,r/(e._stepCount-1))-.5*e.tickwidth,(s?f.tickOffset:f.minorTickOffset)+i.currentValueTotalHeight)}))}function S(t,e,r){for(var n=t.select(\"rect.\"+f.gripRectClass),i=0,a=0;a<e._stepCount;a++)if(e._visibleSteps[a]._index===e.active){i=a;break}var o=E(e,i/(e._stepCount-1));if(!e._invokingCommand){var s=n;r&&e.transition.duration>0&&(s=s.transition().duration(e.transition.duration).ease(e.transition.easing)),s.attr(\"transform\",l(o-.5*f.gripWidth,e._dims.currentValueTotalHeight))}}function E(t,e){var r=t._dims;return r.inputAreaStart+f.stepInset+(r.inputAreaLength-2*f.stepInset)*Math.min(1,Math.max(0,e))}function L(t,e){var r=t._dims;return Math.min(1,Math.max(0,(e-f.stepInset-r.inputAreaStart)/(r.inputAreaLength-2*f.stepInset-2*r.inputAreaStart)))}function C(t,e,r){var 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s(t,e,r){this.gd=t,this.container=e,this.id=r,this.position=null,this.translateX=null,this.translateY=null,this.hbar=null,this.vbar=null,this.bg=this.container.selectAll(\"rect.scrollbox-bg\").data([0]),this.bg.exit().on(\".drag\",null).on(\"wheel\",null).remove(),this.bg.enter().append(\"rect\").classed(\"scrollbox-bg\",!0).style(\"pointer-events\",\"all\").attr({opacity:0,x:0,y:0,width:0,height:0})}s.barWidth=2,s.barLength=20,s.barRadius=2,s.barPad=1,s.barColor=\"#808BA4\",s.prototype.enable=function(t,e,r){var o=this.gd._fullLayout,l=o.width,u=o.height;this.position=t;var c,f,h,p,d=this.position.l,v=this.position.w,g=this.position.t,y=this.position.h,m=this.position.direction,x=\"down\"===m,b=\"left\"===m,_=\"up\"===m,w=v,T=y;x||b||\"right\"===m||_||(this.position.direction=\"down\",x=!0),x||_?(f=(c=d)+w,x?(h=g,T=(p=Math.min(h+T,u))-h):T=(p=g+T)-(h=Math.max(p-T,0))):(p=(h=g)+T,b?w=(f=d+w)-(c=Math.max(f-w,0)):(c=d,w=(f=Math.min(c+w,l))-c)),this._box={l:c,t:h,w:w,h:T};var k=v>w,A=s.barLength+2*s.barPad,M=s.barWidth+2*s.barPad,S=d,E=g+y;E+M>u&&(E=u-M);var L=this.container.selectAll(\"rect.scrollbar-horizontal\").data(k?[0]:[]);L.exit().on(\".drag\",null).remove(),L.enter().append(\"rect\").classed(\"scrollbar-horizontal\",!0).call(i.fill,s.barColor),k?(this.hbar=L.attr({rx:s.barRadius,ry:s.barRadius,x:S,y:E,width:A,height:M}),this._hbarXMin=S+A/2,this._hbarTranslateMax=w-A):(delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax);var C=y>T,P=s.barWidth+2*s.barPad,O=s.barLength+2*s.barPad,I=d+v,D=g;I+P>l&&(I=l-P);var z=this.container.selectAll(\"rect.scrollbar-vertical\").data(C?[0]:[]);z.exit().on(\".drag\",null).remove(),z.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(i.fill,s.barColor),C?(this.vbar=z.attr({rx:s.barRadius,ry:s.barRadius,x:I,y:D,width:P,height:O}),this._vbarYMin=D+O/2,this._vbarTranslateMax=T-O):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=c-.5,B=C?f+P+.5:f+.5,N=h-.5,j=k?p+M+.5:p+.5,U=o._topdefs.selectAll(\"#\"+R).data(k||C?[0]:[]);if(U.exit().remove(),U.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),k||C?(this._clipRect=U.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(N),width:Math.ceil(B)-Math.floor(F),height:Math.ceil(j)-Math.floor(N)}),this.container.call(a.setClipUrl,R,this.gd),this.bg.attr({x:d,y:g,width:v,height:y})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(a.setClipUrl,null),delete this._clipRect),k||C){var V=n.behavior.drag().on(\"dragstart\",(function(){n.event.sourceEvent.preventDefault()})).on(\"drag\",this._onBoxDrag.bind(this));this.container.on(\"wheel\",null).on(\"wheel\",this._onBoxWheel.bind(this)).on(\".drag\",null).call(V);var H=n.behavior.drag().on(\"dragstart\",(function(){n.event.sourceEvent.preventDefault(),n.event.sourceEvent.stopPropagation()})).on(\"drag\",this._onBarDrag.bind(this));k&&this.hbar.on(\".drag\",null).call(H),C&&this.vbar.on(\".drag\",null).call(H)}this.setTranslate(e,r)},s.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(a.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(\".drag\",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(\".drag\",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)},s.prototype._onBoxDrag=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t-=n.event.dx),this.vbar&&(e-=n.event.dy),this.setTranslate(t,e)},s.prototype._onBoxWheel=function(){var 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z=!D;D?(t._dtickInit=t.dtick,t._tick0Init=t.tick0):(t.minor._dtickInit=t.minor.dtick,t.minor._tick0Init=t.minor.tick0);var R=D?t:s.extendFlat({},t,t.minor);if(z?q.prepMinorTicks(R,t,e):q.prepTicks(R,e),\"array\"!==R.tickmode)if(\"sync\"!==R.tickmode){var F=K(c),B=F[0],N=F[1],j=i(R.dtick),U=\"log\"===a&&!(j||\"L\"===R.dtick.charAt(0)),V=q.tickFirst(R,e);if(D){if(t._tmin=V,V<B!==f)break;\"category\"!==a&&\"multicategory\"!==a||(N=f?Math.max(-.5,N):Math.min(t._categories.length-.5,N))}var H,G,Z=null,Y=V;D&&(j?G=t.dtick:\"date\"===a?\"string\"==typeof t.dtick&&\"M\"===t.dtick.charAt(0)&&(G=T*t.dtick.substring(1)):G=t._roughDTick,H=Math.round((t.r2l(Y)-t.r2l(t.tick0))/G)-1);var W=R.dtick;for(R.rangebreaks&&R._tick0Init!==R.tick0&&(Y=zt(Y,t),f||(Y=q.tickIncrement(Y,W,!f,o))),D&&u&&(Y=q.tickIncrement(Y,W,!f,o),H--);f?Y>=N:Y<=N;Y=q.tickIncrement(Y,W,f,o)){if(D&&H++,R.rangebreaks&&!f){if(Y<B)continue;if(R.maskBreaks(Y)===O&&zt(Y,R)>=p)break}if(C.length>d||Y===Z)break;Z=Y;var X={value:Y};D?(U&&Y!==(0|Y)&&(X.simpleLabel=!0),l>1&&H%l&&(X.skipLabel=!0),C.push(X)):(X.minor=!0,P.push(X))}}else C=[],v=at(t);else D?(C=[],v=ot(t)):(P=[],L=ot(t))}if(I&&!(\"inside\"===t.minor.ticks&&\"outside\"===t.ticks||\"outside\"===t.minor.ticks&&\"inside\"===t.ticks)){for(var J=C.map((function(t){return t.value})),$=[],Q=0;Q<P.length;Q++){var tt=P[Q],et=tt.value;if(-1===J.indexOf(et)){for(var rt=!1,nt=0;!rt&&nt<C.length;nt++)1e7+C[nt].value===1e7+et&&(rt=!0);rt||$.push(tt)}}P=$}if(u&&function(t,e,r){for(var n=0;n<t.length;n++){var i=t[n].value,a=n,o=n+1;n<t.length-1?(a=n,o=n+1):n>0?(a=n-1,o=n):(a=n,o=n);var s,l=t[a].value,u=t[o].value,c=Math.abs(u-l),f=r||c,h=0;f>=m?h=c>=m&&c<=g?c:y:r===b&&f>=_?h=c>=_&&c<=x?c:b:f>=k?h=c>=k&&c<=w?c:T:r===A&&f>=A?h=A:f>=M?h=M:r===S&&f>=S?h=S:r===E&&f>=E&&(h=E),h>=c&&(h=c,s=!0);var p=i+h;if(e.rangebreaks&&h>0){for(var d=0,v=0;v<84;v++){var L=(v+.5)/84;e.maskBreaks(i*(1-L)+L*p)!==O&&d++}(h*=d/84)||(t[n].drop=!0),s&&c>A&&(h=c)}(h>0||0===n)&&(t[n].periodX=i+h/2)}}(C,t,t._definedDelta),t.rangebreaks){var it=\"y\"===t._id.charAt(0),st=1;\"auto\"===t.tickmode&&(st=t.tickfont?t.tickfont.size:12);var lt=NaN;for(r=C.length-1;r>-1;r--)if(C[r].drop)C.splice(r,1);else{C[r].value=zt(C[r].value,t);var ut=t.c2p(C[r].value);(it?lt>ut-st:lt<ut+st)?C.splice(f?r+1:r,1):lt=ut}}Dt(t)&&360===Math.abs(c[1]-c[0])&&C.pop(),t._tmax=(C[C.length-1]||{}).value,t._prevDateHead=\"\",t._inCalcTicks=!0;var ct,ft,ht=function(e){e.text=\"\",t._prevDateHead=n};for(C=C.concat(P),r=0;r<C.length;r++){var pt=C[r].minor,dt=C[r].value;pt?L.push({x:dt,minor:!0}):(n=t._prevDateHead,ct=q.tickText(t,dt,!1,C[r].simpleLabel),void 0!==(ft=C[r].periodX)&&(ct.periodX=ft,(ft>p||ft<h)&&(ft>p&&(ct.periodX=p),ft<h&&(ct.periodX=h),ht(ct))),C[r].skipLabel&&ht(ct),v.push(ct))}return v=v.concat(L),t._inCalcTicks=!1,u&&v.length&&(v[0].noTick=!0),v};var st=[2,5,10],lt=[1,2,3,6,12],ut=[1,2,5,10,15,30],ct=[1,2,3,7,14],ft=[-.046,0,.301,.477,.602,.699,.778,.845,.903,.954,1],ht=[-.301,0,.301,.699,1],pt=[15,30,45,90,180];function dt(t,e,r){return e*s.roundUp(t/e,r)}function vt(t){var e=t.dtick;if(t._tickexponent=0,i(e)||\"string\"==typeof e||(e=1),\"category\"!==t.type&&\"multicategory\"!==t.type||(t._tickround=null),\"date\"===t.type){var r=t.r2l(t.tick0),n=t.l2r(r).replace(/(^-|i)/g,\"\"),a=n.length;if(\"M\"===String(e).charAt(0))a>10||\"01-01\"!==n.substr(5)?t._tickround=\"d\":t._tickround=+e.substr(1)%12==0?\"y\":\"m\";else if(e>=M&&a<=10||e>=15*M)t._tickround=\"d\";else if(e>=L&&a<=16||e>=E)t._tickround=\"M\";else if(e>=C&&a<=19||e>=L)t._tickround=\"S\";else{var o=t.l2r(r+e).replace(/^-/,\"\").length;t._tickround=Math.max(a,o)-20,t._tickround<0&&(t._tickround=4)}}else if(i(e)||\"L\"===e.charAt(0)){var s=t.range.map(t.r2d||Number);i(e)||(e=Number(e.substr(1))),t._tickround=2-Math.floor(Math.log(e)/Math.LN10+.01);var l=Math.max(Math.abs(s[0]),Math.abs(s[1])),u=Math.floor(Math.log(l)/Math.LN10+.01),c=void 0===t.minexponent?3:t.minexponent;Math.abs(u)>c&&(mt(t.exponentformat)&&!xt(u)?t._tickexponent=3*Math.round((u-1)/3):t._tickexponent=u)}else t._tickround=null}function gt(t,e,r){var n=t.tickfont||{};return{x:e,dx:0,dy:0,text:r||\"\",fontSize:n.size,font:n.family,fontColor:n.color}}q.autoTicks=function(t,e,r){var n;function a(t){return Math.pow(t,Math.floor(Math.log(e)/Math.LN10))}if(\"date\"===t.type){t.tick0=s.dateTick0(t.calendar,0);var o=2*e;if(o>y)e/=y,n=a(10),t.dtick=\"M\"+12*dt(e,n,st);else if(o>T)e/=T,t.dtick=\"M\"+dt(e,1,lt);else if(o>M){if(t.dtick=dt(e,M,t._hasDayOfWeekBreaks?[1,2,7,14]:ct),!r){var l=q.getTickFormat(t),u=\"period\"===t.ticklabelmode;u&&(t._rawTick0=t.tick0),/%[uVW]/.test(l)?t.tick0=s.dateTick0(t.calendar,2):t.tick0=s.dateTick0(t.calendar,1),u&&(t._dowTick0=t.tick0)}}else o>E?t.dtick=dt(e,E,lt):o>L?t.dtick=dt(e,L,ut):o>C?t.dtick=dt(e,C,ut):(n=a(10),t.dtick=dt(e,n,st))}else if(\"log\"===t.type){t.tick0=0;var c=s.simpleMap(t.range,t.r2l);if(t._isMinor&&(e*=1.5),e>.7)t.dtick=Math.ceil(e);else if(Math.abs(c[1]-c[0])<1){var f=1.5*Math.abs((c[1]-c[0])/e);e=Math.abs(Math.pow(10,c[1])-Math.pow(10,c[0]))/f,n=a(10),t.dtick=\"L\"+dt(e,n,st)}else t.dtick=e>.3?\"D2\":\"D1\"}else\"category\"===t.type||\"multicategory\"===t.type?(t.tick0=0,t.dtick=Math.ceil(Math.max(e,1))):Dt(t)?(t.tick0=0,n=1,t.dtick=dt(e,n,pt)):(t.tick0=0,n=a(10),t.dtick=dt(e,n,st));if(0===t.dtick&&(t.dtick=1),!i(t.dtick)&&\"string\"!=typeof t.dtick){var h=t.dtick;throw t.dtick=1,\"ax.dtick error: \"+String(h)}},q.tickIncrement=function(t,e,r,a){var o=r?-1:1;if(i(e))return s.increment(t,o*e);var l=e.charAt(0),u=o*Number(e.substr(1));if(\"M\"===l)return s.incrementMonth(t,u,a);if(\"L\"===l)return Math.log(Math.pow(10,t)+u)/Math.LN10;if(\"D\"===l){var c=\"D2\"===e?ht:ft,f=t+.01*o,h=s.roundUp(s.mod(f,1),c,r);return Math.floor(f)+Math.log(n.round(Math.pow(10,h),1))/Math.LN10}throw\"unrecognized dtick 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a,o=gt(t,e),l=\"array\"===t.tickmode,u=r||l,c=t.type,f=\"category\"===c?t.d2l_noadd:t.d2l;if(l&&Array.isArray(t.ticktext)){var h=s.simpleMap(t.range,t.r2l),p=(Math.abs(h[1]-h[0])-(t._lBreaks||0))/1e4;for(a=0;a<t.ticktext.length&&!(Math.abs(e-f(t.tickvals[a]))<p);a++);if(a<t.ticktext.length)return o.text=String(t.ticktext[a]),o}function d(n){if(void 0===n)return!0;if(r)return\"none\"===n;var i={first:t._tmin,last:t._tmax}[n];return\"all\"!==n&&e!==i}var v=r?\"never\":\"none\"!==t.exponentformat&&d(t.showexponent)?\"hide\":\"\";if(\"date\"===c?function(t,e,r,n){var a=t._tickround,o=r&&t.hoverformat||q.getTickFormat(t);(n=!o&&n)&&(a=i(a)?4:{y:\"m\",m:\"d\",d:\"M\",M:\"S\",S:4}[a]);var l,u=s.formatDate(e.x,o,a,t._dateFormat,t.calendar,t._extraFormat),c=u.indexOf(\"\\n\");if(-1!==c&&(l=u.substr(c+1),u=u.substr(0,c)),n&&(void 0===l||\"00:00:00\"!==u&&\"00:00\"!==u?8===u.length&&(u=u.replace(/:00$/,\"\")):(u=l,l=\"\")),l)if(r)\"d\"===a?u+=\", \"+l:u=l+(u?\", \"+u:\"\");else if(t._inCalcTicks&&t._prevDateHead===l){var f=Rt(t),h=t._trueSide||t.side;(!f&&\"top\"===h||f&&\"bottom\"===h)&&(u+=\"<br> \")}else t._prevDateHead=l,u+=\"<br>\"+l;e.text=u}(t,o,r,u):\"log\"===c?function(t,e,r,n,a){var o=t.dtick,l=e.x,u=t.tickformat,c=\"string\"==typeof o&&o.charAt(0);if(\"never\"===a&&(a=\"\"),n&&\"L\"!==c&&(o=\"L3\",c=\"L\"),u||\"L\"===c)e.text=bt(Math.pow(10,l),t,a,n);else if(i(o)||\"D\"===c&&s.mod(l+.01,1)<.1){var f=Math.round(l),h=Math.abs(f),p=t.exponentformat;\"power\"===p||mt(p)&&xt(f)?(e.text=0===f?1:1===f?\"10\":\"10<sup>\"+(f>1?\"\":P)+h+\"</sup>\",e.fontSize*=1.25):(\"e\"===p||\"E\"===p)&&h>2?e.text=\"1\"+p+(f>0?\"+\":P)+h:(e.text=bt(Math.pow(10,l),t,\"\",\"fakehover\"),\"D1\"===o&&\"y\"===t._id.charAt(0)&&(e.dy-=e.fontSize/6))}else{if(\"D\"!==c)throw\"unrecognized dtick \"+String(o);e.text=String(Math.round(Math.pow(10,s.mod(l,1)))),e.fontSize*=.75}if(\"D1\"===t.dtick){var d=String(e.text).charAt(0);\"0\"!==d&&\"1\"!==d||(\"y\"===t._id.charAt(0)?e.dx-=e.fontSize/4:(e.dy+=e.fontSize/2,e.dx+=(t.range[1]>t.range[0]?1:-1)*e.fontSize*(l<0?.5:.25)))}}(t,o,0,u,v):\"category\"===c?function(t,e){var r=t._categories[Math.round(e.x)];void 0===r&&(r=\"\"),e.text=String(r)}(t,o):\"multicategory\"===c?function(t,e,r){var n=Math.round(e.x),i=t._categories[n]||[],a=void 0===i[1]?\"\":String(i[1]),o=void 0===i[0]?\"\":String(i[0]);r?e.text=o+\" - \"+a:(e.text=a,e.text2=o)}(t,o,r):Dt(t)?function(t,e,r,n,i){if(\"radians\"!==t.thetaunit||r)e.text=bt(e.x,t,i,n);else{var a=e.x/180;if(0===a)e.text=\"0\";else{var o=function(t){function e(t,e){return Math.abs(t-e)<=1e-6}var r=function(t){for(var r=1;!e(Math.round(t*r)/r,t);)r*=10;return r}(t),n=t*r,i=Math.abs(function t(r,n){return e(n,0)?r:t(n,r%n)}(n,r));return[Math.round(n/i),Math.round(r/i)]}(a);if(o[1]>=100)e.text=bt(s.deg2rad(e.x),t,i,n);else{var 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z=0;z<e.rangebreaks.length;z++)if(e.rangebreaks[z].pattern===y){e._hasDayOfWeekBreaks=!0;break}if(g(e,m),m._has(\"scattergl\")||m._has(\"splom\"))for(var R=0;R<n.data.length;R++){var F=n.data[R];\"scattergl\"!==F.type&&\"splom\"!==F.type||(F.visible=!1,a.warn(F.type+\" traces do not work on axes with rangebreaks. 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k=l._inputDomain,A=(l.domain[1]-l.domain[0])/(k[1]-k[0]),M=(l.r2l(l.range[1])-l.r2l(l.range[0]))/(l.r2l(l._inputRange[1])-l.r2l(l._inputRange[0]));if((h/=A)*M<1){l.domain=l._input.domain=k.slice(),s(l,h);continue}if(M<1&&(l.range=l._input.range=l._inputRange.slice(),h*=M),l.autorange){var S=l.r2l(l.range[0]),E=l.r2l(l.range[1]),L=(S+E)/2,C=L,P=L,O=Math.abs(E-L),I=L-O*h*1.0001,D=L+O*h*1.0001,z=i.makePadFn(p,l,0),R=i.makePadFn(p,l,1);y(l,h);var F,B,N=Math.abs(l._m),j=i.concatExtremes(t,l),U=j.min,V=j.max;for(B=0;B<U.length;B++)(F=U[B].val-z(U[B])/N)>I&&F<C&&(C=F);for(B=0;B<V.length;B++)(F=V[B].val+R(V[B])/N)<D&&F>P&&(P=F);h/=(P-C)/(2*O),C=l.l2r(C),P=l.l2r(P),l.range=l._input.range=S<E?[C,P]:[P,C]}y(l,h)}}},e.getAxisGroup=function(t,e){for(var r=t._axisMatchGroups,n=0;n<r.length;n++)if(r[n][e])return\"g\"+n;return e},e.clean=function(t,e){if(e._inputDomain){for(var 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t.append(\"path\").attr(\"class\",\"zoombox\").style({fill:e>.2?\"rgba(0,0,0,0)\":\"rgba(255,255,255,0)\",\"stroke-width\":0}).attr(\"transform\",u(r,n)).attr(\"d\",i+\"Z\")}function j(t,e,r){return t.append(\"path\").attr(\"class\",\"zoombox-corners\").style({fill:f.background,stroke:f.defaultLine,\"stroke-width\":1,opacity:0}).attr(\"transform\",u(e,r)).attr(\"d\",\"M0,0Z\")}function U(t,e,r,n,i,a){t.attr(\"d\",n+\"M\"+r.l+\",\"+r.t+\"v\"+r.h+\"h\"+r.w+\"v-\"+r.h+\"h-\"+r.w+\"Z\"),V(t,e,i,a)}function V(t,e,r,n){r||(t.transition().style(\"fill\",n>.2?\"rgba(0,0,0,0.4)\":\"rgba(255,255,255,0.3)\").duration(200),e.transition().style(\"opacity\",1).duration(200))}function H(t){n.select(t).selectAll(\".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners\").remove()}function q(t){O&&t.data&&t._context.showTips&&(i.notifier(i._(t,\"Double-click to zoom back out\"),\"long\"),O=!1)}function G(t){var e=Math.floor(Math.min(t.b-t.t,t.r-t.l,P)/2);return\"M\"+(t.l-3.5)+\",\"+(t.t-.5+e)+\"h3v\"+-e+\"h\"+e+\"v-3h-\"+(e+3)+\"ZM\"+(t.r+3.5)+\",\"+(t.t-.5+e)+\"h-3v\"+-e+\"h\"+-e+\"v-3h\"+(e+3)+\"ZM\"+(t.r+3.5)+\",\"+(t.b+.5-e)+\"h-3v\"+e+\"h\"+-e+\"v3h\"+(e+3)+\"ZM\"+(t.l-3.5)+\",\"+(t.b+.5-e)+\"h3v\"+e+\"h\"+e+\"v3h-\"+(e+3)+\"Z\"}function Z(t,e,r,n,a){for(var o,s,l,u,c=!1,f={},h={},p=(a||{}).xaHash,d=(a||{}).yaHash,v=0;v<e.length;v++){var g=e[v];for(o in r)if(g[o]){for(l in g)a&&(p[l]||d[l])||(\"x\"===l.charAt(0)?r:n)[l]||(f[l]=o);for(s in n)a&&(p[s]||d[s])||!g[s]||(c=!0)}for(s in n)if(g[s])for(u in g)a&&(p[u]||d[u])||(\"x\"===u.charAt(0)?r:n)[u]||(h[u]=s)}c&&(i.extendFlat(f,h),h={});var y={},m=[];for(l in f){var x=k(t,l);m.push(x),y[x._id]=x}var b={},_=[];for(u in h){var w=k(t,u);_.push(w),b[w._id]=w}return{xaHash:y,yaHash:b,xaxes:m,yaxes:_,xLinks:f,yLinks:h,isSubplotConstrained:c}}function Y(t,e){if(s){var r=void 0!==t.onwheel?\"wheel\":\"mousewheel\";t._onwheel&&t.removeEventListener(r,t._onwheel),t._onwheel=e,t.addEventListener(r,e,{passive:!1})}else void 0!==t.onwheel?t.onwheel=e:void 0!==t.onmousewheel?t.onmousewheel=e:t.isAddedWheelEvent||(t.isAddedWheelEvent=!0,t.addEventListener(\"wheel\",e,{passive:!1}))}function W(t){var e=[];for(var r in t)e.push(t[r]);return e}t.exports={makeDragBox:function(t,e,r,s,u,f,v,y){var O,I,V,X,J,K,$,Q,tt,et,rt,nt,it,at,ot,st,lt,ut,ct,ft,ht,pt,dt,vt=t._fullLayout._zoomlayer,gt=v+y===\"nsew\",yt=1===(v+y).length;function mt(){if(O=e.xaxis,I=e.yaxis,tt=O._length,et=I._length,$=O._offset,Q=I._offset,(V={})[O._id]=O,(X={})[I._id]=I,v&&y)for(var r=e.overlays,n=0;n<r.length;n++){var i=r[n].xaxis;V[i._id]=i;var a=r[n].yaxis;X[a._id]=a}J=W(V),K=W(X),it=z(J,y),at=z(K,v),ot=!at&&!it,nt=Z(t,t._fullLayout._axisMatchGroups,V,X);var o=(rt=Z(t,t._fullLayout._axisConstraintGroups,V,X,nt)).isSubplotConstrained||nt.isSubplotConstrained;st=y||o,lt=v||o;var s=t._fullLayout;ut=s._has(\"scattergl\"),ct=s._has(\"splom\"),ft=s._has(\"svg\")}r+=e.yaxis._shift,mt();var xt=function(t,e,r){return t?\"nsew\"===t?r?\"\":\"pan\"===e?\"move\":\"crosshair\":t.toLowerCase()+\"-resize\":\"pointer\"}(at+it,t._fullLayout.dragmode,gt),bt=D(e,v+y+\"drag\",xt,r,s,u,f);if(ot&&!gt)return bt.onmousedown=null,bt.style.pointerEvents=\"none\",bt;var _t,wt,Tt,kt,At,Mt,St,Et,Lt,Ct,Pt={element:bt,gd:t,plotinfo:e};function Ot(){Pt.plotinfo.selection=!1,M(t)}function It(t,r){var i=Pt.gd;if(i._fullLayout._activeShapeIndex>=0)i._fullLayout._deactivateShape(i);else{var o=i._fullLayout.clickmode;if(H(i),2!==t||yt||qt(),gt)o.indexOf(\"select\")>-1&&S(r,i,J,K,e.id,Pt),o.indexOf(\"event\")>-1&&p.click(i,r,e.id);else if(1===t&&yt){var s=v?I:O,u=\"s\"===v||\"w\"===y?0:1,f=s._name+\".range[\"+u+\"]\",h=function(t,e){var r,n=t.range[e],i=Math.abs(n-t.range[1-e]);return\"date\"===t.type?n:\"log\"===t.type?(r=Math.ceil(Math.max(0,-Math.log(i)/Math.LN10))+3,a(\".\"+r+\"g\")(Math.pow(10,n))):(r=Math.floor(Math.log(Math.abs(n))/Math.LN10)-Math.floor(Math.log(i)/Math.LN10)+4,a(\".\"+String(r)+\"g\")(n))}(s,u),d=\"left\",g=\"middle\";if(s.fixedrange)return;v?(g=\"n\"===v?\"top\":\"bottom\",\"right\"===s.side&&(d=\"right\")):\"e\"===y&&(d=\"right\"),i._context.showAxisRangeEntryBoxes&&n.select(bt).call(c.makeEditable,{gd:i,immediate:!0,background:i._fullLayout.paper_bgcolor,text:String(h),fill:s.tickfont?s.tickfont.color:\"#444\",horizontalAlign:d,verticalAlign:g}).on(\"edit\",(function(t){var e=s.d2r(t);void 0!==e&&l.call(\"_guiRelayout\",i,f,e)}))}}}function Dt(e,r){if(t._transitioningWithDuration)return!1;var n=Math.max(0,Math.min(tt,pt*e+_t)),i=Math.max(0,Math.min(et,dt*r+wt)),a=Math.abs(n-_t),o=Math.abs(i-wt);function s(){St=\"\",Tt.r=Tt.l,Tt.t=Tt.b,Lt.attr(\"d\",\"M0,0Z\")}if(Tt.l=Math.min(_t,n),Tt.r=Math.max(_t,n),Tt.t=Math.min(wt,i),Tt.b=Math.max(wt,i),rt.isSubplotConstrained)a>P||o>P?(St=\"xy\",a/tt>o/et?(o=a*et/tt,wt>i?Tt.t=wt-o:Tt.b=wt+o):(a=o*tt/et,_t>n?Tt.l=_t-a:Tt.r=_t+a),Lt.attr(\"d\",G(Tt))):s();else if(nt.isSubplotConstrained)if(a>P||o>P){St=\"xy\";var l=Math.min(Tt.l/tt,(et-Tt.b)/et),u=Math.max(Tt.r/tt,(et-Tt.t)/et);Tt.l=l*tt,Tt.r=u*tt,Tt.b=(1-l)*et,Tt.t=(1-u)*et,Lt.attr(\"d\",G(Tt))}else 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a=Pt.dragmode,s=t._fullLayout.dragmode;s!==a&&(Pt.dragmode=s),mt(),pt=t._fullLayout._invScaleX,dt=t._fullLayout._invScaleY,ot||(gt?e.shiftKey?\"pan\"===s?s=\"zoom\":m(s)||(s=\"pan\"):e.ctrlKey&&(s=\"pan\"):s=\"pan\"),x(s)?Pt.minDrag=1:Pt.minDrag=void 0,m(s)?(Pt.xaxes=J,Pt.yaxes=K,A(e,r,n,Pt,s)):(Pt.clickFn=It,m(a)&&Ot(),ot||(\"zoom\"===s?(Pt.moveFn=Dt,Pt.doneFn=Rt,Pt.minDrag=1,function(e,r,n){var a=bt.getBoundingClientRect();_t=r-a.left,wt=n-a.top,t._fullLayout._calcInverseTransform(t);var s=i.apply3DTransform(t._fullLayout._invTransform)(_t,wt);_t=s[0],wt=s[1],Tt={l:_t,r:_t,w:0,t:wt,b:wt,h:0},kt=t._hmpixcount?t._hmlumcount/t._hmpixcount:o(t._fullLayout.plot_bgcolor).getLuminance(),Mt=!1,St=\"xy\",Ct=!1,Et=N(vt,kt,$,Q,At=\"M0,0H\"+tt+\"V\"+et+\"H0V0\"),Lt=j(vt,$,Q)}(0,r,n)):\"pan\"===s&&(Pt.moveFn=Ut,Pt.doneFn=Gt))),t._fullLayout._redrag=function(){var e=t._dragdata;if(e&&e.element===bt){var r=t._fullLayout.dragmode;m(r)||(mt(),Zt([0,0,tt,et]),Pt.moveFn(e.dx,e.dy))}}},g.init(Pt);var Ft=[0,0,tt,et],Bt=null,Nt=L.REDRAWDELAY,jt=e.mainplot?t._fullLayout._plots[e.mainplot]:e;function Ut(e,r){if(e*=pt,r*=dt,!t._transitioningWithDuration){if(t._fullLayout._replotting=!0,\"ew\"===it||\"ns\"===at){var n=it?-e:0,i=at?-r:0;if(nt.isSubplotConstrained){if(it&&at){var a=(e/tt-r/et)/2;n=-(e=a*tt),i=-(r=-a*et)}at?n=-i*tt/et:i=-n*et/tt}return it&&(F(J,e),Vt(\"x\")),at&&(F(K,r),Vt(\"y\")),Zt([n,i,tt,et]),Ht(),void t.emit(\"plotly_relayouting\",ht)}var o,s,l=\"w\"===it==(\"n\"===at)?1:-1;if(it&&at&&(rt.isSubplotConstrained||nt.isSubplotConstrained)){var u=(e/tt+l*r/et)/2;e=u*tt,r=l*u*et}if(\"w\"===it?e=p(J,0,e):\"e\"===it?e=p(J,1,-e):it||(e=0),\"n\"===at?r=p(K,1,r):\"s\"===at?r=p(K,0,-r):at||(r=0),o=\"w\"===it?e:0,s=\"n\"===at?r:0,rt.isSubplotConstrained&&!nt.isSubplotConstrained||nt.isSubplotConstrained&&it&&at&&l>0){var c;if(nt.isSubplotConstrained||!it&&1===at.length){for(c=0;c<J.length;c++)J[c].range=J[c]._r.slice(),E(J[c],1-r/et);o=(e=r*tt/et)/2}if(nt.isSubplotConstrained||!at&&1===it.length){for(c=0;c<K.length;c++)K[c].range=K[c]._r.slice(),E(K[c],1-e/tt);s=(r=e*et/tt)/2}}nt.isSubplotConstrained&&at||Vt(\"x\"),nt.isSubplotConstrained&&it||Vt(\"y\");var f=tt-e,h=et-r;!nt.isSubplotConstrained||it&&at||(it?(s=o?0:e*et/tt,h=f*et/tt):(o=s?0:r*tt/et,f=h*tt/et)),Zt([o,s,f,h]),Ht(),t.emit(\"plotly_relayouting\",ht)}function p(t,e,r){for(var n,i,a=1-e,o=0;o<t.length;o++){var s=t[o];if(!s.fixedrange){n=s,i=s._rl[a]+(s._rl[e]-s._rl[a])/B(r/s._length);var l=s.l2r(i);!1!==l&&void 0!==l&&(s.range[e]=l)}}return n._length*(n._rl[e]-i)/(n._rl[e]-n._rl[a])}}function Vt(t,e){for(var r=nt.isSubplotConstrained?{x:K,y:J}[t]:nt[t+\"axes\"],n=nt.isSubplotConstrained?{x:J,y:K}[t]:[],i=0;i<r.length;i++){var a=r[i],o=a._id,s=nt.xLinks[o]||nt.yLinks[o],l=n[0]||V[s]||X[s];l&&(e?(e[a._name+\".range[0]\"]=e[l._name+\".range[0]\"],e[a._name+\".range[1]\"]=e[l._name+\".range[1]\"]):a.range=l.range.slice())}}function Ht(){var r,n=[];function i(t){for(r=0;r<t.length;r++)t[r].fixedrange||n.push(t[r]._id)}function a(t,e){for(r=0;r<t.length;r++){var i=t[r],a=i[e];i.fixedrange||\"sync\"!==a.tickmode||n.push(a._id)}}for(st&&(i(J),i(rt.xaxes),i(nt.xaxes),a(e.overlays,\"xaxis\")),lt&&(i(K),i(rt.yaxes),i(nt.yaxes),a(e.overlays,\"yaxis\")),ht={},r=0;r<n.length;r++){var o=n[r],s=k(t,o);d.drawOne(t,s,{skipTitle:!0}),ht[s._name+\".range[0]\"]=s.range[0],ht[s._name+\".range[1]\"]=s.range[1]}d.redrawComponents(t,n)}function qt(){if(!t._transitioningWithDuration){var e=t._context.doubleClick,r=[];it&&(r=r.concat(J)),at&&(r=r.concat(K)),nt.xaxes&&(r=r.concat(nt.xaxes)),nt.yaxes&&(r=r.concat(nt.yaxes));var n,i,a={};if(\"reset+autosize\"===e)for(e=\"autosize\",i=0;i<r.length;i++){var o=(n=r[i])._rangeInitial0,s=n._rangeInitial1,u=void 0!==o||void 0!==s;if(u&&(void 0!==o&&o!==n.range[0]||void 0!==s&&s!==n.range[1])||!u&&!0!==n.autorange){e=\"reset\";break}}if(\"autosize\"===e)for(i=0;i<r.length;i++)(n=r[i]).fixedrange||(a[n._name+\".autorange\"]=!0);else if(\"reset\"===e)for((it||rt.isSubplotConstrained)&&(r=r.concat(rt.xaxes)),at&&!rt.isSubplotConstrained&&(r=r.concat(rt.yaxes)),rt.isSubplotConstrained&&(it?at||(r=r.concat(K)):r=r.concat(J)),i=0;i<r.length;i++)if(!(n=r[i]).fixedrange){var c=n._name,f=n._autorangeInitial;void 0===n._rangeInitial0&&void 0===n._rangeInitial1?a[c+\".autorange\"]=!0:void 0===n._rangeInitial0?(a[c+\".autorange\"]=f,a[c+\".range\"]=[null,n._rangeInitial1]):void 0===n._rangeInitial1?(a[c+\".range\"]=[n._rangeInitial0,null],a[c+\".autorange\"]=f):a[c+\".range\"]=[n._rangeInitial0,n._rangeInitial1]}t.emit(\"plotly_doubleclick\",null),l.call(\"_guiRelayout\",t,a)}}function Gt(){Zt([0,0,tt,et]),i.syncOrAsync([T.previousPromises,function(){t._fullLayout._replotting=!1,l.call(\"_guiRelayout\",t,ht)}],t)}function Zt(e){var r,n,a,o,s=t._fullLayout,u=s._plots,c=s._subplots.cartesian;if(ct&&l.subplotsRegistry.splom.drag(t),ut)for(r=0;r<c.length;r++)if(a=(n=u[c[r]]).xaxis,o=n.yaxis,n._scene){var f=i.simpleMap(a.range,a.r2l),p=i.simpleMap(o.range,o.r2l);a.limitRange&&a.limitRange(),o.limitRange&&o.limitRange(),f=a.range,p=o.range,n._scene.update({range:[f[0],p[0],f[1],p[1]]})}if((ct||ut)&&(_(t),w(t)),ft){var d=e[2]/O._length,g=e[3]/I._length;for(r=0;r<c.length;r++){a=(n=u[c[r]]).xaxis,o=n.yaxis;var m,x,b,T,k=(st||nt.isSubplotConstrained)&&!a.fixedrange&&V[a._id],A=(lt||nt.isSubplotConstrained)&&!o.fixedrange&&X[o._id];if(k?(m=d,b=y||nt.isSubplotConstrained?e[0]:Xt(a,m)):nt.xaHash[a._id]?(m=d,b=e[0]*a._length/O._length):nt.yaHash[a._id]?(m=g,b=\"ns\"===at?-e[1]*a._length/I._length:Xt(a,m,{n:\"top\",s:\"bottom\"}[at])):b=Wt(a,m=Yt(a,d,g)),m>1&&(void 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r=function(t,e){return[t[1]*e[2]-t[2]*e[1],t[2]*e[0]-t[0]*e[2],t[0]*e[1]-t[1]*e[0]]}(t,e),n=Math.sqrt(x(r,r)),i=.5*Math.acos(Math.max(-1,Math.min(1,x(t,e)))),a=Math.sin(i)/n;return n&&[Math.cos(i),r[2]*a,-r[1]*a,r[0]*a]}}(r,_),T=function(t){return[Math.atan2(2*(t[0]*t[1]+t[2]*t[3]),1-2*(t[1]*t[1]+t[2]*t[2]))*s,Math.asin(Math.max(-1,Math.min(1,2*(t[0]*t[2]-t[3]*t[1]))))*s,Math.atan2(2*(t[0]*t[3]+t[1]*t[2]),1-2*(t[2]*t[2]+t[3]*t[3]))*s]}((o=(t=C)[0],l=t[1],u=t[2],c=t[3],[o*(f=(a=w)[0])-l*(p=a[1])-u*(d=a[2])-c*(y=a[3]),o*p+l*f+u*y-c*d,o*d-l*y+u*f+c*p,o*y+l*d-u*p+c*f])),k=i.r=function(t,e,r){var n=m(e,2,t[0]);n=m(n,1,t[1]),n=m(n,0,t[2]-r[2]);var i,a,o=e[0],l=e[1],u=e[2],c=n[0],f=n[1],h=n[2],p=Math.atan2(l,o)*s,d=Math.sqrt(o*o+l*l);Math.abs(f)>d?(a=(f>0?90:-90)-p,i=0):(a=Math.asin(f/d)*s-p,i=Math.sqrt(d*d-f*f));var v=180-a-2*p,y=(Math.atan2(h,c)-Math.atan2(u,i))*s,x=(Math.atan2(h,c)-Math.atan2(u,-i))*s;return g(r[0],r[1],a,y)<=g(r[0],r[1],v,x)?[a,y,r[2]]:[v,x,r[2]]}(T,r,E);isFinite(k[0])&&isFinite(k[1])&&isFinite(k[2])||(k=E),e.rotate(k),E=k}}else r=v(e,M=b);h.of(this,arguments)({type:\"zoom\"})})),A=h.of(this,arguments),p++||A({type:\"zoomstart\"})})).on(\"zoomend\",(function(){var r;n.select(this).style(u),d.call(a,\"zoom\",null),r=h.of(this,arguments),--p||r({type:\"zoomend\"}),f(t,e,y)})).on(\"zoom.redraw\",(function(){t.render(!0);var r=e.rotate();t.graphDiv.emit(\"plotly_relayouting\",{\"geo.projection.scale\":e.scale()/t.fitScale,\"geo.projection.rotation.lon\":-r[0],\"geo.projection.rotation.lat\":-r[1]})})),n.rebind(a,h,\"on\")}function v(t,e){var r=t.invert(e);return r&&isFinite(r[0])&&isFinite(r[1])&&function(t){var e=t[0]*o,r=t[1]*o,n=Math.cos(r);return[n*Math.cos(e),n*Math.sin(e),Math.sin(r)]}(r)}function g(t,e,r,n){var i=y(r-t),a=y(n-e);return Math.sqrt(i*i+a*a)}function y(t){return(t%360+540)%360-180}function m(t,e,r){var n=r*o,i=t.slice(),a=0===e?1:0,s=2===e?1:2,l=Math.cos(n),u=Math.sin(n);return i[a]=t[a]*l-t[s]*u,i[s]=t[s]*l+t[a]*u,i}function x(t,e){for(var r=0,n=0,i=t.length;n<i;++n)r+=t[n]*e[n];return r}t.exports=function(t,e){var r=t.projection;return(e._isScoped?h:e._isClipped?d:p)(t,r)}},27659:function(t,e,r){\"use strict\";var n=r(73972),i=r(85555).SUBPLOT_PATTERN;e.AU=function(t,e,r){var i=n.subplotsRegistry[e];if(!i)return[];for(var a=i.attr,o=[],s=0;s<t.length;s++){var l=t[s];l[0].trace[a]===r&&o.push(l)}return o},e.a0=function(t,e){var r,i=[],a=[];if(!(r=\"string\"==typeof e?n.getModule(e).plot:\"function\"==typeof e?e:e.plot))return[i,t];for(var o=0;o<t.length;o++){var s=t[o],l=s[0].trace;!0===l.visible&&0!==l._length&&(l._module&&l._module.plot===r?i.push(s):a.push(s))}return[i,a]},e.NG=function(t,e,r){if(!n.subplotsRegistry[e])return[];var a,o,s,l=n.subplotsRegistry[e].attr,u=[];if(\"gl2d\"===e){var c=r.match(i);o=\"x\"+c[1],s=\"y\"+c[2]}for(var f=0;f<t.length;f++)a=t[f],\"gl2d\"===e&&n.traceIs(a,\"gl2d\")?a[l[0]]===o&&a[l[1]]===s&&u.push(a):a[l]===r&&u.push(a);return u}},75071:function(t,e,r){\"use strict\";var n=r(16825),i=r(1195),a=r(48956),o=r(85555),s=r(38520);function l(t,e){this.element=t,this.plot=e,this.mouseListener=null,this.wheelListener=null,this.lastInputTime=Date.now(),this.lastPos=[0,0],this.boxEnabled=!1,this.boxInited=!1,this.boxStart=[0,0],this.boxEnd=[0,0],this.dragStart=[0,0]}t.exports=function(t){var e=t.mouseContainer,r=t.glplot,u=new l(e,r);function c(){t.xaxis.autorange=!1,t.yaxis.autorange=!1}function f(e,n,i){var a,s,l=t.calcDataBox(),f=r.viewBox,h=u.lastPos[0],p=u.lastPos[1],d=o.MINDRAG*r.pixelRatio,v=o.MINZOOM*r.pixelRatio;function g(e,r,n){var i=Math.min(r,n),a=Math.max(r,n);i!==a?(l[e]=i,l[e+2]=a,u.dataBox=l,t.setRanges(l)):(t.selectBox.selectBox=[0,0,1,1],t.glplot.setDirty())}switch(n*=r.pixelRatio,i*=r.pixelRatio,i=f[3]-f[1]-i,t.fullLayout.dragmode){case\"zoom\":if(e){var y=n/(f[2]-f[0])*(l[2]-l[0])+l[0],m=i/(f[3]-f[1])*(l[3]-l[1])+l[1];u.boxInited||(u.boxStart[0]=y,u.boxStart[1]=m,u.dragStart[0]=n,u.dragStart[1]=i),u.boxEnd[0]=y,u.boxEnd[1]=m,u.boxInited=!0,u.boxEnabled||u.boxStart[0]===u.boxEnd[0]&&u.boxStart[1]===u.boxEnd[1]||(u.boxEnabled=!0);var x=Math.abs(u.dragStart[0]-n)<v,b=Math.abs(u.dragStart[1]-i)<v;if(!function(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,i=0;i<e.length;i++)if(-1!==e[i][r]){if(-1!==e[i][n])return!0;break}return!1}()||x&&b)x&&(u.boxEnd[0]=u.boxStart[0]),b&&(u.boxEnd[1]=u.boxStart[1]);else{a=u.boxEnd[0]-u.boxStart[0],s=u.boxEnd[1]-u.boxStart[1];var _=(l[3]-l[1])/(l[2]-l[0]);Math.abs(a*_)>Math.abs(s)?(u.boxEnd[1]=u.boxStart[1]+Math.abs(a)*_*(s>=0?1:-1),u.boxEnd[1]<l[1]?(u.boxEnd[1]=l[1],u.boxEnd[0]=u.boxStart[0]+(l[1]-u.boxStart[1])/Math.abs(_)):u.boxEnd[1]>l[3]&&(u.boxEnd[1]=l[3],u.boxEnd[0]=u.boxStart[0]+(l[3]-u.boxStart[1])/Math.abs(_))):(u.boxEnd[0]=u.boxStart[0]+Math.abs(s)/_*(a>=0?1:-1),u.boxEnd[0]<l[0]?(u.boxEnd[0]=l[0],u.boxEnd[1]=u.boxStart[1]+(l[0]-u.boxStart[0])*Math.abs(_)):u.boxEnd[0]>l[2]&&(u.boxEnd[0]=l[2],u.boxEnd[1]=u.boxStart[1]+(l[2]-u.boxStart[0])*Math.abs(_)))}}else u.boxEnabled?(a=u.boxStart[0]!==u.boxEnd[0],s=u.boxStart[1]!==u.boxEnd[1],a||s?(a&&(g(0,u.boxStart[0],u.boxEnd[0]),t.xaxis.autorange=!1),s&&(g(1,u.boxStart[1],u.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),u.boxEnabled=!1,u.boxInited=!1):u.boxInited&&(u.boxInited=!1);break;case\"pan\":u.boxEnabled=!1,u.boxInited=!1,e?(u.panning||(u.dragStart[0]=n,u.dragStart[1]=i),Math.abs(u.dragStart[0]-n)<d&&(n=u.dragStart[0]),Math.abs(u.dragStart[1]-i)<d&&(i=u.dragStart[1]),a=(h-n)*(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)*(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),u.panning=!0,u.lastInputTime=Date.now(),c(),t.cameraChanged(),t.handleAnnotations()):u.panning&&(u.panning=!1,t.relayoutCallback())}u.lastPos[0]=n,u.lastPos[1]=i}return u.mouseListener=n(e,f),e.addEventListener(\"touchstart\",(function(t){var r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchmove\",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchend\",(function(t){f(0,u.lastPos[0],u.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),u.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=u.lastPos[0],s=u.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),u.lastInputTime=Date.now(),c(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),u}},82961:function(t,e,r){\"use strict\";var n=r(89298),i=r(78614);function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=[\"xaxis\",\"yaxis\"];o.merge=function(t){var e,r,n,a,o,l,u,c,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?\"\":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]=\"auto\"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;u=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!u,l=this.hasAxisInAltrPos(e,r)&&!u,a=r.mirror||!1,c=u?-1!==String(a).indexOf(\"all\"):!!a,f=u?\"allticks\"===a:-1!==String(a).indexOf(\"ticks\"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||c)&&(this.borderLineEnable[h]=r.showline),(l||c)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},o.getLabelPad=function(t,e){var r=1.5,n=e.title.font.size,i=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?n*(r+(i?1:0))-10:n*(r+(i?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+n*(r+(i?1:.5)):10+n*(r+(i?.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},t.exports=function(t){return new a(t)}},4796:function(t,e,r){\"use strict\";var n=r(30962).overrideAll,i=r(92918),a=r(10820),o=r(77922),s=r(85555),l=r(93612),u=r(528),c=r(27659).NG;e.name=\"gl2d\",e.attr=[\"xaxis\",\"yaxis\"],e.idRoot=[\"x\",\"y\"],e.idRegex=s.idRegex,e.attrRegex=s.attrRegex,e.attributes=r(89502),e.supplyLayoutDefaults=function(t,e,r){e._has(\"cartesian\")||l.supplyLayoutDefaults(t,e,r)},e.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),e.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:u.hoverlabel},\"plot\",\"nested\"),e.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=c(r,\"gl2d\",o),u=s._scene2d;void 0===u&&(u=new i({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=u),u.plot(l,t.calcdata,e,t.layout)}},e.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];s._scene2d&&0===c(t,\"gl2d\",o).length&&(s._scene2d.destroy(),delete n._plots[o])}l.clean.apply(this,arguments)},e.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},e.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":a,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),i.destroy()}},e.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)}},92918:function(t,e,r){\"use strict\";var n,i,a=r(73972),o=r(89298),s=r(30211),l=r(9330).gl_plot2d,u=r(9330).gl_spikes2d,c=r(9330).gl_select_box,f=r(40372),h=r(82961),p=r(75071),d=r(58617),v=r(99082),g=v.enforce,y=v.clean,m=r(71739).doAutoRange,x=r(64505),b=x.drawMode,_=x.selectMode,w=[\"xaxis\",\"yaxis\"],T=r(85555).SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=u(this.glplot),this.selectBox=c(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())}t.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement(\"canvas\"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(\".gl-canvas-focus\"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r);var a=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\";var o=this.mouseContainer=document.createElement(\"div\");o.style.position=\"absolute\",o.style[\"pointer-events\"]=\"auto\",this.pickCanvas=this.container.querySelector(\".gl-canvas-pick\");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener(\"mouseout\",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener(\"mouseover\",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var u=0;u<4;++u){var c=a[4*(r*o+l)+u];a[4*(r*o+l)+u]=a[4*(r*s+l)+u],a[4*(r*s+l)+u]=c}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\",{willReadFrequently:!0}),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+\"\";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r=\"xaxis\"+e[1],n=\"yaxis\"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+\".range\"]=e.range.slice(),s=i[r._name+\".range\"]=r.range.slice();i[e._name+\".autorange\"]=e.autorange,i[r._name+\".autorange\"]=r.autorange,a.call(\"_storeDirectGUIEdit\",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var u=n[r._name];u.range=s,u.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit(\"plotly_relayout\",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};y(s,this.xaxis),y(s,this.yaxis);var l,u,c=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[c.l+f[0]*c.w,c.b+h[0]*c.h,i-c.r-(1-f[1])*c.w,a-c.t-(1-h[1])*c.h],this.mouseContainer.style.width=c.w*(f[1]-f[0])+\"px\",this.mouseContainer.style.height=c.h*(h[1]-h[0])+\"px\",this.mouseContainer.height=c.h*(h[1]-h[0]),this.mouseContainer.style.left=c.l+f[0]*c.w+\"px\",this.mouseContainer.style.top=c.t+(1-h[1])*c.h+\"px\",u=0;u<2;++u)(l=this[w[u]])._length=o.viewBox[u+2]-o.viewBox[u],m(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],u=this.traces[i.uid];u?u.update(i,l):(u=i._module.plot(this,i,l),this.traces[i.uid]=u)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style[\"pointer-events\"]=\"none\",this.mouseContainer.style[\"pointer-events\"]=\"none\"):(this.pickCanvas.style[\"pointer-events\"]=\"auto\",this.mouseContainer.style[\"pointer-events\"]=\"auto\"),this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var u=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])],c=0;c<2;c++)e.boxStart[c]===e.boxEnd[c]&&(u[c]=t.dataBox[c],u[c+2]=t.dataBox[c+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var v=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),v.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var g=this.fullData[v.trace.index]||{},y=v.pointIndex,m=s.castHoverinfo(g,i,y);if(m&&\"all\"!==m){var x=m.split(\"+\");-1===x.indexOf(\"x\")&&(v.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(v.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(v.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(v.textLabel=void 0),-1===x.indexOf(\"name\")&&(v.name=void 0)}s.loneHover({x:v.screenCoord[0],y:v.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",v.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",v.traceCoord[1]),zLabel:v.traceCoord[2],text:v.textLabel,name:v.name,color:s.castHoverOption(g,y,\"bgcolor\")||v.color,borderColor:s.castHoverOption(g,y,\"bordercolor\"),fontFamily:s.castHoverOption(g,y,\"font.family\"),fontSize:s.castHoverOption(g,y,\"font.size\"),fontColor:s.castHoverOption(g,y,\"font.color\"),nameLength:s.castHoverOption(g,y,\"namelength\"),textAlign:s.castHoverOption(g,y,\"align\")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},58547:function(t,e,r){\"use strict\";var n=r(30962).overrideAll,i=r(528),a=r(33539),o=r(27659).NG,s=r(71828),l=r(77922),u=\"gl3d\",c=\"scene\";e.name=u,e.attr=c,e.idRoot=c,e.idRegex=e.attrRegex=s.counterRegex(\"scene\"),e.attributes=r(59084),e.layoutAttributes=r(65500),e.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),e.supplyLayoutDefaults=r(24682),e.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots[u],i=0;i<n.length;i++){var s=n[i],l=o(r,u,s),c=e[s],f=c.camera,h=c._scene;h||(h=new a({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:f},e),c._scene=h),h.viewInitial||(h.viewInitial={up:{x:f.up.x,y:f.up.y,z:f.up.z},eye:{x:f.eye.x,y:f.eye.y,z:f.eye.z},center:{x:f.center.x,y:f.center.y,z:f.center.z}}),h.plot(l,e,t.layout)}},e.clean=function(t,e,r,n){for(var i=n._subplots[u]||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}},e.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots[u],n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}},e.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),c+e}},e.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots[u],n=0;n<r.length;n++)e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}},59084:function(t){\"use strict\";t.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},77894:function(t,e,r){\"use strict\";var n=r(7901),i=r(13838),a=r(1426).extendFlat,o=r(30962).overrideAll;t.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,autorangeoptions:{minallowed:i.autorangeoptions.minallowed,maxallowed:i.autorangeoptions.maxallowed,clipmin:i.autorangeoptions.clipmin,clipmax:i.autorangeoptions.clipmax,include:i.autorangeoptions.include,editType:\"plot\"},rangemode:i.rangemode,minallowed:i.minallowed,maxallowed:i.maxallowed,range:a({},i.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:i.minor.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,labelalias:i.labelalias,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},\"plot\",\"from-root\")},3277:function(t,e,r){\"use strict\";var n=r(84267).mix,i=r(71828),a=r(44467),o=r(77894),s=r(951),l=r(71453),u=[\"xaxis\",\"yaxis\",\"zaxis\"];t.exports=function(t,e,r){var c,f;function h(t,e){return i.coerce(c,f,o,t,e)}for(var p=0;p<u.length;p++){var d=u[p];c=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(c,f,h,r),l(c,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,noInsiderange:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h(\"gridcolor\",n(f.color,r.bgColor,72.72727272727273).toRgbString()),h(\"title.text\",d[0]),f.setScale=i.noop,h(\"showspikes\")&&(h(\"spikesides\"),h(\"spikethickness\"),h(\"spikecolor\",f.color)),h(\"showaxeslabels\"),h(\"showbackground\")&&h(\"backgroundcolor\")}}},30422:function(t,e,r){\"use strict\";var n=r(78614),i=r(71828),a=[\"xaxis\",\"yaxis\",\"zaxis\"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=this,o=0;o<3;++o){var s=e[a[o]];s.visible?(r.labels[o]=t._meta?i.templateString(s.title.text,t._meta):s.title.text,\"font\"in s.title&&(s.title.font.color&&(r.labelColor[o]=n(s.title.font.color)),s.title.font.family&&(r.labelFont[o]=s.title.font.family),s.title.font.size&&(r.labelSize[o]=s.title.font.size)),\"showline\"in s&&(r.lineEnable[o]=s.showline),\"linecolor\"in s&&(r.lineColor[o]=n(s.linecolor)),\"linewidth\"in s&&(r.lineWidth[o]=s.linewidth),\"showgrid\"in s&&(r.gridEnable[o]=s.showgrid),\"gridcolor\"in s&&(r.gridColor[o]=n(s.gridcolor)),\"gridwidth\"in s&&(r.gridWidth[o]=s.gridwidth),\"log\"===s.type?r.zeroEnable[o]=!1:\"zeroline\"in s&&(r.zeroEnable[o]=s.zeroline),\"zerolinecolor\"in s&&(r.zeroLineColor[o]=n(s.zerolinecolor)),\"zerolinewidth\"in s&&(r.zeroLineWidth[o]=s.zerolinewidth),\"ticks\"in s&&s.ticks?r.lineTickEnable[o]=!0:r.lineTickEnable[o]=!1,\"ticklen\"in s&&(r.lineTickLength[o]=r._defaultLineTickLength[o]=s.ticklen),\"tickcolor\"in s&&(r.lineTickColor[o]=n(s.tickcolor)),\"tickwidth\"in s&&(r.lineTickWidth[o]=s.tickwidth),\"tickangle\"in s&&(r.tickAngle[o]=\"auto\"===s.tickangle?-3600:Math.PI*-s.tickangle/180),\"showticklabels\"in s&&(r.tickEnable[o]=s.showticklabels),\"tickfont\"in s&&(s.tickfont.color&&(r.tickColor[o]=n(s.tickfont.color)),s.tickfont.family&&(r.tickFont[o]=s.tickfont.family),s.tickfont.size&&(r.tickSize[o]=s.tickfont.size)),\"mirror\"in s?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(s.mirror)?(r.lineTickMirror[o]=!0,r.lineMirror[o]=!0):!0===s.mirror?(r.lineTickMirror[o]=!1,r.lineMirror[o]=!0):(r.lineTickMirror[o]=!1,r.lineMirror[o]=!1):r.lineMirror[o]=!1,\"showbackground\"in s&&!1!==s.showbackground?(r.backgroundEnable[o]=!0,r.backgroundColor[o]=n(s.backgroundcolor)):r.backgroundEnable[o]=!1):(r.tickEnable[o]=!1,r.labelEnable[o]=!1,r.lineEnable[o]=!1,r.lineTickEnable[o]=!1,r.gridEnable[o]=!1,r.zeroEnable[o]=!1,r.backgroundEnable[o]=!1)}},t.exports=function(t,e){var r=new o;return r.merge(t,e),r}},24682:function(t,e,r){\"use strict\";var n=r(71828),i=r(7901),a=r(73972),o=r(49119),s=r(3277),l=r(65500),u=r(27659).NG,c=\"gl3d\";function f(t,e,r,n){for(var o=r(\"bgcolor\"),l=i.combine(o,n.paper_bgcolor),f=[\"up\",\"center\",\"eye\"],h=0;h<f.length;h++)r(\"camera.\"+f[h]+\".x\"),r(\"camera.\"+f[h]+\".y\"),r(\"camera.\"+f[h]+\".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 v=u(n.fullData,c,n.id);s(t,e,{font:n.font,scene:n.id,data:v,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n);var g=n.getDfltFromLayout(\"dragmode\");if(!1!==g&&!g)if(g=\"orbit\",t.camera&&t.camera.up){var y=t.camera.up.x,m=t.camera.up.y,x=t.camera.up.z;0!==x&&(y&&m&&x?x/Math.sqrt(y*y+m*m+x*x)>.999&&(g=\"turntable\"):g=\"turntable\")}else g=\"turntable\";r(\"dragmode\",g),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}t.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:c,attributes:l,handleDefaults:f,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},65500:function(t,e,r){\"use strict\";var n=r(77894),i=r(27670).Y,a=r(1426).extendFlat,o=r(71828).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\"}}t.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},13133:function(t,e,r){\"use strict\";var n=r(78614),i=[\"xaxis\",\"yaxis\",\"zaxis\"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},t.exports=function(t){var e=new a;return e.merge(t),e}},96085:function(t,e,r){\"use strict\";t.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],u=0;u<3;++u){var c=s[a[u]];if(c._length=(r[u].hi-r[u].lo)*r[u].pixelsPerDataUnit/t.dataScale[u],Math.abs(c._length)===1/0||isNaN(c._length))l[u]=[];else{c._input_range=c.range.slice(),c.range[0]=r[u].lo/t.dataScale[u],c.range[1]=r[u].hi/t.dataScale[u],c._m=1/(t.dataScale[u]*r[u].pixelsPerDataUnit),c.range[0]===c.range[1]&&(c.range[0]-=1,c.range[1]+=1);var f=c.tickmode;if(\"auto\"===c.tickmode){c.tickmode=\"linear\";var h=c.nticks||i.constrain(c._length/40,4,9);n.autoTicks(c,Math.abs(c.range[1]-c.range[0])/h)}for(var p=n.calcTicks(c,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[u],\"date\"===c.type&&(p[d].text=p[d].text.replace(/\\<br\\>/g,\" \"));l[u]=p,c.tickmode=f}}for(e.ticks=l,u=0;u<3;++u)for(o[u]=.5*(t.glplot.bounds[0][u]+t.glplot.bounds[1][u]),d=0;d<2;++d)e.bounds[d][u]=t.glplot.bounds[d][u];t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=r(89298),i=r(71828),a=[\"xaxis\",\"yaxis\",\"zaxis\"],o=[0,0,0]},63538:function(t){\"use strict\";function e(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}t.exports=function(t,r){return e(t.projection,e(t.view,e(t.model,[r[0],r[1],r[2],1])))}},33539:function(t,e,r){\"use strict\";var n,i,a=r(9330).gl_plot3d,o=a.createCamera,s=a.createScene,l=r(40372),u=r(38520),c=r(73972),f=r(71828),h=f.preserveDrawingBuffer(),p=r(89298),d=r(30211),v=r(78614),g=r(58617),y=r(63538),m=r(30422),x=r(13133),b=r(96085),_=r(71739).applyAutorangeOptions,w=!1;function T(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=m(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=c.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=c.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var k=T.prototype;k.prepareOptions=function(){var t=this,e={canvas:t.canvas,gl:t.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:t.camera,pixelRatio:t.pixelRatio};if(t.staticMode){if(!(i||(n=document.createElement(\"canvas\"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");e.gl=i,e.canvas=n}return e};var A=!0;k.tryCreatePlot=function(){var t=this,e=t.prepareOptions(),r=!0;try{t.glplot=s(e)}catch(n){if(t.staticMode||!A||h)r=!1;else{f.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{h=e.glOptions.preserveDrawingBuffer=!0,t.glplot=s(e)}catch(t){h=e.glOptions.preserveDrawingBuffer=!1,r=!1}}}return A=!1,r},k.initializeGLCamera=function(){var t=this,e=t.fullSceneLayout.camera,r=\"orthographic\"===e.projection.type;t.camera=o(t.container,{center:[e.center.x,e.center.y,e.center.z],eye:[e.eye.x,e.eye.y,e.eye.z],up:[e.up.x,e.up.y,e.up.z],_ortho:r,zoomMin:.01,zoomMax:100,mode:\"orbit\"})},k.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+\".camera\"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+\".aspectratio\"]=t.glplot.getAspectratio(),\"manual\"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+\".aspectmode\"]=\"manual\")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit(\"plotly_relayout\",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener(\"mouseup\",(function(){i(t)})),t.glplot.canvas.addEventListener(\"touchstart\",(function(){w=!0})),t.glplot.canvas.addEventListener(\"wheel\",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!u&&{passive:!1}),t.glplot.canvas.addEventListener(\"mousemove\",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit(\"plotly_relayouting\",e)}})),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",(function(r){e&&e.emit&&e.emit(\"plotly_webglcontextlost\",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},k.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),b(e),e.glplot.axes.update(e.axesOptions);for(var u=Object.keys(e.traces),c=null,h=e.glplot.selection,v=0;v<u.length;++v)\"skip\"!==(t=e.traces[u[v]]).data.hoverinfo&&t.handlePick(h)&&(c=t),t.setContourLevels&&t.setContourLevels();function g(t,r,n){var i=e.fullSceneLayout[t+\"axis\"];return\"log\"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==c){var m=y(e.glplot.cameraParams,h.dataCoordinate);t=c.data;var x,_=r._fullData[t.index],T=h.index,k={xLabel:g(\"x\",h.traceCoordinate[0],t.xhoverformat),yLabel:g(\"y\",h.traceCoordinate[1],t.yhoverformat),zLabel:g(\"z\",h.traceCoordinate[2],t.zhoverformat)},A=d.castHoverinfo(_,e.fullLayout,T),M=(A||\"\").split(\"+\"),S=A&&\"all\"===A;_.hovertemplate||S||(-1===M.indexOf(\"x\")&&(k.xLabel=void 0),-1===M.indexOf(\"y\")&&(k.yLabel=void 0),-1===M.indexOf(\"z\")&&(k.zLabel=void 0),-1===M.indexOf(\"text\")&&(h.textLabel=void 0),-1===M.indexOf(\"name\")&&(c.name=void 0));var E=[];\"cone\"===t.type||\"streamtube\"===t.type?(k.uLabel=g(\"x\",h.traceCoordinate[3],t.uhoverformat),(S||-1!==M.indexOf(\"u\"))&&E.push(\"u: \"+k.uLabel),k.vLabel=g(\"y\",h.traceCoordinate[4],t.vhoverformat),(S||-1!==M.indexOf(\"v\"))&&E.push(\"v: \"+k.vLabel),k.wLabel=g(\"z\",h.traceCoordinate[5],t.whoverformat),(S||-1!==M.indexOf(\"w\"))&&E.push(\"w: \"+k.wLabel),k.normLabel=h.traceCoordinate[6].toPrecision(3),(S||-1!==M.indexOf(\"norm\"))&&E.push(\"norm: \"+k.normLabel),\"streamtube\"===t.type&&(k.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(S||-1!==M.indexOf(\"divergence\"))&&E.push(\"divergence: \"+k.divergenceLabel)),h.textLabel&&E.push(h.textLabel),x=E.join(\"<br>\")):\"isosurface\"===t.type||\"volume\"===t.type?(k.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),E.push(\"value: \"+k.valueLabel),h.textLabel&&E.push(h.textLabel),x=E.join(\"<br>\")):x=h.textLabel;var L={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:T};d.appendArrayPointValue(L,_,T),t._module.eventData&&(L=_._module.eventData(L,h,_,{},T));var C={points:[L]};if(e.fullSceneLayout.hovermode){var P=[];d.loneHover({trace:_,x:(.5+.5*m[0]/m[3])*s,y:(.5-.5*m[1]/m[3])*l,xLabel:k.xLabel,yLabel:k.yLabel,zLabel:k.zLabel,text:x,name:c.name,color:d.castHoverOption(_,T,\"bgcolor\")||c.color,borderColor:d.castHoverOption(_,T,\"bordercolor\"),fontFamily:d.castHoverOption(_,T,\"font.family\"),fontSize:d.castHoverOption(_,T,\"font.size\"),fontColor:d.castHoverOption(_,T,\"font.color\"),nameLength:d.castHoverOption(_,T,\"namelength\"),textAlign:d.castHoverOption(_,T,\"align\"),hovertemplate:f.castOption(_,T,\"hovertemplate\"),hovertemplateLabels:f.extendFlat({},L,k),eventData:[L]},{container:n,gd:r,inOut_bbox:P}),L.bbox=P[0]}h.distance<5&&(h.buttons||w)?r.emit(\"plotly_click\",C):r.emit(\"plotly_hover\",C),this.oldEventData=C}else d.loneUnhover(n),this.oldEventData&&r.emit(\"plotly_unhover\",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},k.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")};requestAnimationFrame(e)};var M=[\"xaxis\",\"yaxis\",\"zaxis\"];function S(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=M[i],o=a.charAt(0),s=n[a],l=e[o],u=e[o+\"calendar\"],c=e[\"_\"+o+\"length\"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(c||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,u),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,u),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],c-1)}}k.plot=function(t,e,r){var n=this;if(n.plotArgs=[t,e,r],!n.glplot.contextLost){var i,a,o,s,l,u,c=e[n.id],f=r[n.id];n.fullLayout=e,n.fullSceneLayout=c,n.axesOptions.merge(e,c),n.spikeOptions.merge(c),n.setViewport(c),n.updateFx(c.dragmode,c.hovermode),n.camera.enableWheel=n.graphDiv._context._scrollZoom.gl3d,n.glplot.setClearColor(v(c.bgcolor)),n.setConvert(l),t?Array.isArray(t)||(t=[t]):t=[];var h=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(o=0;o<t.length;++o)!0===(i=t[o]).visible&&0!==i._length&&S(this,i,h);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=M[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var u=n[l];if(u.visible){var c=s.r2l(u[o]);!isNaN(c)&&isFinite(c)&&(e[0][i]=Math.min(e[0][i],c),e[1][i]=Math.max(e[1][i],c))}}}(this,h);var p=[1,1,1];for(s=0;s<3;++s)h[1][s]===h[0][s]?p[s]=1:p[s]=1/(h[1][s]-h[0][s]);for(n.dataScale=p,n.convertAnnotations(this),o=0;o<t.length;++o)!0===(i=t[o]).visible&&0!==i._length&&((a=n.traces[i.uid])?a.data.type===i.type?a.update(i):(a.dispose(),a=i._module.plot(this,i),n.traces[i.uid]=a):(a=i._module.plot(this,i),n.traces[i.uid]=a),a.name=i.name);var d=Object.keys(n.traces);t:for(o=0;o<d.length;++o){for(s=0;s<t.length;++s)if(t[s].uid===d[o]&&!0===t[s].visible&&0!==t[s]._length)continue t;(a=n.traces[d[o]]).dispose(),delete n.traces[d[o]]}n.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var g,y=[[0,0,0],[0,0,0]],m=[],x={};for(o=0;o<3;++o){var b;if((u=(l=c[M[o]]).type)in x?(x[u].acc*=p[o],x[u].count+=1):x[u]={acc:p[o],count:1},l.autorange){y[0][o]=1/0,y[1][o]=-1/0;var w=n.glplot.objects,T=n.fullSceneLayout.annotations||[],k=l._name.charAt(0);for(s=0;s<w.length;s++){var 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n=r(7901),i=r(52075).hasColorscale,a=r(1586),o=r(71828).coercePattern;t.exports=function(t,e,r,s,l){var u=r(\"marker.color\",s),c=i(t,\"marker\");c&&a(t,e,l,r,{prefix:\"marker.\",cLetter:\"c\"}),r(\"marker.line.color\",n.defaultLine),i(t,\"marker.line\")&&a(t,e,l,r,{prefix:\"marker.line.\",cLetter:\"c\"}),r(\"marker.line.width\"),r(\"marker.opacity\"),o(r,\"marker.pattern\",u,c),r(\"selected.marker.color\"),r(\"unselected.marker.color\")}},72597:function(t,e,r){\"use strict\";var n=r(39898),i=r(71828);function a(t){return\"_\"+t+\"Text_minsize\"}t.exports={recordMinTextSize:function(t,e,r){if(r.uniformtext.mode){var n=a(t),i=r.uniformtext.minsize,o=e.scale*e.fontSize;e.hide=o<i,r[n]=r[n]||1/0,e.hide||(r[n]=Math.min(r[n],Math.max(o,i)))}},clearMinTextSize:function(t,e){e[a(t)]=void 0},resizeText:function(t,e,r){var a=t._fullLayout,o=a[\"_\"+r+\"Text_minsize\"];if(o){var 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r=t._fullLayout,o=e.subplot,l=r[o].radialaxis,u=r[o].angularaxis,c=l.makeCalcdata(e,\"r\"),f=u.makeCalcdata(e,\"theta\"),h=e._length,p=new Array(h),d=c,v=f,g=0;g<h;g++)p[g]={p:v[g],s:d[g]};function y(t){var r=e[t];void 0!==r&&(e[\"_\"+t]=Array.isArray(r)?u.makeCalcdata(e,t):u.d2c(r,e.thetaunit))}return\"linear\"===u.type&&(y(\"width\"),y(\"offset\")),n(e,\"marker\")&&i(t,e,{vals:e.marker.color,containerStr:\"marker\",cLetter:\"c\"}),n(e,\"marker.line\")&&i(t,e,{vals:e.marker.line.color,containerStr:\"marker.line\",cLetter:\"c\"}),a(p,e),s(p,e),p},crossTraceCalc:function(t,e,r){for(var n=t.calcdata,i=[],a=0;a<n.length;a++){var s=n[a],c=s[0].trace;!0===c.visible&&l(c,\"bar\")&&c.subplot===r&&i.push(s)}var f=u({},e.radialaxis,{_id:\"x\"}),h=e.angularaxis;o(t,h,f,i,{mode:e.barmode,norm:e.barnorm,gap:e.bargap,groupgap:e.bargroupgap})}}},6135:function(t,e,r){\"use strict\";var n=r(71828),i=r(22184).handleRThetaDefaults,a=r(98340),o=r(55023);t.exports=function(t,e,r,s){function l(r,i){return n.coerce(t,e,o,r,i)}i(t,e,s,l)?(l(\"thetaunit\"),l(\"base\"),l(\"offset\"),l(\"width\"),l(\"text\"),l(\"hovertext\"),l(\"hovertemplate\"),a(t,e,l,r,s),n.coerceSelectionMarkerOpacity(e,l)):e.visible=!1}},27379:function(t,e,r){\"use strict\";var n=r(30211),i=r(71828),a=r(95423).getTraceColor,o=i.fillText,s=r(59150).makeHoverPointText,l=r(10869).isPtInsidePolygon;t.exports=function(t,e,r){var u=t.cd,c=u[0].trace,f=t.subplot,h=f.radialAxis,p=f.angularAxis,d=f.vangles,v=d?l:i.isPtInsideSector,g=t.maxHoverDistance,y=p._period||2*Math.PI,m=Math.abs(h.g2p(Math.sqrt(e*e+r*r))),x=Math.atan2(r,e);if(h.range[0]>h.range[1]&&(x+=Math.PI),n.getClosest(u,(function(t){return v(m,x,[t.rp0,t.rp1],[t.thetag0,t.thetag1],d)?g+Math.min(1,Math.abs(t.thetag1-t.thetag0)/y)-1+(t.rp1-m)/(t.rp1-t.rp0)-1:1/0}),t),!1!==t.index){var b=u[t.index];t.x0=t.x1=b.ct[0],t.y0=t.y1=b.ct[1];var _=i.extendFlat({},b,{r:b.s,theta:b.p});return o(b,c,t),s(_,c,f,t),t.hovertemplate=c.hovertemplate,t.color=a(c,b),t.xLabelVal=t.yLabelVal=void 0,b.s<0&&(t.idealAlign=\"left\"),[t]}}},23381:function(t,e,r){\"use strict\";t.exports={moduleType:\"trace\",name:\"barpolar\",basePlotModule:r(23580),categories:[\"polar\",\"bar\",\"showLegend\"],attributes:r(55023),layoutAttributes:r(40151),supplyDefaults:r(6135),supplyLayoutDefaults:r(19860),calc:r(74692).calc,crossTraceCalc:r(74692).crossTraceCalc,plot:r(60173),colorbar:r(4898),formatLabels:r(98608),style:r(16688).style,styleOnSelect:r(16688).styleOnSelect,hoverPoints:r(27379),selectPoints:r(81974),meta:{}}},40151:function(t){\"use strict\";t.exports={barmode:{valType:\"enumerated\",values:[\"stack\",\"overlay\"],dflt:\"stack\",editType:\"calc\"},bargap:{valType:\"number\",dflt:.1,min:0,max:1,editType:\"calc\"}}},19860:function(t,e,r){\"use strict\";var n=r(71828),i=r(40151);t.exports=function(t,e,r){var a,o={};function s(r,o){return n.coerce(t[a]||{},e[a],i,r,o)}for(var 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R,F,B,N,j,U,V=k+T,H=Boolean(C);if(\"positive\"===P?(M=w*(C?1:.5),R=V,S=R=k):\"negative\"===P?(M=R=k,S=w*(C?1:.5),F=V):(M=S=w,R=F=V),(E.boxpoints||E.points)&&v>0){var q=E.pointpos,G=E.jitter,Z=E.marker.size/2,Y=0;q+G>=0&&((Y=V*(q+G))>M?(H=!0,j=Z,B=Y):Y>R&&(j=Z,B=M)),Y<=M&&(B=M);var W=0;q-G<=0&&((W=-V*(q-G))>S?(H=!0,U=Z,N=W):W>F&&(U=Z,N=S)),W<=S&&(N=S)}else B=M,N=S;var X=new Array(u.length);for(l=0;l<u.length;l++)X[l]=u[l].pos;E._extremes[h]=n.findExtremes(o,X,{padded:H,vpadminus:N,vpadplus:B,vpadLinearized:!0,ppadminus:{x:U,y:j}[p],ppadplus:{x:j,y:U}[p]})}}}t.exports={crossTraceCalc:function(t,e){for(var r=t.calcdata,n=e.xaxis,i=e.yaxis,a=0;a<o.length;a++){for(var l=o[a],u=\"h\"===l?i:n,c=[],f=0;f<r.length;f++){var h=r[f],p=h[0].t,d=h[0].trace;!0!==d.visible||\"box\"!==d.type&&\"candlestick\"!==d.type||p.empty||(d.orientation||\"v\")!==l||d.xaxis!==n._id||d.yaxis!==i._id||c.push(f)}s(\"box\",t,c,u)}},setPositionOffset:s}},36411:function(t,e,r){\"use strict\";var n=r(71828),i=r(73972),a=r(7901),o=r(73927),s=r(26125),l=r(4322),u=r(53522);function c(t,e,r,a){function o(t){var e=0;return t&&t.length&&(e+=1,n.isArrayOrTypedArray(t[0])&&t[0].length&&(e+=1)),e}function s(e){return n.validate(t[e],u[e])}var c,f=r(\"y\"),h=r(\"x\");if(\"box\"===e.type){var p=r(\"q1\"),d=r(\"median\"),v=r(\"q3\");e._hasPreCompStats=p&&p.length&&d&&d.length&&v&&v.length,c=Math.min(n.minRowLength(p),n.minRowLength(d),n.minRowLength(v))}var g,y,m=o(f),x=o(h),b=m&&n.minRowLength(f),_=x&&n.minRowLength(h),w=a.calendar,T={autotypenumbers:a.autotypenumbers};if(e._hasPreCompStats)switch(String(x)+String(m)){case\"00\":var k=s(\"x0\")||s(\"dx\");g=!s(\"y0\")&&!s(\"dy\")||k?\"v\":\"h\",y=c;break;case\"10\":g=\"v\",y=Math.min(c,_);break;case\"20\":g=\"h\",y=Math.min(c,h.length);break;case\"01\":g=\"h\",y=Math.min(c,b);break;case\"02\":g=\"v\",y=Math.min(c,f.length);break;case\"12\":g=\"v\",y=Math.min(c,_,f.length);break;case\"21\":g=\"h\",y=Math.min(c,h.length,b);break;case\"11\":y=0;break;case\"22\":var A,M=!1;for(A=0;A<h.length;A++)if(\"category\"===l(h[A],w,T)){M=!0;break}if(M)g=\"v\",y=Math.min(c,_,f.length);else{for(A=0;A<f.length;A++)if(\"category\"===l(f[A],w,T)){M=!0;break}M?(g=\"h\",y=Math.min(c,h.length,b)):(g=\"v\",y=Math.min(c,_,f.length))}}else m>0?(g=\"v\",y=x>0?Math.min(_,b):Math.min(b)):x>0?(g=\"h\",y=Math.min(_)):y=0;if(y){e._length=y;var 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b=t.selectAll(\"path.box\").data(\"violin\"!==r.type||r.box.visible?i.identity:[]);b.enter().append(\"path\").style(\"vector-effect\",o?\"none\":\"non-scaling-stroke\").attr(\"class\",\"box\"),b.exit().remove(),b.each((function(t){if(t.empty)return n.select(this).attr(\"d\",\"M0,0Z\");var e=f.c2l(t.pos+p,!0),a=f.l2p(e-s)+v,o=f.l2p(e+l)+v,b=h?(a+o)/2:f.l2p(e)+v,_=r.whiskerwidth,w=h?a*_+(1-_)*b:f.l2p(e-d)+v,T=h?o*_+(1-_)*b:f.l2p(e+d)+v,k=f.l2p(e-s*x)+v,A=f.l2p(e+l*x)+v,M=\"sd\"===r.sizemode,S=c.c2p(M?t.mean-t.sd:t.q1,!0),E=M?c.c2p(t.mean+t.sd,!0):c.c2p(t.q3,!0),L=i.constrain(M?c.c2p(t.mean,!0):c.c2p(t.med,!0),Math.min(S,E)+1,Math.max(S,E)-1),C=void 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t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})).attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"})),k(D),E.exit().remove(),S.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var z=e._fullLayout.paper_bgcolor;M.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",f.makeTextShadow(z)).style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return d(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){u.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),S.append(\"text\").attr(\"class\",\"dimlabel\"),M.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"})).attr(\"x\",(function(t){return t.width/2})).attr(\"y\",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){u.font(n.select(this),t.parcatsViewModel.labelfont)})),M.selectAll(\"rect.bandrect\").on(\"mouseover\",L).on(\"mouseout\",C),M.exit().remove(),A.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",P).on(\"drag\",O).on(\"dragend\",I)),h.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\")})),h.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function v(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){s.raiseToTop(this),w(n.select(this));var e=m(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit(\"plotly_hover\",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\")){var i,a,l,u=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),v=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=u&&u<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],y=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+y.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=c.mostReadable(t.model.color,[\"black\",\"white\"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&S.push([\"Count:\",M.countLabel].join(\" \")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&S.push([\"P:\",M.probabilityLabel].join(\" \"));var E=S.join(\"<br>\"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+v.left,y:_-d.top+v.top,text:E,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:T,idealAlign:L<b?\"right\":\"left\",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function y(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(v),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=m(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event,constraints:r})}}function m(t){for(var e=[],r=D(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){var e=m(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:e,event:n.event,constraints:r})}}function _(t){t.attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function w(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",(function(t){return c.mostReadable(t.model.color,[\"black\",\"white\"])})).attr(\"stroke-width\",.3)}function T(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function k(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll(\"rect.bandrect\"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,m(t))}))}));var u={};u[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:u})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,m(t))}));var u={};u[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(u.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:u})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select(\"rect.catrect\"),u=l.node().getBoundingClientRect(),c=l.datum(),f=c.parcatsViewModel,h=f.model.dimensions[c.model.dimensionInd],p=f.trace,d=u.top+u.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=u.left,a=\"left\"):(i=u.left+u.width,a=\"right\");var v=c.model.count,g=c.model.categoryLabel,y=v/c.parcatsViewModel.model.count,m={countLabel:v,categoryLabel:g,probabilityLabel:y.toFixed(3)},x=[];-1!==c.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&x.push([\"Count:\",m.countLabel].join(\" \")),-1!==c.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&x.push([\"P(\"+m.categoryLabel+\"):\",m.probabilityLabel].join(\" \"));var b=x.join(\"<br>\");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:12,fontColor:\"black\",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:m,eventData:[{data:p._input,fullData:p,count:v,category:g,probability:y}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron,u=this;\"color\"===l?(function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll(\"rect.bandrect\").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr(\"stroke\",\"black\").attr(\"stroke-width\",1.5)}))}(u),S(u,\"plotly_hover\",n.event)):(function(t){n.select(t.parentNode).selectAll(\"rect.bandrect\").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}(u),M(u,\"plotly_hover\",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\")&&(\"category\"===l?e=E(r,a,u):\"color\"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),u=n.select(r).datum(),f=u.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,v=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a=\"left\"):(i=l.left+l.width,a=\"right\");var g=f.model.categoryLabel,y=u.parcatsViewModel.model.count,m=0;u.categoryViewModel.bands.forEach((function(t){t.color===u.color&&(m+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===u.color&&(b+=t.model.count)}));var _=m/y,w=m/b,T=m/x,k={countLabel:y,categoryLabel:g,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&A.push([\"Count:\",k.countLabel].join(\" \")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&(A.push(\"P(color ∩ \"+g+\"): \"+k.probabilityLabel),A.push(\"P(\"+g+\" | color): \"+w.toFixed(3)),A.push(\"P(color | \"+g+\"): \"+T.toFixed(3)));var M=A.join(\"<br>\"),S=c.mostReadable(u.color,[\"black\",\"white\"]);return{trace:d,x:o*(i-e.left),y:s*(v-e.top),text:M,color:u.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:y,probability:_,categorycount:x,colorcount:b,bandcolorcount:m}]}}(r,a,u):\"dimension\"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each((function(){i.push(E(t,e,this))})),i}(r,a,u)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r}))}}function C(t){var e=t.parcatsViewModel;e.dragDimension||(_(e.pathSelection),T(e.dimensionSelection.selectAll(\"g.category\")),k(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(v),-1!==e.hoverinfoItems.indexOf(\"skip\"))||(\"color\"===t.parcatsViewModel.hoveron?S(this,\"plotly_unhover\",n.event):M(this,\"plotly_unhover\",n.event))}function P(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function O(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,u=a.categories,c=u[l-1],f=u[l+1];void 0!==c&&s<c.y+c.height/2&&(o.model.displayInd=c.model.displayInd,c.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||\"freeform\"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),z(t.parcatsViewModel)}}function I(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=D(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+i+\"].displayindex\"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var u=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),c=u.map((function(t){return t.categoryValue})),f=u.map((function(t){return t.categoryLabel}));e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[c],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[f],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?S(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):M(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd&&(t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null),t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,j(t.parcatsViewModel),N(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each((function(){R(t.parcatsViewModel,!0),z(t.parcatsViewModel,!0)})).each(\"end\",(function(){(o||s)&&a.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function D(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}),p),(r=t.pathSelection,e?r.transition():r).attr(\"d\",(function(t){return t.svgD}))}function R(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data((function(t){return t.dimensions}),p);var i=t.dimensionSelection.selectAll(\"g.category\").data((function(t){return t.categories}),p);r(t.dimensionSelection).attr(\"transform\",(function(t){return l(t.x,0)})),r(i).attr(\"transform\",(function(t){return l(0,t.y)})),i.select(\".dimlabel\").text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})),i.select(\".catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"x\",(function(t){return d(t)?t.width+5:-5})).each((function(t){var e,r;d(t)?(e=t.width+5,r=\"start\"):(e=-5,r=\"end\"),n.select(this).selectAll(\"tspan\").attr(\"x\",e).attr(\"text-anchor\",r)}));var a=i.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),p),o=a.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"cursor\",\"move\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);a.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})),k(o),a.each((function(){s.raiseToTop(this)})),a.exit().remove()}function F(t,e,r){var n,i=r[0],a=e.margin||{l:80,r:80,t:100,b:80},o=i.trace,s=o.domain,l=e.width,u=e.height,c=Math.floor(l*(s.x[1]-s.x[0])),f=Math.floor(u*(s.y[1]-s.y[0])),h=s.x[0]*l+a.l,p=e.height-s.y[1]*e.height+a.t,d=o.line.shape;n=\"all\"===o.hoverinfo?[\"count\",\"probability\"]:(o.hoverinfo||\"\").split(\"+\");var v={trace:o,key:o.uid,model:i,x:h,y:p,width:c,height:f,hoveron:o.hoveron,hoverinfoItems:n,arrangement:o.arrangement,bundlecolors:o.bundlecolors,sortpaths:o.sortpaths,labelfont:o.labelfont,categorylabelfont:o.tickfont,pathShape:d,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};return i.dimensions&&(j(v),N(v)),v}function B(t,e,r,n,a){var o,s,l=[],u=[];for(s=0;s<r.length-1;s++)o=i(r[s]+t[s],t[s+1]),l.push(o(a)),u.push(o(1-a));var c=\"M \"+t[0]+\",\"+e[0];for(c+=\"l\"+r[0]+\",0 \",s=1;s<r.length;s++)c+=\"C\"+l[s-1]+\",\"+e[s-1]+\" \"+u[s-1]+\",\"+e[s]+\" \"+t[s]+\",\"+e[s],c+=\"l\"+r[s]+\",0 \";for(c+=\"l0,\"+n+\" \",c+=\"l -\"+r[r.length-1]+\",0 \",s=r.length-2;s>=0;s--)c+=\"C\"+u[s]+\",\"+(e[s+1]+n)+\" \"+l[s]+\",\"+(e[s]+n)+\" \"+(t[s]+r[s])+\",\"+(e[s]+n),c+=\"l-\"+r[s]+\",0 \";return c+\"Z\"}function N(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return t.categories.map((function(t){return t.displayInd}))})),a=t.model.dimensions.map((function(t){return t.displayInd})),o=t.dimensions.map((function(t){return t.model.dimensionInd})),s=e.map((function(t){return t.x})),l=e.map((function(t){return t.width})),u=[];for(var c in r.paths)r.paths.hasOwnProperty(c)&&u.push(r.paths[c]);function f(t){var e=t.categoryInds.map((function(t,e){return i[e][t]}));return o.map((function(t){return e[t]}))}u.sort((function(e,r){var n=f(e),i=f(r);return\"backward\"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),n<i?-1:n>i?1:0}));for(var h=new Array(u.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),v=0;v<u.length;v++){var g,y=u[v];g=p>0?d*(y.count/p):0;for(var m,x=new Array(n.length),b=0;b<y.categoryInds.length;b++){var 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n=r(50693),i=r(13838),a=r(41940),o=r(27670).Y,s=r(1426).extendFlat,l=r(44467).templatedArray;t.exports={domain:o({name:\"parcoords\",trace:!0,editType:\"plot\"}),labelangle:{valType:\"angle\",dflt:0,editType:\"plot\"},labelside:{valType:\"enumerated\",values:[\"top\",\"bottom\"],dflt:\"top\",editType:\"plot\"},labelfont:a({editType:\"plot\"}),tickfont:a({editType:\"plot\"}),rangefont:a({editType:\"plot\"}),dimensions:l(\"dimension\",{label:{valType:\"string\",editType:\"plot\"},tickvals:s({},i.tickvals,{editType:\"plot\"}),ticktext:s({},i.ticktext,{editType:\"plot\"}),tickformat:s({},i.tickformat,{editType:\"plot\"}),visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},range:{valType:\"info_array\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},constraintrange:{valType:\"info_array\",freeLength:!0,dimensions:\"1-2\",items:[{valType:\"any\",editType:\"plot\"},{valType:\"any\",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\"})),unselected:{line:{color:{valType:\"color\",dflt:\"#7f7f7f\",editType:\"plot\"},opacity:{valType:\"number\",min:0,max:1,dflt:\"auto\",editType:\"plot\"},editType:\"plot\"},editType:\"plot\"}}},57920:function(t,e,r){\"use 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s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,l,c);o(e,l,c),Array.isArray(v)&&v.length||(e.visible=!1),h(e,v,\"values\",g);var y={family:l.font.family,size:Math.round(l.font.size/1.2),color:l.font.color};n.coerceFont(c,\"labelfont\",y),n.coerceFont(c,\"tickfont\",y),n.coerceFont(c,\"rangefont\",y),c(\"labelangle\"),c(\"labelside\"),c(\"unselected.line.color\"),c(\"unselected.line.opacity\")}},1602:function(t,e,r){\"use strict\";var n=r(71828).isTypedArray;e.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},e.isOrdinal=function(t){return!!t.tickvals},e.isVisible=function(t){return t.visible||!(\"visible\"in t)}},67618:function(t,e,r){\"use strict\";var n=r(71791);n.plot=r(21341),t.exports=n},83398:function(t,e,r){\"use strict\";var n=r(56068),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nattribute vec4 p01_04, p05_08, p09_12, p13_16,\\n               p17_20, p21_24, p25_28, p29_32,\\n               p33_36, p37_40, p41_44, p45_48,\\n               p49_52, p53_56, p57_60, colors;\\n\\nuniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\\n             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\\n\\nuniform vec2 resolution, viewBoxPos, viewBoxSize;\\nuniform float maskHeight;\\nuniform float drwLayer; // 0: context, 1: focus, 2: pick\\nuniform vec4 contextColor;\\nuniform sampler2D maskTexture, palette;\\n\\nbool isPick    = (drwLayer > 1.5);\\nbool isContext = (drwLayer < 0.5);\\n\\nconst vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\\nconst vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\\n\\nfloat val(mat4 p, mat4 v) {\\n    return dot(matrixCompMult(p, v) * UNITS, UNITS);\\n}\\n\\nfloat axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\\n    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\\n    return y1 * (1.0 - ratio) + y2 * ratio;\\n}\\n\\nint iMod(int a, int b) {\\n    return a - b * (a / b);\\n}\\n\\nbool fOutside(float p, float lo, float hi) {\\n    return (lo < hi) && (lo > p || p > hi);\\n}\\n\\nbool vOutside(vec4 p, vec4 lo, vec4 hi) {\\n    return (\\n        fOutside(p[0], lo[0], hi[0]) ||\\n        fOutside(p[1], lo[1], hi[1]) ||\\n        fOutside(p[2], lo[2], hi[2]) ||\\n        fOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool mOutside(mat4 p, mat4 lo, mat4 hi) {\\n    return (\\n        vOutside(p[0], lo[0], hi[0]) ||\\n        vOutside(p[1], lo[1], hi[1]) ||\\n        vOutside(p[2], lo[2], hi[2]) ||\\n        vOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    return mOutside(A, loA, hiA) ||\\n           mOutside(B, loB, hiB) ||\\n           mOutside(C, loC, hiC) ||\\n           mOutside(D, loD, hiD);\\n}\\n\\nbool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    mat4 pnts[4];\\n    pnts[0] = A;\\n    pnts[1] = B;\\n    pnts[2] = C;\\n    pnts[3] = D;\\n\\n    for(int i = 0; i < 4; ++i) {\\n        for(int j = 0; j < 4; ++j) {\\n            for(int k = 0; k < 4; ++k) {\\n                if(0 == iMod(\\n                    int(255.0 * texture2D(maskTexture,\\n                        vec2(\\n                            (float(i * 2 + j / 2) + 0.5) / 8.0,\\n                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\\n                        ))[3]\\n                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\\n                    2\\n                )) return true;\\n            }\\n        }\\n    }\\n    return false;\\n}\\n\\nvec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float x = 0.5 * sign(v) + 0.5;\\n    float y = axisY(x, A, B, C, D);\\n    float z = 1.0 - abs(v);\\n\\n    z += isContext ? 0.0 : 2.0 * float(\\n        outsideBoundingBox(A, B, C, D) ||\\n        outsideRasterMask(A, B, C, D)\\n    );\\n\\n    return vec4(\\n        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\\n        z,\\n        1.0\\n    );\\n}\\n\\nvoid main() {\\n    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\\n    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\\n    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\\n    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\\n\\n    float v = colors[3];\\n\\n    gl_Position = position(isContext, v, A, B, C, D);\\n\\n    fragColor =\\n        isContext ? vec4(contextColor) :\\n        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\\n}\\n\"]),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n    gl_FragColor = fragColor;\\n}\\n\"]),o=r(25706).maxDimensionCount,s=r(71828),l=1e-6,u=new Uint8Array(4),c=new Uint8Array(4),f={shape:[256,1],format:\"rgba\",type:\"uint8\",mag:\"nearest\",min:\"nearest\"};function h(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function p(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(function(t){t.read({x:0,y:0,width:1,height:1,data:u})}(t),r.drawCompleted=!0),function s(l){var u=Math.min(n,i-l*n);0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete r.currentRafs[o],h(t,a.scissorX,a.scissorY,a.scissorWidth,a.viewBoxSize[1])),r.clearOnly||(a.count=2*u,a.offset=2*l*n,e(a),l*n+u<i&&(r.currentRafs[o]=window.requestAnimationFrame((function(){s(l+1)}))),r.drawCompleted=!1)}(0)}function d(t,e){for(var r=new Array(256),n=0;n<256;n++)r[n]=t(n/255).concat(e);return r}function v(t,e){return(t>>>8*e)%256/255}function g(t,e,r){for(var n=new Array(8*e),i=0,a=0;a<e;a++)for(var o=0;o<2;o++)for(var s=0;s<4;s++){var l=4*t+s,u=r[64*a+l];63===l&&0===o&&(u*=-1),n[i++]=u}return n}function y(t){var e=\"0\"+t;return e.substr(e.length-2)}function m(t){return t<o?\"p\"+y(t+1)+\"_\"+y(t+4):\"colors\"}function x(t,e,r,n,i,a,o,l,u,c,f,h,p,d){for(var v=[[],[]],g=0;g<64;g++)v[0][g]=g===i?1:0,v[1][g]=g===a?1:0;o*=d,l*=d,u*=d,c*=d;var y=t.lines.canvasOverdrag*d,m=t.domain,x=t.canvasWidth*d,b=t.canvasHeight*d,_=t.pad.l*d,w=t.pad.b*d,T=t.layoutHeight*d,k=t.layoutWidth*d,A=t.deselectedLines.color,M=t.deselectedLines.opacity;return s.extendFlat({key:f,resolution:[x,b],viewBoxPos:[o+y,l],viewBoxSize:[u,c],i0:i,i1:a,dim0A:v[0].slice(0,16),dim0B:v[0].slice(16,32),dim0C:v[0].slice(32,48),dim0D:v[0].slice(48,64),dim1A:v[1].slice(0,16),dim1B:v[1].slice(16,32),dim1C:v[1].slice(32,48),dim1D:v[1].slice(48,64),drwLayer:h,contextColor:[A[0]/255,A[1]/255,A[2]/255,\"auto\"!==M?A[3]*M:Math.max(1/255,Math.pow(1/t.lines.color.length,1/3))],scissorX:(n===e?0:o+y)+(_-y)+k*m.x[0],scissorWidth:(n===r?x-o+y:u+.5)+(n===e?o+y:0),scissorY:l+w+T*m.y[0],scissorHeight:c,viewportX:_-y+k*m.x[0],viewportY:w+T*m.y[0],viewportWidth:x,viewportHeight:b},p)}function b(t){var e=2047,r=Math.max(0,Math.floor(t[0]*e),0),n=Math.min(e,Math.ceil(t[1]*e),e);return[Math.min(r,n),Math.max(r,n)]}t.exports=function(t,e){var r,n,u,y,_,w=e.context,T=e.pick,k=e.regl,A=k._gl,M=A.getParameter(A.ALIASED_LINE_WIDTH_RANGE),S=Math.max(M[0],Math.min(M[1],e.viewModel.plotGlPixelRatio)),E={currentRafs:{},drawCompleted:!0,clearOnly:!1},L=function(t){for(var e={},r=0;r<=o;r+=4)e[m(r)]=t.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)});return e}(k),C=k.texture(f),P=[];I(e);var O=k({profile:!1,blend:{enable:w,func:{srcRGB:\"src alpha\",dstRGB:\"one minus src 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I(t){r=t.model,n=t.viewModel,u=n.dimensions.slice(),y=u[0]?u[0].values.length:0;var e=r.lines,i=T?e.color.map((function(t,r){return r/e.color.length})):e.color,a=function(t,e,r){for(var n,i=new Array(t*(o+4)),a=0,s=0;s<t;s++){for(var u=0;u<o;u++)i[a++]=u<e.length?e[u].paddedUnitValues[s]:.5;i[a++]=v(s,2),i[a++]=v(s,1),i[a++]=v(s,0),i[a++]=(n=r[s],Math.max(l,Math.min(.999999,n)))}return i}(y,u,i);!function(t,e,r){for(var n=0;n<=o;n+=4)t[m(n)](g(n/4,e,r))}(L,y,a),w||T||(C=k.texture(s.extendFlat({data:d(r.unitToColor,255)},f)))}return{render:function(t,e,n){var i,a,o,s=t.length,l=1/0,c=-1/0;for(i=0;i<s;i++)t[i].dim0.canvasX<l&&(l=t[i].dim0.canvasX,a=i),t[i].dim1.canvasX>c&&(c=t[i].dim1.canvasX,o=i);0===s&&h(k,0,0,r.canvasWidth,r.canvasHeight);var f=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&n<u.length?u[n].brush.filter.getBounds():[-1/0,1/0];i[0][n]=a[0],i[1][n]=a[1]}var o=new Array(16384);for(e=0;e<16384;e++)o[e]=255;if(!t)for(e=0;e<u.length;e++){var s=e%8,l=(e-s)/8,c=Math.pow(2,s),f=u[e].brush.filter.get();if(!(f.length<2)){var h=b(f[0])[1];for(r=1;r<f.length;r++){var p=b(f[r]);for(n=h+1;n<p[0];n++)o[8*n+l]&=~c;h=Math.max(h,p[1])}}}var d={shape:[8,2048],format:\"alpha\",type:\"uint8\",mag:\"nearest\",min:\"nearest\",data:o};return _?_(d):_=k.texture(d),{maskTexture:_,maskHeight:2048,loA:i[0].slice(0,16),loB:i[0].slice(16,32),loC:i[0].slice(32,48),loD:i[0].slice(48,64),hiA:i[1].slice(0,16),hiB:i[1].slice(16,32),hiC:i[1].slice(32,48),hiD:i[1].slice(48,64)}}(w);for(i=0;i<s;i++){var d=t[i],v=d.dim0.crossfilterDimensionIndex,g=d.dim1.crossfilterDimensionIndex,m=d.canvasX,A=d.canvasY,M=m+d.panelSizeX,S=d.plotGlPixelRatio;if(e||!P[v]||P[v][0]!==m||P[v][1]!==M){P[v]=[m,M];var L=x(r,a,o,i,v,g,m,A,d.panelSizeX,d.panelSizeY,d.dim0.crossfilterDimensionIndex,w?0:T?2:1,f,S);E.clearOnly=n;var C=e?r.lines.blockLineCount:y;p(k,O,E,C,y,L)}}},readPixel:function(t,e){return k.read({x:t,y:e,width:1,height:1,data:c}),c},readPixels:function(t,e,r,n){var i=new Uint8Array(4*r*n);return k.read({x:t,y:e,width:r,height:n,data:i}),i},destroy:function(){for(var e in t.style[\"pointer-events\"]=\"none\",C.destroy(),_&&_.destroy(),L)L[e].destroy()},update:I}}},94397:function(t){\"use strict\";t.exports=function(t,e,r,n){var i,a;for(n||(n=1/0),i=0;i<e.length;i++)(a=e[i]).visible&&(n=Math.min(n,a[r].length));for(n===1/0&&(n=0),t._length=n,i=0;i<e.length;i++)(a=e[i]).visible&&(a._length=n);return n}},17171:function(t,e,r){\"use strict\";var n=r(39898),i=r(71828),a=i.numberFormat,o=r(36652),s=r(89298),l=i.strRotate,u=i.strTranslate,c=r(63893),f=r(91424),h=r(21081),p=r(28984),d=p.keyFun,v=p.repeat,g=p.unwrap,y=r(1602),m=r(25706),x=r(57920),b=r(83398);function _(t,e,r){return i.aggNums(t,null,e,r)}function w(t,e){return k(_(Math.min,t,e),_(Math.max,t,e))}function T(t){var e=t.range;return e?k(e[0],e[1]):w(t.values,t._length)}function k(t,e){return!isNaN(t)&&isFinite(t)||(t=0),!isNaN(e)&&isFinite(e)||(e=0),t===e&&(0===t?(t-=1,e+=1):(t*=.9,e*=1.1)),[t,e]}function A(t,e,r,i,o){var s,l,u=T(r);return i?n.scale.ordinal().domain(i.map((s=a(r.tickformat),l=o,l?function(t,e){var r=l[e];return null==r?s(t):r}:s))).range(i.map((function(r){var n=(r-u[0])/(u[1]-u[0]);return t-e+n*(2*e-t)}))):n.scale.linear().domain(u).range([t-e,e])}function M(t){if(t.tickvals){var e=T(t);return n.scale.ordinal().domain(t.tickvals).range(t.tickvals.map((function(t){return(t-e[0])/(e[1]-e[0])})))}}function S(t){var e=t.map((function(t){return t[0]})),r=t.map((function(t){var e=o(t[1]);return n.rgb(\"rgb(\"+e[0]+\",\"+e[1]+\",\"+e[2]+\")\")})),i=\"rgb\".split(\"\").map((function(t){return n.scale.linear().clamp(!0).domain(e).range(r.map((i=t,function(t){return t[i]})));var i}));return function(t){return i.map((function(e){return e(t)}))}}function E(t){return t.dimensions.some((function(t){return t.brush.filterSpecified}))}function L(t,e,r){var a=g(e),s=a.trace,l=y.convertTypedArray(a.lineColor),u=s.line,c={color:o(s.unselected.line.color),opacity:s.unselected.line.opacity},f=h.extractOpts(u),p=f.reversescale?h.flipScale(a.cscale):a.cscale,d=s.domain,v=s.dimensions,x=t.width,b=s.labelangle,_=s.labelside,w=s.labelfont,k=s.tickfont,A=s.rangefont,M=i.extendDeepNoArrays({},u,{color:l.map(n.scale.linear().domain(T({values:l,range:[f.min,f.max],_length:s._length}))),blockLineCount:m.blockLineCount,canvasOverdrag:m.overdrag*m.canvasPixelRatio}),E=Math.floor(x*(d.x[1]-d.x[0])),L=Math.floor(t.height*(d.y[1]-d.y[0])),C=t.margin||{l:80,r:80,t:100,b:80},P=E,O=L;return{key:r,colCount:v.filter(y.isVisible).length,dimensions:v,tickDistance:m.tickDistance,unitToColor:S(p),lines:M,deselectedLines:c,labelAngle:b,labelSide:_,labelFont:w,tickFont:k,rangeFont:A,layoutWidth:x,layoutHeight:t.height,domain:d,translateX:d.x[0]*x,translateY:t.height-d.y[1]*t.height,pad:C,canvasWidth:P*m.canvasPixelRatio+2*M.canvasOverdrag,canvasHeight:O*m.canvasPixelRatio,width:P,height:O,canvasPixelRatio:m.canvasPixelRatio}}function C(t,e,r){var o=r.width,s=r.height,l=r.dimensions,u=r.canvasPixelRatio,c=function(t){return o*t/Math.max(1,r.colCount-1)},f=m.verticalPadding/s,h=function(t,e){return n.scale.linear().range([e,t-e])}(s,m.verticalPadding),p={key:r.key,xScale:c,model:r,inBrushDrag:!1},d={};return p.dimensions=l.filter(y.isVisible).map((function(o,l){var v=function(t,e){return n.scale.linear().domain(T(t)).range([e,1-e])}(o,f),g=d[o.label];d[o.label]=(g||0)+1;var b=o.label+(g?\"__\"+g:\"\"),_=o.constraintrange,w=_&&_.length;w&&!Array.isArray(_[0])&&(_=[_]);var k=w?_.map((function(t){return t.map(v)})):[[-1/0,1/0]],S=o.values;S.length>o._length&&(S=S.slice(0,o._length));var L,C=o.tickvals;function P(t,e){return{val:t,text:L[e]}}function O(t,e){return t.val-e.val}if(Array.isArray(C)&&C.length){L=o.ticktext,Array.isArray(L)&&L.length?L.length>C.length?L=L.slice(0,C.length):C.length>L.length&&(C=C.slice(0,L.length)):L=C.map(a(o.tickformat));for(var I=1;I<C.length;I++)if(C[I]<C[I-1]){for(var D=C.map(P).sort(O),z=0;z<C.length;z++)C[z]=D[z].val,L[z]=D[z].text;break}}else C=void 0;return S=y.convertTypedArray(S),{key:b,label:o.label,tickFormat:o.tickformat,tickvals:C,ticktext:L,ordinal:y.isOrdinal(o),multiselect:o.multiselect,xIndex:l,crossfilterDimensionIndex:l,visibleIndex:o._index,height:s,values:S,paddedUnitValues:S.map(v),unitTickvals:C&&C.map(v),xScale:c,x:c(l),canvasX:c(l)*u,unitToPaddedPx:h,domainScale:A(s,m.verticalPadding,o,C,L),ordinalScale:M(o),parent:p,model:r,brush:x.makeBrush(t,w,k,(function(){t.linePickActive(!1)}),(function(){var e=p;e.focusLayer&&e.focusLayer.render(e.panels,!0);var r=E(e);!t.contextShown()&&r?(e.contextLayer&&e.contextLayer.render(e.panels,!0),t.contextShown(!0)):t.contextShown()&&!r&&(e.contextLayer&&e.contextLayer.render(e.panels,!0,!0),t.contextShown(!1))}),(function(r){if(p.focusLayer.render(p.panels,!0),p.pickLayer&&p.pickLayer.render(p.panels,!0),t.linePickActive(!0),e&&e.filterChanged){var n=v.invert,a=r.map((function(t){return t.map(n).sort(i.sorterAsc)})).sort((function(t,e){return t[0]-e[0]}));e.filterChanged(p.key,o._index,a)}}))}})),p}function P(t){t.classed(m.cn.axisExtentText,!0).attr(\"text-anchor\",\"middle\").style(\"cursor\",\"default\")}function O(t,e){var r=\"top\"===e?1:-1,n=t*Math.PI/180;return{dir:r,dx:Math.sin(n),dy:Math.cos(n),degrees:t}}function I(t,e,r){for(var n=e.panels||(e.panels=[]),i=t.data(),a=0;a<i.length-1;a++){var o=n[a]||(n[a]={}),s=i[a],l=i[a+1];o.dim0=s,o.dim1=l,o.canvasX=s.canvasX,o.panelSizeX=l.canvasX-s.canvasX,o.panelSizeY=e.model.canvasHeight,o.y=0,o.canvasY=0,o.plotGlPixelRatio=r}}function D(t,e){return s.tickText(t._ax,e,!1).text}function z(t,e){if(t.ordinal)return\"\";var r=t.domainScale.domain(),n=r[e?r.length-1:0];return D(t.model.dimensions[t.visibleIndex],n)}t.exports=function(t,e,r,a){var o=t._context.staticPlot,h=t._fullLayout,p=h._toppaper,_=h._glcontainer,T=t._context.plotGlPixelRatio,A=t._fullLayout.paper_bgcolor;!function(t){for(var e=0;e<t.length;e++)for(var r=0;r<t[e].length;r++)for(var n=t[e][r].trace,i=n.dimensions,a=0;a<i.length;a++){var o=i[a].values,l=i[a]._ax;l&&(l.range?l.range=k(l.range[0],l.range[1]):l.range=w(o,n._length),l.dtick||(l.dtick=.01*(Math.abs(l.range[1]-l.range[0])||1)),l.tickformat=i[a].tickformat,s.calcTicks(l),l.cleanRange())}}(e);var M,S,R=(M=!0,S=!1,{linePickActive:function(t){return arguments.length?M=!!t:M},contextShown:function(t){return arguments.length?S=!!t:S}}),F=e.filter((function(t){return g(t).trace.visible})).map(L.bind(0,r)).map(C.bind(0,R,a));_.each((function(t,e){return i.extendFlat(t,F[e])}));var B=_.selectAll(\".gl-canvas\").each((function(t){t.viewModel=F[0],t.viewModel.plotGlPixelRatio=T,t.viewModel.paperColor=A,t.model=t.viewModel?t.viewModel.model:null})),N=null;B.filter((function(t){return t.pick})).style(\"pointer-events\",o?\"none\":\"auto\").on(\"mousemove\",(function(t){if(R.linePickActive()&&t.lineLayer&&a&&a.hover){var e=n.event,r=this.width,i=this.height,o=n.mouse(this),s=o[0],l=o[1];if(s<0||l<0||s>=r||l>=i)return;var u=t.lineLayer.readPixel(s,i-1-l),c=0!==u[3],f=c?u[2]+256*(u[1]+256*u[0]):null,h={x:s,y:l,clientX:e.clientX,clientY:e.clientY,dataIndex:t.model.key,curveNumber:f};f!==N&&(c?a.hover(h):a.unhover&&a.unhover(h),N=f)}})),B.style(\"opacity\",(function(t){return t.pick?0:1})),p.style(\"background\",\"rgba(255, 255, 255, 0)\");var j=p.selectAll(\".\"+m.cn.parcoords).data(F,d);j.exit().remove(),j.enter().append(\"g\").classed(m.cn.parcoords,!0).style(\"shape-rendering\",\"crispEdges\").style(\"pointer-events\",\"none\"),j.attr(\"transform\",(function(t){return u(t.model.translateX,t.model.translateY)}));var U=j.selectAll(\".\"+m.cn.parcoordsControlView).data(v,d);U.enter().append(\"g\").classed(m.cn.parcoordsControlView,!0),U.attr(\"transform\",(function(t){return u(t.model.pad.l,t.model.pad.t)}));var V=U.selectAll(\".\"+m.cn.yAxis).data((function(t){return 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e=t.model.height/t.model.tickDistance,r=t.domainScale,i=r.domain();n.select(this).call(n.svg.axis().orient(\"left\").tickSize(4).outerTickSize(2).ticks(e,t.tickFormat).tickValues(t.ordinal?i:null).tickFormat((function(e){return y.isOrdinal(t)?e:D(t.model.dimensions[t.visibleIndex],e)})).scale(r)),f.font(q.selectAll(\"text\"),t.model.tickFont)})),q.selectAll(\".domain, .tick>line\").attr(\"fill\",\"none\").attr(\"stroke\",\"black\").attr(\"stroke-opacity\",.25).attr(\"stroke-width\",\"1px\"),q.selectAll(\"text\").style(\"text-shadow\",c.makeTextShadow(A)).style(\"cursor\",\"default\");var G=H.selectAll(\".\"+m.cn.axisHeading).data(v,d);G.enter().append(\"g\").classed(m.cn.axisHeading,!0);var Z=G.selectAll(\".\"+m.cn.axisTitle).data(v,d);Z.enter().append(\"text\").classed(m.cn.axisTitle,!0).attr(\"text-anchor\",\"middle\").style(\"cursor\",\"ew-resize\").style(\"pointer-events\",o?\"none\":\"auto\"),Z.text((function(t){return t.label})).each((function(e){var 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J=Y.selectAll(\".\"+m.cn.axisExtentBottom).data(v,d);J.enter().append(\"g\").classed(m.cn.axisExtentBottom,!0),J.attr(\"transform\",(function(t){return u(0,t.model.height+m.axisExtentOffset)}));var K=J.selectAll(\".\"+m.cn.axisExtentBottomText).data(v,d);K.enter().append(\"text\").classed(m.cn.axisExtentBottomText,!0).attr(\"dy\",\"0.75em\").call(P),K.text((function(t){return z(t,!1)})).each((function(t){f.font(n.select(this),t.model.rangeFont)})),x.ensureAxisBrush(H,A,t)}},21341:function(t,e,r){\"use strict\";var n=r(17171),i=r(79749),a=r(1602).isVisible,o={};function s(t,e,r){var n=e.indexOf(r),i=t.indexOf(n);return-1===i&&(i+=e.length),i}(t.exports=function(t,e){var r=t._fullLayout;if(i(t,[],o)){var l={},u={},c={},f={},h=r._size;e.forEach((function(e,r){var n=e[0].trace;c[r]=n.index;var i=f[r]=n._fullInput.index;l[r]=t.data[i].dimensions,u[r]=t.data[i].dimensions.slice()})),n(t,e,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},{filterChanged:function(e,n,i){var a=u[e][n],o=i.map((function(t){return t.slice()})),s=\"dimensions[\"+n+\"].constraintrange\",l=r._tracePreGUI[t._fullData[c[e]]._fullInput.uid];if(void 0===l[s]){var h=a.constraintrange;l[s]=h||null}var p=t._fullData[c[e]].dimensions[n];o.length?(1===o.length&&(o=o[0]),a.constraintrange=o,p.constraintrange=o.slice(),o=[o]):(delete a.constraintrange,delete p.constraintrange,o=null);var d={};d[s]=o,t.emit(\"plotly_restyle\",[d,[f[e]]])},hover:function(e){t.emit(\"plotly_hover\",e)},unhover:function(e){t.emit(\"plotly_unhover\",e)},axesMoved:function(e,r){var n=function(t,e){return function(r,n){return s(t,e,r)-s(t,e,n)}}(r,u[e].filter(a));l[e].sort(n),u[e].filter((function(t){return!a(t)})).sort((function(t){return u[e].indexOf(t)})).forEach((function(t){l[e].splice(l[e].indexOf(t),1),l[e].splice(u[e].indexOf(t),0,t)})),t.emit(\"plotly_restyle\",[{dimensions:[l[e]]},[f[e]]])}})}}).reglPrecompiled=o},34e3:function(t,e,r){\"use strict\";var n=r(9012),i=r(27670).Y,a=r(41940),o=r(22399),s=r(5386).fF,l=r(5386).si,u=r(1426).extendFlat,c=r(79952).u,f=a({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});t.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:o.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},pattern:c,editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:u({},n.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:s({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplate:l({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:u({},f,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:u({},f,{}),outsidetextfont:u({},f,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:u({},f,{}),position:{valType:\"enumerated\",values:[\"top 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n=r(74875);e.name=\"pie\",e.plot=function(t,r,i,a){n.plotBasePlot(e.name,t,r,i,a)},e.clean=function(t,r,i,a){n.cleanBasePlot(e.name,t,r,i,a)}},32354:function(t,e,r){\"use strict\";var n=r(92770),i=r(84267),a=r(7901),o={};function s(t){return function(e,r){return!!e&&!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e)}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r<t.length;r++)a.push(i(t[r]).lighten(20).toHexString());for(r=0;r<t.length;r++)a.push(i(t[r]).darken(20).toHexString());e[n]=a}return a}t.exports={calc:function(t,e){var r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],u=e.labels,c=e.marker.colors||[],f=e.values,h=e._length,p=e._hasValues&&h;if(e.dlabel)for(u=new Array(h),r=0;r<h;r++)u[r]=String(e.label0+r*e.dlabel);var d={},v=s(o[\"_\"+e.type+\"colormap\"]),g=0,y=!1;for(r=0;r<h;r++){var m,x,b;if(p){if(m=f[r],!n(m))continue;m=+m}else m=1;void 0!==(x=u[r])&&\"\"!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(g+=m),a.push({v:m,label:x,color:v(c[r],x),i:r,pts:[r],hidden:b})):(y=!0,(i=a[_]).v+=m,i.pts.push(r),i.hidden||(g+=m),!1===i.color&&c[r]&&(i.color=v(c[r],x)))}return a=a.filter((function(t){return t.v>=0})),(\"funnelarea\"===e.type?y:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var u=0,c=0;c<i.length;c++){var f=i[c];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[u%a.length],u++))}}},makePullColorFn:s,generateExtendedColors:l}},37434:function(t,e,r){\"use strict\";var n=r(92770),i=r(71828),a=r(34e3),o=r(27670).c,s=r(90769).handleText,l=r(71828).coercePattern;function u(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var u=e[l];if(n(u)&&u>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}function c(t,e,r,n,i){n(\"marker.line.width\")&&n(\"marker.line.color\",i?void 0:r.paper_bgcolor);var a=n(\"marker.colors\");l(n,\"marker.pattern\",a),t.marker&&!e.marker.pattern.fgcolor&&(e.marker.pattern.fgcolor=t.marker.colors),e.marker.pattern.bgcolor||(e.marker.pattern.bgcolor=r.paper_bgcolor)}t.exports={handleLabelsAndValues:u,handleMarkerDefaults:c,supplyDefaults:function(t,e,r,n){function l(r,n){return i.coerce(t,e,a,r,n)}var f=u(l(\"labels\"),l(\"values\")),h=f.len;if(e._hasLabels=f.hasLabels,e._hasValues=f.hasValues,!e._hasLabels&&e._hasValues&&(l(\"label0\"),l(\"dlabel\")),h){e._length=h,c(t,e,n,l,!0),l(\"scalegroup\");var p,d=l(\"text\"),v=l(\"texttemplate\");if(v||(p=l(\"textinfo\",Array.isArray(d)?\"text+percent\":\"percent\")),l(\"hovertext\"),l(\"hovertemplate\"),v||p&&\"none\"!==p){var g=l(\"textposition\");s(t,e,n,l,g,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(g)||\"auto\"===g||\"outside\"===g)&&l(\"automargin\"),(\"inside\"===g||\"auto\"===g||Array.isArray(g))&&l(\"insidetextorientation\")}o(e,n,l);var y=l(\"hole\");if(l(\"title.text\")){var m=l(\"title.position\",y?\"middle center\":\"top center\");y||\"middle center\"!==m||(e.title.position=\"top center\"),i.coerceFont(l,\"title.font\",n.font)}l(\"sort\"),l(\"direction\"),l(\"rotation\"),l(\"pull\")}else e.visible=!1}}},20007:function(t,e,r){\"use strict\";var n=r(23469).appendArrayMultiPointValues;t.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},22209:function(t,e,r){\"use strict\";var n=r(91424),i=r(7901);t.exports=function(t,e,r,a){var o=r.marker.pattern;o&&o.shape?n.pointStyle(t,r,a,e):i.fill(t,e.color)}},53581:function(t,e,r){\"use strict\";var n=r(71828);function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}e.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},e.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},e.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}},e.castOption=function(t,r){return Array.isArray(t)?e.getFirstFilled(t,r):t||void 0},e.getRotationAngle=function(t){return(\"auto\"===t?0:t)*Math.PI/180}},58810:function(t,e,r){\"use strict\";t.exports={attributes:r(34e3),supplyDefaults:r(37434).supplyDefaults,supplyLayoutDefaults:r(92097),layoutAttributes:r(92774),calc:r(32354).calc,crossTraceCalc:r(32354).crossTraceCalc,plot:r(14575).plot,style:r(68357),styleOne:r(63463),moduleType:\"trace\",name:\"pie\",basePlotModule:r(13584),categories:[\"pie-like\",\"pie\",\"showLegend\"],meta:{}}},92774:function(t){\"use strict\";t.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},92097:function(t,e,r){\"use strict\";var n=r(71828),i=r(92774);t.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"piecolorway\",e.colorway),r(\"extendpiecolors\")}},14575:function(t,e,r){\"use strict\";var n=r(39898),i=r(74875),a=r(30211),o=r(7901),s=r(91424),l=r(71828),u=l.strScale,c=l.strTranslate,f=r(63893),h=r(72597),p=h.recordMinTextSize,d=h.clearMinTextSize,v=r(97313).TEXTPAD,g=r(53581),y=r(20007),m=r(71828).isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,u=i.trace,c=\"funnelarea\"===u.type;\"_hasHoverLabel\"in u||(u._hasHoverLabel=!1),\"_hasHoverEvent\"in u||(u._hasHoverEvent=!1),t.on(\"mouseover\",(function(t){var r=e._fullLayout,f=e._fullData[u.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:u._module},r,0)),\"all\"===h&&(h=\"label+text+value+percent+name\"),f.hovertemplate||\"none\"!==h&&\"skip\"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),v=s+t.pxmid[1]*(1-p),m=r.separators,x=[];if(h&&-1!==h.indexOf(\"label\")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf(\"text\")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,m),h&&-1!==h.indexOf(\"value\")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,m),h&&-1!==h.indexOf(\"percent\")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:u,x0:d-p*i.r,x1:d+p*i.r,y:v,_x0:c?o+t.TL[0]:d-p*i.r,_x1:c?o+t.TR[0]:d+p*i.r,_y0:c?s+t.TL[1]:v-p*i.r,_y1:c?s+t.BL[1]:v+p*i.r,text:x.join(\"<br>\"),name:f.hovertemplate||-1!==h.indexOf(\"name\")?f.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[y(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],u._hasHoverLabel=!0}u._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[y(t,f)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[u.index],o=n.select(this).datum();u._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[y(o,i)],event:n.event}),u._hasHoverEvent=!1),u._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),u._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[u.index];e._dragging||!1===r.hovermode||(e._hoverdata=[y(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append(\"text\").attr(\"data-notex\",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),u=s.bBox(o.node(),!0);r.titleBox={width:u.width,height:u.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,u=e.midangle,c=r.trace.insidetextorientation,f=\"horizontal\"===c,h=\"tangential\"===c,p=\"radial\"===c,d=\"auto\"===c,v=[];if(!d){var g,y=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),u=s<l?s:l;(a=\"tan\"===i?k(t,n,o,u,0):T(t,n,o,u,Math.PI/2)).textPosAngle=r,v.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)y(Math.PI*g,\"tan\");for(g=4;g>=-4;g-=2)y(Math.PI*(g+1),\"tan\")}if(f||p){for(g=4;g>=-4;g-=2)y(Math.PI*(g+1.5),\"rad\");for(g=4;g>=-4;g-=2)y(Math.PI*(g+.5),\"rad\")}}if(s||d||f){var m=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/m,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;v.push(a)}(d||p)&&((a=T(t,n,o,l,u)).textPosAngle=(e.startangle+e.stopangle)/2,v.push(a)),(d||h)&&((a=k(t,n,o,l,u)).textPosAngle=(e.startangle+e.stopangle)/2,v.push(a));for(var x=0,b=0,_=0;_<v.length;_++){var w=v[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return v[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*v);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*v);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=O(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,u=t.r/(void 0===(l=t.trace.aspectratio)?1:l),c=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(c+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?c*=2:-1!==a.title.position.indexOf(\"right\")&&(c+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=c/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function O(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 I(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&\"middle center\"!==a.title.position&&(l-=P(i,e));var u=s/2,c=l/2;\"funnelarea\"!==a.type||a.scalegroup||(c/=a.aspectratio),i.r=Math.min(u,c)/(1+O(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf(\"bottom\")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if((i=(r=t[n][0]).trace).scalegroup===s){var l;if(\"pie\"===i.type)l=r.r*r.r;else if(\"funnelarea\"===i.type){var u,c;i.aspectratio>1?c=(u=r.r)/i.aspectratio:u=(c=r.r)*i.aspectratio,l=(u*=(1+i.baseratio)/2)*c}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if((i=(r=t[n][0]).trace).scalegroup===s){var f=o*r.vTotal;\"funnelarea\"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function D(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function z(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&\"none\"!==o){var s,u=o.split(\"+\"),c=function(t){return-1!==u.indexOf(t)},f=c(\"label\"),h=c(\"text\"),p=c(\"value\"),d=c(\"percent\"),v=n.separators;if(s=f?[e.label]:[],h){var y=g.getFirstFilled(i.text,e.pts);m(y)&&s.push(y)}p&&s.push(g.formatPieValue(e.v,v)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,v)),e.text=s.join(\"<br>\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=g.getFirstFilled(i.text,e.pts);(m(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}t.exports={plot:function(t,e){var r=t._context.staticPlot,a=t._fullLayout,h=a._size;d(\"pie\",a),_(e,t),I(e,h);var v=l.makeTraceGroups(a._pielayer,e,\"trace\").each((function(e){var d=n.select(this),v=e[0],y=v.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,u=\"px0\",c=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,u=\"px1\",c=\"px0\"}for(n=D(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[u]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=D(a,s),r.midangle=s,n=D(a,s+=l*r.v/2),r.stopangle=s,r[c]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),d.attr(\"stroke-linejoin\",\"round\"),d.each((function(){var m=n.select(this).selectAll(\"g.slice\").data(e);m.enter().append(\"g\").classed(\"slice\",!0),m.exit().remove();var _=[[[],[]],[[],[]]],T=!1;m.each((function(i,o){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=y.index,_[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var u=v.cx,c=v.cy,h=n.select(this),d=h.selectAll(\"path.surface\").data([i]);if(d.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":r?\"none\":\"all\"}),h.call(x,t,e),y.pull){var m=+g.castOption(y.pull,i.pts)||0;m>0&&(u+=m*i.pxmid[0],c+=m*i.pxmid[1])}i.cxFinal=u,i.cyFinal=c;var k=y.hole;if(i.v===v.vTotal){var A=\"M\"+(u+i.px0[0])+\",\"+(c+i.px0[1])+P(i.px0,i.pxmid,!0,1)+P(i.pxmid,i.px0,!0,1)+\"Z\";k?d.attr(\"d\",\"M\"+(u+k*i.px0[0])+\",\"+(c+k*i.px0[1])+P(i.px0,i.pxmid,!1,k)+P(i.pxmid,i.px0,!1,k)+\"Z\"+A):d.attr(\"d\",A)}else{var M=P(i.px0,i.px1,!0,1);if(k){var S=1-k;d.attr(\"d\",\"M\"+(u+k*i.px1[0])+\",\"+(c+k*i.px1[1])+P(i.px1,i.px0,!1,k)+\"l\"+S*i.px0[0]+\",\"+S*i.px0[1]+M+\"Z\")}else d.attr(\"d\",\"M\"+u+\",\"+c+\"l\"+i.px0[0]+\",\"+i.px0[1]+M+\"Z\")}z(t,i,v);var E=g.castOption(y.textposition,i.pts),C=h.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==E?[0]:[]);C.enter().append(\"g\").classed(\"slicetext\",!0),C.exit().remove(),C.each((function(){var r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),h=l.ensureUniformFontSize(t,\"outside\"===E?function(t,e,r){return{color:g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,family:g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,size:g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size}}(y,i,a.font):b(y,i,a.font));r.text(i.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,h).call(f.convertToTspans,t);var d,m=s.bBox(r.node());if(\"outside\"===E)d=L(m,i);else if(d=w(m,i,v),\"auto\"===E&&d.scale<1){var x=l.ensureUniformFontSize(t,y.outsidetextfont);r.call(s.font,x),d=L(m=s.bBox(r.node()),i)}var _=d.textPosAngle,k=void 0===_?i.pxmid:D(v.r,_);if(d.targetX=u+k[0]*d.rCenter+(d.x||0),d.targetY=c+k[1]*d.rCenter+(d.y||0),R(d,m),d.outside){var A=d.targetY;i.yLabelMin=A-m.height/2,i.yLabelMid=A,i.yLabelMax=A+m.height/2,i.labelExtraX=0,i.labelExtraY=0,T=!0}d.fontSize=h.size,p(y.type,d,a),e[o].transform=d,l.setTransormAndDisplay(r,d)}))}function P(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return\"a\"+n*v.r+\",\"+n*v.r+\" 0 \"+i.largeArc+(r?\" 1 \":\" 0 \")+a+\",\"+o}}));var k=n.select(this).selectAll(\"g.titletext\").data(y.title.text?[0]:[]);if(k.enter().append(\"g\").classed(\"titletext\",!0),k.exit().remove(),k.each((function(){var e,r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),i=y.title.text;y._meta&&(i=l.templateString(i,y._meta)),r.text(i).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,y.title.font).call(f.convertToTspans,t),e=\"middle center\"===y.title.position?function(t){var 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t.link.label===i})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})),r&&h(e,t).selectAll(u.sankeyNode).filter(g(t)).call(m)}function _(t,e){var r=t.hoverlabel||{},n=i.nestedProperty(r,e).get();return!Array.isArray(n)&&n}t.exports=function(t,e){for(var r=t._fullLayout,i=r._paper,h=r._size,v=0;v<t._fullData.length;v++)if(t._fullData[v].visible&&t._fullData[v].type===u.sankey&&!t._fullData[v]._viewInitial){var g=t._fullData[v].node;t._fullData[v]._viewInitial={node:{groups:g.groups.slice(),x:g.x.slice(),y:g.y.slice()}}}var w=c(t,\"source:\")+\" \",T=c(t,\"target:\")+\" \",k=c(t,\"concentration:\")+\" \",A=c(t,\"incoming flow count:\")+\" \",M=c(t,\"outgoing flow count:\")+\" \";o(t,i,e,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},{linkEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(x.bind(0,r,i,!0)),\"skip\"!==r.link.trace.link.hoverinfo&&(r.link.fullData=r.link.trace,t.emit(\"plotly_hover\",{event:n.event,points:[r.link]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.link.trace.link;if(\"none\"!==o.hoverinfo&&\"skip\"!==o.hoverinfo){for(var u=[],c=0,h=0;h<i.flow.links.length;h++){var v=i.flow.links[h];if(\"closest\"!==t._fullLayout.hovermode||i.link.pointNumber===v.pointNumber){i.link.pointNumber===v.pointNumber&&(c=h),v.fullData=v.trace,o=i.link.trace.link;var g=m(v),y={valueLabel:a(i.valueFormat)(v.value)+i.valueSuffix};u.push({x:g[0],y:g[1],name:y.valueLabel,text:[v.label||\"\",w+v.source.label,T+v.target.label,v.concentrationscale?k+a(\"%0.2f\")(v.flow.labelConcentration):\"\"].filter(f).join(\"<br>\"),color:_(o,\"bgcolor\")||l.addOpacity(v.color,1),borderColor:_(o,\"bordercolor\"),fontFamily:_(o,\"font.family\"),fontSize:_(o,\"font.size\"),fontColor:_(o,\"font.color\"),nameLength:_(o,\"namelength\"),textAlign:_(o,\"align\"),idealAlign:n.event.x<g[0]?\"right\":\"left\",hovertemplate:o.hovertemplate,hovertemplateLabels:y,eventData:[v]})}}s.loneHover(u,{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t,anchorIndex:c}).each((function(){i.link.concentrationscale||p(this,.65),d(this)}))}}function m(t){var e,r;t.circular?(e=(t.circularPathData.leftInnerExtent+t.circularPathData.rightInnerExtent)/2,r=t.circularPathData.verticalFullExtent):(e=(t.source.x1+t.target.x0)/2,r=(t.y0+t.y1)/2);var n=[e,r];return\"v\"===t.trace.orientation&&n.reverse(),n[0]+=i.parent.translateX,n[1]+=i.parent.translateY,n}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(b.bind(0,i,a,!0)),\"skip\"!==i.link.trace.link.hoverinfo&&(i.link.fullData=i.link.trace,t.emit(\"plotly_unhover\",{event:n.event,points:[i.link]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r){var i=r.link;i.originalEvent=n.event,t._hoverdata=[i],s.click(t,{target:!0})}},nodeEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(y,r,i),\"skip\"!==r.node.trace.node.hoverinfo&&(r.node.fullData=r.node.trace,t.emit(\"plotly_hover\",{event:n.event,points:[r.node]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.node.trace.node;if(\"none\"!==o.hoverinfo&&\"skip\"!==o.hoverinfo){var l=n.select(e).select(\".\"+u.nodeRect),c=t._fullLayout._paperdiv.node().getBoundingClientRect(),h=l.node().getBoundingClientRect(),v=h.left-2-c.left,g=h.right+2-c.left,y=h.top+h.height/4-c.top,m={valueLabel:a(i.valueFormat)(i.node.value)+i.valueSuffix};i.node.fullData=i.node.trace,t._fullLayout._calcInverseTransform(t);var x=t._fullLayout._invScaleX,b=t._fullLayout._invScaleY,w=s.loneHover({x0:x*v,x1:x*g,y:b*y,name:a(i.valueFormat)(i.node.value)+i.valueSuffix,text:[i.node.label,A+i.node.targetLinks.length,M+i.node.sourceLinks.length].filter(f).join(\"<br>\"),color:_(o,\"bgcolor\")||i.tinyColorHue,borderColor:_(o,\"bordercolor\"),fontFamily:_(o,\"font.family\"),fontSize:_(o,\"font.size\"),fontColor:_(o,\"font.color\"),nameLength:_(o,\"namelength\"),textAlign:_(o,\"align\"),idealAlign:\"left\",hovertemplate:o.hovertemplate,hovertemplateLabels:m,eventData:[i.node]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t});p(w,.85),d(w)}}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(m,i,a),\"skip\"!==i.node.trace.node.hoverinfo&&(i.node.fullData=i.node.trace,t.emit(\"plotly_unhover\",{event:n.event,points:[i.node]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r,i){var a=r.node;a.originalEvent=n.event,t._hoverdata=[a],n.select(e).call(m,r,i),s.click(t,{target:!0})}}})}},3393:function(t,e,r){\"use strict\";var n=r(49887),i=r(81684).k4,a=r(39898),o=r(30838),s=r(86781),l=r(85247),u=r(84267),c=r(7901),f=r(91424),h=r(71828),p=h.strTranslate,d=h.strRotate,v=r(28984),g=v.keyFun,y=v.repeat,m=v.unwrap,x=r(63893),b=r(73972),_=r(18783),w=_.CAP_SHIFT,T=_.LINE_SPACING;function k(t,e,r){var n,i=m(e),a=i.trace,c=a.domain,f=\"h\"===a.orientation,p=a.node.pad,d=a.node.thickness,v=t.width*(c.x[1]-c.x[0]),g=t.height*(c.y[1]-c.y[0]),y=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[v,g]:[g,v]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(y).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn(\"node.pad was reduced to \",n.nodePadding(),\" to fit within the figure.\"),i._groupLookup){var M,S=parseInt(i._groupLookup[A]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){M=k.nodes[_];break}if(M){var E={pointNumber:parseInt(A),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+\":\"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=u(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var c=0;for(w=0;w<r.targetLinks.length;w++)c+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/c}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*v,a.node.y[_]*g];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}\"snap\"===a.arrangement&&function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(y=k.nodes).forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p})),n.update(k)}return{circular:b,key:r,trace:a,guid:h.randstr(),horizontal:f,width:v,height:g,nodePad:a.node.pad,nodeLineColor:a.node.line.color,nodeLineWidth:a.node.line.width,linkLineColor:a.link.line.color,linkLineWidth:a.link.line.width,linkArrowLength:a.link.arrowlen,valueFormat:a.valueformat,valueSuffix:a.valuesuffix,textFont:a.textfont,translateX:c.x[0]*t.width+t.margin.l,translateY:t.height-c.y[1]*t.height+t.margin.t,dragParallel:f?g:v,dragPerpendicular:f?v:g,arrangement:a.arrangement,sankey:n,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function A(t,e,r){var n=u(e.color),i=e.source.label+\"|\"+e.target.label+\"__\"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:c.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,linkArrowLength:t.linkArrowLength,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){var e=t.linkArrowLength;if(t.link.circular)return function(t,e){var r=t.width/2,n=t.circularPathData;return\"top\"===t.circularLinkType?\"M \"+(n.targetX-e)+\" \"+(n.targetY+r)+\" L\"+(n.rightInnerExtent-e)+\" \"+(n.targetY+r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 1 \"+(n.rightFullExtent-r-e)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r-e)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 1 \"+(n.rightInnerExtent-e)+\" \"+(n.verticalFullExtent-r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 1 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 0 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+(n.rightInnerExtent-e)+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 0 \"+(n.rightFullExtent+r-e)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r-e)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 0 \"+(n.rightInnerExtent-e)+\" \"+(n.targetY-r)+\"L\"+(n.targetX-e)+\" \"+(n.targetY-r)+(e>0?\"L\"+n.targetX+\" \"+n.targetY:\"\")+\"Z\":\"M \"+(n.targetX-e)+\" \"+(n.targetY-r)+\" L\"+(n.rightInnerExtent-e)+\" \"+(n.targetY-r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 0 \"+(n.rightFullExtent-r-e)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r-e)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 0 \"+(n.rightInnerExtent-e)+\" \"+(n.verticalFullExtent+r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 0 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 1 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+(n.rightInnerExtent-e)+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 1 \"+(n.rightFullExtent+r-e)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r-e)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 1 \"+(n.rightInnerExtent-e)+\" \"+(n.targetY+r)+\"L\"+(n.targetX-e)+\" \"+(n.targetY+r)+(e>0?\"L\"+n.targetX+\" \"+n.targetY:\"\")+\"Z\"}(t.link,e);var r=Math.abs((t.link.target.x0-t.link.source.x1)/2);e>r&&(e=r);var n=t.link.source.x1,a=t.link.target.x0-e,o=i(n,a),s=o(.5),l=o(.5),u=t.link.y0-t.link.width/2,c=t.link.y0+t.link.width/2,f=t.link.y1-t.link.width/2,h=t.link.y1+t.link.width/2,p=\"M\"+n+\",\"+u,d=\"C\"+s+\",\"+u+\" \"+l+\",\"+f+\" \"+a+\",\"+f,v=\"C\"+l+\",\"+h+\" \"+s+\",\"+c+\" \"+n+\",\"+c,g=e>0?\"L\"+(a+e)+\",\"+(f+t.link.width/2):\"\";return p+d+(g+=\"L\"+a+\",\"+h)+v+\"Z\"}}function S(t,e){var r=u(e.color),n=l.nodePadAcross,i=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var a=e.dx,o=Math.max(.5,e.dy),s=\"node_\"+e.pointNumber;return e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:c.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join(\"_\"),interactionState:t.interactionState,figure:t}}function E(t){t.attr(\"transform\",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function 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b=u<2||i[0]!==i[u-1]||o[0]!==o[u-1];b&&(\"tozerox\"===y||\"tonextx\"===y&&(p||\"h\"===d))?m.tozero=!0:(e.error_y||{}).visible||\"tonexty\"!==y&&\"tozeroy\"!==y&&(l.hasMarkers(e)||l.hasText(e))||(m.padded=!1,m.ppad=0),b&&(\"tozeroy\"===y||\"tonexty\"===y&&(p||\"v\"===d))?x.tozero=!0:\"tonextx\"!==y&&\"tozerox\"!==y||(x.padded=!1),f&&(e._extremes[f]=a.findExtremes(r,i,m)),h&&(e._extremes[h]=a.findExtremes(n,o,x))}function p(t,e){if(l.hasMarkers(t)){var r,n=t.marker,o=1.6*(t.marker.sizeref||1);if(r=\"area\"===t.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/o),3)}:function(t){return Math.max((t||0)/o,3)},i.isArrayOrTypedArray(n.size)){var s={type:\"linear\"};a.setConvert(s);for(var u=s.makeCalcdata(t.marker,\"size\"),c=new Array(e),f=0;f<e;f++)c[f]=r(u[f]);return c}return r(n.size)}}function d(t,e){var r=v(e),n=t._firstScatter;n[r]||(n[r]=e.uid)}function v(t){var e=t.stackgroup;return t.xaxis+t.yaxis+t.type+(e?\"-\"+e:\"\")}function g(t,e,r,n){var i=t.stackgroup;if(i){var 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n=r(52075).hasColorscale,i=r(78803),a=r(34098);t.exports=function(t,e){a.hasLines(e)&&n(e,\"line\")&&i(t,e,{vals:e.line.color,containerStr:\"line\",cLetter:\"c\"}),a.hasMarkers(e)&&(n(e,\"marker\")&&i(t,e,{vals:e.marker.color,containerStr:\"marker\",cLetter:\"c\"}),n(e,\"marker.line\")&&i(t,e,{vals:e.marker.line.color,containerStr:\"marker.line\",cLetter:\"c\"}))}},47581:function(t){\"use strict\";t.exports={PTS_LINESONLY:20,minTolerance:.2,toleranceGrowth:10,maxScreensAway:20,eventDataKeys:[]}},72626:function(t,e,r){\"use strict\";var n=r(47761),i=r(11661).setGroupPositions;function a(t,e,r,n,i,a,o){i[n]=!0;var s={i:null,gap:!0,s:0};if(s[o]=r,t.splice(e,0,s),e&&r===t[e-1][o]){var l=t[e-1];s.s=l.s,s.i=l.i,s.gap=l.gap}else a&&(s.s=function(t,e,r,n){var i=t[e-1],a=t[e+1];return a?i?i.s+(a.s-i.s)*(r-i[n])/(a[n]-i[n]):a.s:i.s}(t,e,r,o));e||(t[0].t=t[1].t,t[0].trace=t[1].trace,delete t[1].t,delete 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n=r(82196),i=r(9012),a=r(5386).fF,o=r(5386).si,s=r(50693),l=r(1426).extendFlat,u=n.marker,c=n.line,f=u.line;t.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:c.color,width:c.width,dash:c.dash,backoff:c.backoff,shape:l({},c.shape,{values:[\"linear\",\"spline\"]}),smoothing:c.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,fill:l({},n.fill,{values:[\"none\",\"toself\",\"tonext\"],dflt:\"none\"}),fillcolor:n.fillcolor,marker:l({symbol:u.symbol,opacity:u.opacity,maxdisplayed:u.maxdisplayed,angle:u.angle,angleref:u.angleref,standoff:u.standoff,size:u.size,sizeref:u.sizeref,sizemin:u.sizemin,sizemode:u.sizemode,line:l({width:f.width,editType:\"calc\"},s(\"marker.line\")),gradient:u.gradient,editType:\"calc\"},s(\"marker\")),textfont:n.textfont,textposition:n.textposition,selected:n.selected,unselected:n.unselected,hoverinfo:l({},i.hoverinfo,{flags:[\"a\",\"b\",\"text\",\"name\"]}),hoveron:n.hoveron,hovertemplate:a()}},34618:function(t,e,r){\"use 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r,n,i=e._length,o=e.marker,l={},u=s.isArrayOrTypedArray(o.symbol),f=s.isArrayOrTypedArray(o.angle),d=s.isArrayOrTypedArray(o.color),v=s.isArrayOrTypedArray(o.line.color),g=s.isArrayOrTypedArray(o.opacity),y=s.isArrayOrTypedArray(o.size),m=s.isArrayOrTypedArray(o.line.width);if(u||(n=p.isOpenSymbol(o.symbol)),u||d||v||g||f){l.symbols=new Array(i),l.angles=new Array(i),l.colors=new Array(i),l.borderColors=new Array(i);var x=o.symbol,b=o.angle,_=c(o,o.opacity,i),w=c(o.line,o.opacity,i);if(!Array.isArray(w[0])){var T=w;for(w=Array(i),r=0;r<i;r++)w[r]=T}if(!Array.isArray(_[0])){var k=_;for(_=Array(i),r=0;r<i;r++)_[r]=k}if(!Array.isArray(x)){var A=x;for(x=Array(i),r=0;r<i;r++)x[r]=A}if(!Array.isArray(b)){var M=b;for(b=Array(i),r=0;r<i;r++)b[r]=M}for(l.symbols=x,l.angles=b,l.colors=_,l.borderColors=w,r=0;r<i;r++)u&&(n=p.isOpenSymbol(o.symbol[r])),n&&(w[r]=_[r].slice(),_[r]=_[r].slice(),_[r][3]=0);for(l.opacity=e.opacity,l.markers=new Array(i),r=0;r<i;r++)l.markers[r]=E({mx:l.symbols[r],ma:l.angles[r]},e)}else n?(l.color=a(o.color,\"uint8\"),l.color[3]=0,l.borderColor=a(o.color,\"uint8\")):(l.color=a(o.color,\"uint8\"),l.borderColor=a(o.line.color,\"uint8\")),l.opacity=e.opacity*o.opacity,l.marker=E({mx:o.symbol,ma:o.angle},e);var S,L=h(e,1);if(y||m){var C,P=l.sizes=new Array(i),O=l.borderSizes=new Array(i),I=0;if(y){for(r=0;r<i;r++)P[r]=L(o.size[r]),I+=P[r];C=I/i}else for(S=L(o.size),r=0;r<i;r++)P[r]=S;if(m)for(r=0;r<i;r++)O[r]=o.line.width[r];else for(S=o.line.width,r=0;r<i;r++)O[r]=S;l.sizeAvg=C}else l.size=L(o&&o.size||10),l.borderSizes=L(o.line.width);return l}function b(t,e,r){var n=e.marker,i={};return r?(r.marker&&r.marker.symbol?i=x(0,s.extendFlat({},n,r.marker)):r.marker&&(r.marker.size&&(i.size=r.marker.size),r.marker.color&&(i.colors=r.marker.color),void 0!==r.marker.opacity&&(i.opacity=r.marker.opacity)),i):i}function _(t,e,r){var n={};if(!r)return n;if(r.textfont){var i={opacity:1,text:e.text,texttemplate:e.texttemplate,textposition:e.textposition,textfont:s.extendFlat({},e.textfont)};r.textfont&&s.extendFlat(i.textfont,r.textfont),n=m(t,i)}return n}function w(t,e,r){var n={capSize:2*e.width*r,lineWidth:e.thickness*r,color:e.color};return e.copy_ystyle&&(n=t.error_y),n}var T=d.SYMBOL_SDF_SIZE,k=d.SYMBOL_SIZE,A=d.SYMBOL_STROKE,M={},S=l.symbolFuncs[0](.05*k);function E(t,e){var r,n,a=t.mx;if(\"circle\"===a)return null;var o=l.symbolNumber(a),s=l.symbolFuncs[o%100],u=!!l.symbolNoDot[o%100],c=!!l.symbolNoFill[o%100],f=p.isDotSymbol(a);if(t.ma&&(a+=\"_\"+t.ma),M[a])return M[a];var h=l.getMarkerAngle(t,e);return r=f&&!u?s(1.1*k,h)+S:s(k,h),n=i(r,{w:T,h:T,viewBox:[-k,-k,k,k],stroke:c?A:-A}),M[a]=n,n||null}t.exports={style:function(t,e){var r,n={marker:void 0,markerSel:void 0,markerUnsel:void 0,line:void 0,fill:void 0,errorX:void 0,errorY:void 0,text:void 0,textSel:void 0,textUnsel:void 0},i=t._context.plotGlPixelRatio;if(!0!==e.visible)return n;if(f.hasText(e)&&(n.text=m(t,e),n.textSel=_(t,e,e.selected),n.textUnsel=_(t,e,e.unselected)),f.hasMarkers(e)&&(n.marker=x(0,e),n.markerSel=b(0,e,e.selected),n.markerUnsel=b(0,e,e.unselected),!e.unselected&&s.isArrayOrTypedArray(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]=v*a[r]}if(f.hasLines(e)){n.line={overlay:!0,thickness:e.line.width*i,color:e.line.color,opacity:e.opacity};var o=(d.DASHES[e.line.dash]||[1]).slice();for(r=0;r<o.length;++r)o[r]*=e.line.width*i;n.line.dashes=o}return e.error_x&&e.error_x.visible&&(n.errorX=w(e,e.error_x,i)),e.error_y&&e.error_y.visible&&(n.errorY=w(e,e.error_y,i)),e.fill&&\"none\"!==e.fill&&(n.fill={closed:!0,fill:e.fillcolor,thickness:0}),n},markerStyle:x,markerSelection:b,linePositions:function(t,e,r){var n,i,a=r.length,o=a/2;if(f.hasLines(e)&&o)if(\"hv\"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i+2],r[2*i+1]));n.push(r[a-2],r[a-1])}else if(\"hvh\"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var s=(r[2*i]+r[2*i+2])/2;n.push(r[2*i],r[2*i+1],s,r[2*i+1],s,r[2*i+3])}n.push(r[a-2],r[a-1])}else if(\"vhv\"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var l=(r[2*i+1]+r[2*i+3])/2;n.push(r[2*i],r[2*i+1],r[2*i],l,r[2*i+2],l)}n.push(r[a-2],r[a-1])}else if(\"vh\"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+3]));n.push(r[a-2],r[a-1])}else n=r;var u=!1;for(i=0;i<n.length;i++)if(isNaN(n[i])){u=!0;break}var c=u||n.length>d.TOO_MANY_POINTS||f.hasMarkers(e)?\"rect\":\"round\";if(u&&e.connectgaps){var h=n[0],p=n[1];for(i=0;i<n.length;i+=2)isNaN(n[i])||isNaN(n[i+1])?(n[i]=h,n[i+1]=p):(h=n[i],p=n[i+1])}return{join:c,positions:n}},errorBarPositions:function(t,e,r,i,a){var s=o.getComponentMethod(\"errorbars\",\"makeComputeError\"),l=u.getFromId(t,e.xaxis,\"x\"),c=u.getFromId(t,e.yaxis,\"y\"),f=r.length/2,h={};function p(t,i){var a=i._id.charAt(0),o=e[\"error_\"+a];if(o&&o.visible&&(\"linear\"===i.type||\"log\"===i.type)){for(var l=s(o),u={x:0,y:1}[a],c={x:[0,1,2,3],y:[2,3,0,1]}[a],p=new Float64Array(4*f),d=1/0,v=-1/0,g=0,y=0;g<f;g++,y+=4){var m=t[g];if(n(m)){var x=r[2*g+u],b=l(m,g),_=b[0],w=b[1];if(n(_)&&n(w)){var T=m-_,k=m+w;p[y+c[0]]=x-i.c2l(T),p[y+c[1]]=i.c2l(k)-x,p[y+c[2]]=0,p[y+c[3]]=0,d=Math.min(d,m-_),v=Math.max(v,m+w)}}}h[a]={positions:r,errors:p,_bnds:[d,v]}}}return p(i,l),p(a,c),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(f.hasMarkers(e)){var s=r.font,l=r.align,u=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var c=n.sizes?n.sizes[i]:n.size,h=Array.isArray(s)?s[i].size:s.size,p=Array.isArray(l)?l.length>1?l[i]:l[0]:l,d=Array.isArray(u)?u.length>1?u[i]:u[0]:u,v=g[p],y=g[d],m=c?c/.8+1:0,x=-y*m-.5*y;o.offset[i]=[v*m/h,x/h]}}return o}}},47148:function(t,e,r){\"use strict\";var n=r(71828),i=r(73972),a=r(68645),o=r(42341),s=r(47581),l=r(34098),u=r(67513),c=r(73927),f=r(49508),h=r(11058),p=r(28908),d=r(82410);t.exports=function(t,e,r,v){function g(r,i){return n.coerce(t,e,o,r,i)}var y=!!t.marker&&a.isOpenSymbol(t.marker.symbol),m=l.isBubble(t),x=u(t,e,v,g);if(x){c(t,e,v,g),g(\"xhoverformat\"),g(\"yhoverformat\");var b=x<s.PTS_LINESONLY?\"lines+markers\":\"lines\";g(\"text\"),g(\"hovertext\"),g(\"hovertemplate\"),g(\"mode\",b),l.hasLines(e)&&(g(\"connectgaps\"),h(t,e,r,v,g),g(\"line.shape\")),l.hasMarkers(e)&&(f(t,e,r,v,g,{noAngleRef:!0,noStandOff:!0}),g(\"marker.line.width\",y||m?1:0)),l.hasText(e)&&(g(\"texttemplate\"),d(t,e,v,g));var _=(e.line||{}).color,w=(e.marker||{}).color;g(\"fill\"),\"none\"!==e.fill&&p(t,e,r,g);var T=i.getComponentMethod(\"errorbars\",\"supplyDefaults\");T(t,e,_||w||r,{axis:\"y\"}),T(t,e,_||w||r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},5345:function(t,e,r){\"use strict\";var n=r(71828),i=r(7901),a=r(37822).DESELECTDIM;t.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,u=l._scene,c=l.index,f=u.selectBatch[c],h=u.unselectBatch[c],p=u.textOptions[c],d=u.textSelectedOptions[c]||{},v=u.textUnselectedOptions[c]||{},g=n.extendFlat({},p);if(f.length||h.length){var y=d.color,m=v.color,x=p.color,b=Array.isArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=y||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=m||(y?_:i.addOpacity(_,a))}}u.glText[c].update(g)}}},68101:function(t,e,r){\"use strict\";var n=r(8225);t.exports=function(t,e,r){var i=t.i;return\"x\"in t||(t.x=e._x[i]),\"y\"in t||(t.y=e._y[i]),n(t,e,r)}},68645:function(t,e,r){\"use strict\";var n=r(78232);e.isOpenSymbol=function(t){return\"string\"==typeof t?n.OPEN_RE.test(t):t%200>100},e.isDotSymbol=function(t){return\"string\"==typeof t?n.DOT_RE.test(t):t>200}},20794:function(t,e,r){\"use strict\";var n=r(73972),i=r(71828),a=r(34603);function o(t,e,r,o){var s=t.xa,l=t.ya,u=t.distance,c=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.ma=i.isArrayOrTypedArray(d.angle)?d.angle[f]:d.angle,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var v=d&&d.line;v&&(h.mlc=Array.isArray(v.color)?v.color[f]:v.color,h.mlw=i.isArrayOrTypedArray(v.width)?v.width[f]:v.width);var g=d&&d.gradient;g&&\"none\"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[f]:g.type,h.mgc=Array.isArray(g.color)?g.color[f]:g.color);var y=s.c2p(h.x,!0),m=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:y-x,x1:y+x,xLabelVal:k?k[f]:h.x,y0:m-x,y1:m+x,yLabelVal:A?A[f]:h.y,cd:T,distance:u,spikeDistance:c,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(h,o,M),M}t.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,u,c,f,h,p,d,v=t.cd,g=v[0].t,y=v[0].trace,m=t.xa,x=t.ya,b=g.x,_=g.y,w=m.c2p(e),T=x.c2p(r),k=t.distance;if(g.tree){var A=m.p2c(w-k),M=m.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i=\"x\"===n?g.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):g.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=g.ids;var L=k;if(\"x\"===n){var C=!!y.xperiodalignment,P=!!y.yperiodalignment;for(c=0;c<i.length;c++){if(l=b[a=i[c]],f=Math.abs(m.c2p(l)-w),C){var O=m.c2p(y._xStarts[a]),I=m.c2p(y._xEnds[a]);f=w>=Math.min(O,I)&&w<=Math.max(O,I)?0:1/0}if(f<L){if(L=f,u=_[a],h=x.c2p(u)-T,P){var D=x.c2p(y._yStarts[a]),z=x.c2p(y._yEnds[a]);h=T>=Math.min(D,z)&&T<=Math.max(D,z)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[c]}}}else for(c=i.length-1;c>-1;c--)l=b[a=i[c]],u=_[a],f=m.c2p(l)-w,h=x.c2p(u)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 0===s?[t]:[o(t,b,_,y)]},calcHover:o}},68868:function(t,e,r){\"use strict\";var n=r(72156);n.plot=r(26787),t.exports=n},26787:function(t,e,r){\"use strict\";var n=r(11870),i=r(46075),a=r(3593),o=r(42505),s=r(71828),l=r(64505).selectMode,u=r(79749),c=r(34098),f=r(68687),h=r(5345).styleTextSelection,p={};function d(t,e,r,n){var i=t._size,a=t.width*n,o=t.height*n,s=i.l*n,l=i.b*n,u=i.r*n,c=i.t*n,f=i.w*n,h=i.h*n;return[s+e.domain[0]*f,l+r.domain[0]*h,a-u-(1-e.domain[1])*f,o-c-(1-r.domain[1])*h]}(t.exports=function(t,e,r){if(r.length){var v,g,y=t._fullLayout,m=e._scene,x=e.xaxis,b=e.yaxis;if(m)if(u(t,[\"ANGLE_instanced_arrays\",\"OES_element_index_uint\"],p)){var 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n=r(5386).fF,i=r(5386).si,a=r(19316),o=r(82196),s=r(23585),l=r(9012),u=r(50693),c=r(1426).extendFlat,f=r(30962).overrideAll,h=r(23585),p=a.line,d=a.marker;t.exports=f({lon:a.lon,lat:a.lat,cluster:{enabled:{valType:\"boolean\"},maxzoom:c({},h.layers.maxzoom,{}),step:{valType:\"number\",arrayOk:!0,dflt:-1,min:-1},size:{valType:\"number\",arrayOk:!0,dflt:20,min:0},color:{valType:\"color\",arrayOk:!0},opacity:c({},d.opacity,{dflt:1})},mode:c({},o.mode,{dflt:\"markers\"}),text:c({},o.text,{}),texttemplate:i({editType:\"plot\"},{keys:[\"lat\",\"lon\",\"text\"]}),hovertext:c({},o.hovertext,{}),line:{color:p.color,width:p.width},connectgaps:o.connectgaps,marker:c({symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},angle:{valType:\"number\",dflt:\"auto\",arrayOk:!0},allowoverlap:{valType:\"boolean\",dflt:!1},opacity:d.opacity,size:d.size,sizeref:d.sizeref,sizemin:d.sizemin,sizemode:d.sizemode},u(\"marker\")),fill:a.fill,fillcolor:o.fillcolor,textfont:s.layers.symbol.textfont,textposition:s.layers.symbol.textposition,below:{valType:\"string\"},selected:{marker:o.selected.marker},unselected:{marker:o.unselected.marker},hoverinfo:c({},l.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]}),hovertemplate:n()},\"calc\",\"nested\")},15790:function(t,e,r){\"use 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r,a=e[0].trace,b=!0===a.visible&&0!==a._length,_=\"none\"!==a.fill,w=c.hasLines(a),T=c.hasMarkers(a),k=c.hasText(a),A=T&&\"circle\"===a.marker.symbol,M=T&&\"circle\"!==a.marker.symbol,S=a.cluster&&a.cluster.enabled,E=v(\"fill\"),L=v(\"line\"),C=v(\"circle\"),P=v(\"symbol\"),O={fill:E,line:L,circle:C,symbol:P};if(!b)return O;if((_||w)&&(r=o.calcTraceToLineCoords(e)),_&&(E.geojson=o.makePolygon(r),E.layout.visibility=\"visible\",i.extendFlat(E.paint,{\"fill-color\":a.fillcolor})),w&&(L.geojson=o.makeLine(r),L.layout.visibility=\"visible\",i.extendFlat(L.paint,{\"line-width\":a.line.width,\"line-color\":a.line.color,\"line-opacity\":a.opacity})),A){var I=function(t){var e,r,a,o,c=t[0].trace,f=c.marker,h=c.selectedpoints,p=i.isArrayOrTypedArray(f.color),d=i.isArrayOrTypedArray(f.size),v=i.isArrayOrTypedArray(f.opacity);function g(t){return c.opacity*t}p&&(r=s.hasColorscale(c,\"marker\")?s.makeColorScaleFuncFromTrace(f):i.identity),d&&(a=u(c)),v&&(o=function(t){return 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a.marker&&\"auto\"!==a.marker.angle&&i.extendFlat(P.layout,{\"icon-rotate\":{type:\"identity\",property:\"angle\"},\"icon-rotation-alignment\":\"map\"}),P.layout[\"icon-allow-overlap\"]=a.marker.allowoverlap,i.extendFlat(P.paint,{\"icon-opacity\":a.opacity*a.marker.opacity,\"icon-color\":a.marker.color})),k)){var D=(a.marker||{}).size,z=f(a.textposition,D);i.extendFlat(P.layout,{\"text-size\":a.textfont.size,\"text-anchor\":z.anchor,\"text-offset\":z.offset,\"text-font\":a.textfont.family.split(\", \")}),i.extendFlat(P.paint,{\"text-color\":a.textfont.color,\"text-opacity\":a.opacity})}return O}},76645:function(t,e,r){\"use strict\";var n=r(71828),i=r(34098),a=r(49508),o=r(11058),s=r(82410),l=r(28908),u=r(99181),c=[\"Metropolis Black Italic\",\"Metropolis Black\",\"Metropolis Bold Italic\",\"Metropolis Bold\",\"Metropolis Extra Bold Italic\",\"Metropolis Extra Bold\",\"Metropolis Extra Light Italic\",\"Metropolis Extra Light\",\"Metropolis Light Italic\",\"Metropolis Light\",\"Metropolis Medium Italic\",\"Metropolis Medium\",\"Metropolis Regular Italic\",\"Metropolis Regular\",\"Metropolis Semi Bold Italic\",\"Metropolis Semi Bold\",\"Metropolis Thin Italic\",\"Metropolis Thin\",\"Open Sans Bold Italic\",\"Open Sans Bold\",\"Open Sans Extra Bold Italic\",\"Open Sans Extra Bold\",\"Open Sans Italic\",\"Open Sans Light Italic\",\"Open Sans Light\",\"Open Sans Regular\",\"Open Sans Semibold Italic\",\"Open Sans Semibold\",\"Klokantech Noto Sans Bold\",\"Klokantech Noto Sans CJK Bold\",\"Klokantech Noto Sans CJK Regular\",\"Klokantech Noto Sans Italic\",\"Klokantech Noto Sans Regular\"];t.exports=function(t,e,r,f){function h(r,i){return n.coerce(t,e,u,r,i)}function p(r,i){return n.coerce2(t,e,u,r,i)}var d=function(t,e,r){var n=r(\"lon\")||[],i=r(\"lat\")||[],a=Math.min(n.length,i.length);return 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n=r(35948),i=r(20794).calcHover;t.exports={hoverPoints:function(t,e,r){var a=t.cd[0].trace,o=t.scene.matrixOptions.cdata,s=t.xa,l=t.ya,u=s.c2p(e),c=l.c2p(r),f=t.distance,h=n.getDimIndex(a,s),p=n.getDimIndex(a,l);if(!1===h||!1===p)return[t];for(var d,v,g=o[h],y=o[p],m=f,x=0;x<g.length;x++){var b=g[x],_=y[x],w=s.c2p(b)-u,T=l.c2p(_)-c,k=Math.sqrt(w*w+T*T);k<m&&(m=v=k,d=x)}return t.index=d,t.distance=m,t.dxy=v,void 0===d?[t]:[i(t,g,y,a)]}}},6419:function(t,e,r){\"use strict\";var n=r(65017);n.basePlotModule=r(16947),t.exports=n},79410:function(t,e,r){\"use strict\";var n=r(60487),i=r(71828),a=r(41675),o=r(64505).selectMode;function s(t,e){var r,s,l,u,c,f=t._fullLayout,h=f._size,p=e.trace,d=e.t,v=f._splomScenes[p.uid],g=v.matrixOptions,y=g.cdata,m=f._glcanvas.data()[0].regl,x=f.dragmode;if(0!==y.length){g.lower=p.showupperhalf,g.upper=p.showlowerhalf,g.diagonal=p.diagonal.visible;var b=p._visibleDims,_=y.length,w=v.viewOpts={};for(w.ranges=new Array(_),w.domains=new 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B=i.extendFlat({},g,v.unselectedOptions,w),N=i.extendFlat({},g,v.selectedOptions,w);v.matrix.update(B,N),P=!1}}else d.xpx=d.ypx=null;if(P){var j=i.extendFlat({},g,w);v.matrix.update(j,null)}}}t.exports=function(t,e,r){if(r.length)for(var n=0;n<r.length;n++)s(t,r[n][0])}},10164:function(t,e,r){\"use strict\";var n=r(71828);t.exports=function(t,e){var r=t._fullLayout,i=e.uid,a=r._splomScenes;a||(a=r._splomScenes={});var o={dirty:!0,selectBatch:[],unselectBatch:[]},s=a[e.uid];return s||((s=a[i]=n.extendFlat({},o,{matrix:!1,selectBatch:[],unselectBatch:[]})).draw=function(){s.matrix&&s.matrix.draw&&(s.selectBatch.length||s.unselectBatch.length?s.matrix.draw(s.unselectBatch,s.selectBatch):s.matrix.draw()),s.dirty=!1},s.destroy=function(){s.matrix&&s.matrix.destroy&&s.matrix.destroy(),s.matrixOptions=null,s.selectBatch=null,s.unselectBatch=null,s=null}),s.dirty||n.extendFlat(s,o),s}},8689:function(t,e,r){\"use strict\";var n=r(71828),i=n.pushUnique,a=r(34098),o=r(35948);t.exports=function(t,e){var r=t.cd,s=r[0].trace,l=r[0].t,u=t.scene,c=u.matrixOptions.cdata,f=t.xaxis,h=t.yaxis,p=[];if(!u)return p;var d=!a.hasMarkers(s)&&!a.hasText(s);if(!0!==s.visible||d)return p;var v=o.getDimIndex(s,f),g=o.getDimIndex(s,h);if(!1===v||!1===g)return p;var y=l.xpx[v],m=l.ypx[g],x=c[v],b=c[g],_=(t.scene.selectBatch||[]).slice(),w=[];if(!1!==e&&!e.degenerate)for(var T=0;T<x.length;T++)e.contains([y[T],m[T]],null,T,t)?(p.push({pointNumber:T,x:x[T],y:b[T]}),i(_,T)):-1!==_.indexOf(T)?i(_,T):w.push(T);var k=u.matrixOptions;return _.length||w.length?u.selectBatch.length||u.unselectBatch.length||u.matrix.update(u.unselectedOptions,n.extendFlat({},k,u.selectedOptions,u.viewOpts)):u.matrix.update(k,null),u.selectBatch=_,u.unselectBatch=w,p}},21850:function(t,e,r){\"use strict\";var n=r(50693),i=r(12663).axisHoverFormat,a=r(5386).fF,o=r(2418),s=r(9012),l=r(1426).extendFlat,u={x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},u:{valType:\"data_array\",editType:\"calc\"},v:{valType:\"data_array\",editType:\"calc\"},w:{valType:\"data_array\",editType:\"calc\"},starts:{x:{valType:\"data_array\",editType:\"calc\"},y:{valType:\"data_array\",editType:\"calc\"},z:{valType:\"data_array\",editType:\"calc\"},editType:\"calc\"},maxdisplayed:{valType:\"integer\",min:0,dflt:1e3,editType:\"calc\"},sizeref:{valType:\"number\",editType:\"calc\",min:0,dflt:1},text:{valType:\"string\",dflt:\"\",editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",editType:\"calc\"},hovertemplate:a({editType:\"calc\"},{keys:[\"tubex\",\"tubey\",\"tubez\",\"tubeu\",\"tubev\",\"tubew\",\"norm\",\"divergence\"]}),uhoverformat:i(\"u\",1),vhoverformat:i(\"v\",1),whoverformat:i(\"w\",1),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),showlegend:l({},s.showlegend,{dflt:!1})};l(u,n(\"\",{colorAttr:\"u/v/w 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L=function(){m=0,M=[],S=[],E=[]};(!m||m<M.length*S.length*E.length)&&L();var C=function(t){return\"x\"===t?v:\"y\"===t?g:y},P=function(t){return\"x\"===t?M:\"y\"===t?S:E},O=function(t){return t[m-1]<t[0]?-1:1},I=C(A[1]),D=C(A[3]),z=C(A[5]),R=P(A[1]).length,F=P(A[3]).length,B=P(A[5]).length,N=!1,j=function(t,e,r){return R*(F*t+e)+r},U=O(C(A[1])),V=O(C(A[3])),H=O(C(A[5]));for(e=0;e<B-1;e++){for(r=0;r<F-1;r++){for(i=0;i<R-1;i++){var q=j(e,r,i),G=j(e,r,i+1),Z=j(e,r+1,i),Y=j(e+1,r,i);if(I[q]*U<I[G]*U&&D[q]*V<D[Z]*V&&z[q]*H<z[Y]*H||(N=!0),N)break}if(N)break}if(N)break}return N&&(n.warn(\"Encountered arbitrary coordinates! 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y=n[c];r.xMax=Math.max(r.xMax,y),r.xMin=Math.min(r.xMin,y);var m=o[c];r.yMax=Math.max(r.yMax,m),r.yMin=Math.min(r.yMin,m);var x=l[c];r.zMax=Math.max(r.zMax,x),r.zMin=Math.min(r.zMin,x)}e._slen=u,e._normMax=f,e._xbnds=[r.xMin,r.xMax],e._ybnds=[r.yMin,r.yMax],e._zbnds=[r.zMin,r.zMax]},filter:s,processGrid:a}},90154:function(t,e,r){\"use strict\";var n=r(9330).gl_streamtube3d,i=n.createTubeMesh,a=r(71828),o=r(81697).parseColorScale,s=r(21081).extractOpts,l=r(90060),u={xaxis:0,yaxis:1,zaxis:2};function c(t,e){this.scene=t,this.uid=e,this.mesh=null,this.data=null}var f=c.prototype;function h(t){var e=t.length;return e>2?t.slice(1,e-1):2===e?[(t[0]+t[1])/2]:t}function p(t){var e=t.length;return 1===e?[.5,.5]:[t[1]-t[0],t[e-1]-t[e-2]]}function d(t,e){var r=t.fullSceneLayout,i=t.dataScale,c=e._len,f={};function d(t,e){var n=r[e],o=i[u[e]];return a.simpleMap(t,(function(t){return 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e.listPath(t,\"label\").join(\"/\")+\"/\"},e.formatValue=o.formatPieValue,e.formatPercent=function(t,e){var r=n.formatPercent(t,0);return\"0%\"===r&&(r=o.formatPiePercent(t,e)),r}},87619:function(t,e,r){\"use strict\";t.exports={moduleType:\"trace\",name:\"sunburst\",basePlotModule:r(66888),categories:[],animatable:!0,attributes:r(57564),layoutAttributes:r(2654),supplyDefaults:r(17094),supplyLayoutDefaults:r(57034),calc:r(52147).calc,crossTraceCalc:r(52147).crossTraceCalc,plot:r(24714).plot,style:r(29969).style,colorbar:r(4898),meta:{}}},2654:function(t){\"use strict\";t.exports={sunburstcolorway:{valType:\"colorlist\",editType:\"calc\"},extendsunburstcolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},57034:function(t,e,r){\"use strict\";var n=r(71828),i=r(2654);t.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"sunburstcolorway\",e.colorway),r(\"extendsunburstcolors\")}},24714:function(t,e,r){\"use strict\";var 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s,l=b.getPtId(t.parent);T.each((function(t){if(b.getPtId(t)===l)return s=t}));var u,c=s.children;c.forEach((function(t,e){if(b.getPtId(t)===r)return u=e}));var f=c.length,h=a(s.x0,s.x1);e={rpx0:I,rpx1:I,x0:h(u/f),x1:h((u+1)/f)}}return a(n,e)}(t);return function(t){return Z(e(t))}})),t.select(\"g.slicetext\").attr(\"opacity\",0)})).remove():T.exit().remove(),T.order();var X=null;if(_&&R){var J=b.getPtId(R);T.each((function(t){null===X&&b.getPtId(t)===J&&(X=t.x1)}))}var K=T;function $(t){var e=t.parent,r=F[b.getPtId(e)],n={};if(r){var i=e.children,o=i.indexOf(t),s=i.length,l=a(r.x0,r.x1);n.x0=l(o/s),n.x1=l(o/s)}else n.x0=n.x1=0;return n}_&&(K=K.transition().each(\"end\",(function(){var e=n.select(this);b.setSliceCursor(e,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:!1})}))),K.each((function(i){var u=n.select(this),f=s.ensureSingle(u,\"path\",\"surface\",(function(t){t.style(\"pointer-events\",h?\"none\":\"all\")}));i.rpx0=q(i.y0),i.rpx1=q(i.y1),i.xmid=(i.x0+i.x1)/2,i.pxmid=G(i.rpx1,i.xmid),i.midangle=-(i.xmid-Math.PI/2),i.startangle=-(i.x0-Math.PI/2),i.stopangle=-(i.x1-Math.PI/2),i.halfangle=.5*Math.min(s.angleDelta(i.x0,i.x1)||Math.PI,Math.PI),i.ring=1-i.rpx0/i.rpx1,i.rInscribed=function(t){return 0===t.rpx0&&s.isFullCircle([t.x0,t.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2))}(i),_?f.transition().attrTween(\"d\",(function(t){var e=function(t){var e,r=F[b.getPtId(t)],n={x0:t.x0,x1:t.x1,rpx0:t.rpx0,rpx1:t.rpx1};if(r)e=r;else if(R)if(t.parent)if(X){var i=(t.x1>X?2*Math.PI:0)+V;e={x0:i,x1:i}}else e={rpx0:I,rpx1:I},s.extendFlat(e,$(t));else e={rpx0:0,rpx1:0};else e={x0:V,x1:V};return a(e,n)}(t);return function(t){return Z(e(t))}})):f.attr(\"d\",Z),u.call(m,S,t,r,{eventDataKeys:x.eventDataKeys,transitionTime:x.CLICK_TRANSITION_TIME,transitionEasing:x.CLICK_TRANSITION_EASING}).call(b.setSliceCursor,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:t._transitioning}),f.call(g,i,A,t);var p=s.ensureSingle(u,\"g\",\"slicetext\"),w=s.ensureSingle(p,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),T=s.ensureUniformFontSize(t,b.determineTextFont(A,i,y.font));w.text(e.formatSliceLabel(i,S,A,r,y)).classed(\"slicetext\",!0).attr(\"text-anchor\",\"middle\").call(o.font,T).call(l.convertToTspans,t);var M=o.bBox(w.node());i.transform=v(M,i,k),i.transform.targetX=Y(i),i.transform.targetY=W(i);var E=function(t,e){var r=t.transform;return d(r,e),r.fontSize=T.size,c(A.type,r,y),s.getTextTransform(r)};_?w.transition().attrTween(\"transform\",(function(t){var e=function(t){var e,r=F[b.getPtId(t)],n=t.transform;if(r)e=r;else if(e={rpx1:t.rpx1,transform:{textPosAngle:n.textPosAngle,scale:0,rotate:n.rotate,rCenter:n.rCenter,x:n.x,y:n.y}},R)if(t.parent)if(X){var i=t.x1>X?2*Math.PI:0;e.x0=e.x1=i}else s.extendFlat(e,$(t));else e.x0=e.x1=V;else e.x0=e.x1=V;var o=a(e.transform.textPosAngle,t.transform.textPosAngle),l=a(e.rpx1,t.rpx1),u=a(e.x0,t.x0),f=a(e.x1,t.x1),h=a(e.transform.scale,n.scale),p=a(e.transform.rotate,n.rotate),d=0===n.rCenter?3:0===e.transform.rCenter?1/3:1,v=a(e.transform.rCenter,n.rCenter);return function(t){var e=l(t),r=u(t),i=f(t),a=function(t){return v(Math.pow(t,d))}(t),s={pxmid:G(e,(r+i)/2),rpx1:e,transform:{textPosAngle:o(t),rCenter:a,x:n.x,y:n.y}};return c(A.type,n,y),{transform:{targetX:Y(s),targetY:W(s),scale:h(t),rotate:p(t),rCenter:a}}}}(t);return function(t){return E(e(t),M)}})):w.attr(\"transform\",E(i,M))}))}function w(t){return e=t.rpx1,r=t.transform.textPosAngle,[e*Math.sin(r),-e*Math.cos(r)];var e,r}e.plot=function(t,e,r,i){var a,o,s=t._fullLayout,l=s._sunburstlayer,u=!r,c=!s.uniformtext.mode&&b.hasTransition(r);f(\"sunburst\",s),(a=l.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\"),a.order(),c?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each(\"end\",(function(){o&&o()})).each(\"interrupt\",(function(){o&&o()})).each((function(){l.selectAll(\"g.trace\").each((function(e){_(t,e,this,r)}))}))):(a.each((function(e){_(t,e,this,r)})),s.uniformtext.mode&&y(t,s._sunburstlayer.selectAll(\".trace\"),\"sunburst\")),u&&a.exit().remove()},e.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&\"none\"!==o))return\"\";var l=i.separators,u=n[0],c=t.data.data,f=u.hierarchy,h=b.isHierarchyRoot(t),p=b.getParent(f,t),d=b.getValue(t);if(!a){var v,g=o.split(\"+\"),y=function(t){return-1!==g.indexOf(t)},m=[];if(y(\"label\")&&c.label&&m.push(c.label),c.hasOwnProperty(\"v\")&&y(\"value\")&&m.push(b.formatValue(c.v,l)),!h){y(\"current path\")&&m.push(b.getPath(t.data));var x=0;y(\"percent parent\")&&x++,y(\"percent entry\")&&x++,y(\"percent root\")&&x++;var _=x>1;if(x){var w,T=function(t){v=b.formatPercent(w,l),_&&(v+=\" of \"+t),m.push(v)};y(\"percent parent\")&&!h&&(w=d/b.getValue(p),T(\"parent\")),y(\"percent entry\")&&(w=d/b.getValue(e),T(\"entry\")),y(\"percent root\")&&(w=d/b.getValue(f),T(\"root\"))}}return y(\"text\")&&(v=s.castOption(r,c.i,\"text\"),s.isValidTextValue(v)&&m.push(v)),m.join(\"<br>\")}var k=s.castOption(r,c.i,\"texttemplate\");if(!k)return\"\";var A={};c.label&&(A.label=c.label),c.hasOwnProperty(\"v\")&&(A.value=c.v,A.valueLabel=b.formatValue(c.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),c.hasOwnProperty(\"color\")&&(A.color=c.color);var M=s.castOption(r,c.i,\"text\");return(s.isValidTextValue(M)||\"\"===M)&&(A.text=M),A.customdata=s.castOption(r,c.i,\"customdata\"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},29969:function(t,e,r){\"use strict\";var n=r(39898),i=r(7901),a=r(71828),o=r(72597).resizeText,s=r(43467);function l(t,e,r,n){var 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f=t.exports=u(l({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:o(),xhoverformat:a(\"x\"),yhoverformat:a(\"y\"),zhoverformat:a(\"z\"),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or 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n=r(78803);t.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},43768:function(t,e,r){\"use strict\";var n=r(9330).gl_surface3d,i=r(9330).ndarray,a=r(9330).ndarray_linear_interpolate.d2,o=r(824),s=r(43907),l=r(71828).isArrayOrTypedArray,u=r(81697).parseColorScale,c=r(78614),f=r(21081).extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 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A=[!0,!0,!0],M=[\"x\",\"y\",\"z\"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=m[e],p.highlightColor[e]=w.contourColor[e]=c(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=c(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=A,p.enableHighlight=A,p.snapToData=!0,\"lighting\"in 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n=r(50215),i=r(1426).extendFlat,a=r(30962).overrideAll,o=r(41940),s=r(27670).Y,l=r(12663).descriptionOnlyNumbers;(t.exports=a({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:[],description:l(\"cell value\")},prefix:{valType:\"string\",arrayOk:!0,dflt:null},suffix:{valType:\"string\",arrayOk:!0,dflt:null},height:{valType:\"number\",dflt:28},align:i({},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:i({},o({arrayOk:!0}))},cells:{values:{valType:\"data_array\",dflt:[]},format:{valType:\"data_array\",dflt:[],description:l(\"cell 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L=Math.max(o(e.header.line.width),o(e.cells.line.width)),C={key:e.uid+t._context.staticPlot,translateX:g.x[0]*t._fullLayout._size.w,translateY:t._fullLayout._size.h*(1-g.y[1]),size:t._fullLayout._size,width:y,maxLineWidth:L,height:m,columnOrder:M,groupHeight:m,rowBlocks:k,headerRowBlocks:T,scrollY:0,cells:i({},e.cells,{values:r}),headerCells:i({},e.header,{values:v}),gdColumns:v.map((function(t){return t[0]})),gdColumnsOriginalOrder:v.map((function(t){return t[0]})),prevPages:[0,0],scrollbarState:{scrollbarScrollInProgress:!1},columns:v.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:c,x:void 0,calcdata:void 0,columnWidth:S[e]}}))};return C.columns.forEach((function(t){t.calcdata=C,t.x=c(t)})),C}},56269:function(t,e,r){\"use strict\";var n=r(1426).extendFlat;e.splitToPanels=function(t){var e=[0,0],r=n({},t,{key:\"header\",type:\"header\",page:0,prevPages:e,currentRepaint:[null,null],dragHandle:!0,values:t.calcdata.headerCells.values[t.specIndex],rowBlocks:t.calcdata.headerRowBlocks,calcdata:n({},t.calcdata,{cells:t.calcdata.headerCells})});return[n({},t,{key:\"cells1\",type:\"cells\",page:0,prevPages:e,currentRepaint:[null,null],dragHandle:!1,values:t.calcdata.cells.values[t.specIndex],rowBlocks:t.calcdata.rowBlocks}),n({},t,{key:\"cells2\",type:\"cells\",page:1,prevPages:e,currentRepaint:[null,null],dragHandle:!1,values:t.calcdata.cells.values[t.specIndex],rowBlocks:t.calcdata.rowBlocks}),r]},e.splitToCells=function(t){var e=function(t){var e=t.rowBlocks[t.page],r=e?e.rows[0].rowIndex:0;return[r,e?r+e.rows.length:0]}(t);return(t.values||[]).slice(e[0],e[1]).map((function(r,n){return{keyWithinBlock:n+(\"string\"==typeof r&&r.match(/[<$&> 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a.indexOf(t)})),s=o.length;s<n;s++)o.push(s);e(\"columnorder\",o)}(e,s),s(\"cells.values\"),s(\"cells.format\"),s(\"cells.align\"),s(\"cells.prefix\"),s(\"cells.suffix\"),s(\"cells.height\"),s(\"cells.line.width\"),s(\"cells.line.color\"),s(\"cells.fill.color\"),n.coerceFont(s,\"cells.font\",n.extendFlat({},o.font)),e._length=null}},96595:function(t,e,r){\"use strict\";t.exports={attributes:r(44464),supplyDefaults:r(39754),calc:r(76333),plot:r(36736),moduleType:\"trace\",name:\"table\",basePlotModule:r(99469),categories:[\"noOpacity\"],meta:{}}},36736:function(t,e,r){\"use strict\";var n=r(49850),i=r(39898),a=r(71828).numberFormat,o=r(28984),s=r(91424),l=r(63893),u=r(71828).raiseToTop,c=r(71828).strTranslate,f=r(71828).cancelTransition,h=r(51018),p=r(56269),d=r(7901);function v(t){return Math.ceil(t.calcdata.maxLineWidth/2)}function g(t,e){return\"clip\"+t._fullLayout._uid+\"_scrollAreaBottomClip_\"+e.key}function y(t,e){return\"clip\"+t._fullLayout._uid+\"_columnBoundaryClippath_\"+e.calcdata.key+\"_\"+e.specIndex}function m(t){return[].concat.apply([],t.map((function(t){return t}))).map((function(t){return t.__data__}))}function x(t,e,r){var a=t.selectAll(\".\"+n.cn.scrollbarKit).data(o.repeat,o.keyFun);a.enter().append(\"g\").classed(n.cn.scrollbarKit,!0).style(\"shape-rendering\",\"geometricPrecision\"),a.each((function(t){var e=t.scrollbarState;e.totalHeight=function(t){var e=t.rowBlocks;return z(e,e.length-1)+(e.length?R(e[e.length-1],1/0):1)}(t),e.scrollableAreaHeight=t.groupHeight-S(t),e.currentlyVisibleHeight=Math.min(e.totalHeight,e.scrollableAreaHeight),e.ratio=e.currentlyVisibleHeight/e.totalHeight,e.barLength=Math.max(e.ratio*e.currentlyVisibleHeight,n.goldenRatio*n.scrollbarWidth),e.barWiggleRoom=e.currentlyVisibleHeight-e.barLength,e.wiggleRoom=Math.max(0,e.totalHeight-e.scrollableAreaHeight),e.topY=0===e.barWiggleRoom?0:t.scrollY/e.wiggleRoom*e.barWiggleRoom,e.bottomY=e.topY+e.barLength,e.dragMultiplier=e.wiggleRoom/e.barWiggleRoom})).attr(\"transform\",(function(t){var e=t.width+n.scrollbarWidth/2+n.scrollbarOffset;return c(e,S(t))}));var s=a.selectAll(\".\"+n.cn.scrollbar).data(o.repeat,o.keyFun);s.enter().append(\"g\").classed(n.cn.scrollbar,!0);var l=s.selectAll(\".\"+n.cn.scrollbarSlider).data(o.repeat,o.keyFun);l.enter().append(\"g\").classed(n.cn.scrollbarSlider,!0),l.attr(\"transform\",(function(t){return c(0,t.scrollbarState.topY||0)}));var u=l.selectAll(\".\"+n.cn.scrollbarGlyph).data(o.repeat,o.keyFun);u.enter().append(\"line\").classed(n.cn.scrollbarGlyph,!0).attr(\"stroke\",\"black\").attr(\"stroke-width\",n.scrollbarWidth).attr(\"stroke-linecap\",\"round\").attr(\"y1\",n.scrollbarWidth/2),u.attr(\"y2\",(function(t){return t.scrollbarState.barLength-n.scrollbarWidth/2})).attr(\"stroke-opacity\",(function(t){return t.columnDragInProgress||!t.scrollbarState.barWiggleRoom||r?0:.4})),u.transition().delay(0).duration(0),u.transition().delay(n.scrollbarHideDelay).duration(n.scrollbarHideDuration).attr(\"stroke-opacity\",0);var f=s.selectAll(\".\"+n.cn.scrollbarCaptureZone).data(o.repeat,o.keyFun);f.enter().append(\"line\").classed(n.cn.scrollbarCaptureZone,!0).attr(\"stroke\",\"white\").attr(\"stroke-opacity\",.01).attr(\"stroke-width\",n.scrollbarCaptureWidth).attr(\"stroke-linecap\",\"butt\").attr(\"y1\",0).on(\"mousedown\",(function(r){var n=i.event.y,a=this.getBoundingClientRect(),o=r.scrollbarState,s=n-a.top,l=i.scale.linear().domain([0,o.scrollableAreaHeight]).range([0,o.totalHeight]).clamp(!0);o.topY<=s&&s<=o.bottomY||L(e,t,null,l(s-o.barLength/2))(r)})).call(i.behavior.drag().origin((function(t){return i.event.stopPropagation(),t.scrollbarState.scrollbarScrollInProgress=!0,t})).on(\"drag\",L(e,t)).on(\"dragend\",(function(){}))),f.attr(\"y2\",(function(t){return t.scrollbarState.scrollableAreaHeight})),e._context.staticPlot&&(u.remove(),f.remove())}function b(t,e,r,a){var l=function(t){var e=t.selectAll(\".\"+n.cn.columnCells).data(o.repeat,o.keyFun);return e.enter().append(\"g\").classed(n.cn.columnCells,!0),e.exit().remove(),e}(r),u=function(t){var e=t.selectAll(\".\"+n.cn.columnCell).data(p.splitToCells,(function(t){return t.keyWithinBlock}));return e.enter().append(\"g\").classed(n.cn.columnCell,!0),e.exit().remove(),e}(l);!function(t){t.each((function(t,e){var r=t.calcdata.cells.font,n=t.column.specIndex,i={size:T(r.size,n,e),color:T(r.color,n,e),family:T(r.family,n,e)};t.rowNumber=t.key,t.align=T(t.calcdata.cells.align,n,e),t.cellBorderWidth=T(t.calcdata.cells.line.width,n,e),t.font=i}))}(u);var c=function(t){var e=t.selectAll(\".\"+n.cn.cellRect).data(o.repeat,(function(t){return t.keyWithinBlock}));return e.enter().append(\"rect\").classed(n.cn.cellRect,!0),e}(u);!function(t){t.attr(\"width\",(function(t){return t.column.columnWidth})).attr(\"stroke-width\",(function(t){return t.cellBorderWidth})).each((function(t){var e=i.select(this);d.stroke(e,T(t.calcdata.cells.line.color,t.column.specIndex,t.rowNumber)),d.fill(e,T(t.calcdata.cells.fill.color,t.column.specIndex,t.rowNumber))}))}(c);var f=function(t){var e=t.selectAll(\".\"+n.cn.cellTextHolder).data(o.repeat,(function(t){return t.keyWithinBlock}));return e.enter().append(\"g\").classed(n.cn.cellTextHolder,!0).style(\"shape-rendering\",\"geometricPrecision\"),e}(u),h=function(t){var e=t.selectAll(\".\"+n.cn.cellText).data(o.repeat,(function(t){return t.keyWithinBlock}));return e.enter().append(\"text\").classed(n.cn.cellText,!0).style(\"cursor\",(function(){return\"auto\"})).on(\"mousedown\",(function(){i.event.stopPropagation()})),e}(f);!function(t){t.each((function(t){s.font(i.select(this),t.font)}))}(h),_(h,e,a,t),D(u)}function _(t,e,r,o){t.text((function(t){var e=t.column.specIndex,r=t.rowNumber,i=t.value,o=\"string\"==typeof i,s=o&&i.match(/<br>/i),l=!o||s;t.mayHaveMarkup=o&&i.match(/[<&>]/);var u,c=\"string\"==typeof(u=i)&&u.match(n.latexCheck);t.latex=c;var f,h,p=c?\"\":T(t.calcdata.cells.prefix,e,r)||\"\",d=c?\"\":T(t.calcdata.cells.suffix,e,r)||\"\",v=c?null:T(t.calcdata.cells.format,e,r)||null,g=p+(v?a(v)(t.value):t.value)+d;if(t.wrappingNeeded=!t.wrapped&&!l&&!c&&(f=w(g)),t.cellHeightMayIncrease=s||c||t.mayHaveMarkup||(void 0===f?w(g):f),t.needsConvertToTspans=t.mayHaveMarkup||t.wrappingNeeded||t.latex,t.wrappingNeeded){var y=(\" 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k(t,e,r){t.transition().ease(n.releaseTransitionEase).duration(n.releaseTransitionDuration).attr(\"transform\",c(e.x,r))}function A(t){return\"cells\"===t.type}function M(t){return\"header\"===t.type}function S(t){return(t.rowBlocks.length?t.rowBlocks[0].auxiliaryBlocks:[]).reduce((function(t,e){return t+R(e,1/0)}),0)}function E(t,e,r){var n=m(e)[0];if(void 0!==n){var i=n.rowBlocks,a=n.calcdata,o=z(i,i.length),s=n.calcdata.groupHeight-S(n),l=a.scrollY=Math.max(0,Math.min(o-s,a.scrollY)),u=function(t,e,r){for(var n=[],i=0,a=0;a<t.length;a++){for(var o=t[a],s=o.rows,l=0,u=0;u<s.length;u++)l+=s[u].rowHeight;o.allRowsHeight=l,e<i+l&&e+r>i&&n.push(a),i+=l}return n}(i,l,s);1===u.length&&(u[0]===i.length-1?u.unshift(u[0]-1):u.push(u[0]+1)),u[0]%2&&u.reverse(),e.each((function(t,e){t.page=u[e],t.scrollY=l})),e.attr(\"transform\",(function(t){var e=z(t.rowBlocks,t.page)-t.scrollY;return c(0,e)})),t&&(C(t,r,e,u,n.prevPages,n,0),C(t,r,e,u,n.prevPages,n,1),x(r,t))}}function L(t,e,r,a){return function(o){var s=o.calcdata?o.calcdata:o,l=e.filter((function(t){return s.key===t.key})),u=r||s.scrollbarState.dragMultiplier,c=s.scrollY;s.scrollY=void 0===a?s.scrollY+u*i.event.dy:a;var f=l.selectAll(\".\"+n.cn.yColumn).selectAll(\".\"+n.cn.columnBlock).filter(A);return E(t,f,l),s.scrollY===c}}function C(t,e,r,n,i,a,o){n[o]!==i[o]&&(clearTimeout(a.currentRepaint[o]),a.currentRepaint[o]=setTimeout((function(){var a=r.filter((function(t,e){return e===o&&n[e]!==i[e]}));b(t,e,a,r),i[o]=n[o]})))}function P(t,e,r,a){return function(){var o=i.select(e.parentNode);o.each((function(t){var e=t.fragments;o.selectAll(\"tspan.line\").each((function(t,r){e[r].width=this.getComputedTextLength()}));var 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r=!t._context.staticPlot,a=t._fullLayout._paper.selectAll(\".\"+n.cn.table).data(e.map((function(e){var r=o.unwrap(e).trace;return h(t,r)})),o.keyFun);a.exit().remove(),a.enter().append(\"g\").classed(n.cn.table,!0).attr(\"overflow\",\"visible\").style(\"box-sizing\",\"content-box\").style(\"position\",\"absolute\").style(\"left\",0).style(\"overflow\",\"visible\").style(\"shape-rendering\",\"crispEdges\").style(\"pointer-events\",\"all\"),a.attr(\"width\",(function(t){return t.width+t.size.l+t.size.r})).attr(\"height\",(function(t){return t.height+t.size.t+t.size.b})).attr(\"transform\",(function(t){return c(t.translateX,t.translateY)}));var l=a.selectAll(\".\"+n.cn.tableControlView).data(o.repeat,o.keyFun),d=l.enter().append(\"g\").classed(n.cn.tableControlView,!0).style(\"box-sizing\",\"content-box\");if(r){var _=\"onwheel\"in document?\"wheel\":\"mousewheel\";d.on(\"mousemove\",(function(e){l.filter((function(t){return 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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\"],f=[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 p(t,e,r){r.negative=e.negative^t.negative;var n=t.length+e.length|0;r.length=n,n=n-1|0;var i=0|t.words[0],a=0|e.words[0],o=i*a,s=67108863&o,l=o/67108864|0;r.words[0]=s;for(var u=1;u<n;u++){for(var c=l>>>26,f=67108863&l,h=Math.min(u,e.length-1),p=Math.max(0,u-t.length+1);p<=h;p++){var d=u-p|0;c+=(o=(i=0|t.words[d])*(a=0|e.words[p])+f)/67108864|0,f=67108863&o}r.words[u]=0|f,l=0|c}return 0!==l?r.words[u]=0|l:r.length--,r.strip()}a.prototype.toString=function(t,e){var r;if(e=0|e||1,16===(t=t||10)||\"hex\"===t){r=\"\";for(var i=0,a=0,o=0;o<this.length;o++){var s=this.words[o],l=(16777215&(s<<i|a)).toString(16);r=0!=(a=s>>>24-i&16777215)||o!==this.length-1?c[6-l.length]+l+r:l+r,(i+=2)>=26&&(i-=26,o--)}for(0!==a&&(r=a.toString(16)+r);r.length%e!=0;)r=\"0\"+r;return 0!==this.negative&&(r=\"-\"+r),r}if(t===(0|t)&&t>=2&&t<=36){var u=f[t],p=h[t];r=\"\";var d=this.clone();for(d.negative=0;!d.isZero();){var v=d.modn(p).toString(t);r=(d=d.idivn(p)).isZero()?v+r:c[u-v.length]+v+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)}:a.prototype._countBits=function(t){var e=t,r=0;return e>=4096&&(r+=13,e>>>=13),e>=64&&(r+=7,e>>>=7),e>=8&&(r+=4,e>>>=4),e>=2&&(r+=2,e>>>=2),r+e},a.prototype._zeroBits=function(t){if(0===t)return 26;var e=t,r=0;return 0==(8191&e)&&(r+=13,e>>>=13),0==(127&e)&&(r+=7,e>>>=7),0==(15&e)&&(r+=4,e>>>=4),0==(3&e)&&(r+=2,e>>>=2),0==(1&e)&&r++,r},a.prototype.bitLength=function(){var t=this.words[this.length-1],e=this._countBits(t);return 26*(this.length-1)+e},a.prototype.zeroBits=function(){if(this.isZero())return 0;for(var t=0,e=0;e<this.length;e++){var r=this._zeroBits(this.words[e]);if(t+=r,26!==r)break}return t},a.prototype.byteLength=function(){return Math.ceil(this.bitLength()/8)},a.prototype.toTwos=function(t){return 0!==this.negative?this.abs().inotn(t).iaddn(1):this.clone()},a.prototype.fromTwos=function(t){return this.testn(t-1)?this.notn(t).iaddn(1).ineg():this.clone()},a.prototype.isNeg=function(){return 0!==this.negative},a.prototype.neg=function(){return this.clone().ineg()},a.prototype.ineg=function(){return this.isZero()||(this.negative^=1),this},a.prototype.iuor=function(t){for(;this.length<t.length;)this.words[this.length++]=0;for(var e=0;e<t.length;e++)this.words[e]=this.words[e]|t.words[e];return this.strip()},a.prototype.ior=function(t){return n(0==(this.negative|t.negative)),this.iuor(t)},a.prototype.or=function(t){return this.length>t.length?this.clone().ior(t):t.clone().ior(this)},a.prototype.uor=function(t){return this.length>t.length?this.clone().iuor(t):t.clone().iuor(this)},a.prototype.iuand=function(t){var e;e=this.length>t.length?t:this;for(var r=0;r<e.length;r++)this.words[r]=this.words[r]&t.words[r];return this.length=e.length,this.strip()},a.prototype.iand=function(t){return n(0==(this.negative|t.negative)),this.iuand(t)},a.prototype.and=function(t){return this.length>t.length?this.clone().iand(t):t.clone().iand(this)},a.prototype.uand=function(t){return this.length>t.length?this.clone().iuand(t):t.clone().iuand(this)},a.prototype.iuxor=function(t){var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n(\"number\"==typeof t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n(\"number\"==typeof t&&t>=0);var r=t/26|0,i=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,u=0,c=0|o[0],f=8191&c,h=c>>>13,p=0|o[1],d=8191&p,v=p>>>13,g=0|o[2],y=8191&g,m=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],O=8191&P,I=P>>>13,D=0|o[8],z=8191&D,R=D>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Z=0|s[2],Y=8191&Z,W=Z>>>13,X=0|s[3],J=8191&X,K=X>>>13,$=0|s[4],Q=8191&$,tt=$>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ut=st>>>13,ct=0|s[8],ft=8191&ct,ht=ct>>>13,pt=0|s[9],dt=8191&pt,vt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(u+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;u=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(v,U)|0,a=Math.imul(v,V);var yt=(u+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;u=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(y,U),i=(i=Math.imul(y,V))+Math.imul(m,U)|0,a=Math.imul(m,V),n=n+Math.imul(d,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(v,q)|0,a=a+Math.imul(v,G)|0;var mt=(u+(n=n+Math.imul(f,Y)|0)|0)+((8191&(i=(i=i+Math.imul(f,W)|0)+Math.imul(h,Y)|0))<<13)|0;u=((a=a+Math.imul(h,W)|0)+(i>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(y,q)|0,i=(i=i+Math.imul(y,G)|0)+Math.imul(m,q)|0,a=a+Math.imul(m,G)|0,n=n+Math.imul(d,Y)|0,i=(i=i+Math.imul(d,W)|0)+Math.imul(v,Y)|0,a=a+Math.imul(v,W)|0;var xt=(u+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;u=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(y,Y)|0,i=(i=i+Math.imul(y,W)|0)+Math.imul(m,Y)|0,a=a+Math.imul(m,W)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(v,J)|0,a=a+Math.imul(v,K)|0;var bt=(u+(n=n+Math.imul(f,Q)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,Q)|0))<<13)|0;u=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,Y)|0,i=(i=i+Math.imul(b,W)|0)+Math.imul(_,Y)|0,a=a+Math.imul(_,W)|0,n=n+Math.imul(y,J)|0,i=(i=i+Math.imul(y,K)|0)+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0,n=n+Math.imul(d,Q)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(v,Q)|0,a=a+Math.imul(v,tt)|0;var _t=(u+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;u=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,Y)|0,i=(i=i+Math.imul(T,W)|0)+Math.imul(k,Y)|0,a=a+Math.imul(k,W)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(y,Q)|0,i=(i=i+Math.imul(y,tt)|0)+Math.imul(m,Q)|0,a=a+Math.imul(m,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(v,rt)|0,a=a+Math.imul(v,nt)|0;var wt=(u+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;u=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(O,U),i=(i=Math.imul(O,V))+Math.imul(I,U)|0,a=Math.imul(I,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,Y)|0,i=(i=i+Math.imul(M,W)|0)+Math.imul(S,Y)|0,a=a+Math.imul(S,W)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,Q)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,Q)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(y,rt)|0,i=(i=i+Math.imul(y,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(v,at)|0,a=a+Math.imul(v,ot)|0;var Tt=(u+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ut)|0)+Math.imul(h,lt)|0))<<13)|0;u=((a=a+Math.imul(h,ut)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(z,U),i=(i=Math.imul(z,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(O,q)|0,i=(i=i+Math.imul(O,G)|0)+Math.imul(I,q)|0,a=a+Math.imul(I,G)|0,n=n+Math.imul(L,Y)|0,i=(i=i+Math.imul(L,W)|0)+Math.imul(C,Y)|0,a=a+Math.imul(C,W)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,Q)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,Q)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(y,at)|0,i=(i=i+Math.imul(y,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ut)|0)+Math.imul(v,lt)|0,a=a+Math.imul(v,ut)|0;var kt=(u+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;u=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(z,q)|0,i=(i=i+Math.imul(z,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(O,Y)|0,i=(i=i+Math.imul(O,W)|0)+Math.imul(I,Y)|0,a=a+Math.imul(I,W)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,Q)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,Q)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(y,lt)|0,i=(i=i+Math.imul(y,ut)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ut)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(v,ft)|0,a=a+Math.imul(v,ht)|0;var At=(u+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,vt)|0)+Math.imul(h,dt)|0))<<13)|0;u=((a=a+Math.imul(h,vt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(z,Y)|0,i=(i=i+Math.imul(z,W)|0)+Math.imul(R,Y)|0,a=a+Math.imul(R,W)|0,n=n+Math.imul(O,J)|0,i=(i=i+Math.imul(O,K)|0)+Math.imul(I,J)|0,a=a+Math.imul(I,K)|0,n=n+Math.imul(L,Q)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,Q)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ut)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ut)|0,n=n+Math.imul(y,ft)|0,i=(i=i+Math.imul(y,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var Mt=(u+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,vt)|0)+Math.imul(v,dt)|0))<<13)|0;u=((a=a+Math.imul(v,vt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,Y),i=(i=Math.imul(B,W))+Math.imul(N,Y)|0,a=Math.imul(N,W),n=n+Math.imul(z,J)|0,i=(i=i+Math.imul(z,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(O,Q)|0,i=(i=i+Math.imul(O,tt)|0)+Math.imul(I,Q)|0,a=a+Math.imul(I,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ut)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ut)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(u+(n=n+Math.imul(y,dt)|0)|0)+((8191&(i=(i=i+Math.imul(y,vt)|0)+Math.imul(m,dt)|0))<<13)|0;u=((a=a+Math.imul(m,vt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(z,Q)|0,i=(i=i+Math.imul(z,tt)|0)+Math.imul(R,Q)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(O,rt)|0,i=(i=i+Math.imul(O,nt)|0)+Math.imul(I,rt)|0,a=a+Math.imul(I,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ut)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ut)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(u+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,vt)|0)+Math.imul(_,dt)|0))<<13)|0;u=((a=a+Math.imul(_,vt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,Q),i=(i=Math.imul(B,tt))+Math.imul(N,Q)|0,a=Math.imul(N,tt),n=n+Math.imul(z,rt)|0,i=(i=i+Math.imul(z,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(O,at)|0,i=(i=i+Math.imul(O,ot)|0)+Math.imul(I,at)|0,a=a+Math.imul(I,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ut)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ut)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(u+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,vt)|0)+Math.imul(k,dt)|0))<<13)|0;u=((a=a+Math.imul(k,vt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(z,at)|0,i=(i=i+Math.imul(z,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(O,lt)|0,i=(i=i+Math.imul(O,ut)|0)+Math.imul(I,lt)|0,a=a+Math.imul(I,ut)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(u+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,vt)|0)+Math.imul(S,dt)|0))<<13)|0;u=((a=a+Math.imul(S,vt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(z,lt)|0,i=(i=i+Math.imul(z,ut)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ut)|0,n=n+Math.imul(O,ft)|0,i=(i=i+Math.imul(O,ht)|0)+Math.imul(I,ft)|0,a=a+Math.imul(I,ht)|0;var Pt=(u+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,vt)|0)+Math.imul(C,dt)|0))<<13)|0;u=((a=a+Math.imul(C,vt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ut))+Math.imul(N,lt)|0,a=Math.imul(N,ut),n=n+Math.imul(z,ft)|0,i=(i=i+Math.imul(z,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var Ot=(u+(n=n+Math.imul(O,dt)|0)|0)+((8191&(i=(i=i+Math.imul(O,vt)|0)+Math.imul(I,dt)|0))<<13)|0;u=((a=a+Math.imul(I,vt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var It=(u+(n=n+Math.imul(z,dt)|0)|0)+((8191&(i=(i=i+Math.imul(z,vt)|0)+Math.imul(R,dt)|0))<<13)|0;u=((a=a+Math.imul(R,vt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863;var Dt=(u+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,vt))+Math.imul(N,dt)|0))<<13)|0;return u=((a=Math.imul(N,vt))+(i>>>13)|0)+(Dt>>>26)|0,Dt&=67108863,l[0]=gt,l[1]=yt,l[2]=mt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=Ot,l[17]=It,l[18]=Dt,0!==u&&(l[19]=u,r.length++),r};function v(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r,n=this.length+t.length;return r=10===this.length&&10===t.length?d(this,t,e):n<63?p(this,t,e):n<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),u=Math.max(0,a-t.length+1);u<=l;u++){var c=a-u,f=(0|t.words[c])*(0|e.words[u]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):v(this,t,e),r},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),u=Math.sin(2*Math.PI/s),c=0;c<i;c+=s)for(var f=l,h=u,p=0;p<o;p++){var d=r[c+p],v=n[c+p],g=r[c+p+o],y=n[c+p+o],m=f*g-h*y;y=f*y+h*g,g=m,r[c+p]=d+g,n[c+p]=v+y,r[c+p+o]=d-g,n[c+p+o]=v-y,p!==s&&(m=l*f-u*h,h=l*h+u*f,f=m)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),u=new Array(n),c=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,u,n),this.transform(o,a,s,l,n,i),this.transform(u,a,c,f,n,i);for(var p=0;p<n;p++){var d=s[p]*c[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*c[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),v(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var u=0;u<o;u++)l.words[u]=this.words[u];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,u=0;u<this.length;u++)this.words[u]=this.words[u+o];else this.words[0]=0,this.length=1;var c=0;for(u=this.length-1;u>=0&&(0!==c||u>=i);u--){var f=0|this.words[u];this.words[u]=c<<26-a|f>>>a,c=f&s}return l&&0!==c&&(l.words[l.length++]=c),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r||!(this.words[r]&i))},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!=(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var u=0;u<s.length;u++)s.words[u]=0}var c=n.clone()._ishlnsubmul(i,1,l);0===c.negative&&(n=c,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),u=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++u;for(var c=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(c),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,v=1;0==(r.words[0]&v)&&d<26;++d,v<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(c),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(u)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var u=0,c=1;0==(e.words[0]&c)&&u<26;++u,c<<=1);if(u>0)for(e.iushrn(u);u-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var y={k256:null,p224:null,p192:null,p25519:null};function m(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function x(){m.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function b(){m.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function _(){m.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function w(){m.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function T(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}m.prototype._tmp=function(){var t=new a(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):void 0!==r.strip?r.strip():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)},i(x,m),x.prototype.split=function(t,e){for(var r=4194303,n=Math.min(t.length,9),i=0;i<n;i++)e.words[i]=t.words[i];if(e.length=n,t.length<=9)return t.words[0]=0,void(t.length=1);var a=t.words[9];for(e.words[e.length++]=a&r,i=10;i<t.length;i++){var o=0|t.words[i];t.words[i-10]=(o&r)<<4|a>>>22,a=o}a>>>=22,t.words[i-10]=a,0===a&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,m),i(_,m),i(w,m),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(y[t])return y[t];var e;if(\"k256\"===t)e=new x;else if(\"p224\"===t)e=new b;else if(\"p192\"===t)e=new _;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new w}return y[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),u=this.m.subn(1).iushrn(1),c=this.m.bitLength();for(c=new a(2*c*c).toRed(this);0!==this.pow(c,u).cmp(l);)c.redIAdd(l);for(var f=this.pow(c,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var v=p,g=0;0!==v.cmp(s);g++)v=v.redSqr();n(g<d);var y=this.pow(f,new a(1).iushln(d-g-1));h=h.redMul(y),f=y.redSqr(),p=p.redMul(f),d=g}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var u=e.words[n],c=l-1;c>=0;c--){var 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s=(o=a.left=r(o)).right=r(o.right),a.left=s.right,o.right=s.left,s.right=a,s.left=o,s._color=a._color,a._color=1,o._color=1,e._color=1,i(a),i(o),i(s),l>1&&((u=t[l-2]).right===a?u.right=s:u.left=s),void(t[l-1]=s);if(1===o._color){if(0===a._color)return a._color=1,void(a.left=n(0,o));a.left=n(0,o);continue}var u;o=r(o),a.left=o.right,o.right=a,o._color=a._color,a._color=0,i(a),i(o),l>1&&((u=t[l-2]).right===a?u.right=o:u.left=o),t[l-1]=o,t[l]=a,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(o),p.left===s?p.left=null:p.right=null,new a(this.tree._compare,o[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 r=this._stack;if(0===r.length)throw new Error(\"Can't update empty node!\");var n=new Array(r.length),i=r[r.length-1];n[n.length-1]=new e(i._color,i.key,t,i.left,i.right,i._count);for(var o=r.length-2;o>=0;--o)(i=r[o]).left===r[o+1]?n[o]=new e(i._color,i.key,i.value,n[o+1],i.right,i._count):n[o]=new e(i._color,i.key,i.value,i.left,n[o+1],i._count);return new a(this.tree._compare,n[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}})},7453:function(t,e,r){\"use strict\";t.exports=function(t,e){var r=new c(t);return r.update(e),r};var n=r(9557),i=r(1681),a=r(1011),o=r(2864),s=r(8468),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function u(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function c(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=\"sans-serif\",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=c.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),u=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]})),c=!1,f=!1;if(\"bounds\"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if(\"ticks\"in t)for(r=t.ticks,c=!0,this.autoTicks=!1,p=0;p<3;++p)this.tickSpacing[p]=0;else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),f=!0,c=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),c=!0),c){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?c=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(c=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),u(\"tickColor\");var v=l(\"labels\");l(\"labelFont\")&&(v=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),u(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),u(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),u(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),u(\"gridColor\"),o(\"zeroEnable\"),u(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),u(\"backgroundColor\"),this._text?this._text&&(v||c)&&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&&c&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,u=n[e],c=0;c<3;++c)if(e!==c){var f=a,h=s,p=o,d=l;u&1<<c&&(f=s,h=a,p=l,d=o),f[c]=r[0][c],h[c]=r[1][c],i[c]>0?(p[c]=-1,d[c]=0):(p[c]=0,d[c]=1)}}var v=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var y=[0,0,0],m=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,c=o(r,n,i,a,s),f=c.cubeEdges,h=c.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E,L,C=v;for(A=0;A<3;++A)this.backgroundEnable[A]?C[A]=h[A]:C[A]=0;for(this._background.draw(r,n,i,a,C,this.backgroundColor),this._lines.bind(r,n,i,this),A=0;A<3;++A){var P=[0,0,0];h[A]>0?P[A]=a[1][A]:P[A]=a[0][A];for(var O=0;O<2;++O){var I=(A+1+O)%3,D=(A+1+(1^O))%3;this.gridEnable[I]&&this._lines.drawGrid(I,D,this.bounds,P,this.gridColor[I],this.gridWidth[I]*this.pixelRatio)}for(O=0;O<2;++O)I=(A+1+O)%3,D=(A+1+(1^O))%3,this.zeroEnable[D]&&Math.min(a[0][D],a[1][D])<=0&&Math.max(a[0][D],a[1][D])>=0&&this._lines.drawZero(I,D,this.bounds,P,this.zeroLineColor[D],this.zeroLineWidth[D]*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=u(y,M[A].primalMinor),R=u(m,M[A].mirrorMinor),F=this.lineTickLength;for(O=0;O<3;++O){var B=k/r[5*O];z[O]*=F[O]*B,R[O]*=F[O]*B}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,R,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}function N(t){(L=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio),A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=u(x,M[A].primalOffset);for(O=0;O<3;++O)this.lineTickEnable[A]&&(H[O]+=k*U[O]*Math.max(this.lineTickLength[O],0)/r[5*O]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){for(-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]=\"auto\"):this.tickAlign[A]=-1,E=1,\"auto\"===(S=[this.tickAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),L=[0,0,0],j(A,U,V),O=0;O<3;++O)H[O]+=k*U[O]*this.tickPad[O]/r[5*O];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,L,S)}if(this.labelEnable[A]){for(E=0,L=[0,0,0],this.labels[A].length>4&&(N(A),E=1),\"auto\"===(S=[this.labelAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),O=0;O<3;++O)H[O]+=k*U[O]*this.labelPad[O]/r[5*O];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[0,0,0],L,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},1011:function(t,e,r){\"use strict\";t.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var u=(l+1)%3,c=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[u]=d;for(var v=-1;v<=1;v+=2)f[c]=v,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=u;u=c,c=g}var y=n(t,new Float32Array(e)),m=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:y,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:y,type:t.FLOAT,size:3,offset:12,stride:24}],m),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,y,x,b)};var n=r(5827),i=r(2944),a=r(1943).bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},2864:function(t,e,r){\"use strict\";t.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var m=0,x=0;x<2;++x){c[2]=a[x][2];for(var b=0;b<2;++b){c[1]=a[b][1];for(var _=0;_<2;++_)c[0]=a[_][0],h(l[m],c,s),m+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)u[x][k]=l[x][k]/T;p&&(u[x][2]*=-1),T<0&&(w<0||u[x][2]<u[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=(I=C<<A)+(C<<M)+(1-C<<S),O=I+(1-C<<M)+(C<<S);o(u[I],u[P],u[O],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(I=C<<A)+(C<<M)+(1-C<<S),O=I+(1-C<<M)+(C<<S);var I,D=d([l[I],l[P],l[O],l[I+(1<<M)+(1<<S)]]);C?E=D:L=D}L>E&&(w|=1<<A)}}}var z=7^w,R=-1;for(x=0;x<8;++x)x!==w&&x!==z&&(R<0||u[R][1]>u[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x)(N=R^1<<x)!==w&&N!==z&&(F<0&&(F=N),(S=u[N])[0]<u[F][0]&&(F=N));var B=-1;for(x=0;x<3;++x){var N;(N=R^1<<x)!==w&&N!==z&&N!==F&&(B<0&&(B=N),(S=u[N])[0]>u[B][0]&&(B=N))}var j=v;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===z?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=g,H=w;for(A=0;A<3;++A)V[A]=H&1<<A?-1:1;return y};var n=r(2288),i=r(104),a=r(4670),o=r(417),s=new Array(16),l=new Array(8),u=new Array(8),c=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],u[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],u=s[0]/s[3]-n,c=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(u*h-c*f)}return o}var v=[1,1,1],g=[0,0,0],y={cubeEdges:v,axis:g}},1681:function(t,e,r){\"use strict\";t.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],u=[0,0,0],c=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var v=+r[f][d].x;o.push(v,0,1,v,1,1,v,0,-1,v,0,-1,v,1,1,v,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h,h=o.length/3|0;for(var y=0;y<r[f].length;++y)v=+r[f][y].x,o.push(v,0,1,v,1,1,v,0,-1,v,0,-1,v,1,1,v,1,-1);g=o.length/3|0,u[f]=h,c[f]=g-h}var m=n(t,new Float32Array(o)),x=i(t,[{buffer:m,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,m,x,b,l,s,c,u)};var n=r(5827),i=r(2944),a=r(1943).j,o=[0,0,0],s=[0,0,0],l=[0,0,0],u=[0,0,0],c=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,c[0]=this.gl.drawingBufferWidth,c[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=c,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,c=h(u,r);c[t]+=e[0][t],this.shader.uniforms.offset=c,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var c=f(s);c[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=c;var p=h(u,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var v=f(l);v[t]=1,this.shader.uniforms.screenAxis=v,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var c=h(u,n);c[t]+=r[0][t],this.shader.uniforms.offset=c;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},1943:function(t,e,r){\"use strict\";var n=r(6832),i=r(5158),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n  vec3 major = position.x * majorAxis;\\n  vec3 minor = position.y * minorAxis;\\n\\n  vec3 vPosition = major + minor + offset;\\n  vec3 pPosition = project(vPosition);\\n  vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);e.j=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n  vec3 A = project(a);\\n  vec3 B = project(b);\\n\\n  return atan(\\n    (B.y - A.y) * resolution.y,\\n    (B.x - A.x) * resolution.x\\n  );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio =       alignOpt.y;\\nbool enableAlign =    (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n  return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n  return mod_angle((a < 0.0) ?\\n    a + TWO_PI :\\n    a\\n  );\\n}\\n\\nfloat look_upwards(float a) {\\n  float b = positive_angle(a);\\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n    b - PI :\\n    b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n  // if ratio is set to 0.5 then it is 50%, 50%.\\n  // when using a higher ratio e.g. 0.75 the result would\\n  // likely be more horizontal than vertical.\\n\\n  float b = positive_angle(a);\\n\\n  return\\n    (b < (      ratio) * HALF_PI) ? 0.0 :\\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n                                    0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n  return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n  float b = positive_angle(a);\\n  float div = TWO_PI / float(n);\\n  float c = roundTo(b, div);\\n  return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n  return\\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\\n                    rawAngle;                // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n                   (axis.y == 0.0) &&\\n                   (axis.z == 0.0);\\n\\nvoid main() {\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n\\n  float beta = angle; // i.e. user defined attributes for each tick\\n\\n  float axisAngle;\\n  float clipAngle;\\n  float flip;\\n\\n  if (enableAlign) {\\n    axisAngle = (isAxisTitle) ? HALF_PI :\\n                      computeViewAngle(dataPosition, dataPosition + axis);\\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n    beta += applyAlignOption(clipAngle, flip * PI);\\n  }\\n\\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n\\n  mat2 planeXform = scale * mat2(\\n     cos(beta), sin(beta),\\n    -sin(beta), cos(beta)\\n  );\\n\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute clip position\\n  vec3 clipPosition = project(dataPosition);\\n\\n  //Apply text offset in clip coordinates\\n  clipPosition += vec3(viewOffset, 0.0);\\n\\n  //Done\\n  gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);e.f=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var u=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}\"]),c=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}\"]);e.bg=function(t){return i(t,u,c,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},9557:function(t,e,r){\"use strict\";t.exports=function(t,e,r,i,o,l){var u=n(t),f=a(t,[{buffer:u,size:3}]),h=s(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,i,o,l),p};var n=r(5827),a=r(2944),o=r(875),s=r(1943).f,l=window||i.global||{},u=l.__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}l.__TEXT_CACHE={};var f=c.prototype,h=[0,0];f.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,h[0]=this.gl.drawingBufferWidth,h[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=h},f.unbind=function(){this.vao.unbind()},f.update=function(t,e,r,n,i){var a=[];function s(t,e,r,n,i,s){var l=u[r];l||(l=u[r]={});var c=l[e];c||(c=l[e]=function(t,e){try{return o(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=c.positions,p=c.cells,d=0,v=p.length;d<v;++d)for(var g=p[d],y=2;y>=0;--y){var m=h[g[y]];a.push(f*m[0],-f*m[1],t)}}for(var l=[0,0,0],c=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=a.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(a.length/3|0)-f[d],l[d]=a.length/3|0;for(var v=0;v<n[d].length;++v)n[d][v].text&&s(n[d][v].x,n[d][v].text,n[d][v].font||i,n[d][v].fontSize||12,1.25,p);c[d]=(a.length/3|0)-l[d]}this.buffer.update(a),this.tickOffset=l,this.tickCount=c,this.labelOffset=f,this.labelCount=h},f.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},f.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},f.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}},8468:function(t,e){\"use strict\";function r(t,e){var r=t+\"\",n=r.indexOf(\".\"),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/a,u=o%a;o<0?(l=0|-Math.ceil(l),u=0|-u):(l=0|Math.floor(l),u|=0);var c=\"\"+l;if(o<0&&(c=\"-\"+c),i){for(var f=\"\"+u;f.length<i;)f=\"0\"+f;return c+\".\"+f}return c}e.create=function(t,e){for(var n=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:r(e[i],o)});for(o=-1;o*e[i]>=t[0][i];--o)a.push({x:o*e[i],text:r(e[i],o)});n.push(a)}return n},e.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var i=t[r][n],a=e[r][n];if(i.x!==a.x||i.text!==a.text||i.font!==a.font||i.fontColor!==a.fontColor||i.fontSize!==a.fontSize||i.dx!==a.dx||i.dy!==a.dy)return!1}}return!0}},2771:function(t,e,r){\"use strict\";t.exports=function(t,e,r,l,f){var h=e.model||u,p=e.view||u,y=e.projection||u,m=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,y,x,m)).axis;o(c,p,h),o(c,y,c);for(var _=v,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(c,c));s(c,c);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=m?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var O=d(g,c,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(O[C])))}}}}return _};var n=r(5795),i=r(4670),a=r(2864),o=r(104),s=r(2142),l=r(6342),u=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),c=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,u=0;u<3;++u)s[u]=o[u]=r[u];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var c=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(c*c+f*f)}return t}var v=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},5827:function(t,e,r){\"use strict\";var n=r(5306),i=r(7498),a=r(5050),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function u(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function c(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER&&(r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\"),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=u(this.gl,this.type,this.length,this.usage,t.data,e):this.length=u(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=u(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?c(t,\"uint16\"):c(t,\"float32\"),this.length=u(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=u(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}},t.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},1140:function(t,e,r){\"use strict\";var n=r(2858);t.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,u=1/0,c=-1/0,f=1/0,h=-1/0,p=null,d=null,v=[],g=1/0,y=!1,m=0;m<r.length;m++){var x=r[m];s=Math.min(x[0],s),l=Math.max(x[0],l),u=Math.min(x[1],u),c=Math.max(x[1],c),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[m];if(n.length(b)>o&&(o=n.length(b)),m){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),y=!1):y=!0}y||(p=x,d=b),v.push(b)}var w=[s,u,f],T=[l,c,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A,m=0;for(var M=0;m<r.length;m++)for(var S=(x=r[m])[0],E=x[1],L=x[2],C=v[m],P=n.length(C)*k,O=0;O<8;O++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var I=a.positions.length;a.cells.push([I-6,I-5,I-4],[I-3,I-2,I-1])}return a};var i=r(7234);t.exports.createMesh=r(5028),t.exports.createConeMesh=function(e,r){return t.exports.createMesh(e,r,{shaders:i,traceType:\"cone\"})}},5028:function(t,e,r){\"use strict\";var n=r(5158),i=r(5827),a=r(2944),o=r(8931),s=r(104),l=r(7437),u=r(5050),c=r(9156),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,u,c){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=u,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=c,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;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=c({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,v=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],v=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var y=p[g];d=Math.min(d,y),v=Math.max(v,y)}else for(g=0;g<n.length;++g)y=n[g][2],d=Math.min(d,y),v=Math.max(v,y);for(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]],g=0;g<n.length;++g)for(var m=n[g],x=0;x<3;++x)!isNaN(m[x])&&isFinite(m[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],m[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],m[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];if(3===_.length){for(x=0;x<3;++x){m=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(m[w])||!isFinite(m[w]))continue t}for(x=0;x<3;++x){var T;m=n[T=_[2-x]],a.push(m[0],m[1],m[2],m[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(v-d),0]:[(m[2]-d)/(v-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var u={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};u.inverseModel=l(u.inverseModel,u.model),e.disable(e.CULL_FACE),this.texture.bind(0);var c=new Array(16);for(s(c,u.view,u.model),s(c,u.projection,c),l(c,c),o=0;o<3;++o)u.eyePosition[o]=c[12+o]/c[15];var h=c[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*c[4*o+3];for(o=0;o<3;++o){for(var p=c[12+o],d=0;d<3;++d)p+=c[4*d+o]*this.lightPosition[d];u.lightPosition[o]=p/h}if(this.triangleCount>0){var v=this.triShader;v.bind(),v.uniforms=u,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},t.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),c=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),f=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));f.generateMipmap(),f.minFilter=t.LINEAR_MIPMAP_LINEAR,f.magFilter=t.LINEAR;var p=i(t),d=i(t),v=i(t),g=i(t),y=i(t),m=new h(t,f,l,c,p,d,y,v,g,a(t,[{buffer:p,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:v,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:4}]),r.traceType||\"cone\");return m.update(e),m}},7234:function(t,e,r){var n=r(6832),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * conePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\\n  vec4 t_position  = view * conePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = conePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n  gl_Position = projection * view * conePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},1950:function(t){t.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORMATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}},6603:function(t,e,r){var n=r(1950);t.exports=function(t){return n[t]}},3110:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl,r=n(e),o=i(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=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var u=new s(e,r,o,l);return u.update(t),u};var n=r(5827),i=r(2944),a=r(7667),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 u(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,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],u=n[15],c=(t._ortho?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*u)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]*this.pixelRatio),r.capSize=this.capSize[f]*c,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var c=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=c[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.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 i=[],a=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<a;++l){for(var c=r[l],h=0;h<3;++h)if(isNaN(c[h])||!isFinite(c[h]))continue t;var p,d=n[l],v=e[s];Array.isArray(v[0])&&(v=e[l]),3===v.length?v=[v[0],v[1],v[2],1]:4===v.length&&(v=[v[0],v[1],v[2],v[3]],!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0)),isNaN(d[0][s])||isNaN(d[1][s])||(d[0][s]<0&&((p=c.slice())[s]+=d[0][s],i.push(c[0],c[1],c[2],v[0],v[1],v[2],v[3],0,0,0,p[0],p[1],p[2],v[0],v[1],v[2],v[3],0,0,0),u(this.bounds,p),o+=2+f(i,p,v,s)),d[1][s]>0&&((p=c.slice())[s]+=d[1][s],i.push(c[0],c[1],c[2],v[0],v[1],v[2],v[3],0,0,0,p[0],p[1],p[2],v[0],v[1],v[2],v[3],0,0,0),u(this.bounds,p),o+=2+f(i,p,v,s)))}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},7667:function(t,e,r){\"use strict\";var n=r(6832),i=r(5158),a=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}\"]);t.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},4234:function(t,e,r){\"use strict\";var n=r(8931);t.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var u=t.getExtension(\"WEBGL_draw_buffers\");if(!l&&u&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,u),Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]),\"number\"!=typeof e)throw new Error(\"gl-fbo: Missing shape parameter\");var c=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>c||r<0||r>c)throw new Error(\"gl-fbo: Parameters are too large for FBO\");var f=1;if(\"color\"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(f>1){if(!u)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(f>t.getParameter(u.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+f+\" draw buffers\")}}var h=t.UNSIGNED_BYTE,p=t.getExtension(\"OES_texture_float\");if(n.float&&f>0){if(!p)throw new Error(\"gl-fbo: Context does not support floating point textures\");h=t.FLOAT}else n.preferFloat&&f>0&&p&&(h=t.FLOAT);var v=!0;\"depth\"in n&&(v=!!n.depth);var g=!1;return\"stencil\"in n&&(g=!!n.stencil),new d(t,e,r,h,f,v,g,u)};var i,a,o,s,l=null;function u(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function c(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error(\"gl-fbo: Framebuffer unsupported\");case a: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 h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);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,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var v=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return v._shape[0]},set:function(t){return v.width=t}},1:{get:function(){return v._shape[1]},set:function(t){return v.height=t}}}),this._shapeVector=g,function(t){var e=u(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,v=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var y=0;y<o;++y)t.color[y]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+y);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var m=r.getExtension(\"WEBGL_depth_texture\");m?d?t.depth=h(r,i,a,m.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):v&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):v&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):v?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){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),y=0;y<t.color.length;++y)t.color[y].dispose(),t.color[y]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),c(r,e),f(x)}c(r,e)}(this)}var v=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=u(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(),c(n,a),f(s)),c(n,a)}}Object.defineProperties(v,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),v.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])}},v.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)}}},3530:function(t,e,r){var n=r(8974).sprintf,i=r(6603),a=r(9365),o=r(8008);t.exports=function(t,e,r){\"use strict\";var s=a(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===i.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var u=n(\"Error compiling %s shader %s:\\n\",l,s),c=n(\"%s%s\",u,t),f=t.split(\"\\n\"),h={},p=0;p<f.length;p++){var d=f[p];if(\"\"!==d&&\"\\0\"!==d){var v=parseInt(d.split(\":\")[2]);if(isNaN(v))throw new Error(n(\"Could not parse error: %s\",d));h[v]=d}}var g=o(e).split(\"\\n\");for(p=0;p<g.length;p++)if((h[p+3]||h[p+2]||h[p+1])&&(u+=g[p]+\"\\n\",h[p+1])){var y=h[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),u+=n(\"^^^ %s\\n\\n\",y)}return{long:u.trim(),short:c.trim()}}},6386:function(t,e,r){\"use strict\";t.exports=function(t,e){var r=t.gl,n=new u(t,o(r,l.vertex,l.fragment),o(r,l.pickVertex,l.pickFragment),s(r),s(r),s(r),s(r));return n.update(e),t.addObject(n),n};var n=r(5070),i=r(9560),a=r(5306),o=r(5158),s=r(5827),l=r(1292);function u(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var c,f=u.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(c=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],u=a[3]-a[1];c[0]=2*o/l,c[4]=2*s/u,c[6]=2*(r[0]-a[0])/l-1,c[7]=2*(r[1]-a[1])/u-1,e.bind();var f=e.uniforms;f.viewTransform=c,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,u=a[2]-a[0],c=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*u/f,t[4]=2*c/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var v=i.attributes;return this.positionBuffer.bind(),v.position.pointer(),this.weightBuffer.bind(),v.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),v.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var u,c,f,p,d=t.colorLevels||[0],v=t.colorValues||[0,0,0,1],g=d.length,y=this.bounds;l?(u=y[0]=r[0],c=y[1]=o[0],f=y[2]=r[r.length-1],p=y[3]=o[o.length-1]):(u=y[0]=r[0]+(r[1]-r[0])/2,c=y[1]=o[0]+(o[1]-o[0])/2,f=y[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=y[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var m=1/(f-u),x=1/(p-c),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,O;l?(P=x*(o[C]-c),O=x*(o[C+1]-c)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-c):x*(o[C]-(o[C]-o[C-1])/2-c),O=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-c):x*(o[C]+(o[C]-o[C-1])/2-c));for(var I=0;I<E;++I){var D,z;l?(D=m*(r[I]-u),z=m*(r[I+1]-u)):(D=I<b-1?m*(r[I]-(r[I+1]-r[I])/2-u):m*(r[I]-(r[I]-r[I-1])/2-u),z=I<b-1?m*(r[I]+(r[I+1]-r[I])/2-u):m*(r[I]+(r[I]-r[I-1])/2-u));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],H=s[l?(C+V)*b+(I+U):C*b+I],q=n.le(d,H);if(q<0)F=v[0],B=v[1],N=v[2],j=v[3];else if(q===g-1)F=v[4*g-4],B=v[4*g-3],N=v[4*g-2],j=v[4*g-1];else{var G=(H-d[q])/(d[q+1]-d[q]),Z=1-G,Y=4*q,W=4*(q+1);F=Z*v[Y]+G*v[W],B=Z*v[Y+1]+G*v[W+1],N=Z*v[Y+2]+G*v[W+2],j=Z*v[Y+3]+G*v[W+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*D+.5*z,k[2*S+1]=.5*P+.5*O,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+I,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},1292:function(t,e,r){\"use strict\";var n=r(6832);t.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\"])}},248:function(t,e,r){var n=r(6832),i=r(5158),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  vec4 startPoint = project(position);\\n  vec4 endPoint   = project(nextPosition);\\n\\n  vec2 A = startPoint.xy / startPoint.w;\\n  vec2 B =   endPoint.xy /   endPoint.w;\\n\\n  float clipAngle = atan(\\n    (B.y - A.y) * screenShape.y,\\n    (B.x - A.x) * screenShape.x\\n  );\\n\\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\\n    sin(clipAngle),\\n    -cos(clipAngle)\\n  ) / screenShape;\\n\\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\\n\\n  worldPosition = position;\\n  pixelArcLength = arcLength;\\n  fragColor = color;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3      clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float     dashScale;\\nuniform float     opacity;\\n\\nvarying vec3    worldPosition;\\nvarying float   pixelArcLength;\\nvarying vec4    fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n  if(dashWeight < 0.5) {\\n    discard;\\n  }\\n  gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX  1.70141184e38\\n#define FLOAT_MIN  1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n  float av = abs(v);\\n\\n  //Handle special cases\\n  if(av < FLOAT_MIN) {\\n    return vec4(0.0, 0.0, 0.0, 0.0);\\n  } else if(v > FLOAT_MAX) {\\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n  } else if(v < -FLOAT_MAX) {\\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n  }\\n\\n  vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  float e = floor(log2(av));\\n  float m = av * pow(2.0, -e) - 1.0;\\n\\n  //Unpack mantissa\\n  c[1] = floor(128.0 * m);\\n  m -= c[1] / 128.0;\\n  c[2] = floor(32768.0 * m);\\n  m -= c[2] / 32768.0;\\n  c[3] = floor(8388608.0 * m);\\n\\n  //Unpack exponent\\n  float ebias = e + 127.0;\\n  c[0] = floor(ebias / 2.0);\\n  ebias -= c[0] * 2.0;\\n  c[1] += floor(ebias) * 128.0;\\n\\n  //Unpack sign bit\\n  c[0] += 128.0 * step(0.0, -v);\\n\\n  //Scale back to range\\n  return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];e.createShader=function(t){return i(t,a,o,null,l)},e.createPickShader=function(t){return i(t,a,s,null,l)}},6086:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),c=u(new Array(1024),[256,1,4]),p=0;p<1024;++p)c.data[p]=255;var d=a(e,c);d.wrap=e.REPEAT;var v=new y(e,r,o,s,l,d);return v.update(t),v};var n=r(5827),i=r(2944),a=r(8931),o=new Uint8Array(4),s=new Float32Array(o.buffer),l=r(5070),u=r(5050),c=r(248),f=c.createShader,h=c.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function v(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 y(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var m=y.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||p,view:t.view||p,projection:t.projection||p,clipBounds:v(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||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:v(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 i=[],a=[],o=[],s=0,c=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],v=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var y,m,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);c+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(y=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],m=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):y=m=p,3===y.length&&(y=[y[0],y[1],y[2],1]),3===m.length&&(m=[m[0],m[1],m[2],1]),!this.hasAlpha&&y[3]<1&&(this.hasAlpha=!0),x=Array.isArray(v)?v.length>e-1?v[e-1]:v.length>0?v[v.length-1]:[0,0,0,1]:v;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,y[0],y[1],y[2],y[3]);c+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,y[0],y[1],y[2],y[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,m[0],m[1],m[2],m[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,m[0],m[1],m[2],m[3]),c+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=c,this.points=o,this.arcLength=a,\"dashes\"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=u(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},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=function(t,e,r,n){return o[0]=0,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2]),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),u=1-a,c=[0,0,0],f=0;f<3;++f)c[f]=u*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,c,h,this.points[h])}},7332:function(t){t.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}},9823:function(t){t.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}},7787:function(t){t.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],u=t[8],c=t[9],f=t[10],h=t[11],p=t[12],d=t[13],v=t[14],g=t[15];return(e*o-r*a)*(f*g-h*v)-(e*s-n*a)*(c*g-h*d)+(e*l-i*a)*(c*v-f*d)+(r*s-n*o)*(u*g-h*p)-(r*l-i*o)*(u*v-f*p)+(n*l-i*s)*(u*d-c*p)}},5950:function(t){t.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,u=r*o,c=n*o,f=n*s,h=i*o,p=i*s,d=i*l,v=a*o,g=a*s,y=a*l;return t[0]=1-f-d,t[1]=c+y,t[2]=h-g,t[3]=0,t[4]=c-y,t[5]=1-u-d,t[6]=p+v,t[7]=0,t[8]=h+g,t[9]=p-v,t[10]=1-u-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},7280:function(t){t.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,u=a+a,c=n*s,f=n*l,h=n*u,p=i*l,d=i*u,v=a*u,g=o*s,y=o*l,m=o*u;return t[0]=1-(p+v),t[1]=f+m,t[2]=h-y,t[3]=0,t[4]=f-m,t[5]=1-(c+v),t[6]=d+g,t[7]=0,t[8]=h+y,t[9]=d-g,t[10]=1-(c+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},9947:function(t){t.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}},7437:function(t){t.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],f=e[9],h=e[10],p=e[11],d=e[12],v=e[13],g=e[14],y=e[15],m=r*s-n*o,x=r*l-i*o,b=r*u-a*o,_=n*l-i*s,w=n*u-a*s,T=i*u-a*l,k=c*v-f*d,A=c*g-h*d,M=c*y-p*d,S=f*g-h*v,E=f*y-p*v,L=h*y-p*g,C=m*L-x*E+b*S+_*M-w*A+T*k;return C?(C=1/C,t[0]=(s*L-l*E+u*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(v*T-g*w+y*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-u*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(g*b-d*T-y*x)*C,t[7]=(c*T-h*b+p*x)*C,t[8]=(o*E-s*M+u*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-v*b+y*m)*C,t[11]=(f*b-c*w-p*m)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(v*x-d*_-g*m)*C,t[15]=(c*_-f*x+h*m)*C,t):null}},3012:function(t,e,r){var n=r(9947);t.exports=function(t,e,r,i){var a,o,s,l,u,c,f,h,p,d,v=e[0],g=e[1],y=e[2],m=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];return Math.abs(v-_)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(y-T)<1e-6?n(t):(f=v-_,h=g-w,p=y-T,a=x*(p*=d=1/Math.sqrt(f*f+h*h+p*p))-b*(h*=d),o=b*(f*=d)-m*p,s=m*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0),l=h*s-p*o,u=p*a-f*s,c=f*o-h*a,(d=Math.sqrt(l*l+u*u+c*c))?(l*=d=1/d,u*=d,c*=d):(l=0,u=0,c=0),t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=u,t[6]=h,t[7]=0,t[8]=s,t[9]=c,t[10]=p,t[11]=0,t[12]=-(a*v+o*g+s*y),t[13]=-(l*v+u*g+c*y),t[14]=-(f*v+h*g+p*y),t[15]=1,t)}},104:function(t){t.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],f=e[8],h=e[9],p=e[10],d=e[11],v=e[12],g=e[13],y=e[14],m=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*v,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*u+_*p+w*y,t[3]=x*o+b*c+_*d+w*m,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*v,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*u+_*p+w*y,t[7]=x*o+b*c+_*d+w*m,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*v,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*u+_*p+w*y,t[11]=x*o+b*c+_*d+w*m,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*v,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*u+_*p+w*y,t[15]=x*o+b*c+_*d+w*m,t}},5268:function(t){t.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),u=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*u,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*u,t[15]=1,t}},1120:function(t){t.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},4422:function(t){t.exports=function(t,e,r,n){var i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);return Math.abs(P)<1e-6?null:(E*=P=1/P,L*=P,C*=P,i=Math.sin(r),o=1-(a=Math.cos(r)),s=e[0],l=e[1],u=e[2],c=e[3],f=e[4],h=e[5],p=e[6],d=e[7],v=e[8],g=e[9],y=e[10],m=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+v*_,t[1]=l*x+h*b+g*_,t[2]=u*x+p*b+y*_,t[3]=c*x+d*b+m*_,t[4]=s*w+f*T+v*k,t[5]=l*w+h*T+g*k,t[6]=u*w+p*T+y*k,t[7]=c*w+d*T+m*k,t[8]=s*A+f*M+v*S,t[9]=l*A+h*M+g*S,t[10]=u*A+p*M+y*S,t[11]=c*A+d*M+m*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t)}},6109:function(t){t.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],u=e[8],c=e[9],f=e[10],h=e[11];return 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]),t[4]=a*i+u*n,t[5]=o*i+c*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=u*i-a*n,t[9]=c*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},7115:function(t){t.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[8],c=e[9],f=e[10],h=e[11];return 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]),t[0]=a*i-u*n,t[1]=o*i-c*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+u*i,t[9]=o*n+c*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},5240:function(t){t.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[4],c=e[5],f=e[6],h=e[7];return 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]),t[0]=a*i+u*n,t[1]=o*i+c*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=u*i-a*n,t[5]=c*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},3668:function(t){t.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},998:function(t){t.exports=function(t,e,r){var n,i,a,o,s,l,u,c,f,h,p,d,v=r[0],g=r[1],y=r[2];return e===t?(t[12]=e[0]*v+e[4]*g+e[8]*y+e[12],t[13]=e[1]*v+e[5]*g+e[9]*y+e[13],t[14]=e[2]*v+e[6]*g+e[10]*y+e[14],t[15]=e[3]*v+e[7]*g+e[11]*y+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=u,t[7]=c,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*v+s*g+f*y+e[12],t[13]=i*v+l*g+h*y+e[13],t[14]=a*v+u*g+p*y+e[14],t[15]=o*v+c*g+d*y+e[15]),t}},2142:function(t){t.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},4340:function(t,e,r){\"use strict\";var n=r(957),i=r(7309);function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}t.exports=function(t,e,r,a,l,u){if(1===t.length)return[0,t[0].slice()];for(var c=new Array(t.length),f=0;f<t.length;++f)c[f]=o(t[f],r,a,l,u);var h=0,p=1/0;for(f=0;f<c.length;++f){for(var d=0,v=0;v<2;++v)d+=Math.pow(c[f][v]-e[v],2);d<p&&(p=d,h=f)}var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return(r=Math.sqrt(r))+(a=Math.sqrt(a))<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(c,e),y=0;for(f=0;f<3;++f){if(g[f]<-.001||g[f]>1.0001)return null;y+=g[f]}return Math.abs(y-1)>.001?null:[h,s(t,g),g]}},2056:function(t,e,r){var n=r(6832),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n           , view\\n           , projection\\n           , inverseModel;\\nuniform vec3 eyePosition\\n           , lightPosition;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  gl_Position      = project(position);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  f_color          = color;\\n  f_data           = position;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n            , fresnel\\n            , kambient\\n            , kdiffuse\\n            , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (f_color.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\\n  ) discard;\\n\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_color = color;\\n  f_data  = position;\\n  f_uv    = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n  } else {\\n    gl_Position = projection * view * model * vec4(position, 1.0);\\n  }\\n  gl_PointSize = pointSize;\\n  f_color = color;\\n  f_uv = uv;\\n}\"]),u=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}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_id        = id;\\n  f_position  = position;\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3  position;\\nattribute float pointSize;\\nattribute vec4  id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n  } else {\\n    gl_Position  = projection * view * model * vec4(position, 1.0);\\n    gl_PointSize = pointSize;\\n  }\\n  f_id         = id;\\n  f_position   = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n  gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},e.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},e.pointShader={vertex:l,fragment:u,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},e.pickShader={vertex:c,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},e.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},e.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},8116:function(t,e,r){\"use strict\";var n=r(5158),i=r(5827),a=r(2944),o=r(8931),s=r(115),l=r(104),u=r(7437),c=r(5050),f=r(9156),h=r(7212),p=r(5306),d=r(2056),v=r(4340),g=d.meshShader,y=d.wireShader,m=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=c,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=u,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=y,this.edgeIds=v,this.edgeVAO=m,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,m.vertex,m.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}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var u=r[l],c=0;c<2;++c){var f=u[0];2===u.length&&(f=u[c]);for(var d=n[f][0],v=n[f][1],g=i[f],y=1-g,m=this.positions[d],x=this.positions[v],b=0;b<3;++b)o[s++]=g*m[b]+y*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:\"rgba\"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return c(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],u=[],h=[],p=[],d=[],v=[],g=[],y=[],m=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,O=t.cellUVs,I=t.cellIntensity,D=1/0,z=-1/0;if(!C&&!O)if(P)if(t.vertexIntensityBounds)D=+t.vertexIntensityBounds[0],z=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];D=Math.min(D,F),z=Math.max(z,F)}else if(I)if(t.cellIntensityBounds)D=+t.cellIntensityBounds[0],z=+t.cellIntensityBounds[1];else for(R=0;R<I.length;++R)F=I[R],D=Math.min(D,F),z=Math.max(z,F);else for(R=0;R<n.length;++R)F=n[R][2],D=Math.min(D,F),z=Math.max(z,F);this.intensity=P||I||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.pickVertex=!(I||E);var B=t.pointSizes,N=t.pointSize||1;for(this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,H=0,q=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[Y=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;y.push(j[0],j[1],j[2]),W=S?S[Y]:E?E[R]:L,this.opacityscale&&P?a.push(W[0],W[1],W[2],this.opacity*A((P[Y]-D)/(z-D),this.opacityscale)):3===W.length?m.push(W[0],W[1],W[2],this.opacity):(m.push(W[0],W[1],W[2],W[3]*this.opacity),W[3]<1&&(this.hasAlpha=!0)),X=C?C[Y]:P?[(P[Y]-D)/(z-D),0]:O?O[R]:I?[(I[R]-D)/(z-D),0]:[(j[2]-D)/(z-D),0],x.push(X[0],X[1]),B?b.push(B[Y]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[Y=G[U]];for(var Z=0;Z<3;++Z)if(isNaN(j[Z])||!isFinite(j[Z]))continue t}for(U=0;U<2;++U)j=n[Y=G[U]],p.push(j[0],j[1],j[2]),W=S?S[Y]:E?E[R]:L,this.opacityscale&&P?a.push(W[0],W[1],W[2],this.opacity*A((P[Y]-D)/(z-D),this.opacityscale)):3===W.length?d.push(W[0],W[1],W[2],this.opacity):(d.push(W[0],W[1],W[2],W[3]*this.opacity),W[3]<1&&(this.hasAlpha=!0)),X=C?C[Y]:P?[(P[Y]-D)/(z-D),0]:O?O[R]:I?[(I[R]-D)/(z-D),0]:[(j[2]-D)/(z-D),0],v.push(X[0],X[1]),g.push(R);H+=1;break;case 3:for(U=0;U<3;++U)for(j=n[Y=G[U]],Z=0;Z<3;++Z)if(isNaN(j[Z])||!isFinite(j[Z]))continue t;for(U=0;U<3;++U){var Y,W,X,J;j=n[Y=G[2-U]],i.push(j[0],j[1],j[2]),(W=S?S[Y]:E?E[R]:L)?this.opacityscale&&P?a.push(W[0],W[1],W[2],this.opacity*A((P[Y]-D)/(z-D),this.opacityscale)):3===W.length?a.push(W[0],W[1],W[2],this.opacity):(a.push(W[0],W[1],W[2],W[3]*this.opacity),W[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),X=C?C[Y]:P?[(P[Y]-D)/(z-D),0]:O?O[R]:I?[(I[R]-D)/(z-D),0]:[(j[2]-D)/(z-D),0],u.push(X[0],X[1]),J=w?w[Y]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(y),this.pointColors.update(m),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(v),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(u),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=u(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var c=new Array(16);for(l(c,s.view,s.model),l(c,s.projection,c),u(c,c),o=0;o<3;++o)s.eyePosition[o]=c[12+o]/c[15];var f,h=c[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*c[4*o+3];for(o=0;o<3;++o){for(var p=c[12+o],d=0;d<3;++d)p+=c[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()),this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};(s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],u=this.positions[r[1]],c=this.positions[r[2]],f=[(l[0]+u[0]+c[0])/3,(l[1]+u[1]+c[1])/3,(l[2]+u[2]+c[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=v(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},t.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,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}(t),s=function(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}(t),l=M(t),u=S(t),f=E(t),h=L(t),p=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),v=i(t),m=i(t),x=i(t),b=i(t),_=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:b,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:v,type:t.FLOAT,size:4},{buffer:m,type:t.FLOAT,size:2},{buffer:x,type:t.FLOAT,size:3}]),w=i(t),k=i(t),A=i(t),C=i(t),P=a(t,[{buffer:w,type:t.FLOAT,size:3},{buffer:C,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:k,type:t.FLOAT,size:4},{buffer:A,type:t.FLOAT,size:2}]),O=i(t),I=i(t),D=i(t),z=i(t),R=i(t),F=a(t,[{buffer:O,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:I,type:t.FLOAT,size:4},{buffer:D,type:t.FLOAT,size:2},{buffer:z,type:t.FLOAT,size:1}]),B=i(t),N=new T(t,p,r,s,l,u,f,h,d,b,v,m,x,_,w,C,k,A,P,O,R,I,D,z,F,B,a(t,[{buffer:B,type:t.FLOAT,size:3}]));return N.update(e),N}},4554:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl;return new o(t,n(e,[0,0,0,1,1,0,1,1]),i(e,a.boxVert,a.lineFrag))};var n=r(5827),i=r(5158),a=r(2709);function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,u=o.prototype;u.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},u.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,u=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),u.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},3016:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl;return new s(t,n(e),i(e,o.gridVert,o.gridFrag),i(e,o.tickVert,o.gridFrag))};var n=r(5827),i=r(5158),a=r(5070),o=r(2709);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 u,c,f,h,p,d=s.prototype;d.draw=(u=[0,0],c=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,v=0;v<2;++v){var g=a[v],y=a[v+2]-g,m=.5*(o[v+2]+o[v]),x=o[v+2]-o[v];c[v]=2*y/x,u[v]=2*(g-m)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=u,r.uniforms.dataScale=c;var b=0;for(v=0;v<2;++v){f[0]=f[1]=0,f[v]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[v]/(s[v+2]-s[v])*d,r.uniforms.color=h[v];var _=6*n[v].length;p[v]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,u=this.vbo,c=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,v=s.viewBox,g=s.pixelRatio,y=s.screenBox,m=y[2]-y[0],x=y[3]-y[1],b=v[2]-v[0],_=v[3]-v[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/m,t[0]*=b/m,e[1]*=_/x,t[1]*=_/x,c.bind(),u.bind(),c.attributes.dataCoord.pointer();var S=c.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,O=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),I=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),D=0+6*O,z=6*Math.max(0,I-O),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(v[0]-E[1])/m-1,i[1]=(v[3]+v[1])/x-1,o[0]=E[1]*g/m,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(v[2]+v[0])/m-1,i[1]=2*(v[1]-E[0])/x-1,o[0]=L[0]*g/m,o[1]=E[0]*g/x,z&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,D,z)),i[0]=2*(v[2]+E[3])/m-1,i[1]=(v[3]+v[1])/x-1,o[0]=E[3]*g/m,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(v[2]+v[0])/m-1,i[1]=2*(v[3]+E[2])/x-1,o[0]=L[2]*g/m,o[1]=E[2]*g/x,z&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,D,z))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],u=r[o],c=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-u)/(c-u);s.push(d);for(var v=0;v<6;++v)n[i++]=d,n[i++]=h[v],n[i++]=p[v]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},1154:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl;return new o(t,n(e,[-1,-1,-1,1,1,-1,1,1]),i(e,a.lineVert,a.lineFrag))};var n=r(5827),i=r(5158),a=r(2709);function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,u=o.prototype;u.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},u.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,u=this.shader,c=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,u.uniforms.start=s,u.uniforms.end=l,u.uniforms.width=i*o.pixelRatio,u.uniforms.color=a,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),u.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},2709:function(t,e,r){\"use strict\";var n=r(6832),i=n([\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\"]);t.exports={lineVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n  return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  vec2 delta = normalize(perp(start - end));\\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\"]),lineFrag:i,textVert:n([\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n  float dataOffset  = textCoordinate.z;\\n  vec2 glyphOffset  = textCoordinate.xy;\\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n    glyphMatrix * glyphOffset * textScale + screenOffset;\\n  gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\"]),textFrag:i,gridVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n  gl_Position = vec4(pos, 0, 1);\\n}\\n\"]),gridFrag:i,boxVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\"]),tickVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\\n}\\n\"])}},5613:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl;return new l(t,n(e),i(e,s.textVert,s.textFrag))};var n=r(5827),i=r(5158),a=r(6946),o=r(5070),s=r(2709);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 u,c,f,h,p,d,v=l.prototype;v.drawTicks=(u=[0,0],c=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,v=e.tickPad,g=e.tickColor,y=e.tickAngle,m=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);u[0]=u[1]=0,u[t]=1,c[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];c[1^t]=M*s[1^t]-1,d[t]&&(c[1^t]-=M*p*v[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=u,r.uniforms.screenOffset=c,r.uniforms.color=g[t],r.uniforms.angle=y[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),m[t]&&T&&(c[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=c,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),c[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(c[1^t]+=M*p*v[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=u,r.uniforms.screenOffset=c,r.uniforms.color=g[t+2],r.uniforms.angle=y[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),m[t+2]&&T&&(c[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=c,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),v.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,u=r.pixelRatio;if(this.titleCount){for(var c=0;c<2;++c)e[c]=2*(o[c]*u-a[c])/(a[2+c]-a[c])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),v.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,u=.5*(n[o+2]+n[o]),c=n[o+2]-n[o],f=a[o],v=a[o+2]-f,g=i[o],y=i[o+2]-g;p[o]=2*l/c*v/y,h[o]=2*(s-u)/c*v/y}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),v.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,u=t.bounds;for(o=0;o<2;++o){var c=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,v=p.text,g=p.font||\"sans-serif\";i=p.fontSize||12;for(var y=1/(u[o+2]-u[o]),m=u[o],x=v.split(\"\\n\"),b=0;b<x.length;b++)for(n=a(g,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-m)*y);c.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=c,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:\"center\"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},v.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},2117:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl,r=new l(e,n(e,[e.drawingBufferWidth,e.drawingBufferHeight]));return r.grid=i(r),r.text=a(r),r.line=o(r),r.box=s(r),r.update(t),r};var n=r(2611),i=r(3016),a=r(5613),o=r(1154),s=r(4554);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 u=l.prototype;function c(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}u.setDirty=function(){this.dirty=this.pickDirty=!0},u.setOverlayDirty=function(){this.dirty=!0},u.nextDepthValue=function(){return this._depthCounter++/65536},u.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var u=this.borderColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[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 c=this.backgroundColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var v=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(v,e[1],v,e[3],p[d],h[d]):o.drawLine(e[0],v,e[2],v,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,y=this.borderLineWidth,m=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*y[1]*i,r[0],r[3]+.5*y[3]*i,y[1],m[1]),g[0]&&o.drawLine(r[0]-.5*y[0]*i,r[1],r[2]+.5*y[2]*i,r[1],y[0],m[0]),g[3]&&o.drawLine(r[2],r[1]-.5*y[1]*i,r[2],r[3]+.5*y[3]*i,y[3],m[3]),g[2]&&o.drawLine(r[0]-.5*y[0]*i,r[3],r[2]+.5*y[2]*i,r[3],y[2],m[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)}},u.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()}},u.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),u=this.objects,c=0;c<u.length;++c){var f=u[c].pick(a,o,l);if(f)return f}return null}},u.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()},u.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())},u.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()},u.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]),this.screenBox,this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=c(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=c(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=c(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=c(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=c(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=c(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\"}),this.static=!!t.static,this.setDirty()},u.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();for(this.objects.length=0,t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},u.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},u.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}},u.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},u.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}}},4296:function(t,e,r){\"use strict\";t.exports=function(t,e){t=t||document.body;var r=[.01,1/0];\"distanceLimits\"in(e=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 u=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),c=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",enableWheel:!0,view:u,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:u.modes,_ortho:e._ortho||e.projection&&\"orthographic\"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;u.idle(e-r),u.recalcMatrix(i),u.flush(e-(100+2*r));for(var a=!0,o=u.computedMatrix,s=0;s<16;++s)a=a&&c[s]===o[s],c[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(u.computedRadius[0]),!0)},lookAt:function(t,e,r){u.lookAt(u.lastT(),t,e,r)},rotate:function(t,e,r){u.rotate(u.lastT(),t,e,r)},pan:function(t,e,r){u.pan(u.lastT(),t,e,r)},translate:function(t,e,r){u.translate(u.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return u.computedMatrix},set:function(t){return u.setMatrix(u.lastT(),t),u.computedMatrix},enumerable:!0},mode:{get:function(){return u.getMode()},set:function(t){var e=u.computedUp.slice(),r=u.computedEye.slice(),i=u.computedCenter.slice();if(u.setMode(t),\"turntable\"===t){var a=n();u._active.lookAt(a,r,i,e),u._active.lookAt(a+500,r,i,[0,0,1]),u._active.flush(a)}return u.getMode()},enumerable:!0},center:{get:function(){return u.computedCenter},set:function(t){return u.lookAt(u.lastT(),null,t),u.computedCenter},enumerable:!0},eye:{get:function(){return u.computedEye},set:function(t){return u.lookAt(u.lastT(),t),u.computedEye},enumerable:!0},up:{get:function(){return u.computedUp},set:function(t){return u.lookAt(u.lastT(),null,null,t),u.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return u.setDistance(u.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return u.getDistanceLimits(r)},set:function(t){return u.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,c=\"zoom\"===o,h=!!a.control,p=!!a.alt,v=!!a.shift,g=!!(1&e),y=!!(2&e),m=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!v||g&&!h&&!p&&v)&&u.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!v||y||g&&h&&!p&&!v)&&u.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),c&&g&&!h&&!p&&!v||m||g&&!h&&p&&!v)){var M=-d.zoomSpeed*_/window.innerHeight*(A-u.lastT())*100;u.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener(\"touchstart\",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener(\"touchmove\",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener(\"touchend\",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))u.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-u.lastT())/20;u.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=r(8161),i=r(1152),a=r(6145),o=r(6475),s=r(2565),l=r(5233)},8245:function(t,e,r){var n=r(6832),i=r(5158),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n  uv = position;\\n  gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n  gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);t.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},1059:function(t,e,r){\"use strict\";var n=r(4296),i=r(7453),a=r(2771),o=r(6496),s=r(2611),l=r(4234),u=r(8126),c=r(6145),f=r(1120),h=r(5268),p=r(8245),d=r(2321)({tablet:!0,featureDetect:!0});function v(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}return e>0?(r=Math.round(Math.pow(10,e)),Math.ceil(t/r)*r):Math.ceil(t)}function y(t){return\"boolean\"!=typeof t||t}t.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;e||(e=document.createElement(\"canvas\"),t.container?t.container.appendChild(e):document.body.appendChild(e));var r=t.gl;if(r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d})),!r)throw new Error(\"webgl not supported\");var m=t.bounds||[[-10,-10,-10],[10,10,10]],x=new v,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],O=!0,I=!0,D={view:null,projection:new Array(16),model:new Array(16),_ortho:!1},z=(I=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),R=t.cameraObject||n(e,T),F={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:R,axes:A,axesPixels:null,spikes:S,bounds:m,objects:E,shape:z,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:y(t.autoResize),autoBounds:y(t.autoBounds),autoScale:!!t.autoScale,autoCenter:y(t.autoCenter),clipToBounds:y(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:D,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,I=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},B=[r.drawingBufferWidth/F.pixelRatio|0,r.drawingBufferHeight/F.pixelRatio|0];function N(){if(!F._stopped&&F.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*F.pixelRatio),a=0|Math.ceil(n*F.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",O=!0}}}function j(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,z);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function U(){if(F.contextLost)return!0;r.isContextLost()&&(F.contextLost=!0,F.mouseListener.enabled=!1,F.selection.object=null,F.oncontextloss&&F.oncontextloss())}F.autoResize&&N(),window.addEventListener(\"resize\",N),F.update=function(t){F._stopped||(t=t||{},O=!0,I=!0)},F.add=function(t){F._stopped||(t.axes=A,E.push(t),L.push(-1),O=!0,I=!0,j())},F.remove=function(t){if(!F._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),O=!0,I=!0,j())}},F.dispose=function(){if(!F._stopped&&(F._stopped=!0,window.removeEventListener(\"resize\",N),e.removeEventListener(\"webglcontextlost\",U),F.mouseListener.enabled=!1,!F.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();for(b.dispose(),t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},F._mouseRotating=!1,F._prevButtons=0,F.enableMouseListeners=function(){F.mouseListener=c(e,(function(t,e,r){if(!F._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&F._prevButtons)F._mouseRotating=!0;else{F._mouseRotating&&(I=!0),F._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,B[1]-r-1,F.pickRadius);if(l){if(l.distance>x.distance)continue;for(var u=0;u<i;++u){var c=E[u];if(L[u]===s){var f=c.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=c,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),O=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),O=!0),(o=o||x.object!==a)&&F.onselect&&F.onselect(x),1&t&&!(1&F._prevButtons)&&F.onclick&&F.onclick(x),F._prevButtons=t}}))},e.addEventListener(\"webglcontextlost\",U);var V=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],H=[V[0].slice(),V[1].slice()];function q(){if(!U()){N();var t=F.camera.tick();D.view=F.camera.matrix,O=O||t,I=I||t,A.pixelRatio=F.pixelRatio,S.pixelRatio=F.pixelRatio;var e=E.length,n=V[0],i=V[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=F.pixelRatio,C.axes=F.axes,O=O||!!C.dirty,I=I||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],c=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],c[p])}var d=F.bounds;if(F.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var v=.05*(i[p]-n[p]);n[p]=n[p]-v,i[p]=i[p]+v}d[0][p]=n[p],d[1][p]=i[p]}var y=!1;for(p=0;p<3;++p)y=y||H[0][p]!==d[0][p]||H[1][p]!==d[1][p],H[0][p]=d[0][p],H[1][p]=d[1][p];if(I=I||y,O=O||y){if(y){var m=[0,0,0];for(o=0;o<3;++o)m[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:m}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;for(z[0]=T,z[1]=k,B[0]=0|Math.max(T/F.pixelRatio,1),B[1]=0|Math.max(k/F.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,u=t.zFar,c=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,u),n._ortho=!0):(f(i,c,p,l,u),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var v=0;for(d=0;d<3;++d)v=Math.max(v,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/v,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(F,w),o=0;o<e;++o)(C=E[o]).axesBounds=d,F.clipToBounds&&(C.clipBounds=d);x.object&&(F.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),I&&(I=!1,function(){if(!U()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=B,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(D))}i.end()}}}()),F.axesPixels=a(F.axes,D,T,k),F.onrender&&F.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),F.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;for(A.enable&&(M=M||A.isTransparent(),A.draw(D)),S.axes=A,x.object&&S.draw(D),r.disable(r.CULL_FACE),o=0;o<e;++o)(C=E[o]).axes=A,C.pixelRatio=F.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(D),C.isTransparent&&C.isTransparent()&&(M=!0);if(M){for(b.shape=z,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(D),o=0;o<e;++o)(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(D);for(r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(D),o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(D)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,u(r),r.disable(r.BLEND)}for(O=!1,o=0;o<e;++o)E[o].dirty=!1}}}return F.enableMouseListeners(),function t(){F._stopped||F.contextLost||(q(),requestAnimationFrame(t))}(),F.redraw=function(){F._stopped||(O=!0,q())},F},createCamera:n}},8023:function(t,e,r){var n=r(6832);e.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}\"]),e.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\"]),e.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\"]),e.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\"])},8271:function(t,e,r){\"use strict\";var n=r(5158),i=r(5827),a=r(5306),o=r(8023);function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}t.exports=function(t,e){var r=t.gl,a=new s(t,i(r),i(r),n(r,o.pointVertex,o.pointFragment),n(r,o.pickVertex,o.pickFragment));return a.update(e),t.addObject(a),a};var l,u,c=s.prototype;c.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},c.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),u=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)u[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(u),i||a.free(l),o||a.free(u),this.pointCount=n,this.pickOffset=0},c.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],u=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),c=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=c<5,r.uniforms.pointSize=c,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(u[0]=255&t,u[1]=t>>8&255,u[2]=t>>16&255,u[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=u,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),c.draw=c.unifiedDraw,c.drawPick=c.unifiedDraw,c.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},6093:function(t){t.exports=function(t,e,r,n){var i,a,o,s,l,u=e[0],c=e[1],f=e[2],h=e[3],p=r[0],d=r[1],v=r[2],g=r[3];return(a=u*p+c*d+f*v+h*g)<0&&(a=-a,p=-p,d=-d,v=-v,g=-g),1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n),t[0]=s*u+l*p,t[1]=s*c+l*d,t[2]=s*f+l*v,t[3]=s*h+l*g,t}},8240:function(t){\"use strict\";t.exports=function(t){return t||0===t?t.toString():\"\"}},4123:function(t,e,r){\"use strict\";var n=r(875);t.exports=function(t,e,r){var a=i[e];if(a||(a=i[e]={}),t in a)return a[t];var o={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,u,c=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(u=0;u<s.positions[l].length;++u)s.positions[l][u]/=r;for(l=0;l<c.positions.length;++l)for(u=0;u<c.positions[l].length;++u)c.positions[l][u]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=c.positions.length;for(l=0;l<h;++l){var p=c.positions[l];for(u=0;u<2;++u)f[0][u]=Math.min(f[0][u],p[u]),f[1][u]=Math.max(f[1][u],p[u])}return a[t]=[s,c,f]};var i={}},9282:function(t,e,r){var n=r(5158),i=r(6832),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n\\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (\\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n    interpColor.a * opacity == 0.\\n  ) discard;\\n  gl_FragColor = interpColor * opacity;\\n}\\n\"]),u=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),c=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:c},h={vertex:o,fragment:l,attributes:c},p={vertex:s,fragment:l,attributes:c},d={vertex:a,fragment:u,attributes:c},v={vertex:o,fragment:u,attributes:c},g={vertex:s,fragment:u,attributes:c};function y(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}e.createPerspective=function(t){return y(t,f)},e.createOrtho=function(t){return y(t,h)},e.createProject=function(t){return y(t,p)},e.createPickPerspective=function(t){return y(t,d)},e.createPickOrtho=function(t){return y(t,v)},e.createPickProject=function(t){return y(t,g)}},2182:function(t,e,r){\"use strict\";var n=r(3596),i=r(5827),a=r(2944),o=r(5306),s=r(104),l=r(9282),u=r(4123),c=r(8240),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function v(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,u,c,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=u,this.pickOrthoShader=c,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}t.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),u=l.createPickOrtho(e),c=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),v=new g(e,r,n,o,f,h,p,d,a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),s,u,c);return v.update(t),v};var y=g.prototype;y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},y.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 m=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}var L=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function C(t,e,r,n,i,a,o){var l=r.gl;if((a===r.projectHasAlpha||o)&&function(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,u=r.model||f,c=r.view||f,h=r.projection||f,d=e.axesBounds,v=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],m[0]=2/o.drawingBufferWidth,m[1]=2/o.drawingBufferHeight,t.bind(),l.view=c,l.projection=h,l.screenSize=m,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=v,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var y=T,L=0;L<16;++L)y[L]=0;for(L=0;L<4;++L)y[5*L]=1;y[5*g]=0,i[g]<0?y[12+g]=d[0][g]:y[12+g]=d[1][g],s(y,u,y),l.model=y;var C=(g+1)%3,P=(g+2)%3,O=M(x),I=M(b);O[C]=1,I[P]=1;var D=p(0,0,0,S(_,O)),z=p(0,0,0,S(w,I));if(Math.abs(D[1])>Math.abs(z[1])){var R=D;D=z,z=R,R=O,O=I,I=R;var F=C;C=P,P=F}D[0]<0&&(O[C]=-1),z[1]>0&&(I[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(u[4*C+L],2),N+=Math.pow(u[4*P+L],2);O[C]/=Math.sqrt(B),I[P]/=Math.sqrt(N),l.axes[0]=O,l.axes[1]=I,l.fragClipBounds[0]=E(k,v[0],g,-1e8),l.fragClipBounds[1]=E(k,v[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}(e,r,n,i),a===r.hasAlpha||o){t.bind();var u=t.uniforms;u.model=n.model||f,u.view=n.view||f,u.projection=n.projection||f,m[0]=2/l.drawingBufferWidth,m[1]=2/l.drawingBufferHeight,u.screenSize=m,u.highlightId=r.highlightId,u.highlightScale=r.highlightScale,u.fragClipBounds=L,u.clipBounds=r.axes.bounds,u.opacity=r.opacity,u.pickGroup=r.pickId/255,u.pixelRatio=i,r.vao.bind(),r.vao.draw(l.TRIANGLES,r.vertexCount),r.lineWidth>0&&(l.lineWidth(r.lineWidth*i),r.vao.draw(l.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function P(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=c(a);var o=!0;n(a)&&(a=\"▼\",o=!1);var s=u(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}y.draw=function(t){C(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},y.drawTransparent=function(t){C(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},y.drawPick=function(t){C(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},y.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},y.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){Array.isArray(t.projectOpacity)?this.projectOpacity=t.projectOpacity.slice():(r=+t.projectOpacity,this.projectOpacity=[r,r,r]);for(var n=0;n<3;++n)this.projectOpacity[n]=v(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=v(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||\"normal\",u=t.alignment||[0,0];if(2===u.length)i=u[0],a=u[1];else for(i=[],a=[],n=0;n<u.length;++n)i[n]=u[n][0],a[n]=u[n][1];var c=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,g=t.angle,y=t.lineColor,m=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=P(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;x+=3*k.cells.length,b+=2*A.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),O=o.mallocUint32(S);if(S>0){var I=0,D=x,z=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(y)&&Array.isArray(y[0]);t:for(n=0;n<_;++n){for(m+=1,w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),c[T]=Math.min(c[T],w[T])}k=(N=P(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)z[T]=U[T];z[3]=1}else if(4===U.length){for(T=0;T<4;++T)z[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else z[0]=z[1]=z[2]=0,z[3]=1;else z=[1,1,1,0];if(j)if(Array.isArray(y)){var U;if(3===(U=B?n<y.length?y[n]:[0,0,0,0]:y).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var H=0;Array.isArray(g)?H=n<g.length?+g[n]:0:g&&(H=+g);var q=Math.cos(H),G=Math.sin(H);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),c[T]=Math.min(c[T],w[T]);var Z=i,Y=a;Z=0,Array.isArray(i)?Z=n<i.length?i[n]:0:i&&(Z=i),Y=0,Array.isArray(a)?Y=n<a.length?a[n]:0:a&&(Y=a);var W=[Z*=Z>0?1-M[0][0]:Z<0?1+M[1][0]:1,Y*=Y>0?1-M[0][1]:Y<0?1+M[1][1]:1],X=k.cells||[],J=k.positions||[];for(T=0;T<X.length;++T)for(var K=X[T],$=0;$<3;++$){for(var Q=0;Q<3;++Q)E[3*I+Q]=w[Q];for(Q=0;Q<4;++Q)L[4*I+Q]=z[Q];O[I]=m;var tt=J[K[$]];C[2*I]=V*(q*tt[0]-G*tt[1]+W[0]),C[2*I+1]=V*(G*tt[0]+q*tt[1]+W[1]),I+=1}for(X=A.edges,J=A.positions,T=0;T<X.length;++T)for(K=X[T],$=0;$<2;++$){for(Q=0;Q<3;++Q)E[3*D+Q]=w[Q];for(Q=0;Q<4;++Q)L[4*D+Q]=R[Q];O[D]=m,tt=J[K[$]],C[2*D]=V*(q*tt[0]-G*tt[1]+W[0]),C[2*D+1]=V*(G*tt[0]+q*tt[1]+W[1]),D+=1}}}this.bounds=[c,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(O),o.free(E),o.free(L),o.free(C),o.free(O)},y.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()}},1884:function(t,e,r){\"use strict\";var n=r(6832);e.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\"]),e.boxFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  gl_FragColor = color;\\n}\\n\"])},6623:function(t,e,r){\"use strict\";var n=r(5158),i=r(5827),a=r(1884);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}t.exports=function(t,e){var r=t.gl,s=new o(t,i(r,[0,0,0,1,1,0,1,1]),n(r,a.boxVertex,a.boxFragment));return s.update(e),t.addOverlay(s),s};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,u=t.viewBox,c=t.pixelRatio,f=(e[0]-l[0])*(u[2]-u[0])/(l[2]-l[0])+u[0],h=(e[1]-l[1])*(u[3]-u[1])/(l[3]-l[1])+u[1],p=(e[2]-l[0])*(u[2]-u[0])/(l[2]-l[0])+u[0],d=(e[3]-l[1])*(u[3]-u[1])/(l[3]-l[1])+u[1];if(f=Math.max(f,u[0]),h=Math.max(h,u[1]),p=Math.min(p,u[2]),d=Math.min(d,u[3]),!(p<f||d<h)){o.bind();var v=s[2]-s[0],g=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,v,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,v,g,i),o.drawBox(p,h,v,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var y=r*c;o.drawBox(f-y,h-y,p+y,h+y,a),o.drawBox(f-y,d-y,p+y,d+y,a),o.drawBox(f-y,h-y,f+y,d+y,a),o.drawBox(p-y,h-y,p+y,d+y,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},2611:function(t,e,r){\"use strict\";t.exports=function(t,e){var r=e[0],a=e[1];return new l(t,n(t,r,a,{}),i.mallocUint8(r*a*4))};var n=r(4234),i=r(5306),a=r(5050),o=r(2288).nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var 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h=u,p=c,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);for(f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f,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()}},9578:function(t,e,r){var n=r(6832),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * tubePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\\n  vec4 t_position  = view * tubePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = tubePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  gl_Position = projection * view * tubePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},7307:function(t,e,r){\"use strict\";var n=r(2858),i=r(4020),a=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},s=function(t,e,r){return t<e?e:t>r?r:t},l=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};t.exports=function(t,e){var r=t.startingPositions,u=t.maxLength||1e3,c=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||\"+x+y+z\",p={};-1!==h.indexOf(\"-x\")&&(p.reversedX=!0),-1!==h.indexOf(\"-y\")&&(p.reversedY=!0),-1!==h.indexOf(\"-z\")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,\"\").replace(/\\+/g,\"\"));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,l=t[0],u=t[1],c=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=o(a[0],l),v=o(a[1],u),g=o(a[2],c),y=d+1,m=v+1,x=g+1;if(d=s(d,0,f-1),y=s(y,0,f-1),v=s(v,0,h-1),m=s(m,0,h-1),g=s(g,0,p-1),x=s(x,0,p-1),d<0||v<0||g<0||y>f-1||m>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][y],E=a[1][v],L=a[1][m],C=a[2][g],P=(l-M)/(S-M),O=(u-E)/(L-E),I=(c-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(O)||(O=.5),isFinite(I)||(I=.5),r.reversedX&&(d=f-1-d,y=f-1-y),r.reversedY&&(v=h-1-v,m=h-1-m),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=v*p,T=m*p,b=d*p*h,_=y*p*h;break;case 4:k=g,A=x,b=d*p,_=y*p,w=v*p*f,T=m*p*f;break;case 3:w=v,T=m,k=g*h,A=x*h,b=d*h*p,_=y*h*p;break;case 2:w=v,T=m,b=d*h,_=y*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=y,k=g*f,A=x*f,w=v*f*p,T=m*f*p;break;default:b=d,_=y,w=v*f,T=m*f,k=g*f*h,A=x*f*h}var D=i[b+w+k],z=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),H=n.create(),q=n.create(),G=n.create();n.lerp(V,D,B,P),n.lerp(H,z,N,P),n.lerp(q,R,j,P),n.lerp(G,F,U,P);var Z=n.create(),Y=n.create();n.lerp(Z,V,q,O),n.lerp(Y,H,G,O);var W=n.create();return n.lerp(W,Z,Y,I),W}(e,t,p)},v=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],y=e[0][0],m=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<y||e>b||r<m||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/u,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,u=0;u<s;u++){var c=t[u],f=c[0],h=c[1],p=c[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=l(e),v=l(r),g=l(n),y=Math.min(d,v,g);return isFinite(y)?y:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],O=[],I=d(C),D=C;O.push(I);var z=[],R=v(C,I),F=n.length(R);isFinite(F)&&F>S&&(S=F),z.push(F),g.push({points:P,velocities:O,divergences:z});for(var B=0;B<100*u&&P.length<u&&T(C);){B++;var N=n.clone(I),j=n.squaredLength(N);if(0===j)break;j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),I=d(N),n.squaredDistance(D,N)-A>-1e-4*A&&(P.push(N),D=N,O.push(I),R=v(N,I),F=n.length(R),isFinite(F)&&F>S&&(S=F),z.push(F)),C=N}}var U=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,u=0;u<l.length;u++)o=Math.max(o,n.length(l[u]));var c=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,u=[],c=[],f=[],h=[],p=[],d=[],v=0,g=0,y=i.create(),m=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,y=i.create(),n.copy(y,_),y[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;u.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(m,y,y,y,m,m),d.push(v,g,g,g,v,v);var A=u.length;c.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=m;m=y,y=S;var E=v;v=g,g=E}return{positions:u,cells:c,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<c.length;s++){var v=c[s],g=f.length;for(f=f.concat(v.positions),p=p.concat(v.vectors),d=d.concat(v.vertexIntensity),u=0;u<v.cells.length;u++){var y=v.cells[u],m=[];h.push(m);for(var x=0;x<y.length;x++)m.push(y[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}}(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*c*M/S),U};var u=r(9578),c=r(1140).createMesh;t.exports.createTubeMesh=function(t,e){return c(t,e,{shaders:u,traceType:\"streamtube\"})}},9054:function(t,e,r){var n=r(5158),i=r(6832),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform vec3 objectOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 localCoordinate = vec3(uv.zw, f.x);\\n  worldCoordinate = objectOffset + localCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n  vec4 clipPosition = projection * view * worldPosition;\\n  gl_Position = clipPosition;\\n  kill = f.y;\\n  value = f.z;\\n  planeCoordinate = uv.xy;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * worldPosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  lightDirection = lightPosition - cameraCoordinate.xyz;\\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat beckmannSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness) {\\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  if (\\n    kill > 0.0 ||\\n    vColor.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\\n  ) discard;\\n\\n  vec3 N = normalize(surfaceNormal);\\n  vec3 V = normalize(eyeDirection);\\n  vec3 L = normalize(lightDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  //decide how to interpolate color — in vertex or in fragment\\n  vec4 surfaceColor =\\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\\n    step(.5, vertexColor) * vColor;\\n\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform vec3 objectOffset;\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n  worldCoordinate = objectOffset + dataCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n\\n  vec4 clipPosition = projection * view * worldPosition;\\n  clipPosition.z += zOffset;\\n\\n  gl_Position = clipPosition;\\n  value = f + objectOffset.z;\\n  kill = -1.0;\\n  planeCoordinate = uv.zw;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Don't do lighting for contours\\n  surfaceNormal   = vec3(1,0,0);\\n  eyeDirection    = vec3(0,1,0);\\n  lightDirection  = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n  float vh = 255.0 * v;\\n  float upper = floor(vh);\\n  float lower = fract(vh);\\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n  if ((kill > 0.0) ||\\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);e.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.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},e.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}},3754:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl,r=m(e),n=b(e),s=x(e),l=_(e),u=i(e),c=a(e,[{buffer:u,size:4,stride:w,offset:0},{buffer:u,size:3,stride:w,offset:16},{buffer:u,size:3,stride:w,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),v=o(e,1,S,e.RGBA,e.UNSIGNED_BYTE);v.minFilter=e.LINEAR,v.magFilter=e.LINEAR;var g=new E(e,[0,0],[[0,0,0],[0,0,0]],r,n,u,c,v,s,l,f,h,p,d,[0,0,0]),y={levels:[[],[],[]]};for(var T in t)y[T]=t[T];return y.colormap=y.colormap||\"jet\",g.update(y),g};var n=r(2288),i=r(5827),a=r(2944),o=r(8931),s=r(5306),l=r(9156),u=r(7498),c=r(7382),f=r(5050),h=r(4162),p=r(104),d=r(7437),v=r(5070),g=r(9144),y=r(9054),m=y.createShader,x=y.createContourShader,b=y.createPickShader,_=y.createPickContourShader,w=40,T=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],k=[[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,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=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,i,a,o,l,u,c,h,p,d,v,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=u,this._contourPickShader=c,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new M([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=v,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var L=E.prototype;L.genColormap=function(t,e){var r=!1,n=c([l({colormap:t,nshades:S,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return u.divseq(n,255),this.hasAlphaScale=r,n},L.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},L.isOpaque=function(){return!this.isTransparent()},L.pickSlots=1,L.setPickBase=function(t){this.pickId=t};var C=[0,0,0],P={showSurface:!1,showContour:!1,projections:[T.slice(),T.slice(),T.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,i,a=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[+(a[r]>0)][r],p(l,t.model,l);var u=P.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)u[i][n]=t.clipBounds[i][n];u[0][r]=-1e8,u[1][r]=1e8}return P.showSurface=o,P.showContour=s,P}var I={model:T,view:T,projection:T,inverseModel:T.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},D=T.slice(),z=[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=I;n.model=t.model||T,n.view=t.view||T,n.projection=t.projection||T,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=z,n.vertexColor=this.vertexColor;var s=D;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var u=s[12+i];for(o=0;o<3;++o)u+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=u/l}var c=O(n,this);if(c.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=c.projections[i],this._shader.uniforms.clipBounds=c.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(c.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=A[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=c.projections[i],f.uniforms.clipBounds=c.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=A[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var v=0;v<this.contourLevels[o].length;++v)v===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==v&&v-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][v]&&(f.uniforms.height=this.contourLevels[o][v],h.draw(r.LINES,this._contourCounts[o][v],this._contourOffsets[o][v]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=A[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=c.projections[o],f.uniforms.clipBounds=c.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}L.draw=function(t){return R.call(this,t,!1)},L.drawTransparent=function(t){return R.call(this,t,!0)};var F={model:T,view:T,projection:T,inverseModel:T,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||T,r.view=t.view||T,r.projection=t.projection||T,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=z;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=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(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=A[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=A[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var u=0;u<this.contourLevels[a].length;++u)this._contourCounts[a][u]&&(s.uniforms.height=this.contourLevels[a][u],l.draw(e.LINES,this._contourCounts[a][u],this._contourOffsets[a][u]))}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,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var u=r.position;u[0]=u[1]=u[2]=0;for(var c=0;c<2;++c)for(var f=c?a:1-a,h=0;h<2;++h)for(var p=i+c,d=s+h,g=f*(h?l:1-l),y=0;y<3;++y)u[y]+=this._field[y].get(p,d)*g;for(var m=this._pickResult.level,x=0;x<3;++x)if(m[x]=v.le(this.contourLevels[x],u[x]),m[x]<0)this.contourLevels[x].length>0&&(m[x]=0);else if(m[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][m[x]],_=this.contourLevels[x][m[x]+1];Math.abs(b-u[x])>Math.abs(_-u[x])&&(m[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],y=0;y<3;++y)r.dataCoordinate[y]=this._field[y].get(r.index[0],r.index[1]);return r},L.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();u.assign(t.lo(1,1).hi(r[0],r[1]),e),u.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),u.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),u.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),u.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),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var u=l[o];for(y=0;y<2;++y)if(u.shape[y]!==a[y])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],u)}}else if(t.ticks){var c=t.ticks;if(!Array.isArray(c)||2!==c.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=c[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var v=[0,0];v[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],v,0)}this._field[0].set(0,0,0);for(var y=0;y<a[0];++y)this._field[0].set(y+1,0,y);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),y=0;y<a[1];++y)this._field[1].set(0,y+1,y);this._field[1].set(0,a[1]+1,a[1]-1)}var m=this._field,x=f(s.mallocFloat(3*m[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),m[o],\"mirror\");var b=f(s.mallocFloat(3*m[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(y=0;y<a[1]+2;++y){var _=x.get(0,o,y,0),w=x.get(0,o,y,1),T=x.get(1,o,y,0),A=x.get(1,o,y,1),M=x.get(2,o,y,0),S=x.get(2,o,y,1),E=T*S-A*M,L=M*w-S*_,C=_*A-w*T,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,y,0,E*P),b.set(o,y,1,L*P),b.set(o,y,2,C*P)}s.free(x.data);var O=[1/0,1/0,1/0],I=[-1/0,-1/0,-1/0],D=1/0,z=-1/0,R=(a[0]-1)*(a[1]-1)*6,F=s.mallocFloat(n.nextPow2(10*R)),N=0,U=0;for(o=0;o<a[0]-1;++o)t:for(y=0;y<a[1]-1;++y){for(var V=0;V<2;++V)for(var H=0;H<2;++H)for(var q=0;q<3;++q){var G=this._field[q].get(1+o+V,1+y+H);if(isNaN(G)||!isFinite(G))continue t}for(q=0;q<6;++q){var Z=o+k[q][0],Y=y+k[q][1],W=this._field[0].get(Z+1,Y+1),X=this._field[1].get(Z+1,Y+1);G=this._field[2].get(Z+1,Y+1),E=b.get(Z+1,Y+1,0),L=b.get(Z+1,Y+1,1),C=b.get(Z+1,Y+1,2),t.intensity&&(J=t.intensity.get(Z,Y));var J=t.intensity?t.intensity.get(Z,Y):G+this.objectOffset[2];F[N++]=Z,F[N++]=Y,F[N++]=W,F[N++]=X,F[N++]=G,F[N++]=0,F[N++]=J,F[N++]=E,F[N++]=L,F[N++]=C,O[0]=Math.min(O[0],W+this.objectOffset[0]),O[1]=Math.min(O[1],X+this.objectOffset[1]),O[2]=Math.min(O[2],G+this.objectOffset[2]),D=Math.min(D,J),I[0]=Math.max(I[0],W+this.objectOffset[0]),I[1]=Math.max(I[1],X+this.objectOffset[1]),I[2]=Math.max(I[2],G+this.objectOffset[2]),z=Math.max(z,J),U+=1}}for(t.intensityBounds&&(D=+t.intensityBounds[0],z=+t.intensityBounds[1]),o=6;o<N;o+=10)F[o]=(F[o]-D)/(z-D);this._vertexCount=U,this._coordinateBuffer.update(F.subarray(0,N)),s.freeFloat(F),s.free(b.data),this.bounds=[O,I],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===D&&this.intensityBounds[1]===z||(r=!0),this.intensityBounds=[D,z]}if(\"levels\"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(y=0;y<K[o].length;++y)K[o][y]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(y=0;y<K[o].length;++y)if(K[o][y]!==this.contourLevels[o][y]){r=!0;break t}}this.contourLevels=K}if(r){m=this._field,a=this.shape;for(var $=[],Q=0;Q<3;++Q){var tt=this.contourLevels[Q],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[Q],tt[o]);et.push($.length/5|0),U=0;t:for(y=0;y<it.cells.length;++y){var at=it.cells[y];for(q=0;q<2;++q){var ot=it.positions[at[q]],st=ot[0],lt=0|Math.floor(st),ut=st-lt,ct=ot[1],ft=0|Math.floor(ct),ht=ct-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var vt=(Q+dt+1)%3;for(V=0;V<2;++V){var gt=V?ut:1-ut;for(Z=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var yt=H?ht:1-ht;if(Y=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[vt].get(Z,Y):(this.intensity.get(Z,Y)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var mt=gt*yt;nt[dt]+=mt*G}}}if(pt){if(q>0){for(var xt=0;xt<5;++xt)$.pop();U-=1}continue t}$.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[Q]=et,this._contourCounts[Q]=rt}var bt=s.mallocFloat($.length);for(o=0;o<$.length;++o)bt[o]=$[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},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,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,u=(o+2)%3,c=this._field[o],f=this._field[l],p=this._field[u],d=h(c,r[o]),v=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<v.length;++e)for(var y=v[e],m=0;m<2;++m){var x=g[y[m]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,O=T*L,I=T*E,D=C*f.get(_,M)+P*f.get(_,S)+O*f.get(w,M)+I*f.get(w,S),z=C*p.get(_,M)+P*p.get(_,S)+O*p.get(w,M)+I*p.get(w,S);if(isNaN(D)||isNaN(z)){m&&(n-=1);break}a[2*n+0]=D,a[2*n+1]=z,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},8931:function(t,e,r){\"use strict\";var n=r(5050),i=r(7498),a=r(5306);t.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");if(o||function(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}(t),\"number\"==typeof arguments[1])return g(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return g(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return function(t,e,r,n,i,a){var o=v(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new h(t,o,r,n,i,a)}(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()),u=0;\"float32\"===r?u=t.FLOAT:\"float64\"===r?(u=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?u=t.UNSIGNED_BYTE:(u=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var f,p,g=0;if(2===o.length)g=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])g=t.ALPHA;else if(2===o[2])g=t.LUMINANCE_ALPHA;else if(3===o[2])g=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");g=t.RGBA}}u!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(u=t.UNSIGNED_BYTE,l=!1);var y=e.size;if(l)f=0===e.offset&&e.data.length===y?e.data:e.data.subarray(e.offset,e.offset+y);else{var m=[o[2],o[2]*o[0],1];p=a.malloc(y,r);var x=n(p,o,m,0);\"float32\"!==r&&\"float64\"!==r||u!==t.UNSIGNED_BYTE?i.assign(x,e):c(x,e),f=p.subarray(0,y)}var b=v(t);return t.texImage2D(t.TEXTURE_2D,0,g,o[0],o[1],0,g,u,f),l||a.free(p),new h(t,b,o[0],o[1],g,u)}(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function u(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var c=function(t,e){i.muls(t,e,255)};function f(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function h(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var p=h.prototype;function d(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function v(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function g(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=v(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new h(t,o,e,r,n,i)}Object.defineProperties(p,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var 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n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[8],c=e[9],f=e[10],h=e[11];return 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]),t[0]=a*i-u*n,t[1]=o*i-c*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+u*i,t[9]=o*n+c*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},15692:function(t){t.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[4],c=e[5],f=e[6],h=e[7];return 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]),t[0]=a*i+u*n,t[1]=o*i+c*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=u*i-a*n,t[5]=c*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},10789:function(t){t.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},6726:function(t){t.exports=function(t){return\"mat4(\"+t[0]+\", \"+t[1]+\", \"+t[2]+\", \"+t[3]+\", \"+t[4]+\", \"+t[5]+\", \"+t[6]+\", \"+t[7]+\", \"+t[8]+\", \"+t[9]+\", \"+t[10]+\", \"+t[11]+\", \"+t[12]+\", \"+t[13]+\", \"+t[14]+\", \"+t[15]+\")\"}},31283:function(t){t.exports=function(t,e,r){var n,i,a,o,s,l,u,c,f,h,p,d,v=r[0],g=r[1],y=r[2];return e===t?(t[12]=e[0]*v+e[4]*g+e[8]*y+e[12],t[13]=e[1]*v+e[5]*g+e[9]*y+e[13],t[14]=e[2]*v+e[6]*g+e[10]*y+e[14],t[15]=e[3]*v+e[7]*g+e[11]*y+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=u,t[7]=c,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*v+s*g+f*y+e[12],t[13]=i*v+l*g+h*y+e[13],t[14]=a*v+u*g+p*y+e[14],t[15]=o*v+c*g+d*y+e[15]),t}},88654:function(t){t.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},42505:function(t,e,r){\"use strict\";var n=r(72791),i=r(71299),a=r(98580),o=r(12018),s=r(83522),l=r(25075),u=r(68016),c=r(58404),f=r(18863),h=r(10973),p=r(25677),d=r(75686),v=r(53545),g=r(56131),y=r(32879),m=r(30120),x=r(13547).nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement(\"div\"));w.style.font=\"italic small-caps bold condensed 16px/2 cursive\",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return\"function\"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst 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float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ vec2(positionOffset.x, -positionOffset.y)))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 255.;\\n\\t\\t\\t}\",frag:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tuniform float fontSize, charStep, opacity;\\n\\t\\t\\tuniform vec2 atlasSize;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform sampler2D atlas;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\n\\t\\t\\tfloat lightness(vec4 color) {\\n\\t\\t\\t\\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\\n\\t\\t\\t}\\n\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\\n\\t\\t\\t\\tfloat halfCharStep = floor(charStep * .5 + .5);\\n\\n\\t\\t\\t\\t// invert y and shift by 1px (FF expecially needs that)\\n\\t\\t\\t\\tuv.y = charStep - uv.y;\\n\\n\\t\\t\\t\\t// ignore points outside of character bounding box\\n\\t\\t\\t\\tfloat halfCharWidth = ceil(charWidth * .5);\\n\\t\\t\\t\\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\\n\\t\\t\\t\\t\\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a *= maskY;\\n\\t\\t\\t\\tcolor.a *= opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&(\"number\"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=m(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+\"px \"+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var u=t.family.join(\", \"),c=[t.style];t.style!=t.variant&&c.push(t.variant),t.variant!=t.weight&&c.push(t.weight),_&&t.weight!=t.stretch&&c.push(t.stretch),e.font[r]={baseString:i,family:u,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:y(u,{origin:\"top\",fontSize:T.baseFontSize,fontStyle:c.join(\" \")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var 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Math.atan2(this.y-t.y,this.x-t.x)},angleWith:function(t){return this.angleWithSep(t.x,t.y)},angleWithSep:function(t,e){return Math.atan2(this.x*e-this.y*t,this.x*t+this.y*e)},_matMult:function(t){var e=t[0]*this.x+t[1]*this.y,r=t[2]*this.x+t[3]*this.y;return this.x=e,this.y=r,this},_add:function(t){return this.x+=t.x,this.y+=t.y,this},_sub:function(t){return this.x-=t.x,this.y-=t.y,this},_mult:function(t){return this.x*=t,this.y*=t,this},_div:function(t){return this.x/=t,this.y/=t,this},_multByPoint:function(t){return this.x*=t.x,this.y*=t.y,this},_divByPoint:function(t){return this.x/=t.x,this.y/=t.y,this},_unit:function(){return this._div(this.mag()),this},_perp:function(){var t=this.y;return this.y=this.x,this.x=-t,this},_rotate:function(t){var e=Math.cos(t),r=Math.sin(t),n=e*this.x-r*this.y,i=r*this.x+e*this.y;return this.x=n,this.y=i,this},_rotateAround:function(t,e){var r=Math.cos(t),n=Math.sin(t),i=e.x+r*(this.x-e.x)-n*(this.y-e.y),a=e.y+n*(this.x-e.x)+r*(this.y-e.y);return 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r.width=t.width,r.height=t.height,n.drawImage(t,0,0,t.width,t.height),n.getImageData(-e,-e,t.width+2*e,t.height+2*e)},resolveURL:function(t){return C||(C=self.document.createElement(\"a\")),C.href=t,C.href},hardwareConcurrency:self.navigator.hardwareConcurrency||4,get devicePixelRatio(){return self.devicePixelRatio},get prefersReducedMotion(){return!!self.matchMedia&&(null==P&&(P=self.matchMedia(\"(prefers-reduced-motion: reduce)\")),P.matches)}},B={API_URL:\"https://api.mapbox.com\",get EVENTS_URL(){return this.API_URL?0===this.API_URL.indexOf(\"https://api.mapbox.cn\")?\"https://events.mapbox.cn/events/v2\":0===this.API_URL.indexOf(\"https://api.mapbox.com\")?\"https://events.mapbox.com/events/v2\":null:null},FEEDBACK_URL:\"https://apps.mapbox.com/feedback\",REQUIRE_ACCESS_TOKEN:!0,ACCESS_TOKEN:null,MAX_PARALLEL_IMAGE_REQUESTS:16},N={supported:!1,testSupport:function(t){!j&&I&&(U?V(t):O=t)}},j=!1,U=!1;function V(t){var 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Date.now()>this._skuTokenExpiresAt},q.prototype.transformRequest=function(t,e){return this._transformRequestFn&&this._transformRequestFn(t,e)||{url:t}},q.prototype.normalizeStyleURL=function(t,e){if(!G(t))return t;var r=X(t);return r.path=\"/styles/v1\"+r.path,this._makeAPIURL(r,this._customAccessToken||e)},q.prototype.normalizeGlyphsURL=function(t,e){if(!G(t))return t;var r=X(t);return r.path=\"/fonts/v1\"+r.path,this._makeAPIURL(r,this._customAccessToken||e)},q.prototype.normalizeSourceURL=function(t,e){if(!G(t))return t;var r=X(t);return r.path=\"/v4/\"+r.authority+\".json\",r.params.push(\"secure\"),this._makeAPIURL(r,this._customAccessToken||e)},q.prototype.normalizeSpriteURL=function(t,e,r,n){var i=X(t);return G(t)?(i.path=\"/styles/v1\"+i.path+\"/sprite\"+e+r,this._makeAPIURL(i,this._customAccessToken||n)):(i.path+=\"\"+e+r,J(i))},q.prototype.normalizeTileURL=function(t,e){if(this._isSkuTokenExpired()&&this._createSkuToken(),t&&!G(t))return t;var r=X(t),n=F.devicePixelRatio>=2||512===e?\"@2x\":\"\",i=N.supported?\".webp\":\"$1\";r.path=r.path.replace(/(\\.(png|jpg)\\d*)(?=$)/,\"\"+n+i),r.path=r.path.replace(/^.+\\/v4\\//,\"/\"),r.path=\"/v4\"+r.path;var a=this._customAccessToken||function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e].match(/^access_token=(.*)$/);if(n)return n[1]}return null}(r.params)||B.ACCESS_TOKEN;return B.REQUIRE_ACCESS_TOKEN&&a&&this._skuToken&&r.params.push(\"sku=\"+this._skuToken),this._makeAPIURL(r,a)},q.prototype.canonicalizeTileURL=function(t,e){var r=X(t);if(!r.path.match(/(^\\/v4\\/)/)||!r.path.match(/\\.[\\w]+$/))return t;var n=\"mapbox://tiles/\";n+=r.path.replace(\"/v4/\",\"\");var i=r.params;return e&&(i=i.filter((function(t){return!t.match(/^access_token=/)}))),i.length&&(n+=\"?\"+i.join(\"&\")),n},q.prototype.canonicalizeTileset=function(t,e){for(var r=!!e&&G(e),n=[],i=0,a=t.tiles||[];i<a.length;i+=1){var o=a[i];Y(o)?n.push(this.canonicalizeTileURL(o,r)):n.push(o)}return n},q.prototype._makeAPIURL=function(t,e){var r=\"See https://www.mapbox.com/api-documentation/#access-tokens-and-token-scopes\",n=X(B.API_URL);if(t.protocol=n.protocol,t.authority=n.authority,\"/\"!==n.path&&(t.path=\"\"+n.path+t.path),!B.REQUIRE_ACCESS_TOKEN)return J(t);if(!(e=e||B.ACCESS_TOKEN))throw new Error(\"An API access token is required to use Mapbox GL. \"+r);if(\"s\"===e[0])throw new Error(\"Use a public access token (pk.*) with Mapbox GL, not a secret access token (sk.*). \"+r);return t.params=t.params.filter((function(t){return-1===t.indexOf(\"access_token\")})),t.params.push(\"access_token=\"+e),J(t)};var Z=/^((https?:)?\\/\\/)?([^\\/]+\\.)?mapbox\\.c(n|om)(\\/|\\?|$)/i;function Y(t){return Z.test(t)}var W=/^(\\w+):\\/\\/([^/?]*)(\\/[^?]+)?\\??(.+)?/;function X(t){var e=t.match(W);if(!e)throw new Error(\"Unable to parse URL object\");return{protocol:e[1],authority:e[2],path:e[3]||\"/\",params:e[4]?e[4].split(\"&\"):[]}}function J(t){var e=t.params.length?\"?\"+t.params.join(\"&\"):\"\";return t.protocol+\"://\"+t.authority+t.path+e}var K=\"mapbox.eventData\";function $(t){if(!t)return null;var e,r=t.split(\".\");if(!r||3!==r.length)return null;try{return JSON.parse((e=r[1],decodeURIComponent(self.atob(e).split(\"\").map((function(t){return\"%\"+(\"00\"+t.charCodeAt(0).toString(16)).slice(-2)})).join(\"\"))))}catch(t){return null}}var Q=function(t){this.type=t,this.anonId=null,this.eventData={},this.queue=[],this.pendingRequest=null};Q.prototype.getStorageKey=function(t){var e,r,n=$(B.ACCESS_TOKEN);return e=n&&n.u?(r=n.u,self.btoa(encodeURIComponent(r).replace(/%([0-9A-F]{2})/g,(function(t,e){return String.fromCharCode(Number(\"0x\"+e))})))):B.ACCESS_TOKEN||\"\",t?K+\".\"+t+\":\"+e:K+\":\"+e},Q.prototype.fetchEventData=function(){var t=L(\"localStorage\"),e=this.getStorageKey(),r=this.getStorageKey(\"uuid\");if(t)try{var n=self.localStorage.getItem(e);n&&(this.eventData=JSON.parse(n));var 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r=this.queue.shift(),n=r.id,i=r.timestamp;n&&this.success[n]||(this.anonId||this.fetchEventData(),v(this.anonId)||(this.anonId=d()),this.postEvent(i,{skuToken:this.skuToken},(function(t){t||n&&(e.success[n]=!0)}),t))}},e}(Q),nt=function(t){function e(e){t.call(this,\"appUserTurnstile\"),this._customAccessToken=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.postTurnstileEvent=function(t,e){B.EVENTS_URL&&B.ACCESS_TOKEN&&Array.isArray(t)&&t.some((function(t){return G(t)||Y(t)}))&&this.queueRequest(Date.now(),e)},e.prototype.processRequests=function(t){var e=this;if(!this.pendingRequest&&0!==this.queue.length){this.anonId&&this.eventData.lastSuccess&&this.eventData.tokenU||this.fetchEventData();var r=$(B.ACCESS_TOKEN),n=r?r.u:B.ACCESS_TOKEN,i=n!==this.eventData.tokenU;v(this.anonId)||(this.anonId=d(),i=!0);var a=this.queue.shift();if(this.eventData.lastSuccess){var o=new Date(this.eventData.lastSuccess),s=new 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n(t){return(t=Math.round(t))<0?0:t>255?255:t}function i(t){return t<0?0:t>1?1:t}function a(t){return\"%\"===t[t.length-1]?n(parseFloat(t)/100*255):n(parseInt(t))}function o(t){return\"%\"===t[t.length-1]?i(parseFloat(t)/100):i(parseFloat(t))}function s(t,e,r){return r<0?r+=1:r>1&&(r-=1),6*r<1?t+(e-t)*r*6:2*r<1?e:3*r<2?t+(e-t)*(2/3-r)*6:t}try{e.parseCSSColor=function(t){var e,i=t.replace(/ /g,\"\").toLowerCase();if(i in r)return r[i].slice();if(\"#\"===i[0])return 4===i.length?(e=parseInt(i.substr(1),16))>=0&&e<=4095?[(3840&e)>>4|(3840&e)>>8,240&e|(240&e)>>4,15&e|(15&e)<<4,1]:null:7===i.length&&(e=parseInt(i.substr(1),16))>=0&&e<=16777215?[(16711680&e)>>16,(65280&e)>>8,255&e,1]:null;var l=i.indexOf(\"(\"),u=i.indexOf(\")\");if(-1!==l&&u+1===i.length){var c=i.substr(0,l),f=i.substr(l+1,u-(l+1)).split(\",\"),h=1;switch(c){case\"rgba\":if(4!==f.length)return null;h=o(f.pop());case\"rgb\":return 3!==f.length?null:[a(f[0]),a(f[1]),a(f[2]),h];case\"hsla\":if(4!==f.length)return 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se=function(t,e,r){this.sensitivity=t?e?\"variant\":\"case\":e?\"accent\":\"base\",this.locale=r,this.collator=new Intl.Collator(this.locale?this.locale:[],{sensitivity:this.sensitivity,usage:\"search\"})};se.prototype.compare=function(t,e){return this.collator.compare(t,e)},se.prototype.resolvedLocale=function(){return new Intl.Collator(this.locale?this.locale:[]).resolvedOptions().locale};var le=function(t,e,r,n,i){this.text=t,this.image=e,this.scale=r,this.fontStack=n,this.textColor=i},ue=function(t){this.sections=t};ue.fromString=function(t){return new ue([new le(t,null,null,null,null)])},ue.prototype.isEmpty=function(){return 0===this.sections.length||!this.sections.some((function(t){return 0!==t.text.length||t.image&&0!==t.image.name.length}))},ue.factory=function(t){return t instanceof ue?t:ue.fromString(t)},ue.prototype.toString=function(){return 0===this.sections.length?\"\":this.sections.map((function(t){return t.text})).join(\"\")},ue.prototype.serialize=function(){for(var t=[\"format\"],e=0,r=this.sections;e<r.length;e+=1){var n=r[e];if(n.image)t.push([\"image\",n.image.name]);else{t.push(n.text);var i={};n.fontStack&&(i[\"text-font\"]=[\"literal\",n.fontStack.split(\",\")]),n.scale&&(i[\"font-scale\"]=n.scale),n.textColor&&(i[\"text-color\"]=[\"rgba\"].concat(n.textColor.toArray())),t.push(i)}}return t};var ce=function(t){this.name=t.name,this.available=t.available};function fe(t,e,r,n){return\"number\"==typeof t&&t>=0&&t<=255&&\"number\"==typeof e&&e>=0&&e<=255&&\"number\"==typeof r&&r>=0&&r<=255?void 0===n||\"number\"==typeof n&&n>=0&&n<=1?null:\"Invalid rgba value [\"+[t,e,r,n].join(\", \")+\"]: 'a' must be between 0 and 1.\":\"Invalid rgba value [\"+(\"number\"==typeof n?[t,e,r,n]:[t,e,r]).join(\", \")+\"]: 'r', 'g', and 'b' must be between 0 and 255.\"}function he(t){if(null===t)return!0;if(\"string\"==typeof t)return!0;if(\"boolean\"==typeof t)return!0;if(\"number\"==typeof t)return!0;if(t instanceof oe)return!0;if(t instanceof se)return!0;if(t 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ve=function(t,e){this.type=t,this.value=e};ve.parse=function(t,e){if(2!==t.length)return e.error(\"'literal' expression requires exactly one argument, but found \"+(t.length-1)+\" instead.\");if(!he(t[1]))return e.error(\"invalid value\");var r=t[1],n=pe(r),i=e.expectedType;return\"array\"!==n.kind||0!==n.N||!i||\"array\"!==i.kind||\"number\"==typeof i.N&&0!==i.N||(n=i),new ve(n,r)},ve.prototype.evaluate=function(){return this.value},ve.prototype.eachChild=function(){},ve.prototype.outputDefined=function(){return!0},ve.prototype.serialize=function(){return\"array\"===this.type.kind||\"object\"===this.type.kind?[\"literal\",this.value]:this.value instanceof oe?[\"rgba\"].concat(this.value.toArray()):this.value instanceof ue?this.value.serialize():this.value};var ge=function(t){this.name=\"ExpressionEvaluationError\",this.message=t};ge.prototype.toJSON=function(){return this.message};var ye={string:qt,number:Ht,boolean:Gt,object:Yt},me=function(t,e){this.type=t,this.args=e};me.parse=function(t,e){if(t.length<2)return e.error(\"Expected at least one argument.\");var r,n=1,i=t[0];if(\"array\"===i){var a,o;if(t.length>2){var s=t[1];if(\"string\"!=typeof s||!(s in ye)||\"object\"===s)return e.error('The item type argument of \"array\" must be one of string, number, boolean',1);a=ye[s],n++}else a=Wt;if(t.length>3){if(null!==t[2]&&(\"number\"!=typeof t[2]||t[2]<0||t[2]!==Math.floor(t[2])))return e.error('The length argument to \"array\" must be a positive integer literal',2);o=t[2],n++}r=$t(a,o)}else r=ye[i];for(var l=[];n<t.length;n++){var u=e.parse(t[n],n,Wt);if(!u)return null;l.push(u)}return new me(r,l)},me.prototype.evaluate=function(t){for(var e=0;e<this.args.length;e++){var r=this.args[e].evaluate(t);if(!ee(this.type,pe(r)))return r;if(e===this.args.length-1)throw new ge(\"Expected value to be of type \"+Qt(this.type)+\", but found \"+Qt(pe(r))+\" instead.\")}return null},me.prototype.eachChild=function(t){this.args.forEach(t)},me.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},me.prototype.serialize=function(){var t=this.type,e=[t.kind];if(\"array\"===t.kind){var r=t.itemType;if(\"string\"===r.kind||\"number\"===r.kind||\"boolean\"===r.kind){e.push(r.kind);var n=t.N;(\"number\"==typeof n||this.args.length>1)&&e.push(n)}}return e.concat(this.args.map((function(t){return t.serialize()})))};var xe=function(t){this.type=Jt,this.sections=t};xe.parse=function(t,e){if(t.length<2)return e.error(\"Expected at least one argument.\");var r=t[1];if(!Array.isArray(r)&&\"object\"==typeof r)return e.error(\"First argument must be an image or text section.\");for(var n=[],i=!1,a=1;a<=t.length-1;++a){var o=t[a];if(i&&\"object\"==typeof o&&!Array.isArray(o)){i=!1;var s=null;if(o[\"font-scale\"]&&!(s=e.parse(o[\"font-scale\"],1,Ht)))return null;var l=null;if(o[\"text-font\"]&&!(l=e.parse(o[\"text-font\"],1,$t(qt))))return null;var u=null;if(o[\"text-color\"]&&!(u=e.parse(o[\"text-color\"],1,Zt)))return null;var c=n[n.length-1];c.scale=s,c.font=l,c.textColor=u}else{var f=e.parse(t[a],1,Wt);if(!f)return null;var h=f.type.kind;if(\"string\"!==h&&\"value\"!==h&&\"null\"!==h&&\"resolvedImage\"!==h)return e.error(\"Formatted text type must be 'string', 'value', 'image' or 'null'.\");i=!0,n.push({content:f,scale:null,font:null,textColor:null})}}return new xe(n)},xe.prototype.evaluate=function(t){return new ue(this.sections.map((function(e){var r=e.content.evaluate(t);return pe(r)===Kt?new le(\"\",r,null,null,null):new le(de(r),null,e.scale?e.scale.evaluate(t):null,e.font?e.font.evaluate(t).join(\",\"):null,e.textColor?e.textColor.evaluate(t):null)})))},xe.prototype.eachChild=function(t){for(var e=0,r=this.sections;e<r.length;e+=1){var n=r[e];t(n.content),n.scale&&t(n.scale),n.font&&t(n.font),n.textColor&&t(n.textColor)}},xe.prototype.outputDefined=function(){return!1},xe.prototype.serialize=function(){for(var t=[\"format\"],e=0,r=this.sections;e<r.length;e+=1){var n=r[e];t.push(n.content.serialize());var i={};n.scale&&(i[\"font-scale\"]=n.scale.serialize()),n.font&&(i[\"text-font\"]=n.font.serialize()),n.textColor&&(i[\"text-color\"]=n.textColor.serialize()),t.push(i)}return t};var be=function(t){this.type=Kt,this.input=t};be.parse=function(t,e){if(2!==t.length)return e.error(\"Expected two arguments.\");var r=e.parse(t[1],1,qt);return r?new be(r):e.error(\"No image name provided.\")},be.prototype.evaluate=function(t){var e=this.input.evaluate(t),r=ce.fromString(e);return r&&t.availableImages&&(r.available=t.availableImages.indexOf(e)>-1),r},be.prototype.eachChild=function(t){t(this.input)},be.prototype.outputDefined=function(){return!1},be.prototype.serialize=function(){return[\"image\",this.input.serialize()]};var 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a=Je(e,n),o=e[a],s=e[a+1],l=xr.interpolationFactor(this.interpolation,n,o,s),u=r[a].evaluate(t),c=r[a+1].evaluate(t);return\"interpolate\"===this.operator?Qe[this.type.kind.toLowerCase()](u,c,l):\"interpolate-hcl\"===this.operator?yr.reverse(yr.interpolate(yr.forward(u),yr.forward(c),l)):gr.reverse(gr.interpolate(gr.forward(u),gr.forward(c),l))},xr.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1)t(r[e])},xr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},xr.prototype.serialize=function(){var t;t=\"linear\"===this.interpolation.name?[\"linear\"]:\"exponential\"===this.interpolation.name?1===this.interpolation.base?[\"linear\"]:[\"exponential\",this.interpolation.base]:[\"cubic-bezier\"].concat(this.interpolation.controlPoints);for(var e=[this.operator,t,this.input.serialize()],r=0;r<this.labels.length;r++)e.push(this.labels[r],this.outputs[r].serialize());return e};var _r=function(t,e){this.type=t,this.args=e};_r.parse=function(t,e){if(t.length<2)return e.error(\"Expectected at least one argument.\");var r=null,n=e.expectedType;n&&\"value\"!==n.kind&&(r=n);for(var i=[],a=0,o=t.slice(1);a<o.length;a+=1){var s=o[a],l=e.parse(s,1+i.length,r,void 0,{typeAnnotation:\"omit\"});if(!l)return null;r=r||l.type,i.push(l)}var u=n&&i.some((function(t){return ee(n,t.type)}));return new _r(u?Wt:r,i)},_r.prototype.evaluate=function(t){for(var e,r=null,n=0,i=0,a=this.args;i<a.length&&(n++,(r=a[i].evaluate(t))&&r instanceof ce&&!r.available&&(e||(e=r.name),r=null,n===this.args.length&&(r=e)),null===r);i+=1);return r},_r.prototype.eachChild=function(t){this.args.forEach(t)},_r.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},_r.prototype.serialize=function(){var t=[\"coalesce\"];return this.eachChild((function(e){t.push(e.serialize())})),t};var wr=function(t,e){this.type=e.type,this.bindings=[].concat(t),this.result=e};wr.prototype.evaluate=function(t){return this.result.evaluate(t)},wr.prototype.eachChild=function(t){for(var e=0,r=this.bindings;e<r.length;e+=1)t(r[e][1]);t(this.result)},wr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found \"+(t.length-1)+\" instead.\");for(var r=[],n=1;n<t.length-1;n+=2){var i=t[n];if(\"string\"!=typeof i)return e.error(\"Expected string, but found \"+typeof i+\" instead.\",n);if(/[^a-zA-Z0-9_]/.test(i))return e.error(\"Variable names must contain only alphanumeric characters or '_'.\",n);var a=e.parse(t[n+1],n+1);if(!a)return null;r.push([i,a])}var o=e.parse(t[t.length-1],t.length-1,e.expectedType,r);return o?new wr(r,o):null},wr.prototype.outputDefined=function(){return this.result.outputDefined()},wr.prototype.serialize=function(){for(var t=[\"let\"],e=0,r=this.bindings;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t.push(i,a.serialize())}return 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kr=function(t,e){this.type=Gt,this.needle=t,this.haystack=e};kr.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Wt),n=e.parse(t[2],2,Wt);return r&&n?re(r.type,[Gt,qt,Ht,Vt,Wt])?new kr(r,n):e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Qt(r.type)+\" instead\"):null},kr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!r)return!1;if(!ne(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ge(\"Expected first argument to be of type boolean, string, number or null, but found \"+Qt(pe(e))+\" instead.\");if(!ne(r,[\"string\",\"array\"]))throw new ge(\"Expected second argument to be of type array or string, but found \"+Qt(pe(r))+\" instead.\");return r.indexOf(e)>=0},kr.prototype.eachChild=function(t){t(this.needle),t(this.haystack)},kr.prototype.outputDefined=function(){return!0},kr.prototype.serialize=function(){return[\"in\",this.needle.serialize(),this.haystack.serialize()]};var Ar=function(t,e,r){this.type=Ht,this.needle=t,this.haystack=e,this.fromIndex=r};Ar.parse=function(t,e){if(t.length<=2||t.length>=5)return e.error(\"Expected 3 or 4 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Wt),n=e.parse(t[2],2,Wt);if(!r||!n)return null;if(!re(r.type,[Gt,qt,Ht,Vt,Wt]))return e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Qt(r.type)+\" instead\");if(4===t.length){var i=e.parse(t[3],3,Ht);return i?new Ar(r,n,i):null}return new Ar(r,n)},Ar.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!ne(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ge(\"Expected first argument to be of type boolean, string, number or 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Er(r.type,r,n)},Er.prototype.evaluate=function(t){var e=this.input.evaluate(t),r=this.beginIndex.evaluate(t);if(!ne(e,[\"string\",\"array\"]))throw new ge(\"Expected first argument to be of type array or string, but found \"+Qt(pe(e))+\" instead.\");if(this.endIndex){var n=this.endIndex.evaluate(t);return e.slice(r,n)}return e.slice(r)},Er.prototype.eachChild=function(t){t(this.input),t(this.beginIndex),this.endIndex&&t(this.endIndex)},Er.prototype.outputDefined=function(){return!1},Er.prototype.serialize=function(){if(null!=this.endIndex&&void 0!==this.endIndex){var t=this.endIndex.serialize();return[\"slice\",this.input.serialize(),this.beginIndex.serialize(),t]}return[\"slice\",this.input.serialize(),this.beginIndex.serialize()]};var Or=Pr(\"==\",(function(t,e,r){return e===r}),Cr),Ir=Pr(\"!=\",(function(t,e,r){return e!==r}),(function(t,e,r,n){return!Cr(0,e,r,n)})),Dr=Pr(\"<\",(function(t,e,r){return e<r}),(function(t,e,r,n){return 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e=[],a=t.value,s=t.key;if(\"array\"!==Jr(a))return[new zt(s,a,\"array expected, \"+Jr(a)+\" found\")];if(2!==a.length)return[new zt(s,a,\"array length 2 expected, length \"+a.length+\" found\")];if(u){if(\"object\"!==Jr(a[0]))return[new zt(s,a,\"object expected, \"+Jr(a[0])+\" found\")];if(void 0===a[0].zoom)return[new zt(s,a,\"object stop key must have zoom\")];if(void 0===a[0].value)return[new zt(s,a,\"object stop key must have value\")];if(n&&n>Bt(a[0].zoom))return[new zt(s,a[0].zoom,\"stop zoom values must appear in ascending order\")];Bt(a[0].zoom)!==n&&(n=Bt(a[0].zoom),r=void 0,o={}),e=e.concat(dn({key:s+\"[0]\",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:gn,value:h}}))}else e=e.concat(h({key:s+\"[0]\",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return sn(Nt(a[1]))?e.concat([new zt(s+\"[1]\",a[1],\"expressions are not allowed in function 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t.length<=1?\"any\"!==r:\"==\"===r?Mn(t[1],t[2],\"==\"):\"!=\"===r?Ln(Mn(t[1],t[2],\"==\")):\"<\"===r||\">\"===r||\"<=\"===r||\">=\"===r?Mn(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(An))):\"all\"===r?[\"all\"].concat(t.slice(1).map(An)):\"none\"===r?[\"all\"].concat(t.slice(1).map(An).map(Ln)):\"in\"===r?Sn(t[1],t.slice(2)):\"!in\"===r?Ln(Sn(t[1],t.slice(2))):\"has\"===r?En(t[1]):\"!has\"===r?Ln(En(t[1])):\"within\"!==r||t}function Mn(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function Sn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(Tn)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function 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r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+\"_\"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if(\"paint\"===e&&l&&s[l[1]]&&s[l[1]].transition)return Un({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var u,c=t.valueSpec||s[o];if(!c)return[new zt(r,a,'unknown property \"'+o+'\"')];if(\"string\"===Jr(a)&&Yr(c)&&!c.tokens&&(u=/^{([^}]+)}$/.exec(a)))return[new zt(r,a,'\"'+o+'\" does not support interpolation syntax\\nUse an identity property function instead: `{ \"type\": \"identity\", \"property\": '+JSON.stringify(u[1])+\" }`.\")];var f=[];return\"symbol\"===t.layerType&&(\"text-field\"===o&&n&&!n.glyphs&&f.push(new zt(r,a,'use of \"text-field\" requires a style \"glyphs\" property')),\"text-font\"===o&&Kr(Nt(a))&&\"identity\"===Bt(a.type)&&f.push(new zt(r,a,'\"text-font\" does not support identity 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h=i.sources&&i.sources[r.source],p=h&&Bt(h.type);h?\"vector\"===p&&\"raster\"===s?e.push(new zt(n,r.source,'layer \"'+r.id+'\" requires a raster source')):\"raster\"===p&&\"raster\"!==s?e.push(new zt(n,r.source,'layer \"'+r.id+'\" requires a vector source')):\"vector\"!==p||r[\"source-layer\"]?\"raster-dem\"===p&&\"hillshade\"!==s?e.push(new zt(n,r.source,\"raster-dem source can only be used with layer type 'hillshade'.\")):\"line\"!==s||!r.paint||!r.paint[\"line-gradient\"]||\"geojson\"===p&&h.lineMetrics||e.push(new zt(n,r,'layer \"'+r.id+'\" specifies a line-gradient, which requires a GeoJSON source with `lineMetrics` enabled.')):e.push(new zt(n,r,'layer \"'+r.id+'\" must specify a \"source-layer\"')):e.push(new zt(n,r.source,'source \"'+r.source+'\" not found'))}else e.push(new zt(n,r,'missing required property \"source\"'));return e=e.concat(dn({key:n,value:r,valueSpec:a.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(){return[]},type:function(){return Un({key:n+\".type\",value:r.type,valueSpec:a.layer.type,style:t.style,styleSpec:t.styleSpec,object:r,objectKey:\"type\"})},filter:Cn,layout:function(t){return dn({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(t){return Dn(Ft({layerType:s},t))}}})},paint:function(t){return dn({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(t){return In(Ft({layerType:s},t))}}})}}})),e}function Rn(t){var e=t.value,r=t.key,n=Jr(e);return\"string\"!==n?[new zt(r,e,\"string expected, \"+n+\" found\")]:[]}var Fn={promoteId:function(t){var e=t.key,r=t.value;if(\"string\"===Jr(r))return Rn({key:e,value:r});var n=[];for(var i in r)n.push.apply(n,Rn({key:e+\".\"+i,value:r[i]}));return n}};function Bn(t){var e=t.value,r=t.key,n=t.styleSpec,i=t.style;if(!e.type)return[new zt(r,e,'\"type\" is required')];var a,o=Bt(e.type);switch(o){case\"vector\":case\"raster\":case\"raster-dem\":return dn({key:r,value:e,valueSpec:n[\"source_\"+o.replace(\"-\",\"_\")],style:t.style,styleSpec:n,objectElementValidators:Fn});case\"geojson\":if(a=dn({key:r,value:e,valueSpec:n.source_geojson,style:i,styleSpec:n,objectElementValidators:Fn}),e.cluster)for(var s in e.clusterProperties){var l=e.clusterProperties[s],u=l[0],c=l[1],f=\"string\"==typeof u?[u,[\"accumulated\"],[\"get\",s]]:u;a.push.apply(a,mn({key:r+\".\"+s+\".map\",value:c,expressionContext:\"cluster-map\"})),a.push.apply(a,mn({key:r+\".\"+s+\".reduce\",value:f,expressionContext:\"cluster-reduce\"}))}return a;case\"video\":return dn({key:r,value:e,valueSpec:n.source_video,style:i,styleSpec:n});case\"image\":return dn({key:r,value:e,valueSpec:n.source_image,style:i,styleSpec:n});case\"canvas\":return[new zt(r,null,\"Please use runtime APIs to add canvas sources, rather than including them in stylesheets.\",\"source.canvas\")];default:return xn({key:r+\".type\",value:e.type,valueSpec:{values:[\"vector\",\"raster\",\"raster-dem\",\"geojson\",\"video\",\"image\"]},style:i,styleSpec:n})}}function Nn(t){var e=t.value,r=t.styleSpec,n=r.light,i=t.style,a=[],o=Jr(e);if(void 0===e)return a;if(\"object\"!==o)return a.concat([new zt(\"light\",e,\"object expected, \"+o+\" found\")]);for(var s in e){var l=s.match(/^(.*)-transition$/);a=l&&n[l[1]]&&n[l[1]].transition?a.concat(Un({key:s,value:e[s],valueSpec:r.transition,style:i,styleSpec:r})):n[s]?a.concat(Un({key:s,value:e[s],valueSpec:n[s],style:i,styleSpec:r})):a.concat([new zt(s,e[s],'unknown property \"'+s+'\"')])}return a}var jn={\"*\":function(){return[]},array:vn,boolean:function(t){var e=t.value,r=t.key,n=Jr(e);return\"boolean\"!==n?[new zt(r,e,\"boolean expected, \"+n+\" found\")]:[]},number:gn,color:function(t){var e=t.key,r=t.value,n=Jr(r);return\"string\"!==n?[new zt(e,r,\"color 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o=i[$n+a],s=i[$n+a+1];n.push(o===s?null:i.subarray(o,s))}var l=i[$n+n.length],u=i[$n+n.length+1];this.keys=i.subarray(l,u),this.bboxes=i.subarray(u),this.insert=this._insertReadonly}else{this.d=e+2*r;for(var c=0;c<this.d*this.d;c++)n.push([]);this.keys=[],this.bboxes=[]}this.n=e,this.extent=t,this.padding=r,this.scale=e/t,this.uid=0;var f=r/e*t;this.min=-f,this.max=t+f}Qn.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertCell,this.uid++),this.keys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Qn.prototype._insertReadonly=function(){throw\"Cannot insert into a GridIndex created from an ArrayBuffer.\"},Qn.prototype._insertCell=function(t,e,r,n,i,a){this.cells[i].push(a)},Qn.prototype.query=function(t,e,r,n,i){var a=this.min,o=this.max;if(t<=a&&e<=a&&o<=r&&o<=n&&!i)return Array.prototype.slice.call(this.keys);var s=[];return this._forEachCell(t,e,r,n,this._queryCell,s,{},i),s},Qn.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=this.cells[i];if(null!==l)for(var u=this.keys,c=this.bboxes,f=0;f<l.length;f++){var h=l[f];if(void 0===o[h]){var p=4*h;(s?s(c[p+0],c[p+1],c[p+2],c[p+3]):t<=c[p+2]&&e<=c[p+3]&&r>=c[p+0]&&n>=c[p+1])?(o[h]=!0,a.push(u[h])):o[h]=!1}}},Qn.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToCellCoord(t),u=this._convertToCellCoord(e),c=this._convertToCellCoord(r),f=this._convertToCellCoord(n),h=l;h<=c;h++)for(var p=u;p<=f;p++){var d=this.d*p+h;if((!s||s(this._convertFromCellCoord(h),this._convertFromCellCoord(p),this._convertFromCellCoord(h+1),this._convertFromCellCoord(p+1)))&&i.call(this,t,e,r,n,d,a,o,s))return}},Qn.prototype._convertFromCellCoord=function(t){return(t-this.padding)/this.scale},Qn.prototype._convertToCellCoord=function(t){return Math.max(0,Math.min(this.d-1,Math.floor(t*this.scale)+this.padding))},Qn.prototype.toArrayBuffer=function(){if(this.arrayBuffer)return this.arrayBuffer;for(var t=this.cells,e=$n+this.cells.length+1+1,r=0,n=0;n<this.cells.length;n++)r+=this.cells[n].length;var i=new Int32Array(e+r+this.keys.length+this.bboxes.length);i[0]=this.extent,i[1]=this.n,i[2]=this.padding;for(var a=e,o=0;o<t.length;o++){var s=t[o];i[$n+o]=a,i.set(s,a),a+=s.length}return i[$n+t.length]=a,i.set(this.keys,a),a+=this.keys.length,i[$n+t.length+1]=a,i.set(this.bboxes,a),a+=this.bboxes.length,i.buffer};var ti=self.ImageData,ei=self.ImageBitmap,ri={};function ni(t,e,r){void 0===r&&(r={}),Object.defineProperty(e,\"_classRegistryKey\",{value:t,writeable:!1}),ri[t]={klass:e,omit:r.omit||[],shallow:r.shallow||[]}}for(var ii in ni(\"Object\",Object),Kn.serialize=function(t,e){var r=t.toArrayBuffer();return e&&e.push(r),{buffer:r}},Kn.deserialize=function(t){return new Kn(t.buffer)},ni(\"Grid\",Kn),ni(\"Color\",oe),ni(\"Error\",Error),ni(\"ResolvedImage\",ce),ni(\"StylePropertyFunction\",hn),ni(\"StyleExpression\",on,{omit:[\"_evaluator\"]}),ni(\"ZoomDependentExpression\",cn),ni(\"ZoomConstantExpression\",un),ni(\"CompoundExpression\",Ae,{omit:[\"_evaluate\"]}),jr)jr[ii]._classRegistryKey||ni(\"Expression_\"+ii,jr[ii]);function ai(t){return t&&\"undefined\"!=typeof ArrayBuffer&&(t instanceof ArrayBuffer||t.constructor&&\"ArrayBuffer\"===t.constructor.name)}function oi(t){return ei&&t instanceof ei}function si(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(ai(t)||oi(t))return e&&e.push(t),t;if(ArrayBuffer.isView(t)){var r=t;return e&&e.push(r.buffer),r}if(t instanceof ti)return e&&e.push(t.data.buffer),t;if(Array.isArray(t)){for(var n=[],i=0,a=t;i<a.length;i+=1){var o=a[i];n.push(si(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 u=s.serialize?s.serialize(t,e):{};if(!s.serialize){for(var c in t)if(t.hasOwnProperty(c)&&!(ri[l].omit.indexOf(c)>=0)){var f=t[c];u[c]=ri[l].shallow.indexOf(c)>=0?f:si(f,e)}t instanceof Error&&(u.message=t.message)}if(u.$name)throw new Error(\"$name property is reserved for worker serialization logic.\");return\"Object\"!==l&&(u.$name=l),u}throw new Error(\"can't serialize object of type \"+typeof t)}function li(t){if(null==t||\"boolean\"==typeof t||\"number\"==typeof t||\"string\"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp||ai(t)||oi(t)||ArrayBuffer.isView(t)||t instanceof ti)return t;if(Array.isArray(t))return t.map(li);if(\"object\"==typeof t){var e=t.$name||\"Object\",r=ri[e].klass;if(!r)throw new Error(\"can't deserialize unregistered class 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Aboriginal Syllabics\":function(t){return t>=5120&&t<=5759},Khmer:function(t){return t>=6016&&t<=6143},\"Unified Canadian Aboriginal Syllabics Extended\":function(t){return t>=6320&&t<=6399},\"General Punctuation\":function(t){return t>=8192&&t<=8303},\"Letterlike Symbols\":function(t){return t>=8448&&t<=8527},\"Number Forms\":function(t){return t>=8528&&t<=8591},\"Miscellaneous Technical\":function(t){return t>=8960&&t<=9215},\"Control Pictures\":function(t){return t>=9216&&t<=9279},\"Optical Character Recognition\":function(t){return t>=9280&&t<=9311},\"Enclosed Alphanumerics\":function(t){return t>=9312&&t<=9471},\"Geometric Shapes\":function(t){return t>=9632&&t<=9727},\"Miscellaneous Symbols\":function(t){return t>=9728&&t<=9983},\"Miscellaneous Symbols and Arrows\":function(t){return t>=11008&&t<=11263},\"CJK Radicals Supplement\":function(t){return t>=11904&&t<=12031},\"Kangxi Radicals\":function(t){return t>=12032&&t<=12255},\"Ideographic Description 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t&&t.indexOf(\"NetworkError\")>-1&&(Ti=_i),wi&&wi(t)};function Mi(){Si.fire(new Pt(\"pluginStateChange\",{pluginStatus:Ti,pluginURL:ki}))}var Si=new It,Ei=function(){return Ti},Li=function(){if(Ti!==mi||!ki)throw new Error(\"rtl-text-plugin cannot be downloaded unless a pluginURL is specified\");Ti=xi,Mi(),ki&&kt({url:ki},(function(t){t?Ai(t):(Ti=bi,Mi())}))},Ci={applyArabicShaping:null,processBidirectionalText:null,processStyledBidirectionalText:null,isLoaded:function(){return Ti===bi||null!=Ci.applyArabicShaping},isLoading:function(){return Ti===xi},setState:function(t){Ti=t.pluginStatus,ki=t.pluginURL},isParsed:function(){return null!=Ci.applyArabicShaping&&null!=Ci.processBidirectionalText&&null!=Ci.processStyledBidirectionalText},getPluginURL:function(){return ki}},Pi=function(t,e){this.zoom=t,e?(this.now=e.now,this.fadeDuration=e.fadeDuration,this.zoomHistory=e.zoomHistory,this.transition=e.transition):(this.now=0,this.fadeDuration=0,this.zoomHistory=new 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b(this._values[t].transition)},Di.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new Ii(this._values[t].property)),this._values[t].transition=b(e)||void 0},Di.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i);var a=this.getTransition(n);void 0!==a&&(t[n+\"-transition\"]=a)}return t},Di.prototype.transitioned=function(t,e){for(var r=new Ri(this._properties),n=0,i=Object.keys(this._values);n<i.length;n+=1){var a=i[n];r._values[a]=this._values[a].transitioned(t,e._values[a])}return r},Di.prototype.untransitioned=function(){for(var t=new Ri(this._properties),e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e];t._values[n]=this._values[n].untransitioned()}return t};var zi=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};zi.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var Ri=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};Ri.prototype.possiblyEvaluate=function(t,e,r){for(var n=new Ni(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},Ri.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var Fi=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};Fi.prototype.getValue=function(t){return b(this._values[t].value)},Fi.prototype.setValue=function(t,e){this._values[t]=new Oi(this._values[t].property,null===e?void 0:b(e))},Fi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},Fi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new Ni(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var Bi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};Bi.prototype.isConstant=function(){return\"constant\"===this.value.kind},Bi.prototype.constantOr=function(t){return\"constant\"===this.value.kind?this.value.value:t},Bi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var Ni=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};Ni.prototype.get=function(t){return this._values[t]};var ji=function(t){this.specification=t};ji.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},ji.prototype.interpolate=function(t,e,r){var n=Qe[this.specification.type];return n?n(t,e,r):t};var Ui=function(t,e){this.specification=t,this.overrides=e};Ui.prototype.possiblyEvaluate=function(t,e,r,n){return\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?new Bi(this,{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)},e):new Bi(this,t.expression,e)},Ui.prototype.interpolate=function(t,e,r){if(\"constant\"!==t.value.kind||\"constant\"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new Bi(this,{kind:\"constant\",value:void 0},t.parameters);var n=Qe[this.specification.type];return n?new Bi(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.parameters):t},Ui.prototype.evaluate=function(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new Bi(this,{kind:\"constant\",value:void 0},e);if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a=\"resolvedImage\"===t.property.specification.type&&\"string\"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new Bi(this,{kind:\"constant\",value:o},e)}if(\"camera\"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new Bi(this,{kind:\"constant\",value:s},e)}return new Bi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if(\"source\"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return\"composite\"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Ui),Hi=function(t){this.specification=t};Hi.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new Pi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new Pi(Math.floor(e.zoom),e)),t.expression.evaluate(new Pi(Math.floor(e.zoom+1),e)),e)}},Hi.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Hi.prototype.interpolate=function(t){return t};var qi=function(t){this.specification=t};qi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},qi.prototype.interpolate=function(){return!1};var Gi=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new Oi(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new Ii(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};ni(\"DataDrivenProperty\",Ui),ni(\"DataConstantProperty\",ji),ni(\"CrossFadedDataDrivenProperty\",Vi),ni(\"CrossFadedProperty\",Hi),ni(\"ColorRampProperty\",qi);var Zi=\"-transition\",Yi=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},\"custom\"!==e.type&&(this.metadata=e.metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,\"background\"!==e.type&&(this.source=e.source,this.sourceLayer=e[\"source-layer\"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new Fi(r.layout)),r.paint)){for(var n in this._transitionablePaint=new Di(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new Ni(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".layout.\"+t;if(this._validate(Xn,n,t,e,r))return}\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return y(t,Zi)?this._transitionablePaint.getTransition(t.slice(0,-11)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".paint.\"+t;if(this._validate(Wn,n,t,e,r))return!1}if(y(t,Zi))return this._transitionablePaint.setTransition(t.slice(0,-11),e||void 0),!1;var i=this._transitionablePaint._values[t],a=\"cross-faded-data-driven\"===i.property.specification[\"property-type\"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||!!(this.maxzoom&&t>=this.maxzoom)||\"none\"===this.visibility},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),x(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&Jn(this,t.call(Zn,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Dt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof Bi&&Yr(e.property.specification)&&(\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent)return!0}return!1},e}(It),Wi={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Xi=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Ji=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ki(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Wi[i].BYTES_PER_ELEMENT),o=r=$i(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:$i(r,Math.max(n,e)),alignment:e}}function $i(t,e){return Math.ceil(t/e)*e}Ji.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Ji.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Ji.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Ji.prototype.clear=function(){this.length=0},Ji.prototype.resize=function(t){this.reserve(t),this.length=t},Ji.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Ji.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var Qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Ji);Qi.prototype.bytesPerElement=4,ni(\"StructArrayLayout2i4\",Qi);var ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new 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this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Ji);ea.prototype.bytesPerElement=12,ni(\"StructArrayLayout2i4i12\",ea);var ra=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Ji);ra.prototype.bytesPerElement=8,ni(\"StructArrayLayout2i4ub8\",ra);var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,u){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l,u)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,u,c){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=u,this.uint8[h+17]=c,t},e}(Ji);na.prototype.bytesPerElement=18,ni(\"StructArrayLayout8ui2ub18\",na);var ia=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,u,c,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,u,c,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,u,c,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=u,this.int16[p+9]=c,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Ji);ia.prototype.bytesPerElement=24,ni(\"StructArrayLayout4i4ui4i24\",ia);var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Ji);aa.prototype.bytesPerElement=12,ni(\"StructArrayLayout3f12\",aa);var oa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Ji);oa.prototype.bytesPerElement=4,ni(\"StructArrayLayout1ul4\",oa);var sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,u){var c=10*t,f=5*t;return this.int16[c+0]=e,this.int16[c+1]=r,this.int16[c+2]=n,this.int16[c+3]=i,this.int16[c+4]=a,this.int16[c+5]=o,this.uint32[f+3]=s,this.uint16[c+8]=l,this.uint16[c+9]=u,t},e}(Ji);sa.prototype.bytesPerElement=20,ni(\"StructArrayLayout6i1ul2ui20\",sa);var la=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Ji);la.prototype.bytesPerElement=12,ni(\"StructArrayLayout2i2i2i12\",la);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),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Ji);ua.prototype.bytesPerElement=16,ni(\"StructArrayLayout2f1f2i16\",ua);var ca=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Ji);ca.prototype.bytesPerElement=12,ni(\"StructArrayLayout2ub2f12\",ca);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.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return 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this.int16[m+0]=e,this.int16[m+1]=r,this.uint16[m+2]=n,this.uint16[m+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[m+10]=l,this.uint16[m+11]=u,this.uint16[m+12]=c,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=v,this.uint32[x+10]=g,this.int16[m+22]=y,t},e}(Ji);ha.prototype.bytesPerElement=48,ni(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",ha);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.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,w,T,k,A,M,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,w,T,k,A,M,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,w,T,k,A,M,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=u,this.uint16[L+9]=c,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=v,this.uint16[L+15]=g,this.uint16[L+16]=y,this.uint16[L+17]=m,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=A,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Ji);pa.prototype.bytesPerElement=68,ni(\"StructArrayLayout8i15ui1ul4f68\",pa);var da=function(t){function e(){t.apply(this,arguments)}return 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this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Ji);ba.prototype.bytesPerElement=16,ni(\"StructArrayLayout4f16\",ba);var _a=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return 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this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Xi);Ta.prototype.size=48;var ka=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 Ta(this,t)},e}(ha);ni(\"PlacedSymbolArray\",ka);var Aa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return 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t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){this.current!==t&&(this.current=t,this.gl.uniform1i(this.location,t))},e}(Ga),Ya=function(t){function e(e,r){t.call(this,e,r),this.current=0}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){this.current!==t&&(this.current=t,this.gl.uniform1f(this.location,t))},e}(Ga),Wa=function(t){function e(e,r){t.call(this,e,r),this.current=[0,0]}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){t[0]===this.current[0]&&t[1]===this.current[1]||(this.current=t,this.gl.uniform2f(this.location,t[0],t[1]))},e}(Ga),Xa=function(t){function e(e,r){t.call(this,e,r),this.current=[0,0,0]}return 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Float32Array(16),Qa=function(t){function e(e,r){t.call(this,e,r),this.current=$a}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t[12]!==this.current[12]||t[0]!==this.current[0])return this.current=t,void this.gl.uniformMatrix4fv(this.location,!1,t);for(var e=1;e<16;e++)if(t[e]!==this.current[e]){this.current=t,this.gl.uniformMatrix4fv(this.location,!1,t);break}},e}(Ga);function to(t){return[Ia(255*t.r,255*t.g),Ia(255*t.b,255*t.a)]}var eo=function(t,e,r){this.value=t,this.uniformNames=e.map((function(t){return\"u_\"+t})),this.type=r};eo.prototype.setUniform=function(t,e,r){t.set(r.constantOr(this.value))},eo.prototype.getBinding=function(t,e,r){return\"color\"===this.type?new Ka(t,e):new Ya(t,e)};var ro=function(t,e){this.uniformNames=e.map((function(t){return\"u_\"+t})),this.patternFrom=null,this.patternTo=null,this.pixelRatioFrom=1,this.pixelRatioTo=1};ro.prototype.setConstantPatternPositions=function(t,e){this.pixelRatioFrom=e.pixelRatio,this.pixelRatioTo=t.pixelRatio,this.patternFrom=e.tlbr,this.patternTo=t.tlbr},ro.prototype.setUniform=function(t,e,r,n){var i=\"u_pattern_to\"===n?this.patternTo:\"u_pattern_from\"===n?this.patternFrom:\"u_pixel_ratio_to\"===n?this.pixelRatioTo:\"u_pixel_ratio_from\"===n?this.pixelRatioFrom:null;i&&t.set(i)},ro.prototype.getBinding=function(t,e,r){return\"u_pattern\"===r.substr(0,9)?new Ja(t,e):new Ya(t,e)};var no=function(t,e,r,n){this.expression=t,this.type=r,this.maxValue=0,this.paintVertexAttributes=e.map((function(t){return{name:\"a_\"+t,type:\"Float32\",components:\"color\"===r?2:1,offset:0}})),this.paintVertexArray=new n};no.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.paintVertexArray.length,o=this.expression.evaluate(new Pi(0),e,{},n,[],i);this.paintVertexArray.resize(t),this._setPaintValue(a,t,o)},no.prototype.updatePaintArray=function(t,e,r,n){var i=this.expression.evaluate({zoom:0},r,n);this._setPaintValue(t,e,i)},no.prototype._setPaintValue=function(t,e,r){if(\"color\"===this.type)for(var n=to(r),i=t;i<e;i++)this.paintVertexArray.emplace(i,n[0],n[1]);else{for(var a=t;a<e;a++)this.paintVertexArray.emplace(a,r);this.maxValue=Math.max(this.maxValue,Math.abs(r))}},no.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},no.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()};var io=function(t,e,r,n,i,a){this.expression=t,this.uniformNames=e.map((function(t){return\"u_\"+t+\"_t\"})),this.type=r,this.useIntegerZoom=n,this.zoom=i,this.maxValue=0,this.paintVertexAttributes=e.map((function(t){return{name:\"a_\"+t,type:\"Float32\",components:\"color\"===r?4:2,offset:0}})),this.paintVertexArray=new a};io.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.expression.evaluate(new Pi(this.zoom),e,{},n,[],i),o=this.expression.evaluate(new Pi(this.zoom+1),e,{},n,[],i),s=this.paintVertexArray.length;this.paintVertexArray.resize(t),this._setPaintValue(s,t,a,o)},io.prototype.updatePaintArray=function(t,e,r,n){var i=this.expression.evaluate({zoom:this.zoom},r,n),a=this.expression.evaluate({zoom:this.zoom+1},r,n);this._setPaintValue(t,e,i,a)},io.prototype._setPaintValue=function(t,e,r,n){if(\"color\"===this.type)for(var i=to(r),a=to(n),o=t;o<e;o++)this.paintVertexArray.emplace(o,i[0],i[1],a[0],a[1]);else{for(var s=t;s<e;s++)this.paintVertexArray.emplace(s,r,n);this.maxValue=Math.max(this.maxValue,Math.abs(r),Math.abs(n))}},io.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},io.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()},io.prototype.setUniform=function(t,e){var r=this.useIntegerZoom?Math.floor(e.zoom):e.zoom,n=u(this.expression.interpolationFactor(r,this.zoom,this.zoom+1),0,1);t.set(n)},io.prototype.getBinding=function(t,e,r){return new Ya(t,e)};var ao=function(t,e,r,n,i,a){this.expression=t,this.type=e,this.useIntegerZoom=r,this.zoom=n,this.layerId=a,this.zoomInPaintVertexArray=new i,this.zoomOutPaintVertexArray=new i};ao.prototype.populatePaintArray=function(t,e,r){var n=this.zoomInPaintVertexArray.length;this.zoomInPaintVertexArray.resize(t),this.zoomOutPaintVertexArray.resize(t),this._setPaintValues(n,t,e.patterns&&e.patterns[this.layerId],r)},ao.prototype.updatePaintArray=function(t,e,r,n,i){this._setPaintValues(t,e,r.patterns&&r.patterns[this.layerId],i)},ao.prototype._setPaintValues=function(t,e,r,n){if(n&&r){var i=r.min,a=r.mid,o=r.max,s=n[i],l=n[a],u=n[o];if(s&&l&&u)for(var c=t;c<e;c++)this.zoomInPaintVertexArray.emplace(c,l.tl[0],l.tl[1],l.br[0],l.br[1],s.tl[0],s.tl[1],s.br[0],s.br[1],l.pixelRatio,s.pixelRatio),this.zoomOutPaintVertexArray.emplace(c,l.tl[0],l.tl[1],l.br[0],l.br[1],u.tl[0],u.tl[1],u.br[0],u.br[1],l.pixelRatio,u.pixelRatio)}},ao.prototype.upload=function(t){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=t.createVertexBuffer(this.zoomInPaintVertexArray,Da.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=t.createVertexBuffer(this.zoomOutPaintVertexArray,Da.members,this.expression.isStateDependent))},ao.prototype.destroy=function(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()};var oo=function(t,e,r,n){this.binders={},this.layoutAttributes=n,this._buffers=[];var i=[];for(var a in t.paint._values)if(r(a)){var o=t.paint.get(a);if(o instanceof Bi&&Yr(o.property.specification)){var s=lo(a,t.type),l=o.value,u=o.property.specification.type,c=o.property.useIntegerZoom,f=o.property.specification[\"property-type\"],h=\"cross-faded\"===f||\"cross-faded-data-driven\"===f;if(\"constant\"===l.kind)this.binders[a]=h?new ro(l.value,s):new eo(l.value,s,u),i.push(\"/u_\"+a);else if(\"source\"===l.kind||h){var p=uo(a,u,\"source\");this.binders[a]=h?new ao(l,u,c,e,p,t.id):new no(l,s,u,p),i.push(\"/a_\"+a)}else{var d=uo(a,u,\"composite\");this.binders[a]=new io(l,s,u,c,e,d),i.push(\"/z_\"+a)}}}this.cacheKey=i.sort().join(\"\")};oo.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof no||e instanceof io?e.maxValue:0},oo.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof no||o instanceof io||o instanceof ao)&&o.populatePaintArray(t,e,r,n,i)}},oo.prototype.setConstantPatternPositions=function(t,e){for(var r in this.binders){var n=this.binders[r];n instanceof ro&&n.setConstantPatternPositions(t,e)}},oo.prototype.updatePaintArrays=function(t,e,r,n,i){var a=!1;for(var o in t)for(var s=0,l=e.getPositions(o);s<l.length;s+=1){var u=l[s],c=r.feature(u.index);for(var f in this.binders){var h=this.binders[f];if((h instanceof no||h instanceof io||h instanceof ao)&&!0===h.expression.isStateDependent){var p=n.paint.get(f);h.expression=p.value,h.updatePaintArray(u.start,u.end,c,t[o],i),a=!0}}}return a},oo.prototype.defines=function(){var t=[];for(var e in this.binders){var r=this.binders[e];(r instanceof eo||r instanceof ro)&&t.push.apply(t,r.uniformNames.map((function(t){return\"#define HAS_UNIFORM_\"+t})))}return t},oo.prototype.getPaintVertexBuffers=function(){return this._buffers},oo.prototype.getUniforms=function(t,e){var r=[];for(var n in this.binders){var i=this.binders[n];if(i instanceof eo||i instanceof ro||i instanceof io)for(var a=0,o=i.uniformNames;a<o.length;a+=1){var s=o[a];if(e[s]){var l=i.getBinding(t,e[s],s);r.push({name:s,property:n,binding:l})}}}return r},oo.prototype.setUniforms=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.name,l=o.property,u=o.binding;this.binders[l].setUniform(u,n,r.get(l),s)}},oo.prototype.updatePaintBuffers=function(t){for(var e in this._buffers=[],this.binders){var r=this.binders[e];if(t&&r instanceof ao){var n=2===t.fromScale?r.zoomInPaintVertexBuffer:r.zoomOutPaintVertexBuffer;n&&this._buffers.push(n)}else(r instanceof no||r instanceof io)&&r.paintVertexBuffer&&this._buffers.push(r.paintVertexBuffer)}},oo.prototype.upload=function(t){for(var e in this.binders){var r=this.binders[e];(r instanceof no||r instanceof io||r instanceof ao)&&r.upload(t)}this.updatePaintBuffers()},oo.prototype.destroy=function(){for(var t in this.binders){var e=this.binders[t];(e instanceof no||e instanceof io||e instanceof ao)&&e.destroy()}};var so=function(t,e,r,n){void 0===n&&(n=function(){return!0}),this.programConfigurations={};for(var i=0,a=e;i<a.length;i+=1){var o=a[i];this.programConfigurations[o.id]=new oo(o,r,n,t)}this.needsUpload=!1,this._featureMap=new ja,this._bufferOffset=0};function lo(t,e){return{\"text-opacity\":[\"opacity\"],\"icon-opacity\":[\"opacity\"],\"text-color\":[\"fill_color\"],\"icon-color\":[\"fill_color\"],\"text-halo-color\":[\"halo_color\"],\"icon-halo-color\":[\"halo_color\"],\"text-halo-blur\":[\"halo_blur\"],\"icon-halo-blur\":[\"halo_blur\"],\"text-halo-width\":[\"halo_width\"],\"icon-halo-width\":[\"halo_width\"],\"line-gap-width\":[\"gapwidth\"],\"line-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-extrusion-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"]}[t]||[t.replace(e+\"-\",\"\").replace(/-/g,\"_\")]}function uo(t,e,r){var n={color:{source:xa,composite:ba},number:{source:da,composite:xa}},i=function(t){return{\"line-pattern\":{source:na,composite:na},\"fill-pattern\":{source:na,composite:na},\"fill-extrusion-pattern\":{source:na,composite:na}}[t]}(t);return i&&i[r]||n[e][r]}so.prototype.populatePaintArrays=function(t,e,r,n,i,a){for(var o in this.programConfigurations)this.programConfigurations[o].populatePaintArrays(t,e,n,i,a);void 0!==e.id&&this._featureMap.add(e.id,r,this._bufferOffset,t),this._bufferOffset=t,this.needsUpload=!0},so.prototype.updatePaintArrays=function(t,e,r,n){for(var i=0,a=r;i<a.length;i+=1){var o=a[i];this.needsUpload=this.programConfigurations[o.id].updatePaintArrays(t,this._featureMap,e,o,n)||this.needsUpload}},so.prototype.get=function(t){return this.programConfigurations[t]},so.prototype.upload=function(t){if(this.needsUpload){for(var e in this.programConfigurations)this.programConfigurations[e].upload(t);this.needsUpload=!1}},so.prototype.destroy=function(){for(var t in this.programConfigurations)this.programConfigurations[t].destroy()},ni(\"ConstantBinder\",eo),ni(\"CrossFadedConstantBinder\",ro),ni(\"SourceExpressionBinder\",no),ni(\"CrossFadedCompositeBinder\",ao),ni(\"CompositeExpressionBinder\",io),ni(\"ProgramConfiguration\",oo,{omit:[\"_buffers\"]}),ni(\"ProgramConfigurationSet\",so);var co=8192;var fo,ho=(fo=15,{min:-1*Math.pow(2,fo-1),max:Math.pow(2,fo-1)-1});function po(t){for(var e=co/t.extent,r=t.loadGeometry(),n=0;n<r.length;n++)for(var i=r[n],a=0;a<i.length;a++){var o=i[a];o.x=Math.round(o.x*e),o.y=Math.round(o.y*e),(o.x<ho.min||o.x>ho.max||o.y<ho.min||o.y>ho.max)&&(w(\"Geometry exceeds allowed extent, reduce your vector tile buffer size\"),o.x=u(o.x,ho.min,ho.max),o.y=u(o.y,ho.min,ho.max))}return r}function vo(t,e,r,n,i){t.emplaceBack(2*e+(n+1)/2,2*r+(i+1)/2)}var go=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 Qi,this.indexArray=new fa,this.segments=new Oa,this.programConfigurations=new so(Pa,t.layers,t.zoom),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function yo(t,e){for(var r=0;r<t.length;r++)if(Mo(e,t[r]))return!0;for(var n=0;n<e.length;n++)if(Mo(t,e[n]))return!0;return!!_o(t,e)}function mo(t,e,r){return!!Mo(t,e)||!!To(e,t,r)}function xo(t,e){if(1===t.length)return Ao(e,t[0]);for(var r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++)if(Mo(t,n[i]))return!0;for(var a=0;a<t.length;a++)if(Ao(e,t[a]))return!0;for(var o=0;o<e.length;o++)if(_o(t,e[o]))return!0;return!1}function bo(t,e,r){if(t.length>1){if(_o(t,e))return!0;for(var n=0;n<e.length;n++)if(To(e[n],t,r))return!0}for(var i=0;i<t.length;i++)if(To(t[i],e,r))return!0;return!1}function _o(t,e){if(0===t.length||0===e.length)return!1;for(var r=0;r<t.length-1;r++)for(var 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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 Xs(t,e,r){if(3===t){var n=new Zs(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ys.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 Vs(this._pbf,e,this.extent,this._keys,this._values)};var Js={VectorTile:function(t,e){this.layers=t.readFields(Xs,{},e)},VectorTileFeature:Vs,VectorTileLayer:Zs},Ks=Js.VectorTileFeature.types,$s=Math.pow(2,13);function Qs(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*$s)+o,i*$s*2,a*$s*2,Math.round(s))}var tl=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 ea,this.indexArray=new fa,this.programConfigurations=new so(Us,t.layers,t.zoom),this.segments=new Oa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function el(t,e){return t.x===e.x&&(t.x<0||t.x>co)||t.y===e.y&&(t.y<0||t.y>co)}tl.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=zs(\"fill-extrusion\",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,u=a.sourceLayerIndex,c=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:c?po(o):[]};if(this.layers[0]._featureFilter.filter(new Pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:u,index:l,geometry:c?f.geometry:po(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(Rs(\"fill-extrusion\",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,u,this.index,!0)}}},tl.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},tl.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},tl.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},tl.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},tl.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Us),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},tl.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},tl.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=Is(e,500);a<o.length;a+=1){for(var s=o[a],l=0,u=0,c=s;u<c.length;u+=1)l+=c[u].length;for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!((O=d).every((function(t){return t.x<0}))||O.every((function(t){return t.x>co}))||O.every((function(t){return t.y<0}))||O.every((function(t){return t.y>co}))))for(var v=0,g=0;g<d.length;g++){var y=d[g];if(g>=1){var m=d[g-1];if(!el(y,m)){f.vertexLength+4>Oa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=y.sub(m)._perp()._unit(),b=m.dist(y);v+b>32768&&(v=0),Qs(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,v),Qs(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,v),v+=b,Qs(this.layoutVertexArray,m.x,m.y,x.x,x.y,0,0,v),Qs(this.layoutVertexArray,m.x,m.y,x.x,x.y,0,1,v);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>Oa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),\"Polygon\"===Ks[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Qs(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=es(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(k+C[P],k+C[P+2],k+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}var O;this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},ni(\"FillExtrusionBucket\",tl,{omit:[\"layers\",\"features\"]});var rl={paint:new Gi({\"fill-extrusion-opacity\":new ji(Dt[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new Ui(Dt[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new ji(Dt[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new ji(Dt[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new Vi(Dt[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new Ui(Dt[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new Ui(Dt[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new ji(Dt[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})},nl=function(t){function e(e){t.call(this,e,rl)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new tl(t)},e.prototype.queryRadius=function(){return Lo(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 u=Co(t,this.paint.get(\"fill-extrusion-translate\"),this.paint.get(\"fill-extrusion-translate-anchor\"),o.angle,s),c=this.paint.get(\"fill-extrusion-height\").evaluate(e,r),f=this.paint.get(\"fill-extrusion-base\").evaluate(e,r),h=function(t,e,r,n){for(var i=[],o=0,s=t;o<s.length;o+=1){var l=s[o],u=[l.x,l.y,n,1];No(u,u,e),i.push(new a(u[0]/u[3],u[1]/u[3]))}return i}(u,l,0,0),p=function(t,e,r,n){for(var i=[],o=[],s=n[8]*e,l=n[9]*e,u=n[10]*e,c=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,v=0,g=t;v<g.length;v+=1){for(var y=[],m=[],x=0,b=g[v];x<b.length;x+=1){var _=b[x],w=_.x,T=_.y,k=n[0]*w+n[4]*T+n[12],A=n[1]*w+n[5]*T+n[13],M=n[2]*w+n[6]*T+n[14],S=n[3]*w+n[7]*T+n[15],E=M+u,L=S+c,C=k+f,P=A+h,O=M+p,I=S+d,D=new a((k+s)/L,(A+l)/L);D.z=E/L,y.push(D);var z=new a(C/I,P/I);z.z=O/I,m.push(z)}i.push(y),o.push(m)}return[i,o]}(n,f,c,l);return function(t,e,r){var n=1/0;xo(r,e)&&(n=al(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],u=a[s+1],c=o[s],f=[l,u,o[s+1],c,l];yo(r,f)&&(n=Math.min(n,al(r,f)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Yi);function il(t,e){return t.x*e.x+t.y*e.y}function al(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),u=o.sub(i),c=il(s,s),f=il(s,l),h=il(l,l),p=il(u,s),d=il(u,l),v=c*h-f*f,g=(h*p-f*d)/v,y=(c*d-f*p)/v,m=1-g-y,x=i.z*m+r.z*g+a.z*y;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var ol=Ki([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4).members,sl=Js.VectorTileFeature.types,ll=Math.cos(Math.PI/180*37.5),ul=Math.pow(2,14)/.5,cl=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 ra,this.indexArray=new fa,this.programConfigurations=new so(ol,t.layers,t.zoom),this.segments=new Oa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};cl.prototype.populate=function(t,e,r){this.hasPattern=zs(\"line\",this.layers,e);for(var n=this.layers[0].layout.get(\"line-sort-key\"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,u=s.id,c=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:u,properties:l.properties,geometry:h?po(l):[]};if(this.layers[0]._featureFilter.filter(new Pi(this.zoom),p,r)){h||(p.geometry=po(l));var d=n?n.evaluate(p,{},r):void 0,v={id:u,properties:l.properties,type:l.type,sourceLayerIndex:f,index:c,geometry:p.geometry,patterns:{},sortKey:d};i.push(v)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,y=i;g<y.length;g+=1){var m=y[g],x=m,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=Rs(\"line\",this.layers,m,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(m,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},cl.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},cl.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},cl.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},cl.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},cl.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,ol),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},cl.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},cl.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get(\"line-join\").evaluate(t,{}),s=a.get(\"line-cap\"),l=a.get(\"line-miter-limit\"),u=a.get(\"line-round-limit\"),c=0,f=e;c<f.length;c+=1){var h=f[c];this.addLine(h,t,o,s,l,u)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},cl.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty(\"mapbox_clip_start\")&&e.properties.hasOwnProperty(\"mapbox_clip_end\")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s=\"Polygon\"===sl[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var u=0;u<l-1&&t[u].equals(t[u+1]);)u++;if(!(l<(s?3:2))){\"bevel\"===r&&(i=1.05);var c,f=this.overscaling<=16?15*co/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,v=void 0,g=void 0;this.e1=this.e2=-1,s&&(c=t[l-2],g=t[u].sub(c)._unit()._perp());for(var y=u;y<l;y++)if(!(d=y===l-1?s?t[u+1]:void 0:t[y+1])||!t[y].equals(d)){g&&(v=g),c&&(p=c),c=t[y],g=d?d.sub(c)._unit()._perp():v;var m=(v=v||g).add(g);0===m.x&&0===m.y||m._unit();var x=v.x*g.x+v.y*g.y,b=m.x*g.x+m.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<ll&&p&&d,k=v.x*g.y-v.y*g.x>0;if(T&&y>u){var A=c.dist(p);if(A>2*f){var M=c.sub(c.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,v,0,0,h),p=M}}var S=p&&d,E=S?r:s?\"butt\":n;if(S&&\"round\"===E&&(_<a?E=\"miter\":_<=2&&(E=\"fakeround\")),\"miter\"===E&&_>i&&(E=\"bevel\"),\"bevel\"===E&&(_>2&&(E=\"flipbevel\"),_<i&&(E=\"miter\")),p&&this.updateDistance(p,c),\"miter\"===E)m._mult(_),this.addCurrentVertex(c,m,0,0,h);else if(\"flipbevel\"===E){if(_>100)m=g.mult(-1);else{var L=_*v.add(g).mag()/v.sub(g).mag();m._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(c,m,0,0,h),this.addCurrentVertex(c,m.mult(-1),0,0,h)}else if(\"bevel\"===E||\"fakeround\"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,O=k?0:C;if(p&&this.addCurrentVertex(c,v,P,O,h),\"fakeround\"===E)for(var I=Math.round(180*w/Math.PI/20),D=1;D<I;D++){var z=D/I;if(.5!==z){var R=z-.5;z+=z*R*(z-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(v)._mult(z)._add(v)._unit()._mult(k?-1:1);this.addHalfVertex(c,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(c,g,-P,-O,h)}else if(\"butt\"===E)this.addCurrentVertex(c,m,0,0,h);else if(\"square\"===E){var B=p?1:-1;this.addCurrentVertex(c,m,B,B,h)}else\"round\"===E&&(p&&(this.addCurrentVertex(c,v,0,0,h),this.addCurrentVertex(c,v,1,1,h,!0)),d&&(this.addCurrentVertex(c,g,-1,-1,h,!0),this.addCurrentVertex(c,g,0,0,h)));if(T&&y<l-1){var N=c.dist(d);if(N>2*f){var j=c.add(d.sub(c)._mult(f/N)._round());this.updateDistance(c,j),this.addCurrentVertex(j,g,0,0,h),c=j}}}}},cl.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,u=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,u,a,!0,-n,i),this.distance>ul/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},cl.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,u=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&u)<<2,u>>6);var c=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,c),o.primitiveLength++),i?this.e2=c:this.e1=c},cl.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(ul-1):this.distance},cl.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},ni(\"LineBucket\",cl,{omit:[\"layers\",\"patternFeatures\"]});var fl=new Gi({\"line-cap\":new ji(Dt.layout_line[\"line-cap\"]),\"line-join\":new Ui(Dt.layout_line[\"line-join\"]),\"line-miter-limit\":new ji(Dt.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new ji(Dt.layout_line[\"line-round-limit\"]),\"line-sort-key\":new Ui(Dt.layout_line[\"line-sort-key\"])}),hl={paint:new Gi({\"line-opacity\":new Ui(Dt.paint_line[\"line-opacity\"]),\"line-color\":new Ui(Dt.paint_line[\"line-color\"]),\"line-translate\":new ji(Dt.paint_line[\"line-translate\"]),\"line-translate-anchor\":new ji(Dt.paint_line[\"line-translate-anchor\"]),\"line-width\":new Ui(Dt.paint_line[\"line-width\"]),\"line-gap-width\":new Ui(Dt.paint_line[\"line-gap-width\"]),\"line-offset\":new Ui(Dt.paint_line[\"line-offset\"]),\"line-blur\":new Ui(Dt.paint_line[\"line-blur\"]),\"line-dasharray\":new Hi(Dt.paint_line[\"line-dasharray\"]),\"line-pattern\":new Vi(Dt.paint_line[\"line-pattern\"]),\"line-gradient\":new qi(Dt.paint_line[\"line-gradient\"])}),layout:fl},pl=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new Pi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=f({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Ui),dl=new pl(hl.paint.properties[\"line-width\"].specification);dl.useIntegerZoom=!0;var vl=function(t){function e(e){t.call(this,e,hl)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){\"line-gradient\"===t&&this._updateGradient()},e.prototype._updateGradient=function(){var t=this._transitionablePaint._values[\"line-gradient\"].value.expression;this.gradient=Jo(t,\"lineProgress\"),this.gradientTexture=null},e.prototype.recalculate=function(e,r){t.prototype.recalculate.call(this,e,r),this.paint._values[\"line-floorwidth\"]=dl.possiblyEvaluate(this._transitioningPaint._values[\"line-width\"].value,e)},e.prototype.createBucket=function(t){return new cl(t)},e.prototype.queryRadius=function(t){var e=t,r=gl(Eo(\"line-width\",this,e),Eo(\"line-gap-width\",this,e)),n=Eo(\"line-offset\",this,e);return r/2+Math.abs(n)+Lo(this.paint.get(\"line-translate\"))},e.prototype.queryIntersectsFeature=function(t,e,r,n,i,o,s){var l=Co(t,this.paint.get(\"line-translate\"),this.paint.get(\"line-translate-anchor\"),o.angle,s),u=s/2*gl(this.paint.get(\"line-width\").evaluate(e,r),this.paint.get(\"line-gap-width\").evaluate(e,r)),c=this.paint.get(\"line-offset\").evaluate(e,r);return c&&(n=function(t,e){for(var r=[],n=new a(0,0),i=0;i<t.length;i++){for(var o=t[i],s=[],l=0;l<o.length;l++){var u=o[l-1],c=o[l],f=o[l+1],h=0===l?n:c.sub(u)._unit()._perp(),p=l===o.length-1?n:f.sub(c)._unit()._perp(),d=h._add(p)._unit(),v=d.x*p.x+d.y*p.y;d._mult(1/v),s.push(d._mult(e)._add(c))}r.push(s)}return r}(n,c*s)),function(t,e,r){for(var n=0;n<e.length;n++){var i=e[n];if(t.length>=3)for(var a=0;a<i.length;a++)if(Mo(t,i[a]))return!0;if(bo(t,i,r))return!0}return!1}(l,n,u)},e.prototype.isTileClipped=function(){return!0},e}(Yi);function gl(t,e){return e>0?e+2*t:t}var 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Yl=3;function Wl(t,e,r){1===t&&r.readMessage(Xl,e)}function Xl(t,e,r){if(3===t){var n=r.readMessage(Jl,{}),i=n.id,a=n.bitmap,o=n.width,s=n.height,l=n.left,u=n.top,c=n.advance;e.push({id:i,bitmap:new Yo({width:o+2*Yl,height:s+2*Yl},a),metrics:{width:o,height:s,left:l,top:u,advance:c}})}}function Jl(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 Kl=Yl;function $l(t){for(var e=0,r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];e+=a.w*a.h,r=Math.max(r,a.w)}t.sort((function(t,e){return e.h-t.h}));for(var o=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),r),h:1/0}],s=0,l=0,u=0,c=t;u<c.length;u+=1)for(var f=c[u],h=o.length-1;h>=0;h--){var p=o[h];if(!(f.w>p.w||f.h>p.h)){if(f.x=p.x,f.y=p.y,l=Math.max(l,f.y+f.h),s=Math.max(s,f.x+f.w),f.w===p.w&&f.h===p.h){var d=o.pop();h<o.length&&(o[h]=d)}else 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t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},ru.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl,i=n[0],a=n[1];r.update(e.data,void 0,{x:i,y:a})}},ni(\"ImagePosition\",tu),ni(\"ImageAtlas\",ru);var nu={horizontal:1,vertical:2,horizontalOnly:3},iu=-17;var au=function(){this.scale=1,this.fontStack=\"\",this.imageName=null};au.forText=function(t,e){var r=new au;return r.scale=t||1,r.fontStack=e,r},au.forImage=function(t){var e=new au;return e.imageName=t,e};var ou=function(){this.text=\"\",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function su(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v){var g,y=ou.fromFeature(t,i);f===nu.vertical&&y.verticalizePunctuation();var m=Ci.processBidirectionalText,x=Ci.processStyledBidirectionalText;if(m&&1===y.sections.length){g=[];for(var b=0,_=m(y.toString(),vu(y,u,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new ou;T.text=w,T.sections=y.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);g.push(T)}}else if(x){g=[];for(var A=0,M=x(y.text,y.sectionIndex,vu(y,u,a,e,n,p,d));A<M.length;A+=1){var S=M[A],E=new ou;E.text=S[0],E.sectionIndex=S[1],E.sections=y.sections,g.push(E)}}else g=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(y,vu(y,u,a,e,n,p,d));var L=[],C={positionedLines:L,text:y.toString(),top:c[1],bottom:c[1],left:c[0],right:c[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,u,c,f){for(var h=0,p=iu,d=0,v=0,g=\"right\"===s?1:\"left\"===s?0:.5,y=0,m=0,x=i;m<x.length;m+=1){var b=x[m];b.trim();var _=b.getMaxScale(),w=(_-1)*kl,T={positionedGlyphs:[],lineOffset:0};t.positionedLines[y]=T;var k=T.positionedGlyphs,A=0;if(b.length()){for(var M=0;M<b.length();M++){var S=b.getSection(M),E=b.getSectionIndex(M),L=b.getCharCode(M),C=0,P=null,O=null,I=null,D=kl,z=!(l===nu.horizontal||!c&&!hi(L)||c&&(lu[L]||di(L)));if(S.imageName){var R=n[S.imageName];if(!R)continue;I=S.imageName,t.iconsInText=t.iconsInText||!0,O=R.paddedRect;var F=R.displaySize;S.scale=S.scale*kl/f,P={width:F[0],height:F[1],left:Ql,top:-Kl,advance:z?F[1]:F[0]},C=w+(kl-F[1]*S.scale),D=P.advance;var B=z?F[0]*S.scale-kl*_:F[1]*S.scale-kl*_;B>0&&B>A&&(A=B)}else{var N=r[S.fontStack],j=N&&N[L];if(j&&j.rect)O=j.rect,P=j.metrics;else{var U=e[S.fontStack],V=U&&U[L];if(!V)continue;P=V.metrics}C=(_-S.scale)*kl}z?(t.verticalizable=!0,k.push({glyph:L,imageName:I,x:h,y:p+C,vertical:z,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:O}),h+=D*S.scale+u):(k.push({glyph:L,imageName:I,x:h,y:p+C,vertical:z,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:O}),h+=P.advance*S.scale+u)}if(0!==k.length){var H=h-u;d=Math.max(H,d),yu(k,0,k.length-1,g,A)}h=0;var q=a*_+A;T.lineOffset=Math.max(A,w),p+=q,v=Math.max(q,v),++y}else p+=a,++y}var G=p-iu,Z=gu(o),Y=Z.horizontalAlign,W=Z.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var u=(e-r)*i,c=0;c=a!==o?-s*n-iu:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=0,d=h[f].positionedGlyphs;p<d.length;p+=1){var v=d[p];v.x+=u,v.y+=c}})(t.positionedLines,g,Y,W,d,v,a,G,i.length),t.top+=-W*G,t.bottom=t.top+G,t.left+=-Y*d,t.right=t.left+d}(C,e,r,n,g,o,s,l,f,u,h,v),!function(t){for(var e=0,r=t;e<r.length;e+=1)if(0!==r[e].positionedGlyphs.length)return!1;return!0}(L)&&C}ou.fromFeature=function(t,e){for(var r=new ou,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},ou.prototype.length=function(){return this.text.length},ou.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},ou.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},ou.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},ou.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e=\"\",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;n&&pi(n)&&!Tl[t[r+1]]||i&&pi(i)&&!Tl[t[r-1]]||!Tl[t[r]]?e+=t[r]:e+=Tl[t[r]]}return e}(this.text)},ou.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&lu[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&lu[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},ou.prototype.substring=function(t,e){var r=new ou;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},ou.prototype.toString=function(){return this.text},ou.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return Math.max(e,t.sections[r].scale)}),0)},ou.prototype.addTextSection=function(t,e){this.text+=t.text,this.sections.push(au.forText(t.scale,t.fontStack||e));for(var r=this.sections.length-1,n=0;n<t.text.length;++n)this.sectionIndex.push(r)},ou.prototype.addImageSection=function(t){var e=t.image?t.image.name:\"\";if(0!==e.length){var r=this.getNextImageSectionCharCode();r?(this.text+=String.fromCharCode(r),this.sections.push(au.forImage(e)),this.sectionIndex.push(this.sections.length-1)):w(\"Reached maximum number of images 6401\")}else w(\"Can't add FormattedSection with an empty image.\")},ou.prototype.getNextImageSectionCharCode=function(){return this.imageSectionID?this.imageSectionID>=63743?null:++this.imageSectionID:(this.imageSectionID=57344,this.imageSectionID)};var lu={9:!0,10:!0,11:!0,12:!0,13:!0,32:!0},uu={};function cu(t,e,r,n,i,a){if(e.imageName){var o=n[e.imageName];return o?o.displaySize[0]*e.scale*kl/a+i:0}var s=r[e.fontStack],l=s&&s[t];return l?l.metrics.advance*e.scale+i:0}function fu(t,e,r,n){var i=Math.pow(t-e,2);return n?t<e?i/2:2*i:i+Math.abs(r)*r}function hu(t,e,r){var n=0;return 10===t&&(n-=1e4),r&&(n+=150),40!==t&&65288!==t||(n+=50),41!==e&&65289!==e||(n+=50),n}function pu(t,e,r,n,i,a){for(var o=null,s=fu(e,r,i,a),l=0,u=n;l<u.length;l+=1){var c=u[l],f=fu(e-c.x,r,i,a)+c.badness;f<=s&&(o=c,s=f)}return{index:t,x:e,priorBreak:o,badness:s}}function du(t){return t?du(t.priorBreak).concat(t.index):[]}function vu(t,e,r,n,i,a,o){if(\"point\"!==a)return[];if(!t)return[];for(var s=[],l=function(t,e,r,n,i,a){for(var o=0,s=0;s<t.length();s++){var l=t.getSection(s);o+=cu(t.getCharCode(s),l,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,o),u=t.text.indexOf(\"​\")>=0,c=0,f=0;f<t.length();f++){var h=t.getSection(f),p=t.getCharCode(f);if(lu[p]||(c+=cu(p,h,n,i,e,o)),f<t.length()-1){var d=!((v=p)<11904||!(ci[\"Bopomofo Extended\"](v)||ci.Bopomofo(v)||ci[\"CJK Compatibility Forms\"](v)||ci[\"CJK Compatibility 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yu(t,e,r,n,i){if(n||i)for(var a=t[r],o=a.metrics.advance*a.scale,s=(t[r].x+o)*n,l=e;l<=r;l++)t[l].x-=s,t[l].y+=i}function mu(t,e,r,n,i,a){var o,s=t.image;if(s.content){var l=s.content,u=s.pixelRatio||1;o=[l[0]/u,l[1]/u,s.displaySize[0]-l[2]/u,s.displaySize[1]-l[3]/u]}var c,f,h,p,d=e.left*a,v=e.right*a;\"width\"===r||\"both\"===r?(p=i[0]+d-n[3],f=i[0]+v+n[1]):f=(p=i[0]+(d+v-s.displaySize[0])/2)+s.displaySize[0];var g=e.top*a,y=e.bottom*a;return\"height\"===r||\"both\"===r?(c=i[1]+g-n[0],h=i[1]+y+n[2]):h=(c=i[1]+(g+y-s.displaySize[1])/2)+s.displaySize[1],{image:s,top:c,right:f,bottom:h,left:p,collisionPadding:o}}uu[10]=!0,uu[32]=!0,uu[38]=!0,uu[40]=!0,uu[41]=!0,uu[43]=!0,uu[45]=!0,uu[47]=!0,uu[173]=!0,uu[183]=!0,uu[8203]=!0,uu[8208]=!0,uu[8211]=!0,uu[8231]=!0;var xu=function(t){function e(e,r,n,i){t.call(this,e,r),this.angle=n,void 0!==i&&(this.segment=i)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.clone=function(){return new 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Oa,this.collisionVertexArray=new ca};oc.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,xl.members,!0)},oc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},ni(\"CollisionBuffers\",oc);var sc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=Do([]),this.placementViewportMatrix=Do([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=_u(this.zoom,e[\"text-size\"]),this.iconSizeData=_u(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout,n=r.get(\"symbol-sort-key\"),i=r.get(\"symbol-z-order\");this.sortFeaturesByKey=\"viewport-y\"!==i&&void 0!==n.constantOr(1);var a=\"viewport-y\"===i||\"auto\"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get(\"text-allow-overlap\")||r.get(\"icon-allow-overlap\")||r.get(\"text-ignore-placement\")||r.get(\"icon-ignore-placement\")),\"point\"===r.get(\"symbol-placement\")&&(this.writingModes=r.get(\"text-writing-mode\").map((function(t){return nu[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};sc.prototype.createArrays=function(){this.text=new ac(new so(yl.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new ac(new so(yl.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new Sa,this.lineVertexArray=new Ea,this.symbolInstances=new Ma},sc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=Tl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},sc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get(\"text-font\"),o=i.get(\"text-field\"),s=i.get(\"icon-image\"),l=(\"constant\"!==o.value.kind||o.value.value instanceof ue&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&(\"constant\"!==a.value.kind||a.value.value.length>0),u=\"constant\"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,c=i.get(\"symbol-sort-key\");if(this.features=[],l||u){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new Pi(this.zoom),v=0,g=t;v<g.length;v+=1){var y=g[v],m=y.feature,x=y.id,b=y.index,_=y.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:m.type,id:x,properties:m.properties,geometry:w?po(m):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=po(m));var k=void 0;if(l){var A=n.getValueAndResolveTokens(\"text-field\",T,r,p),M=ue.factory(A);ic(M)&&(this.hasRTLText=!0),(!this.hasRTLText||\"unavailable\"===Ei()||this.hasRTLText&&Ci.isParsed())&&(k=wl(M,n,T))}var S=void 0;if(u){var E=n.getValueAndResolveTokens(\"icon-image\",T,r,p);S=E instanceof ce?E:ce.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?c.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:po(m),properties:m.properties,type:tc[m.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(\",\"),O=\"map\"===i.get(\"text-rotation-alignment\")&&\"point\"!==i.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(nu.vertical)>=0;for(var I=0,D=k.sections;I<D.length;I+=1){var z=D[I];if(z.image)f[z.image.name]=!0;else{var R=fi(k.toString()),F=z.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(z.text,B,O,this.allowVerticalPlacement,R)}}}}}}\"line\"===i.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}for(var u=0;u<t.length;u++){var c=t[u],f=c.geometry,h=c.text?c.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var v=s(p,d,f),g=o(p,d,n[v].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[v].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(u),e[p]=i-1,r[d]=i-1)}else a(u)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},sc.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))},sc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},sc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},sc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},sc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},sc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},sc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var u=a[l];this.lineVertexArray.emplaceBack(u.x,u.y,u.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},sc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,u,c,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),v=this.glyphOffsetArray.length,g=d.vertexLength,y=this.allowVerticalPlacement&&o===nu.vertical?Math.PI/2:0,m=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,O=b.sectionIndex,I=d.vertexLength,D=C[1];rc(p,s.x,s.y,_.x,D+_.y,A.x,A.y,r,P,M.x,M.y,E,L),rc(p,s.x,s.y,w.x,D+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),rc(p,s.x,s.y,T.x,D+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),rc(p,s.x,s.y,k.x,D+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),nc(t.dynamicLayoutVertexArray,s,y),h.emplaceBack(I,I+1,I+2),h.emplaceBack(I+1,I+2,I+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&O===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,m&&m[O])}t.placedSymbolArray.emplaceBack(s.x,s.y,v,this.glyphOffsetArray.length-v,g,l,u,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,c)},sc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},sc.prototype.addCollisionDebugVertices=function(t,e,r,n,i,o,s){var l=i.segments.prepareSegment(4,i.layoutVertexArray,i.indexArray),u=l.vertexLength,c=i.layoutVertexArray,f=i.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(c,f,o,h,p,new a(t,e)),this._addCollisionDebugVertex(c,f,o,h,p,new a(r,e)),this._addCollisionDebugVertex(c,f,o,h,p,new a(r,n)),this._addCollisionDebugVertex(c,f,o,h,p,new a(t,n)),l.vertexLength+=4;var d=i.indexArray;d.emplaceBack(u,u+1),d.emplaceBack(u+1,u+2),d.emplaceBack(u+2,u+3),d.emplaceBack(u+3,u),l.primitiveLength+=4},sc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,u=a.y2;this.addCollisionDebugVertices(o,s,l,u,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},sc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new oc(la,bl.members,ya),this.iconCollisionBox=new oc(la,bl.members,ya);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},sc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var u={},c=e;c<r;c++){var f=t.get(c);u.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},u.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);u.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},u.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var v=t.get(d);u.iconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},u.iconFeatureIndex=v.featureIndex;break}for(var g=s;g<l;g++){var y=t.get(g);u.verticalIconBox={x1:y.x1,y1:y.y1,x2:y.x2,y2:y.y2,anchorPointX:y.anchorPointX,anchorPointY:y.anchorPointY},u.verticalIconFeatureIndex=y.featureIndex;break}return u},sc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},sc.prototype.hasTextData=function(){return this.text.segments.get().length>0},sc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},sc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},sc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},sc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},sc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},sc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},sc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},sc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},ni(\"SymbolBucket\",sc,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),sc.MAX_GLYPHS=65535,sc.addDynamicAttributes=nc;var lc=new Gi({\"symbol-placement\":new ji(Dt.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new ji(Dt.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new ji(Dt.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new Ui(Dt.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new ji(Dt.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new ji(Dt.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new ji(Dt.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new ji(Dt.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new ji(Dt.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new Ui(Dt.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new ji(Dt.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new ji(Dt.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new Ui(Dt.layout_symbol[\"icon-image\"]),\"icon-rotate\":new Ui(Dt.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new ji(Dt.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new ji(Dt.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new Ui(Dt.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new Ui(Dt.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new ji(Dt.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new ji(Dt.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new ji(Dt.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new Ui(Dt.layout_symbol[\"text-field\"]),\"text-font\":new Ui(Dt.layout_symbol[\"text-font\"]),\"text-size\":new Ui(Dt.layout_symbol[\"text-size\"]),\"text-max-width\":new Ui(Dt.layout_symbol[\"text-max-width\"]),\"text-line-height\":new ji(Dt.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new Ui(Dt.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new Ui(Dt.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new Ui(Dt.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new ji(Dt.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new Ui(Dt.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new ji(Dt.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new ji(Dt.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new Ui(Dt.layout_symbol[\"text-rotate\"]),\"text-padding\":new ji(Dt.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new ji(Dt.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new Ui(Dt.layout_symbol[\"text-transform\"]),\"text-offset\":new Ui(Dt.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new ji(Dt.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new ji(Dt.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new ji(Dt.layout_symbol[\"text-optional\"])}),uc={paint:new Gi({\"icon-opacity\":new Ui(Dt.paint_symbol[\"icon-opacity\"]),\"icon-color\":new Ui(Dt.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new Ui(Dt.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new Ui(Dt.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new Ui(Dt.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new ji(Dt.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new ji(Dt.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new Ui(Dt.paint_symbol[\"text-opacity\"]),\"text-color\":new Ui(Dt.paint_symbol[\"text-color\"],{runtimeType:Zt,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new Ui(Dt.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new Ui(Dt.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new Ui(Dt.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new ji(Dt.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new ji(Dt.paint_symbol[\"text-translate-anchor\"])}),layout:lc},cc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Vt,this.defaultValue=t};cc.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},cc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},cc.prototype.outputDefined=function(){return!1},cc.prototype.serialize=function(){return null},ni(\"FormatSectionOverride\",cc,{omit:[\"defaultValue\"]});var fc=function(t){function e(e){t.call(this,e,uc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values[\"text-writing-mode\"]=i}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||sn(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):\"\"}))}(e.properties,i)},e.prototype.createBucket=function(t){return new sc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=uc.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i,a=this.paint.get(n),o=new cc(a),s=new on(o,a.property.specification);i=\"constant\"===a.value.kind||\"source\"===a.value.kind?new un(\"source\",s):new cn(\"composite\",s,a.value.zoomStops,a.value._interpolationType),this.paint._values[n]=new Bi(a.property,i,a.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get(\"text-field\"),n=uc.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if(\"constant\"===r.value.kind&&r.value.value instanceof ue)a(r.value.value.sections);else if(\"source\"===r.value.kind){var o=function(t){if(!i)if(t instanceof ve&&pe(t.value)===Jt){var e=t.value;a(e.sections)}else t instanceof xe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Yi),hc={paint:new Gi({\"background-color\":new ji(Dt.paint_background[\"background-color\"]),\"background-pattern\":new Hi(Dt.paint_background[\"background-pattern\"]),\"background-opacity\":new ji(Dt.paint_background[\"background-opacity\"])})},pc=function(t){function e(e){t.call(this,e,hc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Yi),dc={paint:new Gi({\"raster-opacity\":new ji(Dt.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new ji(Dt.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new ji(Dt.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new ji(Dt.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new ji(Dt.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new ji(Dt.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new ji(Dt.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new ji(Dt.paint_raster[\"raster-fade-duration\"])})},vc=function(t){function e(e){t.call(this,e,dc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Yi);var gc=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}(Yi),yc={circle:Uo,heatmap:Ko,hillshade:Qo,fill:js,\"fill-extrusion\":nl,line:vl,symbol:fc,background:pc,raster:vc};var mc=self.HTMLImageElement,xc=self.HTMLCanvasElement,bc=self.HTMLVideoElement,_c=self.ImageData,wc=self.ImageBitmap,Tc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Tc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof mc||t instanceof xc||t instanceof bc||t instanceof _c||wc&&t instanceof wc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},u=l.x,c=l.y;t instanceof mc||t instanceof xc||t instanceof bc||t instanceof _c||wc&&t instanceof wc?s.texSubImage2D(s.TEXTURE_2D,0,u,c,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,u,c,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Tc.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)},Tc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Tc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var kc=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()})};kc.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))},kc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var Ac=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},g([\"receive\",\"process\"],this),this.invoker=new kc(this.process),this.target.addEventListener(\"message\",this.receive,!1),this.globalScope=A()?t:self};function Mc(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]}Ac.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=E(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:si(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:\"<cancel>\",targetMapId:n,sourceMapId:a.mapId})}}},Ac.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 A()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},Ac.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},Ac.prototype.processTask=function(t,e){var r=this;if(\"<response>\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(li(e.error)):n(null,li(e.data)))}else{var i=!1,a=E(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"<response>\",sourceMapId:r.mapId,error:e?si(e):null,data:si(n,a)},a)}:function(t){i=!0},s=null,l=li(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var u=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,u[0],l.source)[u[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},Ac.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Sc=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]))};Sc.prototype.setNorthEast=function(t){return this._ne=t instanceof Lc?new Lc(t.lng,t.lat):Lc.convert(t),this},Sc.prototype.setSouthWest=function(t){return this._sw=t instanceof Lc?new Lc(t.lng,t.lat):Lc.convert(t),this},Sc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Lc)e=t,r=t;else{if(!(t instanceof Sc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Sc.convert(a))}var o=t;return this.extend(Lc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Lc(e.lng,e.lat),this._ne=new Lc(r.lng,r.lat)),this},Sc.prototype.getCenter=function(){return new Lc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Sc.prototype.getSouthWest=function(){return this._sw},Sc.prototype.getNorthEast=function(){return this._ne},Sc.prototype.getNorthWest=function(){return new Lc(this.getWest(),this.getNorth())},Sc.prototype.getSouthEast=function(){return new Lc(this.getEast(),this.getSouth())},Sc.prototype.getWest=function(){return this._sw.lng},Sc.prototype.getSouth=function(){return this._sw.lat},Sc.prototype.getEast=function(){return this._ne.lng},Sc.prototype.getNorth=function(){return this._ne.lat},Sc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Sc.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Sc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Sc.prototype.contains=function(t){var e=Lc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Sc.convert=function(t){return!t||t instanceof Sc?t:new Sc(t)};var Ec=6371008.8,Lc=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\")};Lc.prototype.wrap=function(){return new Lc(c(this.lng,-180,180),this.lat)},Lc.prototype.toArray=function(){return[this.lng,this.lat]},Lc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Lc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return Ec*Math.acos(Math.min(i,1))},Lc.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 Sc(new Lc(this.lng-r,this.lat-e),new Lc(this.lng+r,this.lat+e))},Lc.convert=function(t){if(t instanceof Lc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Lc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Lc(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 Cc=2*Math.PI*Ec;function Pc(t){return Cc*Math.cos(t*Math.PI/180)}function Oc(t){return(180+t)/360}function Ic(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Dc(t,e){return t/Pc(e)}function zc(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var Rc=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};Rc.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Lc.convert(t);return new Rc(Oc(r.lng),Ic(r.lat),Dc(e,r.lat))},Rc.prototype.toLngLat=function(){return new Lc(360*this.x-180,zc(this.y))},Rc.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Pc(zc(e));var t,e},Rc.prototype.meterInMercatorCoordinateUnits=function(){return 1/Cc*(t=zc(this.y),1/Math.cos(t*Math.PI/180));var t};var Fc=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=jc(0,t,t,e,r)};Fc.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},Fc.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Mc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Mc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",String(this.z)).replace(\"{x}\",String(this.x)).replace(\"{y}\",String(\"tms\"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace(\"{quadkey}\",l).replace(\"{bbox-epsg-3857}\",s)},Fc.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new a((t.x*e-this.x)*co,(t.y*e-this.y)*co)},Fc.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y};var Bc=function(t,e){this.wrap=t,this.canonical=e,this.key=jc(t,e.z,e.z,e.x,e.y)},Nc=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new Fc(r,+n,+i),this.key=jc(e,t,r,n,i)};function jc(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}Nc.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},Nc.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new Nc(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Nc(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},Nc.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?jc(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):jc(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},Nc.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},Nc.prototype.children=function(t){if(this.overscaledZ>=t)return[new Nc(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 Nc(e,this.wrap,e,r,n),new Nc(e,this.wrap,e,r+1,n),new Nc(e,this.wrap,e,r,n+1),new Nc(e,this.wrap,e,r+1,n+1)]},Nc.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))},Nc.prototype.wrapped=function(){return new Nc(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},Nc.prototype.unwrapTo=function(t){return new Nc(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},Nc.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},Nc.prototype.toUnwrapped=function(){return new Bc(this.wrap,this.canonical)},Nc.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},Nc.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new Rc(t.x-this.wrap,t.y))},ni(\"CanonicalTileID\",Fc),ni(\"OverscaledTileID\",Nc,{omit:[\"posMatrix\"]});var Uc=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\".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||\"mapbox\";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};Uc.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return(\"terrarium\"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},Uc.prototype.getUnpackVector=function(){return\"terrarium\"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},Uc.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)},Uc.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},Uc.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},Uc.prototype.getPixels=function(){return new Wo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},Uc.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,u=a;u<o;u++)for(var c=n;c<i;c++)this.data[this._idx(c,u)]=t.data[this._idx(c+s,u+l)]},ni(\"DEMData\",Uc);var Vc=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}};Vc.prototype.encode=function(t){return this._stringToNumber[t]},Vc.prototype.decode=function(t){return this._numberToString[t]};var Hc=function(t,e,r,n,i){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},qc={geometry:{configurable:!0}};qc.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},qc.geometry.set=function(t){this._geometry=t},Hc.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(Hc.prototype,qc);var Gc=function(){this.state={},this.stateChanges={},this.deletedStates={}};Gc.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]||{},f(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r)this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]},Gc.prototype.removeFeatureState=function(t,e,r){if(null!==this.deletedStates[t]){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e)if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null;else this.deletedStates[t]=null}},Gc.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=f({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},Gc.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},Gc.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),f(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var u in this.deletedStates[o]){if(null===this.deletedStates[o][u])this.state[o][u]={};else for(var c=0,h=Object.keys(this.deletedStates[o][u]);c<h.length;c+=1){var p=h[c];delete this.state[o][u][p]}s[u]=this.state[o][u]}r[o]=r[o]||{},f(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t)t[d].setFeatureState(r,e)};var Zc=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new Kn(co,16,0),this.grid3D=new Kn(co,16,0),this.featureIndexArray=new Ca,this.promoteId=e};function Yc(t,e,r,n,i){return m(t,(function(t,a){var o=e instanceof Ni?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function Wc(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function Xc(t,e){return e-t}Zc.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var u=e[l],c=[1/0,1/0,-1/0,-1/0],f=0;f<u.length;f++){var h=u[f];c[0]=Math.min(c[0],h.x),c[1]=Math.min(c[1],h.y),c[2]=Math.max(c[2],h.x),c[3]=Math.max(c[3],h.y)}c[0]<co&&c[1]<co&&c[2]>=0&&c[3]>=0&&s.insert(o,c[0],c[1],c[2],c[3])}},Zc.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Js.VectorTile(new Sl(this.rawTileData)).layers,this.sourceLayerCoder=new Vc(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},Zc.prototype.query=function(t,e,r,n){var i=this;this.loadVTLayers();for(var o=t.params||{},s=co/t.tileSize/t.scale,l=wn(o.filter),u=t.queryGeometry,c=t.queryPadding*s,f=Wc(u),h=this.grid.query(f.minX-c,f.minY-c,f.maxX+c,f.maxY+c),p=Wc(t.cameraQueryGeometry),d=0,v=this.grid3D.query(p.minX-c,p.minY-c,p.maxX+c,p.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 u=[new a(e,r),new a(e,i),new a(n,i),new a(n,r)];if(t.length>2)for(var c=0,f=u;c<f.length;c+=1)if(Mo(t,f[c]))return!0;for(var h=0;h<t.length-1;h++)if(So(t[h],t[h+1],u))return!0;return!1}(t.cameraQueryGeometry,e-c,r-c,n+c,i+c)}));d<v.length;d+=1){var g=v[d];h.push(g)}h.sort(Xc);for(var y,m={},x=function(a){var c=h[a];if(c!==y){y=c;var f=i.featureIndexArray.get(c),p=null;i.loadMatchingFeature(m,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=po(e)),r.queryIntersectsFeature(u,e,n,p,i.z,t.transform,s,t.pixelPosMatrix)}))}},b=0;b<h.length;b++)x(b);return m},Zc.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,u,c){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new Pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),v=0;v<f.length;v++){var g=f[v];if(!(a&&a.indexOf(g)<0)){var y=s[g];if(y){var m={};void 0!==d&&u&&(m=u.getState(y.sourceLayer||\"_geojsonTileLayer\",d));var x=l[g];x.paint=Yc(x.paint,y.paint,p,m,o),x.layout=Yc(x.layout,y.layout,p,m,o);var b=!c||c(p,y,m);if(b){var _=new Hc(p,this.z,this.x,this.y,d);_.layer=x;var w=t[g];void 0===w&&(w=t[g]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},Zc.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var u=wn(i),c=0,f=t;c<f.length;c+=1){var h=f[c];this.loadMatchingFeature(l,r,n,h,u,a,o,s,e)}return l},Zc.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1)if(t===i[n])return!0;return!1},Zc.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n=\"string\"==typeof this.promoteId?this.promoteId:this.promoteId[e];\"boolean\"==typeof(r=t.properties[n])&&(r=Number(r))}return r},ni(\"FeatureIndex\",Zc,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});var Jc=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.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};Jc.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<F.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},Jc.prototype.wasRequested=function(){return\"errored\"===this.state||\"loaded\"===this.state||\"reloading\"===this.state},Jc.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof sc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof sc&&o.hasRTLText){this.hasRTLText=!0,Ci.isLoading()||Ci.isLoaded()||\"deferred\"!==Ei()||Li();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new wa},Jc.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\"},Jc.prototype.getBucket=function(t){return this.buckets[t.id]},Jc.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 Tc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Tc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},Jc.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},Jc.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,u){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:u,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},Jc.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:\"\",a=n._geojsonTileLayer||n[i];if(a)for(var o=wn(e&&e.filter),s=this.tileID.canonical,l=s.z,u=s.x,c=s.y,f={z:l,x:u,y:c},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new Pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),v=new Hc(p,l,u,c,d);v.tile=f,t.push(v)}}}},Jc.prototype.hasData=function(){return\"loaded\"===this.state||\"reloading\"===this.state||\"expired\"===this.state},Jc.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},Jc.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=M(t.cacheControl);r[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*r[\"max-age\"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},Jc.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)},Jc.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||\"_geojsonTileLayer\",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},Jc.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},Jc.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<F.now()},Jc.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},Jc.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=F.now()+t},Jc.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1)r[i[n]]=!0;this.dependencies[t]=r},Jc.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1)if(a[s[o]])return!0}return!1};var Kc=self.performance,$c=function(t){this._marks={start:[t.url,\"start\"].join(\"#\"),end:[t.url,\"end\"].join(\"#\"),measure:t.url.toString()},Kc.mark(this._marks.start)};$c.prototype.finish=function(){Kc.mark(this._marks.end);var t=Kc.getEntriesByName(this._marks.measure);return 0===t.length&&(Kc.measure(this._marks.measure,this._marks.start,this._marks.end),t=Kc.getEntriesByName(this._marks.measure),Kc.clearMarks(this._marks.start),Kc.clearMarks(this._marks.end),Kc.clearMeasures(this._marks.measure)),t},t.Actor=Ac,t.AlphaImage=Yo,t.CanonicalTileID=Fc,t.CollisionBoxArray=wa,t.Color=oe,t.DEMData=Uc,t.DataConstantProperty=ji,t.DictionaryCoder=Vc,t.EXTENT=co,t.ErrorEvent=Ot,t.EvaluationParameters=Pi,t.Event=Pt,t.Evented=It,t.FeatureIndex=Zc,t.FillBucket=Fs,t.FillExtrusionBucket=tl,t.ImageAtlas=ru,t.ImagePosition=tu,t.LineBucket=cl,t.LngLat=Lc,t.LngLatBounds=Sc,t.MercatorCoordinate=Rc,t.ONE_EM=kl,t.OverscaledTileID=Nc,t.Point=a,t.Point$1=a,t.Properties=Gi,t.Protobuf=Sl,t.RGBAImage=Wo,t.RequestManager=q,t.RequestPerformance=$c,t.ResourceType=xt,t.SegmentVector=Oa,t.SourceFeatureState=Gc,t.StructArrayLayout1ui2=ma,t.StructArrayLayout2f1f2i16=ua,t.StructArrayLayout2i4=Qi,t.StructArrayLayout3ui6=fa,t.StructArrayLayout4i8=ta,t.SymbolBucket=sc,t.Texture=Tc,t.Tile=Jc,t.Transitionable=Di,t.Uniform1f=Ya,t.Uniform1i=Za,t.Uniform2f=Wa,t.Uniform3f=Xa,t.Uniform4f=Ja,t.UniformColor=Ka,t.UniformMatrix4f=Qa,t.UnwrappedTileID=Bc,t.ValidationError=zt,t.WritingMode=nu,t.ZoomHistory=ui,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=nc,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=s,t.bindAll=g,t.browser=F,t.cacheEntryPossiblyAdded=function(t){++yt>ct&&(t.getActor().send(\"enforceCacheSizeLimit\",ut),yt=0)},t.clamp=u,t.clearTileCache=function(t){var e=self.caches.delete(lt);t&&e.catch(t).then((function(){return t()}))},t.clipLine=Ou,t.clone=function(t){var e=new Io(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=b,t.clone$2=function(t){var e=new Io(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=_l,t.config=B,t.create=function(){var t=new Io(16);return Io!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0),t[0]=1,t[5]=1,t[10]=1,t[15]=1,t},t.create$1=function(){var t=new Io(9);return Io!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0),t[0]=1,t[4]=1,t[8]=1,t},t.create$2=function(){var t=new Io(4);return Io!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t},t.createCommonjsModule=e,t.createExpression=ln,t.createLayout=Ki,t.createStyleLayer=function(t){return\"custom\"===t.type?new gc(t):new yc[t.type](t)},t.cross=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t},t.deepEqual=function t(e,r){if(Array.isArray(e)){if(!Array.isArray(r)||e.length!==r.length)return!1;for(var n=0;n<e.length;n++)if(!t(e[n],r[n]))return!1;return!0}if(\"object\"==typeof e&&null!==e&&null!==r){if(\"object\"!=typeof r)return!1;if(Object.keys(e).length!==Object.keys(r).length)return!1;for(var i in e)if(!t(e[i],r[i]))return!1;return!0}return e===r},t.dot=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]},t.dot$1=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]+t[3]*e[3]},t.ease=l,t.emitValidationErrors=Jn,t.endsWith=y,t.enforceCacheSizeLimit=function(t){ht(),tt&&tt.then((function(e){e.keys().then((function(r){for(var n=0;n<r.length-t;n++)e.delete(r[n])}))}))},t.evaluateSizeForFeature=wu,t.evaluateSizeForZoom=Tu,t.evaluateVariableOffset=Yu,t.evented=Si,t.extend=f,t.featureFilter=wn,t.filterObject=x,t.fromRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=-r,t[4]=n,t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},t.getAnchorAlignment=gu,t.getAnchorJustification=Wu,t.getArrayBuffer=kt,t.getImage=Et,t.getJSON=function(t,e){return Tt(f(t,{type:\"json\"}),e)},t.getRTLTextPluginStatus=Ei,t.getReferrer=_t,t.getVideo=function(t,e){var r,n,i=self.document.createElement(\"video\");i.muted=!0,i.onloadstart=function(){e(null,i)};for(var a=0;a<t.length;a++){var 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c={},e.tags)c[f]=e.tags[f];c.mapbox_clip_start=i.start/i.size,c.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:\"Polygon\"===a||\"MultiPolygon\"===a?3:\"LineString\"===a||\"MultiLineString\"===a?2:1,tags:c};null!==e.id&&(h.id=e.id),t.features.push(h)}}function wt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function Tt(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++)rt(r,t.features[n],e,n);else\"Feature\"===t.type?rt(r,t,e):rt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",e.indexMaxZoom,e.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=lt(t,1,-1-r,r,0,-1,2,e),a=lt(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=lt(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=gt(i,1).concat(n)),a&&(n=n.concat(gt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}function kt(t,e,r){return 32*((1<<t)*r+e)+t}function At(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}V.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time(\"total time\");var o=\"prepare \"+t.length+\" points\";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(q(t[l],l));this.trees[i+1]=new j(s,J,K,a,Float32Array),r&&console.timeEnd(o);for(var u=i;u>=n;u--){var c=+Date.now();s=this._cluster(s,u),this.trees[u]=new j(s,J,K,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",u,s.length,+Date.now()-c)}return r&&console.timeEnd(\"total time\"),this},V.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],u=[],c=0,f=l.range(Y(r),W(a),Y(i),W(n));c<f.length;c+=1){var h=f[c],p=l.points[h];u.push(p.numPoints?G(p):this.points[p.index])}return u},V.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n=\"No cluster with the specified id.\",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,u=i.within(a.x,a.y,o);l<u.length;l+=1){var c=u[l],f=i.points[c];f.parentId===t&&s.push(f.numPoints?G(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},V.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},V.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,u=(r+1+s)/i,c={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,u),n.points,e,r,i,c),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,u),n.points,i,r,i,c),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,u),n.points,-1,r,i,c),c.features.length?c:null},V.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},V.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},V.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],u=l.numPoints,c={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:u?Z(l):this.points[l.index].properties},f=void 0;u?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(c.id=f),a.features.push(c)}},V.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},V.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var u=t[l];if(!(u.zoom<=e)){u.zoom=e;for(var c=this.trees[e+1],f=c.within(u.x,u.y,s),h=u.numPoints||1,p=u.x*h,d=u.y*h,v=o&&h>1?this._map(u,!0):null,g=(l<<5)+(e+1)+this.points.length,y=0,m=f;y<m.length;y+=1){var x=m[y],b=c.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=g,o&&(v||(v=this._map(u,!0)),o(v,this._map(b)))}}1===h?r.push(u):(u.parentId=g,r.push(H(p/h,d/h,g,h,v)))}}return r},V.prototype._getOriginId=function(t){return t-this.points.length>>5},V.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},V.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},Tt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},Tt.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,u=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var c=1<<e,f=kt(e,r,n),h=this.tiles[f];if(!h&&(u>1&&console.time(\"creation\"),h=this.tiles[f]=bt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),u)){u>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){u>1&&console.time(\"clipping\");var v,g,y,m,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;v=g=y=m=null,x=lt(t,c,r-_,r+T,0,h.minX,h.maxX,l),b=lt(t,c,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(v=lt(x,c,n-_,n+T,1,h.minY,h.maxY,l),g=lt(x,c,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(y=lt(b,c,n-_,n+T,1,h.minY,h.maxY,l),m=lt(b,c,n+w,n+k,1,h.minY,h.maxY,l),b=null),u>1&&console.timeEnd(\"clipping\"),s.push(v||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(y||[],e+1,2*r+1,2*n),s.push(m||[],e+1,2*r+1,2*n+1)}}},Tt.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=kt(t,e=(e%o+o)%o,r);if(this.tiles[s])return mt(this.tiles[s],i);a>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,u=t,c=e,f=r;!l&&u>0;)u--,c=Math.floor(c/2),f=Math.floor(f/2),l=this.tiles[kt(u,c,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",u,c,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,u,c,f,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?mt(this.tiles[s],i):null):null};var Mt=function(e){function r(t,r,n,i){e.call(this,t,r,n,At),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));f(o,!0);try{e._geoJSONIndex=n.cluster?new V(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),u=0,c=l;u<c.length;u+=1){var f=c[u],h=n[f],p=h[0],d=h[1],v=t.createExpression(d),g=t.createExpression(\"string\"==typeof p?[p,[\"accumulated\"],[\"get\",f]]:p);i[f]=v.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new Tt(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){\"Coalescing\"===this._state?this._state=\"Idle\":\"NeedsLoadData\"===this._state&&(this._state=\"Coalescing\",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l);var St=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:Mt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name \"'+t+'\" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error(\"RTL text plugin already registered.\");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return St.prototype.setReferrer=function(t,e){this.referrer=e},St.prototype.setImages=function(t,e,r){for(var n in this.availableImages[t]=e,this.workerSources[t]){var i=this.workerSources[t][n];for(var a in i)i[a].availableImages=e}r()},St.prototype.setLayers=function(t,e,r){this.getLayerIndex(t).replace(e),r()},St.prototype.updateLayers=function(t,e,r){this.getLayerIndex(t).update(e.layers,e.removedIds),r()},St.prototype.loadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).loadTile(e,r)},St.prototype.loadDEMTile=function(t,e,r){this.getDEMWorkerSource(t,e.source).loadTile(e,r)},St.prototype.reloadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).reloadTile(e,r)},St.prototype.abortTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).abortTile(e,r)},St.prototype.removeTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).removeTile(e,r)},St.prototype.removeDEMTile=function(t,e){this.getDEMWorkerSource(t,e.source).removeTile(e)},St.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()}},St.prototype.loadWorkerSource=function(t,e,r){try{this.self.importScripts(e.url),r()}catch(t){r(t.toString())}},St.prototype.syncRTLPluginState=function(e,r,n){try{t.plugin.setState(r);var i=t.plugin.getPluginURL();if(t.plugin.isLoaded()&&!t.plugin.isParsed()&&null!=i){this.self.importScripts(i);var a=t.plugin.isParsed();n(a?void 0:new Error(\"RTL Text Plugin failed to import scripts from \"+i),a)}}catch(t){n(t.toString())}},St.prototype.getAvailableImages=function(t){var e=this.availableImages[t];return e||(e=[]),e},St.prototype.getLayerIndex=function(t){var e=this.layerIndexes[t];return e||(e=this.layerIndexes[t]=new n),e},St.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 i={send:function(e,r,i){n.actor.send(e,r,i,t)}};this.workerSources[t][e][r]=new this.workerSourceTypes[e](i,this.getLayerIndex(t),this.getAvailableImages(t))}return this.workerSources[t][e][r]},St.prototype.getDEMWorkerSource=function(t,e){return this.demWorkerSources[t]||(this.demWorkerSources[t]={}),this.demWorkerSources[t][e]||(this.demWorkerSources[t][e]=new c),this.demWorkerSources[t][e]},St.prototype.enforceCacheSizeLimit=function(e,r){t.enforceCacheSizeLimit(r)},\"undefined\"!=typeof WorkerGlobalScope&&void 0!==t.window&&t.window instanceof WorkerGlobalScope&&(t.window.worker=new St(t.window)),St})),n(0,(function(t){var e=t.createCommonjsModule((function(t){function e(t){return!r(t)}function r(t){return\"undefined\"!=typeof window&&\"undefined\"!=typeof 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t=document.createElement(\"canvas\");t.width=t.height=1;var e=t.getContext(\"2d\");if(!e)return!1;var r=e.getImageData(0,0,1,1);return r&&r.width===t.width}()?(r=t&&t.failIfMajorPerformanceCaveat,void 0===n[r]&&(n[r]=function(t){var r=function(t){var r=document.createElement(\"canvas\"),n=Object.create(e.webGLContextAttributes);return n.failIfMajorPerformanceCaveat=t,r.probablySupportsContext?r.probablySupportsContext(\"webgl\",n)||r.probablySupportsContext(\"experimental-webgl\",n):r.supportsContext?r.supportsContext(\"webgl\",n)||r.supportsContext(\"experimental-webgl\",n):r.getContext(\"webgl\",n)||r.getContext(\"experimental-webgl\",n)}(t);if(!r)return!1;var n=r.createShader(r.VERTEX_SHADER);return!(!n||r.isContextLost())&&(r.shaderSource(n,\"void main() {}\"),r.compileShader(n),!0===r.getShaderParameter(n,r.COMPILE_STATUS))}(r)),n[r]?void 0:\"insufficient WebGL support\"):\"insufficient Canvas/getImageData support\":\"insufficient ArrayBuffer support\":\"insufficient 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s=i([\"transform\",\"WebkitTransform\"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var u=Object.defineProperty({},\"passive\",{get:function(){l=!0}});t.window.addEventListener(\"test\",u,u),t.window.removeEventListener(\"test\",u,u)}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 c=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener(\"click\",c,!0)};function f(t){var e=t.userImage;return!!(e&&e.render&&e.render())&&(t.data.replace(new Uint8Array(e.data.buffer)),!0)}r.suppressClick=function(){t.window.addEventListener(\"click\",c,!0),t.window.setTimeout((function(){t.window.removeEventListener(\"click\",c,!0)}),0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect();return new 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n=r[e],i=n.ids,a=n.callback;this._notify(i,a)}this.requestors=[]}},r.prototype.getImage=function(t){return this.images[t]},r.prototype.addImage=function(t,e){this._validate(t,e)&&(this.images[t]=e)},r.prototype._validate=function(e,r){var n=!0;return this._validateStretch(r.stretchX,r.data&&r.data.width)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchX\" value'))),n=!1),this._validateStretch(r.stretchY,r.data&&r.data.height)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchY\" value'))),n=!1),this._validateContent(r.content,r)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"content\" value'))),n=!1),n},r.prototype._validateStretch=function(t,e){if(!t)return!0;for(var r=0,n=0,i=t;n<i.length;n+=1){var 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s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image \"'+o+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,u=l.x+1,c=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:u,y:c},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:u,y:c-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:u,y:c+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:u-1,y:c},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:u+h,y:c},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=g,d=g,v=1e20;function g(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||\"sans-serif\",this.fontWeight=a||\"normal\",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}function y(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(m(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(m(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function m(t,e,r,n,i){r[0]=0,n[0]=-v,n[1]=+v;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+v}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}g.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?v:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?v:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(y(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),y(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1)(0,o[i])(t,e);delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,u=o.glyph;(n[s]||(n[s]={}))[l]=u&&{id:u.id,bitmap:u.bitmap.clone(),metrics:u.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar[\"CJK Unified Ideographs\"](e)||t.isChar[\"Hangul Syllables\"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=\"-transition\",T=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,w)?this._transitionable.setTransition(n.slice(0,-11),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),k=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};k.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},k.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var u=t[l];i=s*r,a=(s+=u)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===u})}return n},k.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],u=0;u<this.width;u++){u/l.right>1&&(l=t[++s]);var c=Math.abs(u-l.left),f=Math.abs(u-l.right),h=Math.min(c,f),p=void 0,d=i/r*(n+1);if(l.isDash){var v=n-Math.abs(d);p=Math.sqrt(h*h+v*v)}else p=n-Math.sqrt(h*h+d*d);this.data[o+u]=Math.max(0,Math.min(255,p+128))}},k.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],u=0;u<this.width;u++){u/l.right>1&&(l=t[++s]);var c=Math.abs(u-l.left),f=Math.abs(u-l.right),h=Math.min(c,f),p=l.isDash?h:-h;this.data[o+u]=Math.max(0,Math.min(255,p+128))}},k.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var u={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,u},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.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var A=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name=\"Worker \"+a,this.actors.push(s)}};function M(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}A.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},A.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},A.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},A.Actor=t.Actor;var S=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};S.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]},S.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var E=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=t.extend({type:\"vector\"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new S(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&\"expired\"!==e.state?\"loading\"===e.state?e.reloadCallback=r:e.request=e.actor.send(\"reloadTile\",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send(\"loadTile\",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send(\"abortTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:\"raster\"},n),t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new S(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(i)e.state=\"errored\",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state=\"loaded\",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),C=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type=\"raster-dem\",this.maxzoom=22,this._options=t.extend({type:\"raster-dem\"},n),this.encoding=n.encoding||\"mapbox\"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(n)e.state=\"errored\",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&\"expired\"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send(\"loadDEMTile\",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state=\"unloaded\",t.actor&&t.actor.send(\"removeDEMTile\",{uid:t.uid,source:this.id})},r}(L),P=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:\"source\",sourceDataType:\"metadata\"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),O=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var u=i-r,c=a-n,f=Math.max(u,c),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state=\"errored\",e(null))},r.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var D=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type=\"video\",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener(\"playing\",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(I),R={vector:E,raster:L,\"raster-dem\":C,geojson:P,video:D,image:I,canvas:z};function F(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function B(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&\"fill-extrusion\"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&\"fill-extrusion\"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(N);for(var u=[],c=0,f=l;c<f.length;c+=1){var h=f[c];u.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,F(t.transform,h.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var u in o)for(var c=o[u],f=l[u]=l[u]||{},h=e[u]=e[u]||[],p=0,d=c;p<d.length;p+=1){var v=d[p];f[v.featureIndex]||(f[v.featureIndex]=!0,h.push(v))}}return e}(u);for(var d in p)p[d].forEach((function(e){var r=e.feature,n=t.getFeatureState(r.layer[\"source-layer\"],r.id);r.source=r.layer.source,r.layer[\"source-layer\"]&&(r.sourceLayer=r.layer[\"source-layer\"]),r.state=n}));return p}function N(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}var j=function(t,e){this.max=t,this.onRemove=e,this.reset()};j.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},j.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout((function(){n.remove(t,a)}),r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},j.prototype.has=function(t){return t.wrapped().key in this.data},j.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},j.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},j.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},j.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},j.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},j.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},j.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n<i.length;n+=1){var a=i[n];t(a.value)||e.push(a)}for(var o=0,s=e;o<s.length;o+=1){var l=s[o];this.remove(l.value.tileID,l)}};var U=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};U.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},U.prototype.updateData=function(t){var e=this.context.gl;this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},U.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var V={Int8:\"BYTE\",Uint8:\"UNSIGNED_BYTE\",Int16:\"SHORT\",Uint16:\"UNSIGNED_SHORT\",Int32:\"INT\",Uint32:\"UNSIGNED_INT\",Float32:\"FLOAT\"},H=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};H.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},H.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},H.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=this.attributes[r],i=e.attributes[n.name];void 0!==i&&t.enableVertexAttribArray(i)}},H.prototype.setVertexAttribPointers=function(t,e,r){for(var n=0;n<this.attributes.length;n++){var i=this.attributes[n],a=e.attributes[i.name];void 0!==a&&t.vertexAttribPointer(a,i.components,t[V[i.type]],!1,this.itemSize,i.offset+this.itemSize*(r||0))}},H.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var q=function(t){this.gl=t.gl,this.default=this.getDefault(),this.current=this.default,this.dirty=!1};q.prototype.get=function(){return this.current},q.prototype.set=function(t){},q.prototype.getDefault=function(){return this.default},q.prototype.setDefault=function(){this.set(this.default)};var G=function(e){function r(){e.apply(this,arguments)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getDefault=function(){return t.Color.transparent},r.prototype.set=function(t){var e=this.current;(t.r!==e.r||t.g!==e.g||t.b!==e.b||t.a!==e.a||this.dirty)&&(this.gl.clearColor(t.r,t.g,t.b,t.a),this.current=t,this.dirty=!1)},r}(q),Z=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){(t!==this.current||this.dirty)&&(this.gl.clearDepth(t),this.current=t,this.dirty=!1)},e}(q),Y=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 0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.clearStencil(t),this.current=t,this.dirty=!1)},e}(q),W=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[!0,!0,!0,!0]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||t[3]!==e[3]||this.dirty)&&(this.gl.colorMask(t[0],t[1],t[2],t[3]),this.current=t,this.dirty=!1)},e}(q),X=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!0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.depthMask(t),this.current=t,this.dirty=!1)},e}(q),J=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 255},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.stencilMask(t),this.current=t,this.dirty=!1)},e}(q),K=function(t){function e(){t.apply(this,arguments)}return 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t=this.gl;return[t.ONE,t.ZERO]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||this.dirty)&&(this.gl.blendFunc(t[0],t[1]),this.current=t,this.dirty=!1)},e}(q),at=function(e){function r(){e.apply(this,arguments)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getDefault=function(){return t.Color.transparent},r.prototype.set=function(t){var e=this.current;(t.r!==e.r||t.g!==e.g||t.b!==e.b||t.a!==e.a||this.dirty)&&(this.gl.blendColor(t.r,t.g,t.b,t.a),this.current=t,this.dirty=!1)},r}(q),ot=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 this.gl.FUNC_ADD},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.blendEquation(t),this.current=t,this.dirty=!1)},e}(q),st=function(t){function e(){t.apply(this,arguments)}return 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e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||t[3]!==e[3]||this.dirty)&&(this.gl.viewport(t[0],t[1],t[2],t[3]),this.current=t,this.dirty=!1)},e}(q),pt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindFramebuffer(e.FRAMEBUFFER,t),this.current=t,this.dirty=!1}},e}(q),dt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindRenderbuffer(e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(q),vt=function(t){function e(){t.apply(this,arguments)}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),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 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),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_PREMULTIPLY_ALPHA_WEBGL,t),this.current=t,this.dirty=!1}},e}(q),_t=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),wt=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),Tt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.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}(wt),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}(wt),At=function(t,e,r,n){this.context=t,this.width=e,this.height=r;var i=t.gl,a=this.framebuffer=i.createFramebuffer();this.colorAttachment=new Tt(t,a),n&&(this.depthAttachment=new kt(t,a))};At.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r)}t.deleteFramebuffer(this.framebuffer)};var Mt=function(t,e,r){this.func=t,this.mask=e,this.range=r};Mt.ReadOnly=!1,Mt.ReadWrite=!0,Mt.disabled=new Mt(519,Mt.ReadOnly,[0,1]);var St=7680,Et=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Et.disabled=new Et({func:519,mask:0},0,0,St,St,St);var Lt=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};Lt.Replace=[1,0],Lt.disabled=new Lt(Lt.Replace,t.Color.transparent,[!1,!1,!1,!1]),Lt.unblended=new Lt(Lt.Replace,t.Color.transparent,[!0,!0,!0,!0]),Lt.alphaBlended=new Lt([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Ct=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Ct.disabled=new Ct(!1,1029,2305),Ct.backCCW=new Ct(!0,1029,2305);var Pt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.clearColor=new G(this),this.clearDepth=new Z(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new J(this),this.stencilFunc=new K(this),this.stencilOp=new $(this),this.stencilTest=new Q(this),this.depthRange=new tt(this),this.depthTest=new et(this),this.depthFunc=new rt(this),this.blend=new nt(this),this.blendFunc=new it(this),this.blendColor=new at(this),this.blendEquation=new ot(this),this.cullFace=new st(this),this.cullFaceSide=new lt(this),this.frontFace=new ut(this),this.program=new ct(this),this.activeTexture=new ft(this),this.viewport=new ht(this),this.bindFramebuffer=new pt(this),this.bindRenderbuffer=new dt(this),this.bindTexture=new vt(this),this.bindVertexBuffer=new gt(this),this.bindElementBuffer=new yt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new mt(this),this.pixelStoreUnpack=new xt(this),this.pixelStoreUnpackPremultiplyAlpha=new bt(this),this.pixelStoreUnpackFlipY=new _t(this),this.extTextureFilterAnisotropic=t.getExtension(\"EXT_texture_filter_anisotropic\")||t.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||t.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&(t.getExtension(\"OES_texture_half_float_linear\"),this.extRenderToTextureHalfFloat=t.getExtension(\"EXT_color_buffer_half_float\")),this.extTimerQuery=t.getExtension(\"EXT_disjoint_timer_query\")};Pt.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()},Pt.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},Pt.prototype.createIndexBuffer=function(t,e){return new U(this,t,e)},Pt.prototype.createVertexBuffer=function(t,e,r){return new H(this,t,e,r)},Pt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Pt.prototype.createFramebuffer=function(t,e,r){return new At(this,t,e,r)},Pt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Pt.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))},Pt.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)},Pt.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)},Pt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,Lt.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Pt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ot=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on(\"data\",(function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on(\"error\",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new R[r.type](e,r,n,i);if(a.id!==e)throw new Error(\"Expected Source id to be \"+e+\" instead of \"+a.id);return t.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],a),a}(r,n,i,this),this._tiles={},this._cache=new j(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(It).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(It).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)\"errored\"!==this._tiles[t].state&&this._reloadTile(t,\"reloading\")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&(\"loading\"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state=\"errored\",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),\"expired\"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),\"raster-dem\"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event(\"data\",{dataType:\"source\",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(Dt(this._source.type)){for(var u={},c={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],v=this._tiles[p];if(v&&!(v.fadeEndTime&&v.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),u[g.tileID.key]=g.tileID),c[p]=d}}for(var y in this._retainLoadedChildren(c,a,s,l),u)l[y]||(this._coveredTiles[y]=!0,l[y]=u[y])}for(var m in l)this._tiles[m].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,u=t;l<u.length;l+=1){var c=u[l],f=this._addTile(c);n[c.key]=c,f.hasData()||e<this._source.maxzoom&&(s[c.key]=c)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],v=this._tiles[d.key];if(!v.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],y=this.getTile(g);if(y&&y.hasData()){n[g.key]=g;continue}}else{var m=d.children(this._source.maxzoom);if(n[m[0].key]&&n[m[1].key]&&n[m[2].key]&&n[m[3].key])continue}for(var x=v.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(v=this.getTile(_))&&x&&(v=this._addTile(_)),v&&(n[_.key]=_,x=v.wasRequested(),v.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,\"expired\"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),u=s.map((function(t){return o.pointCoordinate(t)})),c=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,v=0,g=u;v<g.length;v+=1){var y=g[v];f=Math.min(f,y.x),h=Math.min(h,y.y),p=Math.max(p,y.x),d=Math.max(d,y.y)}for(var m=function(e){var n=i._tiles[c[e]];if(!n.holdingForFade()){var s=n.tileID,v=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/v,y=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(y[0].x-g<t.EXTENT&&y[0].y-g<t.EXTENT&&y[1].x+g>=0&&y[1].y+g>=0){var m=l.map((function(t){return s.getTilePoint(t)})),x=u.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:m,cameraQueryGeometry:x,scale:v})}}},x=0;x<c.length;x++)m(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(Dt(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles)this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\");this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function It(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function Dt(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function zt(){return new t.window.Worker(na.workerUrl)}Ot.maxOverzooming=10,Ot.maxUnderzooming=3;var Rt=\"mapboxgl_preloaded_worker_pool\",Ft=function(){this.active={}};Ft.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Ft.workerCount;)this.workers.push(new zt);return this.active[t]=!0,this.workers.slice()},Ft.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Ft.prototype.isPreloaded=function(){return!!this.active[Rt]},Ft.prototype.numActive=function(){return Object.keys(this.active).length};var Bt,Nt=Math.floor(t.browser.hardwareConcurrency/2);function jt(){return Bt||(Bt=new Ft),Bt}function Ut(e,r){var n={};for(var i in e)\"ref\"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function Vt(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]=Ut(t[n],e[t[n].ref]));return t}Ft.workerCount=Math.max(Math.min(Nt,6),1);var Ht={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 qt(t,e,r){r.push({command:Ht.addSource,args:[t,e[t]]})}function Gt(t,e,r){e.push({command:Ht.removeSource,args:[t]}),r[t]=!0}function Zt(t,e,r,n){Gt(t,r,n),qt(t,e,r)}function Yt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Wt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Xt(t){return t.id}function Jt(t,e){return t[e.id]=e,t}function Kt(e,r){if(!e)return[{command:Ht.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ht.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ht.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ht.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ht.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ht.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ht.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ht.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ht.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ht.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Gt(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||(\"geojson\"===e[a].type&&\"geojson\"===r[a].type&&Yt(e,r,a)?n.push({command:Ht.setGeoJSONSourceData,args:[a,r[a].data]}):Zt(a,r,n,i)):qt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ht.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,u,c,f=(e=e||[]).map(Xt),h=r.map(Xt),p=e.reduce(Jt,{}),d=r.reduce(Jt,{}),v=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ht.removeLayer,args:[o]}),v.splice(v.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],v[v.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ht.removeLayer,args:[o]}),v.splice(v.lastIndexOf(o,v.length-a),1)):a++,u=v[v.length-i],n.push({command:Ht.addLayer,args:[d[o],u]}),v.splice(v.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s[\"source-layer\"],l[\"source-layer\"])&&t.deepEqual(s.type,l.type)){for(c in Wt(s.layout,l.layout,n,o,null,Ht.setLayoutProperty),Wt(s.paint,l.paint,n,o,null,Ht.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ht.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ht.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(c)&&\"layout\"!==c&&\"paint\"!==c&&\"filter\"!==c&&\"metadata\"!==c&&\"minzoom\"!==c&&\"maxzoom\"!==c&&(0===c.indexOf(\"paint.\")?Wt(s[c],l[c],n,o,c.slice(6),Ht.setPaintProperty):t.deepEqual(s[c],l[c])||n.push({command:Ht.setLayerProperty,args:[o,c,l[c]]}));for(c in l)l.hasOwnProperty(c)&&!s.hasOwnProperty(c)&&\"layout\"!==c&&\"paint\"!==c&&\"filter\"!==c&&\"metadata\"!==c&&\"minzoom\"!==c&&\"maxzoom\"!==c&&(0===c.indexOf(\"paint.\")?Wt(s[c],l[c],n,o,c.slice(6),Ht.setPaintProperty):t.deepEqual(s[c],l[c])||n.push({command:Ht.setLayerProperty,args:[o,c,l[c]]}))}else n.push({command:Ht.removeLayer,args:[o]}),u=v[v.lastIndexOf(o)+1],n.push({command:Ht.addLayer,args:[l,u]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:Ht.setStyle,args:[r]}]}return n}var $t=function(t,e){this.reset(t,e)};$t.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},$t.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Qt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function te(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function ee(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function re(e,r){var n=[e.x,e.y,0,1];pe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ne(t,e){return.5+t/e*.5}function ie(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 ae(e,r,n,i,a,o,s,l){var u=i?e.textSizeData:e.iconSizeData,c=t.evaluateSizeForZoom(u,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,v=n.transform.width/n.transform.height,g=!1,y=0;y<d.length;y++){var m=d.get(y);if(m.hidden||m.writingMode===t.WritingMode.vertical&&!g)he(m.numGlyphs,h);else{g=!1;var x=[m.anchorX,m.anchorY,0,1];if(t.transformMat4(x,x,r),ie(x,f)){var b=x[3],_=ne(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(u,c,m),T=s?w/_:w*_,k=new t.Point(m.anchorX,m.anchorY),A=re(k,a).point,M={},S=le(m,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,v);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&le(m,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,v).notEnoughRoom)&&he(m.numGlyphs,h)}else he(m.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function oe(t,e,r,n,i,a,o,s,l,u,c){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),v=e.getoffsetX(f-1),g=ce(t*d,r,n,i,a,o,s.segment,h,p,l,u,c);if(!g)return null;var y=ce(t*v,r,n,i,a,o,s.segment,h,p,l,u,c);return y?{first:g,last:y}:null}function se(e,r,n,i){return e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i?{useVertical:!0}:(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function le(e,r,n,i,a,o,s,l,u,c,f,h,p,d){var v,g=r/24,y=e.lineOffsetX*g,m=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=oe(g,l,y,m,n,f,h,e,u,o,p);if(!w)return{notEnoughRoom:!0};var T=re(w.first.point,s).point,k=re(w.last.point,s).point;if(i&&!n){var A=se(e.writingMode,T,k,d);if(A)return A}v=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)v.push(ce(g*l.getoffsetX(M),y,m,n,f,h,e.segment,b,_,u,o,p));v.push(w.last)}else{if(i&&!n){var S=re(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(u.getx(E),u.gety(E)),C=re(L,a),P=C.signedDistanceFromCamera>0?C.point:ue(h,L,S,1,a),O=se(e.writingMode,S,P,d);if(O)return O}var I=ce(g*l.getoffsetX(e.glyphStartIndex),y,m,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,u,o,p);if(!I)return{notEnoughRoom:!0};v=[I]}for(var D=0,z=v;D<z.length;D+=1){var R=z[D];t.addDynamicAttributes(c,R.point,R.angle)}return{}}function ue(t,e,r,n,i){var a=re(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function ce(e,r,n,i,a,o,s,l,u,c,f,h){var p=i?e-r:e+r,d=p>0?1:-1,v=0;i&&(d*=-1,v=Math.PI),d<0&&(v+=Math.PI);for(var g=d>0?l+s:l+s+1,y=a,m=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=u)return null;if(m=y,w.push(y),void 0===(y=h[g])){var T=new t.Point(c.getx(g),c.gety(g)),k=re(T,f);if(k.signedDistanceFromCamera>0)y=h[g]=k.point;else{var A=g-d;y=ue(0===x?o:new t.Point(c.getx(A),c.gety(A)),T,m,_-x+1,f)}}x+=b,b=m.dist(y)}var M=(_-x)/b,S=y.sub(m),E=S.mult(M)._add(m);E._add(S._unit()._perp()._mult(n*d));var L=v+Math.atan2(y.y-m.y,y.x-m.x);return w.push(E),{point:E,angle:L,path:w}}Qt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Qt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Qt.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)},Qt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Qt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Qt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var u=this.circles[3*l],c=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:u-f,y1:c-f,x2:u+f,y2:c+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Qt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var u=[],c={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,u,c,i),n?u.length>0:u},Qt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Qt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Qt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Qt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,u=this.boxCells[i];if(null!==u)for(var c=this.bboxes,f=0,h=u;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=c[d+2]&&e<=c[d+3]&&r>=c[d+0]&&n>=c[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:c[d],y1:c[d+1],x2:c[d+2],y2:c[d+3]})}}}var v=this.circleCells[i];if(null!==v)for(var g=this.circles,y=0,m=v;y<m.length;y+=1){var x=m[y];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Qt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,u=o.seenUids,c=this.boxCells[i];if(null!==c)for(var f=this.bboxes,h=0,p=c;h<p.length;h+=1){var d=p[h];if(!u.box[d]){u.box[d]=!0;var v=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[v+0],f[v+1],f[v+2],f[v+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var y=this.circles,m=0,x=g;m<x.length;m+=1){var b=x[m];if(!u.circle[b]){u.circle[b]=!0;var _=3*b;if(this._circlesCollide(y[_],y[_+1],y[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Qt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),u=this._convertToYCellCoord(e),c=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=c;h++)for(var p=u;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Qt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Qt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Qt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Qt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var u=(o-i)/2,c=Math.abs(e-(i+u));if(c>u+r)return!1;if(l<=s||c<=u)return!0;var f=l-s,h=c-u;return f*f+h*h<=r*r};var fe=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function he(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(fe,3*n)}}function pe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var de=100,ve=function(t,e,r){void 0===e&&(e=new Qt(t.width+200,t.height+200,25)),void 0===r&&(r=new Qt(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+de,this.screenBottomBoundary=t.height+de,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function ge(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}ve.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,u=t.x2*o+a.point.x,c=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,u,c)||!e&&this.grid.hitTest(s,l,u,c,i)?{box:[],offscreen:!1}:{box:[s,l,u,c],offscreen:this.isOffscreen(s,l,u,c)}},ve.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,u,c,f,h,p){var d=[],v=new t.Point(r.anchorX,r.anchorY),g=re(v,o),y=ne(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),m=(c?a/y:a*y)/t.ONE_EM,x=re(v,s).point,b=oe(m,i,r.lineOffsetX*m,r.lineOffsetY*m,!1,x,v,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*y+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new $t,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var O=1;O<L.path.length;O++)C.push(L.path[O]);var I=2.5*k;if(l){var D=C.map((function(t){return re(t,l)}));C=D.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:D.map((function(t){return t.point}))}var z=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);z=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=z;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/I)+1;for(var H=0;H<V;H++){var q=H/Math.max(V-1,1),G=S.lerp(q),Z=G.x+de,Y=G.y+de;d.push(Z,Y,k,0);var W=Z-k,X=Y-k,J=Z+k,K=Y+k;if(T=T&&this.isOffscreen(W,X,J,K),w=w||this.isInsideGrid(W,X,J,K),!e&&this.grid.hitTestCircle(Z,Y,k,f)&&(_=!0,!u))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!u&&_||!w?[]:d,offscreen:T,collisionDetected:_}},ve.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var u=l[s],c=new t.Point(u.x+de,u.y+de);n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y),r.push(c)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var v=d[p],g=v.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var y=[new t.Point(v.x1,v.y1),new t.Point(v.x2,v.y1),new t.Point(v.x2,v.y2),new t.Point(v.x1,v.y2)];t.polygonIntersectsPolygon(r,y)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},ve.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},ve.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},ve.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return pe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+de,(-i[1]/i[3]+1)/2*this.transform.height+de),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},ve.prototype.isOffscreen=function(t,e,r,n){return r<de||t>=this.screenRightBoundary||n<de||e>this.screenBottomBoundary},ve.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},ve.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var ye=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};ye.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new ye(t?t.text:null,e,r,i),this.icon=new ye(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var xe=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},be=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},_e=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},we=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function Te(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point(s+u[0]*a,l+u[1]*a)}function ke(e,r,n,i,a,o){var s=e.x1,l=e.x2,u=e.y1,c=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:u+p.y,x2:l+p.x,y2:c+p.y,anchorPointX:f,anchorPointY:h}}we.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 Ae=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new ve(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new we(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Me(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}Ae.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,u=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),c=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h=\"map\"===l.get(\"text-pitch-alignment\"),p=\"map\"===l.get(\"text-rotation-alignment\"),d=ge(n,1,this.transform.zoom),v=te(f,h,p,this.transform,d),g=null;if(h){var y=ee(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,y)}this.retainedQueryData[a.bucketInstanceId]=new _e(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var m={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:v,labelToScreenMatrix:g,scale:u,textPixelRatio:c,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:m})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:m})}},Ae.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d){var v,g=[f.textOffset0,f.textOffset1],y=Te(t,r,n,g,i),m=this.collisionIndex.placeCollisionBox(ke(e,y.x,y.y,a,o,this.transform.angle),c,s,l,u.predicate);if(!d||0!==this.collisionIndex.placeCollisionBox(ke(d,y.x,y.y,a,o,this.transform.angle),c,s,l,u.predicate).box.length)return m.box.length>0?(this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(v=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:v},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:y,placedGlyphBoxes:m}):void 0},Ae.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,u=a.textLabelPlaneMatrix,c=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,v=a.collisionGroup,g=s.get(\"text-optional\"),y=s.get(\"icon-optional\"),m=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=m&&(x||!o.hasIconData()||y),A=x&&(m||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new xe(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,O=null,I=0,D=0,z=0;a.textFeatureIndex?I=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(I=e.featureIndex),a.verticalTextFeatureIndex&&(D=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length&&(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),!(L&&L.box&&L.box.length));i+=1);else L=r()};if(s.get(\"text-variable-anchor\")){var N=s.get(\"text-variable-anchor\");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,u=e.textBoxScale,c=w&&!x?r:null,h={box:[],offscreen:!1},p=m?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],y=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,u,b,_,f,l,v,y,e,o,n,c);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,m,f,l,v.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Z=t.evaluateSizeForFeature(o.textSizeData,d,G),Y=s.get(\"text-padding\"),W=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(m,G,o.lineVertexArray,o.glyphOffsetArray,Z,l,u,c,n,_,v.predicate,W,Y),T=m||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(z=a.iconFeatureIndex),a.iconBox){var X=function(t){var e=w&&E?ke(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,v.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(O=X(a.verticalIconBox)).box.length>0:(O=X(a.iconBox)).box.length>0,S=S&&O.offscreen}var J=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=y||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&D?i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,D,v.ID):i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,I,v.ID)),M&&O&&i.collisionIndex.insertCollisionBox(O.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,z,v.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,I,v.ID),n)){var $=o.bucketInstanceId,Q=i.collisionCircleArrays[$];void 0===Q&&(Q=i.collisionCircleArrays[$]=new be);for(var tt=0;tt<P.circles.length;tt+=4)Q.circles.push(P.circles[tt+0]),Q.circles.push(P.circles[tt+1]),Q.circles.push(P.circles[tt+2]),Q.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new xe(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},Ae.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var u=l[s];u>=0&&(e.text.placedSymbolArray.get(u).crossTileID=a>=0&&u!==a?0:n.crossTileID)}},Ae.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},Ae.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],u=i[s];u?(this.opacities[s]=new me(u,n,l.text,l.icon),r=r||l.text!==u.text.placed||l.icon!==u.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var c in i){var f=i[c];if(!this.opacities[c]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[c]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},Ae.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},Ae.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get(\"text-allow-overlap\"),l=a.get(\"icon-allow-overlap\"),u=a.get(\"text-variable-anchor\"),c=\"map\"===a.get(\"text-rotation-alignment\"),f=\"map\"===a.get(\"text-pitch-alignment\"),h=\"none\"!==a.get(\"icon-text-fit\"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get(\"icon-optional\")),l&&(s||!e.hasTextData()||a.get(\"text-optional\")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},v=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,v=a.crossTileID,g=r[v],y=i.opacities[v];g?y=o:y||(y=p,i.opacities[v]=y),r[v]=!0;var m=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(m){var T=De(y.text),k=_?ze:T;d(e.text,s,k);var A=w?ze:T;d(e.text,l,A);var M=y.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,\"left\",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=De(y.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:ze;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=y.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var O=C?ze:L;d(e.icon,a.numVerticalIconVertices,O),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=y.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var I=e.collisionArrays[n];if(I){var D=new t.Point(0,0);if(I.textBox||I.verticalTextBox){var z=!0;if(u){var R=i.variableOffsets[v];R?(D=Te(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),c&&D._rotate(f?i.transform.angle:-i.transform.angle)):z=!1}I.textBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!z||_,D.x,D.y),I.verticalTextBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!z||w,D.x,D.y)}var F=Boolean(!w&&I.verticalIconBox);I.iconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,F,h?D.x:0,h?D.y:0),I.verticalIconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,!F,h?D.x:0,h?D.y:0)}}},g=0;g<e.symbolInstances.length;g++)v(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var y=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=y.invProjMatrix,e.placementViewportMatrix=y.viewportMatrix,e.collisionCircleArray=y.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},Ae.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},Ae.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},Ae.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},Ae.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},Ae.prototype.setStale=function(){this.stale=!0};var Se=Math.pow(2,25),Ee=Math.pow(2,24),Le=Math.pow(2,17),Ce=Math.pow(2,16),Pe=Math.pow(2,9),Oe=Math.pow(2,8),Ie=Math.pow(2,1);function De(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*Se+e*Ee+r*Le+e*Ce+r*Pe+e*Oe+r*Ie+e}var ze=0,Re=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Re.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var Fe=function(t,e,r,n,i,a,o){this.placement=new Ae(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};Fe.prototype.isDone=function(){return this._done},Fe.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Re(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},Fe.prototype.commit=function(t){return this.placement.commit(t),this.placement};var Be=512/t.EXTENT/2,Ne=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Ne.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=Be/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Ne.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,u=o;l<u.length;l+=1){var c=u[l];if(Math.abs(c.coord.x-s.x)<=n&&Math.abs(c.coord.y-s.y)<=n&&!r[c.crossTileID]){r[c.crossTileID]=!0,a.crossTileID=c.crossTileID;break}}}}};var je=function(){this.maxCrossTileID=0};je.prototype.generate=function(){return++this.maxCrossTileID};var Ue=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Ue.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Ue.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++)e.symbolInstances.get(n).crossTileID=0;this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var u=o[t.scaledTo(Number(a)).key];u&&u.findMatches(e.symbolInstances,t,i)}}for(var c=0;c<e.symbolInstances.length;c++){var f=e.symbolInstances.get(c);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Ne(t,e.symbolInstances,e.bucketInstanceId),!0},Ue.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Ue.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ve=function(){this.layerIndexes={},this.crossTileIDs=new je,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ve.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Ue);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],u=l.getBucket(t);u&&t.id===u.layerIds[0]&&(u.bucketInstanceId||(u.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,u,this.crossTileIDs)&&(i=!0),a[u.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ve.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 He=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return\"source.canvas\"!==t.identifier})))},qe=t.pick(Ht,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Ge=t.pick(Ht,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),Ze=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a;null!=(a=\"version\"===n?r:\"array\"===i.type?[]:{})&&(e[n]=a)}}return e}(),Ye=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new A(jt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new k(256,512),this.crossTileSymbolIndex=new Ve,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast(\"syncRTLPluginState\",r,(function(e,r){if(t.triggerPluginCompletionEvent(e),r&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on(\"data\",(function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"}));var i=\"boolean\"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._load(Ze,!1)},r.prototype._load=function(e,r){if(!r||!He(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=Vt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new T(this.stylesheet.light),this.fire(new t.Event(\"data\",{dataType:\"style\"})),this.fire(new t.Event(\"style.load\"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,c())})),u=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){u=null,o||(o=t,a=e,c())}));function c(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],u=l.width,c=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,v=l.stretchX,g=l.stretchY,y=l.content,m=new t.RGBAImage({width:u,height:c});t.RGBAImage.copy(e,m,{x:f,y:h},{x:0,y:0},{width:u,height:c}),r[s]={data:m,pixelRatio:d,sdf:p,stretchX:v,stretchY:g,content:y}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),u&&(u.cancel(),u=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];\"custom\"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];\"reload\"===o?this._reloadSource(a):\"clear\"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var u=0,c=this._order;u<c.length;u+=1){var f=c[u],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event(\"data\",{dataType:\"style\"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies([\"icons\",\"patterns\"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),He(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=Vt(e.layers);var n=Kt(this.serialize(),e).filter((function(t){return!(t.command in Ge)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in qe)}));if(i.length>0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0&&this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n))){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ot(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(He(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is requred to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var u=l[s][o];if(u)for(var c=0,f=u;c<f.length;c+=1){var h=f[c];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var v=this._order[d];if(r(v))for(var g=i.length-1;g>=0;g--){var y=i[g].feature;if(n[y.layer.id]<d)break;p.push(y),i.pop()}else for(var m=0,x=t;m<x.length;m+=1){var b=x[m][v];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,\"queryRenderedFeatures.filter\",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error(\"The layer '\"+s+\"' does not exist in the map's style and cannot be queried for features.\"))),[];i[l.source]=!0}}var u=[];for(var c in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[c]||u.push(B(this.sourceCaches[c],this._layers,this._serializedLayers,e,r,n));return this.placement&&u.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),u=[],c=0,f=Object.keys(l).map(Number);c<f.length;c+=1){var h=f[c];u.push(o[h])}u.sort(N);for(var p=function(){var r=v[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],u=n[a];u.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var c=0,f=u;c<f.length;c+=1){var h=f[c];o.push(h)}}},d=0,v=u;d<v.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer[\"source-layer\"],i.id);i.source=i.layer.source,i.layer[\"source-layer\"]&&(i.sourceLayer=i.layer[\"source-layer\"]),i.state=o}))};for(var y in s)g(y);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(u)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,\"querySourceFeatures.filter\",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called \"'+t+'\" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&He(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off(\"pluginStateChange\",this._rtlTextPluginCallback),this._layers)this._layers[e].setEventedParent(null);for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},u=0,c=this._order;u<c.length;u+=1){var f=c[u],h=this._layers[f];if(\"symbol\"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new Fe(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var v=0,g=this._order;v<g.length;v+=1){var y=g[v],m=this._layers[y];\"symbol\"===m.type&&this.placement.updateLayerOpacities(m,l[m.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);Ye.getSourceType=function(t){return R[t]},Ye.setSourceType=function(t,e){R[t]=e},Ye.registerForPluginStateChange=t.registerForPluginStateChange;var We=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Xe=_r(\"#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;}\"),Je=_r(\"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);}\"),Ke=_r(\"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);}\"),$e=_r(\"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);}\"),Qe=_r(\"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);}\"),tr=_r(\"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;}\"),er=_r(\"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;}\"),rr=_r(\"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;}\"),nr=_r(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),ir=_r(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),ar=_r(\"#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);}\"),or=_r(\"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;}\"),sr=_r(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),lr=_r(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),ur=_r(\"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;}\"),cr=_r(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),fr=_r(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),hr=_r(\"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;}\"),pr=_r(\"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);}\"),dr=_r(\"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);}\"),vr=_r(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),gr=_r(\"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);}\"),yr=_r(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),mr=_r(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),xr=_r(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),br=_r(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function _r(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o=\"float\"===i?\"vec2\":\"vec4\",s=a.match(/color/)?\"color\":o;return n[a]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"}))}}var wr=Object.freeze({__proto__:null,prelude:Xe,background:Je,backgroundPattern:Ke,circle:$e,clippingMask:Qe,heatmap:tr,heatmapTexture:er,collisionBox:rr,collisionCircle:nr,debug:ir,fill:ar,fillOutline:or,fillOutlinePattern:sr,fillPattern:lr,fillExtrusion:ur,fillExtrusionPattern:cr,hillshadePrepare:fr,hillshade:hr,line:pr,lineGradient:dr,linePattern:vr,lineSDF:gr,raster:yr,symbolIcon:mr,symbolSDF:xr,symbolTextAndIcon:br}),Tr=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};Tr.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,u=0;!l&&u<n.length;u++)this.boundPaintVertexBuffers[u]!==n[u]&&(l=!0);var c=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||c?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},Tr.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,u=this.context,c=u.gl;if(u.extVertexArrayObject)this.vao&&this.destroy(),this.vao=u.extVertexArrayObject.createVertexArrayOES(),u.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=u.currentNumAttributes||0;for(var f=l;f<s;f++)c.disableVertexAttribArray(f)}e.enableAttributes(c,t);for(var h=0,p=r;h<p.length;h+=1)p[h].enableAttributes(c,t);a&&a.enableAttributes(c,t),o&&o.enableAttributes(c,t),e.bind(),e.setVertexAttribPointers(c,t,i);for(var d=0,v=r;d<v.length;d+=1){var g=v[d];g.bind(),g.setVertexAttribPointers(c,t,i)}a&&(a.bind(),a.setVertexAttribPointers(c,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(c,t,i)),u.currentNumAttributes=l},Tr.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var kr=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push(\"#define OVERDRAW_INSPECTOR;\");var s=o.concat(Xe.fragmentSource,e.fragmentSource).join(\"\\n\"),l=o.concat(Xe.vertexSource,e.vertexSource).join(\"\\n\"),u=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,s),a.compileShader(u),a.attachShader(this.program,u);var c=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,l),a.compileShader(c),a.attachShader(this.program,c);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(c),a.deleteShader(u),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var v=a.getActiveAttrib(this.program,d);v&&(this.attributes[v.name]=a.getAttribLocation(this.program,v.name))}for(var g=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),y=0;y<g;y++){var m=a.getActiveUniform(this.program,y);m&&(p[m.name]=a.getUniformLocation(this.program,m.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function Ar(t,e,r){var n=1/ge(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}kr.prototype.draw=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v){var g,y=t.gl;if(!this.failedToCreate){for(var m in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[m].set(o[m]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[y.LINES]=2,g[y.TRIANGLES]=3,g[y.LINE_STRIP]=1,g)[e],b=0,_=c.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new Tr)).bind(t,this,l,p?p.getPaintVertexBuffers():[],u,w.vertexOffset,d,v),y.drawElements(e,w.primitiveLength*x,y.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Mr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var u=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[u.r,u.g,u.b],u_vertical_gradient:+n,u_opacity:i}},Sr=function(e,r,n,i,a,o,s){return t.extend(Mr(e,r,n,i),Ar(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Er=function(t){return{u_matrix:t}},Lr=function(e,r,n,i){return t.extend(Er(e),Ar(n,r,i))},Cr=function(t,e){return{u_matrix:t,u_world:e}},Pr=function(e,r,n,i,a){return t.extend(Lr(e,r,n,i),{u_world:a})},Or=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=ge(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Ir=function(t,e,r){var n=ge(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Dr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},zr=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Rr=function(t){return{u_matrix:t}},Fr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:ge(e,1,r),u_intensity:n}},Br=function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get(\"heatmap-opacity\")}},Nr=function(e,r,n){var i=n.paint.get(\"hillshade-shadow-color\"),a=n.paint.get(\"hillshade-highlight-color\"),o=n.paint.get(\"hillshade-accent-color\"),s=n.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===n.paint.get(\"hillshade-illumination-anchor\")&&(s-=e.transform.angle);var l,u,c,f=!e.options.moving;return{u_matrix:e.transform.calculatePosMatrix(r.tileID.toUnwrapped(),f),u_image:0,u_latrange:(l=r.tileID,u=Math.pow(2,l.canonical.z),c=l.canonical.y,[new t.MercatorCoordinate(0,c/u).toLngLat().lat,new t.MercatorCoordinate(0,(c+1)/u).toLngLat().lat]),u_light:[n.paint.get(\"hillshade-exaggeration\"),s],u_shadow:i,u_highlight:a,u_accent:o}},jr=function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}};var Ur=function(e,r,n){var i=e.transform;return{u_matrix:Zr(e,r,n),u_ratio:1/ge(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Vr=function(e,r,n){return t.extend(Ur(e,r,n),{u_image:0})},Hr=function(e,r,n,i){var a=e.transform,o=Gr(r,a);return{u_matrix:Zr(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/ge(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},qr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=Gr(r,o),u=\"round\"===n.layout.get(\"line-cap\"),c=s.getDash(i.from,u),f=s.getDash(i.to,u),h=c.width*a.fromScale,p=f.width*a.toScale;return t.extend(Ur(e,r,n),{u_patternscale_a:[l/h,-c.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:c.y,u_tex_y_b:f.y,u_mix:a.t})};function Gr(t,e){return 1/ge(t,1,e.tileZoom)}function Zr(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var Yr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Wr(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Wr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var Xr,Jr=function(t,e,r,n,i,a,o,s,l,u){var c=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:c.cameraToCenterDistance,u_pitch:c.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:c.width/c.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:u,u_texture:0}},Kr=function(e,r,n,i,a,o,s,l,u,c,f){var h=a.transform;return t.extend(Jr(e,r,n,i,a,o,s,l,u,c),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},$r=function(e,r,n,i,a,o,s,l,u,c){return t.extend(Kr(e,r,n,i,a,o,s,l,!0,u,!0),{u_texsize_icon:c,u_texture_icon:1})},Qr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},tn=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,u=Math.pow(2,n.tileID.overscaledZ),c=n.tileSize*Math.pow(2,r.transform.tileZoom)/u,f=c*(n.tileID.canonical.x+n.tileID.wrap*u),h=c*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/ge(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},en={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function rn(e,r,n,i,a,o,s){for(var l=e.context,u=l.gl,c=e.useProgram(\"collisionBox\"),f=[],h=0,p=0,d=0;d<i.length;d++){var v=i[d],g=r.getTile(v),y=g.getBucket(n);if(y){var m=v.posMatrix;0===a[0]&&0===a[1]||(m=e.translatePosMatrix(v.posMatrix,g,a,o));var x=s?y.textCollisionBox:y.iconCollisionBox,b=y.collisionCircleArray;if(b.length>0){var _=t.create(),w=m;t.mul(_,y.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,y.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&c.draw(l,u.LINES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,Ir(m,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],O=E.circleArray[C+1],I=E.circleArray[C+2],D=E.circleArray[C+3];k.emplace(A++,P,O,I,D,0),k.emplace(A++,P,O,I,D,1),k.emplace(A++,P,O,I,D,2),k.emplace(A++,P,O,I,D,3)}(!Xr||Xr.length<2*h)&&(Xr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var z=l.createIndexBuffer(Xr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Dr(N.transform,N.invTransform,e.transform);T.draw(l,u.TRIANGLES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,j,n.id,R,z,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),z.destroy()}}var nn=t.identity(new Float32Array(16));function an(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,u=-(s.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point((l/a+c[0])*o,(u/a+c[1])*o)}function on(e,r,n,i,a,o,s,l,u,c,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,v={};p.clear();for(var g=0;g<h.length;g++){var y=h.get(g),m=e.allowVerticalPlacement&&!y.placedOrientation,x=y.hidden||!y.crossTileID||m?null:i[y.crossTileID];if(x){var b=new t.Point(y.anchorX,y.anchorY),_=re(b,n?l:s),w=ne(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,c,y)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/u);for(var k=x.width,A=x.height,M=an(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?re(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&y.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<y.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&y.associatedIconIndex>=0&&(v[y.associatedIconIndex]={shiftedAnchor:S,angle:E})}else he(y.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var O=C.get(P);if(O.hidden)he(O.numGlyphs,d);else{var I=v[P];if(I)for(var D=0;D<O.numGlyphs;D++)t.addDynamicAttributes(d,I.shiftedAnchor,I.angle);else he(O.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function sn(t,e,r){return r.iconsInText&&e?\"symbolTextAndIcon\":t?\"symbolSDF\":\"symbolIcon\"}function ln(e,r,n,i,a,o,s,l,u,c,f,h){for(var p=e.context,d=p.gl,v=e.transform,g=\"map\"===l,y=\"map\"===u,m=g&&\"point\"!==n.layout.get(\"symbol-placement\"),x=g&&!y&&!m,b=void 0!==n.layout.get(\"symbol-sort-key\").constantOr(1),_=e.depthModeForSublayer(0,Mt.ReadOnly),w=n.layout.get(\"text-variable-anchor\"),T=[],k=0,A=i;k<A.length;k+=1){var M=A[k],S=r.getTile(M),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,O=a?E.textSizeData:E.iconSizeData,I=y||0!==v.pitch,D=e.useProgram(sn(P,a,E),C),z=t.evaluateSizeForZoom(O,v.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var V=\"composite\"===O.kind||\"camera\"===O.kind;U=I||e.options.rotating||e.options.zooming||V?d.LINEAR:d.NEAREST}}else{var H=1!==n.layout.get(\"icon-size\").constantOr(0)||E.iconsNeedLinear;B=S.imageAtlasTexture,N=P||e.options.rotating||e.options.zooming||H||I?d.LINEAR:d.NEAREST,R=S.imageAtlasTexture.size}var q=ge(S,1,e.transform.zoom),G=te(M.posMatrix,y,g,e.transform,q),Z=ee(M.posMatrix,y,g,e.transform,q),Y=w&&E.hasTextData(),W=\"none\"!==n.layout.get(\"icon-text-fit\")&&Y&&E.hasIconData();m&&ae(E,M.posMatrix,e,a,G,Z,y,c);var X=e.translatePosMatrix(M.posMatrix,S,o,s),J=m||a&&w||W?nn:G,K=e.translatePosMatrix(Z,S,o,s,!0),$=P&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1),Q={program:D,buffers:L,uniformValues:P?E.iconsInText?$r(O.kind,z,x,y,e,X,J,K,R,F):Kr(O.kind,z,x,y,e,X,J,K,a,R,!0):Jr(O.kind,z,x,y,e,X,J,K,a,R),atlasTexture:B,atlasTextureIcon:j,atlasInterpolation:N,atlasInterpolationIcon:U,isSDF:P,hasHalo:$};if(b)for(var tt=0,et=L.segments.get();tt<et.length;tt+=1){var rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:Q})}else T.push({segments:L.segments,sortKey:0,state:Q})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,un(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}un(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function un(t,e,r,n,i,a,o,s,l){var u=n.context,c=u.gl;i.draw(u,c.TRIANGLES,a,o,s,Ct.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function cn(t,e,r,n,i,a,o){var s,l,u,c,f,h=t.context.gl,p=r.paint.get(\"fill-pattern\"),d=p&&p.constantOr(1),v=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",s=h.LINES):(l=d?\"fillPattern\":\"fill\",s=h.TRIANGLES);for(var g=0,y=n;g<y.length;g+=1){var m=y[g],x=e.getTile(m);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(v));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(m.posMatrix,x,r.paint.get(\"fill-translate\"),r.paint.get(\"fill-translate-anchor\"));if(o){c=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];u=\"fillOutlinePattern\"===l&&d?Pr(S,t,v,x,E):Cr(S,E)}else c=b.indexBuffer,f=b.segments,u=d?Lr(S,t,v,x):Er(S);w.draw(t.context,s,i,t.stencilModeForClipping(m),a,Ct.disabled,u,r.id,b.layoutVertexBuffer,c,f,r.paint,t.transform.zoom,_)}}}}function fn(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,u=r.paint.get(\"fill-extrusion-pattern\"),c=u.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get(\"fill-extrusion-opacity\"),p=0,d=n;p<d.length;p+=1){var v=d[p],g=e.getTile(v),y=g.getBucket(r);if(y){var m=y.programConfigurations.get(r.id),x=t.useProgram(c?\"fillExtrusionPattern\":\"fillExtrusion\",m);c&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),m.updatePaintBuffers(f));var b=u.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&m.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(v.posMatrix,g,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\")),A=r.paint.get(\"fill-extrusion-vertical-gradient\"),M=c?Sr(k,t,A,h,v,f,g):Mr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Ct.backCCW,M,r.id,y.layoutVertexBuffer,y.indexBuffer,y.segments,r.paint,t.transform.zoom,m)}}}function hn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var u=t.useProgram(\"hillshade\");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var c=Nr(t,e,r);u.draw(o,s.TRIANGLES,n,i,a,Ct.disabled,c,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function pn(e,r,n,i,a,o,s){var l=e.context,u=l.gl,c=r.dem;if(c&&c.data){var f=c.dim,h=c.stride,p=c.getPixels();if(l.activeTexture.set(u.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(u.NEAREST,u.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,u.RGBA,{premultiply:!1}),r.demTexture.bind(u.NEAREST,u.CLAMP_TO_EDGE);l.activeTexture.set(u.TEXTURE0);var v=r.fbo;if(!v){var g=new t.Texture(l,{width:f,height:f,data:null},u.RGBA);g.bind(u.LINEAR,u.CLAMP_TO_EDGE),(v=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(g.texture)}l.bindFramebuffer.set(v.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram(\"hillshadePrepare\").draw(l,u.TRIANGLES,a,o,s,Ct.disabled,jr(r.tileID,c,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function dn(e,r,n,i,a){var o=i.paint.get(\"raster-fade-duration\");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,u=r?(s-r.timeAdded)/o:-1,c=n.getSource(),f=a.coveringZoomLevel({tileSize:c.tileSize,roundZoom:c.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-u,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var vn=new t.Color(1,0,0,1),gn=new t.Color(0,1,0,1),yn=new t.Color(0,0,1,1),mn=new t.Color(1,0,1,1),xn=new t.Color(0,1,1,1);function bn(t){var e=t.transform.padding;_n(t,t.transform.height-(e.top||0),3,vn),_n(t,e.bottom||0,3,gn),wn(t,e.left||0,3,yn),wn(t,t.transform.width-(e.right||0),3,mn);var r=t.transform.centerPoint;!function(t,e,r,n){var i=20,a=2;Tn(t,e-a/2,r-i/2,a,i,n),Tn(t,e-i/2,r-a/2,i,a,n)}(t,r.x,t.transform.height-r.y,xn)}function _n(t,e,r,n){Tn(t,0,e+r/2,t.transform.width,r,n)}function wn(t,e,r,n){Tn(t,e-r/2,0,r,t.transform.height,n)}function Tn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function kn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=Mt.disabled,u=Et.disabled,c=e.colorModeForRenderPass(),f=\"$debug\";i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,u,c,Ct.disabled,zr(o,t.Color.red),f,e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var h=r.getTileByID(n.key).latestRawTileData,p=h&&h.byteLength||0,d=Math.floor(p/1024),v=r.getTile(n).tileSize,g=512/Math.min(v,512)*(n.overscaledZ/e.transform.zoom)*.5,y=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(y+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,y+\" \"+d+\"kb\"),s.draw(i,a.TRIANGLES,l,u,Lt.alphaBlended,Ct.disabled,zr(o,t.Color.transparent,g),f,e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var An={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=Et.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,u=\"map\"===a,c=\"map\"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),v=d.getBucket(n);if(v&&v.text&&v.text.segments.get().length){var g=v.textSizeData,y=t.evaluateSizeForZoom(g,l.zoom),m=ge(d,1,r.transform.zoom),x=te(p.posMatrix,c,u,r.transform,m),b=\"none\"!==n.layout.get(\"icon-text-fit\")&&v.hasIconData();if(y){var _=Math.pow(2,l.zoom-d.tileID.overscaledZ);on(v,u,c,s,t.symbolSize,l,x,p.posMatrix,_,y,b)}}}}(i,e,n,r,n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),a),0!==n.paint.get(\"icon-opacity\").constantOr(1)&&ln(e,r,n,i,!1,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),n.layout.get(\"icon-rotation-alignment\"),n.layout.get(\"icon-pitch-alignment\"),n.layout.get(\"icon-keep-upright\"),o,s),0!==n.paint.get(\"text-opacity\").constantOr(1)&&ln(e,r,n,i,!0,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.layout.get(\"text-keep-upright\"),o,s),r.map.showCollisionBoxes&&(rn(e,r,n,i,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),!0),rn(e,r,n,i,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),!1))}},circle:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"circle-opacity\"),o=n.paint.get(\"circle-stroke-width\"),s=n.paint.get(\"circle-stroke-opacity\"),l=void 0!==n.layout.get(\"circle-sort-key\").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var u=e.context,c=u.gl,f=e.depthModeForSublayer(0,Mt.ReadOnly),h=Et.disabled,p=e.colorModeForRenderPass(),d=[],v=0;v<i.length;v++){var g=i[v],y=r.getTile(g),m=y.getBucket(n);if(m){var x=m.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram(\"circle\",x),layoutVertexBuffer:m.layoutVertexBuffer,indexBuffer:m.indexBuffer,uniformValues:Or(e,g,y,n)};if(l)for(var _=0,w=m.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:m.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,O=S.uniformValues,I=M.segments;L.draw(u,c.TRIANGLES,f,h,p,Ct.disabled,O,n.id,C,P,I,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===e.renderPass){var a=e.context,o=a.gl,s=Et.disabled,l=new Lt([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);(function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}})(a,e,n),a.clear({color:t.Color.transparent});for(var u=0;u<i.length;u++){var c=i[u];if(!r.hasRenderableParent(c)){var f=r.getTile(c),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram(\"heatmap\",p),v=e.transform.zoom;d.draw(a,o.TRIANGLES,Mt.disabled,s,l,Ct.disabled,Fr(c.posMatrix,f,v,n.paint.get(\"heatmap-intensity\")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(a){n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA)),o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram(\"heatmapTexture\").draw(n,i.TRIANGLES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,Br(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}}(e,n))},line:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,Mt.ReadOnly),l=e.colorModeForRenderPass(),u=n.paint.get(\"line-dasharray\"),c=n.paint.get(\"line-pattern\"),f=c.constantOr(1),h=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=f?\"linePattern\":u?\"lineSDF\":h?\"lineGradient\":\"line\",v=e.context,g=v.gl,y=!0;if(h){v.activeTexture.set(g.TEXTURE0);var m=n.gradientTexture;if(!n.gradient)return;m||(m=n.gradientTexture=new t.Texture(v,n.gradient,g.RGBA)),m.bind(g.LINEAR,g.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,k),S=y||M.program!==A,E=c.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var O=f?Hr(e,w,n,p):u?qr(e,w,n,u,p):h?Vr(e,w,n):Ur(e,w,n);f?(v.activeTexture.set(g.TEXTURE0),w.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):u&&(S||e.lineAtlas.dirty)&&(v.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(v)),M.draw(v,g.TRIANGLES,s,e.stencilModeForClipping(_),l,Ct.disabled,O,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),y=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get(\"fill-color\"),o=n.paint.get(\"fill-opacity\");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get(\"fill-pattern\"),u=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?\"opaque\":\"translucent\";if(e.renderPass===u){var c=e.depthModeForSublayer(1,\"opaque\"===e.renderPass?Mt.ReadWrite:Mt.ReadOnly);cn(e,r,n,i,c,s,!1)}if(\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")){var f=e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,Mt.ReadOnly);cn(e,r,n,i,f,s,!0)}}},\"fill-extrusion\":function(t,e,r,n){var i=r.paint.get(\"fill-extrusion-opacity\");if(0!==i&&\"translucent\"===t.renderPass){var a=new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get(\"fill-extrusion-pattern\").constantOr(1))fn(t,e,r,n,a,Et.disabled,Lt.disabled),fn(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();fn(t,e,r,n,a,Et.disabled,o)}}},hillshade:function(t,e,r,n){if(\"offscreen\"===t.renderPass||\"translucent\"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,Mt.ReadOnly),s=t.colorModeForRenderPass(),l=\"translucent\"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],u=l[0],c=0,f=l[1];c<f.length;c+=1){var h=f[c],p=e.getTile(h);p.needsHillshadePrepare&&\"offscreen\"===t.renderPass?pn(t,p,r,a,o,Et.disabled,s):\"translucent\"===t.renderPass&&hn(t,p,r,o,u[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram(\"raster\"),l=t.colorModeForRenderPass(),u=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),c=u[0],f=u[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,v=f;d<v.length;d+=1){var g=v[d],y=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get(\"raster-opacity\")?Mt.ReadWrite:Mt.ReadOnly,a.LESS),m=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);m.registerFadeDuration(r.paint.get(\"raster-fade-duration\"));var b=e.findLoadedParent(g,0),_=dn(m,b,e,r,t.transform),w=void 0,T=void 0,k=\"nearest\"===r.paint.get(\"raster-resampling\")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),m.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-m.tileID.overscaledZ),T=[m.tileID.canonical.x*w%1,m.tileID.canonical.y*w%1]):m.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Yr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,y,Et.disabled,l,Ct.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,y,c[g.overscaledZ],l,Ct.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get(\"background-color\"),i=r.paint.get(\"background-opacity\");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,u=r.paint.get(\"background-pattern\");if(!t.isPatternMissing(u)){var c=!u&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(t.renderPass===c){var f=Et.disabled,h=t.depthModeForSublayer(0,\"opaque\"===c?Mt.ReadWrite:Mt.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(u?\"backgroundPattern\":\"background\"),v=s.coveringTiles({tileSize:l});u&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),y=0,m=v;y<m.length;y+=1){var x=m[y],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=u?tn(b,i,t,u,{tileID:x,tileSize:l},g):Qr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Ct.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)kn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if(\"offscreen\"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if(\"translucent\"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Et.disabled);var o=\"3d\"===i.renderingMode?new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,Mt.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},Mn=function(t,e){this.context=new Pt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ot.maxUnderzooming+Ot.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ve,this.gpuTimers={}};Mn.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},Mn.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,We.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,We.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,O.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,We.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Et({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},Mn.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(e,r.TRIANGLES,Mt.disabled,this.stencilClearMode,Lt.disabled,Ct.disabled,Rr(n),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},Mn.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(Lt.disabled),r.setDepthMode(Mt.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,Mt.disabled,new Et({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),Lt.disabled,Ct.disabled,Rr(s.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},Mn.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Et({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},Mn.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Et({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},Mn.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Et({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Et.disabled,e),n]},Mn.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){var r=1/8;return new Lt([e.CONSTANT_COLOR,e.ONE],new t.Color(r,r,r,0),[!0,!0,!0,!0])}return\"opaque\"===this.renderPass?Lt.unblended:Lt.alphaBlended},Mn.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return Mt.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new Mt(r||this.context.gl.LEQUAL,e,[n,n])},Mn.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},Mn.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,u,c={},f={},h={};for(var p in a){var d=a[p];c[p]=d.getVisibleCoordinates(),f[p]=c[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var v=0;v<i.length;v++){var g=i[v];if(this.style._layers[g].is3D()){this.opaquePassCutoff=v;break}}this.renderPass=\"offscreen\";for(var y=0,m=i;y<m.length;y+=1){var x=m[y],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];(\"custom\"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass=\"opaque\",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=c[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=(\"symbol\"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,c[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(u&&u.id)&&(u=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<u.getSource().maxzoom)&&(l=u))})),l&&An.debug(this,l,l.getVisibleCoordinates())),this.options.showPadding&&bn(this),this.context.setDefault()},Mn.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||\"custom\"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),An[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},Mn.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},Mn.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},Mn.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},Mn.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},Mn.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var u=[a?n[0]:ge(r,n[0],this.transform.zoom),a?n[1]:ge(r,n[1],this.transform.zoom),0],c=new Float32Array(16);return t.translate(c,e,u),c},Mn.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},Mn.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},Mn.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},Mn.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new kr(this.context,wr[t],e,en[t],this._showOverdrawInspector)),this.cache[r]},Mn.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()},Mn.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)},Mn.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},Mn.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var Sn=function(t,e){this.points=t,this.planes=e};Sn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new Sn(a,o)};var En=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};En.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new En(n,i)},En.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},En.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},En.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var u=Number.MAX_VALUE,c=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];u=Math.min(u,h),c=Math.max(c,h)}if(c<0||u>this.max[l]-this.min[l])return 0}return 1};var Ln=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};Ln.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},Ln.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},Ln.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},Ln.prototype.clone=function(){return new Ln(this.top,this.bottom,this.left,this.right)},Ln.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var Cn=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new Ln,this._posMatrixCache={},this._alignedPosMatrixCache={}},Pn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};Cn.prototype.clone=function(){var t=new Cn(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Pn.minZoom.get=function(){return this._minZoom},Pn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Pn.maxZoom.get=function(){return this._maxZoom},Pn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Pn.minPitch.get=function(){return this._minPitch},Pn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Pn.maxPitch.get=function(){return this._maxPitch},Pn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Pn.renderWorldCopies.get=function(){return this._renderWorldCopies},Pn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Pn.worldSize.get=function(){return this.tileSize*this.scale},Pn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Pn.size.get=function(){return new t.Point(this.width,this.height)},Pn.bearing.get=function(){return-this.angle/Math.PI*180},Pn.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))},Pn.pitch.get=function(){return this._pitch/Math.PI*180},Pn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Pn.fov.get=function(){return this._fov/Math.PI*180},Pn.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())},Pn.zoom.get=function(){return this._zoom},Pn.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())},Pn.center.get=function(){return this._center},Pn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Pn.padding.get=function(){return this._edgeInsets.toJSON()},Pn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Pn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},Cn.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},Cn.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},Cn.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},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)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),u=s-1;u<=l+1;u++)0!==u&&r.push(new t.UnwrappedTileID(u,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 i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=Sn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var u=function(t){return{aabb:new En([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},c=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)c.push(u(-d)),c.push(u(d));for(c.push(u(0));c.length>0;){var v=c.pop(),g=v.x,y=v.y,m=v.fullyVisible;if(!m){var x=v.aabb.intersects(s);if(0===x)continue;m=2===x}var b=v.aabb.distanceX(o),_=v.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-v.zoom)-2;if(v.zoom===h||w>T&&v.zoom>=l)f.push({tileID:new t.OverscaledTileID(v.zoom===h?p:v.zoom,v.wrap,v.zoom,g,y),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-y])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(y<<1)+(k>>1);c.push({aabb:v.aabb.quadrant(k),zoom:v.zoom+1,x:A,y:M,wrap:v.wrap,fullyVisible:m})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},Cn.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Pn.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()},Pn.point.get=function(){return this.project(this.center)},Cn.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},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 i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,u=n[1]/a,c=r[2]/i,f=n[2]/a,h=c===f?0:(0-c)/(f-c);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,u,h)/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,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},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,i,a=-90,o=90,s=-180,l=180,u=this.size,c=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<u.y?u.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<u.x?u.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=c,void(this._constraining=!1);if(this.latRange){var v=p.y,g=u.y/2;v-g<a&&(i=a+g),v+g>o&&(i=o-g)}if(this.lngRange){var y=p.x,m=u.x/2;y-m<s&&(n=s+m),y+m>l&&(n=l-m)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=c,this._constraining=!1}},Cn.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,u=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),c=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,c,u),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),v=Math.sin(this.angle),g=s-Math.round(s)+d*h+v*p,y=l-Math.round(l)+d*p+v*h,m=new Float64Array(f);if(t.translate(m,m,[g>.5?g-1:g,y>.5?y-1:y,0]),this.alignedProjMatrix=m,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},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,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var u=l[s];n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(Cn.prototype,Pn);var On=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};On.prototype.addTo=function(e){return this._map=e,t.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},On.prototype.remove=function(){return t.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},On.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),u=this._map.getPitch(),c=\"\";if(c+=e?\"/\"+o+\"/\"+s+\"/\"+n:n+\"/\"+s+\"/\"+o,(l||u)&&(c+=\"/\"+Math.round(10*l)/10),u&&(c+=\"/\"+Math.round(u)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split(\"&\").map((function(t){var e=t.split(\"=\")[0];return e===f?(h=!0,e+\"=\"+c):t})).filter((function(t){return t}));return h||p.push(f+\"=\"+c),\"#\"+p.join(\"&\")}return\"#\"+c},On.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace(\"#\",\"\");return this._hashName?(n.split(\"&\").map((function(t){return t.split(\"=\")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||\"\").split(\"/\")):n.split(\"/\")},On.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},On.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,\"\",e)}catch(t){}};var In={linearity:.3,easing:t.bezier(0,0,.3,1)},Dn=t.extend({deceleration:2500,maxSpeed:1400},In),zn=t.extend({deceleration:20,maxSpeed:1400},In),Rn=t.extend({deceleration:1e3,maxSpeed:360},In),Fn=t.extend({deceleration:1e3,maxSpeed:90},In),Bn=function(t){this._map=t,this.clear()};function Nn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function jn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}Bn.prototype.clear=function(){this._inertiaBuffer=[]},Bn.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},Bn.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},Bn.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=jn(r.pan.mag(),o,t.extend({},Dn,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,Nn(s,l)}if(r.zoom){var u=jn(r.zoom,o,zn);s.zoom=this._map.transform.zoom+u.amount,Nn(s,u)}if(r.bearing){var c=jn(r.bearing,o,Rn);s.bearing=this._map.transform.bearing+t.clamp(c.amount,-179,179),Nn(s,c)}if(r.pitch){var f=jn(r.pitch,o,Fn);s.pitch=this._map.transform.pitch+f.amount,Nn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Un=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Vn=function(e){function n(n,i,a){var o=\"touchend\"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),u=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),c=i.unproject(u);e.call(this,n,{points:s,point:u,lngLats:l,lngLat:c,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Hn=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),qn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};qn.prototype.reset=function(){delete this._mousedownPos},qn.prototype.wheel=function(t){return this._firePreventable(new Hn(t.type,this._map,t))},qn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Un(t.type,this._map,t))},qn.prototype.mouseup=function(t){this._map.fire(new Un(t.type,this._map,t))},qn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Un(t.type,this._map,t))},qn.prototype.dblclick=function(t){return this._firePreventable(new Un(t.type,this._map,t))},qn.prototype.mouseover=function(t){this._map.fire(new Un(t.type,this._map,t))},qn.prototype.mouseout=function(t){this._map.fire(new Un(t.type,this._map,t))},qn.prototype.touchstart=function(t){return this._firePreventable(new Vn(t.type,this._map,t))},qn.prototype.touchmove=function(t){this._map.fire(new Vn(t.type,this._map,t))},qn.prototype.touchend=function(t){this._map.fire(new Vn(t.type,this._map,t))},qn.prototype.touchcancel=function(t){this._map.fire(new Vn(t.type,this._map,t))},qn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},qn.prototype.isEnabled=function(){return!0},qn.prototype.isActive=function(){return!1},qn.prototype.enable=function(){},qn.prototype.disable=function(){};var Gn=function(t){this._map=t};Gn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Gn.prototype.mousemove=function(t){this._map.fire(new Un(t.type,this._map,t))},Gn.prototype.mousedown=function(){this._delayContextMenu=!0},Gn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Un(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Gn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Un(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},Gn.prototype.isEnabled=function(){return!0},Gn.prototype.isActive=function(){return!1},Gn.prototype.enable=function(){},Gn.prototype.disable=function(){};var Zn=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Yn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Zn.prototype.isEnabled=function(){return!!this._enabled},Zn.prototype.isActive=function(){return!!this._active},Zn.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Zn.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Zn.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Zn.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,\"translate(\"+a+\"px,\"+s+\"px)\"),this._box.style.width=o-a+\"px\",this._box.style.height=l-s+\"px\"}}},Zn.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event(\"boxzoomend\",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent(\"boxzoomcancel\",e)}},Zn.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))},Zn.prototype.reset=function(){this._active=!1,this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Zn.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Wn=function(t){this.reset(),this.numTouches=t.numTouches};Wn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Wn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Yn(n,r)))},Wn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Yn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Wn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var Xn=function(t){this.singleTap=new Wn(t),this.numTaps=t.numTaps,this.reset()};Xn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},Xn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},Xn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},Xn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Jn=function(){this._zoomIn=new Xn({numTouches:1,numTaps:2}),this._zoomOut=new Xn({numTouches:2,numTaps:1}),this.reset()};Jn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Jn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Jn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Jn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Kn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Kn.prototype._correctButton=function(t,e){return!1},Kn.prototype._move=function(t,e){return{}},Kn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Kn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Kn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Kn.prototype.enable=function(){this._enabled=!0},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};var $n=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Kn),Qn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Kn),ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Kn),ei=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};ei.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},ei.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},ei.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},ei.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},ei.prototype.touchcancel=function(){this.reset()},ei.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Yn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var u=i[l],c=this._touches[l];c&&(a._add(u),o._add(u.sub(c)),s++,i[l]=u)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},ei.prototype.enable=function(){this._enabled=!0},ei.prototype.disable=function(){this._enabled=!1,this.reset()},ei.prototype.isEnabled=function(){return this._enabled},ei.prototype.isActive=function(){return this._active};var ri=function(){this.reset()};function ni(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}ri.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},ri.prototype._start=function(t){},ri.prototype._move=function(t,e,r){return{}},ri.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},ri.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=ni(r,e,i),s=ni(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},ri.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=ni(n,e,a),l=ni(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},ri.prototype.touchcancel=function(){this.reset()},ri.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around},ri.prototype.disable=function(){this._enabled=!1,this.reset()},ri.prototype.isEnabled=function(){return this._enabled},ri.prototype.isActive=function(){return this._active};function ii(t,e){return Math.log(t/e)/Math.LN2}var ai=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs(ii(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:ii(this._distance,r),pinchAround:e}},e}(ri);function oi(t,e){return 180*t.angleWith(e)/Math.PI}var si=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:oi(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=oi(t,this._startVector);return Math.abs(r)<e},e}(ri);function li(t){return Math.abs(t.y)>Math.abs(t.x)}var ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,li(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid)return this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return li(t)&&li(e)&&a}},e}(ri),ci={panStep:100,bearingStep:15,pitchStep:10},fi=function(){var t=ci;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function hi(t){return t*(2-t)}fi.prototype.reset=function(){this._active=!1},fi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:hi,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var pi=4.000244140625,di=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};di.prototype.setZoomRate=function(t){this._defaultZoomRate=t},di.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},di.prototype.isEnabled=function(){return!!this._enabled},di.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},di.prototype.isZooming=function(){return!!this._zooming},di.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},di.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},di.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%pi==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},di.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},di.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},di.prototype.renderFrame=function(){return this._onScrollFrame()},di.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>pi?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,u=this._easing,c=!1;if(\"wheel\"===this._type&&l&&u){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=u(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):c=!0}else o=s,c=!0;return this._active=!0,c&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!c,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},di.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},di.prototype.reset=function(){this._active=!1};var vi=function(t,e){this._clickZoom=t,this._tapZoom=e};vi.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},vi.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},vi.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},vi.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var gi=function(){this.reset()};gi.prototype.reset=function(){this._active=!1},gi.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},gi.prototype.enable=function(){this._enabled=!0},gi.prototype.disable=function(){this._enabled=!1,this.reset()},gi.prototype.isEnabled=function(){return this._enabled},gi.prototype.isActive=function(){return this._active};var yi=function(){this._tap=new Xn({numTouches:1,numTaps:1}),this.reset()};yi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},yi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},yi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},yi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},yi.prototype.touchcancel=function(){this.reset()},yi.prototype.enable=function(){this._enabled=!0},yi.prototype.disable=function(){this._enabled=!1,this.reset()},yi.prototype.isEnabled=function(){return this._enabled},yi.prototype.isActive=function(){return this._active};var mi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};mi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},mi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},mi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},mi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var xi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};xi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},xi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},xi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},xi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var bi=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};bi.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},bi.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},bi.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},bi.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},bi.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},bi.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var _i=function(t){return t.zoom||t.drag||t.pitch||t.rotate},wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function Ti(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var ki=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Bn(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!1}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],u=s[1],c=s[2];r.addEventListener(l,u,l===t.window.document?this.handleWindowEvent:this.handleEvent,c)}};ki.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},ki.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new qn(e,t));var n=e.boxZoom=new Zn(e,t);this._add(\"boxZoom\",n);var i=new Jn,a=new gi;e.doubleClickZoom=new vi(a,i),this._add(\"tapZoom\",i),this._add(\"clickZoom\",a);var o=new yi;this._add(\"tapDragZoom\",o);var s=e.touchPitch=new ui;this._add(\"touchPitch\",s);var l=new Qn(t),u=new ti(t);e.dragRotate=new xi(t,l,u),this._add(\"mouseRotate\",l,[\"mousePitch\"]),this._add(\"mousePitch\",u,[\"mouseRotate\"]);var c=new $n(t),f=new ei(t);e.dragPan=new mi(r,c,f),this._add(\"mousePan\",c),this._add(\"touchPan\",f,[\"touchZoom\",\"touchRotate\"]);var h=new si,p=new ai;e.touchZoomRotate=new bi(r,p,h,o),this._add(\"touchRotate\",h,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",p,[\"touchPan\",\"touchRotate\"]);var d=e.scrollZoom=new di(e,this);this._add(\"scrollZoom\",d,[\"mousePan\"]);var v=e.keyboard=new fi;this._add(\"keyboard\",v),this._add(\"blockableMapEvent\",new Gn(e));for(var g=0,y=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];g<y.length;g+=1){var m=y[g];t.interactive&&t[m]&&e[m].enable(t[m])}},ki.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},ki.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1)e[t].handler.reset();this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},ki.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1)if(e[t].handler.isActive())return!0;return!1},ki.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},ki.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},ki.prototype.isMoving=function(){return Boolean(_i(this._eventsInProgress))||this.isZooming()},ki.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},ki.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+\"Window\")},ki.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},ki.prototype.handleEvent=function(t,e){if(\"blur\"!==t.type){this._updatingCamera=!0;for(var n=\"renderFrame\"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),u=0,c=this._handlers;u<c.length;u+=1){var f=c[u],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var v=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(v=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,v,h,n),v&&v.needsRenderFrame&&this._triggerRenderFrame()),(v||p.isActive())&&(o[h]=p)}}var g={};for(var y in this._previousActiveHandlers)o[y]||(g[y]=n);this._previousActiveHandlers=o,(Object.keys(g).length||Ti(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||Ti(i))&&this._map._stop(!0),this._updatingCamera=!1;var m=i.cameraAnimation;m&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],m(this._map))}else this.stop()},ki.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},ki.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],u=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,u)}this._updateMapTransform(e,r,n),this._changes=[]},ki.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!Ti(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,u=t.around,c=t.pinchAround;void 0!==c&&(u=c),n._stop(!0),u=u||n.transform.centerPoint;var f=i.pointLocation(a?u.sub(a):u);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,u),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},ki.prototype._fireEvents=function(e,r){var n=this,i=_i(this._eventsInProgress),a=_i(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+\"start\"]=l),this._eventsInProgress[s]=e[s]}for(var u in!i&&a&&this._fireEvent(\"movestart\",a.originalEvent),o)this._fireEvent(u,o[u]);for(var c in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent(\"move\",a.originalEvent),e){var f=e[c].originalEvent;this._fireEvent(c,f)}var h,p={};for(var d in this._eventsInProgress){var v=this._eventsInProgress[d],g=v.handlerName,y=v.originalEvent;this._handlersById[g].isActive()||(delete this._eventsInProgress[d],h=r[g]||y,p[d+\"end\"]=h)}for(var m in p)this._fireEvent(m,p[m]);var x=_i(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event(\"moveend\",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},ki.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},ki.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new wi(\"renderFrame\",{timeStamp:e})),t._applyChanges()})))};var Ai=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll([\"_renderFrameCallback\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if(\"number\"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,u=s.project(t.LngLat.convert(e)),c=s.project(t.LngLat.convert(r)),f=u.rotate(-n*Math.PI/180),h=c.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),v=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/v.x,y=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/v.y;if(!(y<0||g<0)){var m=Math.min(s.scaleZoom(s.scale*Math.min(g,y)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(m));return{center:s.unproject(u.add(c).div(2).sub(w)),zoom:m,bearing:n}}t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),i&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),a&&this.fire(new t.Event(\"rotatestart\",r)).fire(new t.Event(\"rotate\",r)).fire(new t.Event(\"rotateend\",r)),o&&this.fire(new t.Event(\"pitchstart\",r)).fire(new t.Event(\"pitch\",r)).fire(new t.Event(\"pitchend\",r)),this.fire(new t.Event(\"moveend\",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),u=\"zoom\"in e?+e.zoom:a,c=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,f=\"pitch\"in e?+e.pitch:s,h=\"padding\"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),v=i.pointLocation(d),g=t.LngLat.convert(e.center||v);this._normalizeCenter(g);var y,m,x=i.project(v),b=i.project(g).sub(x),_=i.zoomScale(u-a);e.around&&(y=t.LngLat.convert(e.around),m=i.locationPoint(y));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||u!==a,this._rotating=this._rotating||o!==c,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,u,e)),n._rotating&&(i.bearing=t.number(o,c,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),y)i.setLocationAtPoint(y,m);else{var v=i.zoomScale(i.zoom-a),g=u>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(v));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),u=this.getPadding(),c=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(c-o),v=t.Point.convert(e.offset),g=a.centerPoint.add(v),y=a.pointLocation(g),m=t.LngLat.convert(e.center||y);this._normalizeCenter(m);var x=a.project(y),b=a.project(m).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var A=t.clamp(Math.min(e.minZoom,o,c),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),O=function(t){return C(P)/C(P+_*t)},I=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},D=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(D)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var z=T<w?-1:1;D=Math.abs(Math.log(T/w))/_,I=function(){return 0},O=function(t){return Math.exp(z*_*t)}}if(\"duration\"in e)e.duration=+e.duration;else{var R=\"screenSpeed\"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*D/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*D,d=1/O(i);a.zoom=1===e?c:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(u,p,e),g=a.centerPoint.add(v));var y=1===e?m:a.unproject(x.add(b.mult(I(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?y.wrap():y,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),Mi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};Mi.prototype.getDefaultPosition=function(){return\"bottom-right\"},Mi.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},Mi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},Mi.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\"}},Mi.prototype._updateData=function(t){!t||\"metadata\"!==t.sourceDataType&&\"style\"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},Mi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return\"string\"!=typeof t?\"\":t}))):\"string\"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},Mi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var Si=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};Si.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},Si.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},Si.prototype.getDefaultPosition=function(){return\"bottom-left\"},Si.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},Si.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}},Si.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 Ei=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};Ei.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},Ei.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},Ei.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},Ei.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Li={\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\"},Ci=t.window.HTMLImageElement,Pi=t.window.HTMLElement,Oi=t.window.ImageBitmap,Ii=60,Di={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:Ii,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:\"sans-serif\",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},zi=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Di,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>Ii)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new Cn(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new Ei,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Li,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof Pi))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1)),this.handlers=new ki(this,e);var a=\"string\"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new On(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new Mi({customAttribution:e.customAttribution})),this.addControl(new Si,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r=\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?Ii:t)>Ii)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Un(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Un(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Un(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||\"number\"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error(\"Missing UI string '\"+t+\"'\");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new Ye(this,e||{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new Ye(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if(\"string\"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else\"object\"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce(\"Unable to perform style diff: \"+(n.message||n.error||n)+\".  Rebuilding the style from scratch.\"),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce(\"There is no style added to the map.\")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error(\"There is no source with ID '\"+e+\"'\")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if(\"loaded\"!==i.state&&\"errored\"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof Ci||Oi&&r instanceof Oi){var u=t.browser.getImageData(r),c=u.width,f=u.height,h=u.data;this.style.addImage(e,{data:new t.RGBAImage({width:c,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var p=r.width,d=r.height,v=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(v)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));var i=r instanceof Ci||Oi&&r instanceof Oi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));var l=!(r instanceof Ci||Oi&&r instanceof Oi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){\"rgb(250, 128, 114)\"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue(\"background-color\")&&t.warnOnce(\"This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.\")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add(\"mapboxgl-map\"),(this._missingCSSCanary=r.create(\"div\",\"mapboxgl-canary\",t)).style.visibility=\"hidden\",this._detectMissingCSS();var e=this._canvasContainer=r.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=r.create(\"canvas\",\"mapboxgl-canvas\",e),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create(\"div\",\"mapboxgl-control-container\",t),a=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((function(t){a[t]=r.create(\"div\",\"mapboxgl-ctrl-\"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext(\"webgl\",r)||this._canvas.getContext(\"experimental-webgl\",r);n?(this.painter=new Mn(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens(\"gpu-timing-frame\")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var u=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),c=u.crossFadingFactor();1===c&&c===this._crossFadingFactor||(o=!0,this._crossFadingFactor=c),this.style.update(u)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens(\"gpu-timing-layer\")}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens(\"gpu-timing-frame\")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event(\"gpu-timing-frame\",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens(\"gpu-timing-layer\")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event(\"gpu-timing-layer\",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event(\"idle\"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1)r[e].onRemove(this);this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener(\"resize\",this._onWindowResize,!1),t.window.removeEventListener(\"online\",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");n&&n.loseContext(),Ri(this._canvasContainer),Ri(this._controlContainer),Ri(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this._removed=!0,this.fire(new t.Event(\"remove\"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(Ai);function Ri(t){t.parentNode&&t.parentNode.removeChild(t)}var Fi={showCompass:!0,showZoom:!0,visualizePitch:!1},Bi=function(e){var n=this;this.options=t.extend({},Fi,e),this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll([\"_setButtonTitle\",\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-zoom-in\",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",!0),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-zoom-out\",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",!0)),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-compass\",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create(\"span\",\"mapboxgl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",!0))};Bi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Bi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?\"scale(\"+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+\") rotateX(\"+this._map.transform.pitch+\"deg) rotateZ(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\":\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassIcon.style.transform=t},Bi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ni(this._map,this._compass,this.options.visualizePitch)),this._container},Bi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Bi.prototype._createButton=function(t,e){var n=r.create(\"button\",t,this._container);return n.type=\"button\",n.addEventListener(\"click\",e),n},Bi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString(\"NavigationControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)};var Ni=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Qn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new ti({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll([\"mousedown\",\"mousemove\",\"mouseup\",\"touchstart\",\"touchmove\",\"touchend\",\"reset\"],this),r.addEventListener(n,\"mousedown\",this.mousedown),r.addEventListener(n,\"touchstart\",this.touchstart,{passive:!1}),r.addEventListener(n,\"touchmove\",this.touchmove),r.addEventListener(n,\"touchend\",this.touchend),r.addEventListener(n,\"touchcancel\",this.reset)};function ji(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ni.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ni.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ni.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ni.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ni.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ni.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ni.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ni.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ni.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ni.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ni.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var Ui={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 Vi(t,e,r){var n=t.classList;for(var i in Ui)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var Hi,qi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\",\"_onKeyPress\"],this),this._anchor=n&&n.anchor||\"center\",this._color=n&&n.color||\"#3FB1CE\",this._draggable=n&&n.draggable||!1,this._state=\"inactive\",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||\"auto\",this._pitchAlignment=n&&n.pitchAlignment&&\"auto\"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");var o=r.createNS(\"http://www.w3.org/2000/svg\",\"svg\");o.setAttributeNS(null,\"display\",\"block\"),o.setAttributeNS(null,\"height\",\"41px\"),o.setAttributeNS(null,\"width\",\"27px\"),o.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var s=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"stroke\",\"none\"),s.setAttributeNS(null,\"stroke-width\",\"1\"),s.setAttributeNS(null,\"fill\",\"none\"),s.setAttributeNS(null,\"fill-rule\",\"evenodd\");var l=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"fill-rule\",\"nonzero\");var u=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");u.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),u.setAttributeNS(null,\"fill\",\"#000000\");for(var c=0,f=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];c<f.length;c+=1){var h=f[c],p=r.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");p.setAttributeNS(null,\"opacity\",\"0.04\"),p.setAttributeNS(null,\"cx\",\"10.5\"),p.setAttributeNS(null,\"cy\",\"5.80029008\"),p.setAttributeNS(null,\"rx\",h.rx),p.setAttributeNS(null,\"ry\",h.ry),u.appendChild(p)}var d=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");d.setAttributeNS(null,\"fill\",this._color);var v=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");v.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),d.appendChild(v);var g=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");g.setAttributeNS(null,\"opacity\",\"0.25\"),g.setAttributeNS(null,\"fill\",\"#000000\");var y=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");y.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(y);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 x=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");x.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var b=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");b.setAttributeNS(null,\"fill\",\"#000000\"),b.setAttributeNS(null,\"opacity\",\"0.25\"),b.setAttributeNS(null,\"cx\",\"5.5\"),b.setAttributeNS(null,\"cy\",\"5.5\"),b.setAttributeNS(null,\"r\",\"5.4999962\");var _=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");_.setAttributeNS(null,\"fill\",\"#FFFFFF\"),_.setAttributeNS(null,\"cx\",\"5.5\"),_.setAttributeNS(null,\"cy\",\"5.5\"),_.setAttributeNS(null,\"r\",\"5.4999962\"),x.appendChild(b),x.appendChild(_),l.appendChild(u),l.appendChild(d),l.appendChild(g),l.appendChild(m),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",(function(t){t.preventDefault()})),this._element.addEventListener(\"mousedown\",(function(t){t.preventDefault()})),this._element.addEventListener(\"focus\",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Vi(this._element,this._anchor,\"marker\"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),t){if(!(\"offset\"in t.options)){var e=13.5,r=Math.sqrt(Math.pow(e,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[r,-1*(24.6+r)],\"bottom-right\":[-r,-1*(24.6+r)],left:[e,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;\"Space\"!==e&&\"Enter\"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=ji(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e=\"\";\"viewport\"===this._rotationAlignment||\"auto\"===this._rotationAlignment?e=\"rotateZ(\"+this._rotation+\"deg)\":\"map\"===this._rotationAlignment&&(e=\"rotateZ(\"+(this._rotation-this._map.getBearing())+\"deg)\");var n=\"\";\"viewport\"===this._pitchAlignment||\"auto\"===this._pitchAlignment?n=\"rotateX(0deg)\":\"map\"===this._pitchAlignment&&(n=\"rotateX(\"+this._map.getPitch()+\"deg)\"),t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,Ui[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px) \"+n+\" \"+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\"))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Gi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Zi=0,Yi=!1,Wi=function(e){function n(r){e.call(this),this.options=t.extend({},Gi,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==Hi?n(Hi):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){Hi=\"denied\"!==t.state,n(Hi)})):(Hi=!!t.window.navigator.geolocation,n(Hi)),this._container;var n},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Zi=0,Yi=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&Yi)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new qi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new qi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){var r=e.originalEvent&&\"resize\"===e.originalEvent.type;e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||r||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Zi--,Yi=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Zi>1?(e={maximumAge:6e5,timeout:0},Yi=!0):(e=this.options.positionOptions,Yi=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Xi={maxWidth:100,unit:\"metric\"},Ji=function(e){this.options=t.extend({},Xi,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function Ki(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;l>5280?$i(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\")):$i(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else r&&\"nautical\"===r.unit?$i(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\")):s>=1e3?$i(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):$i(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function $i(t,e,r,n){var i,a,o,s=(i=r,(a=Math.pow(10,(\"\"+Math.floor(i)).length-1))*((o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o))),l=s/r;t.style.width=e*l+\"px\",t.innerHTML=s+\"&nbsp;\"+n}Ji.prototype.getDefaultPosition=function(){return\"bottom-left\"},Ji.prototype._onMove=function(){Ki(this._map,this._container,this.options)},Ji.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},Ji.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},Ji.prototype.setUnit=function(t){this.options.unit=t,Ki(this._map,this._container,this.options)};var Qi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};Qi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},Qi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Qi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Qi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Qi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},Qi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},Qi.prototype._isFullscreen=function(){return this._fullscreen},Qi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},Qi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var ta={closeButton:!0,closeOnClick:!0,className:\"\",maxWidth:\"240px\"},ea=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(ta),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(t){var e=this,n=this._lngLat||this._trackPointer;if(this._map&&n&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return e._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=ji(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||t)){var i=this._pos=this._trackPointer&&t?t:this._map.project(this._lngLat),a=this.options.anchor,o=ra(this.options.offset);if(!a){var s,l=this._container.offsetWidth,u=this._container.offsetHeight;s=i.y+o.bottom.y<u?[\"top\"]:i.y>this._map.transform.height-u?[\"bottom\"]:[],i.x<l/2?s.push(\"left\"):i.x>this._map.transform.width-l/2&&s.push(\"right\"),a=0===s.length?\"bottom\":s.join(\"-\")}var c=i.add(o[a]).round();r.setTransform(this._container,Ui[a]+\" translate(\"+c.x+\"px,\"+c.y+\"px)\"),Vi(this._container,a,\"popup\")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);function ra(e){if(e){if(\"number\"==typeof e){var r=Math.round(Math.sqrt(.5*Math.pow(e,2)));return{center:new t.Point(0,0),top:new t.Point(0,e),\"top-left\":new t.Point(r,r),\"top-right\":new t.Point(-r,r),bottom:new t.Point(0,-e),\"bottom-left\":new t.Point(r,-r),\"bottom-right\":new t.Point(-r,-r),left:new t.Point(e,0),right:new t.Point(-e,0)}}if(e instanceof t.Point||Array.isArray(e)){var n=t.Point.convert(e);return{center:n,top:n,\"top-left\":n,\"top-right\":n,bottom:n,\"bottom-left\":n,\"bottom-right\":n,left:n,right:n}}return{center:t.Point.convert(e.center||[0,0]),top:t.Point.convert(e.top||[0,0]),\"top-left\":t.Point.convert(e[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(e[\"top-right\"]||[0,0]),bottom:t.Point.convert(e.bottom||[0,0]),\"bottom-left\":t.Point.convert(e[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(e[\"bottom-right\"]||[0,0]),left:t.Point.convert(e.left||[0,0]),right:t.Point.convert(e.right||[0,0])}}return ra(new t.Point(0,0))}var na={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:zi,NavigationControl:Bi,GeolocateControl:Wi,AttributionControl:Mi,ScaleControl:Ji,FullscreenControl:Qi,Popup:ea,Marker:qi,Style:Ye,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){jt().acquire(Rt)},clearPrewarmedResources:function(){var t=Bt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(Rt),Bt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. 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e=[u(t.region,!1)];c.push(e);for(var r=0;r<t.children.length;r++)e.push(h(t.children[r]))}function h(t){for(var e=0;e<t.children.length;e++)f(t.children[e]);return u(t.region,!0)}for(s=0;s<a.children.length;s++)f(a.children[s]);return c.length<=0?{type:\"Polygon\",coordinates:[]}:1==c.length?{type:\"Polygon\",coordinates:c[0]}:{type:\"MultiPolygon\",coordinates:c}}};t.exports=e},87263:function(t,e,r){var n=r(26859);t.exports=function(t,e,r){function i(t,e,n){return{id:r?r.segmentId():-1,start:t,end:e,myFill:{above:n.myFill.above,below:n.myFill.below},otherFill:null}}var a=n.create();function o(t,r){a.insertBefore(t,(function(n){return i=t.isStart,a=t.pt,o=r,s=n.isStart,l=n.pt,u=n.other.pt,(0!==(c=e.pointsCompare(a,l))?c:e.pointsSame(o,u)?0:i!==s?i?1:-1:e.pointAboveOrOnLine(o,s?l:u,s?u:l)?1:-1)<0;var i,a,o,s,l,u,c}))}function s(t,e){var r=function(t,e){var r=n.node({isStart:!0,pt:t.start,seg:t,primary:e,other:null,status:null});return o(r,t.end),r}(t,e);return function(t,e,r){var i=n.node({isStart:!1,pt:e.end,seg:e,primary:r,other:t,status:null});t.other=i,o(i,t.pt)}(r,t,e),r}function l(t,e){var n=i(e,t.seg.end,t.seg);return function(t,e){r&&r.segmentChop(t.seg,e),t.other.remove(),t.seg.end=e,t.other.pt=e,o(t.other,t.pt)}(t,e),s(n,t.primary)}function u(i,o){var s=n.create();function u(t){return s.findTransition((function(r){var n,i,a,o,s,l;return n=t,i=r.ev,a=n.seg.start,o=n.seg.end,s=i.seg.start,l=i.seg.end,(e.pointsCollinear(a,s,l)?e.pointsCollinear(o,s,l)||e.pointAboveOrOnLine(o,s,l)?1:-1:e.pointAboveOrOnLine(a,s,l)?1:-1)>0}))}function c(t,n){var i=t.seg,a=n.seg,o=i.start,s=i.end,u=a.start,c=a.end;r&&r.checkIntersection(i,a);var f=e.linesIntersect(o,s,u,c);if(!1===f){if(!e.pointsCollinear(o,s,u))return!1;if(e.pointsSame(o,c)||e.pointsSame(s,u))return!1;var h=e.pointsSame(o,u),p=e.pointsSame(s,c);if(h&&p)return n;var d=!h&&e.pointBetween(o,u,c),v=!p&&e.pointBetween(s,u,c);if(h)return v?l(n,s):l(t,c),n;d&&(p||(v?l(n,s):l(t,c)),l(n,o))}else 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a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],u=o[0],c=o[1];e.pointsCollinear(l,s,u)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,u,c)&&(r&&r.chainRemoveHead(i,u),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},55968:function(t){function e(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var r={union:function(t,r){return e(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],r)},intersect:function(t,r){return e(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],r)},difference:function(t,r){return e(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],r)},differenceRev:function(t,r){return e(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],r)},xor:function(t,r){return e(t,[0,2,1,0,2,0,0,1,1,0,0,2,0,1,2,0],r)}};t.exports=r},14847:function(t,e,r){\"use 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t&&(t=x[t]),null!=t&&t&&t.count&&t.color&&t.opacity&&t.positions&&t.positions.length>1&&(t.scaleRatio=[t.scale[0]*t.viewport.width,t.scale[1]*t.viewport.height],r(t),t.after&&t.after(t))}function T(t){if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var e=0,r=0;if(b.groups=x=t.map((function(t,u){var c=x[u];return t?(\"function\"==typeof t?t={after:t}:\"number\"==typeof t[0]&&(t={positions:t}),t=o(t,{color:\"color colors fill\",capSize:\"capSize cap capsize cap-size\",lineWidth:\"lineWidth line-width width line thickness\",opacity:\"opacity alpha\",range:\"range dataBox\",viewport:\"viewport viewBox\",errors:\"errors error\",positions:\"positions position data points\"}),c||(x[u]=c={id:u,scale:null,translate:null,scaleFract:null,translateFract:null,draw:!0},t=s({},m,t)),a(c,t,[{lineWidth:function(t){return.5*+t},capSize:function(t){return.5*+t},opacity:parseFloat,errors:function(t){return t=l(t),r+=t.length,t},positions:function(t,r){return t=l(t,\"float64\"),r.count=Math.floor(t.length/2),r.bounds=n(t,2),r.offset=e,e+=r.count,t}},{color:function(t,e){var r=e.count;if(t||(t=\"transparent\"),!Array.isArray(t)||\"number\"==typeof t[0]){var n=t;t=Array(r);for(var a=0;a<r;a++)t[a]=n}if(t.length<r)throw Error(\"Not enough colors\");for(var o=new Uint8Array(4*r),s=0;s<r;s++){var l=i(t[s],\"uint8\");o.set(l,4*s)}return o},range:function(t,e,r){var n=e.bounds;return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=f(e.scale),e.translateFract=f(e.translate),t},viewport:function(t){var e;return Array.isArray(t)?e={x:t[0],y:t[1],width:t[2]-t[0],height:t[3]-t[1]}:t?(e={x:t.x||t.left||0,y:t.y||t.top||0},t.right?e.width=t.right-e.x:e.width=t.w||t.width||0,t.bottom?e.height=t.bottom-e.y:e.height=t.h||t.height||0):e={x:0,y:0,width:y.drawingBufferWidth,height:y.drawingBufferHeight},e}}]),c):c})),e||r){var h=x.reduce((function(t,e,r){return t+(e?e.count:0)}),0),g=new Float64Array(2*h),_=new Uint8Array(4*h),w=new Float32Array(4*h);x.forEach((function(t,e){if(t){var r=t.positions,n=t.count,i=t.offset,a=t.color,o=t.errors;n&&(_.set(a,4*i),w.set(o,4*i),g.set(r,2*i))}}));var T=c(g);u(T);var k=f(g,T);p(k),d(_),v(w)}}}function k(){u.destroy(),p.destroy(),d.destroy(),v.destroy(),g.destroy()}};var h=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]]},46075:function(t,e,r){\"use strict\";var n=r(25075),i=r(21527),a=r(56131),o=r(56068),s=r(71299),l=r(30120),u=r(11474),c=r(54),f=r(57060),h=f.float32,p=f.fract32,d=r(83522),v=r(18863),g=r(6851);function y(t,e){if(!(this instanceof y))return new y(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=y.shaders.has(t)?y.shaders.get(t):y.shaders.set(t,y.createShaders(t)).get(t),this.update(e)}t.exports=y,y.dashMult=2,y.maxPatternLength=256,y.precisionThreshold=3e6,y.maxPoints=1e4,y.maxLines=2048,y.shaders=new d,y.createShaders=function(t){var e,r=t.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),n={primitive:\"triangle strip\",instances:t.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:function(t,e){return\"round\"===e.join?2:1},miterLimit:t.prop(\"miterLimit\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),thickness:t.prop(\"thickness\"),dashTexture:t.prop(\"dashTexture\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),dashLength:t.prop(\"dashLength\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]},depth:t.prop(\"depth\")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:function(t,e){return!e.overlay}},stencil:{enable:!1},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\")},i=t(a({vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\\nattribute vec4 color;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\\n\\t// the order is important\\n\\treturn position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n}\\n\\nvoid main() {\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineOffset = lineTop * 2. - 1.;\\n\\n\\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\\n\\ttangent = normalize(diff * scale * viewport.zw);\\n\\tvec2 normal = vec2(-tangent.y, tangent.x);\\n\\n\\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\\n\\t\\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\\n\\n\\t\\t+ thickness * normal * .5 * lineOffset / viewport.zw;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvoid main() {\\n\\tfloat alpha = 1.;\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:\"back\"},vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\\nattribute vec4 aColor, bColor;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, translate;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\nuniform float miterLimit, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 tangent;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nconst float REVERSE_THRESHOLD = -.875;\\nconst float MIN_DIFF = 1e-6;\\n\\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\\n// TODO: precalculate dot products, normalize things beforehead etc.\\n// TODO: refactor to rectangular algorithm\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nbool isNaN( float val ){\\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\\n}\\n\\nvoid main() {\\n\\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\\n\\n  vec2 adjustedScale;\\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\\n\\n  vec2 scaleRatio = adjustedScale * viewport.zw;\\n\\tvec2 normalWidth = thickness / scaleRatio;\\n\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineBot = 1. - lineTop;\\n\\n\\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\\n\\n\\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\\n\\n\\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\\n\\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\\n\\n\\tvec2 prevDiff = aCoord - prevCoord;\\n\\tvec2 currDiff = bCoord - aCoord;\\n\\tvec2 nextDiff = nextCoord - bCoord;\\n\\n\\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\\n\\tvec2 currTangent = normalize(currDiff * scaleRatio);\\n\\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\\n\\n\\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\\n\\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\\n\\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\\n\\n\\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\\n\\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\\n\\n\\t// collapsed/unidirectional segment cases\\n\\t// FIXME: there should be more elegant solution\\n\\tvec2 prevTanDiff = abs(prevTangent - currTangent);\\n\\tvec2 nextTanDiff = abs(nextTangent - currTangent);\\n\\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\\n\\t\\tstartJoinDirection = currNormal;\\n\\t}\\n\\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\\n\\t\\tendJoinDirection = currNormal;\\n\\t}\\n\\tif (aCoord == bCoord) {\\n\\t\\tendJoinDirection = startJoinDirection;\\n\\t\\tcurrNormal = prevNormal;\\n\\t\\tcurrTangent = prevTangent;\\n\\t}\\n\\n\\ttangent = currTangent;\\n\\n\\t//calculate join shifts relative to normals\\n\\tfloat startJoinShift = dot(currNormal, startJoinDirection);\\n\\tfloat endJoinShift = dot(currNormal, endJoinDirection);\\n\\n\\tfloat startMiterRatio = abs(1. / startJoinShift);\\n\\tfloat endMiterRatio = abs(1. / endJoinShift);\\n\\n\\tvec2 startJoin = startJoinDirection * startMiterRatio;\\n\\tvec2 endJoin = endJoinDirection * endMiterRatio;\\n\\n\\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\\n\\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\\n\\tstartBotJoin = -startTopJoin;\\n\\n\\tendTopJoin = sign(endJoinShift) * endJoin * .5;\\n\\tendBotJoin = -endTopJoin;\\n\\n\\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\\n\\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\\n\\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\\n\\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\\n\\n\\t//miter anti-clipping\\n\\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\\n\\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\\n\\n\\t//prevent close to reverse direction switch\\n\\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\n\\tif (prevReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\\n\\t\\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * 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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 = 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t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>y.precisionThreshold||e.scale[1]*e.viewport.height>y.precisionThreshold||\"rect\"===e.join||!e.join&&(e.thickness<=2||e.count>=y.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},y.prototype.update=function(t){var e=this;if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if(\"number\"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin 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Uint8Array})},t=a({},y.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<y.maxLines&&(d.depth=2*(y.maxLines-1-f%y.maxLines)/y.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,\"uint8\"):null),null!=t.viewport&&(d.viewport=v(t.viewport)),d.viewport||(d.viewport=v([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var m,x;if(t.positions.x&&t.positions.y){var b=t.positions.x,_=t.positions.y;x=d.count=Math.max(b.length,_.length),m=new Float64Array(2*x);for(var w=0;w<x;w++)m[2*w]=b[w],m[2*w+1]=_[w]}else m=l(t.positions,\"float64\"),x=d.count=Math.floor(m.length/2);var T=d.bounds=i(m,2);if(d.fill){for(var k=[],A={},M=0,S=0,E=0,L=d.count;S<L;S++){var C=m[2*S],P=m[2*S+1];isNaN(C)||isNaN(P)||null==C||null==P?(C=m[2*M],P=m[2*M+1],A[S]=M):M=S,k[E++]=C,k[E++]=P}if(t.splitNull){d.count-1 in A||(A[d.count]=d.count-1);var O=Object.keys(A).map(Number).sort((function(t,e){return t-e})),I=[],D=0,z=null!=d.hole?d.hole[0]:null;if(null!=z){var R=g(O,(function(t){return t>=z}));(O=O.slice(0,R)).push(z)}for(var F=function(t){var e=k.slice(2*D,2*O[t]).concat(z?k.slice(2*z):[]),r=(d.hole||[]).map((function(e){return e-z+(O[t]-D)})),n=u(e,r);n=n.map((function(e){return e+D+(e+D<O[t]?0:z-O[t])})),I.push.apply(I,n),D=O[t]+1},B=0;B<O.length;B++)F(B);for(var N=0,j=I.length;N<j;N++)null!=A[I[N]]&&(I[N]=A[I[N]]);d.triangles=I}else{for(var U=u(k,d.hole||[]),V=0,H=U.length;V<H;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);d.triangles=U}}var q=new Float64Array(m);c(q,2,T);var G=new Float64Array(2*x+6);d.close?m[0]===m[2*x-2]&&m[1]===m[2*x-1]?(G[0]=q[2*x-4],G[1]=q[2*x-3]):(G[0]=q[2*x-2],G[1]=q[2*x-1]):(G[0]=q[0],G[1]=q[1]),G.set(q,2),d.close?m[0]===m[2*x-2]&&m[1]===m[2*x-1]?(G[2*x+2]=q[2],G[2*x+3]=q[3],d.count-=1):(G[2*x+2]=q[0],G[2*x+3]=q[1],G[2*x+4]=q[2],G[2*x+5]=q[3]):(G[2*x+2]=q[2*x-2],G[2*x+3]=q[2*x-1],G[2*x+4]=q[2*x-2],G[2*x+5]=q[2*x-1]);var Z=h(G);d.positionBuffer(Z);var Y=p(G,Z);d.positionFractBuffer(Y)}if(t.range?d.range=t.range:d.range||(d.range=d.bounds),(t.range||t.positions)&&d.count){var W=d.bounds,X=W[2]-W[0],J=W[3]-W[1],K=d.range[2]-d.range[0],$=d.range[3]-d.range[1];d.scale=[X/K,J/$],d.translate=[-d.range[0]/K+W[0]/K||0,-d.range[1]/$+W[1]/$||0],d.scaleFract=p(d.scale),d.translateFract=p(d.translate)}if(t.dashes){var Q,tt=0;if(!t.dashes||t.dashes.length<2)tt=1,Q=new Uint8Array([255,255,255,255,255,255,255,255]);else{tt=0;for(var et=0;et<t.dashes.length;++et)tt+=t.dashes[et];Q=new Uint8Array(tt*y.dashMult);for(var rt=0,nt=255,it=0;it<2;it++)for(var at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*y.dashMult*.5;ot<st;++ot)Q[rt++]=nt;nt^=255}}d.dashLength=tt,d.dashTexture({channels:1,data:Q,width:Q.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(t.color){var lt=d.count,ut=t.color;ut||(ut=\"transparent\");var ct=new Uint8Array(4*lt+4);if(Array.isArray(ut)&&\"number\"!=typeof ut[0]){for(var ft=0;ft<lt;ft++){var ht=n(ut[ft],\"uint8\");ct.set(ht,4*ft)}ct.set(n(ut[0],\"uint8\"),4*lt)}else for(var pt=n(ut,\"uint8\"),dt=0;dt<lt+1;dt++)ct.set(pt,4*dt);d.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:ct})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var m=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&m.push(this.passes[x]);return this.passes=m,this}},y.prototype.destroy=function(){return this.passes.forEach((function(t){t.colorBuffer.destroy(),t.positionBuffer.destroy(),t.dashTexture.destroy()})),this.passes.length=0,this}},11870:function(t,e,r){\"use strict\";function n(t,e){return function(t){if(Array.isArray(t))return t}(t)||function(t,e){var r=null==t?null:\"undefined\"!=typeof Symbol&&t[Symbol.iterator]||t[\"@@iterator\"];if(null!=r){var n,i,a,o,s=[],l=!0,u=!1;try{if(a=(r=r.call(t)).next,0===e){if(Object(r)!==r)return;l=!1}else for(;!(l=(n=a.call(r)).done)&&(s.push(n.value),s.length!==e);l=!0);}catch(t){u=!0,i=t}finally{try{if(!l&&null!=r.return&&(o=r.return(),Object(o)!==o))return}finally{if(u)throw i}}return s}}(t,e)||i(t,e)||function(){throw new TypeError(\"Invalid attempt to destructure non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\")}()}function i(t,e){if(t){if(\"string\"==typeof t)return a(t,e);var r=Object.prototype.toString.call(t).slice(8,-1);return\"Object\"===r&&t.constructor&&(r=t.constructor.name),\"Map\"===r||\"Set\"===r?Array.from(t):\"Arguments\"===r||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(r)?a(t,e):void 0}}function a(t,e){(null==e||e>t.length)&&(e=t.length);for(var r=0,n=new Array(e);r<e;r++)n[r]=t[r];return n}var o=r(25075),s=r(21527),l=r(6475),u=r(88294),c=r(56131),f=r(56068),h=r(71299),p=r(93447),d=r(30120),v=r(62683),g=r(57060),y=r(18863),m=x;function x(t,e){var r=this;if(!(this instanceof x))return new x(t,e);\"function\"==typeof t?(e||(e={}),e.regl=t):(e=t,t=null),e&&e.length&&(e.positions=e);var n,i=(t=e.regl)._gl,a=[];this.tooManyColors=v,n=t.texture({data:new Uint8Array(1020),width:255,height:1,type:\"uint8\",format:\"rgba\",wrapS:\"clamp\",wrapT:\"clamp\",mag:\"nearest\",min:\"nearest\"}),c(this,{regl:t,gl:i,groups:[],markerCache:[null],markerTextures:[null],palette:a,paletteIds:{},paletteTexture:n,maxColors:255,maxSize:100,canvas:i.canvas}),this.update(e);var o={uniforms:{constPointSize:!!e.constPointSize,opacity:t.prop(\"opacity\"),paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},pixelRatio:t.context(\"pixelRatio\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translate:t.prop(\"translate\"),translateFract:t.prop(\"translateFract\"),markerTexture:t.prop(\"markerTexture\"),paletteTexture:n},attributes:{x:function(t,e){return e.xAttr||{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return e.borderSize.length?{buffer:e.sizeBuffer,stride:2,offset:1}:{constant:[Math.round(255*e.borderSize/r.maxSize)]}},colorId:function(t,e){return e.color.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:0}:{constant:r.tooManyColors?a.slice(4*e.color,4*e.color+4):[e.color]}},borderColorId:function(t,e){return e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4*e.borderColor,4*e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop(\"elements\"),count:t.prop(\"count\"),offset:t.prop(\"offset\"),primitive:\"points\"},s=c({},o);s.frag=f([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float opacity;\\nuniform sampler2D markerTexture;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragWidth, fragBorderColorLevel, fragColorLevel;\\n\\nfloat smoothStep(float x, float y) {\\n  return 1.0 / (1.0 + exp(50.0*(x - y)));\\n}\\n\\nvoid main() {\\n  float dist = texture2D(markerTexture, gl_PointCoord).r, delta = fragWidth;\\n\\n  // max-distance alpha\\n  if (dist < 0.003) discard;\\n\\n  // null-border case\\n  if (fragBorderColorLevel == fragColorLevel || fragBorderColor.a == 0.) {\\n    float colorAmt = smoothstep(.5 - delta, .5 + delta, dist);\\n    gl_FragColor = vec4(fragColor.rgb, colorAmt * fragColor.a * opacity);\\n  }\\n  else {\\n    float borderColorAmt = smoothstep(fragBorderColorLevel - delta, fragBorderColorLevel + delta, dist);\\n    float colorAmt = smoothstep(fragColorLevel - delta, fragColorLevel + delta, dist);\\n\\n    vec4 color = fragBorderColor;\\n    color.a *= borderColorAmt;\\n    color = mix(color, fragColor, colorAmt);\\n    color.a *= opacity;\\n\\n    gl_FragColor = color;\\n  }\\n\\n}\\n\"]),s.vert=f([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\\nuniform sampler2D paletteTexture;\\n\\nconst float maxSize = 100.;\\nconst float borderLevel = .5;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = 2. * size * pointSizeScale;\\n  fragPointSize = size * pixelRatio;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragColor = color;\\n  fragBorderColor = borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n\\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\\n}\"]),this.drawMarker=t(s);var l=c({},o);l.frag=f([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nuniform float opacity;\\n\\nfloat smoothStep(float edge0, float edge1, float x) {\\n\\tfloat t;\\n\\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\\n\\treturn t * t * (3.0 - 2.0 * t);\\n}\\n\\nvoid main() {\\n\\tfloat radius, alpha = 1.0, delta = fragWidth;\\n\\n\\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\\n\\n\\tif (radius > 1.0 + delta) {\\n\\t\\tdiscard;\\n\\t}\\n\\n\\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\\n\\n\\tfloat borderRadius = fragBorderRadius;\\n\\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\\n\\tvec4 color = mix(fragColor, fragBorderColor, ratio);\\n\\tcolor.a *= alpha * opacity;\\n\\tgl_FragColor = color;\\n}\\n\"]),l.vert=f([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 paletteSize, scale, scaleFract, translate, translateFract;\\nuniform sampler2D paletteTexture;\\n\\nconst float maxSize = 100.;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = (size + borderSize) * pointSizeScale;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\\n  fragColor = color;\\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n}\\n\"]),v&&(l.frag=l.frag.replace(\"smoothstep\",\"smoothStep\"),s.frag=s.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(l)}x.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},x.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},x.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},x.prototype.drawItem=function(t,e){var r,n=this.groups,o=n[t];if(\"number\"==typeof e&&(t=e,o=n[e],e=null),o&&o.count&&o.opacity){o.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,o,e));for(var s=[],l=1;l<o.activation.length;l++)o.activation[l]&&(!0===o.activation[l]||o.activation[l].data.length)&&s.push.apply(s,function(t){if(Array.isArray(t))return a(t)}(r=this.getMarkerDrawOptions(l,o,e))||function(t){if(\"undefined\"!=typeof Symbol&&null!=t[Symbol.iterator]||null!=t[\"@@iterator\"])return Array.from(t)}(r)||i(r)||function(){throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\")}());s.length&&this.drawMarker(s)}},x.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,u=e.count;if(this.regl,!a)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 f=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(u),v=0;v<r.length;v++){var g=r[v];d[g]=p?p[g]:1}l.subdata(d)}for(var y=h.length;y--;){var m=n(h[y],2),x=m[0],b=m[1];f.push(c({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return f},x.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,l=this.maxSize,f=this.maxColors,v=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:\"function\"==typeof e?e={ondraw:e}:\"number\"==typeof e[0]&&(e={positions:e}),null===(e=h(e,{positions:\"positions data points\",snap:\"snap cluster lod tree\",size:\"sizes size radius\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",color:\"colors color fill fill-color fillColor\",borderColor:\"borderColors borderColor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range dataBox databox\",viewport:\"viewport viewPort viewBox viewbox\",opacity:\"opacity alpha transparency\",bounds:\"bound bounds boundaries limits\",tooManyColors:\"tooManyColors palette paletteMode optimizePalette enablePalette\"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:\"stream\",type:\"uint8\"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"})},e=c({},x.defaults,e)),e.positions&&!(\"marker\"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!(\"positions\"in e)&&(e.positions=n.positions,delete n.positions);var m=0,b=0;if(p(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=x.defaults.size),m+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=x.defaults.borderSize),m+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=x.defaults.color),e=t.updateColor(e),b++,e},borderColor:function(e,r){return null==e&&(e=x.defaults.borderColor),e=t.updateColor(e),b++,e},bounds:function(t,e,r){return\"range\"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,l=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=d(t,\"float64\");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?s(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:\"points\",usage:\"static\",data:e.tree,type:\"uint32\"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=g.float32(t);return i({data:p,usage:\"dynamic\"}),a({data:g.fract32(t,p),usage:\"dynamic\"}),l({data:new Uint8Array(c),type:\"uint8\",usage:\"stream\"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&\"number\"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var u=t.addMarker(e[s]);a[u]||(a[u]=new Uint8Array(r.count)),a[u][s]=1}for(var c=0;c<a.length;c++)if(a[c]){var f={data:a[c],type:\"uint8\",usage:\"static\"};i[c]?i[c](f):i[c]=o.buffer(f),i[c].data=a[c]}}else i[t.addMarker(e)]=!0;return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=g.fract(e.scale),e.translateFract=g.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),m){var _=n,w=_.count,T=_.size,k=_.borderSize,A=_.sizeBuffer,M=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)M[2*S]=Math.round(255*(null==T[S]?T:T[S])/l),M[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/l);A({data:M,usage:\"dynamic\"})}if(b){var E,L=n,C=L.count,P=L.color,O=L.borderColor,I=L.colorBuffer;if(t.tooManyColors){if(P.length||O.length){E=new Uint8Array(8*C);for(var D=0;D<C;D++){var z=P[D];E[8*D]=v[4*z],E[8*D+1]=v[4*z+1],E[8*D+2]=v[4*z+2],E[8*D+3]=v[4*z+3];var R=O[D];E[8*D+4]=v[4*R],E[8*D+5]=v[4*R+1],E[8*D+6]=v[4*R+2],E[8*D+7]=v[4*R+3]}}}else if(P.length||O.length){E=new Uint8Array(4*C+2);for(var F=0;F<C;F++)null!=P[F]&&(E[4*F]=P[F]%f,E[4*F+1]=Math.floor(P[F]/f)),null!=O[F]&&(E[4*F+2]=O[F]%f,E[4*F+3]=Math.floor(O[F]/f))}I({data:E||new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return n}))}},x.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:\"linear\",min:\"linear\"})),a},x.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if(\"number\"==typeof t[0]){var a=[];if(Array.isArray(t))for(var s=0;s<t.length;s+=4)a.push(t.slice(s,s+4));else for(var u=0;u<t.length;u+=4)a.push(t.subarray(u,u+4));t=a}for(var c=0;c<t.length;c++){var f=t[c];f=o(f,\"uint8\");var h=l(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[c]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},x.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},x.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var b=r(56131);t.exports=function(t,e){var r=new m(t,e),n=r.render.bind(r);return b(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}},60487:function(t,e,r){\"use strict\";var n=r(11870),i=r(71299),a=r(21527),o=r(5877),s=r(57471),l=r(18863),u=r(30120);function c(t,e){if(!(this instanceof c))return new c(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x,s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y,l.height),[a,n,o,i]}function p(t){if(\"number\"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}t.exports=c,c.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)},c.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,u=0;u<l.length;u++)i.push(this.passes[l[u]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},c.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:\"data items columns rows values dimensions samples x\",snap:\"snap cluster\",size:\"sizes size radius\",color:\"colors color fill fill-color fillColor\",opacity:\"opacity alpha transparency 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Uint8Array}),color:\"black\",marker:null,size:12,borderColor:\"transparent\",borderSize:1,viewport:l([r._gl.drawingBufferWidth,r._gl.drawingBufferHeight]),padding:[0,0,0,0],opacity:1,diagonal:!0,upper:!0,lower:!0});if(null!=o.color&&(s.color=o.color),null!=o.size&&(s.size=o.size),null!=o.marker&&(s.marker=o.marker),null!=o.borderColor&&(s.borderColor=o.borderColor),null!=o.borderSize&&(s.borderSize=o.borderSize),null!=o.opacity&&(s.opacity=o.opacity),o.viewport&&(s.viewport=l(o.viewport)),null!=o.diagonal&&(s.diagonal=o.diagonal),null!=o.upper&&(s.upper=o.upper),null!=o.lower&&(s.lower=o.lower),o.data){s.buffer(u(o.data)),s.columns=o.data.length,s.count=o.data[0].length,s.bounds=[];for(var c=0;c<s.columns;c++)s.bounds[c]=a(o.data[c],1)}o.range&&(s.range=o.range,n=s.range&&\"number\"!=typeof s.range[0]),o.domain&&(s.domain=o.domain);var d=!1;null!=o.padding&&(Array.isArray(o.padding)&&o.padding.length===s.columns&&\"number\"==typeof o.padding[o.padding.length-1]?(s.padding=o.padding.map(p),d=!0):s.padding=p(o.padding));var v=s.columns,g=s.count,y=s.viewport.width,m=s.viewport.height,x=s.viewport.x,b=s.viewport.y,_=y/v,w=m/v;s.passes=[];for(var T=0;T<v;T++)for(var k=0;k<v;k++)if((s.diagonal||k!==T)&&(s.upper||!(T>k))&&(s.lower||!(T<k))){var A=f(s.id,T,k),M=this.passes[A]||(this.passes[A]={});if(o.data&&(o.transpose?M.positions={x:{buffer:s.buffer,offset:k,count:g,stride:v},y:{buffer:s.buffer,offset:T,count:g,stride:v}}:M.positions={x:{buffer:s.buffer,offset:k*g,count:g},y:{buffer:s.buffer,offset:T*g,count:g}},M.bounds=h(s.bounds,T,k)),o.domain||o.viewport||o.data){var S=d?h(s.padding,T,k):s.padding;if(s.domain){var E=h(s.domain,T,k),L=E[0],C=E[1],P=E[2],O=E[3];M.viewport=[x+L*y+S[0],b+C*m+S[1],x+P*y-S[2],b+O*m-S[3]]}else M.viewport=[x+k*_+_*S[0],b+T*w+w*S[1],x+(k+1)*_-_*S[2],b+(T+1)*w-w*S[3]]}o.color&&(M.color=s.color),o.size&&(M.size=s.size),o.marker&&(M.marker=s.marker),o.borderSize&&(M.borderSize=s.borderSize),o.borderColor&&(M.borderColor=s.borderColor),o.opacity&&(M.opacity=s.opacity),o.range&&(M.range=n?h(s.range,T,k):s.range||M.bounds),s.passes.push(A)}return this},c.prototype.draw=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=[],i=0;i<e.length;i++)if(\"number\"==typeof e[i]){var a=this.traces[e[i]],o=a.passes,l=a.passOffset;n.push.apply(n,s(l,l+o.length))}else if(e[i].length){var u=e[i],c=this.traces[i],f=c.passes,h=c.passOffset;f=f.map((function(t,e){n[h+e]=u}))}(t=this.scatter).draw.apply(t,n)}else this.scatter.draw();return this},c.prototype.destroy=function(){return this.traces.forEach((function(t){t.buffer&&t.buffer.destroy&&t.buffer.destroy()})),this.traces=null,this.passes=null,this.scatter.destroy(),this}},98580:function(t){t.exports=function(){function t(t,e){this.id=Z++,this.type=t,this.data=e}function e(t){if(0===t.length)return[];var r=t.charAt(0),n=t.charAt(t.length-1);if(1<t.length&&r===n&&('\"'===r||\"'\"===r))return['\"'+t.substr(1,t.length-2).replace(/\\\\/g,\"\\\\\\\\\").replace(/\"/g,'\\\\\"')+'\"'];if(r=/\\[(false|true|null|\\d+|'[^']*'|\"[^\"]*\")\\]/.exec(t))return e(t.substr(0,r.index)).concat(e(r[1])).concat(e(t.substr(r.index+r[0].length)));if(1===(r=t.split(\".\")).length)return['\"'+t.replace(/\\\\/g,\"\\\\\\\\\").replace(/\"/g,'\\\\\"')+'\"'];for(t=[],n=0;n<r.length;++n)t=t.concat(e(r[n]));return t}function r(t){return\"[\"+e(t).join(\"][\")+\"]\"}function n(t){return\"string\"==typeof t?t.split():t}function i(t){return\"string\"==typeof t?document.querySelector(t):t}function a(t){var e,r,a,o,s=t||{};t={};var 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e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=a},37715:function(t,e,r){var n=r(63489),i=r(56131);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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n=r(56131);function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(u.local.invalidDate||u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(u.local.invalidDate||u.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(u.local.differentCalendars||u.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,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,u.local.invalidMonth||u.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,u.local.invalidMonth||u.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate||u.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,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r||\"w\"===r){var n=t.toJD()+e*(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0);i=t.day(),\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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n=this.newDate(t,e,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,u.local.invalidMonth||u.regionalOptions[\"\"].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 n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate);t=n.year(),e=n.month(),r=n.day(),t<0&&t++,e<3&&(e+=12,t--);var i=Math.floor(t/100),a=2-i+Math.floor(i/4);return Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r+a-1524.5},fromJD:function(t){var e=Math.floor(t+.5),r=Math.floor((e-1867216.25)/36524.25),n=1524+(r=e+1+r-Math.floor(r/4)),i=Math.floor((n-122.1)/365.25),a=Math.floor(365.25*i),o=Math.floor((n-a)/30.6001),s=n-a-Math.floor(30.6001*o),l=o-(o>13.5?13:1),u=i-(l>2.5?4716:4715);return u<=0&&u--,this.newDate(u,l,s)},toJSDate:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),i=new Date(n.year(),n.month()-1,n.day());return i.setHours(0),i.setMinutes(0),i.setSeconds(0),i.setMilliseconds(0),i.setHours(i.getHours()>12?i.getHours()+2:0),i},fromJSDate:function(t){return this.newDate(t.getFullYear(),t.getMonth()+1,t.getDate())}});var u=t.exports=new i;u.cdate=a,u.baseCalendar=s,u.calendars.gregorian=l},94338:function(t,e,r){var n=r(56131),i=r(63489);n(i.regionalOptions[\"\"],{invalidArguments:\"Invalid arguments\",invalidFormat:\"Cannot format a date from another calendar\",missingNumberAt:\"Missing number at position {0}\",unknownNameAt:\"Unknown name at position {0}\",unexpectedLiteralAt:\"Unexpected literal at position {0}\",unexpectedText:\"Additional text found at end\"}),i.local=i.regionalOptions[\"\"],n(i.cdate.prototype,{formatDate:function(t,e){return\"string\"!=typeof 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                       {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('58c5ac63-b484-4f67-94e0-946f8a6a6e3a');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from optuna.visualization import plot_param_importances,plot_edf,plot_optimization_history\n",
    "#plot_edf(study)\n",
    "#plot_optimization_history(study)\n",
    "with open('src/best_params.pkl','rb') as f:\n",
    "    params_final,feat_imp,best_model,study = pickle.load(f)\n",
    "plot_param_importances(study_small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "eea04db3-8653-4be6-8ad8-107133e7236f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'objective': 'multi:softprob',\n",
       " 'nthread': 4,\n",
       " 'num_class': 5,\n",
       " 'seed': 27,\n",
       " 'learning_rate': 0.06267938904293768,\n",
       " 'max_depth': 14,\n",
       " 'min_child_weight': 7,\n",
       " 'gamma': 0.6199971726667972,\n",
       " 'subsample': 0.8015724244693958,\n",
       " 'colsample_bytree': 0.5064972828251288,\n",
       " 'alpha': 0.11096589649454103,\n",
       " 'lambda': 0.5753594958776281}"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d6cb7bd5-35fa-4f80-b04d-b82f4049ef22",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## me li salvo altrimenti ogni volta e' un casino\n",
    "with open('best_params.pkl','wb') as f:\n",
    "    pickle.dump([params_final,feat_imp],f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6f84c38a-138e-499e-a4f8-882fd9ae7750",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('best_params.pkl','rb') as f:\n",
    "    params_final,feat_imp = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7b839d8e-32a4-4417-ba87-3e1f98bf6717",
   "metadata": {},
   "outputs": [],
   "source": [
    "## sklearn interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "88d058e9-f7f2-4ead-8f48-56d97b6cc689",
   "metadata": {},
   "outputs": [],
   "source": [
    "tot = []\n",
    "use_smote = False\n",
    "\n",
    "for i in range(1,X_train_small.shape[1]):\n",
    "    if use_smote:\n",
    "        dtrain_small_FS = xgb.DMatrix(X_train_smote_small[list(feat_imp.head(i).index)],y_train_smote_small, \n",
    "                             enable_categorical=True,)\n",
    "                             #weight=weight_train)\n",
    "    else:\n",
    "        dtrain_small_FS = xgb.DMatrix(X_train_small[list(feat_imp.head(i).index)],y_train_small, \n",
    "                             enable_categorical=True,\n",
    "                             weight=weight_train)\n",
    "    dvalid_small_FS = xgb.DMatrix(X_valid_small[list(feat_imp.head(i).index)],y_valid_small, enable_categorical=True,)\n",
    "   \n",
    "    bst_small_FS = xgb.train(params_final, dtrain_small_FS,verbose_eval=False, num_boost_round=600,\n",
    "                             evals = [(dtrain_small_FS, \"train\"), (dvalid_small_FS, \"valid\")],\n",
    "                             early_stopping_rounds=100,)\n",
    "    preds_class_valid = bst_small_FS.predict(dvalid_small_FS)\n",
    "    mcc = matthews_corrcoef(y_valid_small,preds_class_valid.argmax(1))\n",
    "    acc = accuracy_score(y_valid_small,preds_class_valid.argmax(1))\n",
    "    tot.append({'mcc':mcc,'acc':acc,'FS':i})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "54f915f6-4096-4a29-a3ca-9a5faac065ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>mcc</th>\n",
       "      <th>acc</th>\n",
       "      <th>FS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.024347</td>\n",
       "      <td>0.256763</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.303988</td>\n",
       "      <td>0.585659</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.324149</td>\n",
       "      <td>0.614856</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.374640</td>\n",
       "      <td>0.672821</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.394719</td>\n",
       "      <td>0.687419</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.399983</td>\n",
       "      <td>0.695578</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.431865</td>\n",
       "      <td>0.709532</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.430755</td>\n",
       "      <td>0.710176</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.430538</td>\n",
       "      <td>0.710176</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.434400</td>\n",
       "      <td>0.715758</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.429238</td>\n",
       "      <td>0.713826</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.430244</td>\n",
       "      <td>0.717046</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.435320</td>\n",
       "      <td>0.720481</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.444619</td>\n",
       "      <td>0.728210</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.451718</td>\n",
       "      <td>0.733577</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.449903</td>\n",
       "      <td>0.731000</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.467224</td>\n",
       "      <td>0.745170</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.461310</td>\n",
       "      <td>0.742164</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.466519</td>\n",
       "      <td>0.745814</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.465960</td>\n",
       "      <td>0.745170</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.468924</td>\n",
       "      <td>0.745599</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.468924</td>\n",
       "      <td>0.745599</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.468924</td>\n",
       "      <td>0.745599</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mcc       acc  FS\n",
       "0   0.024347  0.256763   1\n",
       "1   0.303988  0.585659   2\n",
       "2   0.324149  0.614856   3\n",
       "3   0.374640  0.672821   4\n",
       "4   0.394719  0.687419   5\n",
       "5   0.399983  0.695578   6\n",
       "6   0.431865  0.709532   7\n",
       "7   0.430755  0.710176   8\n",
       "8   0.430538  0.710176   9\n",
       "9   0.434400  0.715758  10\n",
       "10  0.429238  0.713826  11\n",
       "11  0.430244  0.717046  12\n",
       "12  0.435320  0.720481  13\n",
       "13  0.444619  0.728210  14\n",
       "14  0.451718  0.733577  15\n",
       "15  0.449903  0.731000  16\n",
       "16  0.467224  0.745170  17\n",
       "17  0.461310  0.742164  18\n",
       "18  0.466519  0.745814  19\n",
       "19  0.465960  0.745170  20\n",
       "20  0.468924  0.745599  21\n",
       "21  0.468924  0.745599  22\n",
       "22  0.468924  0.745599  23"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot = pd.DataFrame(tot)\n",
    "tot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "feb69790-bbcf-4965-92f8-b20e15667edc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f08a6af2a50>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(tot.FS,tot.acc,'o-',label='accuracy')\n",
    "plt.plot(tot.FS,tot.mcc,'o-',label='MCC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b458f312-28f1-4469-95fa-497a7f481c34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FS = int(tot.loc[tot.acc.argmax()].FS)\n",
    "FS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e3cf76fb-2803-40e8-b98f-174f4d55604f",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_area = labeled[labeled.skiarea_name==SKI_AREA_TEST]\n",
    "test_area_small = labeled_small[labeled_small.skiarea_name==SKI_AREA_TEST]\n",
    "\n",
    "test_area_season = labeled[(labeled.skiarea_name==SEASON_TEST_SKIAREA)&(labeled.season>=SEASON_TEST_YEAR)]\n",
    "test_area_season_small = labeled_small[(labeled_small.skiarea_name==SEASON_TEST_SKIAREA)&(labeled_small.season>=SEASON_TEST_YEAR)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c05154d4-c62d-45c7-a8c7-27fe792f82c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mcc=0.3789808968443661, acc=0.7517207472959685, \n",
      "cm=array([[  39,   27,    1,    0,    0],\n",
      "       [  45, 1275,  235,    1,    1],\n",
      "       [   1,  175,  213,    7,    0],\n",
      "       [   0,    0,   11,    2,    0],\n",
      "       [   0,    0,    1,    0,    0]])\n",
      "mcc=0.4520291259107882, acc=0.7425561154374714, cm=array([[  53,   14,    2,    0,    0],\n",
      "       [  34, 1259,  369,    2,    2],\n",
      "       [   0,  107,  306,   21,    0],\n",
      "       [   0,    0,   11,    2,    0],\n",
      "       [   0,    0,    0,    0,    1]])\n"
     ]
    }
   ],
   "source": [
    "\n",
    "if use_smote:\n",
    "    dtrain_small_FS = xgb.DMatrix(X_train_smote_small[list(feat_imp.head(FS).index)],y_train_smote_small, \n",
    "                         enable_categorical=True,)\n",
    "                        # weight=weight_train)\n",
    "else:\n",
    "    dtrain_small_FS = xgb.DMatrix(X_train_small[list(feat_imp.head(FS).index)],y_train_small, \n",
    "                         enable_categorical=True,\n",
    "                         weight=weight_train)\n",
    "dvalid_small_FS = xgb.DMatrix(X_valid_small[list(feat_imp.head(FS).index)],y_valid_small,\n",
    "                     enable_categorical=True,\n",
    "                     )\n",
    "\n",
    "\n",
    "bst_small_FS = xgb.train(params_final, dtrain_small_FS,verbose_eval=False, num_boost_round=600,\n",
    "                evals = [(dtrain_small_FS, \"train\"), (dvalid_small_FS, \"valid\")],\n",
    "                early_stopping_rounds=100,)\n",
    "dtest_small_FS = xgb.DMatrix(test_area_small[list(feat_imp.head(FS).index)],test_area_small.india,\n",
    "                     enable_categorical=True,\n",
    "                     )\n",
    "dtest_season_small_FS = xgb.DMatrix(test_area_season_small[list(feat_imp.head(FS).index)],test_area_season_small.india,\n",
    "                     enable_categorical=True,\n",
    "                     )\n",
    "preds_class_test = bst_small_FS.predict(dtest_small_FS)\n",
    "preds_class_test_season = bst_small_FS.predict(dtest_season_small_FS)\n",
    "\n",
    "mcc = matthews_corrcoef(test_area_small.india,preds_class_test.argmax(1))\n",
    "acc = accuracy_score(test_area_small.india,preds_class_test.argmax(1))\n",
    "cm = confusion_matrix(test_area_small.india,preds_class_test.argmax(1))\n",
    "\n",
    "print(f'{mcc=}, {acc=}, \\n{cm=}')\n",
    "mcc = matthews_corrcoef(test_area_season_small.india,preds_class_test_season.argmax(1))\n",
    "acc = accuracy_score(test_area_season_small.india,preds_class_test_season.argmax(1))\n",
    "cm = confusion_matrix(test_area_season_small.india,preds_class_test_season.argmax(1))\n",
    "\n",
    "print(f'{mcc=}, {acc=}, {cm=}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "6b97ca14-fbd4-41b9-aa99-823dc70cc9ac",
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    {
     "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>skiarea_name</th>\n",
       "      <th>season</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>cause</th>\n",
       "      <th>gender</th>\n",
       "      <th>equipment</th>\n",
       "      <th>helmet</th>\n",
       "      <th>destination</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>india</th>\n",
       "      <th>...</th>\n",
       "      <th>car</th>\n",
       "      <th>ski_lift</th>\n",
       "      <th>akja</th>\n",
       "      <th>other</th>\n",
       "      <th>indipendently</th>\n",
       "      <th>snowmobile</th>\n",
       "      <th>snowmobile_sled</th>\n",
       "      <th>ambulance</th>\n",
       "      <th>skiarea_ambulance</th>\n",
       "      <th>helicopter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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       "Empty DataFrame\n",
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       "Index: []\n",
       "\n",
       "[0 rows x 27 columns]"
      ]
     },
     "execution_count": 96,
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    "test_area_small"
   ]
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   "cell_type": "code",
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     "execution_count": 51,
     "metadata": {},
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   ],
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    "X_train_small.shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "826b6015-d63d-4991-b7fc-6c50d3141278",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "(4658, 24)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_valid_small.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f779a954-7c90-48ad-badc-402f65be389d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   class          p\n",
      "0      0   0.203424\n",
      "1      1   0.013596\n",
      "2      2   0.056109\n",
      "3      3   1.130661\n",
      "4      4  16.206137\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('../') ##accrocchio\n",
    "from src.utils import retrive_data,split\n",
    "import pickle\n",
    "#you can put these parameters in the args but here I keep it simpler\n",
    "num_boost_round = 600\n",
    "SKI_AREA_TEST= 'Klausberg'\n",
    "SEASON_TEST_SKIAREA = 'Kronplatz'\n",
    "SEASON_TEST_YEAR= 2023\n",
    "weight_type = 'sqrt' \n",
    "## get the data\n",
    "labeled,labeled_small,to_remove = retrive_data(reload_data=False,threshold_under_represented=0.5,path='/home/agobbi/Projects/PID/datanalytics/PID/src')\n",
    "with open('to_remove.pkl','wb') as f:\n",
    "    pickle.dump(to_remove,f)\n",
    "\n",
    "#split the data\n",
    "dataset,dataset_test = split(labeled_small  ,\n",
    "                            SKI_AREA_TEST= SKI_AREA_TEST,\n",
    "                            SEASON_TEST_SKIAREA = SEASON_TEST_SKIAREA,\n",
    "                            SEASON_TEST_YEAR= SEASON_TEST_YEAR,\n",
    "                            use_smote = False,\n",
    "                            weight_type = weight_type )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "12b3d65e-bc35-41bc-9a72-c41949e019d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "(18330, 27)"
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     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "labeled_small.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ce5398ad-e35e-43c0-a876-0ecd3d269b3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "(2034, 27)"
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     },
     "execution_count": 41,
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   ],
   "source": [
    "dataset_test.X_test_area.shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2fe29b4c-f7ed-4732-a386-391ae6844c7a",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 42,
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   ],
   "source": [
    "dataset_test.X_test_season.shape"
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  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dd4c470b-e772-448f-8271-3e49aaeb452c",
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   "execution_count": 44,
   "id": "b60ec597-7a3b-4d69-ac3e-e017e76fb5c4",
   "metadata": {},
   "outputs": [
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     "execution_count": 44,
     "metadata": {},
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   ],
   "source": [
    "dataset.X_valid.shape"
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   "cell_type": "code",
   "execution_count": 46,
   "id": "2a7885f6-f40f-4978-ace4-15af317d5b21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14113"
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     "execution_count": 46,
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   "execution_count": 56,
   "id": "92dff3bc-68ac-4765-83ed-8ef449e3af80",
   "metadata": {},
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   "source": [
    "from optuna.visualization import plot_param_importances,plot_edf,plot_optimization_history,plot_intermediate_values,plot_parallel_coordinate,plot_edf\n",
    "#plot_edf(study)\n",
    "#plot_optimization_history(study)\n",
    "with open('../src/best_params.pkl','rb') as f:\n",
    "    params_final,feat_imp,best_model,study = pickle.load(f)\n"
   ]
  },
  {
   "cell_type": "code",
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",
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     "metadata": {},
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   ],
   "source": [
    "plot_edf(study)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d8e7c975-6cd3-4c8f-909a-12ba49037015",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../src/best_params_and_final_model.pkl','rb') as f:\n",
    "    tot,bst_FS,FS = pickle.load(f)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "230160e3-ad1d-469e-8761-d501ea66303a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23, 3)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot.shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "960c03f9-0bb2-4881-8d9a-549454e07f97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(18.5, 0.7783598110777159, 'ACC:0.71\\nMCC:0.45')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(tot.FS, tot.acc,'o-', label='accuracy')\n",
    "plt.plot(tot.FS, tot.mcc,'o-', label='MCC')\n",
    "plt.xlabel('N features')\n",
    "plt.ylabel('Score')\n",
    "bacc = tot.acc[tot.FS==FS].values[0]\n",
    "bmcc = tot.mcc[tot.FS==FS].values[0]\n",
    "plt.vlines(x=FS, ymin=0, ymax=tot.acc.max()*1.2,color='gray',linestyles='dotted')\n",
    "plt.text(x=FS+0.5, y=tot.acc.max()*1.1, s=f'ACC:{round(bacc,2)}\\nMCC:{round(bmcc,2)}',fontdict={'size': 12})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "5dfefae5-2a8c-4d06-9ae7-330ffc1c5aea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19,)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_imp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "600a9d2a-e278-45a7-936a-b675bbc2aded",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18],\n",
       " [Text(0, 0, 'age'),\n",
       "  Text(1, 0, 'diagnosis'),\n",
       "  Text(2, 0, 'injury_general_location'),\n",
       "  Text(3, 0, 'country'),\n",
       "  Text(4, 0, 'difficulty'),\n",
       "  Text(5, 0, 'destination'),\n",
       "  Text(6, 0, 'cause'),\n",
       "  Text(7, 0, 'injury_side'),\n",
       "  Text(8, 0, 'gender'),\n",
       "  Text(9, 0, 'snowmobile'),\n",
       "  Text(10, 0, 'ambulance'),\n",
       "  Text(11, 0, 'helicopter'),\n",
       "  Text(12, 0, 'equipment'),\n",
       "  Text(13, 0, 'indipendently'),\n",
       "  Text(14, 0, 'akja'),\n",
       "  Text(15, 0, 'ski_lift'),\n",
       "  Text(16, 0, 'snowmobile_sled'),\n",
       "  Text(17, 0, 'helmet'),\n",
       "  Text(18, 0, 'skiarea_ambulance')])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (12,5))\n",
    "plt.bar(feat_imp.index,feat_imp.values,)\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "#plt.vlines(x=FS, ymin=0, ymax=300,color='gray',linestyles='dotted')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "422dc553-950c-4af9-a5b7-e482badb7755",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RESULT ON THE TEST SKI AREA mcc=0.353902218427158, acc=0.7143559488692232, \n",
      "cm=array([[  51,   15,    1,    0,    0],\n",
      "       [ 104, 1189,  260,    2,    2],\n",
      "       [   3,  164,  208,   21,    0],\n",
      "       [   0,    0,    8,    5,    0],\n",
      "       [   0,    0,    1,    0,    0]])\n",
      "RESULT ON THE TEST SKI SEASON mcc=0.4284620408598895, acc=0.7095739807604214, cm=array([[  53,   11,    2,    0,    3],\n",
      "       [  65, 1181,  400,   15,    5],\n",
      "       [   0,   91,  308,   35,    0],\n",
      "       [   0,    0,    7,    6,    0],\n",
      "       [   0,    0,    0,    0,    1]])\n"
     ]
    }
   ],
   "source": [
    "dtest_FS = xgb.DMatrix(dataset_test.X_test_area[bst_FS.feature_names],dataset_test.y_test_area,enable_categorical=True,)\n",
    "dtest_season_FS = xgb.DMatrix(dataset_test.X_test_season[bst_FS.feature_names],dataset_test.y_test_season,enable_categorical=True,)\n",
    "preds_class_test = bst_FS.predict(dtest_FS)\n",
    "preds_class_test_season = bst_FS.predict(dtest_season_FS)\n",
    "\n",
    "mcc = matthews_corrcoef(dataset_test.y_test_area,preds_class_test.argmax(1))\n",
    "acc = accuracy_score(dataset_test.y_test_area,preds_class_test.argmax(1))\n",
    "cm = confusion_matrix(dataset_test.y_test_area,preds_class_test.argmax(1))\n",
    "\n",
    "print(f'RESULT ON THE TEST SKI AREA {mcc=}, {acc=}, \\n{cm=}')\n",
    "mcc = matthews_corrcoef(dataset_test.y_test_season,preds_class_test_season.argmax(1))\n",
    "acc = accuracy_score(dataset_test.y_test_season,preds_class_test_season.argmax(1))\n",
    "cm = confusion_matrix(dataset_test.y_test_season,preds_class_test_season.argmax(1))\n",
    "\n",
    "print(f'RESULT ON THE TEST SKI SEASON {mcc=}, {acc=}, {cm=}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "6e2cac2f-b414-45ed-8e26-3fb9786e73aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting seaborn\n",
      "  Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /home/agobbi/miniconda3/envs/pid/lib/python3.11/site-packages (from seaborn) (1.26.3)\n",
      "Requirement already satisfied: pandas>=1.2 in /home/agobbi/miniconda3/envs/pid/lib/python3.11/site-packages (from seaborn) (2.2.0)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /home/agobbi/miniconda3/envs/pid/lib/python3.11/site-packages (from seaborn) (3.8.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/agobbi/miniconda3/envs/pid/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/agobbi/miniconda3/envs/pid/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/agobbi/miniconda3/envs/pid/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.47.2)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/agobbi/miniconda3/envs/pid/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/agobbi/miniconda3/envs/pid/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.2)\n",
      "Requirement already satisfied: pillow>=8 in /home/agobbi/miniconda3/envs/pid/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/agobbi/miniconda3/envs/pid/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/agobbi/miniconda3/envs/pid/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/agobbi/miniconda3/envs/pid/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /home/agobbi/miniconda3/envs/pid/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2023.4)\n",
      "Requirement already satisfied: six>=1.5 in /home/agobbi/miniconda3/envs/pid/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
      "Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n",
      "\u001b[2K   \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mMB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: seaborn\n",
      "Successfully installed seaborn-0.13.2\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "585102a0-0942-48b1-a4d1-ff84393fd70f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "plot a pretty confusion matrix with seaborn\n",
    "Created on Mon Jun 25 14:17:37 2018\n",
    "@author: Wagner Cipriano - wagnerbhbr - gmail - CEFETMG / MMC\n",
    "REFerences:\n",
    "  https://www.mathworks.com/help/nnet/ref/plotconfusion.html\n",
    "  https://stackoverflow.com/questions/28200786/how-to-plot-scikit-learn-classification-report\n",
    "  https://stackoverflow.com/questions/5821125/how-to-plot-confusion-matrix-with-string-axis-rather-than-integer-in-python\n",
    "  https://www.programcreek.com/python/example/96197/seaborn.heatmap\n",
    "  https://stackoverflow.com/questions/19233771/sklearn-plot-confusion-matrix-with-labels/31720054\n",
    "  http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py\n",
    "\"\"\"\n",
    "\n",
    "import matplotlib.font_manager as fm\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sn\n",
    "from matplotlib.collections import QuadMesh\n",
    "\n",
    "\n",
    "def get_new_fig(fn, figsize=[9, 9]):\n",
    "    \"\"\"Init graphics\"\"\"\n",
    "    fig1 = plt.figure(fn, figsize)\n",
    "    ax1 = fig1.gca()  # Get Current Axis\n",
    "    ax1.cla()  # clear existing plot\n",
    "    return fig1, ax1\n",
    "\n",
    "\n",
    "def configcell_text_and_colors(\n",
    "    array_df, lin, col, oText, facecolors, posi, fz, fmt, show_null_values=0\n",
    "):\n",
    "    \"\"\"\n",
    "    config cell text and colors\n",
    "    and return text elements to add and to dell\n",
    "    @TODO: use fmt\n",
    "    \"\"\"\n",
    "    text_add = []\n",
    "    text_del = []\n",
    "    cell_val = array_df[lin][col]\n",
    "    tot_all = array_df[-1][-1]\n",
    "    per = (float(cell_val) / tot_all) * 100\n",
    "    curr_column = array_df[:, col]\n",
    "    ccl = len(curr_column)\n",
    "\n",
    "    # last line  and/or last column\n",
    "    if (col == (ccl - 1)) or (lin == (ccl - 1)):\n",
    "        # tots and percents\n",
    "        if cell_val != 0:\n",
    "            if (col == ccl - 1) and (lin == ccl - 1):\n",
    "                tot_rig = 0\n",
    "                for i in range(array_df.shape[0] - 1):\n",
    "                    tot_rig += array_df[i][i]\n",
    "                per_ok = (float(tot_rig) / cell_val) * 100\n",
    "            elif col == ccl - 1:\n",
    "                tot_rig = array_df[lin][lin]\n",
    "                per_ok = (float(tot_rig) / cell_val) * 100\n",
    "            elif lin == ccl - 1:\n",
    "                tot_rig = array_df[col][col]\n",
    "                per_ok = (float(tot_rig) / cell_val) * 100\n",
    "            per_err = 100 - per_ok\n",
    "        else:\n",
    "            per_ok = per_err = 0\n",
    "\n",
    "        per_ok_s = [\"%.2f%%\" % (per_ok), \"100%\"][per_ok == 100]\n",
    "\n",
    "        # text to DEL\n",
    "        text_del.append(oText)\n",
    "\n",
    "        # text to ADD\n",
    "        font_prop = fm.FontProperties(weight=\"bold\", size=fz)\n",
    "        text_kwargs = dict(\n",
    "            color=\"w\",\n",
    "            ha=\"center\",\n",
    "            va=\"center\",\n",
    "            gid=\"sum\",\n",
    "            fontproperties=font_prop,\n",
    "        )\n",
    "        lis_txt = [\"%d\" % (cell_val), per_ok_s, \"%.2f%%\" % (per_err)]\n",
    "        lis_kwa = [text_kwargs]\n",
    "        dic = text_kwargs.copy()\n",
    "        dic[\"color\"] = \"g\"\n",
    "        lis_kwa.append(dic)\n",
    "        dic = text_kwargs.copy()\n",
    "        dic[\"color\"] = \"r\"\n",
    "        lis_kwa.append(dic)\n",
    "        lis_pos = [\n",
    "            (oText._x, oText._y - 0.3),\n",
    "            (oText._x, oText._y),\n",
    "            (oText._x, oText._y + 0.3),\n",
    "        ]\n",
    "        for i in range(len(lis_txt)):\n",
    "            newText = dict(\n",
    "                x=lis_pos[i][0],\n",
    "                y=lis_pos[i][1],\n",
    "                text=lis_txt[i],\n",
    "                kw=lis_kwa[i],\n",
    "            )\n",
    "            text_add.append(newText)\n",
    "\n",
    "        # set background color for sum cells (last line and last column)\n",
    "        carr = [0.27, 0.30, 0.27, 1.0]\n",
    "        if (col == ccl - 1) and (lin == ccl - 1):\n",
    "            carr = [0.17, 0.20, 0.17, 1.0]\n",
    "        facecolors[posi] = carr\n",
    "\n",
    "    else:\n",
    "        if per > 0:\n",
    "            txt = \"%s\\n%.2f%%\" % (cell_val, per)\n",
    "        else:\n",
    "            if show_null_values == 0:\n",
    "                txt = \"\"\n",
    "            elif show_null_values == 1:\n",
    "                txt = \"0\"\n",
    "            else:\n",
    "                txt = \"0\\n0.0%\"\n",
    "        oText.set_text(txt)\n",
    "\n",
    "        # main diagonal\n",
    "        if col == lin:\n",
    "            # set color of the textin the diagonal to white\n",
    "            oText.set_color(\"w\")\n",
    "            # set background color in the diagonal to blue\n",
    "            facecolors[posi] = [0.35, 0.8, 0.55, 1.0]\n",
    "        else:\n",
    "            oText.set_color(\"r\")\n",
    "\n",
    "    return text_add, text_del\n",
    "\n",
    "\n",
    "def insert_totals(df_cm):\n",
    "    \"\"\"insert total column and line (the last ones)\"\"\"\n",
    "    sum_col = []\n",
    "    for c in df_cm.columns:\n",
    "        sum_col.append(df_cm[c].sum())\n",
    "    sum_lin = []\n",
    "    for item_line in df_cm.iterrows():\n",
    "        sum_lin.append(item_line[1].sum())\n",
    "    df_cm[\"sum_lin\"] = sum_lin\n",
    "    sum_col.append(np.sum(sum_lin))\n",
    "    df_cm.loc[\"sum_col\"] = sum_col\n",
    "\n",
    "\n",
    "def pp_matrix(\n",
    "    df_cm,\n",
    "    annot=True,\n",
    "    cmap=\"Oranges\",\n",
    "    fmt=\".2f\",\n",
    "    fz=11,\n",
    "    lw=0.5,\n",
    "    cbar=False,\n",
    "    figsize=[8, 8],\n",
    "    show_null_values=0,\n",
    "    pred_val_axis=\"y\",\n",
    "):\n",
    "    \"\"\"\n",
    "    print conf matrix with default layout (like matlab)\n",
    "    params:\n",
    "      df_cm          dataframe (pandas) without totals\n",
    "      annot          print text in each cell\n",
    "      cmap           Oranges,Oranges_r,YlGnBu,Blues,RdBu, ... see:\n",
    "      fz             fontsize\n",
    "      lw             linewidth\n",
    "      pred_val_axis  where to show the prediction values (x or y axis)\n",
    "                      'col' or 'x': show predicted values in columns (x axis) instead lines\n",
    "                      'lin' or 'y': show predicted values in lines   (y axis)\n",
    "    \"\"\"\n",
    "    if pred_val_axis in (\"col\", \"x\"):\n",
    "        xlbl = \"Predicted\"\n",
    "        ylbl = \"Actual\"\n",
    "    else:\n",
    "        xlbl = \"Actual\"\n",
    "        ylbl = \"Predicted\"\n",
    "        df_cm = df_cm.T\n",
    "\n",
    "    # create \"Total\" column\n",
    "    insert_totals(df_cm)\n",
    "\n",
    "    # this is for print allways in the same window\n",
    "    fig, ax1 = get_new_fig(\"Conf matrix default\", figsize)\n",
    "\n",
    "    ax = sn.heatmap(\n",
    "        df_cm,\n",
    "        annot=annot,\n",
    "        annot_kws={\"size\": fz},\n",
    "        linewidths=lw,\n",
    "        ax=ax1,\n",
    "        cbar=cbar,\n",
    "        cmap=cmap,\n",
    "        linecolor=\"w\",\n",
    "        fmt=fmt,\n",
    "    )\n",
    "\n",
    "    # set ticklabels rotation\n",
    "    ax.set_xticklabels(ax.get_xticklabels(), rotation=45, fontsize=10)\n",
    "    ax.set_yticklabels(ax.get_yticklabels(), rotation=25, fontsize=10)\n",
    "\n",
    "    # Turn off all the ticks\n",
    "    for t in ax.xaxis.get_major_ticks():\n",
    "        t.tick1On = False\n",
    "        t.tick2On = False\n",
    "    for t in ax.yaxis.get_major_ticks():\n",
    "        t.tick1On = False\n",
    "        t.tick2On = False\n",
    "\n",
    "    # face colors list\n",
    "    quadmesh = ax.findobj(QuadMesh)[0]\n",
    "    facecolors = quadmesh.get_facecolors()\n",
    "\n",
    "    # iter in text elements\n",
    "    array_df = np.array(df_cm.to_records(index=False).tolist())\n",
    "    text_add = []\n",
    "    text_del = []\n",
    "    posi = -1  # from left to right, bottom to top.\n",
    "    for t in ax.collections[0].axes.texts:  # ax.texts:\n",
    "        pos = np.array(t.get_position()) - [0.5, 0.5]\n",
    "        lin = int(pos[1])\n",
    "        col = int(pos[0])\n",
    "        posi += 1\n",
    "\n",
    "        # set text\n",
    "        txt_res = configcell_text_and_colors(\n",
    "            array_df, lin, col, t, facecolors, posi, fz, fmt, show_null_values\n",
    "        )\n",
    "\n",
    "        text_add.extend(txt_res[0])\n",
    "        text_del.extend(txt_res[1])\n",
    "\n",
    "    # remove the old ones\n",
    "    for item in text_del:\n",
    "        item.remove()\n",
    "    # append the new ones\n",
    "    for item in text_add:\n",
    "        ax.text(item[\"x\"], item[\"y\"], item[\"text\"], **item[\"kw\"])\n",
    "\n",
    "    # titles and legends\n",
    "    ax.set_title(\"Confusion matrix\")\n",
    "    ax.set_xlabel(xlbl)\n",
    "    ax.set_ylabel(ylbl)\n",
    "    plt.tight_layout()  # set layout slim\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def pp_matrix_from_data(\n",
    "    y_test,\n",
    "    predictions,\n",
    "    columns=None,\n",
    "    annot=True,\n",
    "    cmap=\"Oranges\",\n",
    "    fmt=\".2f\",\n",
    "    fz=11,\n",
    "    lw=0.5,\n",
    "    cbar=False,\n",
    "    figsize=[8, 8],\n",
    "    show_null_values=0,\n",
    "    pred_val_axis=\"lin\",\n",
    "):\n",
    "    \"\"\"\n",
    "    plot confusion matrix function with y_test (actual values) and predictions (predic),\n",
    "    whitout a confusion matrix yet\n",
    "    \"\"\"\n",
    "    from pandas import DataFrame\n",
    "    from sklearn.metrics import confusion_matrix\n",
    "\n",
    "    # data\n",
    "    if not columns:\n",
    "        from string import ascii_uppercase\n",
    "\n",
    "        columns = [\n",
    "            \"class %s\" % (i)\n",
    "            for i in list(ascii_uppercase)[0 : len(np.unique(y_test))]\n",
    "        ]\n",
    "\n",
    "    confm = confusion_matrix(y_test, predictions)\n",
    "    df_cm = DataFrame(confm, index=columns, columns=columns)\n",
    "    pp_matrix(\n",
    "        df_cm,\n",
    "        fz=fz,\n",
    "        cmap=cmap,\n",
    "        figsize=figsize,\n",
    "        show_null_values=show_null_values,\n",
    "        pred_val_axis=pred_val_axis,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "2bb3ef03-c2fa-4102-ae70-fb11dae3e042",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20879/989477338.py:65: DeprecationWarning:\n",
      "\n",
      "In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "pp_matrix_from_data(dataset_test.y_test_area,preds_class_test.argmax(1),cmap = 'cividis', columns=[f'india{i}' for i in range(5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "73463369-7ffa-4481-a1cf-b8c6bc7142ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20879/989477338.py:65: DeprecationWarning:\n",
      "\n",
      "In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp_matrix_from_data(dataset_test.y_test_season,preds_class_test_season.argmax(1),cmap = 'cividis', columns=[f'india{i}' for i in range(5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33ad200c-1064-4152-bfeb-c679f7966772",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}