{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QSARtuna CLI Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial is intended to provide a new user with the necessary background to start using QSARtuna through a command line interface (CLI).\n",
    "\n",
    "A separate tutorial is available describing the use of the QSARtuna GUI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QSARtuna is a python package originally developed to automate the model building process for *REINVENT*. These models can use a variety of algorithms to fit to your input data and most of them have one or more so-called hyper-parameters (e.g. the maximum number of trees using a *Random Forest* or the C parameter in SVRs, controlling the influence of every support vector).\n",
    "\n",
    "For both regression and classification tasks, QSARtuna allows you to specify input data for which the optimal hyper-parameters and a model can obtained automatically. If you want to get an idea on how the package is structured, read on otherwise you might want to skip it and  The following examples should give you an idea how.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The three-step process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QSARtuna is structured around three steps:\n",
    "1. **Hyperparameter Optimization:** \n",
    "    Train many models with different parameters using Optuna.\n",
    "    Only the training dataset is used here. \n",
    "    Training is usually done with cross-validation.\n",
    "2. **Build (Training):**\n",
    "    Pick the best model from Optimization, \n",
    "    re-train it without cross-validation,\n",
    "    and optionally evaluate its performance on the test dataset.\n",
    "3. **Prod-build (or build merged):** \n",
    "    Re-train the best-performing model on the merged training and test datasets. \n",
    "    This step has a drawback that there is no data left to evaluate the resulting model, \n",
    "    but it has a big benefit that this final model is trained on the all available data.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use QSARtuna from Jupyter Notebook, install it with:\n",
    "```\n",
    "python -m pip install https://github.com/MolecularAI/QSARtuna/releases/download/3.1.2/qsartuna-3.1.2.tar.gz\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression example\n",
    "This is a toy example of training a model that will predict molecular weight for a subset of DRD2 molecules. This example was chosen so that the whole run would take less than a minute.\n",
    "\n",
    "Training dataset is a CSV file. It has SMILES strings in a column named \"canonical\". It has the value that we will try to predict in column \"molwt\".\n",
    "\n",
    "This example has train and test (holdout) dataset ready. If you have single dataset and would like QSARtuna to split it into train and test (holdout) datasets, see the next section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are a few lines from the input file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "canonical,activity,molwt,molwt_gt_330\r\n",
      "Cc1cc(NC(=O)c2cccc(COc3ccc(Br)cc3)c2)no1,0,387.233,True\r\n",
      "O=C(Nc1ccc(F)cc1F)Nc1sccc1-c1nc2ccccc2s1,0,387.4360000000001,True\r\n",
      "COC(=O)c1ccccc1NC(=O)c1cc([N+](=O)[O-])nn1Cc1ccccc1,0,380.36000000000007,True\r\n",
      "CCOC(=O)C(C)Sc1nc(-c2ccccc2)ccc1C#N,0,312.39400000000006,False\r\n",
      "CCC(CC)NC(=O)c1nn(Cc2ccccc2)c(=O)c2ccccc12,0,349.4340000000001,True\r\n",
      "Brc1ccccc1OCCCOc1cccc2cccnc12,0,358.235,True\r\n",
      "CCCCn1c(COc2cccc(OC)c2)nc2ccccc21,0,310.39700000000005,False\r\n",
      "CCOc1cccc(NC(=O)c2sc3nc(-c4ccc(F)cc4)ccc3c2N)c1,0,407.4700000000001,True\r\n",
      "COc1ccc(S(=O)(=O)N(CC(=O)Nc2ccc(C)cc2)c2ccc(C)cc2)cc1OC,0,454.54800000000023,True\r\n"
     ]
    }
   ],
   "source": [
    "!head  ../tests/data/DRD2/subset-50/train.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QSARtuna configuration can be read from a JSON file or created in Python. Here we create it in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"..\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/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": [
    "# Start with the imports.\n",
    "import sklearn\n",
    "from optunaz.three_step_opt_build_merge import (\n",
    "    optimize,\n",
    "    buildconfig_best,\n",
    "    build_best,\n",
    "    build_merged,\n",
    ")\n",
    "from optunaz.config import ModelMode, OptimizationDirection\n",
    "from optunaz.config.optconfig import (\n",
    "    OptimizationConfig,\n",
    "    SVR,\n",
    "    RandomForestRegressor,\n",
    "    Ridge,\n",
    "    Lasso,\n",
    "    PLSRegression,\n",
    "    KNeighborsRegressor\n",
    ")\n",
    "from optunaz.datareader import Dataset\n",
    "from optunaz.descriptors import ECFP, MACCS_keys, ECFP_counts, PathFP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare hyperparameter optimization configuration.\n",
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",  # Typical names are \"SMILES\" and \"smiles\".\n",
    "        response_column=\"molwt\",  # Often a specific name (like here), or just \"activity\".\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-50/train.csv\",\n",
    "        test_dataset_file=\"../tests/data/DRD2/subset-50/test.csv\"  # Hidden during optimization.\n",
    "    ),\n",
    "    descriptors=[\n",
    "        ECFP.new(),\n",
    "        ECFP_counts.new(),\n",
    "        MACCS_keys.new(),\n",
    "        PathFP.new()\n",
    "    ],\n",
    "    algorithms=[\n",
    "        SVR.new(),\n",
    "        RandomForestRegressor.new(n_estimators={\"low\": 5, \"high\": 10}),\n",
    "        Ridge.new(),\n",
    "        Lasso.new(),\n",
    "        PLSRegression.new(),\n",
    "        KNeighborsRegressor.new()\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=3,\n",
    "        n_trials=100,  # Total number of trials.\n",
    "        n_startup_trials=50,  # Number of startup (\"random\") trials.\n",
    "        random_seed=42, # Seed for reproducability\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup basic logging.\n",
    "import logging\n",
    "from importlib import reload\n",
    "reload(logging)\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "logging.getLogger(\"train\").disabled = True # Prevent ChemProp from logging\n",
    "import numpy as np\n",
    "np.seterr(divide=\"ignore\")\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "\n",
    "import tqdm\n",
    "from functools import partialmethod, partial\n",
    "tqdm.__init__ = partialmethod(tqdm.__init__, disable=True) # Prevent tqdm in ChemProp from flooding log\n",
    "\n",
    "# Avoid decpreciated warnings from packages etc\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")\n",
    "def warn(*args, **kwargs):\n",
    "    pass\n",
    "warnings.warn = warn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:13,325] A new study created in memory with name: my_study\n",
      "[I 2024-10-02 17:18:13,600] A new study created in memory with name: study_name_0\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "[I 2024-10-02 17:18:14,374] Trial 0 finished with value: -3594.2228073972638 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 3, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 0 with value: -3594.2228073972638.\n",
      "[I 2024-10-02 17:18:14,689] Trial 1 finished with value: -5029.734616310275 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.039054412752107935, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.1242780840717016e-07, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3594.2228073972638.\n",
      "[I 2024-10-02 17:18:15,164] Trial 2 finished with value: -4242.092751193529 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -3594.2228073972638.\n",
      "[I 2024-10-02 17:18:15,419] Trial 3 finished with value: -3393.577488426015 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06877704223043679, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: -3393.577488426015.\n",
      "[I 2024-10-02 17:18:15,788] Trial 4 finished with value: -427.45250420148204 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:15,841] Trial 5 finished with value: -3387.245629616474 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:15,878] Trial 6 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3661540064603184, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.1799882524170321, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:15,986] Trial 7 finished with value: -9650.026568221794 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': <KNeighborsMetric.MINKOWSKI: 'minkowski'>, 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 7, 'weights__1709d2c39117ae29f6c9debe7241287b': <KNeighborsWeights.UNIFORM: 'uniform'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:16,006] Trial 8 finished with value: -5437.151635569594 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.05083825348819038, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:16,210] Trial 9 finished with value: -2669.8534551928174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 4, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:16,254] Trial 10 finished with value: -4341.586120152291 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7921825998469865, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:16,617] Trial 11 finished with value: -5514.404088878841 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:16,833] Trial 12 finished with value: -5431.6349892392145 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:16,877] Trial 13 finished with value: -3530.549661899129 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:16,899] Trial 14 finished with value: -3497.6833185436317 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:16,922] Trial 15 finished with value: -4382.16208862162 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:16,943] Trial 16 finished with value: -5029.734620031822 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002825619931800395, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.309885135051862e-09, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:16,966] Trial 17 finished with value: -679.3109044887755 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.16827992999009767, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:17,074] Trial 18 finished with value: -2550.114129318373 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': <KNeighborsMetric.MINKOWSKI: 'minkowski'>, 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 7, 'weights__1709d2c39117ae29f6c9debe7241287b': <KNeighborsWeights.UNIFORM: 'uniform'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:17,096] Trial 19 finished with value: -4847.085792360169 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.735431606118867, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:17,329] Trial 20 finished with value: -5029.268760278916 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0014840820994557746, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04671166881768783, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:17,503] Trial 21 finished with value: -4783.047015479679 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 15, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:17,526] Trial 22 finished with value: -3905.0064899852296 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:17,603] Trial 23 finished with value: -4030.4577379164707 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 11, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:17,669] Trial 24 finished with value: -4681.602145939593 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': <KNeighborsMetric.MINKOWSKI: 'minkowski'>, 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 4, 'weights__1709d2c39117ae29f6c9debe7241287b': <KNeighborsWeights.UNIFORM: 'uniform'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:17,705] Trial 25 finished with value: -4398.544034028325 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6452011213193165, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:17,775] Trial 26 finished with value: -4454.143979828407 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 21, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:17,785] Trial 27 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 17:18:17,792] Trial 28 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 17:18:17,849] Trial 29 finished with value: -4397.330360587512 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': <KNeighborsMetric.MINKOWSKI: 'minkowski'>, 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 8, 'weights__1709d2c39117ae29f6c9debe7241287b': <KNeighborsWeights.UNIFORM: 'uniform'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:17,856] Trial 30 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 17:18:17,949] Trial 31 finished with value: -2602.7561184287083 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': <KNeighborsMetric.MINKOWSKI: 'minkowski'>, 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 6, 'weights__1709d2c39117ae29f6c9debe7241287b': <KNeighborsWeights.UNIFORM: 'uniform'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:17,973] Trial 32 finished with value: -5267.388279961089 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2015560027548533, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}, return [-3530.549661899129]\n",
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}, return [-3530.549661899129]\n",
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-3387.245629616474]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:18,060] Trial 33 finished with value: -4863.5817607510535 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 23, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n",
      "[I 2024-10-02 17:18:18,102] Trial 34 finished with value: -388.96473594016675 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.5528259214839937, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:18,154] Trial 35 finished with value: -5539.698232987626 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6400992020612235, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:18,207] Trial 36 finished with value: -5180.5533034102455 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8968910439566395, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:18,245] Trial 37 finished with value: -4989.929984864281 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04458440839692226, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.492108041427977, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:18,259] Trial 38 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 17:18:18,317] Trial 39 finished with value: -6528.215066535042 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.16700143339733753, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:18,408] Trial 40 finished with value: -4168.7955967552625 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': <KNeighborsMetric.MINKOWSKI: 'minkowski'>, 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 8, 'weights__1709d2c39117ae29f6c9debe7241287b': <KNeighborsWeights.UNIFORM: 'uniform'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-4397.330360587512]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:18,479] Trial 41 finished with value: -6177.060727800014 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': <KNeighborsMetric.MINKOWSKI: 'minkowski'>, 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 1, 'weights__1709d2c39117ae29f6c9debe7241287b': <KNeighborsWeights.UNIFORM: 'uniform'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:18,587] Trial 42 finished with value: -3963.906954658342 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 21, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:18,610] Trial 43 finished with value: -5029.6805334166565 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.013186009009851564, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.001008958590140135, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:18,676] Trial 44 finished with value: -9300.86840721566 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': <KNeighborsMetric.MINKOWSKI: 'minkowski'>, 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 9, 'weights__1709d2c39117ae29f6c9debe7241287b': <KNeighborsWeights.UNIFORM: 'uniform'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:18,701] Trial 45 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 83.87968210939489, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.382674443425525e-09, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:18,710] Trial 46 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 17:18:18,719] Trial 47 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 17:18:18,727] Trial 48 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 17:18:18,814] Trial 49 finished with value: -3660.9359502556 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 2, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:18,839] Trial 50 finished with value: -688.5244070398325 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.5267860995545326, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:18,875] Trial 51 finished with value: -690.6494438072099 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8458809314722497, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:18,904] Trial 52 finished with value: -691.1197058420935 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.9167866889210807, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': <KNeighborsMetric.MINKOWSKI: 'minkowski'>, 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 9, 'weights__1709d2c39117ae29f6c9debe7241287b': <KNeighborsWeights.UNIFORM: 'uniform'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}, return [-9300.86840721566]\n",
      "Duplicated trial: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': <KNeighborsMetric.MINKOWSKI: 'minkowski'>, 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 7, 'weights__1709d2c39117ae29f6c9debe7241287b': <KNeighborsWeights.UNIFORM: 'uniform'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [-2550.114129318373]\n",
      "Duplicated trial: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': <KNeighborsMetric.MINKOWSKI: 'minkowski'>, 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 6, 'weights__1709d2c39117ae29f6c9debe7241287b': <KNeighborsWeights.UNIFORM: 'uniform'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [-2602.7561184287083]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:18,931] Trial 53 finished with value: -691.3111710449325 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.945685900574672, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:18,993] Trial 54 finished with value: -690.9665592812149 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8936837761725833, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:19,043] Trial 55 finished with value: -688.4682747008223 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.5183865279530455, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:19,093] Trial 56 finished with value: -687.5230947231512 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3771771681361766, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:19,151] Trial 57 finished with value: -687.4503442069594 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3663259819415374, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:19,186] Trial 58 finished with value: -686.9553733616618 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2925652230875628, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n",
      "[I 2024-10-02 17:18:19,230] Trial 59 finished with value: -370.2038330506566 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3962903248948568, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n",
      "[I 2024-10-02 17:18:19,293] Trial 60 finished with value: -377.25988028857313 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.45237513161879, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n",
      "[I 2024-10-02 17:18:19,343] Trial 61 finished with value: -379.8933285317637 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4741161933311207, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n",
      "[I 2024-10-02 17:18:19,380] Trial 62 finished with value: -374.50897467366013 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4290962207409417, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n",
      "[I 2024-10-02 17:18:19,432] Trial 63 finished with value: -376.5588572940058 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4464295711264585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n",
      "[I 2024-10-02 17:18:19,485] Trial 64 finished with value: -379.237448916406 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4687500034684213, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n",
      "[I 2024-10-02 17:18:19,543] Trial 65 finished with value: -375.7474776359051 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4395650011783436, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n",
      "[I 2024-10-02 17:18:19,603] Trial 66 finished with value: -362.2834906299732 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3326755354190032, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 66 with value: -362.2834906299732.\n",
      "[I 2024-10-02 17:18:19,649] Trial 67 finished with value: -357.3474880122588 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2887212943233457, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -357.3474880122588.\n",
      "[I 2024-10-02 17:18:19,692] Trial 68 finished with value: -354.279045046449 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2577677164664005, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 68 with value: -354.279045046449.\n",
      "[I 2024-10-02 17:18:19,766] Trial 69 finished with value: -347.36894395697703 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1672928587680225, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 69 with value: -347.36894395697703.\n",
      "[I 2024-10-02 17:18:19,815] Trial 70 finished with value: -345.17697390093394 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1242367255308854, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n",
      "[I 2024-10-02 17:18:19,864] Trial 71 finished with value: -347.74610809299037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1728352983905301, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n",
      "[I 2024-10-02 17:18:19,926] Trial 72 finished with value: -345.23464281634324 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1265380781508565, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n",
      "[I 2024-10-02 17:18:19,968] Trial 73 finished with value: -344.6848312222365 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0829896313820404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:20,020] Trial 74 finished with value: -344.9111966504334 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1070414661080543, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n",
      "[I 2024-10-02 17:18:20,073] Trial 75 finished with value: -344.70116419828565 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0875643695329498, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n",
      "[I 2024-10-02 17:18:20,116] Trial 76 finished with value: -344.62647974688133 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0716281620790837, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 76 with value: -344.62647974688133.\n",
      "[I 2024-10-02 17:18:20,193] Trial 77 finished with value: -344.6759429204596 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0456289319914898, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 76 with value: -344.62647974688133.\n",
      "[I 2024-10-02 17:18:20,247] Trial 78 finished with value: -343.58131497761616 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0010195360522613, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 78 with value: -343.58131497761616.\n",
      "[I 2024-10-02 17:18:20,278] Trial 79 finished with value: -342.7290581014813 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9073210715005748, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 79 with value: -342.7290581014813.\n",
      "[I 2024-10-02 17:18:20,338] Trial 80 finished with value: -342.67866114080107 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9166305667100072, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -342.67866114080107.\n",
      "[I 2024-10-02 17:18:20,374] Trial 81 finished with value: -342.6440308445311 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9248722692093634, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n",
      "[I 2024-10-02 17:18:20,407] Trial 82 finished with value: -343.02085648448934 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8776928646870886, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n",
      "[I 2024-10-02 17:18:20,462] Trial 83 finished with value: -343.1662266300702 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.867592364677856, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n",
      "[I 2024-10-02 17:18:20,523] Trial 84 finished with value: -343.30158716569775 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8599491178327108, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n",
      "[I 2024-10-02 17:18:20,570] Trial 85 finished with value: -344.2803074848341 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8396948389352923, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n",
      "[I 2024-10-02 17:18:20,655] Trial 86 finished with value: -344.28301101884045 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8396651775801683, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n",
      "[I 2024-10-02 17:18:20,744] Trial 87 finished with value: -344.6781906268143 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8356021935129933, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n",
      "[I 2024-10-02 17:18:20,839] Trial 88 finished with value: -354.0405418264898 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7430046191126949, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n",
      "[I 2024-10-02 17:18:20,903] Trial 89 finished with value: -342.77203208258476 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9015965341429055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n",
      "[I 2024-10-02 17:18:20,992] Trial 90 finished with value: -363.1622720320929 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6746575663752555, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n",
      "[I 2024-10-02 17:18:21,083] Trial 91 finished with value: -342.7403796626193 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9057564666836629, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n",
      "[I 2024-10-02 17:18:21,160] Trial 92 finished with value: -342.63579667712696 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9332275205203372, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n",
      "[I 2024-10-02 17:18:21,233] Trial 93 finished with value: -342.6886425884964 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9433063264508291, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n",
      "[I 2024-10-02 17:18:21,309] Trial 94 finished with value: -342.9341048659705 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.884739221967487, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:21,417] Trial 95 finished with value: -342.63507445779743 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9381000493689634, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n",
      "[I 2024-10-02 17:18:21,484] Trial 96 finished with value: -343.06021011302374 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.963138023068903, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n",
      "[I 2024-10-02 17:18:21,570] Trial 97 finished with value: -342.9990546212019 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9601651093867907, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n",
      "[I 2024-10-02 17:18:21,648] Trial 98 finished with value: -3821.2267845437514 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n",
      "[I 2024-10-02 17:18:21,730] Trial 99 finished with value: -356.6786067133016 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.721603508336166, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n"
     ]
    }
   ],
   "source": [
    "# Run Optuna Study.\n",
    "study = optimize(config, study_name=\"my_study\")\n",
    "# Optuna will log it's progress to sys.stderr\n",
    "# (usually rendered in red in Jupyter Notebooks)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize optimization progress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "default_reg_scoring= config.settings.scoring\n",
    "ax = sns.scatterplot(data=study.trials_dataframe(), x=\"number\", y=\"value\");\n",
    "ax.set(xlabel=\"Trial number\", ylabel=f\"Ojbective value\\n({default_reg_scoring})\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it might be interesting to look at individual CV scores instead of aggregated score (mean CV score by default). Here we can plot all 3 cross validation scores (neg_mean_squared_error) for each trial (folds highlighted using different colors)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_test = study.trials_dataframe()[\"user_attrs_test_scores\"].map(lambda d: d[default_reg_scoring])\n",
    "x = []\n",
    "y = []\n",
    "fold = []\n",
    "for i, vs in cv_test.items():\n",
    "    for idx, v in enumerate(vs):\n",
    "        x.append(i)\n",
    "        y.append(v)\n",
    "        fold.append(idx)\n",
    "ax = sns.scatterplot(x=x, y=y, hue=fold, style=fold, palette='Set1')\n",
    "ax.set(xlabel=\"Trial number\", ylabel=f\"Ojbective value\\n({default_reg_scoring})\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pick the best trial and build a model for it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We pick the best trial, inspect its configuration, build the best model, and save it as a pickled file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the best Trial from the Study and make a Build (Training) configuration for it.\n",
    "buildconfig = buildconfig_best(study)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optional: write out JSON of the best configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"data\": {\n",
      "    \"training_dataset_file\": \"../tests/data/DRD2/subset-50/train.csv\",\n",
      "    \"input_column\": \"canonical\",\n",
      "    \"response_column\": \"molwt\",\n",
      "    \"response_type\": \"regression\",\n",
      "    \"deduplication_strategy\": {\n",
      "      \"name\": \"KeepMedian\"\n",
      "    },\n",
      "    \"split_strategy\": {\n",
      "      \"name\": \"NoSplitting\"\n",
      "    },\n",
      "    \"test_dataset_file\": \"../tests/data/DRD2/subset-50/test.csv\",\n",
      "    \"save_intermediate_files\": false,\n",
      "    \"log_transform\": false,\n",
      "    \"log_transform_base\": null,\n",
      "    \"log_transform_negative\": null,\n",
      "    \"log_transform_unit_conversion\": null,\n",
      "    \"probabilistic_threshold_representation\": false,\n",
      "    \"probabilistic_threshold_representation_threshold\": null,\n",
      "    \"probabilistic_threshold_representation_std\": null\n",
      "  },\n",
      "  \"metadata\": {\n",
      "    \"name\": \"\",\n",
      "    \"cross_validation\": 3,\n",
      "    \"shuffle\": false,\n",
      "    \"best_trial\": 95,\n",
      "    \"best_value\": -342.63507445779743,\n",
      "    \"n_trials\": 100,\n",
      "    \"visualization\": null\n",
      "  },\n",
      "  \"descriptor\": {\n",
      "    \"name\": \"ECFP_counts\",\n",
      "    \"parameters\": {\n",
      "      \"radius\": 3,\n",
      "      \"useFeatures\": true,\n",
      "      \"nBits\": 2048\n",
      "    }\n",
      "  },\n",
      "  \"settings\": {\n",
      "    \"mode\": \"regression\",\n",
      "    \"scoring\": \"neg_mean_squared_error\",\n",
      "    \"direction\": \"maximize\",\n",
      "    \"n_trials\": 100,\n",
      "    \"tracking_rest_endpoint\": null\n",
      "  },\n",
      "  \"algorithm\": {\n",
      "    \"name\": \"Lasso\",\n",
      "    \"parameters\": {\n",
      "      \"alpha\": 0.9381000493689634\n",
      "    }\n",
      "  },\n",
      "  \"task\": \"building\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import apischema\n",
    "buildconfig_as_dict = apischema.serialize(buildconfig)\n",
    "\n",
    "import json\n",
    "print(json.dumps(buildconfig_as_dict, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build (re-Train) and save the best model. This time training uses all training data, without splitting it into cross-validation folds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_build = build_best(buildconfig, \"../target/best.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the best (or merged) model as following"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 67.43103985, 177.99850936])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "with open(\"../target/best.pkl\", \"rb\") as f:\n",
    "    model = pickle.load(f)\n",
    "model.predict_from_smiles([\"CCC\", \"CC(=O)Nc1ccc(O)cc1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can explore how good the best model performs on the test (holdout) set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(config.data.test_dataset_file)  # Load test data.\n",
    "\n",
    "expected = df[config.data.response_column]\n",
    "predicted = model.predict_from_smiles(df[config.data.input_column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot expected vs predicted values for the best model.\n",
    "import matplotlib.pyplot as plt\n",
    "ax = plt.scatter(expected, predicted)\n",
    "lims = [expected.min(), expected.max()]\n",
    "plt.plot(lims, lims)  # Diagonal line.\n",
    "plt.xlabel(f\"Expected {config.data.response_column}\");\n",
    "plt.ylabel(f\"Predicted {config.data.response_column}\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also calculate custom metrics for the best model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.8566354978126369, RMSE: 26.204909888075044, Mean absolute error: 19.298453946973815\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import (r2_score, mean_squared_error, mean_absolute_error)\n",
    "import numpy as np\n",
    "\n",
    "# R2\n",
    "r2 = r2_score(y_true=expected, y_pred=predicted)\n",
    "\n",
    "# RMSE. sklearn 0.24 added squared=False to get RMSE, here we use np.sqrt().\n",
    "rmse = np.sqrt(mean_squared_error(y_true=expected, y_pred=predicted))  \n",
    "\n",
    "# MAE\n",
    "mae = mean_absolute_error(y_true=expected, y_pred=predicted)\n",
    "\n",
    "print(f\"R2: {r2}, RMSE: {rmse}, Mean absolute error: {mae}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the metrics look acceptable, the model is ready for use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build merged model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can merge train and test data, and build (train) the model again. We will have no more holdout data to evaluate the model, but hopefully the model will be a little better by seeing a little more data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build (Train) and save the model on the merged train+test data.\n",
    "build_merged(buildconfig, \"../target/merged.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing: splitting data into train and test sets, and removing duplicates <a class=\"anchor\" id=\"preprocessing\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting into train and test dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QSARtuna can split data into train and test (holdout) datasets. To do so, send all data in as training_dataset_file, and choose a splitting strategy. Currently QSARtuna supports three splitting strategies: random, temporal and stratified. \n",
    "\n",
    "Random strategy splits data randomly, taking a specified fraction of observations to be test dataset. \n",
    "\n",
    "Temporal strategy takes the first observations as training dataset, and the last specified fraction of observations as test dataset. The input dataset must be already sorted, from oldest in the beginning to newest and the end. This sorting can be done in any external tool (e.g. Excel). \n",
    "\n",
    "Stratified strategy splits data into bins first, and then takes a fraction from each bin to be the test dataset. This ensures that the distributions in the train and test data are similar. This is a better strategy if dataset is unballanced."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the algorithms QSARtuna supports do not work with duplicates. Duplicates can come from multiple measurements for the same compound, or from the fact that the molecular descriptors we use are all disregard stereochemistry, so even if compounds are different, descriptors make them into duplicates. QSARtuna provides several strategies to remove duplicates:\n",
    "* keep median - factors experimental deviation using all replicates into one median value (robust to outliers - recommended)\n",
    "* keep average - use all experimental data acorss all replicates (less robust to outliers vs. median)\n",
    "* keep first / keep last - when the first or the last measurement is the trusted one\n",
    "* keep max / keep min - when we want to keep the most extreme value out of many\n",
    "* keep random - when we are agnostic to which replicate kept"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuration example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from optunaz.utils.preprocessing.splitter import Stratified\n",
    "from optunaz.utils.preprocessing.deduplicator import KeepMedian\n",
    "# Prepare hyperparameter optimization configuration.\n",
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-100/train.csv\",  # This will be split into train and test.\n",
    "        split_strategy=Stratified(fraction=0.2),\n",
    "        deduplication_strategy=KeepMedian(),\n",
    "    ),\n",
    "    descriptors=[\n",
    "        ECFP.new(), \n",
    "        ECFP_counts.new(), \n",
    "        MACCS_keys.new(),\n",
    "    ],\n",
    "    algorithms=[\n",
    "        SVR.new(),\n",
    "        RandomForestRegressor.new(n_estimators={\"low\": 5, \"high\": 10}),\n",
    "        Ridge.new(),\n",
    "        Lasso.new(),\n",
    "        PLSRegression.new(),\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=3,\n",
    "        n_trials=100,\n",
    "        n_startup_trials=50,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "        track_to_mlflow=False,\n",
    "    ),\n",
    ") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:31,184] A new study created in memory with name: my_study_stratified_split\n",
      "[I 2024-10-02 17:18:31,245] A new study created in memory with name: study_name_0\n",
      "[I 2024-10-02 17:18:31,429] Trial 0 finished with value: -4800.860920246363 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -4800.860920246363.\n",
      "[I 2024-10-02 17:18:31,464] Trial 1 finished with value: -4858.3431583833035 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0006453852494528, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -4800.860920246363.\n",
      "[I 2024-10-02 17:18:31,494] Trial 2 finished with value: -5558.496029020844 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.45597544079943825, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.4346415094305745e-08, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -4800.860920246363.\n",
      "[I 2024-10-02 17:18:31,523] Trial 3 finished with value: -5558.496027195924 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0007203408466839294, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.398332667057879e-07, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -4800.860920246363.\n",
      "[I 2024-10-02 17:18:31,711] Trial 4 finished with value: -3754.5080327452138 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -3754.5080327452138.\n",
      "[I 2024-10-02 17:18:31,748] Trial 5 finished with value: -5236.493406720707 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00025294187429725007, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 7.0478236714173725, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -3754.5080327452138.\n",
      "[I 2024-10-02 17:18:31,991] Trial 6 finished with value: -2788.015529102185 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.6751063536155433, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 6 with value: -2788.015529102185.\n",
      "[I 2024-10-02 17:18:32,106] Trial 7 finished with value: -2882.637734911225 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 6 with value: -2788.015529102185.\n",
      "[I 2024-10-02 17:18:32,135] Trial 8 finished with value: -5046.977251718065 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6939577374141297, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 6 with value: -2788.015529102185.\n",
      "[I 2024-10-02 17:18:32,155] Trial 9 finished with value: -4636.310461288903 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7962942118112435, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 6 with value: -2788.015529102185.\n",
      "[I 2024-10-02 17:18:32,209] Trial 10 finished with value: -395.3361537359272 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.17805085674767795, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 10 with value: -395.3361537359272.\n",
      "[I 2024-10-02 17:18:32,239] Trial 11 finished with value: -553.8327456862668 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.258419608745002, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 10 with value: -395.3361537359272.\n",
      "[I 2024-10-02 17:18:32,280] Trial 12 finished with value: -3756.4841168856296 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 10 with value: -395.3361537359272.\n",
      "[I 2024-10-02 17:18:32,320] Trial 13 finished with value: -3878.5996019489726 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.1894168143012962, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 10 with value: -395.3361537359272.\n",
      "[I 2024-10-02 17:18:32,404] Trial 14 finished with value: -5450.506271715585 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 10 with value: -395.3361537359272.\n",
      "[I 2024-10-02 17:18:32,434] Trial 15 finished with value: -5558.496026342885 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00019069508279839618, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.409384302833754e-06, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 10 with value: -395.3361537359272.\n",
      "[I 2024-10-02 17:18:32,460] Trial 16 finished with value: -2539.726030649711 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.551952500241758, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 10 with value: -395.3361537359272.\n",
      "[I 2024-10-02 17:18:32,533] Trial 17 finished with value: -395.1851441224497 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.17941011493676395, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:32,558] Trial 18 finished with value: -2522.86857690139 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.6411053581209198, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:32,590] Trial 19 finished with value: -2990.1801593880864 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.29443918558138327, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 17 with value: -395.1851441224497.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:32,634] Trial 20 finished with value: -5151.671578155611 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.4915389100768701, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:32,729] Trial 21 finished with value: -5177.075814440911 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 23, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:32,759] Trial 22 finished with value: -5558.4960290207355 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.1643538562943172, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00015185295749578292, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:32,843] Trial 23 finished with value: -2783.2573063950963 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 14, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:32,862] Trial 24 finished with value: -656.7509538784298 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.3459707284674065, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:32,869] Trial 25 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 17:18:32,889] Trial 26 finished with value: -3251.269023294797 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.45830123607855855, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:32,918] Trial 27 finished with value: -481.56682269179555 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5891977018007113, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:32,938] Trial 28 finished with value: -3749.429746437208 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:32,943] Trial 29 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 17:18:33,009] Trial 30 finished with value: -3168.773548538337 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,029] Trial 31 finished with value: -4640.839119639873 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.8940670831191575, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,060] Trial 32 finished with value: -589.6417833821303 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.5763051253407128, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,066] Trial 33 pruned. Duplicate parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}, return [-4800.860920246363]\n",
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}, return [-4800.860920246363]\n",
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [-3749.429746437208]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:33,135] Trial 34 finished with value: -5377.083434599547 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 29, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,153] Trial 35 finished with value: -3479.5161579632318 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.31941804567565524, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,221] Trial 36 finished with value: -5679.831553540497 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 12, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,241] Trial 37 finished with value: -2936.132798395687 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,263] Trial 38 finished with value: -3876.4493150756584 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0390781556052506, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,281] Trial 39 finished with value: -3875.0818627742105 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.9434428916113202, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,324] Trial 40 finished with value: -5206.948157138694 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.44374193118596605, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,344] Trial 41 finished with value: -2504.0713169207907 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7474867154059912, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,365] Trial 42 finished with value: -2708.242465728643 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9838565950192151, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,398] Trial 43 finished with value: -4819.991176198922 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0571450238667175, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,430] Trial 44 finished with value: -481.09288047226045 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5866757732447516, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,452] Trial 45 finished with value: -607.3839090109298 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7694356902177404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,474] Trial 46 finished with value: -4813.325194517689 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,547] Trial 47 finished with value: -3168.773548538337 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 17, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,569] Trial 48 finished with value: -2455.1421728064593 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3570683682742817, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,651] Trial 49 finished with value: -2580.5182600331673 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "[I 2024-10-02 17:18:33,735] Trial 50 finished with value: -583.5060167275248 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.000618598864976283, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.812e+01, tolerance: 3.624e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.137e+02, tolerance: 3.727e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.765e+02, tolerance: 3.621e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:33,820] Trial 51 finished with value: -418.8929746573978 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.05980410864739376, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 17 with value: -395.1851441224497.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.990e+02, tolerance: 3.621e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.732e+01, tolerance: 3.624e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.666e+02, tolerance: 3.727e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:33,919] Trial 52 finished with value: -427.7324637771422 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.04579403783746738, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.820e+01, tolerance: 3.624e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.569e+02, tolerance: 3.621e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.601e+02, tolerance: 3.727e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:33,991] Trial 53 finished with value: -424.0172075935404 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.049860882081952745, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.852e+02, tolerance: 3.624e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.002e+02, tolerance: 3.727e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.839e+02, tolerance: 3.621e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:34,065] Trial 54 finished with value: -434.9065871816471 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.035488821685050936, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 17 with value: -395.1851441224497.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.012e+01, tolerance: 3.624e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.358e+01, tolerance: 3.727e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.319e+01, tolerance: 3.621e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:34,139] Trial 55 finished with value: -363.457063392389 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0034229864153010325, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:34,189] Trial 56 finished with value: -392.65210771304714 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.19298856662197952, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:34,228] Trial 57 finished with value: -5612.80084651574 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 22.95297999625734, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 42.644935289746485, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:34,275] Trial 58 finished with value: -391.6911554179665 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.22281608045203982, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:34,322] Trial 59 finished with value: -393.6518920766344 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2347259156988194, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:34,375] Trial 60 finished with value: -398.51379448396074 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2538271702413508, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:34,425] Trial 61 finished with value: -399.23253166598073 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.25813227404797817, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:34,475] Trial 62 finished with value: -395.52936726273145 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.24232560627972968, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:34,526] Trial 63 finished with value: -394.9311494354517 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.23994129348334553, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:34,603] Trial 64 finished with value: -393.7113709861178 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1857664444261702, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:34,693] Trial 65 finished with value: -415.44644244530053 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.32851464534098307, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:34,781] Trial 66 finished with value: -395.70265730644957 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.17228683462293345, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:34,842] Trial 67 finished with value: -5558.4960289920455 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.010242315676871812, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.2834547399495062e-10, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:34,913] Trial 68 finished with value: -394.5907271718441 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1477059143272927, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:34,970] Trial 69 finished with value: -430.01936058528145 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3751961935272725, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:35,064] Trial 70 finished with value: -394.64118398085856 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.14717866352952494, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:35,146] Trial 71 finished with value: -394.92661040791836 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1452515340725078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:35,246] Trial 72 finished with value: -396.4549708250368 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1366524398379856, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:35,341] Trial 73 finished with value: -395.450435112361 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.14209760340774633, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:35,419] Trial 74 finished with value: -397.96093602821253 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1301074171311558, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:35,477] Trial 75 finished with value: -425.78145831556486 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3619155626334043, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:35,539] Trial 76 finished with value: -394.25575410197615 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1550301715483597, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:35,594] Trial 77 finished with value: -398.1344384265276 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.25238586359457954, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:35,664] Trial 78 finished with value: -506.49173012186003 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7991490248575259, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:35,749] Trial 79 finished with value: -396.27736446466787 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13755387170252278, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:35,804] Trial 80 finished with value: -5558.496029020844 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 38.83153434359636, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.012941305174191354, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.510e+01, tolerance: 3.624e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:35,906] Trial 81 finished with value: -402.4885067071505 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.11382015597797501, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:35,982] Trial 82 finished with value: -390.62810821067677 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.20977256266189867, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:36,040] Trial 83 finished with value: -414.1429641716593 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3228378688910579, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:36,115] Trial 84 finished with value: -392.60756488488704 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.22913636273231483, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:36,185] Trial 85 finished with value: -457.0419049482971 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.48407881929129526, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:36,253] Trial 86 finished with value: -392.7729892003058 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.23012775988865936, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:36,308] Trial 87 finished with value: -426.4716375808739 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.36413423030403813, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:36,362] Trial 88 finished with value: -392.6664629214362 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.22950926878022426, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:36,402] Trial 89 finished with value: -440.6400797148971 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.4044127239480447, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:36,444] Trial 90 finished with value: -466.2576692105774 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5216671100745962, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:36,520] Trial 91 finished with value: -392.20868498072076 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.22640448682036937, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:36,575] Trial 92 finished with value: -399.0102335623082 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.25567208651201717, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:36,643] Trial 93 finished with value: -392.7987638434602 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.23030546105659774, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:36,704] Trial 94 finished with value: -408.5495513397237 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.30256461527403555, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:36,780] Trial 95 finished with value: -391.1635887741016 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.21776658272143576, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:36,854] Trial 96 finished with value: -445.06245664446107 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.4248927436146357, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:36,901] Trial 97 finished with value: -673.2581805424892 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.9183185505961307, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "[I 2024-10-02 17:18:36,956] Trial 98 finished with value: -3759.6112020079745 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.189e+01, tolerance: 3.624e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.648e+02, tolerance: 3.621e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.699e+02, tolerance: 3.727e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:37,064] Trial 99 finished with value: -424.78576633112925 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0489979631143741, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -363.457063392389.\n"
     ]
    }
   ],
   "source": [
    "study = optimize(config, study_name=\"my_study_stratified_split\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choosing scoring function <a class=\"anchor\" id=\"scoring\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, QSARtuna uses `neg_mean_squared_error` for regression and `roc_auc` for classification. It is possible to change to other scoring functions that supported by scikit-learn (https://scikit-learn.org/stable/modules/model_evaluation.html) amongst others:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['explained_variance',\n",
       " 'max_error',\n",
       " 'neg_mean_absolute_error',\n",
       " 'neg_mean_squared_error',\n",
       " 'neg_median_absolute_error',\n",
       " 'r2',\n",
       " 'accuracy',\n",
       " 'average_precision',\n",
       " 'balanced_accuracy',\n",
       " 'f1',\n",
       " 'f1_macro',\n",
       " 'f1_micro',\n",
       " 'f1_weighted',\n",
       " 'jaccard',\n",
       " 'jaccard_macro',\n",
       " 'jaccard_micro',\n",
       " 'jaccard_weighted',\n",
       " 'neg_brier_score',\n",
       " 'precision',\n",
       " 'precision_macro',\n",
       " 'precision_micro',\n",
       " 'precision_weighted',\n",
       " 'recall',\n",
       " 'recall_macro',\n",
       " 'recall_micro',\n",
       " 'recall_weighted',\n",
       " 'roc_auc',\n",
       " 'auc_pr_cal',\n",
       " 'bedroc',\n",
       " 'concordance_index']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from optunaz import objective\n",
    "list(objective.regression_scores) + list(objective.classification_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This value can be set using `settings.scoring`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-100/train.csv\",\n",
    "    ),\n",
    "    descriptors=[\n",
    "        ECFP.new(), \n",
    "        ECFP_counts.new(), \n",
    "        MACCS_keys.new(),\n",
    "    ],\n",
    "    algorithms=[\n",
    "        SVR.new(),\n",
    "        RandomForestRegressor.new(n_estimators={\"low\": 5, \"high\": 10}),\n",
    "        Ridge.new(),\n",
    "        Lasso.new(),\n",
    "        PLSRegression.new(),\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=3,\n",
    "        n_trials=100,\n",
    "        n_startup_trials=50,\n",
    "        random_seed=42,\n",
    "        scoring=\"r2\",  # Scoring function name from scikit-learn.\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "        track_to_mlflow=False,\n",
    "    ),\n",
    ") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:40,168] A new study created in memory with name: my_study_r2\n",
      "[I 2024-10-02 17:18:40,170] A new study created in memory with name: study_name_0\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "[I 2024-10-02 17:18:40,948] Trial 0 finished with value: -0.01117186866515977 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.01117186866515977.\n",
      "[I 2024-10-02 17:18:41,277] Trial 1 finished with value: -0.08689402230378156 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.01117186866515977.\n",
      "[I 2024-10-02 17:18:42,194] Trial 2 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.01117186866515977.\n",
      "[I 2024-10-02 17:18:42,728] Trial 3 finished with value: 0.30393095442038176 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: 0.30393095442038176.\n",
      "[I 2024-10-02 17:18:42,769] Trial 4 finished with value: 0.2018274962869717 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: 0.30393095442038176.\n",
      "[I 2024-10-02 17:18:42,835] Trial 5 finished with value: 0.8187194367176578 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: 0.8187194367176578.\n",
      "[I 2024-10-02 17:18:42,880] Trial 6 finished with value: 0.4647239019719945 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: 0.8187194367176578.\n",
      "[I 2024-10-02 17:18:42,918] Trial 7 finished with value: 0.8614818478547979 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: 0.8614818478547979.\n",
      "[I 2024-10-02 17:18:43,033] Trial 8 finished with value: -0.12769795082909816 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 7 with value: 0.8614818478547979.\n",
      "[I 2024-10-02 17:18:43,100] Trial 9 finished with value: 0.8639946428338224 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:43,131] Trial 10 finished with value: -0.12553701248377633 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:43,164] Trial 11 finished with value: -0.12553700871203702 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:43,200] Trial 12 finished with value: 0.29355820424290757 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:43,239] Trial 13 finished with value: 0.18476333152695587 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:43,269] Trial 14 finished with value: 0.8190707459213998 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:43,360] Trial 15 finished with value: 0.12206148974315849 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:43,402] Trial 16 finished with value: 0.3105263811279067 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:43,429] Trial 17 finished with value: 0.3562469062424869 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:43,514] Trial 18 finished with value: 0.04595969590698327 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:43,554] Trial 19 finished with value: 0.8583939656024446 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:43,600] Trial 20 finished with value: 0.30625740785155436 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:43,636] Trial 21 finished with value: -0.11657354998283716 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:43,644] Trial 22 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 17:18:43,689] Trial 23 finished with value: 0.8498478905829554 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:43,784] Trial 24 finished with value: -0.1276979508290982 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:43,833] Trial 25 finished with value: -0.13519830637607919 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [0.29355820424290757]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:43,870] Trial 26 finished with value: 0.8198078293055633 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:43,906] Trial 27 finished with value: 0.8201573964824842 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:44,044] Trial 28 finished with value: 0.045959695906983344 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:44,089] Trial 29 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:44,172] Trial 30 finished with value: 0.11934070343348298 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:44,213] Trial 31 finished with value: 0.4374125584543907 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:44,273] Trial 32 finished with value: 0.3625576518621392 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:44,333] Trial 33 finished with value: 0.36175556871883746 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:44,353] Trial 34 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 17:18:44,387] Trial 35 finished with value: 0.8202473217121523 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:44,416] Trial 36 finished with value: 0.3672927879319306 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:44,423] Trial 37 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 17:18:44,452] Trial 38 finished with value: 0.40076792599874356 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:44,535] Trial 39 finished with value: 0.26560316846701765 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [0.29355820424290757]\n",
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [0.30625740785155436]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:44,631] Trial 40 finished with value: 0.41215254857081174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:44,644] Trial 41 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 17:18:44,768] Trial 42 finished with value: -0.004614143721600776 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:44,794] Trial 43 finished with value: 0.2728253352418362 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [0.30393095442038176]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:44,912] Trial 44 finished with value: -0.10220127407364976 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:44,944] Trial 45 finished with value: 0.3032340413058286 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:44,986] Trial 46 finished with value: 0.3044553805553568 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:45,026] Trial 47 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:45,063] Trial 48 finished with value: 0.36160209098547913 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:45,105] Trial 49 finished with value: 0.2916101445983833 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.434e+02, tolerance: 4.977e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.936e+02, tolerance: 4.782e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:45,194] Trial 50 finished with value: 0.8609413020928532 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.04987590926279814, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.794e+02, tolerance: 4.977e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.830e+02, tolerance: 4.906e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.578e+02, tolerance: 4.782e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:45,322] Trial 51 finished with value: 0.8610289662757457 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.019211413400468974, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+02, tolerance: 4.977e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.507e+02, tolerance: 4.782e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.843e+02, tolerance: 4.906e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:45,415] Trial 52 finished with value: 0.8610070549049179 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.018492644772509947, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.840e+02, tolerance: 4.977e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.513e+02, tolerance: 4.782e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.924e+02, tolerance: 4.906e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:45,546] Trial 53 finished with value: 0.8569771623635769 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.008783442408928633, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.014e+02, tolerance: 4.977e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.243e+02, tolerance: 4.782e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:45,674] Trial 54 finished with value: 0.8624781673814641 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.05782221001517797, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.935e+02, tolerance: 4.782e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.113e+02, tolerance: 4.977e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.122e+02, tolerance: 4.906e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:45,806] Trial 55 finished with value: 0.8618589507037001 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.02487072255316275, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n",
      "[I 2024-10-02 17:18:45,912] Trial 56 finished with value: 0.864754359721037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2079910754941946, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n",
      "[I 2024-10-02 17:18:45,997] Trial 57 finished with value: 0.8622236413326235 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.333215560931422, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n",
      "[I 2024-10-02 17:18:46,054] Trial 58 finished with value: 0.861832165638517 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3628098560209365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n",
      "[I 2024-10-02 17:18:46,149] Trial 59 finished with value: 0.8620108533993581 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.34240779695521706, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n",
      "[I 2024-10-02 17:18:46,214] Trial 60 finished with value: 0.8638540565650902 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.26493714991266293, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n",
      "[I 2024-10-02 17:18:46,276] Trial 61 finished with value: 0.8629799500771645 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.30596394512914815, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n",
      "[I 2024-10-02 17:18:46,335] Trial 62 finished with value: 0.8621408609583922 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.33648829357762355, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n",
      "[I 2024-10-02 17:18:46,402] Trial 63 finished with value: 0.8638132124078156 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2679814646317183, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n",
      "[I 2024-10-02 17:18:46,473] Trial 64 finished with value: 0.863983758876634 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.24062119162159595, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n",
      "[I 2024-10-02 17:18:46,528] Trial 65 finished with value: 0.8627356047945115 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3141728910335158, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n",
      "[I 2024-10-02 17:18:46,591] Trial 66 finished with value: 0.8639203054085788 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.23391390640786494, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n",
      "[I 2024-10-02 17:18:46,632] Trial 67 finished with value: 0.8570103863991635 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6124885145996103, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n",
      "[I 2024-10-02 17:18:46,711] Trial 68 finished with value: 0.8647961976727571 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2059976546070975, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 68 with value: 0.8647961976727571.\n",
      "[I 2024-10-02 17:18:46,823] Trial 69 finished with value: 0.8648312544921793 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.20266060662750784, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 69 with value: 0.8648312544921793.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:46,919] Trial 70 finished with value: 0.8648431452862716 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.20027647978240445, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: 0.8648431452862716.\n",
      "[I 2024-10-02 17:18:47,006] Trial 71 finished with value: 0.8648491459660418 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1968919999787333, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: 0.8648491459660418.\n",
      "[I 2024-10-02 17:18:47,120] Trial 72 finished with value: 0.8650873115156988 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.174598921162764, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "[I 2024-10-02 17:18:47,201] Trial 73 finished with value: 0.8650350577921149 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.16468002989641095, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "[I 2024-10-02 17:18:47,294] Trial 74 finished with value: 0.8649412283687147 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1606717091615047, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.986e+01, tolerance: 4.782e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:47,416] Trial 75 finished with value: 0.8649537211609554 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.14694925097689848, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "[I 2024-10-02 17:18:47,526] Trial 76 finished with value: 0.8649734575435447 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.147612713300643, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.446e+01, tolerance: 4.782e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:47,647] Trial 77 finished with value: 0.8648761002838515 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.14440434705706803, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.398e+02, tolerance: 4.782e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:47,801] Trial 78 finished with value: 0.8639826593122782 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1265357179513065, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.690e+01, tolerance: 4.782e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:47,939] Trial 79 finished with value: 0.864435565531768 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1374245525868926, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "[I 2024-10-02 17:18:48,009] Trial 80 finished with value: 0.8590221951825531 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.49890830155012533, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "[I 2024-10-02 17:18:48,101] Trial 81 finished with value: 0.8649098880804443 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1573428812070292, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.405e+01, tolerance: 4.782e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:48,203] Trial 82 finished with value: 0.864536410656637 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13886104722511608, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "[I 2024-10-02 17:18:48,267] Trial 83 finished with value: 0.8597401050431873 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.47746341180045787, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "[I 2024-10-02 17:18:48,312] Trial 84 finished with value: 0.8537465461603838 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8599491178327108, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.050e+01, tolerance: 4.782e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:48,443] Trial 85 finished with value: 0.8642643827090003 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13446778921611002, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.175e+02, tolerance: 4.782e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:48,574] Trial 86 finished with value: 0.8641621818665252 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1286796719653316, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.446e+01, tolerance: 4.782e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:48,681] Trial 87 finished with value: 0.864182755916388 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13303218726548235, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "[I 2024-10-02 17:18:48,725] Trial 88 finished with value: -0.1255357440899417 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.021711452917433944, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.559714273835951e-05, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "[I 2024-10-02 17:18:48,780] Trial 89 finished with value: 0.8604596648091501 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.43644874418279245, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.463e+02, tolerance: 4.782e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:48,874] Trial 90 finished with value: 0.8635689909135862 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.10940922083495383, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "[I 2024-10-02 17:18:49,058] Trial 91 finished with value: 0.8648544336551733 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1912756875742137, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "[I 2024-10-02 17:18:49,144] Trial 92 finished with value: 0.8648496595672595 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.19628449928540487, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "[I 2024-10-02 17:18:49,201] Trial 93 finished with value: 0.8452625121122099 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4324661283995224, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "[I 2024-10-02 17:18:49,255] Trial 94 finished with value: 0.8378670635846416 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.839206620815206, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.002e+01, tolerance: 4.977e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.082e+02, tolerance: 4.782e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:49,393] Trial 95 finished with value: 0.8649365368153895 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.07270781179126021, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n",
      "[I 2024-10-02 17:18:49,604] Trial 96 finished with value: 0.8875676754699953 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0006995169897945908, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.586e+01, tolerance: 4.977e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.234e+01, tolerance: 4.906e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.618e+01, tolerance: 4.782e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:49,734] Trial 97 finished with value: 0.8730555131061773 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0018186269840273495, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n",
      "[I 2024-10-02 17:18:49,796] Trial 98 finished with value: -0.12553508835019533 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04867556317570456, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.177e+02, tolerance: 4.977e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+02, tolerance: 4.782e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.016e+02, tolerance: 4.906e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 17:18:49,961] Trial 99 finished with value: 0.8586292788613132 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.005078762921098462, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n"
     ]
    }
   ],
   "source": [
    "study = optimize(config, study_name=\"my_study_r2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.scatterplot(data=study.trials_dataframe(), x=\"number\", y=\"value\")\n",
    "ax.set(xlabel=\"Trial number\", ylabel=\"Ojbective value\\n(r2)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced functoinaility: algorithms & runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Various algorithms are available in QSARtuna:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(optunaz.config.optconfig.Lasso,\n",
       " optunaz.config.optconfig.PLSRegression,\n",
       " optunaz.config.optconfig.RandomForestRegressor,\n",
       " optunaz.config.optconfig.Ridge,\n",
       " optunaz.config.optconfig.KNeighborsRegressor,\n",
       " optunaz.config.optconfig.SVR,\n",
       " optunaz.config.optconfig.XGBRegressor,\n",
       " optunaz.config.optconfig.PRFClassifier,\n",
       " optunaz.config.optconfig.ChemPropRegressor,\n",
       " optunaz.config.optconfig.ChemPropRegressorPretrained,\n",
       " optunaz.config.optconfig.ChemPropHyperoptRegressor,\n",
       " optunaz.config.optconfig.CustomRegressionModel,\n",
       " optunaz.config.optconfig.AdaBoostClassifier,\n",
       " optunaz.config.optconfig.KNeighborsClassifier,\n",
       " optunaz.config.optconfig.LogisticRegression,\n",
       " optunaz.config.optconfig.RandomForestClassifier,\n",
       " optunaz.config.optconfig.SVC,\n",
       " optunaz.config.optconfig.ChemPropClassifier,\n",
       " optunaz.config.optconfig.ChemPropHyperoptClassifier,\n",
       " optunaz.config.optconfig.CustomClassificationModel,\n",
       " optunaz.config.optconfig.CalibratedClassifierCVWithVA,\n",
       " optunaz.config.optconfig.Mapie)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from optunaz.config.optconfig import AnyAlgorithm\n",
    "AnyAlgorithm.__args__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial will now look at more complex considerations that should be factored for more advanced functionaility such as the PRF and ChemProp algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probabilistic Random Forest (PRF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PRF is a modification of the long-established Random Forest (RF) algorithm and takes into account uncertainties in features and/or labels (though only uncertainty in labels are currently implemented in QSARtuna), which was first described in[1]. It can be seen as a probabilistic method to factor experimental uncertainty during training, and is considered a hybrid between regression and classification algorithms.\n",
    "\n",
    "In more detail; PRF treats labels as probability distribution functions [PDFs] (denoted as `∆y`), rather than deterministic quantities. In comparison, the traditional RF uses discrete variables for activity (binary y-labels, also referred to as `y`) from the discretised bioactivity scale defining active/inactive sets. \n",
    "\n",
    "PTR integration was added to QSARtuna to afford this probabilistic approach towards modelling, and is particularly useful combined with the PTR (See the preprocessing notebook for details). In this combination, PRF takes as input real-valued probabilities (similar to regression), from a Probabilistic Threshold Representation (PTR). However, similar to classification algorithms, PRF outputs the probability of activity for the active class.\n",
    "\n",
    "Note that QSARtuna runs the PRFClassifier in a regression setting, since the model only outputs class liklihood membership based on `∆y`\n",
    "\n",
    "[1] https://iopscience.iop.org/article/10.3847/1538-3881/aaf101/meta\n",
    "\n",
    "The following code imports the PRFClassifier and sets up a config to use the PRF with PTR:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from optunaz.config.optconfig import PRFClassifier\n",
    "\n",
    "# Prepare hyperparameter optimization configuration.\n",
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"Smiles\",\n",
    "        response_column=\"Measurement\",\n",
    "        training_dataset_file=\"../tests/data/pxc50/P24863.csv\",\n",
    "        probabilistic_threshold_representation=True, # This enables PTR\n",
    "        probabilistic_threshold_representation_threshold=8, # This defines the activity threshold\n",
    "        probabilistic_threshold_representation_std=0.6, # This captures the deviation/uncertainty in the dataset\n",
    "    ),\n",
    "    descriptors=[\n",
    "        ECFP.new(),\n",
    "        ECFP_counts.new(),\n",
    "        MACCS_keys.new(),\n",
    "    ],\n",
    "    algorithms=[\n",
    "        PRFClassifier.new(n_estimators={\"low\": 20, \"high\": 20}), #n_estimators set low for the example to run fast\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=2,\n",
    "        n_trials=15,\n",
    "        random_seed=42,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that QSARtuna is run in regression mode (`ModelMode.REGRESSION`), as outputs from the algorithm are always continuous values.\n",
    "\n",
    "Next we can run the PRF/PTR study:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:18:54,912] A new study created in memory with name: my_study\n",
      "[I 2024-10-02 17:18:54,914] A new study created in memory with name: study_name_0\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "[I 2024-10-02 17:18:59,458] Trial 0 finished with value: -0.08012946445342808 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 13, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': <PRFClassifierMaxFeatures.AUTO: 'auto'>, 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.08012946445342808.\n",
      "[I 2024-10-02 17:19:04,426] Trial 1 finished with value: -0.0728910742204806 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 6, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': <PRFClassifierMaxFeatures.AUTO: 'auto'>, 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -0.0728910742204806.\n",
      "[I 2024-10-02 17:19:09,767] Trial 2 finished with value: -0.08595075398685652 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': <PRFClassifierMaxFeatures.AUTO: 'auto'>, 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -0.0728910742204806.\n",
      "[I 2024-10-02 17:19:14,590] Trial 3 finished with value: -0.07164416449895736 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 7, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': <PRFClassifierMaxFeatures.AUTO: 'auto'>, 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: -0.07164416449895736.\n",
      "[I 2024-10-02 17:19:24,639] Trial 4 finished with value: -0.0688559585497607 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': <PRFClassifierMaxFeatures.AUTO: 'auto'>, 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -0.0688559585497607.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: UserWarning: Persisting input arguments took 0.54s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: UserWarning: Persisting input arguments took 0.58s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "[I 2024-10-02 17:19:38,908] Trial 5 finished with value: -0.05288340414349857 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 26, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': <PRFClassifierMaxFeatures.AUTO: 'auto'>, 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05288340414349857.\n",
      "[I 2024-10-02 17:19:38,925] Trial 6 pruned. Duplicate parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': <PRFClassifierMaxFeatures.AUTO: 'auto'>, 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-0.0688559585497607]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:19:45,347] Trial 7 finished with value: -0.06603614187252341 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 27, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': <PRFClassifierMaxFeatures.AUTO: 'auto'>, 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05288340414349857.\n",
      "[I 2024-10-02 17:19:48,828] Trial 8 finished with value: -0.07732628284491558 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': <PRFClassifierMaxFeatures.AUTO: 'auto'>, 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 3, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05288340414349857.\n",
      "[I 2024-10-02 17:19:54,641] Trial 9 finished with value: -0.06147782414985112 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 22, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': <PRFClassifierMaxFeatures.AUTO: 'auto'>, 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05288340414349857.\n",
      "[I 2024-10-02 17:19:58,656] Trial 10 finished with value: -0.07325856248953562 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 32, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': <PRFClassifierMaxFeatures.AUTO: 'auto'>, 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 4, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.05288340414349857.\n",
      "[I 2024-10-02 17:20:00,365] Trial 11 finished with value: -0.06290477085229226 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 30, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': <PRFClassifierMaxFeatures.AUTO: 'auto'>, 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.05288340414349857.\n",
      "[I 2024-10-02 17:20:02,259] Trial 12 finished with value: -0.06491937561887745 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 14, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': <PRFClassifierMaxFeatures.AUTO: 'auto'>, 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.05288340414349857.\n",
      "[I 2024-10-02 17:20:07,251] Trial 13 finished with value: -0.06872207131337535 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 18, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': <PRFClassifierMaxFeatures.AUTO: 'auto'>, 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.05288340414349857.\n",
      "[I 2024-10-02 17:20:20,598] Trial 14 finished with value: -0.05366258484830945 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 25, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': <PRFClassifierMaxFeatures.AUTO: 'auto'>, 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05288340414349857.\n"
     ]
    }
   ],
   "source": [
    "# Run the PRF/PTR Optuna Study.\n",
    "study = optimize(config, study_name=\"my_study\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now plot obtained performance across the Optuna trials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"darkgrid\")\n",
    "default_reg_scoring = config.settings.scoring\n",
    "ax = sns.scatterplot(data=study.trials_dataframe(), x=\"number\", y=\"value\")\n",
    "ax.set(xlabel=\"Trial number\", ylabel=f\"Ojbective value\\n({default_reg_scoring})\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the best PRF model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "buildconfig = buildconfig_best(study)\n",
    "best_built = build_best(buildconfig, \"../target/best.pkl\")\n",
    "\n",
    "with open(\"../target/best.pkl\", \"rb\") as f:\n",
    "    model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot predictions from the merged model for the (seen) train data for demonstration purposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#predict the input from the trained model (transductive evaluation of the model)\n",
    "example_smiles=config.data.get_sets()[0]\n",
    "expected = config.data.get_sets()[1]\n",
    "predicted = model.predict_from_smiles(example_smiles)\n",
    "\n",
    "# Plot expected vs predicted values for the best model.\n",
    "ax = plt.scatter(expected, predicted)\n",
    "lims = [expected.min(), expected.max()]\n",
    "plt.plot(lims, lims)  # Diagonal line.\n",
    "plt.xlabel(f\"Expected {config.data.response_column} (PTR)\");\n",
    "plt.ylabel(f\"Predicted {config.data.response_column}\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interlude: Cautionary advice for  PRF ∆y (response column) validity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "N.B It is not possible to train on response column values outside the likelihood for y-label memberships (ranging from 0-1), as expected for `∆y`. Doing so will result in the following error from QSARtuna:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:20:29,205] A new study created in memory with name: my_study\n",
      "[I 2024-10-02 17:20:29,248] A new study created in memory with name: study_name_0\n",
      "[W 2024-10-02 17:20:29,249] Trial 0 failed with parameters: {} because of the following error: ValueError('PRFClassifier supplied but response column outside [0.0-1.0] acceptable range. Response max: 9.7, response min: 5.3 ').\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/optuna/study/_optimize.py\", line 196, in _run_trial\n",
      "    value_or_values = func(trial)\n",
      "  File \"/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/objective.py\", line 128, in __call__\n",
      "    self._validate_algos()\n",
      "  File \"/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/objective.py\", line 270, in _validate_algos\n",
      "    raise ValueError(\n",
      "ValueError: PRFClassifier supplied but response column outside [0.0-1.0] acceptable range. Response max: 9.7, response min: 5.3 \n",
      "[W 2024-10-02 17:20:29,257] Trial 0 failed with value None.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As expected, training the PRF on the raw pXC50 values resulted in the following error:\n",
      "\n",
      "\"PRFClassifier supplied but response column outside [0.0-1.0] acceptable range. Response max: 9.7, response min: 5.3 \n"
     ]
    }
   ],
   "source": [
    "# Prepare problematic hyperparameter optimization configuration without PTR.\n",
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"Smiles\",\n",
    "        response_column=\"Measurement\",\n",
    "        training_dataset_file=\"../tests/data/pxc50/P24863.csv\"),\n",
    "    descriptors=[\n",
    "        ECFP.new(),\n",
    "    ],\n",
    "    algorithms=[\n",
    "        PRFClassifier.new(n_estimators={\"low\": 5, \"high\": 10}), #n_estimators set low for the example to run fast\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=2,\n",
    "        n_trials=2,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ")\n",
    "\n",
    "try:\n",
    "    study = optimize(config, study_name=\"my_study\")\n",
    "except ValueError as e:\n",
    "    print(f'As expected, training the PRF on the raw pXC50 values resulted in the following error:\\n\\n\"{e}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To summarise:\n",
    "1. PRF handles probability of `y` or `∆y` labels, which range between [`0-1`]\n",
    "2. PRF is evaluated in a probabilistic setting via conventional regression metrics (e.g. `RMSE`, `R2` etc.), despite the fact that PRF can be considered a modification to the classic Random Forest classifier\n",
    "3. Probabilistic output is the probability of activity at a relevant cutoff, similar to a classification algorithm\n",
    "4. Ouputs reflect liklihoods for a molecular property to be above a relevant threshold, given experimental uncertainty (and arguably a more useful component for within a REINVENT MPO score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ChemProp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QSARtuna has the functionaility to train `ChemProp` deep learning models. These are message passing neural networks (MPNNs) based on a graph representation of training molecules. They are considered by many to offer the state-of-the-art approach for property prediction.\n",
    "\n",
    "`ChemProp` was first described in the paper Analyzing Learned Molecular Representations for Property Prediction: https://pubs.acs.org/doi/full/10.1021/acs.jcim.9b00237\n",
    "\n",
    "More information is available in their slides: https://docs.google.com/presentation/d/14pbd9LTXzfPSJHyXYkfLxnK8Q80LhVnjImg8a3WqCRM/edit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `ChemProp` package expects SMILES as molecule inputs, since it calcaultes a molecule graph directly from these and so expects SMILES as descriptors. The `SmilesFromFile` and `SmilesAndSideInfoFromFile` descriptors (more about this later) are available for this purpose and are _only_ supported by the `ChemProp` algorithms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Smiles based descriptors:\n",
      "(<class 'optunaz.descriptors.SmilesFromFile'>, <class 'optunaz.descriptors.SmilesAndSideInfoFromFile'>)\n"
     ]
    }
   ],
   "source": [
    "from optunaz.config.optconfig import ChemPropRegressor\n",
    "from optunaz.descriptors import SmilesBasedDescriptor, SmilesFromFile\n",
    "print(f\"Smiles based descriptors:\\n{SmilesBasedDescriptor.__args__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple ChemProp example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following is an example of the most basic ChemProp run, which will train the algorithm using the recommended (sensible) defaults for the MPNN architecture:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:20:29,311] A new study created in memory with name: my_study\n",
      "[I 2024-10-02 17:20:29,313] A new study created in memory with name: study_name_0\n",
      "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__668a7428ff5cdb271b01c0925e8fea45': 'ReLU', 'aggregation__668a7428ff5cdb271b01c0925e8fea45': 'mean', 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': 'none', 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45'}\n",
      "[I 2024-10-02 17:21:30,322] Trial 0 finished with value: -4821.6481382641 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': <ChemPropActivation.RELU: 'ReLU'>, 'aggregation__668a7428ff5cdb271b01c0925e8fea45': <ChemPropAggregation.MEAN: 'mean'>, 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': <ChemPropFeatures_Generator.NONE: 'none'>, 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -4821.6481382641.\n",
      "[I 2024-10-02 17:22:35,146] Trial 1 finished with value: -6764.388663243151 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': <ChemPropActivation.RELU: 'ReLU'>, 'aggregation__668a7428ff5cdb271b01c0925e8fea45': <ChemPropAggregation.MEAN: 'mean'>, 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 40.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 15.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 4.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.36, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': <ChemPropFeatures_Generator.NONE: 'none'>, 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 1500.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -1, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 1000.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -1, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -4821.6481382641.\n"
     ]
    }
   ],
   "source": [
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-50/train.csv\",  # This will be split into train and test.\n",
    "        split_strategy=Stratified(fraction=0.50),\n",
    "        deduplication_strategy=KeepMedian(),\n",
    "    ),\n",
    "    descriptors=[\n",
    "        SmilesFromFile.new(),\n",
    "    ],\n",
    "    algorithms=[\n",
    "        ChemPropRegressor.new(epochs=5), #epochs=5 to ensure run finishes quickly\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=2,\n",
    "        n_trials=2,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ")\n",
    "\n",
    "study = optimize(config, study_name=\"my_study\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may safely ignore `ChemProp` warnings such as `Model 0 provided with no test set, no metric evaluation will be performed`, `\"rmse = nan\"` and `1-fold cross validation`, as they are information prompts printed from `ChemProp` due to some (deactivated) CV functionaility (ChemProp can perform it's own cross validation - details for this are still printed despite its deactivation within `QSARtuna`).\n",
    "\n",
    "NB: QSARtuna will first trial the sensible defaults for the MPNN architecture (where possible given the user config). This is communicated to the user, e.g. see the output which advises:\n",
    "\n",
    "`A new study created in memory with name: study_name_0\n",
    "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation': 'ReLU', 'aggregation': 'mean', 'aggregation_norm': 100, 'batch_size': 50, 'depth': 3, 'dropout': 0.0, 'features_generator': 'none', 'ffn_hidden_size': 300, 'ffn_num_layers': 3, 'final_lr_ratio_exp': -1, 'hidden_size': 300, 'init_lr_ratio_exp': -1, 'max_lr_exp': -3, 'warmup_epochs_ratio': 0.1, 'algorithm_name': 'ChemPropRegressor'}`.\n",
    "\n",
    "Enqueuing custom parameters ensures sampling from a sensible hyperparameter space to begin with, and to facilitate further optimisation from this point. Additional trials will not have any further preset enqueing and use Bayesian optimization for trial suggestion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ChemProp optimization separate from shallow methods (default behavior)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, `QSARtuna` separates `ChemProp` from the other shallow methods using the `split_chemprop` flag. When this setting is set, the user must specify the number of `ChemProp` trials using the `n_chemprop_trials` flag if more than 1 (default) trial is desired:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from optunaz.config.optconfig import ChemPropClassifier, RandomForestClassifier\n",
    "\n",
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt_gt_330\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-50/train.csv\",\n",
    "        split_strategy=Stratified(fraction=0.75),\n",
    "        deduplication_strategy=KeepMedian(),\n",
    "    ),\n",
    "    descriptors=[\n",
    "        ECFP.new(),\n",
    "        SmilesFromFile.new(),\n",
    "    ],\n",
    "    algorithms=[\n",
    "        ChemPropClassifier.new(epochs=4),\n",
    "        RandomForestClassifier.new(n_estimators={\"low\": 5, \"high\": 5}),\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.CLASSIFICATION,\n",
    "        cross_validation=2,\n",
    "        n_trials=1, # run only one random forest classifier trial\n",
    "        n_chemprop_trials=2, # run one enqueued chemprop trial and 1 undirected trial\n",
    "        split_chemprop=True, # this is set to true by default (shown here for illustration)\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Turn on Hyperopt within trials (advanced functionaility & very large computational cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QSARtuna optimises all aspects of the ChemProp architecture when using ChemPropRegressor or ChemPropClassifier, however, users can activate the [original hyperparameter-optimization implementation](https://github.com/chemprop/chemprop#hyperparameter-optimization) in the `ChemProp` package, which performs automated Bayesian hyperparameter optimization using the `Hyperopt` package within each trial, at large computational cost.\n",
    "\n",
    "NB: The principal way for users to expand and perform more advanced runs is to extend the available non-network hyperparameters, such as the `features_generator` option or e.g. to trial differnt side information weighting (if side information is available).\n",
    "\n",
    "NB: Please note that when `num_iters`=1 (default behavior), any optimisation of the MPNN architecture (done by Hyperopt) is deactivated - the sensible defaults as specified by the ChemProp authors are applied. i.e. optimisation of the MPNN is only possible when `num_iters`>=2, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from optunaz.config.optconfig import ChemPropHyperoptRegressor, ChemPropHyperoptClassifier\n",
    "\n",
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-50/train.csv\",\n",
    "        split_strategy=Stratified(fraction=0.5),\n",
    "        deduplication_strategy=KeepMedian(),\n",
    "    ),\n",
    "    descriptors=[\n",
    "        SmilesFromFile.new(),\n",
    "    ],\n",
    "    algorithms=[\n",
    "        ChemPropHyperoptRegressor.new(epochs=5, num_iters=2), #num_iters>2: enable hyperopt within ChemProp trials\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=2,\n",
    "        n_trials=1, #just optimise one ChemProp model for this example\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NB: Remember that parameter tuning of the MPNN network is performed _within_ each trial.\n",
    "\n",
    "#### A note on MPNN Hyperopt search space\n",
    "\n",
    "`ChemProp` models trained using Hyperopt use the [original implementation](https://chemprop.readthedocs.io/en/latest/hyperopt.html), but one key difference is the `search_parameter_level` setting created for `QSARtuna`; Instead of using pre-defined search spaces as in the original package, `QSARtuna` can (and will by the default since `search_parameter_level`=`auto` unless changed) alter the space depending on the characteristics of user input data. For example, no. training set compounds, hyperparameter trials (`num_iters`) & epochs (`epochs`) are used by the `auto` setting to ensure search spaces are not too large for limited data/epochs, and _vice-versa_, an extensive search space is trailed when applicable.\n",
    "\n",
    "N.B: Users can also manually define `Hyperopt` search spaces by altering `search_parameter_level` from `auto` to a different level between `[0-8]`, representing the increasing search space size (see the [QSARtuna documentation](https://molecularai.github.io/QSARtuna/optunaz.config.html#optunaz.config.optconfig.ChemPropSearch_Parameter_Level) for details)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Side information and multi-task learning (MTL) <a class=\"anchor\" id=\"sideinfo\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_“Even if you are only optimizing one loss as is the typical case, chances are there is an auxiliary task that will help you improve upon your main task”_ [Caruana, 1998]\n",
    "\n",
    "`QSARtuna` typically optimizes for one particular metric for a given molecule property. While we can generally achieve acceptable performance this way, these single task (ST) models ignore information that may improve the prediction of main task of intent. See option `a.` in the figure below.\n",
    "\n",
    "Signals from relevant related tasks (aka \"auxiliary tasks\" or \"side information\") could come from the training signals of other molecular properties and by sharing representations between related tasks, we can enable a neural network to generalize better on our original task of intent. This approach is called Multi-Task Learning (MTL) See option `b.` in the figure below.\n",
    "\n",
    "<img src=\"../docs/images/st_mt.png\" alt=\"Difference between ST and MTL\" style=\"width: 400px;\"/>\n",
    "\n",
    "_(above) Differences between optimizing one vs. more than one loss function.  a.) Single-task (ST): one model trained to predict one task one model optimised until performance no longer increases b.) Multi-task (MT/MTL): training one model to predict multiple tasks one model optimising more than one loss function at once enables representations to be shared between trained tasks training signals of related tasks shared between all tasks._\n",
    "\n",
    "\n",
    "`ChemProp` performs MTL by using the knowledge learnt during training one task to reduce the loss of other tasks included in training. In order to use this function in `QSARtuna`, a user should provide side information in a separate file, and it should have the same ordering and length as the input/response columns (i.e. length of y should = length of side information for y).\n",
    "\n",
    "E.g: consider the DRD2 example input from earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "canonical,activity,molwt,molwt_gt_330\r\n",
      "Cc1cc(NC(=O)c2cccc(COc3ccc(Br)cc3)c2)no1,0,387.233,True\r\n",
      "O=C(Nc1ccc(F)cc1F)Nc1sccc1-c1nc2ccccc2s1,0,387.4360000000001,True\r\n",
      "COC(=O)c1ccccc1NC(=O)c1cc([N+](=O)[O-])nn1Cc1ccccc1,0,380.36000000000007,True\r\n",
      "CCOC(=O)C(C)Sc1nc(-c2ccccc2)ccc1C#N,0,312.39400000000006,False\r\n",
      "CCC(CC)NC(=O)c1nn(Cc2ccccc2)c(=O)c2ccccc12,0,349.4340000000001,True\r\n",
      "Brc1ccccc1OCCCOc1cccc2cccnc12,0,358.235,True\r\n",
      "CCCCn1c(COc2cccc(OC)c2)nc2ccccc21,0,310.39700000000005,False\r\n",
      "CCOc1cccc(NC(=O)c2sc3nc(-c4ccc(F)cc4)ccc3c2N)c1,0,407.4700000000001,True\r\n",
      "COc1ccc(S(=O)(=O)N(CC(=O)Nc2ccc(C)cc2)c2ccc(C)cc2)cc1OC,0,454.54800000000023,True\r\n"
     ]
    }
   ],
   "source": [
    "!head  ../tests/data/DRD2/subset-50/train.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is an accompying example of side information/auxiliary data inputs (calculated PhysChem properties ) as provided in `train_side_info.csv` within the tests data folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "canonical,cLogP,cLogS,H-Acceptors,H-Donors,Total Surface Area,Relative PSA,H-Acceptors-gt-string\r\n",
      "Cc1cc(NC(=O)c2cccc(COc3ccc(Br)cc3)c2)no1,4.04,-5.293,5,1,265.09,0.22475,Many\r\n",
      "O=C(Nc1ccc(F)cc1F)Nc1sccc1-c1nc2ccccc2s1,4.8088,-5.883,4,2,271.39,0.32297,Few\r\n",
      "COC(=O)c1ccccc1NC(=O)c1cc([N+](=O)[O-])nn1Cc1ccccc1,1.6237,-3.835,9,1,287.39,0.33334,Many\r\n",
      "CCOC(=O)C(C)Sc1nc(-c2ccccc2)ccc1C#N,3.2804,-4.314,4,0,249.51,0.26075,Few\r\n",
      "CCC(CC)NC(=O)c1nn(Cc2ccccc2)c(=O)c2ccccc12,3.2533,-4.498,5,1,278.05,0.18917,Many\r\n",
      "Brc1ccccc1OCCCOc1cccc2cccnc12,4.5102,-4.694,3,0,246.29,0.12575,Few\r\n",
      "CCCCn1c(COc2cccc(OC)c2)nc2ccccc21,3.7244,-2.678,4,0,255.14,0.14831,Few\r\n",
      "CCOc1cccc(NC(=O)c2sc3nc(-c4ccc(F)cc4)ccc3c2N)c1,4.4338,-6.895,5,2,302.18,0.26838,Many\r\n",
      "COc1ccc(S(=O)(=O)N(CC(=O)Nc2ccc(C)cc2)c2ccc(C)cc2)cc1OC,3.2041,-5.057,7,1,343.67,0.22298,Many\r\n"
     ]
    }
   ],
   "source": [
    "!head  ../tests/data/DRD2/subset-50/train_side_info.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I.e. the first column (Smiles) should match between the two files, and any columns after the SMILES within the `train_side_info.csv` side information file will be used as y-label side information in the training of the network.\n",
    "\n",
    "N.B: that calculated PhysChem properties are only one example of side information, and that side information may come from any related property that improves the main task of intent.\n",
    "\n",
    "A classification example can also be found here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "canonical,cLogP_Gt2.5,cLogS_Gt-3.5,H-Acceptors_Gt5,H-Donors_Gt0,Total Surface Area_Gt250,Relative PSA_Lt0.25,H-Acceptors-gt-string\r\n",
      "!Cc1cc(NC(=O)c2cccc(COc3ccc(Br)cc3)c2)no1,1,0,0,1,1,1,Many\r\n",
      "O=C(Nc1ccc(F)cc1F)Nc1sccc1-c1nc2ccccc2s1,1,0,0,1,1,0,Few\r\n",
      "COC(=O)c1ccccc1NC(=O)c1cc([N+](=O)[O-])nn1Cc1ccccc1,0,0,1,1,1,0,Few\r\n",
      "CCOC(=O)C(C)Sc1nc(-c2ccccc2)ccc1C#N,1,0,0,0,0,0,Few\r\n",
      "CCC(CC)NC(=O)c1nn(Cc2ccccc2)c(=O)c2ccccc12,1,0,0,1,1,1,Many\r\n",
      "Brc1ccccc1OCCCOc1cccc2cccnc12,1,0,0,0,0,1,Many\r\n",
      "CCCCn1c(COc2cccc(OC)c2)nc2ccccc21,1,1,0,0,1,1,Many\r\n",
      "CCOc1cccc(NC(=O)c2sc3nc(-c4ccc(F)cc4)ccc3c2N)c1,1,0,0,1,1,0,Few\r\n",
      "COc1ccc(S(=O)(=O)N(CC(=O)Nc2ccc(C)cc2)c2ccc(C)cc2)cc1OC,1,0,1,1,1,1,Many\r\n"
     ]
    }
   ],
   "source": [
    "!head  ../tests/data/DRD2/subset-50/train_side_info_cls.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The contribution or _weight_ of all side information tasks in their contribution to the loss function during training a network is a parameter that can be optimised within QSARtuna, e.g:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:22:37,977] A new study created in memory with name: my_study\n",
      "[I 2024-10-02 17:22:37,986] A new study created in memory with name: study_name_0\n",
      "[I 2024-10-02 17:22:42,900] Trial 0 finished with value: -5492.460529549569 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': <ChemPropFeatures_Generator.NONE: 'none'>, 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': <ChemPropSearch_Parameter_Level.AUTO: 'auto'>, 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 50}. Best is trial 0 with value: -5492.460529549569.\n",
      "[I 2024-10-02 17:22:42,953] Trial 1 pruned. Duplicate parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': <ChemPropFeatures_Generator.NONE: 'none'>, 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': <ChemPropSearch_Parameter_Level.AUTO: 'auto'>, 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 50}, return [-5492.460529549569]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:22:45,348] Trial 2 finished with value: -5488.71615646329 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': <ChemPropFeatures_Generator.NONE: 'none'>, 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': <ChemPropSearch_Parameter_Level.AUTO: 'auto'>, 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 80}. Best is trial 2 with value: -5488.71615646329.\n",
      "[I 2024-10-02 17:22:47,076] Trial 3 finished with value: -5488.166537608711 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': <ChemPropFeatures_Generator.NONE: 'none'>, 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': <ChemPropSearch_Parameter_Level.AUTO: 'auto'>, 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 100}. Best is trial 3 with value: -5488.166537608711.\n",
      "[I 2024-10-02 17:22:47,103] Trial 4 pruned. Duplicate parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': <ChemPropFeatures_Generator.NONE: 'none'>, 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': <ChemPropSearch_Parameter_Level.AUTO: 'auto'>, 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 100}, return [-5488.166537608711]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:22:48,746] Trial 5 finished with value: -5590.6396754537145 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': <ChemPropFeatures_Generator.NONE: 'none'>, 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': <ChemPropSearch_Parameter_Level.AUTO: 'auto'>, 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 0}. Best is trial 3 with value: -5488.166537608711.\n",
      "[I 2024-10-02 17:22:48,772] Trial 6 pruned. Duplicate parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': <ChemPropFeatures_Generator.NONE: 'none'>, 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': <ChemPropSearch_Parameter_Level.AUTO: 'auto'>, 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 100}, return [-5488.166537608711]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:22:52,105] Trial 7 finished with value: -5527.605545861933 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': <ChemPropFeatures_Generator.NONE: 'none'>, 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': <ChemPropSearch_Parameter_Level.AUTO: 'auto'>, 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 10}. Best is trial 3 with value: -5488.166537608711.\n"
     ]
    }
   ],
   "source": [
    "from optunaz.descriptors import SmilesAndSideInfoFromFile\n",
    "\n",
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-50/train.csv\",\n",
    "        test_dataset_file=\"../tests/data/DRD2/subset-50/test.csv\"),  # Hidden during optimization.\n",
    "    descriptors=[\n",
    "        SmilesAndSideInfoFromFile.new(file='../tests/data/DRD2/subset-50/train_side_info.csv',\\\n",
    "                                     input_column='canonical',\n",
    "                                     aux_weight_pc={\"low\": 0, \"high\": 100, \"q\": 10}) #try different aux weights\n",
    "    ],\n",
    "    algorithms=[\n",
    "        ChemPropHyperoptRegressor.new(epochs=4), #epochs=4 to ensure run finishes quickly\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=1,\n",
    "        n_trials=8,\n",
    "        n_startup_trials=0,\n",
    "        random_seed=42,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ")\n",
    "\n",
    "study = optimize(config, study_name=\"my_study\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the toy example above, the `ChemPropRegressor` has been trialed with a variety of auxiliary weights ranging from 0-100%, using the SmilesAndSideInfoFromFile setting `aux_weight_pc={\"low\": 0, \"high\": 100}`.\n",
    "\n",
    "The inlfuence of the weighting of side information on model performance next hence be explored via a scatterplot of the auxiliary weight percent as a product of the objective value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = study.trials_dataframe().query('user_attrs_trial_ran==True') #drop any pruned/erroneous trials\n",
    "data.columns = [i.split('__')[0] for i in data.columns] # remove algorithm hash from columns\n",
    "ax = sns.scatterplot(data=data, x=\"params_aux_weight_pc\", y=\"value\")\n",
    "ax.set(xlabel=\"Aux weight percent (%)\", ylabel=f\"Ojbective value\\n({default_reg_scoring})\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence we can conclude that 100% weighting of the side information produces the most performant `ChemProp` model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining ChemProp & shallow models (only recommended for large no. trials & long run times)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above described how ChemProp is by default split from shallow methods. Users can override this using the advanced functionaility when `split_chemprop`=False. In this situation `QSARtuna` will optimise `ChemProp` with the other shallow methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt_gt_330\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-50/train.csv\",  # This will be split into train and test.\n",
    "        split_strategy=Stratified(fraction=0.75),\n",
    "        deduplication_strategy=KeepMedian(),\n",
    "    ),\n",
    "    descriptors=[\n",
    "        ECFP.new(),\n",
    "        SmilesFromFile.new(),\n",
    "    ],\n",
    "    algorithms=[\n",
    "        ChemPropClassifier.new(epochs=4),\n",
    "        RandomForestClassifier.new(n_estimators={\"low\": 5, \"high\": 5}),\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.CLASSIFICATION,\n",
    "        cross_validation=2,\n",
    "        random_seed=42,\n",
    "        n_trials=10, # run one random forest classifier trial\n",
    "        split_chemprop=False, # set to false to run ChemProp along side other algorithms\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combining ChemProp and traditional methods in this way means the potential search possible in `QSARtuna` comprises some incompatible sub-spaces; for example, `ChemProp` is only compatible with SMILES-based descriptors, whilst traditional methods are incompatible with SMILES. In the following examples, we consider the following search space: <img src=\"../docs/images/subspace.png\" alt=\"Valid subspaces in green\" style=\"width: 400px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pruning is an effective method to handle such incompatible algorithm-desciptor pairs (grey) to skip any incompatible tials that use non-SMILES descriptors with `ChemProp` and _vice-versa_, when any SMILES-based descriptors are trialed with non-`ChemProp` algorithms. \n",
    "\n",
    "This is only recommended for advanced users, when the no. trials is >100, since the optimiser has to learn which descriptors and methods are compatible at the same time as optimising for the most performative pairs.\n",
    "\n",
    "_What is pruning?_\n",
    "\n",
    "Pruning is an `Optuna` feature [originally designed](https://optuna.readthedocs.io/en/v2.0.0/tutorial/pruning.html) to automatically stop unpromising trials early in training.\n",
    "\n",
    "_How/why is pruning implemented in QSARtuna?_\n",
    "\n",
    "Instead of the original intention of pruning _unpromising trials_ due to poor performance, `QSARtuna` ensures `Optuna` prunes _incompatible algorithm-desciptor pairs_, so the sampler will not trial incomptaible settings again. Duplicated trials are also pruned, to avoid repeatedly suggesting the same hyerparameters. Pruning the trial (instead of  returning a cached result) explicitly tasks the sampler to avoid identical parameters.\n",
    "\n",
    "Now let us consider the following run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:22:52,649] A new study created in memory with name: my_study\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:958)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:958)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:958)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:958)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:958)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "[I 2024-10-02 17:22:52,875] Trial 0 finished with value: -inf and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.9525489095524835, 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 40, \"q\": 10}}}', 'aux_weight_pc__cfa1990d5153c8812982f034d788d7ee': 30}. Best is trial 0 with value: -inf.\n",
      "[I 2024-10-02 17:22:53,127] Trial 1 finished with value: -4824.686269039228 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.7731425652872588, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -4824.686269039228.\n",
      "[I 2024-10-02 17:22:53,206] Trial 2 pruned. Incompatible subspace\n",
      "[I 2024-10-02 17:22:53,241] Trial 3 pruned. Incompatible subspace\n",
      "[I 2024-10-02 17:22:53,493] Trial 4 finished with value: -4409.946844928445 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.791002332112292, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -4409.946844928445.\n",
      "[I 2024-10-02 17:22:53,541] Trial 5 pruned. Incompatible subspace\n",
      "[I 2024-10-02 17:22:53,852] Trial 6 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 23.329624779366306, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00015024763718638216, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -4409.946844928445.\n",
      "[I 2024-10-02 17:22:53,890] Trial 7 pruned. Incompatible subspace\n",
      "[I 2024-10-02 17:22:53,929] Trial 8 pruned. Incompatible subspace\n",
      "[I 2024-10-02 17:22:53,950] Trial 9 pruned. Incompatible subspace\n",
      "[I 2024-10-02 17:22:54,335] Trial 10 finished with value: -4396.722635068717 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 17, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 10 with value: -4396.722635068717.\n",
      "[I 2024-10-02 17:22:54,358] Trial 11 pruned. Duplicate parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 17, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-4396.722635068717]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:22:54,717] Trial 12 finished with value: -4030.45773791647 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 30, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -4030.45773791647.\n",
      "[I 2024-10-02 17:22:54,728] Trial 13 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 17:22:54,876] Trial 14 finished with value: -4030.4577379164707 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -4030.45773791647.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 30, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-4030.45773791647]\n"
     ]
    }
   ],
   "source": [
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-50/train.csv\",\n",
    "        test_dataset_file=\"../tests/data/DRD2/subset-50/test.csv\"\n",
    "    ),\n",
    "    descriptors=[\n",
    "        ECFP.new(), # NB: The first 2 descriptors are incompatible with ChemProp \n",
    "        MACCS_keys.new(),\n",
    "        SmilesAndSideInfoFromFile.new(file='../tests/data/DRD2/subset-50/train_side_info.csv',\\\n",
    "                                     input_column='canonical',\n",
    "                                      aux_weight_pc={\"low\": 0, \"high\": 40, \"q\": 10}\n",
    "                                     ), # NB: This is only compatible only with ChemProp\n",
    "    ],\n",
    "    algorithms=[\n",
    "        SVR.new(), # NB: The first 6 regressors are incompatible with SMILES descriptors \n",
    "        RandomForestRegressor.new(n_estimators={\"low\": 5, \"high\": 10}),\n",
    "        Ridge.new(),\n",
    "        Lasso.new(),\n",
    "        PLSRegression.new(),\n",
    "        ChemPropHyperoptRegressor.new(epochs=4) # NB: ChemProp only compatible with SMILES\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=3,\n",
    "        n_trials=15,\n",
    "        random_seed=4,  # seed 4 ensures sampling of incompatible subspace for this example\n",
    "        n_startup_trials=10,\n",
    "        split_chemprop=False,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ")\n",
    "\n",
    "study = optimize(config, study_name=\"my_study\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consulting the `QSARtuna` output, we observe cases of e.g. \"`Trial 3 pruned. Incompatible subspace`\", which indicates an instance when the sampler has sampled an incompitble algo-descriptor pair. \n",
    "\n",
    "\"`Trial 13 pruned. Duplicate parameter set`\" is an example of pruning a duplicated trial parameter suggestion.\n",
    "\n",
    "N.B: \"`Trial 0 finished with value: -inf`\" can occur when the very first `Optuna` trial comprises incompitble algo-descriptor pairs (`-inf` is assigned instead of pruning, since `Optuna` does not allow pruning first trials)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = study.trials_dataframe().query('user_attrs_trial_ran==True') #drop any pruned/erroneous trials\n",
    "ax = sns.scatterplot(data=data, x=\"number\", y=\"value\",hue='params_algorithm_name')\n",
    "ax.set(xlabel=\"Trial number\", ylabel=f\"Ojbective value\\n({default_reg_scoring})\")\n",
    "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown in the overview graph, `Optuna` never successfully samples/trials `ChemProp` with a compatible descriptor, as seen from it's absence in the generated plot. The pruned 8th trial is also missing due to the pruning. This highlights a limitation of combining `ChemProp` (and the associated descriptors) with other algorithms - it makes optimisation more complex, and not all combinations may be suggested. Aside from splitting ChemProp into separate trials, another mitigation strategy is to reduce the complexity of the search space, by removing some incompatible descriptors/traditional methods, to ensure ChemProp has more sampling success. Overall, this serves as a cautionary argument to split `ChemProp` trials (using `split_chemprop`=True)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-training and adapting ChemProp models (Transfer Learning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transfer learning (TL) to adapt pre-trained models on a specific (wider) dataset to a specific dataset of interest in a similar manner to [this publication](https://pubs.acs.org/doi/10.1021/acs.molpharmaceut.3c01124) can be performed in QSARtuna. This option is available for ChemProp models and employs the [original ChemProp package implementation](https://chemprop.readthedocs.io/en/latest/tutorial.html#pretraining).\n",
    "For example, a user can perform optimisation to pre-train a model using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:22:55,736] A new study created in memory with name: my_study\n",
      "[I 2024-10-02 17:22:55,738] A new study created in memory with name: study_name_0\n",
      "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__e0d3a442222d4b38f3aa1434851320db': 'ReLU', 'aggregation__e0d3a442222d4b38f3aa1434851320db': 'mean', 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50, 'depth__e0d3a442222d4b38f3aa1434851320db': 3, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'features_generator__e0d3a442222d4b38f3aa1434851320db': 'none', 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db'}\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:869)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "[I 2024-10-02 17:23:51,502] Trial 0 finished with value: -4937.540075659691 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': <ChemPropActivation.RELU: 'ReLU'>, 'aggregation__e0d3a442222d4b38f3aa1434851320db': <ChemPropAggregation.MEAN: 'mean'>, 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': <ChemPropFeatures_Generator.NONE: 'none'>, 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -4937.540075659691.\n"
     ]
    }
   ],
   "source": [
    "from optunaz.descriptors import SmilesFromFile\n",
    "from optunaz.config.optconfig import ChemPropRegressor\n",
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-50/train.csv\",  # This will be split into train and test.\n",
    "    ),\n",
    "    descriptors=[SmilesFromFile.new()],\n",
    "    algorithms=[\n",
    "        ChemPropRegressor.new(epochs=4),\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=2,\n",
    "        n_trials=1,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ")\n",
    "\n",
    "study = optimize(config, study_name=\"my_study\")\n",
    "_ = build_best(buildconfig_best(study), \"../target/pretrained.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pretrained model saved to `../target/pretrained.pkl` can now be supplied as an input for the `ChemPropRegressorPretrained` algorithm. This model can be retrained with (or adapted to) a new dataset (`../tests/data/DRD2/subset-50/test.csv`) like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:24:08,172] A new study created in memory with name: my_study\n",
      "[I 2024-10-02 17:24:08,222] A new study created in memory with name: study_name_0\n",
      "[I 2024-10-02 17:25:11,638] Trial 0 finished with value: -5114.7131239123555 and parameters: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': 'dfc518a76317f23d95e5aa5a3eac77f0', 'frzn__dfc518a76317f23d95e5aa5a3eac77f0': <ChemPropFrzn.NONE: 'none'>, 'epochs__dfc518a76317f23d95e5aa5a3eac77f0': 4, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5114.7131239123555.\n"
     ]
    }
   ],
   "source": [
    "from optunaz.config.optconfig import ChemPropRegressorPretrained\n",
    "\n",
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-50/test.csv\",\n",
    "    ),\n",
    "    descriptors=[SmilesFromFile.new()],\n",
    "    algorithms=[\n",
    "        ChemPropRegressorPretrained.new(\n",
    "            pretrained_model='../target/pretrained.pkl',\n",
    "            epochs=ChemPropRegressorPretrained.Parameters.ChemPropParametersEpochs(low=4,high=4))\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=2,\n",
    "        n_trials=1,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ")\n",
    "\n",
    "study = optimize(config, study_name=\"my_study\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have the basics covered, we can now provide an example of how QSARtuna can compare the performance of local, adapted and global (no epochs for transfer learning) models within a single optimisation job in the following example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:25:11,764] A new study created in memory with name: my_study\n",
      "[I 2024-10-02 17:25:11,766] A new study created in memory with name: study_name_0\n",
      "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__e0d3a442222d4b38f3aa1434851320db': 'ReLU', 'aggregation__e0d3a442222d4b38f3aa1434851320db': 'mean', 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50, 'depth__e0d3a442222d4b38f3aa1434851320db': 3, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'features_generator__e0d3a442222d4b38f3aa1434851320db': 'none', 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db'}\n",
      "[I 2024-10-02 17:25:40,665] Trial 0 finished with value: -5891.7552821093905 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': <ChemPropActivation.RELU: 'ReLU'>, 'aggregation__e0d3a442222d4b38f3aa1434851320db': <ChemPropAggregation.MEAN: 'mean'>, 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': <ChemPropFeatures_Generator.NONE: 'none'>, 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5891.7552821093905.\n",
      "[I 2024-10-02 17:26:17,759] Trial 1 finished with value: -5891.7552821093905 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': <ChemPropActivation.RELU: 'ReLU'>, 'aggregation__e0d3a442222d4b38f3aa1434851320db': <ChemPropAggregation.MEAN: 'mean'>, 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 105.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 60.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': <ChemPropFeatures_Generator.NONE: 'none'>, 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5891.7552821093905.\n",
      "[I 2024-10-02 17:26:59,666] Trial 2 finished with value: -5846.8674879655655 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': <ChemPropActivation.RELU: 'ReLU'>, 'aggregation__e0d3a442222d4b38f3aa1434851320db': <ChemPropAggregation.MEAN: 'mean'>, 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 14.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 10.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 2.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.24, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': <ChemPropFeatures_Generator.NONE: 'none'>, 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 1600.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 900.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -1, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -2, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 2 with value: -5846.8674879655655.\n",
      "[I 2024-10-02 17:27:35,570] Trial 3 finished with value: -5890.94653501547 and parameters: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': '77dfc8230317e08504ed5e643243fbc2', 'frzn__77dfc8230317e08504ed5e643243fbc2': <ChemPropFrzn.NONE: 'none'>, 'epochs__77dfc8230317e08504ed5e643243fbc2': 0, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 2 with value: -5846.8674879655655.\n",
      "[I 2024-10-02 17:28:04,410] Trial 4 finished with value: -5890.881210303758 and parameters: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': 'dfc518a76317f23d95e5aa5a3eac77f0', 'frzn__dfc518a76317f23d95e5aa5a3eac77f0': <ChemPropFrzn.NONE: 'none'>, 'epochs__dfc518a76317f23d95e5aa5a3eac77f0': 4, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 2 with value: -5846.8674879655655.\n"
     ]
    }
   ],
   "source": [
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-50/train.csv\", #  test.csv supplied for fair comparison\n",
    "        test_dataset_file=\"../tests/data/DRD2/subset-50/test.csv\", #  test.csv supplied for fair comparison\n",
    "    ),\n",
    "    descriptors=[SmilesFromFile.new()],\n",
    "    algorithms=[\n",
    "        ChemPropRegressor.new(epochs=4), # local\n",
    "        ChemPropRegressorPretrained.new(\n",
    "            pretrained_model='../target/pretrained.pkl',\n",
    "            epochs=ChemPropRegressorPretrained.Parameters.ChemPropParametersEpochs(low=0,high=0)) # global\n",
    "    ,        \n",
    "        ChemPropRegressorPretrained.new(\n",
    "            pretrained_model='../target/pretrained.pkl',\n",
    "            epochs=ChemPropRegressorPretrained.Parameters.ChemPropParametersEpochs(low=4,high=4)) #adapted\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=1,\n",
    "        n_trials=5,\n",
    "        n_startup_trials=0,\n",
    "        random_seed=0, # ensure all model types trialed\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ")\n",
    "tl_study = optimize(config, study_name=\"my_study\").trials_dataframe()\n",
    "\n",
    "tl_study['epochs'] = tl_study.loc[:,tl_study.columns.str.contains('params_epochs'\n",
    "            )].fillna(''\n",
    "            ).astype(str\n",
    "            ).agg(''.join, axis=1).astype(float) # merge epochs into one column\n",
    "\n",
    "tl_study.loc[~tl_study['params_ChemPropRegressor_algorithm_hash'].isna(),\n",
    "             \"Model type\"]='Local' # Annotate the local model\n",
    "\n",
    "tl_study.loc[tl_study['params_ChemPropRegressor_algorithm_hash'].isna() \n",
    "             & (tl_study['epochs'] == 4), \"Model type\"] = 'Adapted' # Annotate the adapted model (TL to new data)\n",
    "\n",
    "tl_study.loc[tl_study['params_ChemPropRegressor_algorithm_hash'].isna() \n",
    "             & (tl_study['epochs'] == 0), \"Model type\"] = 'Global' # Annotate the global model (no TL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"darkgrid\")\n",
    "default_reg_scoring= config.settings.scoring\n",
    "ax = sns.scatterplot(data=tl_study, x=\"number\", y=\"value\",hue='Model type')\n",
    "ax.set(xlabel=\"Trial number\",ylabel=f\"Ojbective value\\n({default_reg_scoring})\")\n",
    "sns.move_legend(ax, \"upper right\", bbox_to_anchor=(1.6, 1), ncol=1, title=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this toy example we do not observe a large difference between the adapted and global model types, but in a real world setting a user can build the best model from the three model types evaluated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ChemProp fingerprints (encode latent representation as descriptors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible for ChemProp to provide generate outputs in the form intended for use as a fingerprint using the [original package implementation](https://github.com/chemprop/chemprop/tree/master?tab=readme-ov-file#encode-fingerprint-latent-representation). Fingerprints are derived from the latent representation from the MPNN or penultimate FFN output layer, which can be used as a form of learned descriptor or fingerprint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open(\"../target/pretrained.pkl\", \"rb\") as f:\n",
    "    chemprop_model = pickle.load(f)\n",
    "\n",
    "ax = sns.heatmap(\n",
    "    chemprop_model.predictor.chemprop_fingerprint(\n",
    "    df[config.data.input_column].head(5),\n",
    "    fingerprint_type=\"MPN\",\n",
    "    ), # MPN specified for illustration purposes - this is the default method in QSARtuna\n",
    "    cbar_kws={'label': 'Fingerprint value'}\n",
    ")\n",
    "ax.set(ylabel=\"Compound query\", xlabel=f\"Latent representation\\n(ChemProp descriptor/fingerprint)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output is n compounds as the input query in the rows by n latent representation features from the MPN in the columns. This output can then be used for any semi-supervise learning approach outside of QSARtuna, as required. Alternatively the last layer of the FFN can be used as so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.heatmap(\n",
    "    chemprop_model.predictor.chemprop_fingerprint(\n",
    "    df[config.data.input_column].head(5),\n",
    "    fingerprint_type=\"last_FFN\"), # Last FFN\n",
    "    cbar_kws={'label': 'Fingerprint value'}\n",
    ")\n",
    "ax.set(ylabel=\"Compound query\", xlabel=f\"Latent representation\\n(ChemProp descriptor)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 5 compounds in the user query are also represented by the rows, howeever the 300 features are now derived from the last output layer of the FFN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probability calibration (classification)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When performing classification you often want not only to predict the class label, but also obtain a probability of the respective label. This probability gives you some kind of confidence on the prediction. Some models can give you poor estimates of the class probabilities. The `CalibratedClassifierCV` QSARtuna models allow better calibration for the probabilities of a given model.\n",
    "\n",
    "First, we should understand that well calibrated classifiers are probabilistic classifiers for which the output of the predict_proba method can be directly interpreted as a confidence level. For instance, a well calibrated (binary) classifier should classify the samples such that among the samples to which it gave a predict_proba value close to 0.8, approximately 80% actually belong to the positive class.\n",
    "\n",
    "See the [Scikit-learn documentation](https://scikit-learn.org/stable/modules/calibration.html#calibration) on the topic for more details.\n",
    "\n",
    "The available methods are `Sigmoid`, `Isotonic regression` and `VennABERS`, and a review of those calibration methods for QSAR has been performed [here](https://pubs.acs.org/doi/10.1021/acs.jcim.0c00476).\n",
    "\n",
    "we can review the effect of e.g. `sigmoid` calibration on the Random Forest algorithm by doing a calibrated run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:28:34,653] A new study created in memory with name: calibrated_rf\n",
      "[I 2024-10-02 17:28:34,656] A new study created in memory with name: study_name_0\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:993: FutureWarning: probas_pred was deprecated in version 1.5 and will be removed in 1.7.Please use ``y_score`` instead.\n",
      "  warnings.warn(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:993: FutureWarning: probas_pred was deprecated in version 1.5 and will be removed in 1.7.Please use ``y_score`` instead.\n",
      "  warnings.warn(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:993: FutureWarning: probas_pred was deprecated in version 1.5 and will be removed in 1.7.Please use ``y_score`` instead.\n",
      "  warnings.warn(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:993: FutureWarning: probas_pred was deprecated in version 1.5 and will be removed in 1.7.Please use ``y_score`` instead.\n",
      "  warnings.warn(\n",
      "[I 2024-10-02 17:28:36,107] Trial 0 finished with value: 0.8353535353535354 and parameters: {'algorithm_name': 'CalibratedClassifierCVWithVA', 'CalibratedClassifierCVWithVA_algorithm_hash': 'e788dfbfc5075967acb5ddf9d971ea20', 'n_folds__e788dfbfc5075967acb5ddf9d971ea20': 5, 'max_depth__e788dfbfc5075967acb5ddf9d971ea20': 16, 'n_estimators__e788dfbfc5075967acb5ddf9d971ea20': 100, 'max_features__e788dfbfc5075967acb5ddf9d971ea20': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8353535353535354.\n"
     ]
    }
   ],
   "source": [
    "from optunaz.config.optconfig import CalibratedClassifierCVWithVA, RandomForestClassifier\n",
    "from sklearn.calibration import calibration_curve\n",
    "import seaborn as sns\n",
    "\n",
    "from collections import defaultdict\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.metrics import (\n",
    "    precision_score,\n",
    "    recall_score,\n",
    "    f1_score,\n",
    "    brier_score_loss,\n",
    "    log_loss,\n",
    "    roc_auc_score,\n",
    ")\n",
    "\n",
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt_gt_330\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-100/train.csv\"),\n",
    "    descriptors=[ECFP.new()],\n",
    "    algorithms=[ # the CalibratedClassifierCVWithVA is used here\n",
    "        CalibratedClassifierCVWithVA.new(\n",
    "            estimator=RandomForestClassifier.new(\n",
    "                n_estimators=RandomForestClassifier.Parameters.RandomForestClassifierParametersNEstimators(\n",
    "                    low=100, high=100\n",
    "                )\n",
    "            ),\n",
    "            n_folds=5,\n",
    "            ensemble=\"True\",\n",
    "            method=\"sigmoid\", \n",
    "        )\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.CLASSIFICATION,\n",
    "        cross_validation=2,\n",
    "        n_trials=1,\n",
    "        n_startup_trials=0,\n",
    "        n_jobs=-1,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "        random_seed=42,\n",
    "    ),\n",
    ")\n",
    "\n",
    "study = optimize(config, study_name=\"calibrated_rf\")\n",
    "build_best(buildconfig_best(study), \"../target/best.pkl\")\n",
    "with open(\"../target/best.pkl\", \"rb\") as f:\n",
    "    calibrated_model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "followed by an uncalibrated run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:28:37,168] A new study created in memory with name: uncalibrated_rf\n",
      "[I 2024-10-02 17:28:37,228] A new study created in memory with name: study_name_0\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:993: FutureWarning: probas_pred was deprecated in version 1.5 and will be removed in 1.7.Please use ``y_score`` instead.\n",
      "  warnings.warn(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:993: FutureWarning: probas_pred was deprecated in version 1.5 and will be removed in 1.7.Please use ``y_score`` instead.\n",
      "  warnings.warn(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:993: FutureWarning: probas_pred was deprecated in version 1.5 and will be removed in 1.7.Please use ``y_score`` instead.\n",
      "  warnings.warn(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:993: FutureWarning: probas_pred was deprecated in version 1.5 and will be removed in 1.7.Please use ``y_score`` instead.\n",
      "  warnings.warn(\n",
      "[I 2024-10-02 17:28:37,650] Trial 0 finished with value: 0.8185858585858585 and parameters: {'algorithm_name': 'RandomForestClassifier', 'RandomForestClassifier_algorithm_hash': '167e1e88dd2a80133e317c78f009bdc9', 'max_depth__167e1e88dd2a80133e317c78f009bdc9': 16, 'n_estimators__167e1e88dd2a80133e317c78f009bdc9': 100, 'max_features__167e1e88dd2a80133e317c78f009bdc9': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8185858585858585.\n"
     ]
    }
   ],
   "source": [
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt_gt_330\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-100/train.csv\"),\n",
    "    descriptors=[ECFP.new()],\n",
    "    algorithms=[ # an uncalibrated RandomForestClassifier is used here\n",
    "        RandomForestClassifier.new(\n",
    "                n_estimators=RandomForestClassifier.Parameters.RandomForestClassifierParametersNEstimators(\n",
    "                    low=100, high=100\n",
    "                )\n",
    "        )\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.CLASSIFICATION,\n",
    "        cross_validation=2,\n",
    "        n_trials=1,\n",
    "        n_startup_trials=0,\n",
    "        n_jobs=-1,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "        random_seed=42,\n",
    "    ),\n",
    ")\n",
    "\n",
    "study = optimize(config, study_name=\"uncalibrated_rf\")\n",
    "build_best(buildconfig_best(study), \"../target/best.pkl\")\n",
    "with open(\"../target/best.pkl\", \"rb\") as f:\n",
    "    uncalibrated_model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sigmoid calibration assigns more conservative probability estimates compared to the default RF, as shown by the lower median:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(\n",
    "    '../tests/data/DRD2/subset-1000/train.csv'\n",
    "    ).sample(500, random_state=123)  # Load and sample test data.\n",
    "expected = df[config.data.response_column]\n",
    "input_column = df[config.data.input_column]\n",
    "calibrated_predicted = uncalibrated_model.predict_from_smiles(input_column)\n",
    "uncalibrated_predicted = calibrated_model.predict_from_smiles(input_column)\n",
    "\n",
    "cal_df=pd.DataFrame(data={\"default\":uncalibrated_predicted,\"sigmoid\":calibrated_predicted})\n",
    "sns.boxplot(data=cal_df.melt(),x='value',y='variable').set_ylabel('');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the (sigmoid) calibrated predictions as a function of uncalibrated (default) values further highlights the behaviour of the probability calibration scaling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot expected vs predicted values for the best model.\n",
    "import matplotlib.pyplot as plt\n",
    "ax = plt.scatter(calibrated_predicted, uncalibrated_predicted)\n",
    "lims = [expected.min(), expected.max()]\n",
    "plt.plot(lims, lims)  # Diagonal line.\n",
    "plt.xlabel(f\"Calibrated {config.data.response_column}\");\n",
    "plt.ylabel(f\"Uncalibrated {config.data.response_column}\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now visualize how well calibrated the predicted probabilities are using calibration curves. A calibration curve, also known as a reliability diagram, uses inputs from a binary classifier and plots the average predicted probability for each bin against the fraction of positive classes, on the y-axis. See [here](https://scikit-learn.org/stable/modules/calibration.html) for more info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.calibration import calibration_curve\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)\n",
    "ax2 = plt.subplot2grid((3, 1), (2, 0))\n",
    "\n",
    "ax1.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n",
    "for pred, name in [(uncalibrated_predicted, 'default'),\n",
    "                  (calibrated_predicted, 'sigmoid')]:\n",
    "\n",
    "    fraction_of_positives, mean_predicted_value = \\\n",
    "        calibration_curve(expected, pred, n_bins=10)\n",
    "    \n",
    "    brier=brier_score_loss(expected,pred)\n",
    "\n",
    "    ax1.plot(mean_predicted_value, fraction_of_positives, \"s-\",\n",
    "             label=\"%s, brier=%.2f\" % (name, brier))\n",
    "\n",
    "    ax2.hist(pred, range=(0, 1), bins=10, label=name,\n",
    "             histtype=\"step\", lw=2)\n",
    "\n",
    "ax1.set_ylabel(\"Fraction of positives\")\n",
    "ax1.set_ylim([-0.05, 1.05])\n",
    "ax1.legend(loc=\"lower right\")\n",
    "ax1.set_title('Calibration plots  (reliability curve)')\n",
    "\n",
    "ax2.set_xlabel(\"Mean predicted value\")\n",
    "ax2.set_ylabel(\"Count\")\n",
    "ax2.legend(loc=\"upper center\", ncol=2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The diagonal line on the calibration (scatter) plot indicates the situation when a classifier is perfectly calibrationed, when the proportion of active instances annotated by the model are perfectly captured by the probability generated by the model. Deviation above this line indicates when a classifier is under-confident, since the proportion of actives obtaining that score is higher than the score itself, and vice-versa, lines below indicate over-confident estimators, when the proportion of actives obtaining a given score is lower.\n",
    "\n",
    "Brier score loss (a metric composed of calibration term and refinement term) is one way to capture calibration calibration improvement (this is recorded in the legend above). Notice that this metric does not significantly alter the prediction accuracy measures (precision, recall and F1 score) as shown in the cell below. This is because calibration should not significantly change prediction probabilities at the location of the decision threshold (at x = 0.5 on the graph). Calibration should however, make the predicted probabilities more accurate and thus more useful for making allocation decisions under uncertainty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Brier  loss</th>\n",
       "      <th>Log loss</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Roc auc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calibration</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>0.184705</td>\n",
       "      <td>0.547129</td>\n",
       "      <td>0.830565</td>\n",
       "      <td>0.744048</td>\n",
       "      <td>0.784929</td>\n",
       "      <td>0.716536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no</th>\n",
       "      <td>0.175297</td>\n",
       "      <td>0.529474</td>\n",
       "      <td>0.811209</td>\n",
       "      <td>0.818452</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>0.714104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Brier  loss  Log loss  Precision    Recall        F1   Roc auc \n",
       "calibration                                                                 \n",
       "yes             0.184705  0.547129    0.830565  0.744048  0.784929  0.716536\n",
       "no              0.175297  0.529474    0.811209  0.818452  0.814815  0.714104"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.metrics import (\n",
    "    precision_score,\n",
    "    recall_score,\n",
    "    f1_score,\n",
    "    brier_score_loss,\n",
    "    log_loss,\n",
    "    roc_auc_score,\n",
    ")\n",
    "\n",
    "scores = defaultdict(list)\n",
    "for i, (name, y_prob) in enumerate([('yes',calibrated_predicted), ('no',uncalibrated_predicted)]):\n",
    "    \n",
    "    y_pred = y_prob > 0.5\n",
    "    scores[\"calibration\"].append(name)\n",
    "\n",
    "    for metric in [brier_score_loss, log_loss]:\n",
    "        score_name = metric.__name__.replace(\"_\", \" \").replace(\"score\", \"\").capitalize()\n",
    "        scores[score_name].append(metric(expected, y_prob))\n",
    "\n",
    "    for metric in [precision_score, recall_score, f1_score, roc_auc_score]:\n",
    "        score_name = metric.__name__.replace(\"_\", \" \").replace(\"score\", \"\").capitalize()\n",
    "        scores[score_name].append(metric(expected, y_pred))\n",
    "\n",
    "    score_df = pd.DataFrame(scores).set_index(\"calibration\")\n",
    "    score_df.round(decimals=3)\n",
    "\n",
    "score_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uncertainty estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QSARtuna offers three different ways to calculate uncertainty estimates and they are returned along with the normal predictions in the format `[[predictions], [uncertainties]]`. The currently implemented methods are:\n",
    "\n",
    "1. VennABERS calibration (a probability calibration covered in the section above).\n",
    "2. Ensemble uncertainty (ChemProp models trained with random initialisations).\n",
    "3. MAPIE (uncertainty for regression)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VennABERS uncertainty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VennABERS (VA) uncertainty is implemented as in the section \"Uses for the Multipoint Probabilities from the VA Predictors\" from https://pubs.acs.org/doi/10.1021/acs.jcim.0c00476. This is based on the margin between the upper (p1) and lower (p0) probability bounary, output by the VennABERS algorithm. More details on this can be found in [this](https://cml.rhul.ac.uk/people/ptocca/HomePage/Toccaceli_CP___Venn_Tutorial.pdf) tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:28:39,211] A new study created in memory with name: calibrated_rf\n",
      "[I 2024-10-02 17:28:39,260] A new study created in memory with name: study_name_0\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:993: FutureWarning: probas_pred was deprecated in version 1.5 and will be removed in 1.7.Please use ``y_score`` instead.\n",
      "  warnings.warn(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:993: FutureWarning: probas_pred was deprecated in version 1.5 and will be removed in 1.7.Please use ``y_score`` instead.\n",
      "  warnings.warn(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:993: FutureWarning: probas_pred was deprecated in version 1.5 and will be removed in 1.7.Please use ``y_score`` instead.\n",
      "  warnings.warn(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:993: FutureWarning: probas_pred was deprecated in version 1.5 and will be removed in 1.7.Please use ``y_score`` instead.\n",
      "  warnings.warn(\n",
      "[I 2024-10-02 17:28:40,275] Trial 0 finished with value: 0.8213131313131313 and parameters: {'algorithm_name': 'CalibratedClassifierCVWithVA', 'CalibratedClassifierCVWithVA_algorithm_hash': '79765fbec1586f3c917ff30de274fdb4', 'n_folds__79765fbec1586f3c917ff30de274fdb4': 5, 'max_depth__79765fbec1586f3c917ff30de274fdb4': 16, 'n_estimators__79765fbec1586f3c917ff30de274fdb4': 100, 'max_features__79765fbec1586f3c917ff30de274fdb4': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8213131313131313.\n"
     ]
    }
   ],
   "source": [
    "from optunaz.config.optconfig import CalibratedClassifierCVWithVA, RandomForestClassifier\n",
    "from sklearn.calibration import calibration_curve\n",
    "import seaborn as sns\n",
    "\n",
    "from collections import defaultdict\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.metrics import (\n",
    "    precision_score,\n",
    "    recall_score,\n",
    "    f1_score,\n",
    "    brier_score_loss,\n",
    "    log_loss,\n",
    "    roc_auc_score,\n",
    ")\n",
    "\n",
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt_gt_330\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-100/train.csv\"),\n",
    "    descriptors=[ECFP.new()],\n",
    "    algorithms=[ # the CalibratedClassifierCVWithVA is used here\n",
    "        CalibratedClassifierCVWithVA.new(\n",
    "            estimator=RandomForestClassifier.new(\n",
    "                n_estimators=RandomForestClassifier.Parameters.RandomForestClassifierParametersNEstimators(\n",
    "                    low=100, high=100\n",
    "                )\n",
    "            ),\n",
    "            n_folds=5,\n",
    "            ensemble=\"True\",\n",
    "            method=\"vennabers\", \n",
    "        )\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.CLASSIFICATION,\n",
    "        cross_validation=2,\n",
    "        n_trials=1,\n",
    "        n_startup_trials=0,\n",
    "        n_jobs=-1,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "        random_seed=42,\n",
    "    ),\n",
    ")\n",
    "\n",
    "study = optimize(config, study_name=\"calibrated_rf\")\n",
    "build_best(buildconfig_best(study), \"../target/best.pkl\")\n",
    "with open(\"../target/best.pkl\", \"rb\") as f:\n",
    "    calibrated_model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VennABERS uncertainty can now be obtained by running inference and supplying `uncert=True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit.Chem import AllChem\n",
    "from rdkit.Chem import PandasTools\n",
    "from rdkit import RDConfig\n",
    "from rdkit import DataStructs\n",
    "\n",
    "# get training data, mols & fingerprints\n",
    "train_df = pd.read_csv('../tests/data/DRD2/subset-100/train.csv')  # Load test data.\n",
    "PandasTools.AddMoleculeColumnToFrame(train_df,'canonical','molecule',includeFingerprints=True)\n",
    "train_df[\"fp\"]=train_df[\"molecule\"].apply(lambda x: AllChem.GetMorganFingerprint(x,2 ))\n",
    "\n",
    "# get test data, mols & fingerprints and calculate the nn to training set\n",
    "df = pd.read_csv('../tests/data/DRD2/subset-1000/train.csv')  # Load test data.\n",
    "PandasTools.AddMoleculeColumnToFrame(df,'canonical','molecule',includeFingerprints=True)\n",
    "df[\"fp\"]=df[\"molecule\"].apply(lambda x: AllChem.GetMorganFingerprint(x,2 ))\n",
    "df['nn']=df[\"fp\"].apply(lambda x: max(DataStructs.BulkTanimotoSimilarity(x,[i for i in train_df[\"fp\"]])))\n",
    "\n",
    "# add uncertainty & prediction to the df \n",
    "df['va_pred'], df['va_uncert'] = calibrated_model.predict_from_smiles(df[config.data.input_column], uncert=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to relate the uncertainty to the nearest neighbor (nn) to look for distance-to-model (DTM) effect and to the probabilistic output from the RF model scaled by VA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot uncertainty as a function of nn or true label in a trellis for overview.\n",
    "fig, ax =plt.subplots(1,2, figsize=(10, 5), sharey=True)\n",
    "sns.regplot(data=df,y='va_uncert',x='nn', ax=ax[0])\n",
    "sns.regplot(data=df,y='va_uncert',x='va_pred', ax=ax[1]).set_ylabel(\"\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the findings in the referenced scaling evaluation paper above, the lower and upper probability boundary intervals are shown to produce large discordance for test set molecules that are neither very similar nor very dissimilar to the active training set, which were hence difficult to predict."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ensemble uncertainty (ChemProp Only)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training a ChemProp model with `ensemble_size` >1 will enable uncertainty estimation based on the implementation in the original ChemProp package, using the deviation of predictions from the ensemble of models trained with different random initialisation of the weights. This can be done like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:28:43,031] A new study created in memory with name: my_study\n",
      "[I 2024-10-02 17:28:43,079] A new study created in memory with name: study_name_0\n",
      "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__fd833c2dde0b7147e6516ea5eebb2657': 'ReLU', 'aggregation__fd833c2dde0b7147e6516ea5eebb2657': 'mean', 'aggregation_norm__fd833c2dde0b7147e6516ea5eebb2657': 100, 'batch_size__fd833c2dde0b7147e6516ea5eebb2657': 50, 'depth__fd833c2dde0b7147e6516ea5eebb2657': 3, 'dropout__fd833c2dde0b7147e6516ea5eebb2657': 0.0, 'features_generator__fd833c2dde0b7147e6516ea5eebb2657': 'none', 'ffn_hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300, 'ffn_num_layers__fd833c2dde0b7147e6516ea5eebb2657': 2, 'final_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300, 'init_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'max_lr_exp__fd833c2dde0b7147e6516ea5eebb2657': -3, 'warmup_epochs_ratio__fd833c2dde0b7147e6516ea5eebb2657': 0.1, 'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'fd833c2dde0b7147e6516ea5eebb2657'}\n",
      "[I 2024-10-02 17:39:21,822] Trial 0 finished with value: 0.65625 and parameters: {'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'fd833c2dde0b7147e6516ea5eebb2657', 'activation__fd833c2dde0b7147e6516ea5eebb2657': <ChemPropActivation.RELU: 'ReLU'>, 'aggregation__fd833c2dde0b7147e6516ea5eebb2657': <ChemPropAggregation.MEAN: 'mean'>, 'aggregation_norm__fd833c2dde0b7147e6516ea5eebb2657': 100.0, 'batch_size__fd833c2dde0b7147e6516ea5eebb2657': 50.0, 'depth__fd833c2dde0b7147e6516ea5eebb2657': 3.0, 'dropout__fd833c2dde0b7147e6516ea5eebb2657': 0.0, 'ensemble_size__fd833c2dde0b7147e6516ea5eebb2657': 5, 'epochs__fd833c2dde0b7147e6516ea5eebb2657': 4, 'features_generator__fd833c2dde0b7147e6516ea5eebb2657': <ChemPropFeatures_Generator.NONE: 'none'>, 'ffn_hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'ffn_num_layers__fd833c2dde0b7147e6516ea5eebb2657': 2.0, 'final_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'init_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'max_lr_exp__fd833c2dde0b7147e6516ea5eebb2657': -3, 'warmup_epochs_ratio__fd833c2dde0b7147e6516ea5eebb2657': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: 0.65625.\n"
     ]
    }
   ],
   "source": [
    "# Start with the imports.\n",
    "import sklearn\n",
    "from optunaz.three_step_opt_build_merge import (\n",
    "    optimize,\n",
    "    buildconfig_best,\n",
    "    build_best,\n",
    "    build_merged,\n",
    ")\n",
    "from optunaz.config import ModelMode, OptimizationDirection\n",
    "from optunaz.config.optconfig import (\n",
    "    OptimizationConfig,\n",
    "    ChemPropHyperoptRegressor,\n",
    "    ChemPropHyperoptClassifier\n",
    ")\n",
    "from optunaz.datareader import Dataset\n",
    "from optunaz.descriptors import SmilesFromFile\n",
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt_gt_330\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-50/train.csv\",  # This will be split into train and test.\n",
    "    ),\n",
    "    descriptors=[\n",
    "        SmilesFromFile.new(),\n",
    "    ],\n",
    "    algorithms=[\n",
    "        ChemPropClassifier.new(epochs=4, ensemble_size=5), #epochs=4 to ensure run finishes quickly\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.CLASSIFICATION,\n",
    "        cross_validation=2,\n",
    "        n_trials=1,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ")\n",
    "\n",
    "study = optimize(config, study_name=\"my_study\")\n",
    "\n",
    "build_best(buildconfig_best(study), \"../target/best.pkl\")\n",
    "with open(\"../target/best.pkl\", \"rb\") as f:\n",
    "    chemprop_model = pickle.load(f)\n",
    "\n",
    "# add chemprop uncertainty & prediction to the df \n",
    "df[\"cp_pred_ensemble\"], df[\"cp_uncert_ensemble\"] = chemprop_model.predict_from_smiles(df[config.data.input_column], uncert=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot uncertainty as a function of nn or true label in a trellis for overview.\n",
    "fig, ax =plt.subplots(1,2, figsize=(10, 5), sharey=True)\n",
    "sns.regplot(data=df,y='cp_uncert_ensemble',x='nn', ax=ax[0])\n",
    "sns.regplot(data=df,y='cp_uncert_ensemble',x='cp_pred_ensemble', ax=ax[1]).set(ylabel='')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the VA uncertainty, the largest ensemble uncertainty is observed for test set molecules that are neither very similar nor very dissimilar to the active training set, which are hence difficult to predict. Larger uncertainty is also seen toward the midpoint of the ChemProp predictions, for cases when the probabilistic output from models is also neither very high nor very low."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ChemProp dropout uncertainty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ChemProp uncertainty based on dropout is available for single model and not an ensemble (i.e. when ChemProp is provided with `ensemble_size=1`. It is based on the implementation in the [original ChemProp package](https://github.com/chemprop/chemprop#uncertainty-estimation)\n",
    "\n",
    "The method uses Monte Carlo dropout to generate a virtual ensemble of models and reports the ensemble variance of the predictions.\n",
    "\n",
    "Note that this dropout is distinct from dropout regularization used during training, which is not active during predictions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 17:53:06,941] A new study created in memory with name: my_study\n",
      "[I 2024-10-02 17:53:07,009] A new study created in memory with name: study_name_0\n",
      "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__c73885c5d5a4182168b8b002d321965a': 'ReLU', 'aggregation__c73885c5d5a4182168b8b002d321965a': 'mean', 'aggregation_norm__c73885c5d5a4182168b8b002d321965a': 100, 'batch_size__c73885c5d5a4182168b8b002d321965a': 50, 'depth__c73885c5d5a4182168b8b002d321965a': 3, 'dropout__c73885c5d5a4182168b8b002d321965a': 0.0, 'features_generator__c73885c5d5a4182168b8b002d321965a': 'none', 'ffn_hidden_size__c73885c5d5a4182168b8b002d321965a': 300, 'ffn_num_layers__c73885c5d5a4182168b8b002d321965a': 2, 'final_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'hidden_size__c73885c5d5a4182168b8b002d321965a': 300, 'init_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'max_lr_exp__c73885c5d5a4182168b8b002d321965a': -3, 'warmup_epochs_ratio__c73885c5d5a4182168b8b002d321965a': 0.1, 'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'c73885c5d5a4182168b8b002d321965a'}\n",
      "[I 2024-10-02 17:55:19,174] Trial 0 finished with value: 0.46875 and parameters: {'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'c73885c5d5a4182168b8b002d321965a', 'activation__c73885c5d5a4182168b8b002d321965a': <ChemPropActivation.RELU: 'ReLU'>, 'aggregation__c73885c5d5a4182168b8b002d321965a': <ChemPropAggregation.MEAN: 'mean'>, 'aggregation_norm__c73885c5d5a4182168b8b002d321965a': 100.0, 'batch_size__c73885c5d5a4182168b8b002d321965a': 50.0, 'depth__c73885c5d5a4182168b8b002d321965a': 3.0, 'dropout__c73885c5d5a4182168b8b002d321965a': 0.0, 'ensemble_size__c73885c5d5a4182168b8b002d321965a': 1, 'epochs__c73885c5d5a4182168b8b002d321965a': 5, 'features_generator__c73885c5d5a4182168b8b002d321965a': <ChemPropFeatures_Generator.NONE: 'none'>, 'ffn_hidden_size__c73885c5d5a4182168b8b002d321965a': 300.0, 'ffn_num_layers__c73885c5d5a4182168b8b002d321965a': 2.0, 'final_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'hidden_size__c73885c5d5a4182168b8b002d321965a': 300.0, 'init_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'max_lr_exp__c73885c5d5a4182168b8b002d321965a': -3, 'warmup_epochs_ratio__c73885c5d5a4182168b8b002d321965a': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: 0.46875.\n"
     ]
    }
   ],
   "source": [
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt_gt_330\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-50/train.csv\",  # This will be split into train and test.\n",
    "    ),\n",
    "    descriptors=[\n",
    "        SmilesFromFile.new(),\n",
    "    ],\n",
    "    algorithms=[\n",
    "        ChemPropClassifier.new(epochs=5), #ensemble_size not supplied (defaults back to 1) \n",
    "                                                  #to ensure uncertainty will be based on dropout\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.CLASSIFICATION,\n",
    "        cross_validation=2,\n",
    "        n_trials=1,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ")\n",
    "\n",
    "study = optimize(config, study_name=\"my_study\")\n",
    "build_best(buildconfig_best(study), \"../target/best.pkl\")\n",
    "with open(\"../target/best.pkl\", \"rb\") as f:\n",
    "    chemprop_model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# add chemprop uncertainty & prediction to the df \n",
    "df[\"cp_pred_dropout\"], df[\"cp_uncert_dropout\"] = chemprop_model.predict_from_smiles(df[config.data.input_column], uncert=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to previous findings using ensembling, the dropout approach toward uncertainty shows largest uncertainty for marginal cases neither similar not dissimilar to training, and with proabilities toward the midpoint (0.5):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot uncertainty as a function of nn or true label in a trellis for overview.\n",
    "fig, ax =plt.subplots(1,2, figsize=(10, 5), sharey=True)\n",
    "sns.regplot(data=df,y='cp_uncert_dropout',x='nn', ax=ax[0])\n",
    "sns.regplot(data=df,y='cp_uncert_dropout',x='cp_pred_dropout', ax=ax[1]).set(ylabel='')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison of dropout vs. ensemble uncertainties can be performed as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 correlation between drouput and ensemble uncertatinties:-96.38\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot uncertainty as a function of va_prediction and true label in a trellis for overview.\n",
    "r2 = r2_score(y_true=df['cp_uncert_dropout'], y_pred=df['cp_uncert_ensemble'])\n",
    "print(f\"R2 correlation between drouput and ensemble uncertatinties:{r2:.2f}\")\n",
    "\n",
    "fig, ax =plt.subplots(1,2, figsize=(10, 5))\n",
    "df['cp_uncert_delta']=df['cp_uncert_dropout']-df['cp_uncert_ensemble']\n",
    "sns.regplot(data=df,y='cp_uncert_dropout',x='cp_uncert_ensemble', ax=ax[0])\n",
    "sns.boxplot(data=df,y='cp_uncert_delta',x='activity', ax=ax[1])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Findings show that a limited correlation between dropout and ensemble uncertainty for the toy example (real world examples with more epochs/more predictive models will be different)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MAPIE (regression uncertainty)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For regression uncertainty, the MAPIE package is available within QSARtuna for regression algorithms, and is selected like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:15:04,953] A new study created in memory with name: my_study\n",
      "[I 2024-10-02 18:15:05,007] A new study created in memory with name: study_name_0\n",
      "[I 2024-10-02 18:15:07,190] Trial 0 finished with value: -4497.021799854763 and parameters: {'algorithm_name': 'Mapie', 'Mapie_algorithm_hash': '976d211e4ac64e5568d369bcddd3aeb1', 'mapie_alpha__976d211e4ac64e5568d369bcddd3aeb1': 0.05, 'max_depth__976d211e4ac64e5568d369bcddd3aeb1': 8, 'n_estimators__976d211e4ac64e5568d369bcddd3aeb1': 50, 'max_features__976d211e4ac64e5568d369bcddd3aeb1': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -4497.021799854763.\n"
     ]
    }
   ],
   "source": [
    "from optunaz.config.optconfig import Mapie\n",
    "    \n",
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-300/train.csv\",  # This will be split into train and test.\n",
    "    ),\n",
    "    descriptors=[\n",
    "        ECFP.new(),\n",
    "    ],\n",
    "    algorithms=[Mapie.new( # mapie 'wraps' around a regressor of choice\n",
    "                estimator=RandomForestRegressor.new(n_estimators={\"low\": 50, \"high\": 50})\n",
    "    )\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=2,\n",
    "        n_trials=1,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ")\n",
    "\n",
    "study = optimize(config, study_name=\"my_study\")\n",
    "build_best(buildconfig_best(study), \"../target/best.pkl\")\n",
    "with open(\"../target/best.pkl\", \"rb\") as f:\n",
    "    mapie = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis of the nn's and behaviour of uncertainty vs. predicted values can be perfomed like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get training data, mols & fingerprints\n",
    "train_df = pd.read_csv('../tests/data/DRD2/subset-300/train.csv')  # Load test data.\n",
    "PandasTools.AddMoleculeColumnToFrame(train_df,'canonical','molecule',includeFingerprints=True)\n",
    "train_df[\"fp\"]=train_df[\"molecule\"].apply(lambda x: AllChem.GetMorganFingerprint(x,2 ))\n",
    "\n",
    "# get test data, mols & fingerprints and calculate the nn to training set\n",
    "df = pd.read_csv('../tests/data/DRD2/subset-50/train.csv')  # Load test data.\n",
    "PandasTools.AddMoleculeColumnToFrame(df,'canonical','molecule',includeFingerprints=True)\n",
    "df[\"fp\"]=df[\"molecule\"].apply(lambda x: AllChem.GetMorganFingerprint(x,2 ))\n",
    "df['nn']=df[\"fp\"].apply(lambda x: max(DataStructs.BulkTanimotoSimilarity(x,[i for i in train_df[\"fp\"]])))\n",
    "\n",
    "mapie.predictor.mapie_alpha=0.99 # it is possible to alter the alpha of mapie post-train using this approach\n",
    "\n",
    "# add uncertainty & prediction to the df \n",
    "df['mapie_pred'], df['mapie_unc'] = mapie.predict_from_smiles(df[config.data.input_column], uncert=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting mapie uncertainty as a product of the nearest neighbors/mapie predictions is performed here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot uncertainty as a function of nn or true label in a trellis for overview.\n",
    "fig, ax =plt.subplots(1,3, figsize=(10, 5))\n",
    "sns.regplot(data=df,y='mapie_unc',x='nn', ax=ax[0])\n",
    "sns.regplot(data=df,y='mapie_unc',x='mapie_pred', ax=ax[1])\n",
    "sns.regplot(data=df,y=df[config.data.response_column],x='mapie_pred', ax=ax[2])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further analysis of the uncertainty using error bars is shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot true value as a function of predicted value, with MAPIE uncertainty error bars for visualisation.\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.errorbar(df[config.data.response_column], df['mapie_pred'], yerr=df['mapie_unc'].abs(), fmt='o',color='black', alpha=.8, ecolor='gray', elinewidth=1, capsize=10);\n",
    "plt.xlabel('Predicted Mw');\n",
    "plt.ylabel('Expected Mw');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where more certain predictions have smaller error bars.\n",
    "\n",
    "The same analysis can be performed by plotting similarity to nn's (increasing similarity to the training set moving from left to right on the x-axis):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot true value as a function of predicted value, with MAPIE uncertainty error bars for visualisation.\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.errorbar(df['nn'], df['mapie_pred'], yerr=df['mapie_unc'].abs(), fmt='o',color='black', alpha=.8, ecolor='gray', elinewidth=1, capsize=10);\n",
    "plt.xlabel('Nearest neighbor (NN) similarity');\n",
    "plt.ylabel('Expected Mw');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MAPIE package uses the `alpha` parameter to set the uncertainty of the confidence interval, see here for details. It is possible to alter the uncertainty of the confidence interval by setting the `mapie_alpha` parameter of the QSARtuna model predictor. Here lower alpha produce larger (more conservative) prediction intervals. N.B: `alpha` is set to 0.05 by default and will hence provide more conservative predictions if not changed.\n",
    "\n",
    "The alpha settings as a function of uncertainty (over all point predictions) can be analysed for our toy example using the following (error bars denote deviations across all point predictions which have been extended by two standard error widths):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha_impact=[]\n",
    "for ma in range(1,100,5):\n",
    "    mapie.predictor.mapie_alpha=ma/100\n",
    "    preds = mapie.predict_from_smiles(df[config.data.input_column], uncert=True)\n",
    "    unc_df = pd.DataFrame(\n",
    "    data={\n",
    "        \"pred\": preds[0],\n",
    "        \"unc\": preds[1],\n",
    "        \"alpha\": ma,\n",
    "        }\n",
    "    )\n",
    "    alpha_impact.append(unc_df.reset_index())\n",
    "alpha_impact=pd.concat(alpha_impact).reset_index(drop=True)\n",
    "\n",
    "sns.lineplot(data=alpha_impact[alpha_impact['index']<=20],x='alpha',y='unc',err_style=\"bars\", errorbar=(\"se\", 2))\n",
    "plt.xlabel('MAPIE Alpha');\n",
    "plt.ylabel('MAPIE uncertainty (±MW)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected larger alpha values produce smaller (less conservative) prediction intervals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explainability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model explainability is incorporated into QSARtuna using two different approaches, depending on the algorithm chosen:\n",
    "1. **SHAP:** \n",
    "    Any shallow algorithm is compatible with the SHAP package (even traditionally unsupported packages use the `KernelExplainer`)\n",
    "2. **ChemProp interpret:**\n",
    "    This explainability approach is based on the interpret function in the original ChemProp package"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SHAP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[SHAP](https://proceedings.neurips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html) (SHapley Additive exPlanations) are available in `QSARtuna` based on the implementation available at https://github.com/slundberg/shap. The method uses a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see [here](https://shap.readthedocs.io/en/latest/index.html) for more details on the published tool and [here](https://github.com/slundberg/shap#methods-unified-by-shap) for papers using the approach).\n",
    "\n",
    "In the following example, a `RIDGE` regressor is trained using the a comopsite descriptor based on the `ECFP`, `MACCS` keys and `PhysChem` descriptors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:15:13,266] A new study created in memory with name: my_study\n",
      "[I 2024-10-02 18:15:13,311] A new study created in memory with name: study_name_0\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_ridge.py:255: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n",
      "  warnings.warn(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_ridge.py:255: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n",
      "  warnings.warn(\n",
      "[I 2024-10-02 18:15:15,004] Trial 0 finished with value: -0.3655099730804518 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.1404851303400703, 'descriptor': '{\"parameters\": {\"descriptors\": [{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}, {\"name\": \"MACCS_keys\", \"parameters\": {}}, {\"name\": \"UnscaledJazzyDescriptors\", \"parameters\": {\"jazzy_names\": [\"dga\", \"dgp\", \"dgtot\", \"sa\", \"sdc\", \"sdx\"], \"jazzy_filters\": {\"NumHAcceptors\": 25, \"NumHDonors\": 25, \"MolWt\": 1000}}}, {\"name\": \"UnscaledPhyschemDescriptors\", \"parameters\": {\"rdkit_names\": [\"MaxAbsEStateIndex\", \"MaxEStateIndex\", \"MinAbsEStateIndex\", \"MinEStateIndex\", \"qed\", \"SPS\", \"MolWt\", \"HeavyAtomMolWt\", \"ExactMolWt\", \"NumValenceElectrons\", \"NumRadicalElectrons\", \"MaxPartialCharge\", \"MinPartialCharge\", \"MaxAbsPartialCharge\", \"MinAbsPartialCharge\", \"FpDensityMorgan1\", \"FpDensityMorgan2\", \"FpDensityMorgan3\", \"BCUT2D_MWHI\", \"BCUT2D_MWLOW\", \"BCUT2D_CHGHI\", \"BCUT2D_CHGLO\", \"BCUT2D_LOGPHI\", \"BCUT2D_LOGPLOW\", \"BCUT2D_MRHI\", \"BCUT2D_MRLOW\", \"AvgIpc\", \"BalabanJ\", \"BertzCT\", \"Chi0\", \"Chi0n\", \"Chi0v\", \"Chi1\", \"Chi1n\", \"Chi1v\", \"Chi2n\", \"Chi2v\", \"Chi3n\", \"Chi3v\", \"Chi4n\", \"Chi4v\", \"HallKierAlpha\", \"Ipc\", \"Kappa1\", \"Kappa2\", \"Kappa3\", \"LabuteASA\", \"PEOE_VSA1\", \"PEOE_VSA10\", \"PEOE_VSA11\", \"PEOE_VSA12\", \"PEOE_VSA13\", \"PEOE_VSA14\", \"PEOE_VSA2\", \"PEOE_VSA3\", \"PEOE_VSA4\", \"PEOE_VSA5\", \"PEOE_VSA6\", \"PEOE_VSA7\", \"PEOE_VSA8\", \"PEOE_VSA9\", \"SMR_VSA1\", \"SMR_VSA10\", \"SMR_VSA2\", \"SMR_VSA3\", \"SMR_VSA4\", \"SMR_VSA5\", \"SMR_VSA6\", \"SMR_VSA7\", \"SMR_VSA8\", \"SMR_VSA9\", \"SlogP_VSA1\", \"SlogP_VSA10\", \"SlogP_VSA11\", \"SlogP_VSA12\", \"SlogP_VSA2\", \"SlogP_VSA3\", \"SlogP_VSA4\", \"SlogP_VSA5\", \"SlogP_VSA6\", \"SlogP_VSA7\", \"SlogP_VSA8\", \"SlogP_VSA9\", \"TPSA\", \"EState_VSA1\", \"EState_VSA10\", \"EState_VSA11\", \"EState_VSA2\", \"EState_VSA3\", \"EState_VSA4\", \"EState_VSA5\", \"EState_VSA6\", \"EState_VSA7\", \"EState_VSA8\", \"EState_VSA9\", \"VSA_EState1\", \"VSA_EState10\", \"VSA_EState2\", \"VSA_EState3\", \"VSA_EState4\", \"VSA_EState5\", \"VSA_EState6\", \"VSA_EState7\", \"VSA_EState8\", \"VSA_EState9\", \"FractionCSP3\", \"HeavyAtomCount\", \"NHOHCount\", \"NOCount\", \"NumAliphaticCarbocycles\", \"NumAliphaticHeterocycles\", \"NumAliphaticRings\", \"NumAromaticCarbocycles\", \"NumAromaticHeterocycles\", \"NumAromaticRings\", \"NumHAcceptors\", \"NumHDonors\", \"NumHeteroatoms\", \"NumRotatableBonds\", \"NumSaturatedCarbocycles\", \"NumSaturatedHeterocycles\", \"NumSaturatedRings\", \"RingCount\", \"MolLogP\", \"MolMR\", \"fr_Al_COO\", \"fr_Al_OH\", \"fr_Al_OH_noTert\", \"fr_ArN\", \"fr_Ar_COO\", \"fr_Ar_N\", \"fr_Ar_NH\", \"fr_Ar_OH\", \"fr_COO\", \"fr_COO2\", \"fr_C_O\", \"fr_C_O_noCOO\", \"fr_C_S\", \"fr_HOCCN\", \"fr_Imine\", \"fr_NH0\", \"fr_NH1\", \"fr_NH2\", \"fr_N_O\", \"fr_Ndealkylation1\", \"fr_Ndealkylation2\", \"fr_Nhpyrrole\", \"fr_SH\", \"fr_aldehyde\", \"fr_alkyl_carbamate\", \"fr_alkyl_halide\", \"fr_allylic_oxid\", \"fr_amide\", \"fr_amidine\", \"fr_aniline\", \"fr_aryl_methyl\", \"fr_azide\", \"fr_azo\", \"fr_barbitur\", \"fr_benzene\", \"fr_benzodiazepine\", \"fr_bicyclic\", \"fr_diazo\", \"fr_dihydropyridine\", \"fr_epoxide\", \"fr_ester\", \"fr_ether\", \"fr_furan\", \"fr_guanido\", \"fr_halogen\", \"fr_hdrzine\", \"fr_hdrzone\", \"fr_imidazole\", \"fr_imide\", \"fr_isocyan\", \"fr_isothiocyan\", \"fr_ketone\", \"fr_ketone_Topliss\", \"fr_lactam\", \"fr_lactone\", \"fr_methoxy\", \"fr_morpholine\", \"fr_nitrile\", \"fr_nitro\", \"fr_nitro_arom\", \"fr_nitro_arom_nonortho\", \"fr_nitroso\", \"fr_oxazole\", \"fr_oxime\", \"fr_para_hydroxylation\", \"fr_phenol\", \"fr_phenol_noOrthoHbond\", \"fr_phos_acid\", \"fr_phos_ester\", \"fr_piperdine\", \"fr_piperzine\", \"fr_priamide\", \"fr_prisulfonamd\", \"fr_pyridine\", \"fr_quatN\", \"fr_sulfide\", \"fr_sulfonamd\", \"fr_sulfone\", \"fr_term_acetylene\", \"fr_tetrazole\", \"fr_thiazole\", \"fr_thiocyan\", \"fr_thiophene\", \"fr_unbrch_alkane\", \"fr_urea\"]}}]}, \"name\": \"CompositeDescriptor\"}'}. Best is trial 0 with value: -0.3655099730804518.\n"
     ]
    }
   ],
   "source": [
    "from optunaz.descriptors import CompositeDescriptor, UnscaledPhyschemDescriptors, UnscaledJazzyDescriptors\n",
    "    \n",
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-50/train.csv\",  # This will be split into train and test.\n",
    "    ),\n",
    "    descriptors=[\n",
    "        CompositeDescriptor.new(\n",
    "            descriptors=[\n",
    "                ECFP.new(),\n",
    "                MACCS_keys.new(),\n",
    "                UnscaledJazzyDescriptors.new(),\n",
    "                UnscaledPhyschemDescriptors.new(),\n",
    "            ]\n",
    "        )\n",
    "    ],\n",
    "    algorithms=[\n",
    "        Ridge.new(),\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=2,\n",
    "        n_trials=1,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ")\n",
    "\n",
    "study = optimize(config, study_name=\"my_study\")\n",
    "build_best(buildconfig_best(study), \"../target/best.pkl\")\n",
    "with open(\"../target/best.pkl\", \"rb\") as f:\n",
    "    ridge = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predictions from the algorithms can be explained like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "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>shap_value</th>\n",
       "      <th>descriptor</th>\n",
       "      <th>bit</th>\n",
       "      <th>info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2227</th>\n",
       "      <td>2.042840e+01</td>\n",
       "      <td>UnscaledPhyschemDescriptors</td>\n",
       "      <td>7.0</td>\n",
       "      <td>MolWt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2229</th>\n",
       "      <td>2.025345e+01</td>\n",
       "      <td>UnscaledPhyschemDescriptors</td>\n",
       "      <td>9.0</td>\n",
       "      <td>ExactMolWt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2228</th>\n",
       "      <td>1.804976e+01</td>\n",
       "      <td>UnscaledPhyschemDescriptors</td>\n",
       "      <td>8.0</td>\n",
       "      <td>HeavyAtomMolWt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2267</th>\n",
       "      <td>2.370409e+00</td>\n",
       "      <td>UnscaledPhyschemDescriptors</td>\n",
       "      <td>47.0</td>\n",
       "      <td>LabuteASA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2230</th>\n",
       "      <td>2.106017e+00</td>\n",
       "      <td>UnscaledPhyschemDescriptors</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NumValenceElectrons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>4.243703e-07</td>\n",
       "      <td>ECFP</td>\n",
       "      <td>846.0</td>\n",
       "      <td>c(c(c)C)c(O)c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>4.243703e-07</td>\n",
       "      <td>ECFP</td>\n",
       "      <td>805.0</td>\n",
       "      <td>N(S(N)(=O)=O)(C)C(C)=C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>4.243703e-07</td>\n",
       "      <td>ECFP</td>\n",
       "      <td>424.0</td>\n",
       "      <td>N(=C(c)C)S(=O)(=O)N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>4.243703e-07</td>\n",
       "      <td>ECFP</td>\n",
       "      <td>996.0</td>\n",
       "      <td>C(C(N)=C)(=O)N(C)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1375</th>\n",
       "      <td>4.243703e-07</td>\n",
       "      <td>ECFP</td>\n",
       "      <td>1376.0</td>\n",
       "      <td>S1(=O)(=O)N=C(c)C=C(C)N1C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1570 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        shap_value                   descriptor     bit  \\\n",
       "2227  2.042840e+01  UnscaledPhyschemDescriptors     7.0   \n",
       "2229  2.025345e+01  UnscaledPhyschemDescriptors     9.0   \n",
       "2228  1.804976e+01  UnscaledPhyschemDescriptors     8.0   \n",
       "2267  2.370409e+00  UnscaledPhyschemDescriptors    47.0   \n",
       "2230  2.106017e+00  UnscaledPhyschemDescriptors    10.0   \n",
       "...            ...                          ...     ...   \n",
       "845   4.243703e-07                         ECFP   846.0   \n",
       "804   4.243703e-07                         ECFP   805.0   \n",
       "423   4.243703e-07                         ECFP   424.0   \n",
       "995   4.243703e-07                         ECFP   996.0   \n",
       "1375  4.243703e-07                         ECFP  1376.0   \n",
       "\n",
       "                           info  \n",
       "2227                      MolWt  \n",
       "2229                 ExactMolWt  \n",
       "2228             HeavyAtomMolWt  \n",
       "2267                  LabuteASA  \n",
       "2230        NumValenceElectrons  \n",
       "...                         ...  \n",
       "845               c(c(c)C)c(O)c  \n",
       "804      N(S(N)(=O)=O)(C)C(C)=C  \n",
       "423         N(=C(c)C)S(=O)(=O)N  \n",
       "995          C(C(N)=C)(=O)N(C)C  \n",
       "1375  S1(=O)(=O)N=C(c)C=C(C)N1C  \n",
       "\n",
       "[1570 rows x 4 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.predict_from_smiles(df[config.data.input_column], explain=True).query('shap_value > 0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outputs are ordered by shap_value (higher is more important). We see that the `UnscaledPhyschemDescriptors` bits corresponding to e.g. `MolWt`, `ExactMolWt`, `HeavyAtomMolWt` and `NumValenceElectrons`. We can hence interpret these as the most important features contrinubting to predicting the MolWt for the DRD2 datset. `UnscaledPhyschemJazzy` descriptors are also ranked relatively high in the list.\n",
    "\n",
    "Other descriptor types in the composite descriptor such as the ECFP fingerprints are also shown in the output. ECFP bits are translated to the atom environments for which the bit was turned on within the training set. \n",
    "\n",
    "Other descriptors are less interpretable as no additional information is available in the `info` column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ChemProp interpret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ChemProp explainability is based on the `interpret` in the [original package](https://chemprop.readthedocs.io/en/latest/interpret.html#interpretation).\n",
    "\n",
    "The follow example shows the usage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:15:20,410] A new study created in memory with name: my_study\n",
      "[I 2024-10-02 18:15:20,658] A new study created in memory with name: study_name_0\n",
      "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__e0d3a442222d4b38f3aa1434851320db': 'ReLU', 'aggregation__e0d3a442222d4b38f3aa1434851320db': 'mean', 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50, 'depth__e0d3a442222d4b38f3aa1434851320db': 3, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'features_generator__e0d3a442222d4b38f3aa1434851320db': 'none', 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db'}\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:869)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "[I 2024-10-02 18:16:10,167] Trial 0 finished with value: -4937.540075659691 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': <ChemPropActivation.RELU: 'ReLU'>, 'aggregation__e0d3a442222d4b38f3aa1434851320db': <ChemPropAggregation.MEAN: 'mean'>, 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': <ChemPropFeatures_Generator.NONE: 'none'>, 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -4937.540075659691.\n"
     ]
    }
   ],
   "source": [
    "config = OptimizationConfig(\n",
    "    data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-50/train.csv\",  # This will be split into train and test.\n",
    "    ),\n",
    "    descriptors=[SmilesFromFile.new()],\n",
    "    algorithms=[\n",
    "        ChemPropRegressor.new(epochs=4),\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=2,\n",
    "        n_trials=1,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ")\n",
    "\n",
    "study = optimize(config, study_name=\"my_study\")\n",
    "build_best(buildconfig_best(study), \"../target/best.pkl\")\n",
    "with open(\"../target/best.pkl\", \"rb\") as f:\n",
    "    chemprop = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to SHAP, ChemProp explainability inference is called using the `explain` flag from the `predict_from_smiles`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 8 9 18 19 20 21 22 23 24\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 4 5 14 15 16 17 18 19 20\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 12 13 14 15 16 17 18\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 12 13 14 15 16 17 18\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 9 10 11 12 13 14 15\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10 11 12 13 14\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 11\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 10\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 7 8 17 18 19 20 21 22 23\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 12 13 14 15 16 17 18\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 10 11 12\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 9 10 11\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 13 14 15 16 17 18 19\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 13 14 15\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 12 13 14\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 11 12 13\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 7 8 9\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 6 7 8\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 5 6 7\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 4 6 7\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 0 1 3 4 5\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 8 9 18 19 20\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 16 17 18\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 16 17 18\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 5 6 15 16 17\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 4 5 14 15 16\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 12 13 14\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 12 13 14\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 7 8 9\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 4 7 8\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 6 7 8\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 8 9 13 14 15 16 17 18 19\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 4 5 9 10 11 12 13 14 15\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 7 8 9 10 11 12 13\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 7 8 9 10 11 12 13\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 8 12\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 8 11\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 4 6 7\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 5 6 15 16 17\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 5 6 15 16 17\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 5 6 10 11 12\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 8 9 10\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 4 9 10\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 0 1 6 7 8\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 4 8 9\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 0 1 5 6 7\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 4 7 8\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 0 1 4 5 6\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 11 14\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 10 13\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 9 12\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 2 3 4 7 8\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 7 8 9 11 12\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 16 17 18\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 0 1 3 4 5\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 0 1 3 4 5\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 16 17 18\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 6 7 11 12 13\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 5 6 10 11 12\n",
      "[18:16:26] Can't kekulize mol.  Unkekulized atoms: 4 5 9 10 11\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 7 8 9\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 4 7 8\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 6 7 8\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 7 8 17 18 19\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 7 8 12 13 14\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 5 6 10 11 12\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 9 10 11\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 4 10 11\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 0 1 7 8 9\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 8 9 12 13 14 15 16 17 18\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 6 7 8 11 12\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 5 6 7 10 11\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 4 5 6 9 10\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 4 7 8\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 7 8 17 18 19\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 5 6 10 11 12\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 5 6 7 10 11\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 5 6 9 10 11\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 5 6 7 10 11\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 5 6 9 10 11\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 8 9 10 12 13\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 12 16\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 11 15\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 10 14\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 9 13\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 4 13 14\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 6 7 11 12 13\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 4 8 9\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 6 7 8 11 12\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 4 7 8\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 6 7 10 11 12\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 4 6 7\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 6 7 8 11 12\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 6 7 10 11 12\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 5 6 7 10 11\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 5 6 9 10 11\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 4 5 6 9 10\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 4 5 8 9 10\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 6 7 16 17 18\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 5 6 15 16 17\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 8 9 10\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 4 8 9\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 0 1 5 6 7\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 10 11 12 14 15\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 7 8 12 13 14 15 16 17 18\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 7 8 11 12 13 14 15 16 17\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 5 6 7 10 11\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 5 6 7 10 11\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 4 9 10\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 4 8 9\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 7 8 12 13 14 15 16 17 18\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 12 15\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 4 10 11\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 6 7 16 17 18\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 6 7 12 13 14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 6 7 8\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 6 7 13 14 15 16 17 18 19\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 6 7 12 13 14\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 4 7 8\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 0 1 4 5 6\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 8 9 13 14 15\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 8 9 10 13 14\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 8 9 12 13 14\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 7 8 9 12 13\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 7 8 11 12 13\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 9 10 11 13 14\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 5 6 7 10 11\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 10 11 12 15 16\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 9 10 11 14 15\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 4 13 14\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 4 8 9\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 4 7 8\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 4 6 7\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 10 11 12 14 15\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 4 15 16\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 2 3 4 14 15\n",
      "[18:16:27] Can't kekulize mol.  Unkekulized atoms: 11 12 13 15 16\n"
     ]
    },
    {
     "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>smiles</th>\n",
       "      <th>score</th>\n",
       "      <th>rationale</th>\n",
       "      <th>rationale_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cc1cc(NC(=O)c2cccc(COc3ccc(Br)cc3)c2)no1</td>\n",
       "      <td>386.097</td>\n",
       "      <td>c1cc(CO[CH3:1])c[cH:1]c1</td>\n",
       "      <td>389.151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>O=C(Nc1ccc(F)cc1F)Nc1sccc1-c1nc2ccccc2s1</td>\n",
       "      <td>389.485</td>\n",
       "      <td>c1c[cH:1]c[cH:1]c1N[CH2:1][NH2:1]</td>\n",
       "      <td>388.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>COC(=O)c1ccccc1NC(=O)c1cc([N+](=O)[O-])nn1Cc1c...</td>\n",
       "      <td>384.720</td>\n",
       "      <td>CO[CH2:1]c1cccc[cH:1]1</td>\n",
       "      <td>389.151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CCOC(=O)C(C)Sc1nc(-c2ccccc2)ccc1C#N</td>\n",
       "      <td>387.110</td>\n",
       "      <td>c1c[cH:1]c(S[CH2:1][CH3:1])n[cH:1]1</td>\n",
       "      <td>388.871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CCC(CC)NC(=O)c1nn(Cc2ccccc2)c(=O)c2ccccc12</td>\n",
       "      <td>388.997</td>\n",
       "      <td>n1c([CH2:1]N[CH3:1])[cH:1][cH:1][cH:1][n:1]1</td>\n",
       "      <td>387.854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              smiles    score  \\\n",
       "0           Cc1cc(NC(=O)c2cccc(COc3ccc(Br)cc3)c2)no1  386.097   \n",
       "0           O=C(Nc1ccc(F)cc1F)Nc1sccc1-c1nc2ccccc2s1  389.485   \n",
       "0  COC(=O)c1ccccc1NC(=O)c1cc([N+](=O)[O-])nn1Cc1c...  384.720   \n",
       "0                CCOC(=O)C(C)Sc1nc(-c2ccccc2)ccc1C#N  387.110   \n",
       "0         CCC(CC)NC(=O)c1nn(Cc2ccccc2)c(=O)c2ccccc12  388.997   \n",
       "\n",
       "                                      rationale rationale_score  \n",
       "0                      c1cc(CO[CH3:1])c[cH:1]c1         389.151  \n",
       "0             c1c[cH:1]c[cH:1]c1N[CH2:1][NH2:1]         388.565  \n",
       "0                        CO[CH2:1]c1cccc[cH:1]1         389.151  \n",
       "0           c1c[cH:1]c(S[CH2:1][CH3:1])n[cH:1]1         388.871  \n",
       "0  n1c([CH2:1]N[CH3:1])[cH:1][cH:1][cH:1][n:1]1         387.854  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chemprop.predict_from_smiles(df[config.data.input_column].head(5), explain=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output contians the following:\n",
    "\n",
    "* The first column is a molecule and second column is its predicted property (in this dummy case MolWt).\n",
    "\n",
    "* The third column is the smallest substructure that made this molecule obtain that MolWt prediction (called rationale).\n",
    "\n",
    "* The fourth column is the predicted MolWt of that substructure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QSARtuna can be used to transform input labels so that log-scaled or irregularly distributed data can be transformed to a normal distribution as required for most Machine Learning inputs. The following example shows how XC50 values can be scaled to pXC50 values by using the -Log10 to the 6th unit conversion, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:29,451] A new study created in memory with name: transform_example\n",
      "[I 2024-10-02 18:16:29,498] A new study created in memory with name: study_name_0\n",
      "[I 2024-10-02 18:16:29,601] Trial 0 finished with value: -0.5959493772536109 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.5959493772536109.\n",
      "[I 2024-10-02 18:16:29,851] Trial 1 finished with value: -0.6571993250300608 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.5959493772536109.\n",
      "[I 2024-10-02 18:16:29,896] Trial 2 finished with value: -4.1511102853256885 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.5959493772536109.\n",
      "[I 2024-10-02 18:16:29,937] Trial 3 finished with value: -1.248706331711276 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -0.5959493772536109.\n",
      "[I 2024-10-02 18:16:29,953] Trial 4 finished with value: -0.6714912461080983 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.5959493772536109.\n",
      "[I 2024-10-02 18:16:29,973] Trial 5 finished with value: -0.2725944467796781 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n",
      "[I 2024-10-02 18:16:31,326] Trial 6 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.2725944467796781.\n",
      "[I 2024-10-02 18:16:31,346] Trial 7 finished with value: -0.7520919188596032 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n",
      "[I 2024-10-02 18:16:31,539] Trial 8 finished with value: -0.7803723847416691 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n",
      "[I 2024-10-02 18:16:31,572] Trial 9 finished with value: -0.6397753979196248 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n",
      "[I 2024-10-02 18:16:31,592] Trial 10 finished with value: -4.151110299986041 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n",
      "[I 2024-10-02 18:16:31,612] Trial 11 finished with value: -4.151110111437006 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n",
      "[I 2024-10-02 18:16:31,633] Trial 12 finished with value: -0.5410418750776741 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n",
      "[I 2024-10-02 18:16:31,669] Trial 13 finished with value: -0.7183231137124538 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n",
      "[I 2024-10-02 18:16:31,706] Trial 14 finished with value: -0.2721824844856162 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n",
      "[I 2024-10-02 18:16:31,786] Trial 15 finished with value: -1.1900929470222508 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n",
      "[I 2024-10-02 18:16:31,824] Trial 16 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n",
      "[I 2024-10-02 18:16:31,854] Trial 17 finished with value: -0.5585323973564646 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n",
      "[I 2024-10-02 18:16:31,927] Trial 18 finished with value: -1.3169218304262786 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:31,948] Trial 19 finished with value: -0.7974925066137679 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n",
      "[I 2024-10-02 18:16:31,967] Trial 20 finished with value: -1.2183952264663362 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n",
      "[I 2024-10-02 18:16:31,987] Trial 21 finished with value: -1.1474226942497083 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n",
      "[I 2024-10-02 18:16:31,992] Trial 22 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 18:16:32,014] Trial 23 finished with value: -1.0239005731675412 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n",
      "[I 2024-10-02 18:16:32,098] Trial 24 finished with value: -0.7803723847416691 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n",
      "[I 2024-10-02 18:16:32,140] Trial 25 finished with value: -2.178901060853144 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n",
      "[I 2024-10-02 18:16:32,162] Trial 26 finished with value: -0.27137790098830755 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 26 with value: -0.27137790098830755.\n",
      "[I 2024-10-02 18:16:32,186] Trial 27 finished with value: -0.2710284516876423 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -0.2710284516876423.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [-0.5410418750776741]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:32,274] Trial 28 finished with value: -1.3169218304262786 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n",
      "[I 2024-10-02 18:16:32,303] Trial 29 finished with value: -3.6273152492418945 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.2710284516876423.\n",
      "[I 2024-10-02 18:16:32,396] Trial 30 finished with value: -1.1900929470222508 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n",
      "[I 2024-10-02 18:16:32,420] Trial 31 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n",
      "[I 2024-10-02 18:16:32,441] Trial 32 finished with value: -2.1907041717628215 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.2710284516876423.\n",
      "[I 2024-10-02 18:16:32,465] Trial 33 finished with value: -1.3209075619139279 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n",
      "[I 2024-10-02 18:16:32,474] Trial 34 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 18:16:32,499] Trial 35 finished with value: -0.2709423025014604 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n",
      "[I 2024-10-02 18:16:32,520] Trial 36 finished with value: -1.3133943310851415 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n",
      "[I 2024-10-02 18:16:32,528] Trial 37 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 18:16:32,551] Trial 38 finished with value: -1.257769959239938 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n",
      "[I 2024-10-02 18:16:32,627] Trial 39 finished with value: -0.40359637945134746 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [-0.5410418750776741]\n",
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-1.2183952264663362]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:32,723] Trial 40 finished with value: -0.4127882135896648 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n",
      "[I 2024-10-02 18:16:32,736] Trial 41 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 18:16:32,819] Trial 42 finished with value: -0.5959493772536109 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n",
      "[I 2024-10-02 18:16:32,847] Trial 43 finished with value: -0.9246005133276615 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n",
      "[I 2024-10-02 18:16:32,935] Trial 44 finished with value: -0.8908739215746118 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-1.248706331711276]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:32,974] Trial 45 finished with value: -1.107536316777608 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n",
      "[I 2024-10-02 18:16:33,000] Trial 46 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n",
      "[I 2024-10-02 18:16:33,024] Trial 47 finished with value: -4.054360360588395 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n",
      "[I 2024-10-02 18:16:33,048] Trial 48 finished with value: -0.5428179904345867 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n",
      "[I 2024-10-02 18:16:33,073] Trial 49 finished with value: -0.569627364221335 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n",
      "[I 2024-10-02 18:16:33,103] Trial 50 finished with value: -0.27099769667470536 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1580741708125475, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n",
      "[I 2024-10-02 18:16:33,134] Trial 51 finished with value: -0.2709564785634315 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10900413894771653, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n",
      "[I 2024-10-02 18:16:33,164] Trial 52 finished with value: -0.2709799905898163 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.13705914456987853, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n",
      "[I 2024-10-02 18:16:33,194] Trial 53 finished with value: -0.27097230608092054 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.12790870116376127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n",
      "[I 2024-10-02 18:16:33,224] Trial 54 finished with value: -0.2709499903064464 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10123180962907431, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n",
      "[I 2024-10-02 18:16:33,255] Trial 55 finished with value: -0.2710895886052581 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.26565663774320425, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n",
      "[I 2024-10-02 18:16:33,286] Trial 56 finished with value: -0.2708711012023424 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.005637048678674678, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n",
      "[I 2024-10-02 18:16:33,321] Trial 57 finished with value: -0.27092322402109364 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06902647427781451, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n",
      "[I 2024-10-02 18:16:33,353] Trial 58 finished with value: -0.2712140349882 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4076704953178294, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n",
      "[I 2024-10-02 18:16:33,396] Trial 59 finished with value: -0.27090080367174 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.04187106800188596, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n",
      "[I 2024-10-02 18:16:33,441] Trial 60 finished with value: -0.27086925247190047 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.003371853599610078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n",
      "[I 2024-10-02 18:16:33,481] Trial 61 finished with value: -0.2708933298483799 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.032781796328385376, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n",
      "[I 2024-10-02 18:16:33,514] Trial 62 finished with value: -0.27087205624489635 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.006806773659187283, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n",
      "[I 2024-10-02 18:16:33,554] Trial 63 finished with value: -0.2708869511176179 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.025009489814943348, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n",
      "[I 2024-10-02 18:16:33,580] Trial 64 finished with value: -0.2711465077924297 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.3311125627707556, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n",
      "[I 2024-10-02 18:16:33,622] Trial 65 finished with value: -0.2708756855936628 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011249102380159387, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:33,652] Trial 66 finished with value: -0.27087301924224993 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.007985924302396141, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n",
      "[I 2024-10-02 18:16:33,683] Trial 67 finished with value: -0.2708685399954944 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00249856291483601, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n",
      "[I 2024-10-02 18:16:33,713] Trial 68 finished with value: -0.27121879554836553 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4130244908975993, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n",
      "[I 2024-10-02 18:16:33,747] Trial 69 finished with value: -0.2708693196600531 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0034541978803366022, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n",
      "[I 2024-10-02 18:16:33,784] Trial 70 finished with value: -0.27110195265802334 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.27994943662091765, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n",
      "[I 2024-10-02 18:16:33,824] Trial 71 finished with value: -0.2708682582859318 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0021532199144365088, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:33,858] Trial 72 finished with value: -0.27087024523986086 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0045884092728113585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:33,889] Trial 73 finished with value: -0.27087351807632193 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.008596600952859433, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:33,936] Trial 74 finished with value: -0.2710818633795896 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2567049271070902, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:33,973] Trial 75 finished with value: -0.27103241786565463 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1990111983307052, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,003] Trial 76 finished with value: -0.2710350879598171 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.20214459724424078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,036] Trial 77 finished with value: -0.2708688328221868 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00285750520671645, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,083] Trial 78 finished with value: -0.27100832234449684 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.17064008990759916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,168] Trial 79 finished with value: -0.27268613236193845 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8725420109733135, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,203] Trial 80 finished with value: -0.27119617446689237 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.387533542012365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,236] Trial 81 finished with value: -0.2708691110831552 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0031985656730512953, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,268] Trial 82 finished with value: -0.27086852174155146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.002476186542950981, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,302] Trial 83 finished with value: -0.27135383618835024 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5626643670396761, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,335] Trial 84 finished with value: -0.2709819654433871 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1394077979875128, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,370] Trial 85 finished with value: -0.2718548944510965 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0858347526799794, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,408] Trial 86 finished with value: -4.1508084699212935 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03329943145150872, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00025672309762227527, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:34,440] Trial 87 finished with value: -0.27249853374634975 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.702026434077893, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,470] Trial 88 finished with value: -0.27095660957755363 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10916094511173127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,520] Trial 89 finished with value: -0.27102160995407715 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.18630665884100353, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,556] Trial 90 finished with value: -0.27095708822582026 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10973377642487026, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,591] Trial 91 finished with value: -0.27088222008661084 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.019235980282946118, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,623] Trial 92 finished with value: -0.2708703086029017 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.004666043957133775, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,658] Trial 93 finished with value: -0.27095279044622245 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1045877457096882, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,695] Trial 94 finished with value: -0.2709408288690431 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.09023455456986404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,737] Trial 95 finished with value: -0.9289218260898663 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8200088368788958, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n",
      "[I 2024-10-02 18:16:34,774] Trial 96 finished with value: -0.27086675101898655 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00030502148265565063, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n",
      "[I 2024-10-02 18:16:34,812] Trial 97 finished with value: -0.2710491243757999 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.21858260742423916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n",
      "[I 2024-10-02 18:16:34,846] Trial 98 finished with value: -4.1491615840508995 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.024725853754515203, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n",
      "[I 2024-10-02 18:16:34,879] Trial 99 finished with value: -0.2709462479577586 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0967427718847167, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n"
     ]
    }
   ],
   "source": [
    "from optunaz.utils.preprocessing.transform import (\n",
    "    LogBase,\n",
    "    LogNegative,\n",
    "    ModelDataTransform\n",
    ")\n",
    "\n",
    "config = OptimizationConfig(\n",
    "        data=Dataset(\n",
    "        input_column=\"Smiles\",\n",
    "        response_column=\"Measurement\",\n",
    "        response_type=\"regression\",\n",
    "        training_dataset_file=\"../tests/data/sdf/example.sdf\",\n",
    "        split_strategy=Stratified(fraction=0.4),\n",
    "        deduplication_strategy=KeepMedian(),\n",
    "        log_transform=True, # Set to True to perform\n",
    "        log_transform_base=LogBase.LOG10, # Log10 base will be used\n",
    "        log_transform_negative=LogNegative.TRUE, # Negated transform for the pXC50 calculation\n",
    "        log_transform_unit_conversion=6, # 6 units used for pXC50 conversion\n",
    "    ),\n",
    "    descriptors=[\n",
    "        ECFP.new(), \n",
    "        ECFP_counts.new(), \n",
    "        MACCS_keys.new(),\n",
    "    ],\n",
    "    algorithms=[\n",
    "        SVR.new(),\n",
    "        RandomForestRegressor.new(n_estimators={\"low\": 5, \"high\": 10}),\n",
    "        Ridge.new(),\n",
    "        Lasso.new(),\n",
    "        PLSRegression.new(),\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=3,\n",
    "        n_trials=100,\n",
    "        n_startup_trials=50,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "        track_to_mlflow=False,\n",
    "        random_seed=42,\n",
    "    ),\n",
    ") \n",
    "\n",
    "transformed_study = optimize(config, study_name=\"transform_example\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In comparison, QSARtuna does not normally transform the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:38,216] A new study created in memory with name: non-transform_example\n",
      "[I 2024-10-02 18:16:38,218] A new study created in memory with name: study_name_0\n",
      "[I 2024-10-02 18:16:38,320] Trial 0 finished with value: -3501.942111261296 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n",
      "[I 2024-10-02 18:16:38,389] Trial 1 finished with value: -5451.207265576796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n",
      "[I 2024-10-02 18:16:38,431] Trial 2 finished with value: -208.1049201007814 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n",
      "[I 2024-10-02 18:16:38,476] Trial 3 finished with value: -9964.541364058234 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -208.1049201007814.\n",
      "[I 2024-10-02 18:16:38,492] Trial 4 finished with value: -3543.9536085399013 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n",
      "[I 2024-10-02 18:16:38,512] Trial 5 finished with value: -6837.057544630979 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n",
      "[I 2024-10-02 18:16:38,531] Trial 6 finished with value: -2507.1794330606067 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -208.1049201007814.\n",
      "[I 2024-10-02 18:16:38,571] Trial 7 finished with value: -21534.719219668405 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n",
      "[I 2024-10-02 18:16:38,638] Trial 8 finished with value: -2899.736555614694 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.294e+02, tolerance: 2.760e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 18:16:38,690] Trial 9 finished with value: -21674.445000284228 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n",
      "[I 2024-10-02 18:16:38,708] Trial 10 finished with value: -208.1049203123567 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n",
      "[I 2024-10-02 18:16:38,723] Trial 11 finished with value: -208.1049192609138 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n",
      "[I 2024-10-02 18:16:38,739] Trial 12 finished with value: -3630.727680937561 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n",
      "[I 2024-10-02 18:16:38,768] Trial 13 finished with value: -3431.9428169672683 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n",
      "[I 2024-10-02 18:16:38,786] Trial 14 finished with value: -6908.462045154488 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n",
      "[I 2024-10-02 18:16:38,852] Trial 15 finished with value: -5964.65935954044 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n",
      "[I 2024-10-02 18:16:38,871] Trial 16 finished with value: -21070.107195348774 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n",
      "[I 2024-10-02 18:16:38,888] Trial 17 finished with value: -4977.068508997133 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:38,944] Trial 18 finished with value: -8873.669262669626 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n",
      "[I 2024-10-02 18:16:38,970] Trial 19 finished with value: -21387.63697424318 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n",
      "[I 2024-10-02 18:16:38,989] Trial 20 finished with value: -9958.573006910125 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n",
      "[I 2024-10-02 18:16:39,006] Trial 21 finished with value: -180.5182695600183 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 21 with value: -180.5182695600183.\n",
      "[I 2024-10-02 18:16:39,011] Trial 22 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 18:16:39,040] Trial 23 finished with value: -20684.56412138056 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 21 with value: -180.5182695600183.\n",
      "[I 2024-10-02 18:16:39,097] Trial 24 finished with value: -2899.736555614694 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 21 with value: -180.5182695600183.\n",
      "[I 2024-10-02 18:16:39,115] Trial 25 finished with value: -150.3435882510586 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,132] Trial 26 finished with value: -7068.705383113378 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,151] Trial 27 finished with value: -7150.482090052133 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,206] Trial 28 finished with value: -8873.669262669626 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [-3630.727680937561]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:39,224] Trial 29 finished with value: -203.93637462922368 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,289] Trial 30 finished with value: -5964.65935954044 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,308] Trial 31 finished with value: -2570.5111262532305 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,335] Trial 32 finished with value: -21987.659957192194 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,353] Trial 33 finished with value: -9889.493204596083 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,358] Trial 34 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 18:16:39,376] Trial 35 finished with value: -7172.208490771303 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,395] Trial 36 finished with value: -9804.512701665093 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,399] Trial 37 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 18:16:39,418] Trial 38 finished with value: -9165.74081120673 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,481] Trial 39 finished with value: -543.0280270800017 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,550] Trial 40 finished with value: -161.1602933782954 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,556] Trial 41 pruned. Duplicate parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [-3630.727680937561]\n",
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-9958.573006910125]\n",
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-9964.541364058234]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:39,623] Trial 42 finished with value: -3501.888460860864 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,643] Trial 43 finished with value: -8414.932694243476 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,710] Trial 44 finished with value: -2270.5407991891466 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,729] Trial 45 finished with value: -10383.79559309305 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,749] Trial 46 finished with value: -20815.025469865475 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,768] Trial 47 finished with value: -206.7560385808573 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,788] Trial 48 finished with value: -5264.4700789389035 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,806] Trial 49 finished with value: -3668.2550641354246 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,831] Trial 50 finished with value: -156.12174877890536 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 56.793408178086295, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 9.99902820845678, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,856] Trial 51 finished with value: -157.371632749506 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 57.88307313087517, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 8.140915461519354, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,883] Trial 52 finished with value: -153.66773675231477 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 46.177324126813716, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 40.77906017834145, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,909] Trial 53 finished with value: -186.52056745848623 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 89.4565714180547, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 93.6710444346508, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,936] Trial 54 finished with value: -153.30976119334312 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 35.62916671166313, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 40.023639423189294, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,963] Trial 55 finished with value: -181.053696900694 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 23.914617418880486, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 86.31140591484044, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:39,989] Trial 56 finished with value: -201.33573874994386 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 12.569769302718845, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.5781354926491789, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:40,016] Trial 57 finished with value: -190.1384885119049 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 95.87666716965626, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.2537791489618, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:40,043] Trial 58 finished with value: -208.076949848299 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.9559574710535281, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0032830967319653665, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:40,068] Trial 59 finished with value: -170.764974036324 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 15.03910427457823, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.406811480459925, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:40,095] Trial 60 finished with value: -164.4477304958181 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 17.701690847791482, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.819274780536123, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:40,122] Trial 61 finished with value: -157.87939164358104 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 28.32187661108304, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 7.660320437878754, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:40,150] Trial 62 finished with value: -157.01705178481896 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 38.61397716361812, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 8.603665957830847, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:40,178] Trial 63 finished with value: -155.73257312230092 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 40.759645965959294, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 11.503212714246787, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:40,204] Trial 64 finished with value: -154.46848394144124 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 93.8546740801317, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 15.35327336610912, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:40,233] Trial 65 finished with value: -161.20421802817864 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 93.57596974747163, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 51.84756262407801, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:40,264] Trial 66 finished with value: -190.51233215278089 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 6.3564642040401464, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.5034542273159819, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:40,294] Trial 67 finished with value: -207.68667089892196 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 24.034895878929095, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03653571911285094, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n",
      "[I 2024-10-02 18:16:40,324] Trial 68 finished with value: -102.52277054278186 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.01961499216484045, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 17.670937191883546, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 68 with value: -102.52277054278186.\n",
      "[I 2024-10-02 18:16:40,354] Trial 69 finished with value: -97.28722475694815 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.012434370509176538, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 19.34222704431493, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 69 with value: -97.28722475694815.\n",
      "[I 2024-10-02 18:16:40,383] Trial 70 finished with value: -93.87402050281146 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.008452015347522093, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 24.914863578437455, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 70 with value: -93.87402050281146.\n",
      "[I 2024-10-02 18:16:40,412] Trial 71 finished with value: -89.38847505937936 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.01573542234868893, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.99307522974174, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 71 with value: -89.38847505937936.\n",
      "[I 2024-10-02 18:16:40,443] Trial 72 finished with value: -81.96336195786391 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009845516063879428, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 80.59422914099683, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n",
      "[I 2024-10-02 18:16:40,472] Trial 73 finished with value: -89.19345618324213 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009382525091504246, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.35573659237662, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n",
      "[I 2024-10-02 18:16:40,506] Trial 74 finished with value: -86.30772721342525 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.010579672066291478, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 84.35550323165882, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n",
      "[I 2024-10-02 18:16:40,541] Trial 75 finished with value: -90.23970902543148 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.013369359066405863, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 87.4744102498801, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n",
      "[I 2024-10-02 18:16:40,589] Trial 76 finished with value: -81.34331248758777 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.011398351701814368, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 72.54146340620301, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 76 with value: -81.34331248758777.\n",
      "[I 2024-10-02 18:16:40,647] Trial 77 finished with value: -208.104535853341 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.011708779850509646, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.682286191624579e-05, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 76 with value: -81.34331248758777.\n",
      "[I 2024-10-02 18:16:40,702] Trial 78 finished with value: -80.0653774146952 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009806826677473646, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 76.90274406278985, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 78 with value: -80.0653774146952.\n",
      "[I 2024-10-02 18:16:40,739] Trial 79 finished with value: -81.64646042813787 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0038598153381434685, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 73.20918134828555, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 78 with value: -80.0653774146952.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:40,776] Trial 80 finished with value: -78.68420472011734 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0032474576673554513, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.35551178979624, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n",
      "[I 2024-10-02 18:16:40,822] Trial 81 finished with value: -80.85985201823172 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.003187930738019005, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 89.29431603544847, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n",
      "[I 2024-10-02 18:16:40,860] Trial 82 finished with value: -80.21583898009355 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.003122319313153475, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 93.83526418992966, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n",
      "[I 2024-10-02 18:16:40,903] Trial 83 finished with value: -83.34787242859676 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002781955938462633, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 89.76228981520067, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n",
      "[I 2024-10-02 18:16:40,954] Trial 84 finished with value: -194.70914272129673 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0023173546614751305, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.3000082904498813, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n",
      "[I 2024-10-02 18:16:40,998] Trial 85 finished with value: -208.10492031097328 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002606064524407, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.7861330234653922e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n",
      "[I 2024-10-02 18:16:41,031] Trial 86 finished with value: -208.1049154281806 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0029210589377408366, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.200933937391094e-07, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n",
      "[I 2024-10-02 18:16:41,074] Trial 87 finished with value: -208.10492028002287 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.06431564840324226, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.2981641934644904e-09, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n",
      "[I 2024-10-02 18:16:41,109] Trial 88 finished with value: -196.56066541774658 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0010848843623839548, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.151493073951163, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n",
      "[I 2024-10-02 18:16:41,144] Trial 89 finished with value: -76.76337597039308 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.004134805589645341, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 90.88115336652716, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n",
      "[I 2024-10-02 18:16:41,190] Trial 90 finished with value: -108.58009587759925 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.004763418454688096, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 22.02920758025023, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n",
      "[I 2024-10-02 18:16:41,234] Trial 91 finished with value: -113.35230417583477 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009098023238189749, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 79.57100980886017, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n",
      "[I 2024-10-02 18:16:41,270] Trial 92 finished with value: -113.30807467406214 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03739791555156691, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.12818940557025, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n",
      "[I 2024-10-02 18:16:41,307] Trial 93 finished with value: -76.44100655116532 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.006380481141720477, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 88.4882351186755, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n",
      "[I 2024-10-02 18:16:41,346] Trial 94 finished with value: -150.35181001564942 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0036244007454981787, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.608797806921866, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n",
      "[I 2024-10-02 18:16:41,385] Trial 95 finished with value: -124.3719027482892 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0014198536004321608, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 35.05588994284273, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n",
      "[I 2024-10-02 18:16:41,424] Trial 96 finished with value: -95.28568052794907 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.005434972462746285, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 30.215759789700954, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n",
      "[I 2024-10-02 18:16:41,477] Trial 97 finished with value: -20325.66479442037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.9696417046589247, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n",
      "[I 2024-10-02 18:16:41,512] Trial 98 finished with value: -132.21507621375022 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0004528978867024753, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 84.80386923876023, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:41,555] Trial 99 finished with value: -166.85570350846885 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0016948043699497222, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.455627755557016, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n"
     ]
    }
   ],
   "source": [
    "config = OptimizationConfig(\n",
    "        data=Dataset(\n",
    "        input_column=\"Smiles\",\n",
    "        response_column=\"Measurement\",\n",
    "        response_type=\"regression\",\n",
    "        training_dataset_file=\"../tests/data/sdf/example.sdf\",\n",
    "        split_strategy=Stratified(fraction=0.4),\n",
    "        deduplication_strategy=KeepMedian(),\n",
    "        log_transform=False, # Shown for illustration: Log transform defaults to False\n",
    "        log_transform_base=None, # Shown for illustration: Log10 base is None/ignored if not log scaled\n",
    "        log_transform_negative=None, # Shown for illustration: negation is None/ignored if not log scaled\n",
    "        log_transform_unit_conversion=None, # Shown for illustration: conversion is None/ignored if not log scaled\n",
    "    ),\n",
    "    descriptors=[\n",
    "        ECFP.new(), \n",
    "        ECFP_counts.new(), \n",
    "        MACCS_keys.new(),\n",
    "    ],\n",
    "    algorithms=[\n",
    "        SVR.new(),\n",
    "        RandomForestRegressor.new(n_estimators={\"low\": 5, \"high\": 10}),\n",
    "        Ridge.new(),\n",
    "        Lasso.new(),\n",
    "        PLSRegression.new(),\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=3,\n",
    "        n_trials=100,\n",
    "        n_startup_trials=50,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "        track_to_mlflow=False,\n",
    "        random_seed=42,\n",
    "    ),\n",
    ") \n",
    "\n",
    "default_study = optimize(config, study_name=\"non-transform_example\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The importance of scaling can be analysed by directly contrasting the two different studies with and without log transformation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fe0712622c0>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1226.88x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "comparison = pd.concat((default_study.trials_dataframe().assign(run=f'no transform (best ={study.best_value:.2f})'),\n",
    "            transformed_study.trials_dataframe().assign(run=f'transform (best ={transformed_study.best_value:.2f})')))\n",
    "\n",
    "default_reg_scoring= config.settings.scoring\n",
    "ax = sns.relplot(data=comparison, x=\"number\", y=\"value\", \n",
    "                 col='run',hue='params_algorithm_name', \n",
    "                 facet_kws={\"sharey\":False})\n",
    "ax.set(xlabel=\"Trial number\",ylabel=f\"Ojbective value\\n({default_reg_scoring})\")\n",
    "ax.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example shows the influence of scaling the pXC50 values to the log scale. The non-noramlised distribution of the unlogged data yields very large (negative) model evaluation scores, since evaluation metrics such as MSE are relative, and the scale of the error is reported in performance values.\n",
    "\n",
    "Users generate predictions for a model trained on log transformed data in the same way as the normal models, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1126.56968721,  120.20237903])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the best Trial from the log transformed study and build the model.\n",
    "buildconfig = buildconfig_best(transformed_study)\n",
    "best_build = build_best(buildconfig, \"../target/best.pkl\")\n",
    "\n",
    "# generate predictions\n",
    "import pickle\n",
    "with open(\"../target/best.pkl\", \"rb\") as f:\n",
    "    model = pickle.load(f)\n",
    "model.predict_from_smiles([\"CCC\", \"CC(=O)Nc1ccc(O)cc1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NB: Please note that outputs have automatically been reversed transformed at inference, back onto the original XC50 scale, as shown by large values outside the log pXC50.\n",
    "\n",
    "This is the default behaviour of QSARtuna; reverse transform is performed at inference when log transformation was applied, so that users can action on prediction the original input data scale. Importantly, a user can easily override this behaviour by providing the transform parameter as `None`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.94824194, 3.92008694])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_from_smiles([\"CCC\", \"CC(=O)Nc1ccc(O)cc1\"], transform=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will instruct QSARtuna to avoid the reverse transform on the predictions. This transform parameter is ignored if no transformation was applied in the user config.\n",
    "\n",
    "Log transformation can also be combined with the PTR transform. In this situation, all user inputs are expected to be on the untransformed scale. For example, if a user wishes to create a PTR model, trained on pXC50 data and a cut-off for pXC50 values of 5 (10um), the following config can be used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:47,381] A new study created in memory with name: ptr_and_transform_example\n",
      "[I 2024-10-02 18:16:47,424] A new study created in memory with name: study_name_0\n",
      "[I 2024-10-02 18:16:47,523] Trial 0 finished with value: -0.002341918451736245 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n",
      "[I 2024-10-02 18:16:47,589] Trial 1 finished with value: -0.0024908979029632686 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n",
      "[I 2024-10-02 18:16:47,634] Trial 2 finished with value: -0.007901407671048116 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.002341918451736245.\n",
      "[I 2024-10-02 18:16:47,678] Trial 3 finished with value: -0.00496231674623194 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -0.002341918451736245.\n",
      "[I 2024-10-02 18:16:47,695] Trial 4 finished with value: -0.0026848278110363512 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n",
      "[I 2024-10-02 18:16:47,717] Trial 5 finished with value: -0.0010872728889471893 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n",
      "[I 2024-10-02 18:16:47,738] Trial 6 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.0010872728889471893.\n",
      "[I 2024-10-02 18:16:47,754] Trial 7 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n",
      "[I 2024-10-02 18:16:47,818] Trial 8 finished with value: -0.002999462459688867 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n",
      "[I 2024-10-02 18:16:47,835] Trial 9 finished with value: -0.00825680029907454 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n",
      "[I 2024-10-02 18:16:47,852] Trial 10 finished with value: -0.007901407993550248 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n",
      "[I 2024-10-02 18:16:47,869] Trial 11 finished with value: -0.007901405163828307 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n",
      "[I 2024-10-02 18:16:47,886] Trial 12 finished with value: -0.0021653695362066753 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n",
      "[I 2024-10-02 18:16:47,902] Trial 13 finished with value: -0.0028691694869710156 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n",
      "[I 2024-10-02 18:16:47,918] Trial 14 finished with value: -0.0010855652626111146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n",
      "[I 2024-10-02 18:16:47,981] Trial 15 finished with value: -0.005505338042993082 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n",
      "[I 2024-10-02 18:16:48,000] Trial 16 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n",
      "[I 2024-10-02 18:16:48,016] Trial 17 finished with value: -0.002236800860454562 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n",
      "[I 2024-10-02 18:16:48,079] Trial 18 finished with value: -0.006105985607235417 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:48,098] Trial 19 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n",
      "[I 2024-10-02 18:16:48,117] Trial 20 finished with value: -0.004846526544994462 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n",
      "[I 2024-10-02 18:16:48,136] Trial 21 finished with value: -0.006964668794465202 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n",
      "[I 2024-10-02 18:16:48,140] Trial 22 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 18:16:48,156] Trial 23 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n",
      "[I 2024-10-02 18:16:48,222] Trial 24 finished with value: -0.002999462459688867 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n",
      "[I 2024-10-02 18:16:48,241] Trial 25 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n",
      "[I 2024-10-02 18:16:48,260] Trial 26 finished with value: -0.001082194093844804 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 26 with value: -0.001082194093844804.\n",
      "[I 2024-10-02 18:16:48,281] Trial 27 finished with value: -0.0010807084256204563 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -0.0010807084256204563.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [-0.0021653695362066753]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:48,341] Trial 28 finished with value: -0.006105985607235417 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n",
      "[I 2024-10-02 18:16:48,360] Trial 29 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.0010807084256204563.\n",
      "[I 2024-10-02 18:16:48,425] Trial 30 finished with value: -0.005505338042993082 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n",
      "[I 2024-10-02 18:16:48,445] Trial 31 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n",
      "[I 2024-10-02 18:16:48,462] Trial 32 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.0010807084256204563.\n",
      "[I 2024-10-02 18:16:48,478] Trial 33 finished with value: -0.005247934991526694 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n",
      "[I 2024-10-02 18:16:48,483] Trial 34 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 18:16:48,502] Trial 35 finished with value: -0.0010803393728928605 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n",
      "[I 2024-10-02 18:16:48,519] Trial 36 finished with value: -0.005218354425190125 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n",
      "[I 2024-10-02 18:16:48,524] Trial 37 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 18:16:48,543] Trial 38 finished with value: -0.004999207507691546 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n",
      "[I 2024-10-02 18:16:48,610] Trial 39 finished with value: -0.0015694919308122928 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n",
      "[I 2024-10-02 18:16:48,678] Trial 40 finished with value: -0.001975769419400139 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [-0.0021653695362066753]\n",
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-0.004846526544994462]\n",
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-0.00496231674623194]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:48,683] Trial 41 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 18:16:48,750] Trial 42 finished with value: -0.002341918451736245 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n",
      "[I 2024-10-02 18:16:48,771] Trial 43 finished with value: -0.00368328296527152 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n",
      "[I 2024-10-02 18:16:48,839] Trial 44 finished with value: -0.003412828259848677 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n",
      "[I 2024-10-02 18:16:48,860] Trial 45 finished with value: -0.004412110711416997 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n",
      "[I 2024-10-02 18:16:48,879] Trial 46 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n",
      "[I 2024-10-02 18:16:48,898] Trial 47 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n",
      "[I 2024-10-02 18:16:48,918] Trial 48 finished with value: -0.0021743798524909573 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n",
      "[I 2024-10-02 18:16:48,938] Trial 49 finished with value: -0.0022761245849848527 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n",
      "[I 2024-10-02 18:16:48,963] Trial 50 finished with value: -0.0010805768178458735 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1580741708125475, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n",
      "[I 2024-10-02 18:16:48,988] Trial 51 finished with value: -0.001080400188305814 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10900413894771653, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n",
      "[I 2024-10-02 18:16:49,011] Trial 52 finished with value: -0.0010805009783570441 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.13705914456987853, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n",
      "[I 2024-10-02 18:16:49,034] Trial 53 finished with value: -0.0010804680472500541 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.12790870116376127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n",
      "[I 2024-10-02 18:16:49,059] Trial 54 finished with value: -0.0010803723579987025 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10123180962907431, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n",
      "[I 2024-10-02 18:16:49,081] Trial 55 finished with value: -0.001080969596032512 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.26565663774320425, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n",
      "[I 2024-10-02 18:16:49,105] Trial 56 finished with value: -0.0010800333715082816 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.005637048678674678, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n",
      "[I 2024-10-02 18:16:49,128] Trial 57 finished with value: -0.0010802574700236845 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06902647427781451, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n",
      "[I 2024-10-02 18:16:49,155] Trial 58 finished with value: -0.0010814994986419817 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4076704953178294, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n",
      "[I 2024-10-02 18:16:49,181] Trial 59 finished with value: -0.001080161136846237 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.04187106800188596, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n",
      "[I 2024-10-02 18:16:49,205] Trial 60 finished with value: -0.0010800254136811547 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.003371853599610078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n",
      "[I 2024-10-02 18:16:49,230] Trial 61 finished with value: -0.0010801290036870739 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.032781796328385376, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:49,255] Trial 62 finished with value: -0.001080037482216557 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.006806773659187283, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n",
      "[I 2024-10-02 18:16:49,280] Trial 63 finished with value: -0.0010801015705851358 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.025009489814943348, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n",
      "[I 2024-10-02 18:16:49,305] Trial 64 finished with value: -0.0010812122378841013 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.3311125627707556, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n",
      "[I 2024-10-02 18:16:49,329] Trial 65 finished with value: -0.0010800531021304936 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011249102380159387, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n",
      "[I 2024-10-02 18:16:49,352] Trial 66 finished with value: -0.00108004162698813 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.007985924302396141, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n",
      "[I 2024-10-02 18:16:49,375] Trial 67 finished with value: -0.0010800223466649803 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00249856291483601, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n",
      "[I 2024-10-02 18:16:49,401] Trial 68 finished with value: -0.0010815197263834202 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4130244908975993, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n",
      "[I 2024-10-02 18:16:49,425] Trial 69 finished with value: -0.0010800257029027847 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0034541978803366022, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n",
      "[I 2024-10-02 18:16:49,449] Trial 70 finished with value: -0.0010810223438672223 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.27994943662091765, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n",
      "[I 2024-10-02 18:16:49,475] Trial 71 finished with value: -0.0010800211339555509 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0021532199144365088, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:49,500] Trial 72 finished with value: -0.0010800296871141684 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0045884092728113585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:49,526] Trial 73 finished with value: -0.0010800437739166451 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.008596600952859433, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:49,550] Trial 74 finished with value: -0.0010809366267195716 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2567049271070902, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:49,576] Trial 75 finished with value: -0.001080725386603206 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1990111983307052, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:49,605] Trial 76 finished with value: -0.0010807368035830652 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.20214459724424078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:49,631] Trial 77 finished with value: -0.0010800236072155854 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00285750520671645, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:49,658] Trial 78 finished with value: -0.0010806223050773966 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.17064008990759916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:49,683] Trial 79 finished with value: -0.0010876516369772728 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8725420109733135, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:49,708] Trial 80 finished with value: -0.00108142358144501 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.387533542012365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:49,734] Trial 81 finished with value: -0.0010800248050489667 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0031985656730512953, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:49,760] Trial 82 finished with value: -0.001080022268085466 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.002476186542950981, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:49,787] Trial 83 finished with value: -0.0010820922958715991 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5626643670396761, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:49,816] Trial 84 finished with value: -0.0010805094397523254 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1394077979875128, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:49,846] Trial 85 finished with value: -0.0010841993753324146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0858347526799794, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:49,873] Trial 86 finished with value: -0.007899735988203994 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03329943145150872, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00025672309762227527, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:49,900] Trial 87 finished with value: -0.0010868762004637347 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.702026434077893, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:49,925] Trial 88 finished with value: -0.001080400750193767 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10916094511173127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:49,950] Trial 89 finished with value: -0.0010806791616300314 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.18630665884100353, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:49,976] Trial 90 finished with value: -0.0010804028029753213 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10973377642487026, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:50,004] Trial 91 finished with value: -0.0010800812188506515 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.019235980282946118, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:50,031] Trial 92 finished with value: -0.0010800299598580359 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.004666043957133775, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:50,058] Trial 93 finished with value: -0.0010803843696362083 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1045877457096882, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:50,086] Trial 94 finished with value: -0.001080333048974234 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.09023455456986404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:50,114] Trial 95 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8200088368788958, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n",
      "[I 2024-10-02 18:16:50,142] Trial 96 finished with value: -0.001080014645182176 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00030502148265565063, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n",
      "[I 2024-10-02 18:16:50,170] Trial 97 finished with value: -0.0010807968027851892 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.21858260742423916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n",
      "[I 2024-10-02 18:16:50,200] Trial 98 finished with value: -0.007907028395366658 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.024725853754515203, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n",
      "[I 2024-10-02 18:16:50,227] Trial 99 finished with value: -0.0010803563024666294 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0967427718847167, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n"
     ]
    }
   ],
   "source": [
    "ptr_config_log_transform = OptimizationConfig(\n",
    "        data=Dataset(\n",
    "        input_column=\"Smiles\",\n",
    "        response_column=\"Measurement\",\n",
    "        response_type=\"regression\",\n",
    "        training_dataset_file=\"../tests/data/sdf/example.sdf\",\n",
    "        split_strategy=Stratified(fraction=0.4),\n",
    "        deduplication_strategy=KeepMedian(),\n",
    "        log_transform=True, # Set to True to perform\n",
    "        log_transform_base=LogBase.LOG10, # Log10 base will be used\n",
    "        log_transform_negative=LogNegative.TRUE, # Negated transform for the pXC50 calculation\n",
    "        log_transform_unit_conversion=6, # 6 units used for pXC50 conversion\n",
    "        probabilistic_threshold_representation=True, # This enables PTR\n",
    "        probabilistic_threshold_representation_threshold=5, # This defines the activity threshold for 10um\n",
    "        probabilistic_threshold_representation_std=0.6, # This captures the deviation/uncertainty in the dataset\n",
    "\n",
    "    ),\n",
    "    descriptors=[\n",
    "        ECFP.new(), \n",
    "        ECFP_counts.new(), \n",
    "        MACCS_keys.new(),\n",
    "    ],\n",
    "    algorithms=[\n",
    "        SVR.new(),\n",
    "        RandomForestRegressor.new(n_estimators={\"low\": 5, \"high\": 10}),\n",
    "        Ridge.new(),\n",
    "        Lasso.new(),\n",
    "        PLSRegression.new(),\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=3,\n",
    "        n_trials=100,\n",
    "        n_startup_trials=50,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "        track_to_mlflow=False,\n",
    "        random_seed=42,\n",
    "    ),\n",
    ") \n",
    "\n",
    "ptr_transformed_study = optimize(ptr_config_log_transform, study_name=\"ptr_and_transform_example\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis of the study is performed in the same manner as above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"darkgrid\")\n",
    "default_reg_scoring= config.settings.scoring\n",
    "ax = sns.scatterplot(data=ptr_transformed_study.trials_dataframe(), x=\"number\", \n",
    "                     y=\"value\",style='params_algorithm_name',hue='params_algorithm_name')\n",
    "ax.set(xlabel=\"Trial number\",ylabel=f\"Ojbective value\\n({default_reg_scoring})\")\n",
    "sns.move_legend(ax, \"upper right\", bbox_to_anchor=(1.6, 1), ncol=1, title=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to log scaled models trained without the PRF transform, log-transformed models trained with PTR functions will reverse both the probabilistic class membership likelihoods from the PTR function and reverse the subsequent log transform from any log scaling, scaling predictions back inline with original data: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.3506154])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the best Trial from the log transformed study and build the model.\n",
    "buildconfig = buildconfig_best(ptr_transformed_study)\n",
    "best_build = build_best(buildconfig, \"../target/best.pkl\")\n",
    "\n",
    "# generate predictions\n",
    "import pickle\n",
    "with open(\"../target/best.pkl\", \"rb\") as f:\n",
    "    model = pickle.load(f)\n",
    "model.predict_from_smiles([\"CCC\"], transform=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Covariate modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling one simple covariate, e.g. dose or time point"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A covariate, such as dose or timepoint, can be used as an auxiliary descriptor to account for the effect of this parameter in predictions. In this situation, a compound can be represented more than once across n distinct covariate measurements. Each of the covariate response values can now be used in training an algorithm in this approach. Replicates across each compound-covariate pair may be deduplicated using the standard deduplication approaches.\n",
    "\n",
    "To activate this function in QSARtuna, the `aux_column` setting can be used according to the column denoting the covariate to be modelled, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:53,613] A new study created in memory with name: covariate_example\n",
      "[I 2024-10-02 18:16:53,676] A new study created in memory with name: study_name_0\n",
      "[I 2024-10-02 18:16:53,882] Trial 0 finished with value: -5186.767663956718 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -5186.767663956718.\n",
      "[I 2024-10-02 18:16:53,982] Trial 1 finished with value: -4679.740824270968 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -4679.740824270968.\n",
      "[I 2024-10-02 18:16:54,105] Trial 2 finished with value: -4890.6705099499995 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 1 with value: -4679.740824270968.\n",
      "[I 2024-10-02 18:16:54,241] Trial 3 finished with value: -3803.9324375833758 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: -3803.9324375833758.\n",
      "[I 2024-10-02 18:16:54,259] Trial 4 finished with value: -3135.6497388676926 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -3135.6497388676926.\n",
      "[I 2024-10-02 18:16:54,302] Trial 5 finished with value: -551.2518812859375 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -551.2518812859375.\n",
      "[I 2024-10-02 18:16:54,335] Trial 6 finished with value: -4309.124112370974 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -551.2518812859375.\n",
      "[I 2024-10-02 18:16:54,379] Trial 7 finished with value: -362.30159424580074 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: -362.30159424580074.\n",
      "[I 2024-10-02 18:16:54,464] Trial 8 finished with value: -4357.02827013125 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 7 with value: -362.30159424580074.\n",
      "[I 2024-10-02 18:16:54,514] Trial 9 finished with value: -386.1437929337522 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: -362.30159424580074.\n"
     ]
    }
   ],
   "source": [
    "aux_col_config = OptimizationConfig(\n",
    "        data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt\",\n",
    "        response_type=\"regression\",\n",
    "        training_dataset_file=\"../tests/data/aux_descriptors_datasets/train_with_conc.csv\",\n",
    "        aux_column=\"aux1\" # use column aux1 as a co-variate in modelling\n",
    "    ),\n",
    "    descriptors=[\n",
    "        ECFP.new(), \n",
    "        ECFP_counts.new(), \n",
    "        MACCS_keys.new(),\n",
    "    ],\n",
    "    algorithms=[\n",
    "        SVR.new(),\n",
    "        RandomForestRegressor.new(n_estimators={\"low\": 5, \"high\": 10}),\n",
    "        Ridge.new(),\n",
    "        Lasso.new(),\n",
    "        PLSRegression.new(),\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=2,\n",
    "        n_trials=10,\n",
    "        random_seed=42,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ") \n",
    "\n",
    "aux_col_study = optimize(aux_col_config, study_name=\"covariate_example\")\n",
    "build_best(buildconfig_best(aux_col_study), \"../target/aux1_model.pkl\")\n",
    "with open(\"../target/aux1_model.pkl\", \"rb\") as f:\n",
    "    aux1_model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predictions from a covariate-trained model can now be generated like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([52.45281013, 52.45281013])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aux1_model.predict_from_smiles([\"CCC\", \"CCC\"], aux=[10,5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where the `aux` parameter of `predict_from_smiles` is used (and required) to generate predictions for a an input covariate auxiliary query, and the shape of the `aux` query must be the same shape as the SMILES input query, otherwise a ValueError will be thrown.\n",
    "\n",
    "So, for this toy example query the predicitons are for the SMILES `CCC` and two separate auxiliary covariate queries of `10` and `5`.\n",
    "\n",
    "\n",
    "N.B: For this particular toy training example, the molecular weight response column (`molwt`) is the same regardless of the modelled covariate value, and so the predictions are the same regardless the `aux` query, as expected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Transformation of co-variates: Proteochemometric (PCM) modelling + more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### VectorFromSmiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to utilise more than one type of covariate value at a time, an auxiliary transformation must be applied to process co-variates in a manner expected for the algorithms.\n",
    "\n",
    "Pre-computed covariates (in a similar manner to pre-computed descriptors), can be processed using the `VectorFromColumn`. Similar to pre-computed descriptors, the VectorFromColumn will split covariates on `,` or comma seperations like so: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:16:54,988] A new study created in memory with name: vector_aux_example\n",
      "[I 2024-10-02 18:16:55,037] A new study created in memory with name: study_name_0\n",
      "[I 2024-10-02 18:16:55,141] Trial 0 finished with value: -3043.4473106576693 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011994365911634164, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -3043.4473106576693.\n",
      "[I 2024-10-02 18:16:55,229] Trial 1 finished with value: -3043.39919100435 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.029071783512897825, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -3043.39919100435.\n",
      "[I 2024-10-02 18:16:55,344] Trial 2 finished with value: -4621.489703141113 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.022631709120790048, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.2198637677605415, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -3043.39919100435.\n",
      "[I 2024-10-02 18:16:55,439] Trial 3 finished with value: -770.039055241578 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4846814642387681, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 3 with value: -770.039055241578.\n",
      "[I 2024-10-02 18:16:55,487] Trial 4 finished with value: -284.9739534365022 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5914093983615214, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -284.9739534365022.\n",
      "[I 2024-10-02 18:16:55,536] Trial 5 finished with value: -283.8670891604803 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6201811079699818, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -283.8670891604803.\n",
      "[I 2024-10-02 18:16:55,574] Trial 6 finished with value: -3460.0018663351607 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -283.8670891604803.\n",
      "[I 2024-10-02 18:16:55,616] Trial 7 finished with value: -5320.591589729147 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.5789541326389298, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -283.8670891604803.\n",
      "[I 2024-10-02 18:16:55,694] Trial 8 finished with value: -3513.686346750008 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -283.8670891604803.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.097e+01, tolerance: 1.829e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.118e+01, tolerance: 1.868e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.116e+01, tolerance: 1.971e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 18:16:55,758] Trial 9 finished with value: -272.85385648295437 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.012507356254519486, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: -272.85385648295437.\n"
     ]
    }
   ],
   "source": [
    "from optunaz.utils.preprocessing.transform import VectorFromColumn\n",
    "\n",
    "vector_covariate_config = OptimizationConfig(\n",
    "        data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt\",\n",
    "        response_type=\"regression\",\n",
    "        training_dataset_file=\"../tests/data/precomputed_descriptor/train_with_fp.csv\",\n",
    "        aux_column=\"fp\", # use a comma separated co-variate vector in column `fp`\n",
    "        aux_transform=VectorFromColumn.new(), # split the comma separated values into a vector\n",
    "        split_strategy=Stratified(fraction=0.2),\n",
    "    ),\n",
    "    descriptors=[\n",
    "        ECFP.new(), \n",
    "        ECFP_counts.new(), \n",
    "        MACCS_keys.new(),\n",
    "    ],\n",
    "    algorithms=[\n",
    "        SVR.new(),\n",
    "        RandomForestRegressor.new(n_estimators={\"low\": 5, \"high\": 10}),\n",
    "        Ridge.new(),\n",
    "        Lasso.new(),\n",
    "        PLSRegression.new(),\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=3,\n",
    "        n_trials=10,\n",
    "        n_startup_trials=0,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "        track_to_mlflow=False,\n",
    "        random_seed=42,\n",
    "    ),\n",
    ") \n",
    "\n",
    "vector_covariate_study = optimize(vector_covariate_config, study_name=\"vector_aux_example\")\n",
    "build_best(buildconfig_best(vector_covariate_study), \"../target/vector_covariate_model.pkl\")\n",
    "with open(\"../target/vector_covariate_model.pkl\", \"rb\") as f:\n",
    "    vector_covariate_model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can inspect the input query for the auxiliary co-variates used in the modelling like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0., 1., 0., ..., 0., 0., 0.],\n",
       "        [1., 1., 1., ..., 0., 0., 0.],\n",
       "        [1., 0., 0., ..., 1., 0., 1.],\n",
       "        ...,\n",
       "        [0., 1., 0., ..., 0., 0., 1.],\n",
       "        [0., 1., 0., ..., 1., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]]),\n",
       " (40, 512))"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_smiles, train_y, train_aux, test_smiles, test_y, test_aux = vector_covariate_config.data.get_sets()\n",
    "\n",
    "train_aux, train_aux.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this toy example, the co-variate descriptors 512 in legth for the 40 training instances are used in training. Inference for the model can be performed on the test like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([494.24615136, 343.57199899, 243.66555938, 499.15698703,\n",
       "       444.45019502, 340.58194786, 463.055959  , 350.78471292,\n",
       "       412.86726453, 376.11106444])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector_covariate_model.predict_from_smiles(test_smiles, aux=test_aux)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Z-Scales (for PCM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proteochemometric modelling (PCM) is the term used for the approach of training protein-descriptors as a distinct input space alongside the chemical ones. This can be performed in QSARtuna by providing Z-Scales as an auxiliary transformation to a user input column containing sequence information. Protein sequence is  transformed to Z-Scales based on [this publication](https://pubs.acs.org/doi/10.1021/jm9700575) using the `Peptides` Python package.\n",
    "\n",
    "N:B. Note that Z-Scales as covariates are a distinct method separate to `ZScales` descriptors, since the former treats Z-Scales as a distinct input parameter (for PCM modelling), whereas the latter treates them as a descriptor trial that may or may not be selected during optimisation (e.g. for Protein-peptide interaction modelling). In other words, Z-scales will always be an input descriptor parameter when applied as a covariate and duplicates are treated on a compound-`ZScale` pair basis).\n",
    "\n",
    "Now let us consider the following toy data set file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peptide,Class,Smiles\r\n",
      "MDLITITWASVMVAFTFSLSLVVWGRSGL,0,N[C@@H](CCSC)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H]([C@H](CC)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H]([C@H](CC)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)N[C@@H](C)C(=O)N[C@@H](CO)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](C)C(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)NCC(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)O\r\n",
      "ARRGGVLNFGQFGLQALECGFVTNR,0,N[C@@H](C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCCNC(=N)N)C(=O)NCC(=O)NCC(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](Cc1ccccc1)C(=O)NCC(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](Cc1ccccc1)C(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CS)C(=O)NCC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCCNC(=N)N)C(=O)O\r\n",
      "GWCGDPGATCGKLRLYCCSGACDCYTKTCKDKSSA,1,NCC(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)N[C@@H](CS)C(=O)NCC(=O)N[C@@H](CC(=O)O)C(=O)N1[C@@H](CCC1)C(=O)NCC(=O)N[C@@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CS)C(=O)NCC(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H](CS)C(=O)N[C@@H](CS)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](C)C(=O)N[C@@H](CS)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CS)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CS)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CO)C(=O)N[C@@H](CO)C(=O)N[C@@H](C)C(=O)O\r\n",
      "NGNLLGGLLRPVLGVVKGLTGGLGKK,1,N[C@@H](CC(=O)N)C(=O)NCC(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N1[C@@H](CCC1)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)N[C@@H](C(C)C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCCCN)C(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCC(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCCCN)C(=O)O\r\n"
     ]
    }
   ],
   "source": [
    "!head -n 5 ../tests/data/peptide/toxinpred3/train.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following example demponstrates how Z-Scales may be utilised for PCM by specifying the `ZScales` data transform on the \"Peptide\" column containing our peptide sequence, like so: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:17:00,612] A new study created in memory with name: zscale_aux_example\n",
      "[I 2024-10-02 18:17:00,666] A new study created in memory with name: study_name_0\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:993: FutureWarning: probas_pred was deprecated in version 1.5 and will be removed in 1.7.Please use ``y_score`` instead.\n",
      "  warnings.warn(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:993: FutureWarning: probas_pred was deprecated in version 1.5 and will be removed in 1.7.Please use ``y_score`` instead.\n",
      "  warnings.warn(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:993: FutureWarning: probas_pred was deprecated in version 1.5 and will be removed in 1.7.Please use ``y_score`` instead.\n",
      "  warnings.warn(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:993: FutureWarning: probas_pred was deprecated in version 1.5 and will be removed in 1.7.Please use ``y_score`` instead.\n",
      "  warnings.warn(\n",
      "[I 2024-10-02 18:17:34,922] Trial 0 finished with value: 0.8735224395254063 and parameters: {'algorithm_name': 'KNeighborsClassifier', 'KNeighborsClassifier_algorithm_hash': 'e51ca55089f389fc37a736adb2aa0e42', 'metric__e51ca55089f389fc37a736adb2aa0e42': <KNeighborsMetric.MINKOWSKI: 'minkowski'>, 'n_neighbors__e51ca55089f389fc37a736adb2aa0e42': 5, 'weights__e51ca55089f389fc37a736adb2aa0e42': <KNeighborsWeights.UNIFORM: 'uniform'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 128, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8735224395254063.\n"
     ]
    }
   ],
   "source": [
    "from optunaz.utils.preprocessing.transform import ZScales\n",
    "from optunaz.config.optconfig import KNeighborsClassifier\n",
    "\n",
    "zscale_covariate_config = OptimizationConfig(\n",
    "        data=Dataset(\n",
    "        input_column=\"Smiles\",\n",
    "        response_column=\"Class\",\n",
    "        response_type=\"classification\",\n",
    "        training_dataset_file=\"../tests/data/peptide/toxinpred3/train.csv\",\n",
    "        aux_column=\"Peptide\", # Name of the column containing peptide/protein amino acid sequence\n",
    "        aux_transform=ZScales.new(), # Zscales transform is used to transform sequence into a Z-scales vector\n",
    "        split_strategy=Stratified(fraction=0.2),\n",
    "    ),\n",
    "    descriptors=[\n",
    "        ECFP.new(nBits=128), \n",
    "    ],\n",
    "    algorithms=[\n",
    "        KNeighborsClassifier.new(),\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.CLASSIFICATION,\n",
    "        cross_validation=2,\n",
    "        n_trials=1,\n",
    "        n_startup_trials=0,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "        track_to_mlflow=False,\n",
    "        random_seed=42,\n",
    "    ),\n",
    ") \n",
    "\n",
    "zscale_covariate_study = optimize(zscale_covariate_config, study_name=\"zscale_aux_example\")\n",
    "build_best(buildconfig_best(zscale_covariate_study), \"../target/zscale_covariate_model.pkl\")\n",
    "with open(\"../target/zscale_covariate_model.pkl\", \"rb\") as f:\n",
    "    zscale_covariate_model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "N:B. Unlike the `ZScale` descriptor (which works on SMILES level of a peptide/protein), the `ZScale` data transform expects amino acid sequence as inputs.\n",
    "\n",
    "We can inspect the input query for the auxiliary co-variates used in the modelling like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 1.31176471,  0.08058824, -0.27176471,  0.56470588, -0.62529412],\n",
       "        [-0.99521739, -0.59826087, -0.34695652, -0.03086957,  0.13391304],\n",
       "        [ 0.08083333, -0.6125    ,  0.82916667, -0.05083333, -0.56083333],\n",
       "        ...,\n",
       "        [ 0.93357143, -0.02785714, -0.04214286, -0.36      , -0.02785714],\n",
       "        [ 0.30461538, -0.55307692,  0.31307692, -0.11076923,  0.00846154],\n",
       "        [-0.1232    , -0.3364    ,  0.2328    , -0.1368    ,  0.2304    ]]),\n",
       " (7060, 5))"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_smiles, train_y, train_aux, test_smiles, test_y, test_aux = zscale_covariate_config.data.get_sets()\n",
    "\n",
    "train_aux, train_aux.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this toy example, the Z-scale co-variate descriptors 7062 with the expected length of 5 Z-Scale descriptors  used in training. Inference for the model can be performed on the test by providing the auxiliary co-variate Z-Scales like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.2, 0. , 0.8, ..., 0.2, 0.2, 0. ])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zscale_covariate_model.predict_from_smiles(test_smiles, aux=test_aux)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may also inspect the X-matrix (descriptor) used to train the toy model like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.heatmap(zscale_covariate_model.predictor.X_,\n",
    "            vmin=-1, vmax=1, cmap='Spectral',\n",
    "            cbar_kws={'label': 'Fingerprint value'})\n",
    "ax.set(ylabel=\"Compound input\", xlabel=f\"Input descriptor (248bit ECFP & Z-Scale))\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the (continuous) Z-scales covariates can be seen in the final columns (129-132) after the 128bit ECFP fingerprints used in this example  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Advanced options for QSARtuna runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-objective prioritization of performance and standard deviation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QSARtuna can optimize for the minimzation of the standard deviation of performance across the folds. This should in theory prioritize hyperparameters that are consistently performative across different splits of the data, and so should be more generalizable/performative in production. This can be performed with the `minimize_std_dev` in the example below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:18:17,078] A new study created in memory with name: example_multi-parameter_analysis\n",
      "[I 2024-10-02 18:18:17,122] A new study created in memory with name: study_name_0\n",
      "[I 2024-10-02 18:18:17,259] Trial 0 finished with values: [-1.4008740644240856, 0.9876203329634794] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n",
      "[I 2024-10-02 18:18:17,326] Trial 1 finished with values: [-1.3561484909673422, 0.9875061220991905] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n",
      "[I 2024-10-02 18:18:17,378] Trial 2 finished with values: [-2.7856521165563053, 0.21863029956806662] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n",
      "[I 2024-10-02 18:18:17,434] Trial 3 finished with values: [-0.9125905675311808, 0.7861693342190089] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n",
      "[I 2024-10-02 18:18:17,451] Trial 4 finished with values: [-0.5238765412750028, 0.2789424384877304] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n",
      "[I 2024-10-02 18:18:17,471] Trial 5 finished with values: [-0.5348363849100434, 0.5741725628917808] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n",
      "[I 2024-10-02 18:18:17,492] Trial 6 finished with values: [-2.0072511048320134, 0.2786318125997387] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n",
      "[I 2024-10-02 18:18:17,509] Trial 7 finished with values: [-0.9625764609276656, 0.27575381401822424] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n",
      "[I 2024-10-02 18:18:17,575] Trial 8 finished with values: [-1.1114006274062538, 0.7647766019001522] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n",
      "[I 2024-10-02 18:18:17,594] Trial 9 finished with values: [-0.7801680863916906, 0.2725738454485389] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n",
      "[I 2024-10-02 18:18:17,610] Trial 10 finished with values: [-2.785652116470164, 0.21863029955530786] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n",
      "[I 2024-10-02 18:18:17,627] Trial 11 finished with values: [-2.785651973436432, 0.21863032832257323] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n",
      "[I 2024-10-02 18:18:17,644] Trial 12 finished with values: [-0.6101359993004856, 0.3011280543457062] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n",
      "[I 2024-10-02 18:18:17,659] Trial 13 finished with values: [-0.5361950698070445, 0.2356078652319563] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n",
      "[I 2024-10-02 18:18:17,675] Trial 14 finished with values: [-0.5356113574175657, 0.5769721187181905] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n",
      "[I 2024-10-02 18:18:17,741] Trial 15 finished with values: [-0.543430366921729, 0.514747412346662] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n",
      "[I 2024-10-02 18:18:17,759] Trial 16 finished with values: [-2.0072511048320134, 0.2786318125997387] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n",
      "[I 2024-10-02 18:18:17,777] Trial 17 finished with values: [-0.5194661889628072, 0.40146744515282495] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n",
      "[I 2024-10-02 18:18:17,841] Trial 18 finished with values: [-0.659749443628722, 0.6659085938841998] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n",
      "[I 2024-10-02 18:18:17,857] Trial 19 finished with values: [-1.1068495306229729, 0.24457822094737378] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:18:17,876] Trial 20 finished with values: [-0.8604898820838102, 0.7086875504668665] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n",
      "[I 2024-10-02 18:18:17,895] Trial 21 finished with values: [-0.5919869916997383, 0.2367498627927979] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n",
      "[I 2024-10-02 18:18:17,900] Trial 22 pruned. Duplicate parameter set\n",
      "[I 2024-10-02 18:18:17,918] Trial 23 finished with values: [-1.2497762395862362, 0.10124660026536195] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n",
      "[I 2024-10-02 18:18:17,984] Trial 24 finished with values: [-1.1114006274062536, 0.7647766019001522] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n",
      "[I 2024-10-02 18:18:18,039] A new study created in memory with name: study_name_1\n",
      "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__668a7428ff5cdb271b01c0925e8fea45': 'ReLU', 'aggregation__668a7428ff5cdb271b01c0925e8fea45': 'mean', 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': 'none', 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45'}\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:869)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:869)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:869)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:869)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:869)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:869)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [-0.6101359993004856, 0.3011280543457062]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:19:54,957] Trial 0 finished with values: [-2.0621601907738047, 0.2749020946925899] and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': <ChemPropActivation.RELU: 'ReLU'>, 'aggregation__668a7428ff5cdb271b01c0925e8fea45': <ChemPropAggregation.MEAN: 'mean'>, 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': <ChemPropFeatures_Generator.NONE: 'none'>, 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. \n",
      "[I 2024-10-02 18:21:23,281] Trial 1 finished with values: [-2.0621601907738047, 0.2749020946925899] and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': <ChemPropActivation.RELU: 'ReLU'>, 'aggregation__668a7428ff5cdb271b01c0925e8fea45': <ChemPropAggregation.MEAN: 'mean'>, 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 45.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': <ChemPropFeatures_Generator.NONE: 'none'>, 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. \n"
     ]
    }
   ],
   "source": [
    "config = OptimizationConfig(\n",
    "        data=Dataset(\n",
    "        input_column=\"Smiles\",\n",
    "        response_column=\"pXC50\",\n",
    "        response_type=\"regression\",\n",
    "        training_dataset_file=\"../tests/data/sdf/example.sdf\",\n",
    "    ),\n",
    "    descriptors=[\n",
    "        ECFP.new(), \n",
    "        ECFP_counts.new(), \n",
    "        MACCS_keys.new(),\n",
    "        SmilesFromFile.new(),\n",
    "    ],\n",
    "    algorithms=[\n",
    "        SVR.new(),\n",
    "        RandomForestRegressor.new(n_estimators={\"low\": 5, \"high\": 10}),\n",
    "        Ridge.new(),\n",
    "        Lasso.new(),\n",
    "        PLSRegression.new(),\n",
    "        ChemPropRegressor.new(epochs=5),\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=3,\n",
    "        n_trials=25,\n",
    "        n_startup_trials=25,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "        track_to_mlflow=False,\n",
    "        random_seed=42,\n",
    "        n_chemprop_trials=3,\n",
    "        minimise_std_dev=True # Multi-objective optimization for performance and std. dev.\n",
    "    ),\n",
    ") \n",
    "\n",
    "study = optimize(config, study_name=\"example_multi-parameter_analysis\")\n",
    "default_reg_scoring= config.settings.scoring\n",
    "study.set_metric_names([default_reg_scoring.value,'Standard deviation']) # Set the names of the multi-parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the multi-parameter performance reported for each trial, e.g. ` Trial 1 finished with values: [XXX, XXX]`, which correspond to negated MSE and deviation of negated MSE performance across the 3-folds, respectively. The two objectives may be plot as a function of trial number, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Standard Deviation across folds')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = study.trials_dataframe()\n",
    "df.number = df.number+1\n",
    "fig=plt.figure(figsize=(12,4))\n",
    "ax = sns.scatterplot(data=df, x=\"number\", y=\"values_neg_mean_squared_error\",\n",
    "                     legend=False, color=\"b\")\n",
    "ax2 = sns.scatterplot(data=df, x=\"number\", y=\"values_Standard deviation\",\n",
    "                      ax=ax.axes.twinx(), legend=False, color=\"r\")\n",
    "\n",
    "a = df['values_neg_mean_squared_error'].apply(np.floor).min()\n",
    "b = df['values_neg_mean_squared_error'].apply(np.ceil).max()\n",
    "c = df['values_Standard deviation'].apply(np.floor).min()\n",
    "d = df['values_Standard deviation'].apply(np.ceil).max()\n",
    "\n",
    "# Align both axes\n",
    "ax.set_ylim(a,b);\n",
    "ax.set_yticks(np.linspace(a,b, 7));\n",
    "ax2.set_ylim(c,d);\n",
    "ax2.set_yticks(np.linspace(c,d, 7));\n",
    "ax.set_xticks(df.number);\n",
    "\n",
    "# Set the colors of labels\n",
    "ax.set_xlabel('Trial Number')\n",
    "ax.set_ylabel('(Performance) Negated MSE', color='b')\n",
    "ax2.set_ylabel('Standard Deviation across folds', color='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may plot the Pareto front of this multi-objective study using the Optuna plotting functionaility directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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 e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}};e.showarrow&&At(0,0),D&&d.init({element:R.node(),gd:t,prepFn:function(){kt=I.attr(\"transform\")},moveFn:function(t,r){var n=\"pointer\";if(e.showarrow)e.axref===e.xref?S(\"ax\",m(y,t,\"ax\",T,e)):S(\"ax\",e.ax+t),e.ayref===e.yref?S(\"ay\",m(x,r,\"ay\",T.w,e)):S(\"ay\",e.ay+r),At(t,r);else{if(a)return;var i,o;if(y)i=m(y,t,\"x\",T,e);else{var l=e._xsize/T.w,u=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(u+t/T.w,l,0,1,e.xanchor)}if(x)o=m(x,r,\"y\",T,e);else{var c=e._ysize/T.h,f=e.y-(e._yshift+e.yshift)/T.h-c/2;o=d.align(f-r/T.h,c,0,1,e.yanchor)}S(\"x\",i),S(\"y\",o),y&&x||(n=d.getCursor(y?.5:i,x?.5:o,e.xanchor,e.yanchor))}I.attr({transform:s(t,r)+kt}),p(R,n)},clickFn:function(r,n){e.captureevents&&t.emit(\"plotly_clickannotation\",Z(n))},doneFn:function(){p(R),i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}}t.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(\".annotation\").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&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 a._fullLayout[n]=r[n],i.coercePosition(e,a,l,t,t,.5)}l(\"visible\")&&(o(t,e,a.fullLayout,l),u(\"x\"),u(\"y\"),u(\"z\"),n.noneOrAll(t,e,[\"x\",\"y\",\"z\"]),e.xref=\"x\",e.yref=\"y\",e.zref=\"z\",l(\"xanchor\"),l(\"yanchor\"),l(\"xshift\"),l(\"yshift\"),e.showarrow&&(e.axref=\"pixel\",e.ayref=\"pixel\",l(\"ax\",-10),l(\"ay\",-30),n.noneOrAll(t,e,[\"ax\",\"ay\"])))}t.exports=function(t,e,r){a(t,e,{name:\"annotations\",handleItemDefaults:l,fullLayout:r.fullLayout})}},82188:function(t,e,r){\"use strict\";var n=r(92605).drawRaw,i=r(63538),a=[\"x\",\"y\",\"z\"];t.exports=function(t){for(var e=t.fullSceneLayout,r=t.dataScale,o=e.annotations,s=0;s<o.length;s++){for(var l=o[s],u=!1,c=0;c<3;c++){var f=a[c],h=l[f],p=e[f+\"axis\"].r2fraction(h);if(p<0||p>1){u=!0;break}}u?t.fullLayout._infolayer.select(\".annotation-\"+t.id+'[data-index=\"'+s+'\"]').remove():(l._pdata=i(t.glplot.cameraParams,[e.xaxis.r2l(l.x)*r[0],e.yaxis.r2l(l.y)*r[1],e.zaxis.r2l(l.z)*r[2]]),n(t.graphDiv,l,s,t.id,l._xa,l._ya))}}},2468:function(t,e,r){\"use strict\";var n=r(73972),i=r(71828);t.exports={moduleType:\"component\",name:\"annotations3d\",schema:{subplots:{scene:{annotations:r(26997)}}},layoutAttributes:r(26997),handleDefaults:r(20226),includeBasePlot:function(t,e){var r=n.subplotsRegistry.gl3d;if(r)for(var a=r.attrRegex,o=Object.keys(t),s=0;s<o.length;s++){var l=o[s];a.test(l)&&(t[l].annotations||[]).length&&(i.pushUnique(e._basePlotModules,r),i.pushUnique(e._subplots.gl3d,l))}},convert:r(5485),draw:r(82188)}},7561:function(t,e,r){\"use strict\";t.exports=r(63489),r(94338),r(3961),r(38751),r(86825),r(37715),r(99384),r(43805),r(88874),r(83290),r(29108),r(55422),r(94320),r(31320),r(51367),r(21457)},72201:function(t,e,r){\"use strict\";var n=r(7561),i=r(71828),a=r(50606),o=a.EPOCHJD,s=a.ONEDAY,l={valType:\"enumerated\",values:i.sortObjectKeys(n.calendars),editType:\"calc\",dflt:\"gregorian\"},u=function(t,e,r,n){var a={};return a[r]=l,i.coerce(t,e,a,r,n)},c=\"##\",f={d:{0:\"dd\",\"-\":\"d\"},e:{0:\"d\",\"-\":\"d\"},a:{0:\"D\",\"-\":\"D\"},A:{0:\"DD\",\"-\":\"DD\"},j:{0:\"oo\",\"-\":\"o\"},W:{0:\"ww\",\"-\":\"w\"},m:{0:\"mm\",\"-\":\"m\"},b:{0:\"M\",\"-\":\"M\"},B:{0:\"MM\",\"-\":\"MM\"},y:{0:\"yy\",\"-\":\"yy\"},Y:{0:\"yyyy\",\"-\":\"yyyy\"},U:c,w:c,c:{0:\"D M d %X yyyy\",\"-\":\"D M d %X yyyy\"},x:{0:\"mm/dd/yyyy\",\"-\":\"mm/dd/yyyy\"}},h={};function p(t){var e=h[t];return e||(h[t]=n.instance(t))}function d(t){return i.extendFlat({},l,{description:t})}function v(t){return\"Sets the calendar system to use with `\"+t+\"` date data.\"}var g={xcalendar:d(v(\"x\"))},y=i.extendFlat({},g,{ycalendar:d(v(\"y\"))}),m=i.extendFlat({},y,{zcalendar:d(v(\"z\"))}),x=d([\"Sets the calendar system to use for `range` and `tick0`\",\"if this is a date axis. This does not set the calendar for\",\"interpreting data on this axis, that's specified in the trace\",\"or via the global `layout.calendar`\"].join(\" \"));t.exports={moduleType:\"component\",name:\"calendars\",schema:{traces:{scatter:y,bar:y,box:y,heatmap:y,contour:y,histogram:y,histogram2d:y,histogram2dcontour:y,scatter3d:m,surface:m,mesh3d:m,scattergl:y,ohlc:g,candlestick:g},layout:{calendar:d([\"Sets the default calendar system to use for interpreting and\",\"displaying dates throughout the plot.\"].join(\" \"))},subplots:{xaxis:{calendar:x},yaxis:{calendar:x},scene:{xaxis:{calendar:x},yaxis:{calendar:x},zaxis:{calendar:x}},polar:{radialaxis:{calendar:x}}},transforms:{filter:{valuecalendar:d([\"WARNING: All transforms are deprecated and may be removed from the API in next major version.\",\"Sets the calendar system to use for `value`, if it is a date.\"].join(\" \")),targetcalendar:d([\"WARNING: All transforms are deprecated and may be removed from the API in next major version.\",\"Sets the calendar system to use for `target`, if it is an\",\"array of dates. If `target` is a string (eg *x*) we use the\",\"corresponding trace attribute (eg `xcalendar`) if it exists,\",\"even if `targetcalendar` is provided.\"].join(\" \"))}}},layoutAttributes:l,handleDefaults:u,handleTraceDefaults:function(t,e,r,n){for(var i=0;i<r.length;i++)u(t,e,r[i]+\"calendar\",n.calendar)},CANONICAL_SUNDAY:{chinese:\"2000-01-02\",coptic:\"2000-01-03\",discworld:\"2000-01-03\",ethiopian:\"2000-01-05\",hebrew:\"5000-01-01\",islamic:\"1000-01-02\",julian:\"2000-01-03\",mayan:\"5000-01-01\",nanakshahi:\"1000-01-05\",nepali:\"2000-01-05\",persian:\"1000-01-01\",jalali:\"1000-01-01\",taiwan:\"1000-01-04\",thai:\"2000-01-04\",ummalqura:\"1400-01-06\"},CANONICAL_TICK:{chinese:\"2000-01-01\",coptic:\"2000-01-01\",discworld:\"2000-01-01\",ethiopian:\"2000-01-01\",hebrew:\"5000-01-01\",islamic:\"1000-01-01\",julian:\"2000-01-01\",mayan:\"5000-01-01\",nanakshahi:\"1000-01-01\",nepali:\"2000-01-01\",persian:\"1000-01-01\",jalali:\"1000-01-01\",taiwan:\"1000-01-01\",thai:\"2000-01-01\",ummalqura:\"1400-01-01\"},DFLTRANGE:{chinese:[\"2000-01-01\",\"2001-01-01\"],coptic:[\"1700-01-01\",\"1701-01-01\"],discworld:[\"1800-01-01\",\"1801-01-01\"],ethiopian:[\"2000-01-01\",\"2001-01-01\"],hebrew:[\"5700-01-01\",\"5701-01-01\"],islamic:[\"1400-01-01\",\"1401-01-01\"],julian:[\"2000-01-01\",\"2001-01-01\"],mayan:[\"5200-01-01\",\"5201-01-01\"],nanakshahi:[\"0500-01-01\",\"0501-01-01\"],nepali:[\"2000-01-01\",\"2001-01-01\"],persian:[\"1400-01-01\",\"1401-01-01\"],jalali:[\"1400-01-01\",\"1401-01-01\"],taiwan:[\"0100-01-01\",\"0101-01-01\"],thai:[\"2500-01-01\",\"2501-01-01\"],ummalqura:[\"1400-01-01\",\"1401-01-01\"]},getCal:p,worldCalFmt:function(t,e,r){for(var 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lt=e._axis=function(t,e,r){var n=t._fullLayout,i=\"v\"===e.orientation,a={type:\"linear\",range:r,tickmode:e.tickmode,nticks:e.nticks,tick0:e.tick0,dtick:e.dtick,tickvals:e.tickvals,ticktext:e.ticktext,ticks:e.ticks,ticklen:e.ticklen,tickwidth:e.tickwidth,tickcolor:e.tickcolor,showticklabels:e.showticklabels,labelalias:e.labelalias,ticklabelposition:e.ticklabelposition,ticklabeloverflow:e.ticklabeloverflow,ticklabelstep:e.ticklabelstep,tickfont:e.tickfont,tickangle:e.tickangle,tickformat:e.tickformat,exponentformat:e.exponentformat,minexponent:e.minexponent,separatethousands:e.separatethousands,showexponent:e.showexponent,showtickprefix:e.showtickprefix,tickprefix:e.tickprefix,showticksuffix:e.showticksuffix,ticksuffix:e.ticksuffix,title:e.title,showline:!0,anchor:\"free\",side:i?\"right\":\"bottom\",position:1},o=i?\"y\":\"x\",s={type:\"linear\",_id:o+e._id},l={letter:o,font:n.font,noHover:!0,noTickson:!0,noTicklabelmode:!0,calendar:n.calendar};function c(t,e){return 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n=l(t*f*.8,2),i=l(.8*t,2),a=l(1.6*t,2),o=l(4*t,2),s=\"A \"+o+\",\"+o+\" 0 0 1 \";return g(e,r,\"M-\"+n+\",\"+i+s+n+\",\"+i+s+\"0,-\"+a+s+\"-\"+n+\",\"+i+\"Z\")}},\"star-triangle-down\":{n:20,f:function(t,e,r){if(v(e))return u;var n=l(t*f*.8,2),i=l(.8*t,2),a=l(1.6*t,2),o=l(4*t,2),s=\"A \"+o+\",\"+o+\" 0 0 1 \";return g(e,r,\"M\"+n+\",-\"+i+s+\"-\"+n+\",-\"+i+s+\"0,\"+a+s+n+\",-\"+i+\"Z\")}},\"star-square\":{n:21,f:function(t,e,r){if(v(e))return u;var n=l(1.1*t,2),i=l(2*t,2),a=\"A \"+i+\",\"+i+\" 0 0 1 \";return g(e,r,\"M-\"+n+\",-\"+n+a+\"-\"+n+\",\"+n+a+n+\",\"+n+a+n+\",-\"+n+a+\"-\"+n+\",-\"+n+\"Z\")}},\"star-diamond\":{n:22,f:function(t,e,r){if(v(e))return u;var n=l(1.4*t,2),i=l(1.9*t,2),a=\"A \"+i+\",\"+i+\" 0 0 1 \";return g(e,r,\"M-\"+n+\",0\"+a+\"0,\"+n+a+n+\",0\"+a+\"0,-\"+n+a+\"-\"+n+\",0Z\")}},\"diamond-tall\":{n:23,f:function(t,e,r){if(v(e))return u;var n=l(.7*t,2),i=l(1.4*t,2);return g(e,r,\"M0,\"+i+\"L\"+n+\",0L0,-\"+i+\"L-\"+n+\",0Z\")}},\"diamond-wide\":{n:24,f:function(t,e,r){if(v(e))return u;var n=l(1.4*t,2),i=l(.7*t,2);return g(e,r,\"M0,\"+i+\"L\"+n+\",0L0,-\"+i+\"L-\"+n+\",0Z\")}},hourglass:{n:25,f:function(t,e,r){if(v(e))return u;var n=l(t,2);return g(e,r,\"M\"+n+\",\"+n+\"H-\"+n+\"L\"+n+\",-\"+n+\"H-\"+n+\"Z\")},noDot:!0},bowtie:{n:26,f:function(t,e,r){if(v(e))return u;var n=l(t,2);return g(e,r,\"M\"+n+\",\"+n+\"V-\"+n+\"L-\"+n+\",\"+n+\"V-\"+n+\"Z\")},noDot:!0},\"circle-cross\":{n:27,f:function(t,e,r){if(v(e))return u;var n=l(t,2);return g(e,r,\"M0,\"+n+\"V-\"+n+\"M\"+n+\",0H-\"+n+\"M\"+n+\",0A\"+n+\",\"+n+\" 0 1,1 0,-\"+n+\"A\"+n+\",\"+n+\" 0 0,1 \"+n+\",0Z\")},needLine:!0,noDot:!0},\"circle-x\":{n:28,f:function(t,e,r){if(v(e))return u;var n=l(t,2),i=l(t/c,2);return g(e,r,\"M\"+i+\",\"+i+\"L-\"+i+\",-\"+i+\"M\"+i+\",-\"+i+\"L-\"+i+\",\"+i+\"M\"+n+\",0A\"+n+\",\"+n+\" 0 1,1 0,-\"+n+\"A\"+n+\",\"+n+\" 0 0,1 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ot,st,lt,ut,ct=d.select(\"g.legend\"),ft=H(r,ct.node()),ht=ft.width+2*S,pt=ft.height+2*S,dt=W[0],vt=(dt.x0+dt.x1)/2,gt=(dt.y0+dt.y1)/2,yt=!(g.traceIs(dt.trace,\"bar-like\")||g.traceIs(dt.trace,\"box-violin\"));\"y\"===P?yt?(st=gt-S,ot=gt+S):(st=Math.min.apply(null,W.map((function(t){return Math.min(t.y0,t.y1)}))),ot=Math.max.apply(null,W.map((function(t){return Math.max(t.y0,t.y1)})))):st=ot=o.mean(W.map((function(t){return(t.y0+t.y1)/2})))-pt/2,\"x\"===P?yt?(lt=vt+S,ut=vt-S):(lt=Math.max.apply(null,W.map((function(t){return Math.max(t.x0,t.x1)}))),ut=Math.min.apply(null,W.map((function(t){return Math.min(t.x0,t.x1)})))):lt=ut=o.mean(W.map((function(t){return(t.x0+t.x1)/2})))-ht/2;var mt,xt,bt=E._offset,_t=L._offset;return ut+=bt-ht,st+=_t-pt,mt=(lt+=bt)+ht<B&&lt>=0?lt:ut+ht<B&&ut>=0?ut:bt+ht<B?bt:lt-vt<vt-ut+ht?B-ht:0,mt+=S,xt=(ot+=_t)+pt<N&&ot>=0?ot:st+pt<N&&st>=0?st:_t+pt<N?_t:ot-gt<gt-st+pt?N-pt:0,xt+=S,ct.attr(\"transform\",s(mt-1,xt-1)),ct}var wt=d.selectAll(\"g.hovertext\").data(t,(function(t){return C(t)}));return wt.enter().append(\"g\").classed(\"hovertext\",!0).each((function(){var t=n.select(this);t.append(\"rect\").call(p.fill,p.addOpacity(f,.8)),t.append(\"text\").classed(\"name\",!0),t.append(\"path\").style(\"stroke-width\",\"1px\"),t.append(\"text\").classed(\"nums\",!0).call(h.font,T,k)})),wt.exit().remove(),wt.each((function(t){var e=n.select(this).attr(\"transform\",\"\"),o=t.color;Array.isArray(o)&&(o=o[t.eventData[0].pointNumber]);var d=t.bgcolor||o,v=p.combine(p.opacity(d)?d:p.defaultLine,f),g=p.combine(p.opacity(o)?o:p.defaultLine,f),y=t.borderColor||p.contrast(v),m=I(t,j,a,i,D,e),x=m[0],b=m[1],w=e.select(\"text.nums\").call(h.font,t.fontFamily||T,t.fontSize||k,t.fontColor||y).text(x).attr(\"data-notex\",1).call(c.positionText,0,0).call(c.convertToTspans,r),A=e.select(\"text.name\"),E=0,L=0;if(b&&b!==x){A.call(h.font,t.fontFamily||T,t.fontSize||k,g).text(b).attr(\"data-notex\",1).call(c.positionText,0,0).call(c.convertToTspans,r);var C=H(r,A.node());E=C.width+2*S,L=C.height+2*S}else A.remove(),e.select(\"rect\").remove();e.select(\"path\").style({fill:v,stroke:y});var P=t.xa._offset+(t.x0+t.x1)/2,O=t.ya._offset+(t.y0+t.y1)/2,z=Math.abs(t.x1-t.x0),R=Math.abs(t.y1-t.y0),U=H(r,w.node()),V=U.width/i._invScaleX,q=U.height/i._invScaleY;t.ty0=(F-U.top)/i._invScaleY,t.bx=V+2*S,t.by=Math.max(q+2*S,L),t.anchor=\"start\",t.txwidth=V,t.tx2width=E,t.offset=0;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(85555),i=r(44467).templatedArray;r(24695),t.exports=i(\"image\",{visible:{valType:\"boolean\",dflt:!0,editType:\"arraydraw\"},source:{valType:\"string\",editType:\"arraydraw\"},layer:{valType:\"enumerated\",values:[\"below\",\"above\"],dflt:\"above\",editType:\"arraydraw\"},sizex:{valType:\"number\",dflt:0,editType:\"arraydraw\"},sizey:{valType:\"number\",dflt:0,editType:\"arraydraw\"},sizing:{valType:\"enumerated\",values:[\"fill\",\"contain\",\"stretch\"],dflt:\"contain\",editType:\"arraydraw\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,editType:\"arraydraw\"},x:{valType:\"any\",dflt:0,editType:\"arraydraw\"},y:{valType:\"any\",dflt:0,editType:\"arraydraw\"},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\",editType:\"arraydraw\"},yanchor:{valType:\"enumerated\",values:[\"top\",\"middle\",\"bottom\"],dflt:\"top\",editType:\"arraydraw\"},xref:{valType:\"enumerated\",values:[\"paper\",n.idRegex.x.toString()],dflt:\"paper\",editType:\"arraydraw\"},yref:{valType:\"enumerated\",values:[\"paper\",n.idRegex.y.toString()],dflt:\"paper\",editType:\"arraydraw\"},editType:\"arraydraw\"})},75378:function(t,e,r){\"use 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b=Object.keys(l._plots);for(r=0;r<b.length;r++){e=b[r];var _=l._plots[e];if(_.imagelayer){var w=_.imagelayer.selectAll(\"image\").data(c[e]||[]);w.enter().append(\"image\"),w.exit().remove(),w.each((function(t){g.bind(this)(t),y.bind(this)(t)}))}}}},68804:function(t,e,r){\"use strict\";t.exports={moduleType:\"component\",name:\"images\",layoutAttributes:r(69819),supplyLayoutDefaults:r(81603),includeBasePlot:r(76325)(\"images\"),draw:r(80750),convertCoords:r(75378)}},33030:function(t,e,r){\"use strict\";var 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n=r(73972),i=r(10130);t.exports=function(t,e,r){var a,o,s=e._inHover,l=i.isGrouped(e),u=i.isReversed(e),c={},f=[],h=!1,p={},d=0,v=0;function g(t,n,a){if(!1!==e.visible&&(!r||t===e._id))if(\"\"!==n&&i.isGrouped(e))-1===f.indexOf(n)?(f.push(n),h=!0,c[n]=[a]):c[n].push(a);else{var o=\"~~i\"+d;f.push(o),c[o]=[a],d++}}for(a=0;a<t.length;a++){var y=t[a],m=y[0],x=m.trace,b=x.legend,_=x.legendgroup;if(s||x.visible&&x.showlegend)if(n.traceIs(x,\"pie-like\"))for(p[_]||(p[_]={}),o=0;o<y.length;o++){var w=y[o].label;p[_][w]||(g(b,_,{label:w,color:y[o].color,i:y[o].i,trace:x,pts:y[o].pts}),p[_][w]=!0,v=Math.max(v,(w||\"\").length))}else g(b,_,m),v=Math.max(v,(x.name||\"\").length)}if(!f.length)return[];var T=!h||!l,k=[];for(a=0;a<f.length;a++){var A=c[f[a]];T?k.push(A[0]):k.push(A)}for(T&&(k=[k]),a=0;a<k.length;a++){var M=1/0;for(o=0;o<k[a].length;o++){var S=k[a][o].trace.legendrank;M>S&&(M=S)}k[a][0]._groupMinRank=M,k[a][0]._preGroupSort=a}var E=function(t,e){return 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rectangle\")},attr:\"dragmode\",val:\"drawrect\",icon:o.drawrect,click:f},c.drawcircle={name:\"drawcircle\",title:function(t){return u(t,\"Draw circle\")},attr:\"dragmode\",val:\"drawcircle\",icon:o.drawcircle,click:f},c.eraseshape={name:\"eraseshape\",title:function(t){return u(t,\"Erase active shape\")},icon:o.eraseshape,click:s},c.zoomIn2d={name:\"zoomIn2d\",_cat:\"zoomin\",title:function(t){return u(t,\"Zoom in\")},attr:\"zoom\",val:\"in\",icon:o.zoom_plus,click:f},c.zoomOut2d={name:\"zoomOut2d\",_cat:\"zoomout\",title:function(t){return u(t,\"Zoom out\")},attr:\"zoom\",val:\"out\",icon:o.zoom_minus,click:f},c.autoScale2d={name:\"autoScale2d\",_cat:\"autoscale\",title:function(t){return u(t,\"Autoscale\")},attr:\"zoom\",val:\"auto\",icon:o.autoscale,click:f},c.resetScale2d={name:\"resetScale2d\",_cat:\"resetscale\",title:function(t){return u(t,\"Reset axes\")},attr:\"zoom\",val:\"reset\",icon:o.home,click:f},c.hoverClosestCartesian={name:\"hoverClosestCartesian\",_cat:\"hoverclosest\",title:function(t){return u(t,\"Show closest data on hover\")},attr:\"hovermode\",val:\"closest\",icon:o.tooltip_basic,gravity:\"ne\",click:f},c.hoverCompareCartesian={name:\"hoverCompareCartesian\",_cat:\"hoverCompare\",title:function(t){return u(t,\"Compare data on hover\")},attr:\"hovermode\",val:function(t){return t._fullLayout._isHoriz?\"y\":\"x\"},icon:o.tooltip_compare,gravity:\"ne\",click:f},c.zoom3d={name:\"zoom3d\",_cat:\"zoom\",title:function(t){return u(t,\"Zoom\")},attr:\"scene.dragmode\",val:\"zoom\",icon:o.zoombox,click:h},c.pan3d={name:\"pan3d\",_cat:\"pan\",title:function(t){return u(t,\"Pan\")},attr:\"scene.dragmode\",val:\"pan\",icon:o.pan,click:h},c.orbitRotation={name:\"orbitRotation\",title:function(t){return u(t,\"Orbital rotation\")},attr:\"scene.dragmode\",val:\"orbit\",icon:o[\"3d_rotate\"],click:h},c.tableRotation={name:\"tableRotation\",title:function(t){return u(t,\"Turntable rotation\")},attr:\"scene.dragmode\",val:\"turntable\",icon:o[\"z-axis\"],click:h},c.resetCameraDefault3d={name:\"resetCameraDefault3d\",_cat:\"resetCameraDefault\",title:function(t){return u(t,\"Reset camera to default\")},attr:\"resetDefault\",icon:o.home,click:p},c.resetCameraLastSave3d={name:\"resetCameraLastSave3d\",_cat:\"resetCameraLastSave\",title:function(t){return u(t,\"Reset camera to last save\")},attr:\"resetLastSave\",icon:o.movie,click:p},c.hoverClosest3d={name:\"hoverClosest3d\",_cat:\"hoverclosest\",title:function(t){return u(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:o.tooltip_basic,gravity:\"ne\",click:function(t,e){var r=d(t,e);n.call(\"_guiRelayout\",t,r)}},c.zoomInGeo={name:\"zoomInGeo\",_cat:\"zoomin\",title:function(t){return u(t,\"Zoom in\")},attr:\"zoom\",val:\"in\",icon:o.zoom_plus,click:v},c.zoomOutGeo={name:\"zoomOutGeo\",_cat:\"zoomout\",title:function(t){return u(t,\"Zoom out\")},attr:\"zoom\",val:\"out\",icon:o.zoom_minus,click:v},c.resetGeo={name:\"resetGeo\",_cat:\"reset\",title:function(t){return u(t,\"Reset\")},attr:\"reset\",val:null,icon:o.autoscale,click:v},c.hoverClosestGeo={name:\"hoverClosestGeo\",_cat:\"hoverclosest\",title:function(t){return u(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:o.tooltip_basic,gravity:\"ne\",click:y},c.hoverClosestGl2d={name:\"hoverClosestGl2d\",_cat:\"hoverclosest\",title:function(t){return u(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:o.tooltip_basic,gravity:\"ne\",click:y},c.hoverClosestPie={name:\"hoverClosestPie\",_cat:\"hoverclosest\",title:function(t){return u(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:\"closest\",icon:o.tooltip_basic,gravity:\"ne\",click:y},c.resetViewSankey={name:\"resetSankeyGroup\",title:function(t){return u(t,\"Reset view\")},icon:o.home,click:function(t){for(var e={\"node.groups\":[],\"node.x\":[],\"node.y\":[]},r=0;r<t._fullData.length;r++){var i=t._fullData[r]._viewInitial;e[\"node.groups\"].push(i.node.groups.slice()),e[\"node.x\"].push(i.node.x.slice()),e[\"node.y\"].push(i.node.y.slice())}n.call(\"restyle\",t,e)}},c.toggleHover={name:\"toggleHover\",title:function(t){return u(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:o.tooltip_basic,gravity:\"ne\",click:function(t,e){var r=d(t,e);r.hovermode=g(t),n.call(\"_guiRelayout\",t,r)}},c.resetViews={name:\"resetViews\",title:function(t){return u(t,\"Reset views\")},icon:o.home,click:function(t,e){var r=e.currentTarget;r.setAttribute(\"data-attr\",\"zoom\"),r.setAttribute(\"data-val\",\"reset\"),f(t,e),r.setAttribute(\"data-attr\",\"resetLastSave\"),p(t,e),x(t,\"geo\"),x(t,\"mapbox\")}},c.toggleSpikelines={name:\"toggleSpikelines\",title:function(t){return u(t,\"Toggle Spike Lines\")},icon:o.spikeline,attr:\"_cartesianSpikesEnabled\",val:\"on\",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled=\"on\"===r?\"off\":\"on\",n.call(\"_guiRelayout\",t,function(t){for(var e=\"on\"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+\".showspikes\"]=!!e||o._showSpikeInitial}return n}(t))}},c.resetViewMapbox={name:\"resetViewMapbox\",_cat:\"resetView\",title:function(t){return u(t,\"Reset view\")},attr:\"reset\",icon:o.home,click:function(t){x(t,\"mapbox\")}},c.zoomInMapbox={name:\"zoomInMapbox\",_cat:\"zoomin\",title:function(t){return u(t,\"Zoom in\")},attr:\"zoom\",val:\"in\",icon:o.zoom_plus,click:m},c.zoomOutMapbox={name:\"zoomOutMapbox\",_cat:\"zoomout\",title:function(t){return u(t,\"Zoom out\")},attr:\"zoom\",val:\"out\",icon:o.zoom_minus,click:m}},93348:function(t,e,r){\"use strict\";var n=r(26023),i=Object.keys(n),a=[\"drawline\",\"drawopenpath\",\"drawclosedpath\",\"drawcircle\",\"drawrect\",\"eraseshape\"],o=[\"v1hovermode\",\"hoverclosest\",\"hovercompare\",\"togglehover\",\"togglespikelines\"].concat(a),s=[];i.forEach((function(t){!function(t){if(-1===o.indexOf(t._cat||t.name)){var e=t.name,r=(t._cat||t.name).toLowerCase();-1===s.indexOf(e)&&s.push(e),-1===s.indexOf(r)&&s.push(r)}}(n[t])})),s.sort(),t.exports={DRAW_MODES:a,backButtons:o,foreButtons:s}},35750:function(t,e,r){\"use strict\";var n=r(71828),i=r(7901),a=r(44467),o=r(42068);t.exports=function(t,e){var r=t.modebar||{},s=a.newContainer(e,\"modebar\");function l(t,e){return n.coerce(r,s,o,t,e)}l(\"orientation\"),l(\"bgcolor\",i.addOpacity(e.paper_bgcolor,.5));var u=i.contrast(i.rgb(e.modebar.bgcolor));l(\"color\",i.addOpacity(u,.3)),l(\"activecolor\",i.addOpacity(u,.7)),l(\"uirevision\",e.uirevision),l(\"add\"),l(\"remove\")}},64168:function(t,e,r){\"use strict\";t.exports={moduleType:\"component\",name:\"modebar\",layoutAttributes:r(42068),supplyLayoutDefaults:r(35750),manage:r(14192)}},14192:function(t,e,r){\"use strict\";var n=r(41675),i=r(34098),a=r(73972),o=r(23469).isUnifiedHover,s=r(37676),l=r(26023),u=r(93348).DRAW_MODES,c=r(71828).extendDeep;t.exports=function(t){var e=t._fullLayout,r=t._context,f=e._modeBar;if(r.displayModeBar||r.watermark){if(!Array.isArray(r.modeBarButtonsToRemove))throw new Error([\"*modeBarButtonsToRemove* configuration options\",\"must be an array.\"].join(\" \"));if(!Array.isArray(r.modeBarButtonsToAdd))throw new Error([\"*modeBarButtonsToAdd* configuration options\",\"must be an array.\"].join(\" \"));var h,p=r.modeBarButtons;h=Array.isArray(p)&&p.length?function(t){for(var e=c([],t),r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++){var a=n[i];if(\"string\"==typeof a){if(void 0===l[a])throw new Error([\"*modeBarButtons* configuration options\",\"invalid button name\"].join(\" \"));e[r][i]=l[a]}}return e}(p):!r.displayModeBar&&r.watermark?[]:function(t){var e=t._fullLayout,r=t._fullData,s=t._context;function c(t,e){if(\"string\"==typeof e){if(e.toLowerCase()===t.toLowerCase())return!0}else{var r=e.name,n=e._cat||e.name;if(r===t||n===t.toLowerCase())return!0}return!1}var f=e.modebar.add;\"string\"==typeof f&&(f=[f]);var h=e.modebar.remove;\"string\"==typeof h&&(h=[h]);var p=s.modeBarButtonsToAdd.concat(f.filter((function(t){for(var e=0;e<s.modeBarButtonsToRemove.length;e++)if(c(t,s.modeBarButtonsToRemove[e]))return!1;return!0}))),d=s.modeBarButtonsToRemove.concat(h.filter((function(t){for(var e=0;e<s.modeBarButtonsToAdd.length;e++)if(c(t,s.modeBarButtonsToAdd[e]))return!1;return!0}))),v=e._has(\"cartesian\"),g=e._has(\"gl3d\"),y=e._has(\"geo\"),m=e._has(\"pie\"),x=e._has(\"funnelarea\"),b=e._has(\"gl2d\"),_=e._has(\"ternary\"),w=e._has(\"mapbox\"),T=e._has(\"polar\"),k=e._has(\"smith\"),A=e._has(\"sankey\"),M=function(t){for(var e=n.list({_fullLayout:t},null,!0),r=0;r<e.length;r++)if(!e[r].fixedrange)return!1;return!0}(e),S=o(e.hovermode),E=[];function L(t){if(t.length){for(var e=[],r=0;r<t.length;r++){for(var n=t[r],i=l[n],a=i.name.toLowerCase(),o=(i._cat||i.name).toLowerCase(),s=!1,u=0;u<d.length;u++){var c=d[u].toLowerCase();if(c===a||c===o){s=!0;break}}s||e.push(l[n])}E.push(e)}}var C=[\"toImage\"];s.showEditInChartStudio?C.push(\"editInChartStudio\"):s.showSendToCloud&&C.push(\"sendDataToCloud\"),L(C);var P=[],O=[],I=[],D=[];(v||b||m||x||_)+y+g+w+T+k>1?(O=[\"toggleHover\"],I=[\"resetViews\"]):y?(P=[\"zoomInGeo\",\"zoomOutGeo\"],O=[\"hoverClosestGeo\"],I=[\"resetGeo\"]):g?(O=[\"hoverClosest3d\"],I=[\"resetCameraDefault3d\",\"resetCameraLastSave3d\"]):w?(P=[\"zoomInMapbox\",\"zoomOutMapbox\"],O=[\"toggleHover\"],I=[\"resetViewMapbox\"]):b?O=[\"hoverClosestGl2d\"]:m?O=[\"hoverClosestPie\"]:A?(O=[\"hoverClosestCartesian\",\"hoverCompareCartesian\"],I=[\"resetViewSankey\"]):O=[\"toggleHover\"],v&&(O=[\"toggleSpikelines\",\"hoverClosestCartesian\",\"hoverCompareCartesian\"]),(function(t){for(var e=0;e<t.length;e++)if(!a.traceIs(t[e],\"noHover\"))return!1;return!0}(r)||S)&&(O=[]),!v&&!b||M||(P=[\"zoomIn2d\",\"zoomOut2d\",\"autoScale2d\"],\"resetViews\"!==I[0]&&(I=[\"resetScale2d\"])),g?D=[\"zoom3d\",\"pan3d\",\"orbitRotation\",\"tableRotation\"]:(v||b)&&!M||_?D=[\"zoom2d\",\"pan2d\"]:w||y?D=[\"pan2d\"]:T&&(D=[\"zoom2d\"]),function(t){for(var e=!1,r=0;r<t.length&&!e;r++){var n=t[r];n._module&&n._module.selectPoints&&(a.traceIs(n,\"scatter-like\")?(i.hasMarkers(n)||i.hasText(n))&&(e=!0):a.traceIs(n,\"box-violin\")&&\"all\"!==n.boxpoints&&\"all\"!==n.points||(e=!0))}return e}(r)&&D.push(\"select2d\",\"lasso2d\");var z=[],R=function(t){-1===z.indexOf(t)&&-1!==O.indexOf(t)&&z.push(t)};if(Array.isArray(p)){for(var F=[],B=0;B<p.length;B++){var N=p[B];\"string\"==typeof N?(N=N.toLowerCase(),-1!==u.indexOf(N)?(e._has(\"mapbox\")||e._has(\"cartesian\"))&&D.push(N):\"togglespikelines\"===N?R(\"toggleSpikelines\"):\"togglehover\"===N?R(\"toggleHover\"):\"hovercompare\"===N?R(\"hoverCompareCartesian\"):\"hoverclosest\"===N?(R(\"hoverClosestCartesian\"),R(\"hoverClosestGeo\"),R(\"hoverClosest3d\"),R(\"hoverClosestGl2d\"),R(\"hoverClosestPie\")):\"v1hovermode\"===N&&(R(\"toggleHover\"),R(\"hoverClosestCartesian\"),R(\"hoverCompareCartesian\"),R(\"hoverClosestGeo\"),R(\"hoverClosest3d\"),R(\"hoverClosestGl2d\"),R(\"hoverClosestPie\"))):F.push(N)}p=F}return L(D),L(P.concat(I)),L(z),function(t,e){if(e.length)if(Array.isArray(e[0]))for(var r=0;r<e.length;r++)t.push(e[r]);else t.push(e);return t}(E,p)}(t),f?f.update(t,h):e._modeBar=s(t,h)}else f&&(f.destroy(),delete e._modeBar)}},37676:function(t,e,r){\"use strict\";var n=r(39898),i=r(92770),a=r(71828),o=r(24255),s=r(11506).version,l=new DOMParser;function u(t){this.container=t.container,this.element=document.createElement(\"div\"),this.update(t.graphInfo,t.buttons),this.container.appendChild(this.element)}var c=u.prototype;c.update=function(t,e){this.graphInfo=t;var r=this.graphInfo._context,n=this.graphInfo._fullLayout,i=\"modebar-\"+n._uid;this.element.setAttribute(\"id\",i),this._uid=i,this.element.className=\"modebar\",\"hover\"===r.displayModeBar&&(this.element.className+=\" modebar--hover ease-bg\"),\"v\"===n.modebar.orientation&&(this.element.className+=\" vertical\",e=e.reverse());var o=n.modebar,s=\"hover\"===r.displayModeBar?\".js-plotly-plot .plotly:hover \":\"\";a.deleteRelatedStyleRule(i),a.addRelatedStyleRule(i,s+\"#\"+i+\" .modebar-group\",\"background-color: \"+o.bgcolor),a.addRelatedStyleRule(i,\"#\"+i+\" .modebar-btn .icon path\",\"fill: \"+o.color),a.addRelatedStyleRule(i,\"#\"+i+\" .modebar-btn:hover .icon path\",\"fill: \"+o.activecolor),a.addRelatedStyleRule(i,\"#\"+i+\" .modebar-btn.active .icon path\",\"fill: \"+o.activecolor);var l=!this.hasButtons(e),u=this.hasLogo!==r.displaylogo,c=this.locale!==r.locale;if(this.locale=r.locale,(l||u||c)&&(this.removeAllButtons(),this.updateButtons(e),r.watermark||r.displaylogo)){var f=this.getLogo();r.watermark&&(f.className=f.className+\" watermark\"),\"v\"===n.modebar.orientation?this.element.insertBefore(f,this.element.childNodes[0]):this.element.appendChild(f),this.hasLogo=!0}this.updateActiveButton()},c.updateButtons=function(t){var e=this;this.buttons=t,this.buttonElements=[],this.buttonsNames=[],this.buttons.forEach((function(t){var r=e.createGroup();t.forEach((function(t){var n=t.name;if(!n)throw new Error(\"must provide button 'name' in button config\");if(-1!==e.buttonsNames.indexOf(n))throw new Error(\"button name '\"+n+\"' is taken\");e.buttonsNames.push(n);var i=e.createButton(t);e.buttonElements.push(i),r.appendChild(i)})),e.element.appendChild(r)}))},c.createGroup=function(){var t=document.createElement(\"div\");return t.className=\"modebar-group\",t},c.createButton=function(t){var e=this,r=document.createElement(\"a\");r.setAttribute(\"rel\",\"tooltip\"),r.className=\"modebar-btn\";var i=t.title;void 0===i?i=t.name:\"function\"==typeof i&&(i=i(this.graphInfo)),(i||0===i)&&r.setAttribute(\"data-title\",i),void 0!==t.attr&&r.setAttribute(\"data-attr\",t.attr);var a=t.val;if(void 0!==a&&(\"function\"==typeof a&&(a=a(this.graphInfo)),r.setAttribute(\"data-val\",a)),\"function\"!=typeof t.click)throw new Error(\"must provide button 'click' function in button config\");r.addEventListener(\"click\",(function(r){t.click(e.graphInfo,r),e.updateActiveButton(r.currentTarget)})),r.setAttribute(\"data-toggle\",t.toggle||!1),t.toggle&&n.select(r).classed(\"active\",!0);var s=t.icon;return\"function\"==typeof s?r.appendChild(s()):r.appendChild(this.createIcon(s||o.question)),r.setAttribute(\"data-gravity\",t.gravity||\"n\"),r},c.createIcon=function(t){var e,r=i(t.height)?Number(t.height):t.ascent-t.descent,n=\"http://www.w3.org/2000/svg\";if(t.path){(e=document.createElementNS(n,\"svg\")).setAttribute(\"viewBox\",[0,0,t.width,r].join(\" \")),e.setAttribute(\"class\",\"icon\");var a=document.createElementNS(n,\"path\");a.setAttribute(\"d\",t.path),t.transform?a.setAttribute(\"transform\",t.transform):void 0!==t.ascent&&a.setAttribute(\"transform\",\"matrix(1 0 0 -1 0 \"+t.ascent+\")\"),e.appendChild(a)}return t.svg&&(e=l.parseFromString(t.svg,\"application/xml\").childNodes[0]),e.setAttribute(\"height\",\"1em\"),e.setAttribute(\"width\",\"1em\"),e},c.updateActiveButton=function(t){var e=this.graphInfo._fullLayout,r=void 0!==t?t.getAttribute(\"data-attr\"):null;this.buttonElements.forEach((function(t){var i=t.getAttribute(\"data-val\")||!0,o=t.getAttribute(\"data-attr\"),s=\"true\"===t.getAttribute(\"data-toggle\"),l=n.select(t);if(s)o===r&&l.classed(\"active\",!l.classed(\"active\"));else{var u=null===o?o:a.nestedProperty(e,o).get();l.classed(\"active\",u===i)}}))},c.hasButtons=function(t){var e=this.buttons;if(!e)return!1;if(t.length!==e.length)return!1;for(var r=0;r<t.length;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;n++)if(t[r][n].name!==e[r][n].name)return!1}return!0},c.getLogo=function(){var t=this.createGroup(),e=document.createElement(\"a\");return e.href=\"https://plotly.com/\",e.target=\"_blank\",e.setAttribute(\"data-title\",a._(this.graphInfo,\"Produced 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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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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 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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 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s=A.selections[o];s.xref!==t||s.yref!==e?n.push(r[o]):i=!0}i&&(k._fullLayout._noEmitSelectedAtStart=!0,a.call(\"_guiRelayout\",k,{selections:n}))}});var Lt=function(t){return t.plotinfo.fillRangeItems||ct(t.xaxes.concat(t.yaxes))}(n);n.moveFn=function(t,e){n._clearSubplotSelections&&(n._clearSubplotSelections(),n._clearSubplotSelections=void 0),lt=Math.max(0,Math.min(yt,ot*t+F)),pt=Math.max(0,Math.min(mt,st*e+B));var r=Math.abs(lt-F),i=Math.abs(pt-B);if(g){var a,o,s;if(b){var 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s=[],u=k._fullLayout.selections,c=0;c<u.length;c++){var f=u[c];f&&(f.xref===i._id&&f.yref===o._id||s.push(f))}s.length<u.length&&(k._fullLayout._noEmitSelectedAtStart=!0,a.call(\"_guiRelayout\",k,{selections:s}))}}}else r.indexOf(\"select\")>-1&&V(e,k,n.xaxes,n.yaxes,n.subplot,n,bt),\"event\"===r&&ft(k,void 0);l.click(k,e)})).catch(M.error)}},n.doneFn=function(){kt.remove(),L.done(Mt).then((function(){L.clear(Mt),!S&&J&&n.selectionDefs&&(J.subtract=xt,n.selectionDefs.push(J),n.mergedPolygons.length=0,[].push.apply(n.mergedPolygons,W)),(S||x)&&Y(n,S),n.doneFnCompleted&&n.doneFnCompleted(St),b&&ft(k,at)})).catch(M.error)}},clearOutline:x,clearSelectionsCache:Y,selectOnClick:V}},89827:function(t,e,r){\"use strict\";var n=r(50215),i=r(41940),a=r(82196).line,o=r(79952).P,s=r(1426).extendFlat,l=r(44467).templatedArray,u=(r(24695),r(5386).R),c=r(37281);t.exports=l(\"shape\",{visible:{valType:\"boolean\",dflt:!0,editType:\"calc+arraydraw\"},type:{valType:\"enumerated\",values:[\"circle\",\"rect\",\"path\",\"line\"],editType:\"calc+arraydraw\"},layer:{valType:\"enumerated\",values:[\"below\",\"above\"],dflt:\"above\",editType:\"arraydraw\"},xref:s({},n.xref,{}),xsizemode:{valType:\"enumerated\",values:[\"scaled\",\"pixel\"],dflt:\"scaled\",editType:\"calc+arraydraw\"},xanchor:{valType:\"any\",editType:\"calc+arraydraw\"},x0:{valType:\"any\",editType:\"calc+arraydraw\"},x1:{valType:\"any\",editType:\"calc+arraydraw\"},yref:s({},n.yref,{}),ysizemode:{valType:\"enumerated\",values:[\"scaled\",\"pixel\"],dflt:\"scaled\",editType:\"calc+arraydraw\"},yanchor:{valType:\"any\",editType:\"calc+arraydraw\"},y0:{valType:\"any\",editType:\"calc+arraydraw\"},y1:{valType:\"any\",editType:\"calc+arraydraw\"},path:{valType:\"string\",editType:\"calc+arraydraw\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,editType:\"arraydraw\"},line:{color:s({},a.color,{editType:\"arraydraw\"}),width:s({},a.width,{editType:\"calc+arraydraw\"}),dash:s({},o,{editType:\"arraydraw\"}),editType:\"calc+arraydraw\"},fillcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"arraydraw\"},fillrule:{valType:\"enumerated\",values:[\"evenodd\",\"nonzero\"],dflt:\"evenodd\",editType:\"arraydraw\"},editable:{valType:\"boolean\",dflt:!1,editType:\"calc+arraydraw\"},label:{text:{valType:\"string\",dflt:\"\",editType:\"arraydraw\"},texttemplate:u({},{keys:Object.keys(c)}),font:i({editType:\"calc+arraydraw\",colorEditType:\"arraydraw\"}),textposition:{valType:\"enumerated\",values:[\"top 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n=r(7901),i=r(71828);t.exports=function(t,e,r){if(r(\"newshape.drawdirection\"),r(\"newshape.layer\"),r(\"newshape.fillcolor\"),r(\"newshape.fillrule\"),r(\"newshape.opacity\"),r(\"newshape.line.width\")){var a=(t||{}).plot_bgcolor||\"#FFF\";r(\"newshape.line.color\",n.contrast(a)),r(\"newshape.line.dash\")}var o=\"drawline\"===t.dragmode,s=r(\"newshape.label.text\"),l=r(\"newshape.label.texttemplate\");if(s||l){r(\"newshape.label.textangle\");var u=r(\"newshape.label.textposition\",o?\"middle\":\"middle center\");r(\"newshape.label.xanchor\"),r(\"newshape.label.yanchor\",function(t,e){return t?\"bottom\":-1!==e.indexOf(\"top\")?\"top\":-1!==e.indexOf(\"bottom\")?\"bottom\":\"middle\"}(o,u)),r(\"newshape.label.padding\"),i.coerceFont(r,\"newshape.label.font\",e.font)}r(\"activeshape.fillcolor\"),r(\"activeshape.opacity\")}},60165:function(t,e,r){\"use strict\";var n=r(95616),i=r(89995),a=i.CIRCLE_SIDES,o=i.SQRT2,s=r(75549),l=s.p2r,u=s.r2p,c=[0,3,4,5,6,1,2],f=[0,3,4,1,2];function 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m,x,b,_,w=[],T=f[y][0],k=T;switch(T){case\"M\":h[++p]=[],d=+f[y][1],v=+f[y][2],w.push([k,d,v]),g();break;case\"Q\":case\"S\":m=+f[y][1],b=+f[y][2],d=+f[y][3],v=+f[y][4],w.push([k,d,v,m,b]);break;case\"C\":m=+f[y][1],b=+f[y][2],x=+f[y][3],_=+f[y][4],d=+f[y][5],v=+f[y][6],w.push([k,d,v,m,b,x,_]);break;case\"T\":case\"L\":d=+f[y][1],v=+f[y][2],w.push([k,d,v]);break;case\"H\":k=\"L\",d=+f[y][1],w.push([k,d,v]);break;case\"V\":k=\"L\",v=+f[y][1],w.push([k,d,v]);break;case\"A\":k=\"L\";var A=+f[y][1],M=+f[y][2];+f[y][4]||(A=-A,M=-M);var S=d-A,E=v;for(o=1;o<=a/2;o++){var L=2*Math.PI*o/a;w.push([k,S+A*Math.cos(L),E+M*Math.sin(L)])}break;case\"Z\":d===s&&v===c||(d=s,v=c,w.push([k,d,v]))}for(var C=(r||{}).domain,P=e._fullLayout._size,O=r&&\"pixel\"===r.xsizemode,I=r&&\"pixel\"===r.ysizemode,D=!1===i,z=0;z<w.length;z++){for(o=0;o+2<7;o+=2){var R=w[z][o+1],F=w[z][o+2];void 0!==R&&void 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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,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 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n=r._dims;o.setTranslate(e,n.lx+r.pad.l,n.ly+r.pad.t),e.call(S,r,!1),e.call(x,r)}(t,n.select(this),e)}))}}},23243:function(t,e,r){\"use strict\";var n=r(98292);t.exports={moduleType:\"component\",name:n.name,layoutAttributes:r(75067),supplyLayoutDefaults:r(12343),draw:r(44504)}},92998:function(t,e,r){\"use strict\";var n=r(39898),i=r(92770),a=r(74875),o=r(73972),s=r(71828),l=s.strTranslate,u=r(91424),c=r(7901),f=r(63893),h=r(37822),p=r(18783).OPPOSITE_SIDE,d=/ [XY][0-9]* /;t.exports={draw:function(t,e,r){var v,g=r.propContainer,y=r.propName,m=r.placeholder,x=r.traceIndex,b=r.avoid||{},_=r.attributes,w=r.transform,T=r.containerGroup,k=t._fullLayout,A=1,M=!1,S=g.title,E=(S&&S.text?S.text:\"\").trim(),L=S&&S.font?S.font:{},C=L.family,P=L.size,O=L.color;\"title.text\"===y?v=\"titleText\":-1!==y.indexOf(\"axis\")?v=\"axisTitleText\":y.indexOf(!0)&&(v=\"colorbarTitleText\");var I=t._context.edits[v];\"\"===E?A=0:E.replace(d,\" % \")===m.replace(d,\" % 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n=r(41940),i=r(22399),a=r(1426).extendFlat,o=r(30962).overrideAll,s=r(35025),l=r(44467).templatedArray,u=l(\"button\",{visible:{valType:\"boolean\"},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},args2:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\",dflt:\"\"},execute:{valType:\"boolean\",dflt:!0}});t.exports=o(l(\"updatemenu\",{_arrayAttrRegexps:[/^updatemenus\\[(0|[1-9][0-9]+)\\]\\.buttons/],visible:{valType:\"boolean\"},type:{valType:\"enumerated\",values:[\"dropdown\",\"buttons\"],dflt:\"dropdown\"},direction:{valType:\"enumerated\",values:[\"left\",\"right\",\"up\",\"down\"],dflt:\"down\"},active:{valType:\"integer\",min:-1,dflt:0},showactive:{valType:\"boolean\",dflt:!0},buttons:u,x:{valType:\"number\",min:-2,max:3,dflt:-.05},xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"right\"},y:{valType:\"number\",min:-2,max:3,dflt:1},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},pad:a(s({editType:\"arraydraw\"}),{}),font:n({}),bgcolor:{valType:\"color\"},bordercolor:{valType:\"color\",dflt:i.borderLine},borderwidth:{valType:\"number\",min:0,dflt:1,editType:\"arraydraw\"}}),\"arraydraw\",\"from-root\")},75909:function(t){\"use strict\";t.exports={name:\"updatemenus\",containerClassName:\"updatemenu-container\",headerGroupClassName:\"updatemenu-header-group\",headerClassName:\"updatemenu-header\",headerArrowClassName:\"updatemenu-header-arrow\",dropdownButtonGroupClassName:\"updatemenu-dropdown-button-group\",dropdownButtonClassName:\"updatemenu-dropdown-button\",buttonClassName:\"updatemenu-button\",itemRectClassName:\"updatemenu-item-rect\",itemTextClassName:\"updatemenu-item-text\",menuIndexAttrName:\"updatemenu-active-index\",autoMarginIdRoot:\"updatemenu-\",blankHeaderOpts:{label:\"  \"},minWidth:30,minHeight:30,textPadX:24,arrowPadX:16,rx:2,ry:2,textOffsetX:12,textOffsetY:3,arrowOffsetX:4,gapButtonHeader:5,gapButton:2,activeColor:\"#F4FAFF\",hoverColor:\"#F4FAFF\",arrowSymbol:{left:\"◄\",right:\"►\",up:\"▲\",down:\"▼\"}}},64897:function(t,e,r){\"use strict\";var n=r(71828),i=r(85501),a=r(7163),o=r(75909).name,s=a.buttons;function l(t,e,r){function o(r,i){return n.coerce(t,e,a,r,i)}o(\"visible\",i(t,e,{name:\"buttons\",handleItemDefaults:u}).length>0)&&(o(\"active\"),o(\"direction\"),o(\"type\"),o(\"showactive\"),o(\"x\"),o(\"y\"),n.noneOrAll(t,e,[\"x\",\"y\"]),o(\"xanchor\"),o(\"yanchor\"),o(\"pad.t\"),o(\"pad.r\"),o(\"pad.b\"),o(\"pad.l\"),n.coerceFont(o,\"font\",r.font),o(\"bgcolor\",r.paper_bgcolor),o(\"bordercolor\"),o(\"borderwidth\"))}function u(t,e){function r(r,i){return n.coerce(t,e,s,r,i)}r(\"visible\",\"skip\"===t.method||Array.isArray(t.args))&&(r(\"method\"),r(\"args\"),r(\"args2\"),r(\"label\"),r(\"execute\"))}t.exports=function(t,e){i(t,e,{name:o,handleItemDefaults:l})}},13689:function(t,e,r){\"use strict\";var n=r(39898),i=r(74875),a=r(7901),o=r(91424),s=r(71828),l=r(63893),u=r(44467).arrayEditor,c=r(18783).LINE_SPACING,f=r(75909),h=r(25849);function p(t){return t._index}function d(t,e){return+t.attr(f.menuIndexAttrName)===e._index}function 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y(t,e,r,a,o){r||(r=e).attr(\"pointer-events\",\"all\");var l=function(t){return-1==+t.attr(f.menuIndexAttrName)}(r)&&\"buttons\"!==o.type?[]:o.buttons,u=\"dropdown\"===o.type?f.dropdownButtonClassName:f.buttonClassName,c=r.selectAll(\"g.\"+u).data(s.filterVisible(l)),h=c.enter().append(\"g\").classed(u,!0),p=c.exit();\"dropdown\"===o.type?(h.attr(\"opacity\",\"0\").transition().attr(\"opacity\",\"1\"),p.transition().attr(\"opacity\",\"0\").remove()):p.remove();var d=0,g=0,y=o._dims,x=-1!==[\"up\",\"down\"].indexOf(o.direction);\"dropdown\"===o.type&&(x?g=y.headerHeight+f.gapButtonHeader:d=y.headerWidth+f.gapButtonHeader),\"dropdown\"===o.type&&\"up\"===o.direction&&(g=-f.gapButtonHeader+f.gapButton-y.openHeight),\"dropdown\"===o.type&&\"left\"===o.direction&&(d=-f.gapButtonHeader+f.gapButton-y.openWidth);var b={x:y.lx+d+o.pad.l,y:y.ly+g+o.pad.t,yPad:f.gapButton,xPad:f.gapButton,index:0},k={l:b.x+o.borderwidth,t:b.y+o.borderwidth};c.each((function(s,l){var 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e=!!t.hbar,r=!!t.vbar;e&&t.hbar.transition().attr(\"opacity\",\"0\").each(\"end\",(function(){e=!1,r||t.disable()})),r&&t.vbar.transition().attr(\"opacity\",\"0\").each(\"end\",(function(){r=!1,e||t.disable()}))}(a))}function m(t,e,r,n){t.call(x,e).call(b,e,r,n)}function x(t,e){s.ensureSingle(t,\"rect\",f.itemRectClassName,(function(t){t.attr({rx:f.rx,ry:f.ry,\"shape-rendering\":\"crispEdges\"})})).call(a.stroke,e.bordercolor).call(a.fill,e.bgcolor).style(\"stroke-width\",e.borderwidth+\"px\")}function b(t,e,r,n){var i=s.ensureSingle(t,\"text\",f.itemTextClassName,(function(t){t.attr({\"text-anchor\":\"start\",\"data-notex\":1})})),a=r.label,u=n._fullLayout._meta;u&&(a=s.templateString(a,u)),i.call(o.font,e.font).text(a).call(l.convertToTspans,n)}function _(t,e){var r=e.active;t.each((function(t,i){var o=n.select(this);i===r&&e.showactive&&o.select(\"rect.\"+f.itemRectClassName).call(a.fill,f.activeColor)}))}function w(t){t.select(\"rect.\"+f.itemRectClassName).call(a.fill,f.hoverColor)}function T(t,e){t.select(\"rect.\"+f.itemRectClassName).call(a.fill,e.bgcolor)}function k(t,e){var r=e._dims={width1:0,height1:0,heights:[],widths:[],totalWidth:0,totalHeight:0,openWidth:0,openHeight:0,lx:0,ly:0},a=o.tester.selectAll(\"g.\"+f.dropdownButtonClassName).data(s.filterVisible(e.buttons));a.enter().append(\"g\").classed(f.dropdownButtonClassName,!0);var u=-1!==[\"up\",\"down\"].indexOf(e.direction);a.each((function(i,a){var s=n.select(this);s.call(m,e,i,t);var h=s.select(\".\"+f.itemTextClassName),p=h.node()&&o.bBox(h.node()).width,d=Math.max(p+f.textPadX,f.minWidth),v=e.font.size*c,g=l.lineCount(h),y=Math.max(v*g,f.minHeight)+f.textOffsetY;y=Math.ceil(y),d=Math.ceil(d),r.widths[a]=d,r.heights[a]=y,r.height1=Math.max(r.height1,y),r.width1=Math.max(r.width1,d),u?(r.totalWidth=Math.max(r.totalWidth,d),r.openWidth=r.totalWidth,r.totalHeight+=y+f.gapButton,r.openHeight+=y+f.gapButton):(r.totalWidth+=d+f.gapButton,r.openWidth+=d+f.gapButton,r.totalHeight=Math.max(r.totalHeight,y),r.openHeight=r.totalHeight)})),u?r.totalHeight-=f.gapButton:r.totalWidth-=f.gapButton,r.headerWidth=r.width1+f.arrowPadX,r.headerHeight=r.height1,\"dropdown\"===e.type&&(u?(r.width1+=f.arrowPadX,r.totalHeight=r.height1):r.totalWidth=r.width1,r.totalWidth+=f.arrowPadX),a.remove();var h=r.totalWidth+e.pad.l+e.pad.r,p=r.totalHeight+e.pad.t+e.pad.b,d=t._fullLayout._size;r.lx=d.l+d.w*e.x,r.ly=d.t+d.h*(1-e.y);var 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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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t.constructor(0),s=D(t,e);r<0?(n=s,i=a):(n=a,i=s)}else if(n=new t.constructor(r),i=new t.constructor(t.length+e.length-r),r===e.length)n.set(e),i.set(t);else if(r<e.length){var l=e.length-r;n.set(e.subarray(l)),i.set(t),i.set(e.subarray(0,l),t.length)}else{var u=r-e.length,c=t.length-u;n.set(t.subarray(c)),n.set(e,u),i.set(t.subarray(0,c))}else n=t.concat(e),i=r>=0&&r<n.length?n.splice(0,n.length-r):[];return[n,i]})),l=e.redraw(r),c=[r,s.update,i,s.maxPoints];return u.add(r,e.prependTraces,c,t,arguments),l},e.moveTraces=function t(r,n,i){var a,s=[],l=[],c=t,f=t,h=[r=o.getGraphDiv(r),i,n],p=[r,n,i];if(O(r,n,i),n=Array.isArray(n)?n:[n],void 0===i)for(i=[],a=0;a<n.length;a++)i.push(-n.length+a);for(i=Array.isArray(i)?i:[i],n=C(n,r.data.length-1),i=C(i,r.data.length-1),a=0;a<r.data.length;a++)-1===n.indexOf(a)&&s.push(r.data[a]);for(a=0;a<n.length;a++)l.push({newIndex:i[a],trace:r.data[n[a]]});for(l.sort((function(t,e){return t.newIndex-e.newIndex})),a=0;a<l.length;a+=1)s.splice(l[a].newIndex,0,l[a].trace);r.data=s;var d=e.redraw(r);return u.add(r,c,h,f,p),d},e.prependTraces=function t(r,n,i,a){var s=I(r=o.getGraphDiv(r),n,i,a,(function(t,e,r){var n,i;if(o.isTypedArray(t))if(r<=0){var a=new t.constructor(0),s=D(e,t);r<0?(n=s,i=a):(n=a,i=s)}else if(n=new t.constructor(r),i=new t.constructor(t.length+e.length-r),r===e.length)n.set(e),i.set(t);else if(r<e.length){var l=e.length-r;n.set(e.subarray(0,l)),i.set(e.subarray(l)),i.set(t,l)}else{var u=r-e.length;n.set(e),n.set(t.subarray(0,u),e.length),i.set(t.subarray(u))}else n=e.concat(t),i=r>=0&&r<n.length?n.splice(r,n.length):[];return[n,i]})),l=e.redraw(r),c=[r,s.update,i,s.maxPoints];return u.add(r,e.extendTraces,c,t,arguments),l},e.newPlot=function(t,r,n,i){return t=o.getGraphDiv(t),h.cleanPlot([],{},t._fullData||[],t._fullLayout||{}),h.purge(t),e._doPlot(t,r,n,i)},e._doPlot=function(t,r,i,a){var s;if(t=o.getGraphDiv(t),l.init(t),o.isPlainObject(r)){var u=r;r=u.data,i=u.layout,a=u.config,s=u.frames}if(!1===l.triggerHandler(t,\"plotly_beforeplot\",[r,i,a]))return Promise.reject();r||i||o.isPlotDiv(t)||o.warn(\"Calling _doPlot as if redrawing but this container doesn't yet have a plot.\",t),L(t,a),i||(i={}),n.select(t).classed(\"js-plotly-plot\",!0),d.makeTester(),Array.isArray(t._promises)||(t._promises=[]);var f=0===(t.data||[]).length&&Array.isArray(r);Array.isArray(r)&&(_.cleanData(r),f?t.data=r:t.data.push.apply(t.data,r),t.empty=!1),t.layout&&!f||(t.layout=_.cleanLayout(i)),h.supplyDefaults(t);var v=t._fullLayout,m=v._has(\"cartesian\");v._replotting=!0,(f||v._shouldCreateBgLayer)&&(function(t){var e=n.select(t),r=t._fullLayout;if(r._calcInverseTransform=ot,r._calcInverseTransform(t),r._container=e.selectAll(\".plot-container\").data([0]),r._container.enter().insert(\"div\",\":first-child\").classed(\"plot-container\",!0).classed(\"plotly\",!0),r._paperdiv=r._container.selectAll(\".svg-container\").data([0]),r._paperdiv.enter().append(\"div\").classed(\"user-select-none\",!0).classed(\"svg-container\",!0).style(\"position\",\"relative\"),r._glcontainer=r._paperdiv.selectAll(\".gl-container\").data([{}]),r._glcontainer.enter().append(\"div\").classed(\"gl-container\",!0),r._paperdiv.selectAll(\".main-svg\").remove(),r._paperdiv.select(\".modebar-container\").remove(),r._paper=r._paperdiv.insert(\"svg\",\":first-child\").classed(\"main-svg\",!0),r._toppaper=r._paperdiv.append(\"svg\").classed(\"main-svg\",!0),r._modebardiv=r._paperdiv.append(\"div\"),delete r._modeBar,r._hoverpaper=r._paperdiv.append(\"svg\").classed(\"main-svg\",!0),!r._uid){var i={};n.selectAll(\"defs\").each((function(){this.id&&(i[this.id.split(\"-\")[1]]=1)})),r._uid=o.randstr(i)}r._paperdiv.selectAll(\".main-svg\").attr(y.svgAttrs),r._defs=r._paper.append(\"defs\").attr(\"id\",\"defs-\"+r._uid),r._clips=r._defs.append(\"g\").classed(\"clips\",!0),r._topdefs=r._toppaper.append(\"defs\").attr(\"id\",\"topdefs-\"+r._uid),r._topclips=r._topdefs.append(\"g\").classed(\"clips\",!0),r._bgLayer=r._paper.append(\"g\").classed(\"bglayer\",!0),r._draggers=r._paper.append(\"g\").classed(\"draglayer\",!0);var a=r._paper.append(\"g\").classed(\"layer-below\",!0);r._imageLowerLayer=a.append(\"g\").classed(\"imagelayer\",!0),r._shapeLowerLayer=a.append(\"g\").classed(\"shapelayer\",!0),r._cartesianlayer=r._paper.append(\"g\").classed(\"cartesianlayer\",!0),r._polarlayer=r._paper.append(\"g\").classed(\"polarlayer\",!0),r._smithlayer=r._paper.append(\"g\").classed(\"smithlayer\",!0),r._ternarylayer=r._paper.append(\"g\").classed(\"ternarylayer\",!0),r._geolayer=r._paper.append(\"g\").classed(\"geolayer\",!0),r._funnelarealayer=r._paper.append(\"g\").classed(\"funnelarealayer\",!0),r._pielayer=r._paper.append(\"g\").classed(\"pielayer\",!0),r._iciclelayer=r._paper.append(\"g\").classed(\"iciclelayer\",!0),r._treemaplayer=r._paper.append(\"g\").classed(\"treemaplayer\",!0),r._sunburstlayer=r._paper.append(\"g\").classed(\"sunburstlayer\",!0),r._indicatorlayer=r._toppaper.append(\"g\").classed(\"indicatorlayer\",!0),r._glimages=r._paper.append(\"g\").classed(\"glimages\",!0);var s=r._toppaper.append(\"g\").classed(\"layer-above\",!0);r._imageUpperLayer=s.append(\"g\").classed(\"imagelayer\",!0),r._shapeUpperLayer=s.append(\"g\").classed(\"shapelayer\",!0),r._selectionLayer=r._toppaper.append(\"g\").classed(\"selectionlayer\",!0),r._infolayer=r._toppaper.append(\"g\").classed(\"infolayer\",!0),r._menulayer=r._toppaper.append(\"g\").classed(\"menulayer\",!0),r._zoomlayer=r._toppaper.append(\"g\").classed(\"zoomlayer\",!0),r._hoverlayer=r._hoverpaper.append(\"g\").classed(\"hoverlayer\",!0),r._modebardiv.classed(\"modebar-container\",!0).style(\"position\",\"absolute\").style(\"top\",\"0px\").style(\"right\",\"0px\"),t.emit(\"plotly_framework\")}(t),v._shouldCreateBgLayer&&delete v._shouldCreateBgLayer),d.initGradients(t),d.initPatterns(t),f&&p.saveShowSpikeInitial(t);var x=!t.calcdata||t.calcdata.length!==(t._fullData||[]).length;x&&h.doCalcdata(t);for(var b=0;b<t.calcdata.length;b++)t.calcdata[b][0].trace=t._fullData[b];t._context.responsive?t._responsiveChartHandler||(t._responsiveChartHandler=function(){o.isHidden(t)||h.resize(t)},window.addEventListener(\"resize\",t._responsiveChartHandler)):o.clearResponsive(t);var T=o.extendFlat({},v._size),k=0;function A(){if(h.clearAutoMarginIds(t),w.drawMarginPushers(t),p.allowAutoMargin(t),t._fullLayout.title.text&&t._fullLayout.title.automargin&&h.allowAutoMargin(t,\"title.automargin\"),v._has(\"pie\"))for(var e=t._fullData,r=0;r<e.length;r++){var n=e[r];\"pie\"===n.type&&n.automargin&&h.allowAutoMargin(t,\"pie.\"+n.uid+\".automargin\")}return h.doAutoMargin(t),h.previousPromises(t)}function S(){t._transitioning||(w.doAutoRangeAndConstraints(t),f&&p.saveRangeInitial(t),c.getComponentMethod(\"rangeslider\",\"calcAutorange\")(t))}var E=[h.previousPromises,function(){if(s)return e.addFrames(t,s)},function e(){for(var r=v._basePlotModules,n=0;n<r.length;n++)r[n].drawFramework&&r[n].drawFramework(t);!v._glcanvas&&v._has(\"gl\")&&(v._glcanvas=v._glcontainer.selectAll(\".gl-canvas\").data([{key:\"contextLayer\",context:!0,pick:!1},{key:\"focusLayer\",context:!1,pick:!1},{key:\"pickLayer\",context:!1,pick:!0}],(function(t){return t.key})),v._glcanvas.enter().append(\"canvas\").attr(\"class\",(function(t){return\"gl-canvas gl-canvas-\"+t.key.replace(\"Layer\",\"\")})).style({position:\"absolute\",top:0,left:0,overflow:\"visible\",\"pointer-events\":\"none\"}));var i=t._context.plotGlPixelRatio;if(v._glcanvas){v._glcanvas.attr(\"width\",v.width*i).attr(\"height\",v.height*i).style(\"width\",v.width+\"px\").style(\"height\",v.height+\"px\");var a=v._glcanvas.data()[0].regl;if(a&&(Math.floor(v.width*i)!==a._gl.drawingBufferWidth||Math.floor(v.height*i)!==a._gl.drawingBufferHeight)){var s=\"WebGL context buffer and canvas dimensions do not match due to browser/WebGL bug.\";if(!k)return o.log(s+\" Clearing 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g){if(c=Q(i,K)){if(d=c.head,v=c.tail,a=c.attr||d+\".uirevision\",(u=(l=s(n,a).get())&&tt(a,e))&&u===l){if(null===(f=g[i])&&(f=void 0),nt(p=(h=s(e,i)).get(),f)){void 0===p&&\"autorange\"===v&&y.push(d),h.set(R(s(n,i).get()));continue}if(\"autorange\"===v||\"range[\"===v.substr(0,6)){var b=g[d+\".range[0]\"],_=g[d+\".range[1]\"],w=g[d+\".autorange\"];if(w||null===w&&null===b&&null===_){if(!(d in m)){var T=s(e,d).get();m[d]=T&&(T.autorange||!1!==T.autorange&&(!T.range||2!==T.range.length))}if(m[d]){h.set(R(s(n,i).get()));continue}}}}}else o.warn(\"unrecognized GUI edit: \"+i);delete g[i],c&&\"range[\"===c.tail.substr(0,6)&&(x[c.head]=1)}for(var k=0;k<y.length;k++){var A=y[k];if(x[A]){var M=s(e,A).get();M&&delete M.autorange}}var S=n._tracePreGUI;for(var E in S){var L,C=S[E],P=null;for(i in C){if(!P){var O=et(E,r);if(O<0){delete S[E];break}var I=rt(E,t,(L=r[O]._fullInput).index);if(I<0){delete 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c={getValObject:function(t){var e=f.getTraceValObject(l,t);return!l._module.animatable&&e.anim&&(e.anim=!1),e},flags:u,immutable:n,transition:i,newDataRevision:a,gd:t},p={};for(s=0;s<e.length;s++)if(r[s]){if(l=r[s]._fullInput,h.hasMakesDataTransform(l)&&(l=r[s]),p[l.uid])continue;p[l.uid]=1,it(e[s]._fullInput,l,[],c)}return(u.calc||u.plot)&&(u.fullReplot=!0),i&&u.nChanges&&u.nChangesAnim&&(u.anim=u.nChanges===u.nChangesAnim&&o?\"all\":\"some\"),u}(t,u,y,x,b,A);if(W(t)&&(k.layoutReplot=!0),S.calc||k.calc){t.calcdata=void 0;for(var E=Object.getOwnPropertyNames(m),C=0;C<E.length;C++){var P=E[C],O=P.substring(0,5);if(\"xaxis\"===O||\"yaxis\"===O){var I=m[P]._emptyCategories;I&&I()}}}else h.supplyDefaultsUpdateCalc(t.calcdata,y);var D=[];if(a&&(t._transitionData={},h.createTransitionData(t),D.push((function(){return e.addFrames(t,a)}))),m.transition&&!v&&(S.anim||k.anim))k.ticks&&D.push(w.doTicksRelayout),h.doCalcdata(t),w.doAutoRangeAndConstraints(t),D.push((function(){return h.transitionFromReact(t,S,k,p)}));else if(S.fullReplot||k.layoutReplot||v)t._fullLayout._skipDefaults=!0,D.push(e._doPlot);else{for(var z in k.arrays){var F=k.arrays[z];if(F.length){var B=c.getComponentMethod(z,\"drawOne\");if(B!==o.noop)for(var N=0;N<F.length;N++)B(t,F[N]);else{var j=c.getComponentMethod(z,\"draw\");if(j===o.noop)throw new Error(\"cannot draw components: \"+z);j(t)}}}D.push(h.previousPromises),S.style&&D.push(w.doTraceStyle),(S.colorbars||k.colorbars)&&D.push(w.doColorBars),k.legend&&D.push(w.doLegend),k.layoutstyle&&D.push(w.layoutStyles),k.axrange&&H(D),k.ticks&&D.push(w.doTicksRelayout),k.modebar&&D.push(w.doModeBar),k.camera&&D.push(w.doCamera),D.push(M)}D.push(h.rehover,h.redrag,h.reselect),(l=o.syncOrAsync(D,t))&&l.then||(l=Promise.resolve(t))}else l=e.newPlot(t,r,n,i);return l.then((function(){return t.emit(\"plotly_react\",{data:r,layout:n}),t}))},e.redraw=function(t){if(t=o.getGraphDiv(t),!o.isPlotDiv(t))throw new Error(\"This element is not a Plotly plot: 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u=i(e.dtick),c=i(t.dtick),f=u?e.dtick:+e.dtick.substring(1),h=c?t.dtick:+t.dtick.substring(1);u&&c?et(f,h)?f===2*A&&h===2*M&&(t.dtick=A):f===2*A&&h===3*M?t.dtick=A:f!==A||(e._input.minor||{}).nticks?rt(f/h,2.5)?t.dtick=f/2:t.dtick=f:t.dtick=M:\"M\"===String(e.dtick).charAt(0)?c?t.dtick=\"M1\":et(f,h)?f>=12&&2===h&&(t.dtick=\"M3\"):t.dtick=e.dtick:\"L\"===String(t.dtick).charAt(0)?\"L\"===String(e.dtick).charAt(0)?et(f,h)||(t.dtick=rt(f/h,2.5)?e.dtick/2:e.dtick):t.dtick=\"D1\":\"D2\"===t.dtick&&+e.dtick>1&&(t.dtick=1)}t.range=e.range}void 0===e.minor._tick0Init&&(t.tick0=e.tick0)},q.prepTicks=function(t,e){var r=s.simpleMap(t.range,t.r2l,void 0,void 0,e);if(\"auto\"===t.tickmode||!t.dtick){var n,a=t.nticks;a||(\"category\"===t.type||\"multicategory\"===t.type?(n=t.tickfont?s.bigFont(t.tickfont.size||12):15,a=t._length/n):(n=\"y\"===t._id.charAt(0)?40:80,a=s.constrain(t._length/n,4,9)+1),\"radialaxis\"===t._name&&(a*=2)),t.minor&&\"array\"!==t.minor.tickmode||\"array\"===t.tickmode&&(a*=100),t._roughDTick=Math.abs(r[1]-r[0])/a,q.autoTicks(t,t._roughDTick),t._minDtick>0&&t.dtick<2*t._minDtick&&(t.dtick=t._minDtick,t.tick0=t.l2r(t._forceTick0))}\"period\"===t.ticklabelmode&&function(t){var e;function r(){return!(i(t.dtick)||\"M\"!==t.dtick.charAt(0))}var n=r(),a=q.getTickFormat(t);if(a){var o=t._dtickInit!==t.dtick;/%[fLQsSMX]/.test(a)||(/%[HI]/.test(a)?(e=E,o&&!n&&t.dtick<E&&(t.dtick=E)):/%p/.test(a)?(e=S,o&&!n&&t.dtick<S&&(t.dtick=S)):/%[Aadejuwx]/.test(a)?(e=M,o&&!n&&t.dtick<M&&(t.dtick=M)):/%[UVW]/.test(a)?(e=A,o&&!n&&t.dtick<A&&(t.dtick=A)):/%[Bbm]/.test(a)?(e=T,o&&(n?nt(t.dtick)<1:t.dtick<k)&&(t.dtick=\"M1\")):/%[q]/.test(a)?(e=b,o&&(n?nt(t.dtick)<3:t.dtick<_)&&(t.dtick=\"M3\")):/%[Yy]/.test(a)&&(e=y,o&&(n?nt(t.dtick)<12:t.dtick<m)&&(t.dtick=\"M12\")))}(n=r())&&t.tick0===t._dowTick0&&(t.tick0=t._rawTick0),t._definedDelta=e}(t),t.tick0||(t.tick0=\"date\"===t.type?\"2000-01-01\":0),\"date\"===t.type&&t.dtick<.1&&(t.dtick=.1),vt(t)},q.calcTicks=function(t,e){for(var r,n,a=t.type,o=t.calendar,l=t.ticklabelstep,u=\"period\"===t.ticklabelmode,c=s.simpleMap(t.range,t.r2l,void 0,void 0,e),f=c[1]<c[0],h=Math.min(c[0],c[1]),p=Math.max(c[0],c[1]),d=Math.max(1e3,t._length||0),v=[],L=[],C=[],P=[],I=t.minor&&(t.minor.ticks||t.minor.showgrid),D=1;D>=(I?0:1);D--){var 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 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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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r=n.select(this),i=t._promises.length;r.call(u.positionText,d.xFn(e),d.yFn(e)).call(h.font,e.font,e.fontSize,e.fontColor).text(e.text).call(u.convertToTspans,t),t._promises[i]?m.push(t._promises.pop().then((function(){x(r,v)}))):x(r,v)})),Ft(e,[F]),y.exit().remove(),r.repositionOnUpdate&&y.each((function(t){n.select(this).select(\"text\").call(u.positionText,d.xFn(t),d.yFn(t))})),e._adjustTickLabelsOverflow=function(){var r=e.ticklabeloverflow;if(r&&\"allow\"!==r){var i=-1!==r.indexOf(\"hide\"),o=\"x\"===e._id.charAt(0),l=0,u=o?t._fullLayout.width:t._fullLayout.height;if(-1!==r.indexOf(\"domain\")){var c=s.simpleMap(e.range,e.r2l);l=e.l2p(c[0])+e._offset,u=e.l2p(c[1])+e._offset}var f=Math.min(l,u),p=Math.max(l,u),d=e.side,v=1/0,g=-1/0;for(var m in y.each((function(t){var r=n.select(this);if(r.select(\".text-math-group\").empty()){var a=h.bBox(r.node()),s=0;o?(a.right>p||a.left<f)&&(s=1):(a.bottom>p||a.top+(e.tickangle?0:t.fontSize/4)<f)&&(s=1);var l=r.select(\"text\");s?i&&l.style(\"opacity\",0):(l.style(\"opacity\",1),v=\"bottom\"===d||\"right\"===d?Math.min(v,o?a.top:a.left):-1/0,g=\"top\"===d||\"left\"===d?Math.max(g,o?a.bottom:a.right):1/0)}})),a._plots){var x=a._plots[m];if(e._id===x.xaxis._id||e._id===x.yaxis._id){var b=o?x.yaxis:x.xaxis;b&&(b[\"_visibleLabelMin_\"+e._id]=v,b[\"_visibleLabelMax_\"+e._id]=g)}}}},e._hideCounterAxisInsideTickLabels=function(t){var r=\"x\"===e._id.charAt(0),i=[];for(var o in a._plots){var s=a._plots[o];e._id!==s.xaxis._id&&e._id!==s.yaxis._id||i.push(r?s.yaxis:s.xaxis)}i.forEach((function(r,i){r&&Rt(r)&&(t||[I,z,D,R,F]).forEach((function(t){var o=\"tick\"===t.K&&\"text\"===t.L&&\"period\"===e.ticklabelmode,s=a._plots[e._mainSubplot];(t.K===I.K?s.zerolinelayer.selectAll(\".\"+e._id+\"zl\"):t.K===z.K?s.minorGridlayer.selectAll(\".\"+e._id):t.K===D.K?s.gridlayer.selectAll(\".\"+e._id):s[e._id.charAt(0)+\"axislayer\"]).each((function(){var 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v=Object.keys(n),m=1/0,x=0,b=1/0,_={},w={},T=!1;for(r=0;r<v.length;r++)w[o=v[r]]=l=p[a(o)],l._inputDomain?l.domain=l._inputDomain.slice():l._inputDomain=l.domain.slice(),l._inputRange||(l._inputRange=l.range.slice()),l.setScale(),_[o]=c=Math.abs(l._m)/n[o],m=Math.min(m,c),\"domain\"!==l.constrain&&l._constraintShrinkable||(b=Math.min(b,c)),delete l._constraintShrinkable,x=Math.max(x,c),\"domain\"===l.constrain&&(T=!0);if(!(m>u*x)||T)for(r=0;r<v.length;r++)if(c=_[o=v[r]],f=(l=w[o]).constrain,c!==b||\"domain\"===f)if(h=c/b,\"range\"===f)s(l,h);else{var 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 r=!1,n=e._id,i=t._fullLayout._axisConstraintGroups,a=0;a<i.length;a++)if(i[a][n]){r=!0;break}r&&\"domain\"===e.constrain||(e._input.domain=e.domain=e._inputDomain,delete e._inputDomain)}}},29323:function(t,e,r){\"use strict\";var n=r(39898),i=r(71828),a=i.numberFormat,o=r(84267),s=r(38520),l=r(73972),u=i.strTranslate,c=r(63893),f=r(7901),h=r(91424),p=r(30211),d=r(89298),v=r(6964),g=r(28569),y=r(64505),m=y.selectingOrDrawing,x=y.freeMode,b=r(18783).FROM_TL,_=r(33306),w=r(61549).redrawReglTraces,T=r(74875),k=r(41675).getFromId,A=r(47322).prepSelect,M=r(47322).clearOutline,S=r(47322).selectOnClick,E=r(42449),L=r(85555),C=L.MINDRAG,P=L.MINZOOM,O=!0;function I(t,e,r,n){var a=i.ensureSingle(t.draglayer,e,r,(function(e){e.classed(\"drag\",!0).style({fill:\"transparent\",\"stroke-width\":0}).attr(\"data-subplot\",t.id)}));return a.call(v,n),a.node()}function D(t,e,r,i,a,o,s){var l=I(t,\"rect\",e,r);return n.select(l).call(h.setRect,i,a,o,s),l}function z(t,e){for(var r=0;r<t.length;r++)if(!t[r].fixedrange)return e;return\"\"}function R(t,e,r,n,i){for(var a=0;a<t.length;a++){var o=t[a];if(!o.fixedrange)if(o.rangebreaks){var 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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 s();else!at||o<Math.min(Math.max(.6*a,C),P)?a<C||!it?s():(Tt.t=0,Tt.b=et,St=\"x\",Lt.attr(\"d\",function(t,e){return\"M\"+(t.l-.5)+\",\"+(e-P-.5)+\"h-3v\"+(2*P+1)+\"h3ZM\"+(t.r+.5)+\",\"+(e-P-.5)+\"h3v\"+(2*P+1)+\"h-3Z\"}(Tt,wt))):!it||a<Math.min(.6*o,P)?(Tt.l=0,Tt.r=tt,St=\"y\",Lt.attr(\"d\",function(t,e){return\"M\"+(e-P-.5)+\",\"+(t.t-.5)+\"v-3h\"+(2*P+1)+\"v3ZM\"+(e-P-.5)+\",\"+(t.b+.5)+\"v3h\"+(2*P+1)+\"v-3Z\"}(Tt,_t))):(St=\"xy\",Lt.attr(\"d\",G(Tt)));Tt.w=Tt.r-Tt.l,Tt.h=Tt.b-Tt.t,St&&(Ct=!0),t._dragged=Ct,U(Et,Lt,Tt,At,Mt,kt),zt(),t.emit(\"plotly_relayouting\",ht),Mt=!0}function zt(){ht={},\"xy\"!==St&&\"x\"!==St||(R(J,Tt.l/tt,Tt.r/tt,ht,rt.xaxes),Vt(\"x\",ht)),\"xy\"!==St&&\"y\"!==St||(R(K,(et-Tt.b)/et,(et-Tt.t)/et,ht,rt.yaxes),Vt(\"y\",ht))}function Rt(){zt(),H(t),Gt(),q(t)}Pt.prepFn=function(e,r,n){var 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0)&&r(\"lakecolor\"),r(\"showrivers\",!!x&&void 0)&&(r(\"rivercolor\"),r(\"riverwidth\")),r(\"showcountries\",d&&\"usa\"!==c&&x)&&(r(\"countrycolor\"),r(\"countrywidth\")),(\"usa\"===c||\"north america\"===c&&50===u)&&(r(\"showsubunits\",x),r(\"subunitcolor\"),r(\"subunitwidth\")),d||r(\"showframe\",x)&&(r(\"framecolor\"),r(\"framewidth\")),r(\"bgcolor\"),r(\"fitbounds\")&&(delete e.projection.scale,d?(delete e.center.lon,delete e.center.lat):y?(delete e.center.lon,delete e.center.lat,delete e.projection.rotation.lon,delete e.projection.rotation.lat,delete e.lonaxis.range,delete e.lataxis.range):(delete e.center.lon,delete e.center.lat,delete e.projection.rotation.lon))}t.exports=function(t,e,r){i(t,e,r,{type:\"geo\",attributes:s,handleDefaults:u,fullData:r,partition:\"y\"})}},74455:function(t,e,r){\"use strict\";var n=r(39898),i=r(71828),a=r(73972),o=Math.PI/180,s=180/Math.PI,l={cursor:\"pointer\"},u={cursor:\"auto\"};function c(t,e){return n.behavior.zoom().translate(e.translate()).scale(e.scale())}function f(t,e,r){var n=t.id,o=t.graphDiv,s=o.layout,l=s[n],u=o._fullLayout,c=u[n],f={},h={};function p(t,e){f[n+\".\"+t]=i.nestedProperty(l,t).get(),a.call(\"_storeDirectGUIEdit\",s,u._preGUI,f);var r=i.nestedProperty(c,t);r.get()!==e&&(r.set(e),i.nestedProperty(l,t).set(e),h[n+\".\"+t]=e)}r(p),p(\"projection.scale\",e.scale()/t.fitScale),p(\"fitbounds\",!1),o.emit(\"plotly_relayout\",h)}function h(t,e){var r=c(0,e);function i(r){var n=e.invert(t.midPt);r(\"center.lon\",n[0]),r(\"center.lat\",n[1])}return r.on(\"zoomstart\",(function(){n.select(this).style(l)})).on(\"zoom\",(function(){e.scale(n.event.scale).translate(n.event.translate),t.render(!0);var r=e.invert(t.midPt);t.graphDiv.emit(\"plotly_relayouting\",{\"geo.projection.scale\":e.scale()/t.fitScale,\"geo.center.lon\":r[0],\"geo.center.lat\":r[1]})})).on(\"zoomend\",(function(){n.select(this).style(u),f(t,e,i)})),r}function p(t,e){var r,i,a,o,s,h,p,d,v,g=c(0,e);function y(t){return e.invert(t)}function m(r){var n=e.rotate(),i=e.invert(t.midPt);r(\"projection.rotation.lon\",-n[0]),r(\"center.lon\",i[0]),r(\"center.lat\",i[1])}return g.on(\"zoomstart\",(function(){n.select(this).style(l),r=n.mouse(this),i=e.rotate(),a=e.translate(),o=i,s=y(r)})).on(\"zoom\",(function(){if(h=n.mouse(this),function(t){var r=y(t);if(!r)return!0;var n=e(r);return Math.abs(n[0]-t[0])>2||Math.abs(n[1]-t[1])>2}(r))return g.scale(e.scale()),void g.translate(e.translate());e.scale(n.event.scale),e.translate([a[0],n.event.translate[1]]),s?y(h)&&(d=y(h),p=[o[0]+(d[0]-s[0]),i[1],i[2]],e.rotate(p),o=p):s=y(r=h),v=!0,t.render(!0);var l=e.rotate(),u=e.invert(t.midPt);t.graphDiv.emit(\"plotly_relayouting\",{\"geo.projection.scale\":e.scale()/t.fitScale,\"geo.center.lon\":u[0],\"geo.center.lat\":u[1],\"geo.projection.rotation.lon\":-l[0]})})).on(\"zoomend\",(function(){n.select(this).style(u),v&&f(t,e,m)})),g}function d(t,e){var r,i={r:e.rotate(),k:e.scale()},a=c(0,e),h=function(t){for(var e=0,r=arguments.length,i=[];++e<r;)i.push(arguments[e]);var a=n.dispatch.apply(null,i);return a.of=function(e,r){return function(i){var o;try{o=i.sourceEvent=n.event,i.target=t,n.event=i,a[i.type].apply(e,r)}finally{n.event=o}}},a}(a,\"zoomstart\",\"zoom\",\"zoomend\"),p=0,d=a.on;function y(t){var r=e.rotate();t(\"projection.rotation.lon\",-r[0]),t(\"projection.rotation.lat\",-r[1])}return a.on(\"zoomstart\",(function(){n.select(this).style(l);var t,u,c,f,y,b,_,w,T,k,A,M=n.mouse(this),S=e.rotate(),E=S,L=e.translate(),C=(u=.5*(t=S)[0]*o,c=.5*t[1]*o,f=.5*t[2]*o,y=Math.sin(u),b=Math.cos(u),_=Math.sin(c),w=Math.cos(c),T=Math.sin(f),[b*w*(k=Math.cos(f))+y*_*T,y*w*k-b*_*T,b*_*k+y*w*T,b*w*T-y*_*k]);r=v(e,M),d.call(a,\"zoom\",(function(){var t,a,o,l,u,c,f,p,d,y,b=n.mouse(this);if(e.scale(i.k=n.event.scale),r){if(v(e,b)){e.rotate(S).translate(L);var _=v(e,b),w=function(t,e){if(t&&e){var 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.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,rangemode:i.rangemode,range:a({},i.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:i.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,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),_=!1;function w(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 T=w.prototype;T.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 k=!0;T.tryCreatePlot=function(){var t=this,e=t.prepareOptions(),r=!0;try{t.glplot=s(e)}catch(n){if(t.staticMode||!k||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 k=!1,r},T.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\"})},T.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(){_=!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},T.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,w=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(w,e.fullLayout,T),M=(A||\"\").split(\"+\"),S=A&&\"all\"===A;w.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:w._input,fullData:w,curveNumber:w.index,pointNumber:T};d.appendArrayPointValue(L,w,T),t._module.eventData&&(L=w._module.eventData(L,h,w,{},T));var C={points:[L]};if(e.fullSceneLayout.hovermode){var P=[];d.loneHover({trace:w,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(w,T,\"bgcolor\")||c.color,borderColor:d.castHoverOption(w,T,\"bordercolor\"),fontFamily:d.castHoverOption(w,T,\"font.family\"),fontSize:d.castHoverOption(w,T,\"font.size\"),fontColor:d.castHoverOption(w,T,\"font.color\"),nameLength:d.castHoverOption(w,T,\"namelength\"),textAlign:d.castHoverOption(w,T,\"align\"),hovertemplate:f.castOption(w,T,\"hovertemplate\"),hovertemplateLabels:f.extendFlat({},L,k),eventData:[L]},{container:n,gd:r,inOut_bbox:P}),L.bbox=P[0]}h.distance<5&&(h.buttons||_)?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)},T.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. 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i.isPlainObject(t)?(e.id=t.id,e.style=t):\"string\"==typeof t?(e.id=t,-1!==y.styleValuesMapbox.indexOf(t)?e.style=T(t):y.stylesNonMapbox[t]?e.style=y.stylesNonMapbox[t]:e.style=t):(e.id=y.styleValueDflt,e.style=T(y.styleValueDflt)),e.transition={duration:0,delay:0},e}function T(t){return y.styleUrlPrefix+t+\"-\"+y.styleUrlSuffix}function k(t){return[t.lon,t.lat]}b.updateData=function(t){var e,r,n,i,a=this.traceHash,o=t.slice().sort((function(t,e){return _[t[0].trace.type]-_[e[0].trace.type]}));for(n=0;n<o.length;n++){var s=o[n],l=!1;(e=a[(r=s[0].trace).uid])&&(e.type===r.type?(e.update(s),l=!0):e.dispose()),!l&&r._module&&(a[r.uid]=r._module.plot(this,s))}var u=Object.keys(a);t:for(n=0;n<u.length;n++){var c=u[n];for(i=0;i<t.length;i++)if(c===(r=t[i][0].trace).uid)continue t;(e=a[c]).dispose(),delete a[c]}},b.updateLayout=function(t){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=n.select(this),s=a.ensureSingle(r,\"g\",\"points\").selectAll(\"g.point\").data(a.identity);s.enter().append(\"g\").style(\"vector-effect\",l?\"none\":\"non-scaling-stroke\").style(\"stroke-miterlimit\",2).classed(\"point\",!0),s.exit().remove(),s.each((function(t){var e,r=n.select(this),o=t.rp0=f.c2p(t.s0),s=t.rp1=f.c2p(t.s1),l=t.thetag0=h.c2g(t.p0),d=t.thetag1=h.c2g(t.p1);if(i(o)&&i(s)&&i(l)&&i(d)&&o!==s&&l!==d){var v=f.c2g(t.s1),g=(l+d)/2;t.ct=[u.c2p(v*Math.cos(g)),c.c2p(v*Math.sin(g))],e=p(o,s,l,d)}else e=\"M0,0Z\";a.ensureSingle(r,\"path\").attr(\"d\",e)})),o.setClipUrl(r,e._hasClipOnAxisFalse?e.clipIds.forTraces:null,t)}))}},53522:function(t,e,r){\"use strict\";var 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ct,ft;(E={}).pos=E[_]=B[r],L=E.pts=nt[r].sort(f),P=(C=E[x]=L.map(h)).length,E.min=C[0],E.max=C[P-1],E.mean=o.mean(C,P),E.sd=o.stdev(C,P,E.mean),E.med=o.interp(C,.5),P%2&&(lt||ut)?(lt?(ct=C.slice(0,P/2),ft=C.slice(P/2+1)):ut&&(ct=C.slice(0,P/2+1),ft=C.slice(P/2)),E.q1=o.interp(ct,.5),E.q3=o.interp(ft,.5)):(E.q1=o.interp(C,.25),E.q3=o.interp(C,.75)),E.lf=p(E,C,P),E.uf=d(E,C,P),E.lo=v(E),E.uo=g(E);var ht=y(E,P);E.ln=E.med-ht,E.un=E.med+ht,at=Math.min(at,E.ln),ot=Math.max(ot,E.un),E.pts2=L.filter(j),M.push(E)}e._extremes[m._id]=i.findExtremes(m,e.notched?tt.concat([at,ot]):tt,{padded:!0})}return function(t,e){if(o.isArrayOrTypedArray(e.selectedpoints))for(var r=0;r<t.length;r++){for(var n=t[r].pts||[],i={},a=0;a<n.length;a++)i[n[a].i]=a;o.tagSelected(n,e,i)}}(M,e),M.length>0?(M[0].t={num:T[S],dPos:N,posLetter:_,valLetter:x,labels:{med:l(t,\"median:\"),min:l(t,\"min:\"),q1:l(t,\"q1:\"),q3:l(t,\"q3:\"),max:l(t,\"max:\"),mean:\"sd\"===e.boxmean?l(t,\"mean ± σ:\"):l(t,\"mean:\"),lf:l(t,\"lower 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g=i.distinctVals(d);\"category\"!==o.type&&\"multicategory\"!==o.type||(g.minDiff=1);var y=g.minDiff/2;n.minDtick(o,g.minDiff,g.vals[0],!0);var m=f[\"violin\"===t?\"_numViolins\":\"_numBoxes\"],x=\"group\"===f[t+\"mode\"]&&m>1,b=1-f[t+\"gap\"],_=1-f[t+\"groupgap\"];for(s=0;s<r.length;s++){var w,T,k,A,M,S,E=(u=c[r[s]])[0].trace,L=u[0].t,C=E.width,P=E.side;if(C)w=T=A=C/2,k=0;else if(w=y,x){var O=a(f,o._id)+E.orientation,I=(f._alignmentOpts[O]||{})[E.alignmentgroup]||{},D=Object.keys(I.offsetGroups||{}).length,z=D||m;T=w*b*_/z,k=2*w*(((D?E._offsetIndex:L.num)+.5)/z-.5)*b,A=w*b/z}else T=w*b*_,k=0,A=w;L.dPos=w,L.bPos=k,L.bdPos=T,L.wHover=A;var 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 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c=r(a+\"points\",l);c?(r(\"jitter\",\"all\"===c?.3:0),r(\"pointpos\",\"all\"===c?-1.5:0),r(\"marker.symbol\"),r(\"marker.opacity\"),r(\"marker.size\"),r(\"marker.angle\"),r(\"marker.color\",e.line.color),r(\"marker.line.color\"),r(\"marker.line.width\"),\"suspectedoutliers\"===c&&(r(\"marker.line.outliercolor\",e.marker.color),r(\"marker.line.outlierwidth\")),r(\"selected.marker.color\"),r(\"unselected.marker.color\"),r(\"selected.marker.size\"),r(\"unselected.marker.size\"),r(\"text\"),r(\"hovertext\")):delete e.marker;var f=r(\"hoveron\");\"all\"!==f&&-1===f.indexOf(\"points\")||r(\"hovertemplate\"),n.coerceSelectionMarkerOpacity(e,r)}t.exports={supplyDefaults:function(t,e,r,i){function s(r,i){return n.coerce(t,e,u,r,i)}if(c(t,e,s,i),!1!==e.visible){o(t,e,i,s),s(\"xhoverformat\"),s(\"yhoverformat\");var l=e._hasPreCompStats;l&&(s(\"lowerfence\"),s(\"upperfence\")),s(\"line.color\",(t.marker||{}).color||r),s(\"line.width\"),s(\"fillcolor\",a.addOpacity(e.line.color,.5));var 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e=f.c2l(t.pos+p,!0),a=f.l2p(e-s)+v,o=f.l2p(e+l)+v,x=h?(a+o)/2:f.l2p(e)+v,b=r.whiskerwidth,_=h?a*b+(1-b)*x:f.l2p(e-d)+v,w=h?o*b+(1-b)*x:f.l2p(e+d)+v,T=f.l2p(e-s*m)+v,k=f.l2p(e+l*m)+v,A=c.c2p(t.q1,!0),M=c.c2p(t.q3,!0),S=i.constrain(c.c2p(t.med,!0),Math.min(A,M)+1,Math.max(A,M)-1),E=void 0===t.lf||!1===r.boxpoints,L=c.c2p(E?t.min:t.lf,!0),C=c.c2p(E?t.max:t.uf,!0),P=c.c2p(t.ln,!0),O=c.c2p(t.un,!0);u?n.select(this).attr(\"d\",\"M\"+S+\",\"+T+\"V\"+k+\"M\"+A+\",\"+a+\"V\"+o+(y?\"H\"+P+\"L\"+S+\",\"+k+\"L\"+O+\",\"+o:\"\")+\"H\"+M+\"V\"+a+(y?\"H\"+O+\"L\"+S+\",\"+T+\"L\"+P+\",\"+a:\"\")+\"ZM\"+A+\",\"+x+\"H\"+L+\"M\"+M+\",\"+x+\"H\"+C+(0===g?\"\":\"M\"+L+\",\"+_+\"V\"+w+\"M\"+C+\",\"+_+\"V\"+w)):n.select(this).attr(\"d\",\"M\"+T+\",\"+S+\"H\"+k+\"M\"+a+\",\"+A+\"H\"+o+(y?\"V\"+P+\"L\"+k+\",\"+S+\"L\"+o+\",\"+O:\"\")+\"V\"+M+\"H\"+a+(y?\"V\"+O+\"L\"+T+\",\"+S+\"L\"+a+\",\"+P:\"\")+\"ZM\"+x+\",\"+A+\"V\"+L+\"M\"+x+\",\"+M+\"V\"+C+(0===g?\"\":\"M\"+_+\",\"+L+\"H\"+w+\"M\"+_+\",\"+C+\"H\"+w))}))}function s(t,e,r,n){var o=e.x,s=e.y,l=n.bdPos,u=n.bPos,c=r.boxpoints||r.points;i.seedPseudoRandom();var f=t.selectAll(\"g.points\").data(c?function(t){return t.forEach((function(t){t.t=n,t.trace=r})),t}:[]);f.enter().append(\"g\").attr(\"class\",\"points\"),f.exit().remove();var h=f.selectAll(\"path\").data((function(t){var e,n,a=t.pts2,o=Math.max((t.max-t.min)/10,t.q3-t.q1),s=1e-9*o,f=.01*o,h=[],p=0;if(r.jitter){if(0===o)for(p=1,h=new Array(a.length),e=0;e<a.length;e++)h[e]=1;else for(e=0;e<a.length;e++){var d=Math.max(0,e-5),v=a[d].v,g=Math.min(a.length-1,e+5),y=a[g].v;\"all\"!==c&&(a[e].v<t.lf?y=Math.min(y,t.lf):v=Math.max(v,t.uf));var m=Math.sqrt(f*(g-d)/(y-v+s))||0;m=i.constrain(Math.abs(m),0,1),h.push(m),p=Math.max(m,p)}n=2*r.jitter/(p||1)}for(e=0;e<a.length;e++){var x=a[e],b=x.v,_=r.jitter?n*h[e]*(i.pseudoRandom()-.5):0,w=t.pos+u+l*(r.pointpos+_);\"h\"===r.orientation?(x.y=w,x.x=b):(x.x=w,x.y=b),\"suspectedoutliers\"===c&&b<t.uo&&b>t.lo&&(x.so=!0)}return a}));h.enter().append(\"path\").classed(\"point\",!0),h.exit().remove(),h.call(a.translatePoints,o,s)}function l(t,e,r,a){var 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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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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}),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);t.exports=function(t,e,r,c){function f(r,i){return n.coerce(t,e,u,r,i)}function h(r,i){return n.coerce2(t,e,u,r,i)}var p=function(t,e,r){var n=r(\"lon\")||[],i=r(\"lat\")||[],a=Math.min(n.length,i.length);return e._length=a,a}(0,e,f);if(p){if(f(\"text\"),f(\"texttemplate\"),f(\"hovertext\"),f(\"hovertemplate\"),f(\"mode\"),f(\"below\"),i.hasLines(e)&&(o(t,e,r,c,f,{noDash:!0}),f(\"connectgaps\")),i.hasMarkers(e)){a(t,e,r,c,f,{noLine:!0,noAngle:!0}),f(\"marker.allowoverlap\"),f(\"marker.angle\");var d=e.marker;\"circle\"!==d.symbol&&(n.isArrayOrTypedArray(d.size)&&(d.size=d.size[0]),n.isArrayOrTypedArray(d.color)&&(d.color=d.color[0]))}var v=h(\"cluster.maxzoom\"),g=h(\"cluster.step\"),y=h(\"cluster.color\",e.marker&&e.marker.color||r),m=h(\"cluster.size\"),x=h(\"cluster.opacity\");f(\"cluster.enabled\",!1!==v||!1!==g||!1!==y||!1!==m||!1!==x),i.hasText(e)&&s(t,e,c,f,{noSelect:!0}),f(\"fill\"),\"none\"!==e.fill&&l(t,e,r,f),n.coerceSelectionMarkerOpacity(e,f)}else e.visible=!1}},53353:function(t){\"use strict\";t.exports=function(t,e){return t.lon=e.lon,t.lat=e.lat,t}},15636:function(t,e,r){\"use strict\";var n=r(89298);t.exports=function(t,e,r){var i={},a=r[e.subplot]._subplot.mockAxis,o=t.lonlat;return i.lonLabel=n.tickText(a,a.c2l(o[0]),!0).text,i.latLabel=n.tickText(a,a.c2l(o[1]),!0).text,i}},28178:function(t,e,r){\"use strict\";var n=r(30211),i=r(71828),a=r(34603),o=i.fillText,s=r(50606).BADNUM,l=r(77734).traceLayerPrefix;function u(t,e,r){if(!t.hovertemplate){var n=(e.hi||t.hoverinfo).split(\"+\"),i=-1!==n.indexOf(\"all\"),a=-1!==n.indexOf(\"lon\"),s=-1!==n.indexOf(\"lat\"),l=e.lonlat,u=[];return i||a&&s?u.push(\"(\"+c(l[1])+\", \"+c(l[0])+\")\"):a?u.push(r.lon+c(l[0])):s&&u.push(r.lat+c(l[1])),(i||-1!==n.indexOf(\"text\"))&&o(e,t,u),u.join(\"<br>\")}function c(t){return t+\"°\"}}t.exports={hoverPoints:function(t,e,r){var o=t.cd,c=o[0].trace,f=t.xa,h=t.ya,p=t.subplot,d=[],v=l+c.uid+\"-circle\",g=c.cluster&&c.cluster.enabled;if(g){var y=p.map.queryRenderedFeatures(null,{layers:[v]});d=y.map((function(t){return t.id}))}var m=360*(e>=0?Math.floor((e+180)/360):Math.ceil((e-180)/360)),x=e-m;if(n.getClosest(o,(function(t){var 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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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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 o=e.data.data,l=!e.children,u=o.i,c=a.castOption(r,u,\"marker.line.color\")||i.defaultLine,f=a.castOption(r,u,\"marker.line.width\")||0;t.call(s,e,r,n).style(\"stroke-width\",f).call(i.stroke,c).style(\"opacity\",l?r.leaf.opacity:null)}t.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(\".trace\");o(t,e,\"sunburst\"),e.each((function(e){var r=n.select(this),i=e[0].trace;r.style(\"opacity\",i.opacity),r.selectAll(\"path.surface\").each((function(e){n.select(this).call(l,e,i,t)}))}))},styleOne:l}},54532:function(t,e,r){\"use strict\";var n=r(7901),i=r(50693),a=r(12663).axisHoverFormat,o=r(5386).fF,s=r(9012),l=r(1426).extendFlat,u=r(30962).overrideAll;function c(t){return{show:{valType:\"boolean\",dflt:!1},start:{valType:\"number\",dflt:null,editType:\"plot\"},end:{valType:\"number\",dflt:null,editType:\"plot\"},size:{valType:\"number\",dflt:null,min:0,editType:\"plot\"},project:{x:{valType:\"boolean\",dflt:!1},y:{valType:\"boolean\",dflt:!1},z:{valType:\"boolean\",dflt:!1}},color:{valType:\"color\",dflt:n.defaultLine},usecolormap:{valType:\"boolean\",dflt:!1},width:{valType:\"number\",min:1,max:16,dflt:2},highlight:{valType:\"boolean\",dflt:!0},highlightcolor:{valType:\"color\",dflt:n.defaultLine},highlightwidth:{valType:\"number\",min:1,max:16,dflt:2}}}var 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 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){null!=t.dataCoordinate[a]&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var o=this.data.hovertext||this.data.text;return Array.isArray(o)&&o[i]&&void 0!==o[i][n]?t.textLabel=o[i][n]:t.textLabel=o||\"\",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function 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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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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 f=u>>c&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4==++s||0===n&&0===c)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var 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if(h.right===s){if(!(d=p.left)||0!==d._color){p._color=0,p.right=h.left,h._color=1,h.left=p,l[f-2]=h,l[f-1]=s,i(p),i(h),f>=3&&((v=l[f-3]).right===p?v.right=h:v.left=h);break}h._color=1,p.left=n(1,d),p._color=0,f-=1}else{var d;if(!(d=p.left)||0!==d._color){var v;h.left=s.right,p._color=0,p.right=s.left,s._color=1,s.right=h,s.left=p,l[f-2]=s,l[f-1]=h,i(p),i(h),i(s),f>=3&&((v=l[f-3]).right===p?v.right=s:v.left=s);break}h._color=1,p.left=n(1,d),p._color=0,f-=1}}return l[0]._color=1,new a(o,l[0])},o.forEach=function(t,e,r){if(this.root)switch(arguments.length){case 1:return s(t,this.root);case 2:return l(e,this._compare,t,this.root);case 3:if(this._compare(e,r)>=0)return;return u(e,r,this._compare,t,this.root)}},Object.defineProperty(o,\"begin\",{get:function(){for(var t=[],e=this.root;e;)t.push(e),e=e.left;return new c(this,t)}}),Object.defineProperty(o,\"end\",{get:function(){for(var t=[],e=this.root;e;)t.push(e),e=e.right;return new c(this,t)}}),o.at=function(t){if(t<0)return new c(this,[]);for(var e=this.root,r=[];;){if(r.push(e),e.left){if(t<e.left._count){e=e.left;continue}t-=e.left._count}if(!t)return new c(this,r);if(t-=1,!e.right)break;if(t>=e.right._count)break;e=e.right}return new c(this,[])},o.ge=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<=0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new c(this,n)},o.gt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new c(this,n)},o.lt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new c(this,n)},o.le=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>=0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new c(this,n)},o.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var i=e(t,r.key);if(n.push(r),0===i)return new c(this,n);r=i<=0?r.left:r.right}return new c(this,[])},o.remove=function(t){var e=this.find(t);return e?e.remove():this},o.get=function(t){for(var e=this._compare,r=this.root;r;){var n=e(t,r.key);if(0===n)return r.value;r=n<=0?r.left:r.right}};var f=c.prototype;function h(t,e){t.key=e.key,t.value=e.value,t.left=e.left,t.right=e.right,t._color=e._color,t._count=e._count}function p(t,e){return t<e?-1:t>e?1:0}Object.defineProperty(f,\"valid\",{get:function(){return this._stack.length>0}}),Object.defineProperty(f,\"node\",{get:function(){return this._stack.length>0?this._stack[this._stack.length-1]:null},enumerable:!0}),f.clone=function(){return new c(this.tree,this._stack.slice())},f.remove=function(){var t=this._stack;if(0===t.length)return this.tree;var o=new Array(t.length),s=t[t.length-1];o[o.length-1]=new e(s._color,s.key,s.value,s.left,s.right,s._count);for(var l=t.length-2;l>=0;--l)(s=t[l]).left===t[l+1]?o[l]=new e(s._color,s.key,s.value,o[l+1],s.right,s._count):o[l]=new e(s._color,s.key,s.value,s.left,o[l+1],s._count);if((s=o[o.length-1]).left&&s.right){var u=o.length;for(s=s.left;s.right;)o.push(s),s=s.right;var c=o[u-1];for(o.push(new e(s._color,c.key,c.value,s.left,s.right,s._count)),o[u-1].key=s.key,o[u-1].value=s.value,l=o.length-2;l>=u;--l)s=o[l],o[l]=new e(s._color,s.key,s.value,s.left,o[l+1],s._count);o[u-1].left=o[u]}if(0===(s=o[o.length-1])._color){var f=o[o.length-2];for(f.left===s?f.left=null:f.right===s&&(f.right=null),o.pop(),l=0;l<o.length;++l)o[l]._count--;return new a(this.tree._compare,o[0])}if(s.left||s.right){for(s.left?h(s,s.left):s.right&&h(s,s.right),s._color=1,l=0;l<o.length-1;++l)o[l]._count--;return new a(this.tree._compare,o[0])}if(1===o.length)return new a(this.tree._compare,null);for(l=0;l<o.length;++l)o[l]._count--;var p=o[o.length-2];return function(t){for(var e,a,o,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((a=t[l-1]).left===e){if((o=a.right).right&&0===o.right._color)return s=(o=a.right=r(o)).right=r(o.right),a.right=o.left,o.left=a,o.right=s,o._color=a._color,e._color=1,a._color=1,s._color=1,i(a),i(o),l>1&&((u=t[l-2]).left===a?u.left=o:u.right=o),void(t[l-1]=o);if(o.left&&0===o.left._color)return s=(o=a.right=r(o)).left=r(o.left),a.right=s.left,o.left=s.right,s.left=a,s.right=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]).left===a?u.left=s:u.right=s),void(t[l-1]=s);if(1===o._color){if(0===a._color)return a._color=1,void(a.right=n(0,o));a.right=n(0,o);continue}o=r(o),a.right=o.left,o.left=a,o._color=a._color,a._color=0,i(a),i(o),l>1&&((u=t[l-2]).left===a?u.left=o:u.right=o),t[l-1]=o,t[l]=a,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((o=a.left).left&&0===o.left._color)return s=(o=a.left=r(o)).left=r(o.left),a.left=o.right,o.right=a,o.left=s,o._color=a._color,e._color=1,a._color=1,s._color=1,i(a),i(o),l>1&&((u=t[l-2]).right===a?u.right=o:u.left=o),void(t[l-1]=o);if(o.right&&0===o.right._color)return 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 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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 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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 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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 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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 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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 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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 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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 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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 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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 e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,t),this._minFilter=t}},magFilter:{get:function(){return this._magFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,t),this._magFilter=t}},mipSamples:{get:function(){return this._anisoSamples},set:function(t){var e=this._anisoSamples;if(this._anisoSamples=0|Math.max(t,1),e!==this._anisoSamples){var r=this.gl.getExtension(\"EXT_texture_filter_anisotropic\");r&&this.gl.texParameterf(this.gl.TEXTURE_2D,r.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,t),this._wrapS=t}},wrapT:{get:function(){return this._wrapT},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,t),this._wrapT=t}},wrap:{get:function(){return this._wrapVector},set:function(t){if(Array.isArray(t)||(t=[t,t]),2!==t.length)throw new Error(\"gl-texture2d: Must specify wrap mode for rows and columns\");for(var e=0;e<2;++e)if(l.indexOf(t[e])<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);this._wrapS=t[0],this._wrapT=t[1];var r=this.gl;return this.bind(),r.texParameteri(r.TEXTURE_2D,r.TEXTURE_WRAP_S,this._wrapS),r.texParameteri(r.TEXTURE_2D,r.TEXTURE_WRAP_T,this._wrapT),t}},shape:{get:function(){return this._shapeVector},set:function(t){if(Array.isArray(t)){if(2!==t.length)throw new Error(\"gl-texture2d: Invalid texture shape\")}else t=[0|t,0|t];return f(this,0|t[0],0|t[1]),[0|t[0],0|t[1]]}},width:{get:function(){return this._shape[0]},set:function(t){return f(this,t|=0,this._shape[1]),t}},height:{get:function(){return this._shape[1]},set:function(t){return t|=0,f(this,this._shape[0],t),t}}}),p.bind=function(t){var e=this.gl;return void 0!==t&&e.activeTexture(e.TEXTURE0+(0|t)),e.bindTexture(e.TEXTURE_2D,this.handle),void 0!==t?0|t:e.getParameter(e.ACTIVE_TEXTURE)-e.TEXTURE0},p.dispose=function(){this.gl.deleteTexture(this.handle)},p.generateMipmap=function(){this.bind(),this.gl.generateMipmap(this.gl.TEXTURE_2D);for(var t=Math.min(this._shape[0],this._shape[1]),e=0;t>0;++e,t>>>=1)this._mipLevels.indexOf(e)<0&&this._mipLevels.push(e)},p.setPixels=function(t,e,r,o){var s=this.gl;this.bind(),Array.isArray(e)?(o=r,r=0|e[1],e=0|e[0]):(e=e||0,r=r||0),o=o||0;var l=u(t)?t:t.raw;if(l)this._mipLevels.indexOf(o)<0?(s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,this.type,l),this._mipLevels.push(o)):s.texSubImage2D(s.TEXTURE_2D,o,e,r,this.format,this.type,l);else{if(!(t.shape&&t.stride&&t.data))throw new Error(\"gl-texture2d: Unsupported data type\");if(t.shape.length<2||e+t.shape[1]>this._shape[1]>>>o||r+t.shape[0]>this._shape[0]>>>o||e<0||r<0)throw new Error(\"gl-texture2d: Texture dimensions are out of bounds\");!function(t,e,r,o,s,l,u,f){var h=f.dtype,p=f.shape.slice();if(p.length<2||p.length>3)throw new Error(\"gl-texture2d: Invalid ndarray, must be 2d or 3d\");var v=0,g=0,y=d(p,f.stride.slice());if(\"float32\"===h?v=t.FLOAT:\"float64\"===h?(v=t.FLOAT,y=!1,h=\"float32\"):\"uint8\"===h?v=t.UNSIGNED_BYTE:(v=t.UNSIGNED_BYTE,y=!1,h=\"uint8\"),2===p.length)g=t.LUMINANCE,p=[p[0],p[1],1],f=n(f.data,p,[f.stride[0],f.stride[1],1],f.offset);else{if(3!==p.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===p[2])g=t.ALPHA;else if(2===p[2])g=t.LUMINANCE_ALPHA;else if(3===p[2])g=t.RGB;else{if(4!==p[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");g=t.RGBA}p[2]}if(g!==t.LUMINANCE&&g!==t.ALPHA||s!==t.LUMINANCE&&s!==t.ALPHA||(g=s),g!==s)throw new Error(\"gl-texture2d: Incompatible texture format for setPixels\");var m=f.size,x=u.indexOf(o)<0;if(x&&u.push(o),v===l&&y)0===f.offset&&f.data.length===m?x?t.texImage2D(t.TEXTURE_2D,o,s,p[0],p[1],0,s,l,f.data):t.texSubImage2D(t.TEXTURE_2D,o,e,r,p[0],p[1],s,l,f.data):x?t.texImage2D(t.TEXTURE_2D,o,s,p[0],p[1],0,s,l,f.data.subarray(f.offset,f.offset+m)):t.texSubImage2D(t.TEXTURE_2D,o,e,r,p[0],p[1],s,l,f.data.subarray(f.offset,f.offset+m));else{var b;b=l===t.FLOAT?a.mallocFloat32(m):a.mallocUint8(m);var _=n(b,p,[p[2],p[2]*p[0],1]);v===t.FLOAT&&l===t.UNSIGNED_BYTE?c(_,f):i.assign(_,f),x?t.texImage2D(t.TEXTURE_2D,o,s,p[0],p[1],0,s,l,b.subarray(0,m)):t.texSubImage2D(t.TEXTURE_2D,o,e,r,p[0],p[1],s,l,b.subarray(0,m)),l===t.FLOAT?a.freeFloat32(b):a.freeUint8(b)}}(s,e,r,o,this.format,this.type,this._mipLevels,t)}}},3056:function(t){\"use strict\";t.exports=function(t,e,r){e?e.bind():t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null);var n=0|t.getParameter(t.MAX_VERTEX_ATTRIBS);if(r){if(r.length>n)throw new Error(\"gl-vao: Too many vertex attributes\");for(var i=0;i<r.length;++i){var a=r[i];if(a.buffer){var o=a.buffer,s=a.size||4,l=a.type||t.FLOAT,u=!!a.normalized,c=a.stride||0,f=a.offset||0;o.bind(),t.enableVertexAttribArray(i),t.vertexAttribPointer(i,s,l,u,c,f)}else{if(\"number\"==typeof a)t.vertexAttrib1f(i,a);else if(1===a.length)t.vertexAttrib1f(i,a[0]);else if(2===a.length)t.vertexAttrib2f(i,a[0],a[1]);else if(3===a.length)t.vertexAttrib3f(i,a[0],a[1],a[2]);else{if(4!==a.length)throw new Error(\"gl-vao: Invalid vertex attribute\");t.vertexAttrib4f(i,a[0],a[1],a[2],a[3])}t.disableVertexAttribArray(i)}}for(;i<n;++i)t.disableVertexAttribArray(i)}else for(t.bindBuffer(t.ARRAY_BUFFER,null),i=0;i<n;++i)t.disableVertexAttribArray(i)}},7220:function(t,e,r){\"use strict\";var n=r(3056);function i(t){this.gl=t,this._elements=null,this._attributes=null,this._elementsType=t.UNSIGNED_SHORT}i.prototype.bind=function(){n(this.gl,this._elements,this._attributes)},i.prototype.update=function(t,e,r){this._elements=e,this._attributes=t,this._elementsType=r||this.gl.UNSIGNED_SHORT},i.prototype.dispose=function(){},i.prototype.unbind=function(){},i.prototype.draw=function(t,e,r){r=r||0;var n=this.gl;this._elements?n.drawElements(t,e,this._elementsType,r):n.drawArrays(t,r,e)},t.exports=function(t){return new i(t)}},3778:function(t,e,r){\"use strict\";var n=r(3056);function i(t,e,r,n,i,a){this.location=t,this.dimension=e,this.a=r,this.b=n,this.c=i,this.d=a}function 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strict\";t.exports=function(t,e,r,n,i,a,o,s,l,u){var c=e+a+u;if(f>0){var f=Math.sqrt(c+1);t[0]=.5*(o-l)/f,t[1]=.5*(s-n)/f,t[2]=.5*(r-a)/f,t[3]=.5*f}else{var h=Math.max(e,a,u);f=Math.sqrt(2*h-c+1),e>=h?(t[0]=.5*f,t[1]=.5*(i+r)/f,t[2]=.5*(s+n)/f,t[3]=.5*(o-l)/f):a>=h?(t[0]=.5*(r+i)/f,t[1]=.5*f,t[2]=.5*(l+o)/f,t[3]=.5*(s-n)/f):(t[0]=.5*(n+s)/f,t[1]=.5*(o+l)/f,t[2]=.5*f,t[3]=.5*(r-i)/f)}return t}},7774:function(t,e,r){\"use strict\";t.exports=function(t){var e=(t=t||{}).center||[0,0,0],r=t.rotation||[0,0,0,1],n=t.radius||1;e=[].slice.call(e,0,3),c(r=[].slice.call(r,0,4),r);var i=new f(r,e,Math.log(n));return i.setDistanceLimits(t.zoomMin,t.zoomMax),(\"eye\"in t||\"up\"in t)&&i.lookAt(0,t.eye,t.center,t.up),i};var n=r(8444),i=r(3012),a=r(5950),o=r(7437),s=r(567);function l(t,e,r){return Math.sqrt(Math.pow(t,2)+Math.pow(e,2)+Math.pow(r,2))}function u(t,e,r,n){return Math.sqrt(Math.pow(t,2)+Math.pow(e,2)+Math.pow(r,2)+Math.pow(n,2))}function c(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=u(r,n,i,a);o>1e-6?(t[0]=r/o,t[1]=n/o,t[2]=i/o,t[3]=a/o):(t[0]=t[1]=t[2]=0,t[3]=1)}function f(t,e,r){this.radius=n([r]),this.center=n(e),this.rotation=n(t),this.computedRadius=this.radius.curve(0),this.computedCenter=this.center.curve(0),this.computedRotation=this.rotation.curve(0),this.computedUp=[.1,0,0],this.computedEye=[.1,0,0],this.computedMatrix=[.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],this.recalcMatrix(0)}var h=f.prototype;h.lastT=function(){return Math.max(this.radius.lastT(),this.center.lastT(),this.rotation.lastT())},h.recalcMatrix=function(t){this.radius.curve(t),this.center.curve(t),this.rotation.curve(t);var e=this.computedRotation;c(e,e);var r=this.computedMatrix;a(r,e);var n=this.computedCenter,i=this.computedEye,o=this.computedUp,s=Math.exp(this.computedRadius[0]);i[0]=n[0]+s*r[2],i[1]=n[1]+s*r[6],i[2]=n[2]+s*r[10],o[0]=r[1],o[1]=r[5],o[2]=r[9];for(var l=0;l<3;++l){for(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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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from optuna.visualization import plot_pareto_front\n",
    "\n",
    "plot_pareto_front(study)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further visualization of QSARtuna runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to evaluate the parameter importances on regression metric performance across descriptor vs. algorithm choice, based on the completed trials in our study:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "from optuna.visualization import plot_param_importances\n",
    "\n",
    "plot_param_importances(study, target=lambda t: t.values[0])"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "Parameter importances are represented by non-negative floating point numbers, where higher values mean that the parameters are more important. The returned dictionary is of type collections.OrderedDict and is ordered by its values in a descending order (the sum of the importance values are normalized to 1.0). Hence we can conclude that choice of algortihm is more important than choice of descriptor for our current study.\n",
    "\n",
    "It is also possible to analyse the importance of these hyperparameter choices on the impact on trial duration:"
   ]
  },
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    "    study, target=lambda t: t.duration.total_seconds(), target_name=\"duration\"\n",
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    "Optuna also allows us to plot the parameter relationships for our study, like so:"
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     "metadata": {},
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    }
   ],
   "source": [
    "from optuna.visualization import plot_parallel_coordinate\n",
    "\n",
    "plot_parallel_coordinate(study,\n",
    "                         params=[\"algorithm_name\", \"descriptor\"],\n",
    "                         target=lambda t: t.values[0]) # First performance value taken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same can be done for the relationships for the standard deviation of performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
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otypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('44d87d1d-124f-4403-9176-d8e8aec6bf4a');\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_parallel_coordinate\n",
    "\n",
    "plot_parallel_coordinate(study,\n",
    "                         params=[\"algorithm_name\", \"descriptor\"],\n",
    "                         target=lambda t: t.values[1]) # Second standard deviation value taken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precomputed descriptors from a file example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Precomputed descriptors can be supplied to models using the \"PrecomputedDescriptorFromFile\" descriptor, and supplying the `input_column` and `response_column` like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(512,)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from optunaz.descriptors import PrecomputedDescriptorFromFile\n",
    "\n",
    "descriptor=PrecomputedDescriptorFromFile.new(\n",
    "            file=\"../tests/data/precomputed_descriptor/train_with_fp.csv\",\n",
    "            input_column=\"canonical\", # Name of the identifier for the compound\n",
    "            response_column=\"fp\") # Name of the column with the pretrained (comma separated) descriptors\n",
    "\n",
    "descriptor.calculate_from_smi(\"Cc1cc(NC(=O)c2cccc(COc3ccc(Br)cc3)c2)no1\").shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this toy example there are 512 precomputed bit descriptor vectors, and a model can be trained with precomputed descriptors from a file (in a composite descriptor with ECFP), like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:21:26,393] A new study created in memory with name: precomputed_example\n",
      "[I 2024-10-02 18:21:26,395] A new study created in memory with name: study_name_0\n",
      "[I 2024-10-02 18:21:26,506] Trial 0 finished with value: -3704.729132396259 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011994365911634164, 'descriptor': '{\"parameters\": {\"descriptors\": [{\"name\": \"PrecomputedDescriptorFromFile\", \"parameters\": {\"file\": \"../tests/data/precomputed_descriptor/train_with_fp.csv\", \"input_column\": \"canonical\", \"response_column\": \"fp\"}}, {\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}]}, \"name\": \"CompositeDescriptor\"}'}. Best is trial 0 with value: -3704.729132396259.\n",
      "[I 2024-10-02 18:21:26,547] Trial 1 finished with value: -3704.801507216346 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.03592375122963953, 'descriptor': '{\"parameters\": {\"descriptors\": [{\"name\": \"PrecomputedDescriptorFromFile\", \"parameters\": {\"file\": \"../tests/data/precomputed_descriptor/train_with_fp.csv\", \"input_column\": \"canonical\", \"response_column\": \"fp\"}}, {\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}]}, \"name\": \"CompositeDescriptor\"}'}. Best is trial 0 with value: -3704.729132396259.\n",
      "[I 2024-10-02 18:21:26,585] Trial 2 finished with value: -3710.34494982837 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8153295905650357, 'descriptor': '{\"parameters\": {\"descriptors\": [{\"name\": \"PrecomputedDescriptorFromFile\", \"parameters\": {\"file\": \"../tests/data/precomputed_descriptor/train_with_fp.csv\", \"input_column\": \"canonical\", \"response_column\": \"fp\"}}, {\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}]}, \"name\": \"CompositeDescriptor\"}'}. Best is trial 0 with value: -3704.729132396259.\n",
      "[I 2024-10-02 18:21:26,671] Trial 3 finished with value: -3215.0017434643833 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 14, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"parameters\": {\"descriptors\": [{\"name\": \"PrecomputedDescriptorFromFile\", \"parameters\": {\"file\": \"../tests/data/precomputed_descriptor/train_with_fp.csv\", \"input_column\": \"canonical\", \"response_column\": \"fp\"}}, {\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}]}, \"name\": \"CompositeDescriptor\"}'}. Best is trial 3 with value: -3215.0017434643833.\n"
     ]
    }
   ],
   "source": [
    "precomputed_config = OptimizationConfig(\n",
    "        data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt\",\n",
    "        response_type=\"regression\",\n",
    "        training_dataset_file=\"../tests/data/precomputed_descriptor/train_with_fp.csv\",\n",
    "        split_strategy=Stratified(fraction=0.2),\n",
    "    ),\n",
    "    descriptors=[\n",
    "        CompositeDescriptor.new(\n",
    "            descriptors=[\n",
    "                PrecomputedDescriptorFromFile.new(file=\"../tests/data/precomputed_descriptor/train_with_fp.csv\",\n",
    "                                                 input_column=\"canonical\", response_column=\"fp\"),\n",
    "                ECFP.new()])\n",
    "    ],\n",
    "    algorithms=[\n",
    "        RandomForestRegressor.new(n_estimators={\"low\": 5, \"high\": 10}),\n",
    "        Ridge.new(),\n",
    "        Lasso.new(),\n",
    "        PLSRegression.new(),\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=2,\n",
    "        n_trials=4,\n",
    "        n_startup_trials=0,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "        track_to_mlflow=False,\n",
    "        random_seed=42,\n",
    "    ),\n",
    ") \n",
    "\n",
    "precomputed_study = optimize(precomputed_config, study_name=\"precomputed_example\")\n",
    "build_best(buildconfig_best(precomputed_study), \"../target/precomputed_model.pkl\")\n",
    "with open(\"../target/precomputed_model.pkl\", \"rb\") as f:\n",
    "    precomputed_model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "N.B: The `qsartuna-predict` CLI command for QSARtuna contains the options `--input-precomputed-file`, `input-precomputed-input-column` and `--input-precomputed-response-column` for generating predictions at inference time. However this is not available within python notebooks and calling predict on a new set of unseen molecules will cause \"Could not find descriptor errors\" like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Could not find descriptor for CCC in file ../tests/data/precomputed_descriptor/train_with_fp.csv.\n",
      "Could not find descriptor for CC(=O)Nc1ccc(O)cc1 in file ../tests/data/precomputed_descriptor/train_with_fp.csv.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([nan, nan])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_molecules = [\"CCC\", \"CC(=O)Nc1ccc(O)cc1\"]\n",
    "\n",
    "precomputed_model.predict_from_smiles(new_molecules)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A precomputed desciptors from a file should be provided, and the `inference_parameters` function called, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([335.0447, 335.3502])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tempfile # For this example we use a temp file to store a temporary inference dataset\n",
    "\n",
    "# extract precomputed descriptor (i.e the 1st descriptor in the composite descriptor for this example)\n",
    "precomputed_descriptor = precomputed_model.descriptor.parameters.descriptors[0]\n",
    "\n",
    "# example fp with 0's for illustration purposes\n",
    "example_fp = str([0] * 512)[1:-1]\n",
    "\n",
    "with tempfile.NamedTemporaryFile() as temp_file:\n",
    "    # write the query data to a new file\n",
    "    X = pd.DataFrame(\n",
    "        data={\"canonical\": new_molecules,\n",
    "              \"fp\": [example_fp for i in range(len(new_molecules))]})\n",
    "    X.to_csv(temp_file.name)\n",
    "    \n",
    "    # set precomputed descriptor to the new file\n",
    "    precomputed_descriptor.inference_parameters(temp_file.name, \"canonical\", \"fp\")\n",
    "    preds = precomputed_model.predict_from_smiles([\"CCC\", \"CC(=O)Nc1ccc(O)cc1\"])\n",
    "\n",
    "preds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preexisting models: Convert scikit-learn(-like) models to QSARtuna models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to convert existing scikit-learn or scikit-learn like models (which support the relevant `fit`, `predict`, `predict_proba` functions) to QSARtuna models for use with external tools expecting QSARtuna objects (e.g. REINVENT) or for reuse as a preexisting model into new Optimisation runs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple QSARtuna conversion example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CLI tool `qsartuna-convert` is available for converting preexisting models, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "usage: prog [-h] --input-model-file INPUT_MODEL_FILE --input-model-mode\n",
      "            INPUT_MODEL_MODE --output-model-path OUTPUT_MODEL_PATH\n",
      "            [--input-json-descriptor-file INPUT_JSON_DESCRIPTOR_FILE]\n",
      "            [--wrap-for-uncertainty]\n",
      "\n",
      "Convert an existing sklearn(-like) model into a QSARtuna model\n",
      "\n",
      "options:\n",
      "  -h, --help            show this help message and exit\n",
      "  --input-json-descriptor-file INPUT_JSON_DESCRIPTOR_FILE\n",
      "                        Name of input JSON file with descriptor configuration.\n",
      "                        Defaults to PrecomputedDescriptorFromFile\n",
      "  --wrap-for-uncertainty\n",
      "                        Whether to wrap regression in MAPIE or classification\n",
      "                        in VennAbers Calibrated Classifiers for uncertainty\n",
      "                        support\n",
      "\n",
      "required named arguments:\n",
      "  --input-model-file INPUT_MODEL_FILE\n",
      "                        Model file name.\n",
      "  --input-model-mode INPUT_MODEL_MODE\n",
      "                        Classification or regression mode for the existing\n",
      "                        model.\n",
      "  --output-model-path OUTPUT_MODEL_PATH\n",
      "                        Path where to write the converted model.\n"
     ]
    }
   ],
   "source": [
    "from optunaz import convert\n",
    "from unittest.mock import patch\n",
    "import sys\n",
    "\n",
    "convert_args = [\n",
    "    \"prog\",\n",
    "    \"-h\",\n",
    "]\n",
    "with patch.object(sys, \"argv\", convert_args):\n",
    "    try:\n",
    "        convert.main()\n",
    "    except SystemExit:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CLI helper function expects an `input-model-file`, the `input-model-mode` (classification or regression) and an `output-model-path` for the new QSARtuna model. In the following example, we convert a toy regression (which returns the sum of the input descriptors as a prediction) as a QSARtuna model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-10-02 18:21:27,199.199 WARNING convert - convert: input-json-descriptor-file not provided, default (PrecomputedDescriptorFromFile) will be used\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-02 18:21:27,199.199 WARNING convert - convert: input-json-descriptor-file not provided, default (PrecomputedDescriptorFromFile) will be used\n"
     ]
    }
   ],
   "source": [
    "from optunaz.model_writer import Predictor\n",
    "\n",
    "# This is a simple custom predict model, \n",
    "# which is used for demonstration purposes\n",
    "class RegModel(Predictor):\n",
    "    def predict(self, xs):\n",
    "        return np.sum(xs, axis=1)\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        pass\n",
    "\n",
    "\n",
    "with open(str(\"../target/regmodel_convert_example.pkl\"), \"wb\") as model_f:\n",
    "    pickle.dump(RegModel(), model_f)\n",
    "    \n",
    "convert_args = [\n",
    "    \"prog\",\n",
    "    \"--input-model-file\",\n",
    "    \"../target/regmodel_convert_example.pkl\",\n",
    "    \"--input-model-mode\",\n",
    "    \"regression\",\n",
    "    \"--output-model-path\",\n",
    "    \"../target/regmodel_convert_example_converted.pkl\",\n",
    "]\n",
    "\n",
    "with patch.object(sys, \"argv\", convert_args):\n",
    "    convert.main()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the logs we see `input-json-descriptor-file not provided, default (PrecomputedDescriptorFromFile) will be used` reported.\n",
    "\n",
    "Since no descriptor JSON configuration was provided, the default behaviour is for converted model to require preompcuted descriptors (as we presume that the descriptors used to train the preexisting model are not implemented in QSARtuna).\n",
    "\n",
    "It is possible to also provide a JSON containing the details of a descriptor to wrap with the model, if this is a QSARtuna compatible descriptor already implemented in QSARtuna, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-10-02 18:21:27,232.232 INFO convert - convert: Descriptor ECFP(name='ECFP', parameters=ECFP.Parameters(radius=3, nBits=2048, returnRdkit=False)) will be used from provided JSON\n"
     ]
    }
   ],
   "source": [
    "# create a JSON with the default ECFP configuration for --input-json-descriptor-file\n",
    "with open(\"../target/ecfp.json\", \"wt\") as ecfp_f:\n",
    "    json.dump(apischema.serialize(ECFP.new()), ecfp_f)\n",
    "\n",
    "convert_args = [\n",
    "    \"prog\",\n",
    "    \"--input-model-file\",\n",
    "    \"../target/regmodel_convert_example.pkl\",\n",
    "    \"--input-model-mode\",\n",
    "    \"regression\",\n",
    "    \"--input-json-descriptor-file\",\n",
    "    \"../target/ecfp.json\",   # the default ECFP configuration is provided to qsartuna-convert \n",
    "    \"--output-model-path\",\n",
    "    \"../target/ecfp_convert_example.pkl\",\n",
    "]\n",
    "\n",
    "with patch.object(sys, \"argv\", convert_args):\n",
    "    convert.main()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a QSARtuna model which will return the sum of ECFP bits turned on as a prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add uncertainty estimation to preexisting and QSARtuna models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to add uncertainty estimation to models by 'wrapping' preexisitng models (so long as they have the X/y labels stored in self.X_ and self.y_) with compatible uncertainty quantification methods using the `----wrap-for-uncertainty` flag.\n",
    "\n",
    "In this scenario MAPIE (for regression) or VennAbers calibrated classifiers (for classification) will be used to retrain the custom model.\n",
    "\n",
    "It even possible to add uncertainty estimation to preexisting QSARtuna models, where the descriptor JSON is not required since the QSARtuna descriptor coming with the model will simply be used. For example, we may convert the previous model we generated from the covariate section above at `../target/vector_covariate_model` like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "convert_args = [\n",
    "    \"prog\",\n",
    "    \"--input-model-file\",\n",
    "    \"../target/vector_covariate_model.pkl\",\n",
    "    \"--input-model-mode\",\n",
    "    \"regression\",\n",
    "    \"--output-model-path\",\n",
    "    str(\"../target/qsartuna_add_uncertainty.pkl\"),\n",
    "]\n",
    "\n",
    "with patch.object(sys, \"argv\", convert_args):\n",
    "    convert.main()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preexisting models: Using prexisting models as custom models for Optimisation or Build"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`CustomRegressionModel` and `CustromClassifiationModel` are available to use existing `.pkl` files in at Optimisation and Build time. Custom models can also be prexisting QSARtuna models (including converted QSARtuna models as per the previous section).\n",
    "\n",
    "These methods require the location of a preexisting model pkl file (`preexisting_model`) and whether you would like the custom estimator to be refit during Optimisation or Build (N.B: most sklearn models will refit to a brand new estimator by default, so any previous fitting data would be lost if this is set to `1`).\n",
    "\n",
    "This function can be used like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:21:27,307] A new study created in memory with name: custom_example\n",
      "[I 2024-10-02 18:21:27,351] A new study created in memory with name: study_name_0\n",
      "[I 2024-10-02 18:21:30,025] Trial 0 finished with value: -104472.28996365008 and parameters: {'algorithm_name': 'CustomRegressionModel', 'CustomRegressionModel_algorithm_hash': '28a16a59398d185b3254cc83cdaf7ca8', 'preexisting_model__28a16a59398d185b3254cc83cdaf7ca8': '../target/regmodel_convert_example.pkl', 'refit_model__28a16a59398d185b3254cc83cdaf7ca8': 0, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -104472.28996365008.\n"
     ]
    }
   ],
   "source": [
    "from optunaz.config.optconfig import CustomRegressionModel\n",
    "\n",
    "\n",
    "custom_config = OptimizationConfig(\n",
    "        data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt\",\n",
    "        response_type=\"regression\",\n",
    "        training_dataset_file=\"../tests/data/DRD2/subset-50/train.csv\",\n",
    "    ),\n",
    "    descriptors=[\n",
    "        ECFP.new()\n",
    "    ],\n",
    "    algorithms=[\n",
    "        CustomRegressionModel.new(preexisting_model=\"../target/regmodel_convert_example.pkl\",\n",
    "                                 refit_model=0    # example parameter, showing how to prevent the \n",
    "                                                  #retraining of the model during optimisation\n",
    "                                 )\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=2,\n",
    "        n_trials=1,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "    ),\n",
    ") \n",
    "\n",
    "custom_study = optimize(custom_config, study_name=\"custom_example\")\n",
    "build_best(buildconfig_best(custom_study), \"../target/custom_model.pkl\") # Build the optimal custom model\n",
    "with open(\"../target/custom_model.pkl\", \"rb\") as f:\n",
    "    custom_model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to combine the above configuration with other algorithms and descriptors to trial custom models with the default QSARtuna options, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-10-02 18:21:30,324] A new study created in memory with name: custom_mixed_example\n",
      "[I 2024-10-02 18:21:30,383] A new study created in memory with name: study_name_0\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/optunaz/descriptors.py:180)\n",
      "  return self._cached_call(args, kwargs, shelving=False)[0]\n",
      "[I 2024-10-02 18:21:30,528] Trial 0 finished with value: -3043.4473106576693 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011994365911634164, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -3043.4473106576693.\n",
      "[I 2024-10-02 18:21:30,590] Trial 1 finished with value: -3043.39919100435 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.029071783512897825, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -3043.39919100435.\n",
      "[I 2024-10-02 18:21:30,731] Trial 2 finished with value: -4621.489703141113 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.022631709120790048, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.2198637677605415, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -3043.39919100435.\n",
      "[I 2024-10-02 18:21:30,842] Trial 3 finished with value: -770.039055241578 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4846814642387681, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 3 with value: -770.039055241578.\n",
      "[I 2024-10-02 18:21:30,887] Trial 4 finished with value: -3465.318936926075 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 3 with value: -770.039055241578.\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.979e+01, tolerance: 1.868e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.776e+01, tolerance: 1.971e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.609e+01, tolerance: 1.829e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "[I 2024-10-02 18:21:30,996] Trial 5 finished with value: -255.7472744013205 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.02838446058299282, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -255.7472744013205.\n",
      "[I 2024-10-02 18:21:31,057] Trial 6 finished with value: -294.89857105510674 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1638638684773267, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -255.7472744013205.\n",
      "[I 2024-10-02 18:21:31,112] Trial 7 finished with value: -5093.524730054582 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7436415797926526, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -255.7472744013205.\n",
      "[I 2024-10-02 18:21:34,944] Trial 8 finished with value: -51690.21185741673 and parameters: {'algorithm_name': 'CustomRegressionModel', 'CustomRegressionModel_algorithm_hash': '28a16a59398d185b3254cc83cdaf7ca8', 'preexisting_model__28a16a59398d185b3254cc83cdaf7ca8': '../target/regmodel_convert_example.pkl', 'refit_model__28a16a59398d185b3254cc83cdaf7ca8': 0, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -255.7472744013205.\n",
      "[I 2024-10-02 18:21:35,029] Trial 9 finished with value: -3356.140674214637 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': <RandomForestMaxFeatures.AUTO: 'auto'>, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -255.7472744013205.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from optunaz.utils.preprocessing.transform import VectorFromColumn\n",
    "\n",
    "custom_mixed_config = OptimizationConfig(\n",
    "        data=Dataset(\n",
    "        input_column=\"canonical\",\n",
    "        response_column=\"molwt\",\n",
    "        response_type=\"regression\",\n",
    "        training_dataset_file=\"../tests/data/precomputed_descriptor/train_with_fp.csv\",\n",
    "        aux_column=\"fp\", # use a comma separated co-variate vector in column `fp`\n",
    "        aux_transform=VectorFromColumn.new(), # split the comma separated values into a vector\n",
    "        split_strategy=Stratified(fraction=0.2),\n",
    "    ),\n",
    "    descriptors=[\n",
    "        ECFP.new(), \n",
    "        ECFP_counts.new(), \n",
    "        MACCS_keys.new(),\n",
    "    ],\n",
    "    algorithms=[\n",
    "        SVR.new(),\n",
    "        RandomForestRegressor.new(n_estimators={\"low\": 5, \"high\": 10}),\n",
    "        Ridge.new(),\n",
    "        Lasso.new(),\n",
    "        PLSRegression.new(),\n",
    "        CustomRegressionModel.new(preexisting_model=\"../target/regmodel_convert_example.pkl\", refit_model=0)\n",
    "    ],\n",
    "    settings=OptimizationConfig.Settings(\n",
    "        mode=ModelMode.REGRESSION,\n",
    "        cross_validation=3,\n",
    "        n_trials=10,\n",
    "        n_startup_trials=0,\n",
    "        direction=OptimizationDirection.MAXIMIZATION,\n",
    "        track_to_mlflow=False,\n",
    "        random_seed=42,\n",
    "    ),\n",
    ")\n",
    "custom_study = optimize(custom_mixed_config, study_name=\"custom_mixed_example\")\n",
    "\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "ax = sns.scatterplot(data=custom_study.trials_dataframe(), x=\"number\", \n",
    "                     y=\"value\",style='params_algorithm_name',hue='params_algorithm_name')\n",
    "ax.set(xlabel=\"Trial number\",ylabel=f\"Ojbective value\\n({default_reg_scoring})\")\n",
    "sns.move_legend(ax, \"upper right\", bbox_to_anchor=(1.6, 1), ncol=1, title=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML (Automated model retraining)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The AutoML daemon functionaility in QSARtuna automates the process of preparing data for model training, including data cleaning, feature extraction, and data formatting, streamlining the data preprocessing stage. The main aspects of this workflow are the following:\n",
    "\n",
    "* __Automated Data Preparation__: Automated process of preparing data for model training, including cleaning, feature extraction, formatting and quorum checks, streamlining data preprocessing\n",
    "\n",
    "* __Model Training with SLURM__: Integration with SLURM to dispatch tasks, leveraging distributed computing resources for efficient and scalable model training\n",
    "\n",
    "* __Scalable and Efficient with Dynamic Resource Allocation__: Workflow designed to handle large datasets (with multiple prediction tasks) and dynamically utilize CPU/GPU/memory HPC resources\n",
    "\n",
    "* __Customizable SLURM and QSARtuna Templates__: SLURM templates can be tailored for different use cases. Both initial training and retraining QSARtuna JSON configurations are used, allowing users customise which algorithms and descriptors should be trialed. The default configuration will for e.g. train an initial ChemProp model, and subsequent models will automatically trial Transfer Learning (TL) from previous models for new data, when appropriate\n",
    "\n",
    "* __Metadata, Prediction and Model Tracking__: The code includes functionality for tracking temporal performance, raw test predictions, active learning predictions and exported QSARtuna models, aiding monitoring and evaluating pseudo-prospective model performance over time\n",
    "\n",
    "* __Automatic Job Resubmission__: In case of SLURM job failures, the code provides functionality to automatically resubmit failed jobs with modified resource allocations, enhancing the robustness of the model training process\n",
    "\n",
    "* __Parallel Task Processing__: Supports for parallel processing training tasks, allowing for efficient handling of multiple retraining tasks simultaneously, reducing overall processing time\n",
    "\n",
    "* __Dry Run Mode__: Dry run mode option enables users to simulate the process without actually submitting jobs, useful for verifying configurations and testing the workflow\n",
    "\n",
    "The following is an example from the QSARtuna unit tests:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "usage: prog [-h] --output-path OUTPUT_PATH --email EMAIL --user_name USER_NAME\n",
      "            --input-data INPUT_DATA --input-smiles-csv-column\n",
      "            INPUT_SMILES_CSV_COLUMN --input-activity-csv-column\n",
      "            INPUT_ACTIVITY_CSV_COLUMN --input-task-csv-column\n",
      "            INPUT_TASK_CSV_COLUMN --input-initial-template\n",
      "            INPUT_INITIAL_TEMPLATE --input-retrain-template\n",
      "            INPUT_RETRAIN_TEMPLATE --input-slurm-template INPUT_SLURM_TEMPLATE\n",
      "            [--quorum QUORUM] [--n-cores N_CORES] [--dry-run] [-v]\n",
      "            [--slurm-req-cores SLURM_REQ_CORES]\n",
      "            [--slurm-req-mem SLURM_REQ_MEM] --slurm-req-partition\n",
      "            SLURM_REQ_PARTITION --slurm-al-pool SLURM_AL_POOL\n",
      "            --slurm-al-smiles-csv-column SLURM_AL_SMILES_CSV_COLUMN\n",
      "            --slurm-job-prefix SLURM_JOB_PREFIX\n",
      "            [--slurm-failure-cores-increment SLURM_FAILURE_CORES_INCREMENT]\n",
      "            [--slurm-failure-mem-increment SLURM_FAILURE_MEM_INCREMENT]\n",
      "            [--slurm-failure-mins-increment SLURM_FAILURE_MINS_INCREMENT]\n",
      "            [--slurm-failure-max-retries SLURM_FAILURE_MAX_RETRIES]\n",
      "            [--slurm-failure-max-mem SLURM_FAILURE_MAX_MEM]\n",
      "            [--slurm-failure-max-cpu SLURM_FAILURE_MAX_CPU]\n",
      "            [--save-previous-models]\n",
      "\n",
      "AutoML scheduling for temporal automatic retraining of QSARtuna models\n",
      "\n",
      "options:\n",
      "  -h, --help            show this help message and exit\n",
      "  --quorum QUORUM\n",
      "  --n-cores N_CORES\n",
      "  --dry-run\n",
      "  -v, --verbose\n",
      "  --slurm-req-cores SLURM_REQ_CORES\n",
      "  --slurm-req-mem SLURM_REQ_MEM\n",
      "  --slurm-failure-cores-increment SLURM_FAILURE_CORES_INCREMENT\n",
      "  --slurm-failure-mem-increment SLURM_FAILURE_MEM_INCREMENT\n",
      "  --slurm-failure-mins-increment SLURM_FAILURE_MINS_INCREMENT\n",
      "  --slurm-failure-max-retries SLURM_FAILURE_MAX_RETRIES\n",
      "  --slurm-failure-max-mem SLURM_FAILURE_MAX_MEM\n",
      "  --slurm-failure-max-cpu SLURM_FAILURE_MAX_CPU\n",
      "  --save-previous-models\n",
      "\n",
      "required named arguments:\n",
      "  --output-path OUTPUT_PATH\n",
      "                        Path to the output AutoML directory\n",
      "  --email EMAIL         Email for SLURM job notifications\n",
      "  --user_name USER_NAME\n",
      "                        HPC 'username' for the AutoML user\n",
      "  --input-data INPUT_DATA\n",
      "                        Name of the input file[s]. For multiple files use '*'\n",
      "                        in wildcard expression\n",
      "  --input-smiles-csv-column INPUT_SMILES_CSV_COLUMN\n",
      "                        Column name of SMILES column in csv file\n",
      "  --input-activity-csv-column INPUT_ACTIVITY_CSV_COLUMN\n",
      "                        Column name of activity column in data file\n",
      "  --input-task-csv-column INPUT_TASK_CSV_COLUMN\n",
      "                        Column name of task column in data file\n",
      "  --input-initial-template INPUT_INITIAL_TEMPLATE\n",
      "  --input-retrain-template INPUT_RETRAIN_TEMPLATE\n",
      "  --input-slurm-template INPUT_SLURM_TEMPLATE\n",
      "  --slurm-req-partition SLURM_REQ_PARTITION\n",
      "  --slurm-al-pool SLURM_AL_POOL\n",
      "  --slurm-al-smiles-csv-column SLURM_AL_SMILES_CSV_COLUMN\n",
      "  --slurm-job-prefix SLURM_JOB_PREFIX\n"
     ]
    }
   ],
   "source": [
    "from optunaz import automl\n",
    "import sys\n",
    "\n",
    "aml_args = [\n",
    "    \"prog\",\n",
    "    \"-h\",\n",
    "]\n",
    "with patch.object(sys, \"argv\", aml_args):\n",
    "    try:\n",
    "        automl.main()\n",
    "    except SystemExit:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Note on High-Performance Computing (HPC) Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This workflow is designed for use with one particular SLURM setup used in-house. If you intend to functionilaity with a different job scheduling system, significant modifications to the SLURM-specific components are necessary. Please ensure thorough understanding and necessary adaptations. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data extraction options"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QSARtuna AutoML expects temporal data (`--input-data`) to have been exported from warehouses/databases in a flat file structure in CSV format (which can also be gz compressed), containing  SMILES, activity and task (which denotes each distinct property to be modelled) CSV columns. \n",
    "\n",
    "Exports are expected to be temporal in nature, with the naming convention `%Y-%m-%d` (see [here](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior) for details). Data can be exported in two ways:\n",
    "\n",
    "* 1.) __Multiple files__: Each extraction date gets an distinct/unique file with `%Y-%m-%d` format within the filename, which denotes that point in temporal train time, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 128\r\n",
      "-rw-r--r--@ 1 kljk345  staff   9.2K Aug 27 12:08 2024-01-01.csv\r\n",
      "-rw-r--r--@ 1 kljk345  staff    12K Aug 27 12:08 2024-02-01.csv\r\n",
      "-rw-r--r--@ 1 kljk345  staff    12K Aug 27 12:08 2024-03-01.csv\r\n",
      "-rw-r--r--@ 1 kljk345  staff    12K Aug 27 12:08 2024-04-01.csv\r\n",
      "-rw-r--r--@ 1 kljk345  staff   438B Aug 27 12:08 2024-05-01.csv\r\n"
     ]
    }
   ],
   "source": [
    "ls -lrth ../tests/data/automl/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 2.) __Single file__: Data extraction is written to a single file (with any naming convention). The last modified date of the file is compared to the most recent round of AutoML training. If the file has undergone an update, then a new model building procedure is triggered."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Walkthough running an AutoML pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main files that should be configured by users are the following:\n",
    "\n",
    "* `--input-initial-template` dictates the first optimisation configuration for a property\n",
    "* `--input-retrain-template` defines any further optimisation configurations, which e.g. could be configured to perform transfer learning on a compatible model from the `--input-initial-template`\n",
    "* `--input-slurm-template` is the bash script to orchestrate model [re]-training. The default provided in `../examples/slurm-scripts/automl.template` is setup to use singularity commands, and for easy configuration within our current HPC setup (e.g. different partitions/memory allocations can be easily modified)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us consider the following AutoML setup:\n",
    "\n",
    "* Training data is located in `../tests/data/automl/`\n",
    "* The config for an initial round of model training is in `../examples/automl/config.initial.template`\n",
    "* A config for any subsequent models is in `../examples/automl/config.retrain.template`\n",
    "* Our slurm template is in `../examples/slurm-scripts/automl.template`\n",
    "* HPC environment dictates our maximum requested resources should not exceed 50G of memory and 26 cpu\n",
    "* We would like to retain all previous models\n",
    "* A pool of compounds that could be added tested and added to a future model is in `../tests/data/DRD2/subset-1000/train.csv `\n",
    "\n",
    "Then our configuration would be:\n",
    "\n",
    "```\n",
    "qsartuna-automl \n",
    "   --input-data \"../tests/data/automl/*\"  \\\n",
    "   --email <example>@<example>.com  --user_name <example>  \\ # username should be accurate to monitor jobs\n",
    "   --input-smiles-csv-column canonical  --input-activity-csv-column molwt \\\n",
    "   --input-task-csv-column one_taskid  \\ # one_taskid in the toy data set has only one example task \n",
    "   --input-initial-template ../examples/automl/config.initial.template \\\n",
    "   --input-retrain-template ../examples/automl/config.retrain.template \\\n",
    "   --input-slurm-template ../examples/slurm-scripts/automl.template \\\n",
    "   --n-cores 1 -vvv --slurm-al-pool ../tests/data/DRD2/subset-1000/train.csv \\\n",
    "   --slurm-al-smiles-csv-column canonical  --output-path ../example_automl \\\n",
    "   --slurm-failure-max-cpu 26 --slurm-failure-max-mem 50 --save-previous-models \\\n",
    "   --slurm-job-prefix testaml --slurm-req-partition testpartition\n",
    "```\n",
    "\n",
    "and we can perform a test dry-run of this command from within our workbook like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-10-02 18:21:35,723.723 INFO automl - main: Namespace(output_path='../target/automl_example', email='test@test.com', user_name='test', input_data='../tests/data/automl/*', input_smiles_csv_column='canonical', input_activity_csv_column='molwt', input_task_csv_column='one_taskid', input_initial_template='../examples/automl/config.initial.template', input_retrain_template='../examples/automl/config.retrain.template', input_slurm_template='../examples/slurm-scripts/automl.template', quorum=25, n_cores=1, dry_run=True, verbose=2, slurm_req_cores=12, slurm_req_mem=None, slurm_req_partition='testpartition', slurm_al_pool='../tests/data/DRD2/subset-1000/train.csv', slurm_al_smiles_csv_column='canonical', slurm_job_prefix='testaml', slurm_failure_cores_increment=4, slurm_failure_mem_increment=20, slurm_failure_mins_increment=720, slurm_failure_max_retries=5, slurm_failure_max_mem=200, slurm_failure_max_cpu=20, save_previous_models=False)\n",
      "2024-10-02 18:21:35,729.729 DEBUG automl - main: Processing timepoint 24_01_01\n",
      "2024-10-02 18:21:35,730.730 DEBUG automl - first_run: ../target/automl_example does not exist, creating it\n",
      "2024-10-02 18:21:35,731.731 DEBUG automl - first_run: ../target/automl_example/processed_timepoints.json not set\n",
      "2024-10-02 18:21:35,731.731 DEBUG automl - initProcessedTimepoints: Init first processed timepoint to: ../target/automl_example/processed_timepoints.json\n",
      "2024-10-02 18:21:35,733.733 DEBUG automl - checkSkipped: ../target/automl_example/data/TID1/.skip not present\n",
      "2024-10-02 18:21:35,733.733 DEBUG automl - checkisLocked: 24_01_01: Lockfile [../target/automl_example/data/TID1/.24_01_01] not set; no lock for taskcode [TID1]\n",
      "2024-10-02 18:21:35,737.737 DEBUG automl - processRetraining: TID1: Dynamic resource allocation mem: 60G\n",
      "2024-10-02 18:21:35,740.740 DEBUG automl - processRetraining: TID1: 24_01_01: No temporal predictions since [No previous model found for [../target/automl_example/data/TID1/TID1.pkl]]\n",
      "2024-10-02 18:21:35,742.742 DEBUG automl - writeDataset: wrote dataset to ../target/automl_example/data/TID1/TID1.csv\n",
      "2024-10-02 18:21:35,746.746 DEBUG automl - writeSlurm: wrote slurm to ../target/automl_example/data/TID1/TID1.sh\n",
      "2024-10-02 18:21:35,749.749 DEBUG automl - writeJson: wrote json to ../target/automl_example/data/TID1/TID1.json\n",
      "2024-10-02 18:21:35,750.750 DEBUG automl - setJobLocked: lock_file for ../target/automl_example/data/TID1/.24_01_01 was set\n",
      "2024-10-02 18:21:35,750.750 DEBUG automl - submitJob: Dry run of /usr/bin/sbatch ../target/automl_example/data/TID1/TID1.sh\n",
      "2024-10-02 18:21:35,751.751 INFO automl - main: Exiting at this timepoint since there is work to do\n",
      "2024-10-02 18:21:35,751.751 DEBUG automl - main: Work: ['TID1']\n",
      "2024-10-02 18:21:35,751.751 INFO automl - main: AutoML script took [0.030505419] seconds.\n",
      "2024-10-02 18:21:35,752.752 DEBUG base - close: <pid.posix.PidFile object at 0x7fe081795840> closing pidfile: /Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/notebooks/prog.pid\n"
     ]
    }
   ],
   "source": [
    "aml_args = [\n",
    "    \"prog\",\n",
    "    \"--output-path\",\n",
    "    \"../target/automl_example\",\n",
    "    \"--email\",\n",
    "    \"test@test.com\",\n",
    "    \"--user_name\",\n",
    "    \"test\",\n",
    "    \"--input-data\",\n",
    "    \"../tests/data/automl/*\",\n",
    "    \"--input-smiles-csv-column\",\n",
    "    \"canonical\",\n",
    "    \"--input-activity-csv-column\",\n",
    "    \"molwt\",\n",
    "    \"--input-task-csv-column\",\n",
    "    \"one_taskid\",\n",
    "    \"--input-initial-template\",\n",
    "    \"../examples/automl/config.initial.template\",\n",
    "    \"--input-retrain-template\",\n",
    "    \"../examples/automl/config.retrain.template\",\n",
    "    \"--input-slurm-template\",\n",
    "    \"../examples/slurm-scripts/automl.template\",\n",
    "    \"--n-cores\",\n",
    "    \"1\",\n",
    "    \"--dry-run\", # The dry-run option is enabled, so the AutoML pipeline does not submit to SLURM\n",
    "    \"-vv\", # Use this CLI option to enable detailed debugging logging to observe QSARtuna AutoML behaviour \n",
    "    \"--slurm-al-pool\",\n",
    "    \"../tests/data/DRD2/subset-1000/train.csv\",\n",
    "    \"--slurm-al-smiles-csv-column\",\n",
    "    \"canonical\",\n",
    "    \"--slurm-job-prefix\",\n",
    "    \"testaml\",\n",
    "    \"--slurm-req-partition\",\n",
    "    \"testpartition\"\n",
    "]\n",
    "with patch.object(sys, \"argv\", aml_args):\n",
    "    automl.main()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the verbose `-vv` enables us to see what the automl script did behind the scenes.\n",
    "\n",
    "Let's check the `testaml` directory to see what the automl script has generated in the `../target/automl_example` output directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " | | |\r\n",
      " | | |-/processed_timepoints.json\r\n",
      " | | |-/data\r\n",
      " | | | |-TID1\r\n",
      " | | | | |-TID1.csv\r\n",
      " | | | | |-TID1.sh\r\n",
      " | | | | |-.24_01_01\r\n",
      " | | | | |-TID1.json\r\n"
     ]
    }
   ],
   "source": [
    "!find ../target/automl_example/ | sed -e \"s/[^-][^\\/]*\\// |/g\" -e \"s/|\\([^ ]\\)/|-\\1/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taken together, the above log and directory structure of `automl_example` shows that the:\n",
    "\n",
    "* first temporal point of training data has been correctly ingested (`24_01_01`)\n",
    "* one available task in this example (for which data is available) at the `24_01_01` timepoint, denoted as `TID1` (for `molwt` prediction) meets quourum and is hence indexed at `../tests/data/automl/data/TID1/`\n",
    "* resulting folder `data/TID1` comprises the following processed data:\n",
    "    * `TID1.csv` : molecular property data set ready for modelling\n",
    "    * `TID1.json`: config for an initial round of model training\n",
    "    * `TID1.sh`: used to run run QSARtuna AutoML via an `sbatch` command, though `-- dry-run` prevented this happening\n",
    "    * `.24_01_01` lock file initiated to track the status of the training at this timepoint\n",
    "* `processed_timepoints.json` is created to track which timepoints are processed\n",
    "\n",
    "The script stopped at this point, to allow for HPC resources to submit the initial optimisation job. Subsequent runs of the QSARtuna AutoML are required to progress past the initial optimisation run, and so could be scheduled (e.g. using `cron` or similar).\n",
    "\n",
    "Running the AutoML workflow does a dry-run check of the status of the run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-10-02 18:21:36,148.148 DEBUG base - setup: <pid.posix.PidFile object at 0x7fe0817962c0> entering setup\n",
      "2024-10-02 18:21:36,149.149 DEBUG base - create: <pid.posix.PidFile object at 0x7fe0817962c0> create pidfile: /Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/notebooks/prog.pid\n",
      "2024-10-02 18:21:36,151.151 DEBUG base - check: <pid.posix.PidFile object at 0x7fe0817962c0> check pidfile: /Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/notebooks/prog.pid\n",
      "2024-10-02 18:21:36,153.153 INFO automl - main: Namespace(output_path='../target/automl_example', email='test@test.com', user_name='test', input_data='../tests/data/automl/*', input_smiles_csv_column='canonical', input_activity_csv_column='molwt', input_task_csv_column='one_taskid', input_initial_template='../examples/automl/config.initial.template', input_retrain_template='../examples/automl/config.retrain.template', input_slurm_template='../examples/slurm-scripts/automl.template', quorum=25, n_cores=1, dry_run=True, verbose=2, slurm_req_cores=12, slurm_req_mem=None, slurm_req_partition='testpartition', slurm_al_pool='../tests/data/DRD2/subset-1000/train.csv', slurm_al_smiles_csv_column='canonical', slurm_job_prefix='testaml', slurm_failure_cores_increment=4, slurm_failure_mem_increment=20, slurm_failure_mins_increment=720, slurm_failure_max_retries=5, slurm_failure_max_mem=200, slurm_failure_max_cpu=20, save_previous_models=False)\n",
      "2024-10-02 18:21:36,159.159 DEBUG automl - main: Processing timepoint 24_01_01\n",
      "2024-10-02 18:21:36,160.160 DEBUG automl - first_run: ../target/automl_example/processed_timepoints.json exists\n",
      "2024-10-02 18:21:36,162.162 DEBUG automl - checkSkipped: ../target/automl_example/data/TID1/.skip not present\n",
      "2024-10-02 18:21:36,162.162 DEBUG automl - checkisLocked: 24_01_01: Lockfile [../target/automl_example/data/TID1/.24_01_01] locks the taskcode [TID1]\n",
      "2024-10-02 18:21:36,164.164 DEBUG automl - checkRunningSlurmJobs: Dry run of /usr/bin/squeue\n",
      "2024-10-02 18:21:36,164.164 WARNING automl - resubmitAnyFailedJobs: ../target/automl_example/data/TID1/TID1.sh never ran, so will be resubmit\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-02 18:21:36,164.164 WARNING automl - resubmitAnyFailedJobs: ../target/automl_example/data/TID1/TID1.sh never ran, so will be resubmit\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-10-02 18:21:36,167.167 DEBUG automl - submitJob: Dry run of /usr/bin/sbatch ../target/automl_example/data/TID1/TID1.sh\n",
      "2024-10-02 18:21:36,167.167 INFO automl - resubmitAnyFailedJobs: ../target/automl_example/data/TID1/TID1.sh resubmit (0 retrys)\n",
      "2024-10-02 18:21:36,168.168 INFO automl - resubmitAnyFailedJobs: Some jobs were resubmitted: ['TID1']\n",
      "2024-10-02 18:21:36,168.168 INFO automl - main: Exiting: 24_01_01 lock(s) indicate(s) work ongoing\n",
      "2024-10-02 18:21:36,168.168 INFO automl - main: AutoML script took [0.016326904] seconds.\n",
      "2024-10-02 18:21:36,168.168 DEBUG base - close: <pid.posix.PidFile object at 0x7fe0817962c0> closing pidfile: /Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/notebooks/prog.pid\n"
     ]
    }
   ],
   "source": [
    "with patch.object(sys, \"argv\", aml_args):\n",
    "    automl.main()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The subsequent run of the code above correctly identifies the job was never correctly submitted to the SLURM queue (due to the dry-run), and correctly assessed the previous number of retires: \"`resubmitAnyFailedJobs: ... resubmit (0 retrys)`\".\n",
    "\n",
    "NB: if an acutal job fails with a reported reason, dynamic resource allocation will attempt to increase job time/mem/cpu and resubmit to slurm, in an attempt to facilitate a successful run due to insufficient resources.\n",
    "\n",
    "We can now emulate a sucessful run by copying an example trained model to the directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-10-02 18:21:36,175.175 DEBUG base - setup: <pid.posix.PidFile object at 0x7fe081796080> entering setup\n",
      "2024-10-02 18:21:36,175.175 DEBUG base - create: <pid.posix.PidFile object at 0x7fe081796080> create pidfile: /Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/notebooks/prog.pid\n",
      "2024-10-02 18:21:36,176.176 DEBUG base - check: <pid.posix.PidFile object at 0x7fe081796080> check pidfile: /Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/notebooks/prog.pid\n",
      "2024-10-02 18:21:36,178.178 INFO automl - main: Namespace(output_path='../target/automl_example', email='test@test.com', user_name='test', input_data='../tests/data/automl/*', input_smiles_csv_column='canonical', input_activity_csv_column='molwt', input_task_csv_column='one_taskid', input_initial_template='../examples/automl/config.initial.template', input_retrain_template='../examples/automl/config.retrain.template', input_slurm_template='../examples/slurm-scripts/automl.template', quorum=25, n_cores=1, dry_run=True, verbose=2, slurm_req_cores=12, slurm_req_mem=None, slurm_req_partition='testpartition', slurm_al_pool='../tests/data/DRD2/subset-1000/train.csv', slurm_al_smiles_csv_column='canonical', slurm_job_prefix='testaml', slurm_failure_cores_increment=4, slurm_failure_mem_increment=20, slurm_failure_mins_increment=720, slurm_failure_max_retries=5, slurm_failure_max_mem=200, slurm_failure_max_cpu=20, save_previous_models=False)\n",
      "2024-10-02 18:21:36,181.181 DEBUG automl - main: Processing timepoint 24_01_01\n",
      "2024-10-02 18:21:36,182.182 DEBUG automl - first_run: ../target/automl_example/processed_timepoints.json exists\n",
      "2024-10-02 18:21:36,183.183 DEBUG automl - checkSkipped: ../target/automl_example/data/TID1/.skip not present\n",
      "2024-10-02 18:21:36,183.183 DEBUG automl - checkisLocked: 24_01_01: Lockfile [../target/automl_example/data/TID1/.24_01_01] not set; no lock for taskcode [TID1]\n",
      "2024-10-02 18:21:36,188.188 DEBUG automl - processRetraining: TID1: Fist timepoint\n",
      "2024-10-02 18:21:36,189.189 INFO automl - main: Work appears complete for timepoint 24_01_01\n",
      "2024-10-02 18:21:36,189.189 DEBUG automl - setProcessedTimepoints: Appended processed timepoint 24_01_01 to ../target/automl_example/processed_timepoints.json\n",
      "2024-10-02 18:21:36,192.192 DEBUG automl - getRetrainingData: 24_01_01 is in processed_timepoints.json\n",
      "2024-10-02 18:21:36,194.194 DEBUG automl - main: Processing timepoint 24_02_01\n",
      "2024-10-02 18:21:36,195.195 DEBUG automl - first_run: ../target/automl_example/processed_timepoints.json exists\n",
      "2024-10-02 18:21:36,196.196 DEBUG automl - checkSkipped: ../target/automl_example/data/TID1/.skip not present\n",
      "2024-10-02 18:21:36,197.197 DEBUG automl - checkisLocked: 24_02_01: Lockfile [../target/automl_example/data/TID1/.24_02_01] not set; no lock for taskcode [TID1]\n",
      "2024-10-02 18:21:36,201.201 DEBUG automl - processTrain: TID1: 152 new data points found\n",
      "2024-10-02 18:21:36,204.204 DEBUG automl - processRetraining: TID1: Dynamic resource allocation mem: 60G\n",
      "2024-10-02 18:21:36,206.206 DEBUG automl - processRetraining: TID1: 24_02_01: No temporal predictions since [No previous model found for [../target/automl_example/data/TID1/TID1.pkl]]\n",
      "2024-10-02 18:21:36,208.208 DEBUG automl - writeDataset: wrote dataset to ../target/automl_example/data/TID1/TID1.csv\n",
      "2024-10-02 18:21:36,209.209 DEBUG automl - writeSlurm: wrote slurm to ../target/automl_example/data/TID1/TID1.sh\n",
      "2024-10-02 18:21:36,209.209 DEBUG automl - writeJson: wrote json to ../target/automl_example/data/TID1/TID1.json\n",
      "2024-10-02 18:21:36,210.210 DEBUG automl - setJobLocked: lock_file for ../target/automl_example/data/TID1/.24_02_01 was set\n",
      "2024-10-02 18:21:36,210.210 DEBUG automl - submitJob: Dry run of /usr/bin/sbatch ../target/automl_example/data/TID1/TID1.sh\n",
      "2024-10-02 18:21:36,211.211 INFO automl - main: Exiting at this timepoint since there is work to do\n",
      "2024-10-02 18:21:36,212.212 DEBUG automl - main: Work: ['TID1']\n",
      "2024-10-02 18:21:36,212.212 INFO automl - main: AutoML script took [0.035192013] seconds.\n",
      "2024-10-02 18:21:36,212.212 DEBUG base - close: <pid.posix.PidFile object at 0x7fe081796080> closing pidfile: /Users/kljk345/PycharmProjects/Public_QSARtuna/D/QSARtuna/notebooks/prog.pid\n"
     ]
    }
   ],
   "source": [
    "import shutil\n",
    "import os\n",
    "\n",
    "os.remove('../target/automl_example/data/TID1/.24_01_01') # Remove the lock\n",
    "shutil.copy('../tests/data/DRD2/drd2_reg.pkl', '../target/automl_example/data/TID1/') # Add the example model\n",
    "\n",
    "with patch.object(sys, \"argv\", aml_args):\n",
    "    automl.main()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that after lock file removal and emulation of a trained model, the AutoML pipeline correctly identifies that \" Work appears complete for timepoint 24_01_01\". \n",
    "\n",
    "The output shows how the pipeline proceeds to the next timepoint, `24_02_01`, which has 152 new datapoints and is allocated dynamically allocated 60G of requested SLURM memory. A dry run to generate predictions for the pseodu-prospective performance of a \"24_01_01\" model vs. new data from \"24_02_01\" timepoint is now initiated.\n",
    "\n",
    "Upon generation of pseodu-prospective performance, the next next round of optimisation, build and active learning predictions are initiated for the next timepoint, and the process continues like so."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Schedule AutoML as a daemon for up-to-date models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This pipeline is intented to be run frequently to monitor input data and ensure models are trained and tested. How often this should occur will depend on the size/numer of prediction tasks to be processed. As an example you could create a `slurm_update_models_parallel.sh` file with the following:\n",
    "\n",
    "```\n",
    "#!/bin/bash\n",
    "#SBATCH --job-name=qsartuna_automl-%j\n",
    "#SBATCH -N 1\n",
    "#SBATCH -c 1\n",
    "#SBATCH --time=24:00:00\n",
    "#SBATCH --mem=12G\n",
    "#SBATCH --output=<your_path>/logs/update_it-%j.log\n",
    "#SBATCH --error=<your_path>/logs/update_it-%j.log\n",
    "\n",
    "\n",
    "ml Miniconda3\n",
    "conda activate my_env_with_qsartuna\n",
    "\n",
    "qsartuna-automl \n",
    "   --input-data \"<your_path>/tests/data/automl/*\"  \\\n",
    "   --email <example>@<example>.com  --user_name <example>  \\\n",
    "   --input-smiles-csv-column canonical  --input-activity-csv-column molwt \\\n",
    "   --input-task-csv-column one_taskid  \\\n",
    "   --input-initial-template <your_path>/examples/automl/config.initial.template \\\n",
    "   --input-retrain-template <your_path>/examples/automl/config.retrain.template \\\n",
    "   --input-slurm-template <your_path>/examples/slurm-scripts/automl.template \\\n",
    "   --n-cores 1 -vvv --slurm-al-pool <your_path>/tests/data/DRD2/subset-1000/train.csv \\\n",
    "   --slurm-al-smiles-csv-column canonical  --output-path <your_path>/example_automl \\\n",
    "   --slurm-failure-max-cpu 26 --slurm-failure-max-mem 50\n",
    "```\n",
    "\n",
    "and the following may be added to cron to run e.g. hourly:\n",
    "\n",
    "`@hourly /usr/bin/sbatch <your_path>/slurm_update_models_parallel.sh`"
   ]
  }
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