diff --git a/3a.train_dmso_logistic_regression_models/train_platemapwise_model/3.inference_on_treated.ipynb b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/3.inference_on_treated.ipynb
new file mode 100644
index 0000000..621b7a9
--- /dev/null
+++ b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/3.inference_on_treated.ipynb
@@ -0,0 +1,840 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d932fd4e",
+   "metadata": {},
+   "source": [
+    "## Perform inference on treated single cell profile dataset for hit-calling \n",
+    "Loads:\n",
+    "- All fold non-shuffled platemapwise models trained. \n",
+    "- All treated (non-DMSO) failing single cell profiles unseen during training.\n",
+    "\n",
+    "Computes inference between all models against all single profiles to yield k-fold split scorings on each single predicting whether it looks more like a cell from control patient with dilated cardiomyopathy or from healthy individual. \n",
+    "\n",
+    "Scores will be used in subsequent analysis assessing candidate treatments."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c54f8ea",
+   "metadata": {},
+   "source": [
+    "## Import libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "da34d623",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pathlib\n",
+    "import yaml\n",
+    "import json\n",
+    "from joblib import load\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import polars as pl\n",
+    "import statsmodels.api as sm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b18c58f8",
+   "metadata": {},
+   "source": [
+    "## Pathing and global parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c7603f78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random_state = 0\n",
+    "np.random.seed(random_state)\n",
+    "metadata_prefix = \"Metadata_\"\n",
+    "label_col = \"Metadata_cell_type\"\n",
+    "\n",
+    "# Path to directory with feature selected profiles\n",
+    "path_to_feature_selected_data = pathlib.Path().home() / \"mnt\" / \"bandicoot\" /\\\n",
+    "    \"CFReT_screening_data\" / \"screen_profiles\"\n",
+    "if not path_to_feature_selected_data.exists() and\\\n",
+    "    not path_to_feature_selected_data.is_dir():\n",
+    "    raise FileNotFoundError(\n",
+    "        f\"Directory {path_to_feature_selected_data} does not exist or is not a directory.\"\n",
+    "    )\n",
+    "\n",
+    "# Find all batch folders\n",
+    "batch_folders = list(path_to_feature_selected_data.glob(\"batch*\"))\n",
+    "batch_folders = [folder for folder in batch_folders if folder.is_dir()]\n",
+    "if not batch_folders:\n",
+    "    raise FileNotFoundError(\n",
+    "        f\"No batch folders found in {path_to_feature_selected_data}.\"\n",
+    "    )\n",
+    "\n",
+    "## Other needed paths\n",
+    "fitted_model_dir = pathlib.Path(\".\") / \"models\"\n",
+    "if not fitted_model_dir.exists():\n",
+    "    raise FileNotFoundError(f\"Fitted model directory not found: {fitted_model_dir}\")\n",
+    "\n",
+    "datasplit_dir = pathlib.Path(\".\") / \"datasplits\"\n",
+    "if not datasplit_dir.exists():\n",
+    "    raise FileNotFoundError(f\"Datasplit directory not found: {datasplit_dir}\")\n",
+    "\n",
+    "encoding_path = datasplit_dir / \"cell_type_encoding.json\"\n",
+    "if not encoding_path.exists():\n",
+    "    raise FileNotFoundError(f\"Cell type encoding file not found: {encoding_path}\")\n",
+    "\n",
+    "platemap_csv_dir = pathlib.Path(\".\").resolve().parent.parent  / \"metadata\" / \"updated_platemaps\"\n",
+    "if not platemap_csv_dir.exists():\n",
+    "    raise FileNotFoundError(\n",
+    "        f\"Directory {platemap_csv_dir} does not exist.\"\n",
+    "    )\n",
+    "\n",
+    "barcode_platemap_file = platemap_csv_dir / \"updated_barcode_platemap.csv\"\n",
+    "if not barcode_platemap_file.exists():\n",
+    "    raise FileNotFoundError(\n",
+    "        f\"Barcode platemap file not found: {barcode_platemap_file}\"\n",
+    "    )\n",
+    "platemap_files = [\n",
+    "    file \n",
+    "    for file in platemap_csv_dir.glob(\"*.csv\")\n",
+    "    if (file.stem not in [\"updated_barcode_platemap\", \"pathways_platemap\"])    \n",
+    "]\n",
+    "if not platemap_files:\n",
+    "    raise FileNotFoundError(\n",
+    "        f\"No platemap CSV files found in {platemap_csv_dir}.\"\n",
+    "    )\n",
+    "\n",
+    "# output path\n",
+    "output_dir = pathlib.Path(\".\") / \"inference_results\"\n",
+    "output_dir.mkdir(exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c566e147",
+   "metadata": {},
+   "source": [
+    "## Read in encodings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "088a2b10",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded cell type encoding for 2 cell types.\n"
+     ]
+    }
+   ],
+   "source": [
+    "encoding_dict = json.loads(encoding_path.read_text())\n",
+    "print(f\"Loaded cell type encoding for {len(encoding_dict)} cell types.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "634f263d",
+   "metadata": {},
+   "source": [
+    "## Read and wrangle platemap metadata\n",
+    "This later helps with collecting treatment wells completely missing single cell profiles,\n",
+    "which may indicate problems with toxicity. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5b42026a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Metadata_Well",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "cell_type",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "treatment",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "platemap_file",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "Metadata_Plate",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "platemap_num",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "Pathway",
+         "rawType": "str",
+         "type": "string"
+        }
+       ],
+       "ref": "ef01a616-f1f1-432c-948d-d57abe2d1072",
+       "rows": [
+        [
+         "4",
+         "B03",
+         "failing",
+         "UCD-0000253",
+         "Target_Selective_Library_Screen_Plate_3_with_pathways",
+         "CARD-CelIns-CX7_251205100001",
+         "3",
+         "Neuronal Signaling"
+        ],
+        [
+         "5",
+         "B03",
+         "failing",
+         "UCD-0000253",
+         "Target_Selective_Library_Screen_Plate_3_with_pathways",
+         "CARD-CelIns-CX7_251203170001",
+         "3",
+         "Neuronal Signaling"
+        ],
+        [
+         "6",
+         "B03",
+         "failing",
+         "UCD-0000253",
+         "Target_Selective_Library_Screen_Plate_3_with_pathways",
+         "CARD-CelIns-CX7_251208160001",
+         "3",
+         "Neuronal Signaling"
+        ],
+        [
+         "7",
+         "B03",
+         "failing",
+         "UCD-0000253",
+         "Target_Selective_Library_Screen_Plate_3_with_pathways",
+         "CARD-CelIns-CX7_251210180001",
+         "3",
+         "Neuronal Signaling"
+        ],
+        [
+         "8",
+         "B04",
+         "failing",
+         "UCD-0000872",
+         "Target_Selective_Library_Screen_Plate_3_with_pathways",
+         "CARD-CelIns-CX7_251205100001",
+         "3",
+         "Metabolism"
+        ]
+       ],
+       "shape": {
+        "columns": 7,
+        "rows": 5
+       }
+      },
+      "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>Metadata_Well</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>platemap_file</th>\n",
+       "      <th>Metadata_Plate</th>\n",
+       "      <th>platemap_num</th>\n",
+       "      <th>Pathway</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B03</td>\n",
+       "      <td>failing</td>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>Target_Selective_Library_Screen_Plate_3_with_p...</td>\n",
+       "      <td>CARD-CelIns-CX7_251205100001</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>B03</td>\n",
+       "      <td>failing</td>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>Target_Selective_Library_Screen_Plate_3_with_p...</td>\n",
+       "      <td>CARD-CelIns-CX7_251203170001</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>B03</td>\n",
+       "      <td>failing</td>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>Target_Selective_Library_Screen_Plate_3_with_p...</td>\n",
+       "      <td>CARD-CelIns-CX7_251208160001</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>B03</td>\n",
+       "      <td>failing</td>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>Target_Selective_Library_Screen_Plate_3_with_p...</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>B04</td>\n",
+       "      <td>failing</td>\n",
+       "      <td>UCD-0000872</td>\n",
+       "      <td>Target_Selective_Library_Screen_Plate_3_with_p...</td>\n",
+       "      <td>CARD-CelIns-CX7_251205100001</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Metabolism</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Metadata_Well cell_type    treatment  \\\n",
+       "4           B03   failing  UCD-0000253   \n",
+       "5           B03   failing  UCD-0000253   \n",
+       "6           B03   failing  UCD-0000253   \n",
+       "7           B03   failing  UCD-0000253   \n",
+       "8           B04   failing  UCD-0000872   \n",
+       "\n",
+       "                                       platemap_file  \\\n",
+       "4  Target_Selective_Library_Screen_Plate_3_with_p...   \n",
+       "5  Target_Selective_Library_Screen_Plate_3_with_p...   \n",
+       "6  Target_Selective_Library_Screen_Plate_3_with_p...   \n",
+       "7  Target_Selective_Library_Screen_Plate_3_with_p...   \n",
+       "8  Target_Selective_Library_Screen_Plate_3_with_p...   \n",
+       "\n",
+       "                 Metadata_Plate platemap_num             Pathway  \n",
+       "4  CARD-CelIns-CX7_251205100001            3  Neuronal Signaling  \n",
+       "5  CARD-CelIns-CX7_251203170001            3  Neuronal Signaling  \n",
+       "6  CARD-CelIns-CX7_251208160001            3  Neuronal Signaling  \n",
+       "7  CARD-CelIns-CX7_251210180001            3  Neuronal Signaling  \n",
+       "8  CARD-CelIns-CX7_251205100001            3          Metabolism  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "barcode_platemap_df = pd.read_csv(barcode_platemap_file)\n",
+    "\n",
+    "platemap_dfs = []\n",
+    "\n",
+    "for platemap_file in platemap_files:\n",
+    "\n",
+    "    platemap_repr = platemap_file.stem\n",
+    "    platemap_num = platemap_repr.split(\"_\")[-3]\n",
+    "\n",
+    "    df = pd.read_csv(platemap_file)\n",
+    "    df[\"platemap_file\"] = platemap_repr\n",
+    "    df[\"platemap_num\"] = platemap_num\n",
+    "    platemap_dfs.append(df)\n",
+    "\n",
+    "platemap_df = pd.concat(platemap_dfs, ignore_index=True)\n",
+    "platemap_df = pd.merge(\n",
+    "    platemap_df, \n",
+    "    barcode_platemap_df, \n",
+    "    on=[\"platemap_file\"], \n",
+    "    how=\"inner\",\n",
+    "    validate=\"many_to_many\"\n",
+    ")\n",
+    "\n",
+    "treatment_well_df = platemap_df.loc[\n",
+    "    :,\n",
+    "    [\"well_position\", \"cell_type\", \"treatment\", \"platemap_file\", \"plate_barcode\", \"platemap_num\", \"Pathway\"]\n",
+    "]\n",
+    "treatment_well_df = treatment_well_df[\n",
+    "    (treatment_well_df[\"treatment\"] != \"DMSO\") &\n",
+    "    (treatment_well_df[\"cell_type\"] == \"failing\")    \n",
+    "].rename(columns={\n",
+    "        \"plate_barcode\": \"Metadata_Plate\",\n",
+    "        \"well_position\": \"Metadata_Well\"\n",
+    "    }\n",
+    ")\n",
+    "treatment_well_df.to_csv(output_dir / \"treatment_well.csv\")\n",
+    "treatment_well_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "326d7bd0",
+   "metadata": {},
+   "source": [
+    "## Load all treatment profiles and models and inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "610eb578",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing batch: batch_1\n",
+      "Processing batch: batch_2\n",
+      "Processing batch: batch_3\n"
+     ]
+    }
+   ],
+   "source": [
+    "score_dfs = []\n",
+    "cell_counts = []\n",
+    "missing_profile_wells = {}\n",
+    "\n",
+    "for batch_folder in batch_folders:\n",
+    "\n",
+    "    print(f\"Processing batch: {batch_folder.name}\")\n",
+    "\n",
+    "    platemap_folders = list(\n",
+    "        f \n",
+    "        for f in batch_folder.glob(\"*\")\n",
+    "        if f.is_dir() \n",
+    "    )\n",
+    "\n",
+    "    for platemap_folder in platemap_folders:\n",
+    "\n",
+    "        platemap_repr = platemap_folder.name\n",
+    "        if not platemap_repr.startswith(\"platemap_\"):\n",
+    "            continue\n",
+    "        \n",
+    "        # Resolve fitted model checkpoints\n",
+    "        saved_model_files = (\n",
+    "            fitted_model_dir / platemap_repr\n",
+    "        ).resolve().glob(\"*original_statsmodels_logit.joblib\")\n",
+    "        if not saved_model_files:\n",
+    "            continue\n",
+    "        saved_model_files = sorted(list(saved_model_files))\n",
+    "\n",
+    "        # Resolve single cell profile folder\n",
+    "        sc_profile_folder = (platemap_folder / \"single_cell_profiles\").resolve()\n",
+    "        if not sc_profile_folder.exists() or not sc_profile_folder.is_dir():\n",
+    "            print(f\"\\tNo single_cell_profiles folder for {platemap_repr}. Skipping.\")\n",
+    "            continue\n",
+    "\n",
+    "        # Find all feature selected profile files for this platemap\n",
+    "        plate_files = list(\n",
+    "            f\n",
+    "            for f in sc_profile_folder.glob(\"*_sc_feature_selected.parquet\")\n",
+    "            if f.is_file()\n",
+    "        )\n",
+    "\n",
+    "        # Inference on each plate file using the fitted models and collect scores and metadata\n",
+    "        for plate_file in plate_files:\n",
+    "\n",
+    "            plate_repr = plate_file.stem\n",
+    "            \n",
+    "            # Only inference on non-DMSO treated failing cells\n",
+    "            # Note that training is done on cells that are DMSO treated and both failing and healthy\n",
+    "            df = (\n",
+    "                pl.scan_parquet(plate_file)\n",
+    "                .filter(\n",
+    "                    (pl.col(\"Metadata_treatment\") != \"DMSO\") & \n",
+    "                    (pl.col(\"Metadata_cell_type\") == \"failing\")\n",
+    "                )\n",
+    "                .collect(engine=\"cpu\")\n",
+    "                .to_pandas()\n",
+    "            )\n",
+    "            if df.empty:\n",
+    "                print(f\"\\t\\tNo non DMSO rows for {plate_file.stem}. Skipping.\")\n",
+    "                continue\n",
+    "            cell_counts.append(\n",
+    "                df.groupby(['Metadata_treatment', 'Metadata_Well', 'Metadata_Plate']).size().reset_index(name='row_count')\n",
+    "            )\n",
+    "\n",
+    "            # Compute missing treatments wells by cross-referencing with the platemap\n",
+    "            plate_name = df['Metadata_Plate'].unique()\n",
+    "            if len(plate_name) != 1:\n",
+    "                raise ValueError(f\"Multiple plate names found in {plate_file.stem}: {plate_name}.\")            \n",
+    "            plate_name = plate_name[0]\n",
+    "            \n",
+    "            treatment_well_df_subset = treatment_well_df[\n",
+    "                treatment_well_df[\"Metadata_Plate\"] == plate_name\n",
+    "            ].copy()\n",
+    "            treatment_wells = treatment_well_df_subset[\"Metadata_Well\"].unique()\n",
+    "            missing_treatment_wells = set(treatment_wells) - set(df[\"Metadata_Well\"].unique())            \n",
+    "            missing_treatments = treatment_well_df_subset[\n",
+    "                treatment_well_df_subset['Metadata_Well'].isin(missing_treatment_wells)\n",
+    "            ]['treatment'].unique()\n",
+    "            # collect missing profile wells info for this plate\n",
+    "            missing_profile_wells[plate_name] = list(missing_treatments)\n",
+    "\n",
+    "            # Iterate over fitted models for this platemap and compute scores\n",
+    "            scores = []\n",
+    "            treatment = df.loc[:, 'Metadata_treatment'].copy()\n",
+    "            for saved_model_file in saved_model_files:\n",
+    "\n",
+    "                model = load(saved_model_file)\n",
+    "\n",
+    "                feats = list(model.params.index)\n",
+    "                feats = [feat for feat in feats if feat != 'const']\n",
+    "                X = df.loc[:, feats].copy()\n",
+    "                X = sm.add_constant(X, has_constant=\"add\")\n",
+    "\n",
+    "                score = model.predict(X)\n",
+    "                if encoding_dict['healthy'] == 0:\n",
+    "                    # convert to larger means more healthy by inverting the scores\n",
+    "                    score_df['mean_score'] = 1 - score_df['mean_score']\n",
+    "                elif encoding_dict['healthy'] == 1:\n",
+    "                    pass\n",
+    "                else:\n",
+    "                    raise ValueError(\n",
+    "                        f\"Unexpected encoding for healthy cell type: {encoding_dict['healthy']}\"\n",
+    "                    )\n",
+    "                scores.append(score)\n",
+    "\n",
+    "            score_df = pd.concat(\n",
+    "                [treatment] + scores,\n",
+    "                axis=1\n",
+    "            )\n",
+    "            score_df.columns = [score_df.columns[0]] + [\n",
+    "                f\"fold_{col}_score\" for col in score_df.columns[1:]\n",
+    "            ]\n",
+    "            score_df['mean_score'] = score_df.iloc[:, 1:].mean(axis=1)\n",
+    "            score_df['plate'] =  plate_repr\n",
+    "            score_df['platemap'] = platemap_repr\n",
+    "            score_dfs.append(score_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4843c23f",
+   "metadata": {},
+   "source": [
+    "## Write results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0b52e8b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Metadata_treatment",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "fold_0_score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fold_1_score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fold_2_score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fold_3_score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "mean_score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "plate",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "platemap",
+         "rawType": "str",
+         "type": "string"
+        }
+       ],
+       "ref": "06b9d3a1-337f-442c-bf11-275cab5224d2",
+       "rows": [
+        [
+         "0",
+         "UCD-0000253",
+         "1.405144898043835e-05",
+         "3.255575500088818e-07",
+         "3.6096343148901472e-06",
+         "4.423902129767936e-06",
+         "5.602635743776329e-06",
+         "CARD-CelIns-CX7_251210180001_sc_feature_selected",
+         "platemap_3"
+        ],
+        [
+         "1",
+         "UCD-0000253",
+         "0.10630568541382204",
+         "0.999926697246038",
+         "0.9234447051934638",
+         "0.9952903744311449",
+         "0.7562418655711173",
+         "CARD-CelIns-CX7_251210180001_sc_feature_selected",
+         "platemap_3"
+        ],
+        [
+         "2",
+         "UCD-0000253",
+         "0.00019862336552110477",
+         "0.10104494406971701",
+         "0.8204221581684658",
+         "0.009254381210928728",
+         "0.23273002670365814",
+         "CARD-CelIns-CX7_251210180001_sc_feature_selected",
+         "platemap_3"
+        ],
+        [
+         "3",
+         "UCD-0000253",
+         "0.00010897232190528963",
+         "0.003034445604985777",
+         "0.0011685837332062038",
+         "0.0003438957279956733",
+         "0.001163974347023236",
+         "CARD-CelIns-CX7_251210180001_sc_feature_selected",
+         "platemap_3"
+        ],
+        [
+         "4",
+         "UCD-0000253",
+         "0.9827479875444656",
+         "0.9973340765490463",
+         "0.9999463052826586",
+         "0.9986012231214456",
+         "0.994657398124404",
+         "CARD-CelIns-CX7_251210180001_sc_feature_selected",
+         "platemap_3"
+        ]
+       ],
+       "shape": {
+        "columns": 8,
+        "rows": 5
+       }
+      },
+      "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>Metadata_treatment</th>\n",
+       "      <th>fold_0_score</th>\n",
+       "      <th>fold_1_score</th>\n",
+       "      <th>fold_2_score</th>\n",
+       "      <th>fold_3_score</th>\n",
+       "      <th>mean_score</th>\n",
+       "      <th>plate</th>\n",
+       "      <th>platemap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>0.000014</td>\n",
+       "      <td>3.255576e-07</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.000006</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001_sc_feature_selected</td>\n",
+       "      <td>platemap_3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>0.106306</td>\n",
+       "      <td>9.999267e-01</td>\n",
+       "      <td>0.923445</td>\n",
+       "      <td>0.995290</td>\n",
+       "      <td>0.756242</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001_sc_feature_selected</td>\n",
+       "      <td>platemap_3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>0.000199</td>\n",
+       "      <td>1.010449e-01</td>\n",
+       "      <td>0.820422</td>\n",
+       "      <td>0.009254</td>\n",
+       "      <td>0.232730</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001_sc_feature_selected</td>\n",
+       "      <td>platemap_3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>0.000109</td>\n",
+       "      <td>3.034446e-03</td>\n",
+       "      <td>0.001169</td>\n",
+       "      <td>0.000344</td>\n",
+       "      <td>0.001164</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001_sc_feature_selected</td>\n",
+       "      <td>platemap_3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>0.982748</td>\n",
+       "      <td>9.973341e-01</td>\n",
+       "      <td>0.999946</td>\n",
+       "      <td>0.998601</td>\n",
+       "      <td>0.994657</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001_sc_feature_selected</td>\n",
+       "      <td>platemap_3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Metadata_treatment  fold_0_score  fold_1_score  fold_2_score  fold_3_score  \\\n",
+       "0        UCD-0000253      0.000014  3.255576e-07      0.000004      0.000004   \n",
+       "1        UCD-0000253      0.106306  9.999267e-01      0.923445      0.995290   \n",
+       "2        UCD-0000253      0.000199  1.010449e-01      0.820422      0.009254   \n",
+       "3        UCD-0000253      0.000109  3.034446e-03      0.001169      0.000344   \n",
+       "4        UCD-0000253      0.982748  9.973341e-01      0.999946      0.998601   \n",
+       "\n",
+       "   mean_score                                             plate    platemap  \n",
+       "0    0.000006  CARD-CelIns-CX7_251210180001_sc_feature_selected  platemap_3  \n",
+       "1    0.756242  CARD-CelIns-CX7_251210180001_sc_feature_selected  platemap_3  \n",
+       "2    0.232730  CARD-CelIns-CX7_251210180001_sc_feature_selected  platemap_3  \n",
+       "3    0.001164  CARD-CelIns-CX7_251210180001_sc_feature_selected  platemap_3  \n",
+       "4    0.994657  CARD-CelIns-CX7_251210180001_sc_feature_selected  platemap_3  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with (datasplit_dir / \"missing_profiles.yaml\").open(\"w\") as f:\n",
+    "    yaml.safe_dump(\n",
+    "        missing_profile_wells,\n",
+    "        f,\n",
+    "        default_flow_style=False,\n",
+    "        sort_keys=False,\n",
+    "    )\n",
+    "\n",
+    "pd.concat(cell_counts, ignore_index=True).to_csv(\n",
+    "    datasplit_dir / \"cell_counts.csv\", index=False)\n",
+    "\n",
+    "all_score_df = pd.concat(\n",
+    "    score_dfs,\n",
+    "    axis=0,\n",
+    "    ignore_index=True\n",
+    ")\n",
+    "all_score_df.to_parquet(output_dir / 'all_logit_scores.parquet')\n",
+    "all_score_df.head()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cfret_ml",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/3a.train_dmso_logistic_regression_models/train_platemapwise_model/4.summarize_inferences.ipynb b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/4.summarize_inferences.ipynb
new file mode 100644
index 0000000..3f605c1
--- /dev/null
+++ b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/4.summarize_inferences.ipynb
@@ -0,0 +1,1603 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ddc04e02",
+   "metadata": {},
+   "source": [
+    "## Summarize inference results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8c413588",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pathlib\n",
+    "import yaml\n",
+    "import json\n",
+    "from joblib import load\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.transforms import blended_transform_factory\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af0df51a",
+   "metadata": {},
+   "source": [
+    "## Pathing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "443cab8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasplit_dir = pathlib.Path(\".\") / \"datasplits\"\n",
+    "if not datasplit_dir.exists():\n",
+    "    raise FileNotFoundError(f\"Datasplit directory not found: {datasplit_dir}\")\n",
+    "missing_yaml = datasplit_dir / \"missing_profiles.yaml\"\n",
+    "if not missing_yaml.exists():\n",
+    "    raise FileNotFoundError(f\"Missing profiles YAML file not found: {missing_yaml}\")\n",
+    "cell_count_file = datasplit_dir / 'cell_counts.csv'\n",
+    "if not cell_count_file.exists():\n",
+    "    raise FileNotFoundError(f\"Cell count file not found: {cell_count_file}\")\n",
+    "control_cell_count_file = datasplit_dir / 'control_cell_counts.csv'\n",
+    "if not control_cell_count_file.exists():\n",
+    "    raise FileNotFoundError(f\"Control cell count file not found: {control_cell_count_file}\")\n",
+    "\n",
+    "output_dir = pathlib.Path(\".\") / \"inference_results\"\n",
+    "if not output_dir.exists():\n",
+    "    raise FileNotFoundError(f\"Output directory not found: {output_dir}\")\n",
+    "treatment_well_file = output_dir / \"treatment_well.csv\"\n",
+    "if not treatment_well_file.exists():\n",
+    "    raise FileNotFoundError(f\"Treatment well file not found: {treatment_well_file}\")\n",
+    "logit_score_file = output_dir / 'all_logit_scores.parquet'\n",
+    "if not logit_score_file.exists():\n",
+    "    raise FileNotFoundError(f\"Logit score file not found: {logit_score_file}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f6cb839",
+   "metadata": {},
+   "source": [
+    "## Read treatment well platemap summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c22263e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Unnamed: 0",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Metadata_Well",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "cell_type",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "treatment",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "platemap_file",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "Metadata_Plate",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "platemap_num",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Pathway",
+         "rawType": "str",
+         "type": "string"
+        }
+       ],
+       "ref": "65256fa0-e190-4ede-a218-bd84466af022",
+       "rows": [
+        [
+         "0",
+         "4",
+         "B03",
+         "failing",
+         "UCD-0000253",
+         "Target_Selective_Library_Screen_Plate_3_with_pathways",
+         "CARD-CelIns-CX7_251205100001",
+         "3",
+         "Neuronal Signaling"
+        ],
+        [
+         "1",
+         "5",
+         "B03",
+         "failing",
+         "UCD-0000253",
+         "Target_Selective_Library_Screen_Plate_3_with_pathways",
+         "CARD-CelIns-CX7_251203170001",
+         "3",
+         "Neuronal Signaling"
+        ],
+        [
+         "2",
+         "6",
+         "B03",
+         "failing",
+         "UCD-0000253",
+         "Target_Selective_Library_Screen_Plate_3_with_pathways",
+         "CARD-CelIns-CX7_251208160001",
+         "3",
+         "Neuronal Signaling"
+        ],
+        [
+         "3",
+         "7",
+         "B03",
+         "failing",
+         "UCD-0000253",
+         "Target_Selective_Library_Screen_Plate_3_with_pathways",
+         "CARD-CelIns-CX7_251210180001",
+         "3",
+         "Neuronal Signaling"
+        ],
+        [
+         "4",
+         "8",
+         "B04",
+         "failing",
+         "UCD-0000872",
+         "Target_Selective_Library_Screen_Plate_3_with_pathways",
+         "CARD-CelIns-CX7_251205100001",
+         "3",
+         "Metabolism"
+        ]
+       ],
+       "shape": {
+        "columns": 8,
+        "rows": 5
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>Metadata_Well</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>platemap_file</th>\n",
+       "      <th>Metadata_Plate</th>\n",
+       "      <th>platemap_num</th>\n",
+       "      <th>Pathway</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>B03</td>\n",
+       "      <td>failing</td>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>Target_Selective_Library_Screen_Plate_3_with_p...</td>\n",
+       "      <td>CARD-CelIns-CX7_251205100001</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>B03</td>\n",
+       "      <td>failing</td>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>Target_Selective_Library_Screen_Plate_3_with_p...</td>\n",
+       "      <td>CARD-CelIns-CX7_251203170001</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6</td>\n",
+       "      <td>B03</td>\n",
+       "      <td>failing</td>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>Target_Selective_Library_Screen_Plate_3_with_p...</td>\n",
+       "      <td>CARD-CelIns-CX7_251208160001</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7</td>\n",
+       "      <td>B03</td>\n",
+       "      <td>failing</td>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>Target_Selective_Library_Screen_Plate_3_with_p...</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8</td>\n",
+       "      <td>B04</td>\n",
+       "      <td>failing</td>\n",
+       "      <td>UCD-0000872</td>\n",
+       "      <td>Target_Selective_Library_Screen_Plate_3_with_p...</td>\n",
+       "      <td>CARD-CelIns-CX7_251205100001</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Metabolism</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0 Metadata_Well cell_type    treatment  \\\n",
+       "0           4           B03   failing  UCD-0000253   \n",
+       "1           5           B03   failing  UCD-0000253   \n",
+       "2           6           B03   failing  UCD-0000253   \n",
+       "3           7           B03   failing  UCD-0000253   \n",
+       "4           8           B04   failing  UCD-0000872   \n",
+       "\n",
+       "                                       platemap_file  \\\n",
+       "0  Target_Selective_Library_Screen_Plate_3_with_p...   \n",
+       "1  Target_Selective_Library_Screen_Plate_3_with_p...   \n",
+       "2  Target_Selective_Library_Screen_Plate_3_with_p...   \n",
+       "3  Target_Selective_Library_Screen_Plate_3_with_p...   \n",
+       "4  Target_Selective_Library_Screen_Plate_3_with_p...   \n",
+       "\n",
+       "                 Metadata_Plate  platemap_num             Pathway  \n",
+       "0  CARD-CelIns-CX7_251205100001             3  Neuronal Signaling  \n",
+       "1  CARD-CelIns-CX7_251203170001             3  Neuronal Signaling  \n",
+       "2  CARD-CelIns-CX7_251208160001             3  Neuronal Signaling  \n",
+       "3  CARD-CelIns-CX7_251210180001             3  Neuronal Signaling  \n",
+       "4  CARD-CelIns-CX7_251205100001             3          Metabolism  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "treatment_well_df = pd.read_csv(treatment_well_file)\n",
+    "treatment_well_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1df05fd",
+   "metadata": {},
+   "source": [
+    "## Read missing profiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c7661f3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of unique missing profiles: 160\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(missing_yaml, \"r\") as f:\n",
+    "    missing_profiles = yaml.safe_load(f)\n",
+    "\n",
+    "all_missing = []\n",
+    "for plate, missing_trts in missing_profiles.items():\n",
+    "    for trt in missing_trts:\n",
+    "        all_missing.append(trt)\n",
+    "all_missing = list(set(all_missing))\n",
+    "print(f\"Total number of unique missing profiles: {len(all_missing)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2476ec0",
+   "metadata": {},
+   "source": [
+    "## Missing treatment well diagnostics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cd658ad8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "expected = (\n",
+    "    treatment_well_df[\n",
+    "        [\"Metadata_Plate\", \"platemap_num\", \"Metadata_Well\", \"treatment\", \"Pathway\"]\n",
+    "    ]\n",
+    "    .drop_duplicates()\n",
+    "    .copy()\n",
+    ")\n",
+    "\n",
+    "missing_long = pd.DataFrame(\n",
+    "    [\n",
+    "        {\"Metadata_Plate\": plate, \"treatment\": treatment, \"missing\": True}\n",
+    "        for plate, treatments in missing_profiles.items()\n",
+    "        for treatment in treatments\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "miss_df = expected.merge(\n",
+    "    missing_long,\n",
+    "    on=[\"Metadata_Plate\", \"treatment\"],\n",
+    "    how=\"left\",\n",
+    ")\n",
+    "\n",
+    "miss_df[\"missing\"] = miss_df[\"missing\"].fillna(False)\n",
+    "miss_df[\"missing_int\"] = miss_df[\"missing\"].astype(int)\n",
+    "\n",
+    "treatment_order = (\n",
+    "    expected\n",
+    "    .sort_values([\"platemap_num\", \"Pathway\", \"Metadata_Well\", \"treatment\"])\n",
+    "    [\"treatment\"]\n",
+    "    .drop_duplicates()\n",
+    "    .tolist()\n",
+    ")\n",
+    "\n",
+    "plate_order = (\n",
+    "    expected\n",
+    "    .sort_values([\"platemap_num\", \"Metadata_Plate\"])\n",
+    "    [\"Metadata_Plate\"]\n",
+    "    .drop_duplicates()\n",
+    "    .tolist()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8c3da8b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "platemap_summary = (\n",
+    "    miss_df\n",
+    "    .groupby([\"platemap_num\", \"Metadata_Plate\"], as_index=False)\n",
+    "    .agg(\n",
+    "        n_expected=(\"treatment\", \"nunique\"),\n",
+    "        n_missing=(\"missing_int\", \"sum\"),\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "platemap_summary[\"missing_fraction\"] = (\n",
+    "    platemap_summary[\"n_missing\"] / platemap_summary[\"n_expected\"]\n",
+    ")\n",
+    "\n",
+    "pm_heat = platemap_summary.pivot(\n",
+    "    index=\"platemap_num\",\n",
+    "    columns=\"Metadata_Plate\",\n",
+    "    values=\"missing_fraction\",\n",
+    ")\n",
+    "\n",
+    "plt.figure(figsize=(max(12, 0.25 * pm_heat.shape[1]), max(4, 0.4 * pm_heat.shape[0])))\n",
+    "sns.heatmap(\n",
+    "    pm_heat,\n",
+    "    cmap=\"viridis\",\n",
+    "    vmin=0,\n",
+    "    vmax=1,\n",
+    "    cbar_kws={\"label\": \"Fraction missing\"},\n",
+    "    annot=False,\n",
+    ")\n",
+    "plt.xlabel(\"Plate\")\n",
+    "plt.ylabel(\"Platemap number\")\n",
+    "plt.title(\"Missingness fraction by platemap and plate\")\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7542a907",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x690 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pathway_summary = (\n",
+    "    miss_df\n",
+    "    .groupby(\"Pathway\", as_index=False)\n",
+    "    .agg(\n",
+    "        n_expected_plate_treatment=(\"missing_int\", \"size\"),\n",
+    "        n_missing=(\"missing_int\", \"sum\"),\n",
+    "        n_treatments=(\"treatment\", \"nunique\"),\n",
+    "        n_plates=(\"Metadata_Plate\", \"nunique\"),\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "pathway_summary[\"missing_fraction\"] = (\n",
+    "    pathway_summary[\"n_missing\"] / pathway_summary[\"n_expected_plate_treatment\"]\n",
+    ")\n",
+    "\n",
+    "pathway_summary = pathway_summary.sort_values(\"missing_fraction\", ascending=False)\n",
+    "\n",
+    "plt.figure(figsize=(8, max(5, 0.3 * len(pathway_summary))))\n",
+    "sns.barplot(\n",
+    "    data=pathway_summary,\n",
+    "    y=\"Pathway\",\n",
+    "    x=\"missing_fraction\",\n",
+    ")\n",
+    "plt.xlabel(\"Fraction of expected plate-treatment profiles missing\")\n",
+    "plt.ylabel(\"Pathway\")\n",
+    "plt.title(\"Pathway-level complete profile missingness\")\n",
+    "plt.xlim(0, min(1, pathway_summary[\"missing_fraction\"].max() * 1.1))\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a10028b3",
+   "metadata": {},
+   "source": [
+    "## Read cell counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0bb7ff11",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Metadata_treatment",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "Metadata_Well",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "Metadata_Plate",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "row_count",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "ref": "ae471308-65b7-49ad-91df-f580ab5c1833",
+       "rows": [
+        [
+         "0",
+         "UCD-0000083",
+         "F10",
+         "CARD-CelIns-CX7_251210180001",
+         "193"
+        ],
+        [
+         "1",
+         "UCD-0000101",
+         "F02",
+         "CARD-CelIns-CX7_251210180001",
+         "192"
+        ],
+        [
+         "2",
+         "UCD-0000253",
+         "B03",
+         "CARD-CelIns-CX7_251210180001",
+         "155"
+        ],
+        [
+         "3",
+         "UCD-0000324",
+         "G11",
+         "CARD-CelIns-CX7_251210180001",
+         "227"
+        ],
+        [
+         "4",
+         "UCD-0000469",
+         "G02",
+         "CARD-CelIns-CX7_251210180001",
+         "239"
+        ]
+       ],
+       "shape": {
+        "columns": 4,
+        "rows": 5
+       }
+      },
+      "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>Metadata_treatment</th>\n",
+       "      <th>Metadata_Well</th>\n",
+       "      <th>Metadata_Plate</th>\n",
+       "      <th>row_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>UCD-0000083</td>\n",
+       "      <td>F10</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001</td>\n",
+       "      <td>193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>UCD-0000101</td>\n",
+       "      <td>F02</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001</td>\n",
+       "      <td>192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>B03</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001</td>\n",
+       "      <td>155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>UCD-0000324</td>\n",
+       "      <td>G11</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001</td>\n",
+       "      <td>227</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>UCD-0000469</td>\n",
+       "      <td>G02</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001</td>\n",
+       "      <td>239</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Metadata_treatment Metadata_Well                Metadata_Plate  row_count\n",
+       "0        UCD-0000083           F10  CARD-CelIns-CX7_251210180001        193\n",
+       "1        UCD-0000101           F02  CARD-CelIns-CX7_251210180001        192\n",
+       "2        UCD-0000253           B03  CARD-CelIns-CX7_251210180001        155\n",
+       "3        UCD-0000324           G11  CARD-CelIns-CX7_251210180001        227\n",
+       "4        UCD-0000469           G02  CARD-CelIns-CX7_251210180001        239"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cell_count_df = pd.read_csv(cell_count_file)\n",
+    "cell_count_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cadf4b2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Metadata_treatment",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "Metadata_Well",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "Metadata_Plate",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "row_count",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "ref": "6c6bcbb7-d629-4efe-aa58-ad31e7faf7af",
+       "rows": [
+        [
+         "0",
+         "DMSO",
+         "E02",
+         "CARD-CelIns-CX7_251210180001",
+         "212"
+        ],
+        [
+         "1",
+         "DMSO",
+         "E05",
+         "CARD-CelIns-CX7_251210180001",
+         "200"
+        ],
+        [
+         "2",
+         "DMSO",
+         "E08",
+         "CARD-CelIns-CX7_251210180001",
+         "303"
+        ],
+        [
+         "3",
+         "DMSO",
+         "E11",
+         "CARD-CelIns-CX7_251210180001",
+         "244"
+        ],
+        [
+         "4",
+         "DMSO",
+         "E02",
+         "CARD-CelIns-CX7_251203170001",
+         "74"
+        ]
+       ],
+       "shape": {
+        "columns": 4,
+        "rows": 5
+       }
+      },
+      "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>Metadata_treatment</th>\n",
+       "      <th>Metadata_Well</th>\n",
+       "      <th>Metadata_Plate</th>\n",
+       "      <th>row_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>E02</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001</td>\n",
+       "      <td>212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>E05</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001</td>\n",
+       "      <td>200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>E08</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001</td>\n",
+       "      <td>303</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>E11</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001</td>\n",
+       "      <td>244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>E02</td>\n",
+       "      <td>CARD-CelIns-CX7_251203170001</td>\n",
+       "      <td>74</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Metadata_treatment Metadata_Well                Metadata_Plate  row_count\n",
+       "0               DMSO           E02  CARD-CelIns-CX7_251210180001        212\n",
+       "1               DMSO           E05  CARD-CelIns-CX7_251210180001        200\n",
+       "2               DMSO           E08  CARD-CelIns-CX7_251210180001        303\n",
+       "3               DMSO           E11  CARD-CelIns-CX7_251210180001        244\n",
+       "4               DMSO           E02  CARD-CelIns-CX7_251203170001         74"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "control_cell_count_df = pd.read_csv(control_cell_count_file)\n",
+    "control_cell_count_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ab63b70a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plate-level control summary\n",
+    "control_summary = (\n",
+    "    control_cell_count_df.groupby(\"Metadata_Plate\", as_index=False)\n",
+    "    .agg(\n",
+    "        control_mean=(\"row_count\", \"mean\"),\n",
+    "        control_median=(\"row_count\", \"median\"),\n",
+    "        control_std=(\"row_count\", \"std\"),\n",
+    "        control_n=(\"row_count\", \"size\"),\n",
+    "        control_q25=(\"row_count\", lambda x: x.quantile(0.25)),\n",
+    "        control_q75=(\"row_count\", lambda x: x.quantile(0.75)),\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# merge control summary onto treated wells\n",
+    "treated_norm = cell_count_df.merge(control_summary, on=\"Metadata_Plate\", how=\"left\")\n",
+    "control_norm = control_cell_count_df.merge(control_summary, on=\"Metadata_Plate\", how=\"left\")\n",
+    "\n",
+    "# normalize to plate-level control median\n",
+    "treated_norm[\"relative_to_control\"] = (\n",
+    "    treated_norm[\"row_count\"] / treated_norm[\"control_median\"]\n",
+    ")\n",
+    "control_norm[\"relative_to_control\"] = (\n",
+    "    control_norm[\"row_count\"] / control_norm[\"control_median\"]\n",
+    ")\n",
+    "\n",
+    "# optional log2 version, often easier to read if spread is wide\n",
+    "treated_norm[\"log2_relative_to_control\"] = np.log2(treated_norm[\"relative_to_control\"])\n",
+    "control_norm[\"log2_relative_to_control\"] = np.log2(control_norm[\"relative_to_control\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "842cc0e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KFMPhcBibN28OTlu1apVhsViafX4N+Xw+IyoqyrjxxhuD++3Ro4dx5plnGhaLxaioqDAMwzAefPBBw2w2G3v27DEMwzCWLFliAMZ//vOfRtt79913m0yfMGHCfo+Zltx2220GYLzyyitN5tW/Jm05zlNTU41p06YFH+/7eW7J+eefb5jNZuOrr75qMY5rrrnGABrFUVFRYaSnpxtpaWnBz0Jzx1tLsdSfb//9738Hp9XW1hrJycnG6aefHpzW0mdoxYoVBmAsWLBgv8+vOS2d6w/l9T7UY8IwAueRhuf7hQsXGoDxv//9r9FyJ510kpGRkXFQcTenpevMnDlzDMB47rnngtM8Ho9x9NFHGy6XyygvL9/vdlt7/m7u89HcNXL27NmGyWRqdC5o6TzXlmN4f/E3fI//8pe/GBEREca6desaLXfTTTcZFovF2LJli2EYHfO+7RtLcz766CMDMGbMmNFkXv2x/O233xqAcfHFFzeaP3PmTAMwPvroo0b7BIxPPvkkOG3Xrl1GWFiYcf311wenLViwoMVzTf023n333UbTW3tOOdjvICIiIiJyeGhzafFLLrmErVu3cuedd3LmmWcyZcqU4L+DbYEtIiIiInKoTjjhBBISEkhJSeHss8/G5XLx6quv0rt3b1555RX8fj9Tp06luLg4+C85OZnMzEw+/vjjRtsKCwtrtiRoS44++mhyc3ODj/v27cuvfvUrFi5cuN+S0g3Hh6yoqKC4uJhx48bhdrtZs2bNAfe7YMECsrOzycrKavS86suq7/u8Gvr666/ZtWsXl19+eaPeYtOnTz9gL8bw8HDsdjuLFi06pPKsl1xySavHw+7Vq1ej3xtRUVGcf/75rFixgoKCgoOO4UDqX6crr7yy0djZJ598MllZWc2WQL388ssbPR43bhwbN27c734++OADPB4P11xzDWbzTz/TLrnkEqKiolpVavVgnH766SQkJAQfl5SU8NFHHzF16tTgZ7K4uJjdu3czceJE8vLygiXVw8LCgrH6fD52796Ny+Vi4MCBfPPNNwfc97fffkteXh6//e1v2b17d3BfVVVV/OIXv+CTTz7B7/fj8/lYuHAhU6ZMoW/fvsH1s7OzmThx4gH3YzabGTNmTLDc/+rVq9m9ezc33XQThmGwbNkyINBLe8iQIcEe9gsWLCA6OpoTTzyx0fGVm5uLy+Xa7/HVWi+//DJDhw5t9rd0fXnkQznOW8Pv9/Paa69x6qmnBntrNhfH22+/zahRozjmmGOC81wuF5deeimbNm0KlqVvK5fLxbnnnht8bLfbGTVq1AGPGSB4rlq4cCFut7vN+27uXH8or/ehHhPNOf7444mPj+eFF14ITtuzZw/vv/8+Z511VrvEvT9vv/02ycnJnHPOOcFpNpuNGTNmUFlZyeLFiw+4jYM9fze8RlZVVVFcXMyYMWMwDIMVK1YccL8dcQwvWLCAcePGERsb22ibJ5xwAj6fL3ieCdX79vLLL2MymYJDUDTU8FgGGg2tAHD99dcDNLneDBo0KFg5AALDWgwcOLBVx2i99PT0Jufrgz2ntNd3EBERERHpHtpcWrx+zB8RERERka7kkUceYcCAAVitVpKSkhg4cGAwoZCXl4dhGGRmZja77r6lRXv37t0ouVtWVtZoTE673U5cXFzwcXPbHTBgAG63m6KiIpKTk5vd748//sif//xnPvroI8rLyxvNO9D4rvXPa/Xq1Y0SkQ3t2rWrxXU3b97cbOw2m+2AwwiFhYVx3333cf3115OUlMRRRx3FKaecwvnnn9/ic21Oenp6q5ft379/k/GvBwwYAATGe27Lftui/nUaOHBgk3lZWVksXbq00bT68Ugbio2NPeDN9pb2Y7fbycjICM5vb/u+B+vXr8cwDG699VZuvfXWZtfZtWsXvXv3Do7D+uijj5Kfn9+o0UZ9Sf/9ycvLA2DatGktLlNWVkZtbS3V1dXNHmcDBw4MJmX2Z9y4cdxxxx1UV1ezZMkSevbsyYgRIxg6dChLlizhxBNPZOnSpUydOrVRfGVlZSQmJja7zf0dX621YcMGTj/99P0ucyjHeWsUFRVRXl6+32EQIPAZHT16dJPp9eWDN2/efMBtNKdPnz5Nju3Y2Fi+//77A66bnp7Oddddx4MPPsh//vMfxo0bx2mnnca55557wAY50PRcD4f2eh/qMdEcq9XK6aefzvPPP09tbS1hYWG88sor1NXVNUqIdtTnZPPmzWRmZjZqYAON3/cDOdjz95YtW7jtttt44403mpxDW3uNbM0xfKBr/L7b/P777w/4OofqfduwYQO9evVqMX4IvGdms5n+/fs3mp6cnExMTEyT97RhA6J6rbmuNdTc9f5gzynt9R1ERERERLqHNieyRURERES6olGjRjXbmxACyQWTycQ777zTbA/g+rFA6zXsBQbwhz/8gWeeeSb4eMKECSxatOiQ4i0tLWXChAlERUVx55130q9fPxwOB9988w1//OMfW9WA1O/3k5OTw4MPPtjs/AONc30orrnmGk499VRee+01Fi5cyK233srs2bP56KOPGD58eKu2se/rfKj2TZTU21+v+PbW2h7mXcW+70H9527mzJkt9nauT37cc8893HrrrVx44YX85S9/IS4uDrPZzDXXXNPqzy/A/fffz7Bhw5pdxuVyUVtb29qn06JjjjmGuro6li1bxpIlS4K9C8eNG8eSJUtYs2YNRUVFjXod+v1+EhMT+c9//tPsNltKPLW3UB7nB6Otx2FLx4xhGK3a3wMPPMD06dN5/fXXee+995gxYwazZ8/m888/p0+fPvtdt7lz0KG83od6TLTk7LPP5vHHH+edd95hypQpvPjii2RlZTF06NB2ibsr8vl8nHjiiZSUlPDHP/6RrKwsIiIi2L59O9OnT2/1OaY1x3BbrvF+v58TTzyRG2+8sdn59Ql66PrvW0vH6r4O9RiF9r/et8d3EBERERHpHg4qkV1VVcXixYvZsmULHo+n0bwZM2a0S2AiIiIiIu2lX79+GIZBenp6o5vMrXXjjTc2Kn0bGxvbaH59z9KG1q1bh9PpbDHZtWjRInbv3s0rr7zC+PHjg9Pz8/ObLNvSzeZ+/frx3Xff8Ytf/KLVN6TrpaamBmOvL2EKUFdXR35+fqMb7S3p168f119/Pddffz15eXkMGzaMBx54gOeee26/cR+M+p7CDbe5bt06ANLS0oCf3pfS0tJgeWhovsdga2Orf53Wrl3b6HWqn1Y//1A13E/DHvEej4f8/HxOOOGEg9puW9+D+n3bbLYD7vOll17iuOOO4+mnn240vbS0lPj4+APG0K9fPyBQZnh/+0pISCA8PLzZ42zt2rX7jbHeqFGjsNvtLFmyhCVLlnDDDTcAMH78eJ588kk+/PDD4OOG8X3wwQeMHTu23ZMwDffxww8/HHCZgz3OWyMhIYGoqKgDxpGamtrs610/DEL9Z7jhcdjQoVQVONDzzsnJIScnhz//+c989tlnjB07lrlz53LXXXe1eV+H8nof6jHRkvHjx9OzZ09eeOEFjjnmGD766CP+9Kc/tVvc+4spNTWV77//Hr/f36hX9r7v+/605vy9r5UrV7Ju3TqeeeYZzj///OD0999/v9Wxt/YYPtA1ft9tVlZWtuqc3BnvW3PxLVy4kJKSkhZ7ZaempuL3+8nLywv2fgYoLCyktLT0oK5rBxN7a88pLTnQdxAREREROTy0eYzsFStW0L9/f8455xyuvvpq7rrrLq655hpuueUW5syZ0wEhioiIiIgcmt/85jdYLBZmzZrVpAeRYRjs3r17v+sPGjSIE044Ifiv4XjYAMuWLWs0/unWrVt5/fXX+eUvf9liT6b66Q3j8Xg8PProo02WjYiIaLaM6tSpU9m+fTtPPvlkk3nV1dVUVVW1+JxGjhxJQkICc+fObdQ4df78+U0SUPtyu93U1NQ0mtavXz8iIyMb9Z6NiIg44LZaa8eOHbz66qvBx+Xl5fz73/9m2LBhwVKi9YnR+jFKIdAIt2FPu7bGNnLkSBITE5k7d26j5/bOO++wevVqTj755IN9So2ccMIJ2O12/vGPfzT6TDz99NOUlZUd9H4iIiKApknFliQmJnLsscfy+OOPs3Pnzibzi4qKgn9bLJYmx9OCBQuCY2gfKIbc3Fz69evH3/72NyorK1vcl8ViYeLEibz22mts2bIlOH/16tUsXLiwVc/L4XBw5JFH8n//939s2bKlUY/s6upq/vGPf9CvXz969uwZXGfq1Kn4fD7+8pe/NNme1+ttl8/26aefznfffdfos12v/rU9lOO8NcxmM1OmTOF///sfX3/9dYtxnHTSSXz55ZfBMcUhcHw98cQTpKWlMWjQIKD549Dn8/HEE08cdIwtfYbKy8vxer2NpuXk5GA2mw+6J/+hvN6Heky0xGw2c8YZZ/C///2PZ599Fq/X26g89aHGXR9Tc9eZk046iYKCgkZjPXu9Xh5++GFcLhcTJkw4YPytOX/vq7lrpGEYPPTQQ83GDk1fz9Yewwe6xu+7zWXLljV77iktLW30eeyM921fp59+OoZhMGvWrCbzGh7LQJN7ePW9wg/metPWz3R9HK05p+yrtd9BREREROTw0OYe2ddeey2nnnoqc+fOJTo6ms8//xybzca5557LH/7wh46IUURERETkkPTr14+77rqLm2++mU2bNjFlyhQiIyPJz8/n1Vdf5dJLL2XmzJkHvf0hQ4YwceJEZsyYQVhYWDAZ3dyN5HpjxowhNjaWadOmMWPGDEwmE88++2yzpTpzc3N54YUXuO666zjyyCNxuVyceuqpnHfeebz44otcfvnlfPzxx4wdOxafz8eaNWt48cUXWbhwYYvl1m02G3fddReXXXYZxx9/PGeddRb5+fnMmzfvgGNkr1u3jl/84hdMnTqVQYMGYbVaefXVVyksLOTss89uFPdjjz3GXXfdRf/+/UlMTGzSq7m1BgwYwEUXXcRXX31FUlIS//rXvygsLGTevHnBZX75y1/St29fLrroIm644QYsFgv/+te/SEhIaJQAbUtsNpuN++67jwsuuIAJEyZwzjnnUFhYyEMPPURaWhrXXnvtQT2ffSUkJHDzzTcza9YsJk2axGmnncbatWt59NFHOfLIIxv1FmyL+oTMjBkzmDhxIhaLpdF71JxHHnmEY445hpycHC655BIyMjIoLCxk2bJlbNu2je+++w6AU045hTvvvJMLLriAMWPGsHLlSv7zn/80+fz069ePmJgY5s6dS2RkJBEREYwePZr09HSeeuopJk+ezODBg7ngggvo3bs327dv5+OPPyYqKor//e9/QOBYevfddxk3bhxXXnllMJE2ePDgVo2lDIGk9b333kt0dDQ5OTlAIHE/cOBA1q5dy/Tp0xstP2HCBC677DJmz57Nt99+yy9/+UtsNht5eXksWLCAhx56iDPOOKNV+27JDTfcwEsvvcSZZ57JhRdeSG5uLiUlJbzxxhvMnTuXoUOHHtJx3lr33HMP7733HhMmTODSSy8lOzubnTt3smDBApYuXUpMTAw33XQT//d//8fkyZOZMWMGcXFxPPPMM+Tn5/Pyyy8He+sOHjyYo446iptvvjnYK/S///1vk4RzW7T0Gfruu++4+uqrOfPMMxkwYABer5dnn30Wi8VywLHHW3Ior3d7HBMtOeuss3j44Ye5/fbbycnJadST9lDjhpavM5deeimPP/4406dPZ/ny5aSlpfHSSy/x6aefMmfOHCIjIw/4mrbm/L2vrKws+vXrx8yZM9m+fTtRUVG8/PLLzY7L3NJ5riOO4RtuuIE33niDU045henTp5Obm0tVVRUrV67kpZdeYtOmTY1633f0+7av4447jvPOO49//OMf5OXlMWnSJPx+P0uWLOG4447j6quvZujQoUybNo0nnngiOMzJl19+yTPPPMOUKVM47rjj2vSaAAwbNgyLxcJ9991HWVkZYWFhHH/88S2OTw60+pyyr9Z+BxERERGRw4TRRtHR0caaNWuCf69atcowDMP4/PPPjYEDB7Z1cyIiIiIih2TevHkGYHz11VcHXPbll182jjnmGCMiIsKIiIgwsrKyjKuuuspYu3ZtcJkJEyYYgwcPbvX+AeOqq64ynnvuOSMzM9MICwszhg8fbnz88cfNxpmfnx+c9umnnxpHHXWUER4ebvTq1cu48cYbjYULFxpAo/UrKyuN3/72t0ZMTIwBGKmpqcF5Ho/HuO+++4zBgwcbYWFhRmxsrJGbm2vMmjXLKCsrO2D8jz76qJGenm6EhYUZI0eOND755BNjwoQJxoQJE4LL5OfnG4Axb948wzAMo7i42LjqqquMrKwsIyIiwoiOjjZGjx5tvPjii422XVBQYJx88slGZGSkAQS3ub/3rLnXKTU11Tj55JONhQsXGkcccYQRFhZmZGVlGQsWLGiy/vLly43Ro0cbdrvd6Nu3r/Hggw82u82WYvv444+bvP6GYRgvvPCCMXz4cCMsLMyIi4szfve73xnbtm1rtMy0adOMiIiIJjHdfvvtRmt/ev3zn/80srKyDJvNZiQlJRlXXHGFsWfPnma3V1RUdMDteb1e4/e//72RkJBgmEymYBz17+n999/f7HobNmwwzj//fCM5Odmw2WxG7969jVNOOcV46aWXgsvU1NQY119/vdGzZ08jPDzcGDt2rLFs2bImnx/DMIzXX3/dGDRokGG1Wht9lgzDMFasWGH85je/MXr06GGEhYUZqampxtSpU40PP/yw0TYWL15s5ObmGna73cjIyDDmzp3bptf2rbfeMgBj8uTJjaZffPHFBmA8/fTTza73xBNPGLm5uUZ4eLgRGRlp5OTkGDfeeKOxY8eO4DIHOmb2Z/fu3cbVV19t9O7d27Db7UafPn2MadOmGcXFxcFlWnucp6amGtOmTQs+bunz3JzNmzcb559/vpGQkGCEhYUZGRkZxlVXXWXU1tYGl9mwYYNxxhlnGDExMYbD4TBGjRplvPnmm022tWHDBuOEE04wwsLCjKSkJOOWW24x3n///SaxtHS+nTZtWqPznGE0/xnauHGjceGFFxr9+vUzHA6HERcXZxx33HHGBx98cMDnu79z/cG+3u1xTDT33A3DMPx+v5GSkmIAxl133XVIcTdnf9eZwsJC44ILLjDi4+MNu91u5OTktOqzbRitP38391ldtWqVccIJJxgul8uIj483LrnkEuO7775rcmy1dJ6r15pjeH/xN3yPDcMwKioqjJtvvtno37+/Ybfbjfj4eGPMmDHG3/72N8Pj8TRatj3ft+ZiaY7X6zXuv/9+Iysry7Db7UZCQoIxefJkY/ny5cFl6urqjFmzZhnp6emGzWYzUlJSjJtvvtmoqalp8vxPPvnkJvto7jP95JNPGhkZGYbFYmn0Xra0DcNo3TnlYL+DiIiIiMjhwWQYzXT52I+EhAQ+++wzMjMzGTBgAA8//DATJ05kzZo1wZaoIiIiIiI/FyaTiauuuop//vOfoQ5FRESkS0lLS2PIkCG8+eaboQ5FRERERES6oTaXFh8+fDhfffUVmZmZTJgwgdtuu43i4mKeffZZhgwZ0hExioiIiIiIiIiIiIiIiIjIz0jzA87sxz333EPPnj0BuPvuu4mNjeWKK66gqKiIJ554ot0DFBERERERERERERERERGRn5c298geOXJk8O/ExETefffddg1IRERERERERERERERERER+3to8RraIiIiIiIiIiIiIiIiIiEhHalWP7BEjRvDhhx8SGxvL8OHDMZlMLS77zTfftFtwIiIiIiIiIiIiIiIiIiLy89OqRPavfvUrwsLCAJgyZUpHxiMiIiIiIiIiIiIiIiIiIj9zIS0tPnv2bF555RXWrFlDeHg4Y8aM4b777mPgwIH7XW/BggXceuutbNq0iczMTO677z5OOumk4HzDMLj99tt58sknKS0tZezYsTz22GNkZma2Ki6/38+OHTuIjIzcb+9zERERERERERERERERERFpHcMwqKiooFevXpjN5v0uG9JE9qRJkzj77LM58sgj8Xq93HLLLfzwww+sWrWKiIiIZtf57LPPGD9+PLNnz+aUU07h+eef57777uObb75hyJAhANx3333Mnj2bZ555hvT0dG699VZWrlzJqlWrcDgcB4xr27ZtpKSktOtzFRERERERERERERERERER2Lp1K3369NnvMq1KZMfGxra6Z3JJSUnromtGUVERiYmJLF68mPHjxze7zFlnnUVVVRVvvvlmcNpRRx3FsGHDmDt3LoZh0KtXL66//npmzpwJQFlZGUlJScyfP5+zzz77gHGUlZURExPD1q1biYqKOujnIyIiIiIiIiIiIiIiIiIiAeXl5aSkpFBaWkp0dPR+l23VGNlz5swJ/r17927uuusuJk6cyNFHHw3AsmXLWLhwIbfeeuvBR00ggQwQFxfX4jLLli3juuuuazRt4sSJvPbaawDk5+dTUFDACSecEJwfHR3N6NGjWbZsWbOJ7NraWmpra4OPKyoqAIiKilIiW0RERERERERERERERESkHbWmE3WrEtnTpk0L/n366adz5513cvXVVwenzZgxg3/+85988MEHXHvttQcRamBc6muuuYaxY8cGS4Q3p6CggKSkpEbTkpKSKCgoCM6vn9bSMvuaPXs2s2bNOqi4RURERERERERERERERESkfe1/BO1mLFy4kEmTJjWZPmnSJD744IODDuSqq67ihx9+4L///e9Bb+Ng3XzzzZSVlQX/bd26tdNjEBERERERERERERERERGRgDYnsnv06MHrr7/eZPrrr79Ojx49DiqIq6++mjfffJOPP/74gIN6JycnU1hY2GhaYWEhycnJwfn101paZl9hYWHBMuIqJy4iIiIiIiIiIiIiIiIiElqtKi3e0KxZs7j44otZtGgRo0ePBuCLL77g3Xff5cknn2zTtgzD4Pe//z2vvvoqixYtIj09/YDrHH300Xz44Ydcc801wWnvv/9+cLzu9PR0kpOT+fDDDxk2bBgQGDT8iy++4IorrmhTfAfi8/moq6tr123K4cNut2M2t7mtiIiIiIiIiIiIiIiIiMjPXpsT2dOnTyc7O5t//OMfvPLKKwBkZ2ezdOnSYGK7ta666iqef/55Xn/9dSIjI4NjWEdHRxMeHg7A+eefT+/evZk9ezYAf/jDH5gwYQIPPPAAJ598Mv/973/5+uuveeKJJ4DAwODXXHMNd911F5mZmaSnp3PrrbfSq1cvpkyZ0tan2yzDMCgoKKC0tLRdtieHJ7PZTHp6Ona7PdShiIiIiIiIiIiIiIiIiHQrJsMwjJDt3GRqdvq8efOYPn06AMceeyxpaWnMnz8/OH/BggX8+c9/ZtOmTWRmZvLXv/6Vk046KTjfMAxuv/12nnjiCUpLSznmmGN49NFHGTBgQKviKi8vJzo6mrKysmbLjO/cuZPS0lISExNxOp0tPg/5+fL7/ezYsQObzUbfvn31GREREREREREREREREZGfvQPlYRs6qET2hg0bmDdvHhs3bmTOnDkkJibyzjvv0LdvXwYPHnzQgXcV+3sBfT4f69atIzEx8aDHBJefh7KyMnbs2EH//v2x2WyhDkdEREREREREREREREQkpNqSyG7zAL6LFy8mJyeHL774gpdffpnKykoAvvvuO26//faDi7gbqR8T2+l0hjgS6erqS4r7fL4QRyIiIiIiIiIiIiIiIiLSer7KSqq//56KRYuo/v57fHtzwp2pzYnsm266ibvuuov333+/0di/xx9/PJ9//nm7BteVqVS0HIg+IyIiIiIiIiIiIiIiItLd+CorKV+4kIqPPqLmx1VUfPQR5QsXdnoy29rWFVauXMnzzz/fZHpiYiLFxcXtEpSIiIiIiIiIiIiIiIiIiHQst9vNmjVrGk2rycvD/eWX+BIS2bJ1G30TErB89hlOrxdHZmZwuaysrA6tYt3mRHZMTAw7d+4kPT290fQVK1bQu3fvdgtMpKHp06dTWlrKa6+9FupQRERERERERERERERERA4La9asITc396DWXb58OSNGjGjniH7S5kT22WefzR//+EcWLFiAyWTC7/fz6aefMnPmTM4///yOiFEO0YFKXN9+++3ccccd7b5fJZ9FREREREREREREREREuq6srCyWL1/eaFp9j+x8Ay79+4M8ce11pJvAOWpUkx7ZHanNiex77rmHq666ipSUFHw+H4MGDcLn8/Hb3/6WP//5zx0RoxyinTt3Bv9+4YUXuO2221i7dm1wmsvlCv5tGAY+nw+rtc0fDRERERERERERERERERHpRpxOZ5Ne1b4BAyi3WuGzzwBIN0HumDFETZyIpUFesaOZ27qC3W7nySefZMOGDbz55ps899xzrFmzhmeffRaLxdIRMcohSk5ODv6Ljo7GZDIFH69Zs4bIyEjeeecdcnNzCQsLY+nSpfj9fmbPnk16ejrh4eEMHTqUl156KbhNn8/HRRddFJw/cOBAHnrooeD8O+64g2eeeYbXX38dk8mEyWRi0aJFAGzdupWpU6cSExNDXFwcv/rVr9i0aVOjbV933XXExMTQo0cPbrzxRgzD6KyXS0RERERERERERERERORny+JyETVxIs5Ro4BAT+zOTmLDQfTIrte3b1/69u3bnrH8rFTU1LGhqIrdlbX0cIXRLyGCSIctZPHcdNNN/O1vfyMjI4PY2Fhmz57Nc889x9y5c8nMzOSTTz7h3HPPJSEhgQkTJuD3++nTpw8LFiygR48efPbZZ1x66aX07NmTqVOnMnPmTFavXk15eTnz5s0DIC4ujrq6OiZOnMjRRx/NkiVLsFqt3HXXXUyaNInvv/8eu93OAw88wPz58/nXv/5FdnY2DzzwAK+++irHH398yF4fERERERERERERERERkZ8Li8sVLCPuyMzs9CQ2HEQi2zAMXnrpJT7++GN27dqF3+9vNP+VV15pt+AOVxU1dbyzsoAtJVXYLGbqfH7WxUUwOSc5ZMnsO++8kxNPPBGA2tpa7rnnHj744AOOPvpoADIyMli6dCmPP/44EyZMwGazMWvWrOD66enpLFu2jBdffJGpU6ficrkIDw+ntraW5OTk4HLPPfccfr+fp556Kjh297x584iJiWHRokX88pe/ZM6cOdx888385je/AWDu3LksXLiws14KEREREREREREREREREQmxNieyr7nmGh5//HGOO+44kpKSgslIab0NRVVsKakiI8GFxWzC5zfYWFTJhqIqhqXEhCSmkSNHBv9ev349brc7mNiu5/F4GD58ePDxI488wr/+9S+2bNlCdXU1Ho+HYcOG7Xc/3333HevXrycyMrLR9JqaGjZs2EBZWRk7d+5k9OjRwXlWq5WRI0eqvLiIiIiIiIiIiIiIiIjIz0SbE9nPPvssr7zyCieddFJHxPOzsLuyFpvFjMUcaARgMZuwWczsrqwNWUwRERHBvysrKwF466236N27d6PlwsLCAPjvf//LzJkzeeCBBzj66KOJjIzk/vvv54svvtjvfiorK8nNzeU///lPk3kJCQmH+jRERERERERERERERERE5DDQ5kR2dHQ0GRkZHRHLz0YPVxh1Pj8+vxHskV3n89PDFRbq0AAYNGgQYWFhbNmyhQkTJjS7zKeffsqYMWO48sorg9M2bNjQaBm73Y7P52s0bcSIEbzwwgskJiYSFRXV7LZ79uzJF198wfjx4wHwer0sX76cESNGHMrTEhEREREREREREZFurtbtpmTHVtzl5TijoojrlUKY0xnqsEREpAOY27rCHXfcwaxZs6iuru6IeH4W+iVE0Dcugo1FlWwqrmJjUSV94yLolxBx4JU7QWRkJDNnzuTaa6/lmWeeYcOGDXzzzTc8/PDDPPPMMwBkZmby9ddfs3DhQtatW8ett97KV1991Wg7aWlpfP/996xdu5bi4mLq6ur43e9+R3x8PL/61a9YsmQJ+fn5LFq0iBkzZrBt2zYA/vCHP3Dvvffy2muvsWbNGq688kpKS0s7+2UQERERERERERERkS6k1u0m74tP2fD1lxRuXM+Gr78k74tPqXW7Qx2aiIh0gDb3yJ46dSr/93//R2JiImlpadhstkbzv/nmm3YL7nAV6bAxOSeZDUVV7K6spYcrjH4JEUQ6bAdeuZP85S9/ISEhgdmzZ7Nx40ZiYmIYMWIEt9xyCwCXXXYZK1as4KyzzsJkMnHOOedw5ZVX8s477wS3cckll7Bo0SJGjhxJZWUlH3/8McceeyyffPIJf/zjH/nNb35DRUUFvXv35he/+EWwh/b111/Pzp07mTZtGmazmQsvvJBf//rXlJWVheS1EBEREREREREREZHQcrvdLHn/Xbat+pGohATMZgt+v4+1+Z+zYVcxlV4/aWlphIeHN1k3KysLp3pti4h0OybDMIy2rDB16lQ+/vhjzjjjDJKSkjCZTI3m33777e0aYCiUl5cTHR1NWVlZk/LXNTU15Ofnk56ejsPhCFGE0h3osyIiIiIiIiIi0jkqvD7y3DXs9njpYbeS6XQQabWEOiwRaUfffPMNubm5B7Wuhq4UETkwX2Ulno0b8ZaUYI2Lw56RwXfr1pGbm9uu59H95WH31eYe2W+99RYLFy7kmGOOOegARURERERERERERNpDhdfHm0WlbKquxW4y4zH8rA2v4ZSEGCWzRQ4jWVlZvPvay016ZJcXFVEdFs7vr7+B5557juzs7GbXFRGRlvkqKylfuJC6rVsx2ewYdR5seXn4kpJCGlebE9kpKSkHzI6LiIiIiIiIiIiIdIY8dw2bqmvJdDqwmEz4DIM8dw157hpGREWEOjwRaSdOp5NjT5xEXlQkpYUFmK1W/F4v2f36UWkPlA3Pzs5Wz2sRkYPg2biRuq1bsadnYLJYMHw+PPkbqfN6QxqXua0rPPDAA9x4441s2rSpA8IRERERERERERERab3dHi92kxnL3iEQLSYTdpOZ3Z7Q3ngVkfYX5nSSOXos/UaOIimjP/1GjiJz9FjszYyLLSIirectKcFks2OyBKrZmCwWTDY7vrKykMbV5h7Z5557Lm63m379+uF0OrHZbI3ml5SUtFtwIiIiIiIiIiIiIvvTw27FY/jxGUawR7bH8NPD3uZbnyLSDYQ5nfTsPzDUYYhIV1FTDsV5UFUEEQkQnwkOVZZuK2tcHEadB8PnC/bINuo8WOJ7hDautq4wZ86cDghDREREREREREREpO0ynQ7WhgdKidePkZ0WHkam0xHq0ERERKQj1ZTD6jegJB8sdvB5oCgdsk9TMruN7BkZ2PLy8ORv/GmM7JQUbN1tjOxp06Z1RBwiIiIiIiIiIiIibRZptXBKQgx57hp2e7z0sFvJdDqItFpCHZqIiIh0pOK8QBI7PhPMVvB7A9OK86BPbqij61YsLhdREyfi2bgRb0kJ1rg47BkZWNatC2lcqq8jIiIiIiIiIiIi3Vqk1cKIqIhQhyEiIiKdqaoo0BPbvDfdabYGHlcVhTaubsrichF+xBGhDqMRc6gDEBERERERERERERERERFpk4iEQDlxvzfw2O8NPI5ICG1c0m5Cmsj+5JNPOPXUU+nVqxcmk4nXXnttv8tPnz4dk8nU5N/gwYODy9xxxx1N5mdlZXXwMxERERERERERERERERGRThOfCXHpgVLiuzcE/o9LD0yXw0JIE9lVVVUMHTqURx55pFXLP/TQQ+zcuTP4b+vWrcTFxXHmmWc2Wm7w4MGNllu6dGlHhC+dYNGiRZhMJkpLSztlf5s2bcJkMvHtt9+GZP8iIiIiIiIiIiIiIiLSCo4oyD4NBp4EPYcG/s8+LTBdDgshHSN78uTJTJ48udXLR0dHEx0dHXz82muvsWfPHi644IJGy1mtVpKTk9stzo7i8/nZkVdKr8wYLJaOb1NQUFDA3XffzVtvvcX27dtJTExk2LBhXHPNNfziF79ot/0ce+yxDBs2jDlz5rTbNkVEREREREREREREREQacURBn9xQRyEdpFWJ7N/85jet3uArr7xy0MG01dNPP80JJ5xAampqo+l5eXn06tULh8PB0UcfzezZs+nbt2+L26mtraW2tjb4uLy8vMNirufz+fnk+bWs/2YX/UckMv63Azs0mb1p0ybGjh1LTEwM999/Pzk5OdTV1bFw4UKuuuoq1qxZ02H7bo5hGPh8PqzWkLalEBEREREREREREREREZEuqFWZ0/qe0K3511l27NjBO++8w8UXX9xo+ujRo5k/fz7vvvsujz32GPn5+YwbN46KiooWtzV79uxGzyElJaVDY69PYq/7qhDDD+u+KuST59fi8/k7bJ9XXnklJpOJL7/8ktNPP50BAwYwePBgrrvuOj7//PPgclu2bOFXv/oVLpeLqKgopk6dSmFhYXD+HXfcwbBhw3j22WdJS0sjOjqas88+O/j6Tp8+ncWLF/PQQw8FxyjftGlTsET3O++8Q25uLmFhYSxdupTa2lpmzJhBYmIiDoeDY445hq+++qrVz2vmzJmccsopwcdz5szBZDLx7rvvBqf179+fp556Kvj4qaeeIjs7G4fDQVZWFo8++mir97d582ZOPfVUYmNjiYiIYPDgwbz99tutXl9EREREREREREREREREDqxV3WHnzZvX0XG02TPPPENMTAxTpkxpNL1hqfIjjjiC0aNHk5qayosvvshFF13U7LZuvvlmrrvuuuDj8vLyDktmN0xiW6xmLDYzvjo/674KJIs7omd2SUkJ7777LnfffTcRERFN5sfExADg9/uDSezFixfj9Xq56qqrOOuss1i0aFFw+Q0bNvDaa6/x5ptvsmfPHqZOncq9997L3XffzUMPPcS6desYMmQId955JwAJCQls2rQJgJtuuom//e1vZGRkEBsby4033sjLL7/MM888Q2pqKn/961+ZOHEi69evJy4u7oDPbcKECTz11FP4fD4sFguLFy8mPj6eRYsWMWnSJLZv386GDRs49thjAfjPf/7Dbbfdxj//+U+GDx/OihUruOSSS4iIiGDatGkH3N9VV12Fx+Phk08+ISIiglWrVuFyuQ64noiIiIiIiIiIiIiIiIi03kHXdS4qKmLt2rUADBw4kISEhHYL6kAMw+Bf//oX5513Hna7fb/LxsTEMGDAANavX9/iMmFhYYSFhbV3mE00l8QGgv93VDJ7/fr1GIZBVlbWfpf78MMPWblyJfn5+cFE/r///W8GDx7MV199xZFHHgkEEt7z588nMjISgPPOO48PP/yQu+++m+joaOx2O06ns9lxyu+8805OPPFEAKqqqnjssceYP39+sAHCk08+yfvvv8/TTz/NDTfccMDnVt/bfsWKFeTm5vLJJ59www038NprrwGwaNEievfuTf/+/QG4/fbbeeCBB4Ll8tPT01m1ahWPP/54qxLZW7Zs4fTTTycnJweAjIyMA64jIiIiIiIiIiIiIiIiIm3T5mxpVVUVF154IT179mT8+PGMHz+eXr16cdFFF+F2uzsixiYWL17M+vXrW+xh3VBlZSUbNmygZ8+enRBZy1pKYtez2MxYrOYOKTNuGEarllu9ejUpKSmNeqMPGjSImJgYVq9eHZyWlpYWTGID9OzZk127drVqHyNHjgz+vWHDBurq6hg7dmxwms1mY9SoUY32tz8xMTEMHTqURYsWsXLlSux2O5deeikrVqygsrKSxYsXM2HCBCDw2d2wYQMXXXQRLpcr+O+uu+5iw4YNrdrfjBkzuOuuuxg7diy3334733//favWExEREREREREREREREemufJWVVH//PRWLFlH9/ff4Kis7fJ9tTmRfd911LF68mP/973+UlpZSWlrK66+/zuLFi7n++uvbtK3Kykq+/fZbvv32WwDy8/P59ttv2bJlCxAo+X3++ec3We/pp59m9OjRDBkypMm8mTNnsnjxYjZt2sRnn33Gr3/9aywWC+ecc05bn2q72pFXyvpvdmEymZoksetZbGZMJhPrv9nFjrzSdtt3ZmYmJpOJNWvWtMv2bDZbo8cmkwm/v3WJ9+ZKmx+qY489lkWLFgWT1nFxcWRnZ7N06dJGiezKvQfUk08+Gfzcffvtt/zwww+Nxgnfn4svvpiNGzdy3nnnsXLlSkaOHMnDDz/c7s9JREREREREREREREREpCvwud2UL1xIxUcfUfPjKio++ojyhQs7PJnd5kT2yy+/zNNPP83kyZOJiooiKiqKk046iSeffJKXXnqpTdv6+uuvGT58OMOHDwcCSfLhw4dz2223AbBz585gUrteWVkZL7/8cou9sbdt28Y555zDwIEDmTp1Kj169ODzzz/v1NLnzemVGUP/EYkYhoGvrvmkr6/Oj2EY9B+RSK/MmHbbd1xcHBMnTuSRRx6hqqqqyfzS0lIAsrOz2bp1K1u3bg3OW7VqFaWlpQwaNKjV+7Pb7fh8vgMu169fP+x2O59++mlwWl1dHV999VWb9jdhwgSWLl3Khx9+GBwL+9hjj+X//u//WLduXXBaUlISvXr1YuPGjfTv37/Rv/T09FbvLyUlhcsvv5xXXnmF66+/nieffLLV64qIiIiIiIiIiIiIiIh0J3Xbt1O3dSv29AzsqanY0zOo27oVz8aNHbrfNo+R7Xa7SUpKajI9MTGxzaXFjz322P2WvZ4/f36TadHR0fvdz3//+982xdBZLBYz4387EPhpLOyGPbN9dX58Xj8Djkxq9zGyAR555BHGjh3LqFGjuPPOOzniiCPwer28//77PPbYY6xevZoTTjiBnJwcfve73zFnzhy8Xi9XXnklEyZMaFQS/EDS0tL44osv2LRpEy6Xi7i4uGaXi4iI4IorruCGG24gLi6Ovn378te//hW3292qsvH1xo8fT0VFBW+++Sb33nsvEPhsnXHGGfTs2ZMBAwYEl501axYzZswgOjqaSZMmUVtby9dff82ePXu47rrrDriva665hsmTJzNgwAD27NnDxx9/THZ2dqtjFREREREREREREREREelOfGVlmGx2TBYLACaLBZPNjrekpEP32+Zs6dFHH83tt99OTU1NcFp1dTWzZs3i6KOPbtfgDjf1yewBRybh8/qDPbM7OokNkJGRwTfffMNxxx3H9ddfz5AhQzjxxBP58MMPeeyxx4BAifDXX3+d2NhYxo8fzwknnEBGRgYvvPBCm/Y1c+ZMLBYLgwYNIiEhoUmv+obuvfdeTj/9dM477zxGjBjB+vXrWbhwIbGxsa3eX2xsLDk5OSQkJJCVlQUEktt+vz9YVrzexRdfzFNPPcW8efPIyclhwoQJzJ8/v9U9sn0+H1dddRXZ2dlMmjSJAQMG8Oijj7Y6VhERERERERERaX9GXR1Vy5Zh1NWFOhQRERGRw44lOhqjzoOxtyKz4fNh1HmwttCZtb2YjP11iW7GypUrgz1Zhw4dCsB3332Hw+Fg4cKFDB48uEMC7Uzl5eVER0dTVlZGVFRUo3k1NTXk5+eTnp6Ow+E4qO37fH4+eX4t674qxGQyYRhGhyaxJTTa47MiIiIiIiIiIiL7Z9TVUXDnnZS/8y5RkyeRfNttmGy2UIclIp3om2++ITc3l+XLlzNixIhQhyMictioP79+uWQJ/QsLqdu6FZPNjlHnwZaSQtTEiVhcrjZtc3952H21ubR4Tk4OeXl5/Oc//2HNmjUAnHPOOfzud78jPDy8rZv7WWpYZnz9N7vIHKEktoiIiIiIiIiISFvVJ7HL3nwLk8lE2ZtvASiZLSIiItKOLE4nURMn4tm4EW9JCda4OOwZGW1OYrdVmxLZdXV1ZGVl8eabb3LJJZd0VEw/C/XJ7P5HJtErM0ZJbBERERERERERkTZolMS22zHb7fg9HiWzRURERDqAxeUi/IgjOnWfbUpk22y2RmNjy6GxWMykZHVs7XgREREREREREZHDTXNJbCCQzAYls0VEREQOA23uBnzVVVdx33334fV6OyIeERERERERERERkRa1lMSuZ7bbMdntlL35FgV33olRVxeiSEVERETkULR5jOyvvvqKDz/8kPfee4+cnBwiIiIazX/llVfaLTgRERERERERkbbwVVZ2+rhtItK53F9/Tfk772IymZokseuZ7Xb8dXWUv/MuUSedRMTRR3dylCIiIiJyqNqcyI6JieH000/viFi6Fb/fH+oQpIszDCPUIYiIiIiIiPys+CorKV+4kLqtWzHZ7Bh1Hmx5eURNnKhk9mHKX+PFW1SNr9KDxWXHmhCO2dHm213SzThHjiRq8iTK3nwLv8fTbDLb7/FgGAbRkyfhHDkyBFGKiIiIyKFq8zf7efPmdUQc3YbdbsdsNrNjxw4SEhKw2+2YTKZQhyVdjGEYFBUVYTKZsGkcJhERERERkUPmdrtZs2ZNk+nV1dVs2rSJtLQ0TNu24f7yS2y9+0BdHfgN6j77jOF9+tBj9OgQRC0dyV/jxb2yGF9JDSaLCY/PwBLnwJkTr2T2Yc5ks5F8221AYCxsPzRKZvs9HgyPh+hTTtYY2SIiIiLdWJu/1R9//PG88sorxMTENJpeXl7OlClT+Oijj9orti7JbDaTnp7Ozp072bFjR6jDkS7MZDLRp08fLBZLqEMRERERERHp9tasWUNubu5BrfvJwIGMUyK729u3MUNdQRU1G8vwOg02F2wjNbkP1jwTjqJobMmNh8LLysrC6XR2dsjSgVpKZiuJLSIiInL4aHMie9GiRXg8nibTa2pqWLJkSbsE1dXZ7Xb69u2L1+vF5/OFOhzpomw2m5LYIiIiIiIi7SQrK4vly5c3mb569WrOPfdcnnvuOdKt1p96ZJvN4PdTt30bg4YODUHE0t4OpTHD8uXLGTFiRDtHJKHWJJldVxcoJ64ktojIz0adv47lhcvJTcrFZtZ5X+Rw0+pE9vfffx/8e9WqVRQUFAQf+3w+3n33XXr37t2+0XVh9SWjVTZaREREREREpOM5nc79JiKzs7MZOmAA5Vbr3jGybYExsseMIWrw4E6MVDrKvo0Z6gqqqMnbw7qdG7n0vmt54o9/Z0DPDByZsc32yJbDU8Nkdvk77xI9eZKS2Ie5Sk8l+WX5lNSUEOeIIz06HZfdFeqwRCQE6vx13P353SzctJCJaRP501F/UjJb5DDT6kT2sGHDMJlMmEwmjj/++Cbzw8PDefjhh9s1OBERERERERGR1rK4XERNnIhn40a8JSVY4+KwZ2RgcSnBcTjYtzGDt7SGssrN1BVUAZBuSmJIz4FEj0nFGuMIVZgSAvXJ7KiTTsI5cqSS2IexSk8l729+n20V27CarXj9XtaXrufE1BOVzBb5malPYr+d/zYmTLyd/zaAktkih5lWJ7Lz8/MxDIOMjAy+/PJLEhISgvPsdjuJiYkqoywiIiIiIiIiIWVxuQg/4ohQhyGdwF9RhyXChi0lCgBbShSWCBv+ijpQIvtnx2SzEXH00aEOQzpYflk+2yq2kRaVhsVswef3sal8E/ll+eQk5IQ6PBHpJA2T2HazHbvFjsfnUTJbpJ34KiuDjYNrCgtDGkurE9mpqakA+P3+DgtGRERERERERESkNXyVHsxhFqyxYQBYY8Mwh1nwVXpCHJmIdJSSmhKsZisWc6BDlcVswWq2UlJTEuLIRKSzNJfEBoL/K5ktcmh8lZWUL1y4d7gmO+6NGwLT3e6QxNPqRHZDeXl5fPzxx+zatatJYvu2vWPSiIiIiIiIiIiIdBSLy47HZ4DfCEzwGxg+A4vLHtrARKTDxDni8Pq9+Py+YI9sr99LnCMu1KGJSCdoKYldT8lskUPn2biRuq1bsadnYLJYsNXWAlC3fXtI4mlzIvvJJ5/kiiuuID4+nuTkZEwmU3CeyWRSIltERERERERERDqcNSEcS5wDb17g5pp3Ty2WAQ6sCeEhjkxEOkp6dDrrS9ezqXxTcIzsPpF9SI9OD3VoItIJlhcuZ+GmhZgwNUli17Nb7NT561i4aSGT0idxVM+jOjlKke7NW1KCyWbHVD+ctNkMgK+sLCTxtDmRfdddd3H33Xfzxz/+sSPiEREREREREREROSCzw4ozJx5HUTQAjoxonDnxmB0HVYBQ5OerphyK86CqCCISID4THFGhjqpZLruLE1NPJL8sn5KaEuIccaRHp+Oyu0Idmoh0gtykXCamTeTt/Lfx+DzNJrM9Pg8GBhPTJpKblBuCKEW6N2tcHEadB8PnCySz91bmtkRHhyaetq6wZ88ezjzzzI6IRUREREREREREpNXMDiu25AgAbMkRSmKLtFVNOax+A0rywWIHnweK0iH7tC6dzM5JyAl1GCISAjazjT8d9Sfgp/LhDZPZHp8Hj9/DSeknqay4yEGyZ2Rgy8vDk78Rk81O3fZtANh69w5JPG3+dn/mmWfy3nvvcfnll3dEPCIiIiIiIiIiIiLSGYrzcO9Yy5oKJ5hMYFgh71MoMFEdlcamTZtIS0sjPLz5kv1ZWVk4nc5ODlqksVq3m6KtmwAo2rqJ2qwswvS5PGy1lMxWElukfVhcLqImTsSzcSPekhKc8T0C00N0Xm1zIrt///7ceuutfP755+Tk5GCzNT4ZzJgxo92CExEREREREREREZEOUlXEmu1l5F521z4z5rZq9eXLlzNixIj2j0uklWrdbvK++JRtq34EYNuqH8mLiiRz9Fglsw9j+yaz6/x1GBhKYou0E4vLRfgRRwDg+OabkMbS5kT2E088gcvlYvHixSxevLjRPJPJpES2iIiIiHQqT7WXPQVuqis8hEfaiU12Yg9XWVERERERkQOKSCCrdzTL/+8eMFnA8EHpFkg9htUlcO655/Lcc8+RnZ3d7OpZWVmdHLBIYyU7tlJaWEBUQgIAUQkJlBYWULJjKz37DwxxdNKRGiazF25ayMS0iUpiixyG2nyHLz8/v912/sknn3D//fezfPlydu7cyauvvsqUKVNaXH7RokUcd9xxTabv3LmT5OTk4ONHHnmE+++/n4KCAoYOHcrDDz/MqFGj2i1uEREREekaPNVeNnyzi7LiasxWE36vQcmOcPqNSFQyW0RERETkQOIzcfYayIiSfLCYweeFgWMh+1RYtR6A7Oxs9bqWLstdXo7ZasVstgBgNlswW624y8tDHJl0hvpk9qT0SeQm5XabJHaF10eeu4bdHi897FYynQ4irZZQhyXSJR3S3T3DMIBAT+yDUVVVxdChQ7nwwgv5zW9+0+r11q5dS1RUVPBxYmJi8O8XXniB6667jrlz5zJ69GjmzJnDxIkTWbt2baPlRERERKT7cLvdrFmzpsn03dsryf+xgLLaXaT07ovNFsbaDTVsK4mlR28XoHH7RERERERa5IiC7NOgOA+qiiAiAeIzA9NFugFnVBR+rxe/3weA3+/D7/XijNJn+OfCZrZxVM+jQh1Gq+2o8fDvHcVscNcSbbXgspoZGBHOKQkxSmaLNOOgEtn//ve/uf/++8nLywNgwIAB3HDDDZx33nlt2s7kyZOZPHlym/efmJhITExMs/MefPBBLrnkEi644AIA5s6dy1tvvcW//vUvbrrppjbvS0RERERCb82aNeTm5h7Uuhq3T0RERERkPxxR0OfgvmuLhFpcrxR2J21hbf7nAJQXFZHdrx9xvVJCHJn83DXXIL/K6+O5ncV8VVSCv2AnUb1S6BXpYqfdjD82imxXOKAG+SINtTmR/eCDD3Lrrbdy9dVXM3bsWACWLl3K5ZdfTnFxMddee227B7mvYcOGUVtby5AhQ7jjjjuCcXg8HpYvX87NN98cXNZsNnPCCSewbNmyDo9LRERERDpGVlYWy5cvbzJ99/ZKPvv4a+74+/Xcf+c/Se/bn8rdNfTKbNwjW0RERERERA4/YU4nmaPHsr28AoA+gwaTduQYvqzxcZTDwGY+uGqyIoeqrQ3yn2jwtxrki/ykzYnshx9+mMcee4zzzz8/OO20005j8ODB3HHHHR2ayO7Zsydz585l5MiR1NbW8tRTT3HsscfyxRdfMGLECIqLi/H5fCQlJTVaLykpqdlSlPVqa2upra0NPi7X+BkiIiIiXYrT6Wz2R5wn20t5UTUAiVEp9I7pT3R/jZH9c1JTU0NxcTFVVVVEREQQHx+Pw+EIdVgiIiIiItJJwpxOElLSAIjtncrt23bzxq5STkuM4d4BKUpmS0g01yD/pZ0lfLC7DPfmjfx4240cedcD1PbpS7zNyoV9Ehr1yBbpamry8qgoL8caF4c9IwOLy9Up+23z3b2dO3cyZsyYJtPHjBnDzp072yWolgwcOJCBAwc22ueGDRv4+9//zrPPPnvQ2509ezazZs1qjxBFREREpBPZw630yYoFIC45gvQjEohNdiqJ/TNRU1PD6tWrKSkpwWKx4PP5KCoqIjs7W8lsEREREZGfoX9sLuCzeBsmE7xaWAqgZLaERHMN8r/cUkhSURlVVgs/Ata+aXj7DaBfVASnZfXVGNnSJfncbgDcX35JTUY/jDoPtrw8oiZO7JRkdpvv8PXv358XX3yRW265pdH0F154gczMzHYLrLVGjRrF0qVLAYiPj8disVBYWNhomcLCQpKTk1vcxs0338x1110XfFxeXk5KisbQEBEREekObI7AV9pembEkpUeFOJrDh7/Gi7eoGl+lB4vLjjUhHLMjdA0EmhtfrLCwkM2bNxMeHs7OnTvp2bMneXl5FBQUNKrSpPHFRKQr8VR72VPgprrCQ3ikXQ2wRER+RnyVlXg2bsRbUtLpPdoOJ7VuNyU7tuIuL8cZFUVcrxS8fgOAj0sqiUyEMLOZWr9fyWzpUoZEOvm0tJI6U/1n0aB3mJ0zkuKUxJYuq277dgBsvftgT03F8Pnw5G/Es3Ej4Ucc0eH7b/MvpVmzZnHWWWfxySefBMem/vTTT/nwww958cUX2z3AA/n222/p2bMnAHa7ndzcXD788EOmTJkCgN/v58MPP+Tqq69ucRthYWGEhYV1RrgiIiIiIl2ev8aLe2UxvpIaTBYTHp+BJc6BMyc+ZMnsto4v1pDGFxORrsJT7WXDN7soK67GbDXh9xqU7NCQGIfKX+sFoCa/FE9CRcgbX4lI56v0VJJflk9JTQlxjjjSo9Nx2btWgthXWUn5woXUbd2KyWbv9B5th4tat5u8Lz6ltLAAs9WK3+ulMGkLc/YOOWUzBZLYUP+/ktnSdQyNdHJ8XBTv2QPfU1KdDn6ZFMvRsToHSNflKysL/LH33GqyWDDZ7HhLSjpl/23+Vn/66afzxRdf8Pe//53XXnsNgOzsbL788kuGDx/epm1VVlayfv364OP8/Hy+/fZb4uLi6Nu3LzfffDPbt2/n3//+NwBz5swhPT2dwYMHU1NTw1NPPcVHH33Ee++9F9zGddddx7Rp0xg5ciSjRo1izpw5VFVVccEFF7T1qYqIiIiIHNaa6+UMUFdQRemaXWx3F5Lapy8OWxjevFocRdHYkiOAzu/l3Nz4YvU9svfs2cMtt9zCPffcQ2xsLKmpqU16ZIuIdAV7CtyUFVcTk+zEbDbh9xuUFrjZU+BWVZGD5K/xUrO+FADvrmqqV+0OeeMrEelclZ5K3t/8PtsqtmE1W/H6vawvXc+JqSeGNJm973ftmrw83F9+iS8hkS1bt9E3IQHLZ5/h9HpxNKh0qmpC+1eyYyulhQXE9uqN2WLB4/Xx10qDRaVVAE0S1UpmS1cSabVwRnIc9oQY/guckhDDacnqjS1dmykyEgCfzweA4fNh1HmwxsV1yv4P6ht9bm4uzz333CHv/Ouvv+a4444LPq4v7z1t2jTmz5/Pzp072bJlS3C+x+Ph+uuvZ/v27TidTo444gg++OCDRts466yzKCoq4rbbbqOgoIBhw4bx7rvvNrqRJSIiIiIi3auXc3Pji9WPkf3FF18AEBsby+jRozVGtoh0WdUVHsxWE+a9N9DNZhNmq4nqCk+II+u+vEXV+MoCr58lJgxrQjjeomq8RdXYUyJDHJ1IN1BTDsV5UFUEEQkQnwmOrtuwprmGmHl78vh88+eYS830TOmJ1W5lWeUyPFs8ZMYGEsShSA4f7HdtVRPaP3d5OWarFbMlkPhbabazzBEORmGL64SZzdQZft7YVcqUxFjGxen6IKETabWQ7QoHINsVriS2dGk+n48vt20D4MuNGxgUFobZW4ctJQV7RkanxNDmRPbbb7+NxWJh4sSJjaYvXLgQv9/P5MmTW72tY489FsMwWpw/f/78Ro9vvPFGbrzxxgNu9+qrr95vKXEREREREWm+lzMEemR/v3g5l/71Wp6++1EGpvbHu6cWR0bjHtmh5nA4yM7OpqCgAIDU1FQlsUWkSwuPtOP3Gvj9RrBHtt9rEB5pD3Vo3Zav0oPJ8lPPOpPZhMliwlepxgEiB1RTDqvfgJJ8sNjB54GidMg+rcsms7tTcnjf79r1PbLzDbj07w/yxLXXkW4C56hRTXpkS8ucUVH4vV78Ph9mi4Ucv4eja2r52NRyL+tavx/DgNOSYjgqRiWcRURaw+fz8dZbb7Fh0yYAtvr9fGq1MPGYsYT3799pw2K0OZF90003ce+99zaZbhgGN910U5sS2SIibVXrdlOyYyvu8nKcUVHE9UohTOWWREREDkpzvZwhUKa1bndgjLn+sX0ZEp+JZUDXLNPqcDiC1ZeSkpKUxBaRLi022UnJjnBKC9zBMbKj48OJTdZvmoNlcdkxfD91kjD8BobPwOJS4wCRAyrOCySx4zPBbAW/NzCtOA/6HFzVno7WXEPMvD15vL3sbf5967+Z+eBMemf0ZkflDnKTcxv1yO5s+37X9g0YQLnVCp99BkC6CXLHjOnyY2R7qr3sKXBTXeEhPNJObLITe3jofhPE9Uphd9IW9uzYHhwj+4akZIyYCP4PqPM37jhX6/fj8cOvk2JUVly6hAqvj9WVgd/bqyuryfT61Ctbupz6JPbKlSsx7x0b22y1smb3bmxbtnByTk6nxdLmK05eXh6DBg1qMj0rK6vReNciIu2t1u0m74tPKS0sCH5R3Z20hczRY5XMFhERaUdmhxVH/xgArInhhA/qgTUhvMslsUVEuht7uJV+IxK7VEKgu7MmhGOJDiStfaW1eIuqscQ5sCaEhzgykW6gqijQE9u89xxktgYeVxWFNq79aK4h5gDPALZWbAXAmmzFkmLh6MijQz5G9r4sLhdREyfi9HqBQE/s7pDE3vDNLsqKq4MNsEp2hNNvRGLIrl1hTieZo8c26ehyzarVgUS2EUheh5nNSmJLl1Ph9fFmUSnLSisBWFZaibmolFMSYpTMli6jYRLbYrFg2TuUQ/3fK1euBODkk08OzutIbb7aREdHs3HjRtLS0hpNX79+PREREe0Vl4hII263myXvv8u2VT8SlZCA2WzB7/excu0nLFrxHcNHH014ePM3KkIxDpKIiEh3Zw4L/FRwpMdojFERkXZkD7eSlN41S/Z2R2p8JXIIIhIC5cT93p96ZPs8gekAtYFEC5s+g0Sjy46f7bK7OKrnUQBkRGdwVMpRpEend6kkdj2LyxUsI+7IzOwSSezmxh2vq/FSvruGbRuKyVu9nuyhA3BFOvH7DdZuqGFbSSw9ertCds8tzOmkZ/+BjaZZ9yapj4tz8Zkf6oxAOXElsaUryXPXsKm6liR74HuKx2/weWklKQ4bx8R2vfNrl1VTHqgeUlUUuGZ10etTd7RvEttqbfyduv5xZyaz2/yt/le/+hXXXHMNr776Kv369QMCSezrr7+e0047rd0DFBGBwBhIk6acflDrhmIcJBEREREREekcanwlcpDiMwNjYhfn/TRGdlx6YHpNOeR/EliueB2s3dOlx8922gLJ1NzkXHISOq/c6eHgYMcdh655z21GajJJrhje2FXKaUpiSxez2+PFMCCvqgaAXZ46qPbwblE5QyMj1Cu7NWrKYfUbgaEx6q9dXfj61N1s3ryZH3/8EZPJ1CSJXc9qteL3+/nxxx8ZMmQIGRkZHRpTmxPZf/3rX5k0aRJZWVn06dMHgG3btjFu3Dj+9re/tXuAIiIQ6FX97msvN+2R/e13/OWRuTz33HNkZ2e3uK6IiIiIiIiIiDTgiArc+G+uV9u25VC+PbCcyQR11bD5M4juCxnjQxu3tKt9xx3fvb2SHXl7cPVwsGrlam574Fqun3YPQ0cMwRltp3J3Db0yf+qR3dVYzSbuHZDClMRYjopxKYktXUoPu5VCTx0lXh8AMVYLfquZCp+PPHcNI6JU9fhA3FtXsuaLTyGmb+D6ZFgh71MoMFEdlcamTZtIS0trtnqrKrceWGpqKoMHD2blypV4vd5mk9lerxfDMBgyZAipqakdHtNBlRb/7LPPeP/99/nuu+8IDw/niCOOYPx4fYERkY7jdDo59sRJ5EVFNhoj28gJtLLNzs7uci1ARURERESCVP5ORKTdVXgDN/53e7z0sFvJdDrUm62tHFHQp5neuFVFYBiBv4vXQnQiVBbB2reg1zBdww4jDccd91R7+XH3DozoOGLjI/BmBpJtyTF9SYpIJyomnOj+oR0juzVsZhPj4lShQ7qeTKeDSIuFyr2J7N11XrLCbMRarez2eEMcXfewZuUKcq+Y28yc5qY11hWrSHQ1FouFk08+GfipfHhDXq8Xn89HTk5O1x0jG8BkMvHLX/6SX/7yl+0dj4hIi8KcTjJHj6Vkx1bc5eU4o6KwFhaHOiwRERHpIgoLC1m7di0RERHEx8fjcDhCHZJIgMrfiYi0uwqvjzeLStlUXYvdZMZj+FkbXsMpCTFKZreHiARw7w78HR4PznjweqC2ItAwq7nkt7SKz+dr9H9X4an2suGbXZTsqKSytJa6Wh+lJdUAuGLDSOwbRc/+McQmO7t0ElukK4u0WpiUEM0Ghx2AzAgHR7icbKv10MOu46o1snKGs/yxy/f2yLaA4YPSLZB6DKtL4Nxzz22xemtXrCIRCiUlJVRWVu53mZycHIqKilizZg2lpaUAlJaWYrPZyMrKIicnh+3bt7e4vsvlIi4url3i1ZEhIt1KmNNJz/4Dg493lleFMBoRERHpCmpra4HAWE7h4eH4fD6KiorIzs5WMltCwu12s2bNmp8mFK6GzYHyd9WeOjZtLyTNuZnwAhMkNb7BonJ3IiIta3h+XV1ZzbLSSvo67HhqaynYsplNicn4k+LJdqmc6CGLzww0vgLYvR6iSyA2DVxJgd7aclB8Ph9Lly4FYOnSpYwYMaJTerO1xp4CN2XF1SRlRGG2mnCXeagoDozjm5ASyaBjenWLBHbR1k1swIczKoq4XimE6bg/aDU1NRQXF1NVVaXGwu1oaKSTHFc4/yVQ+GJbrYe08DAynXptW8OZksOI0WP3NhI2g88LA8dC9qmwaj2g6q37U1JSwjU33kJJufuAyxqGwa6iIop37QRg8ZcriE/syapNBby6cPF+142LcjLnr/e0SzK76195RERERCRkfJWVeDZuxFtSgjUuDntGBhaXK9RhiTRS3zo4JiaGHj164Pf7KS4upri4mD59+oQ2OPlZWrNmDbm5remp9nSTKSp3JyLSstacX59oYbrOrwfBtrdBgOH/6X9/XaC3trSZz+fjrbfeYv36QKJl/fr1vPXWW51WmvVAqis8mK0m7A4rSWlRVJV52FRgA6BPVmyXT2J7qgO9x7et+pHIulr8Xi+7k7aQOXqsktkHoaamhtWrV1NSUoLFYlFj4XYUabUwLjZQ+j7TGcZR8dEaGqMtHFGBylYatumgVFZWUlLupseIiUTEHvh6nmr42bw5n7ih60lO609qajqYzPtdp2pPEbu/WUhlZaUS2SIi+6rzG3xeWslRMS5sZlOowxER6dZ8lZWUL1xI3datmGx2jDoPtrw8oiZO7NLJbE+1lz0FbqorPIRH2lX67jBW30Ng8+bNwE/lGc1mMxaLhaoqVW6R0MjKymL58uU/TShcDZuXQkxfVm8q4Nyb/8FzN59O9rhfN9sjW0REmtfw/NqwR/aO9Xk8dNVlTH3wYU7LHd5ij2xpg+K8n8bIjoiD8Dgo2QhRfQIJA2mT+iT2ypUrMZsDCQCz2Rwcf7QrJLPDI+34vQZ+v4HVbiGyhwNXTBgANkfX/z1VVlwIQFRCArE9e+H3+dizYzslO7Y2qu4oTTWpJkRg2Kb6ilc7d+6kZ8+e5OXlUVBQQFJSUnA5Vbs4OBF7k9ZHxUYyIioixNF0Q44oDXFxiCJiE4hO6NmqZXMSepI+8AhckS5MB0hi19t9KMHto+tfgUREWqnOb3DTuq28sauU0xJjuHdAipLZIiL70dyP1XrV1dWsW7KEhJ07iUxNg7o68BvUffYZTq+XYaee2iV/rNaP61ZWXI3ZasLvNSjZEU6/EYlKZh9Gampq2LFjB2vXrqW2tjY4tlNhYSF1dXXBHgMREbohIKHhdDob9/qr6Q+rjUD5u/JAUiB7yDBGHHeqeg6IiLRBw/NrpteHp6CEr8qqWL+7HIDYjH4cN+pIeu0de1QOQVUR2Pd+l4ofCHFR4OwBSYN07Wojn8/Hm6+9xg+rVmE2mahPV1ssFiwWS5dJZscmOynZEU5pgTv4W8oV03163tZUBhqxms2B19BssWC2WnGXl4cyrG6h9dWEmlK1C5HDn8lkJjIqdNf+g7qbt2HDBubNm8eGDRt46KGHSExM5J133qFv374MHjy4vWMUETkg794k9quFpZhM8GphKYCS2SKHIZ/Xy7bVP9AnewgWqxKTh+Jw/LFaP65bTLITs9mE329QWuBmT4GbpHTdcDsc1Je427x5MyUlJTgcDkymwLW+tLSUbdu2ERERQVxcHPHx8SGOVmSvhuXvypYFpqWPVyJAROQQ1fj8bKvxUOypA2Cj28PC4jLOSI5TidZDFZEQKCMOENUbkvoGrmMxqaGNq5sJJrF/+AGzYWAxmzE8nsBMw8BqsYDPx8pvv8VbUsIpv/419ujokMRqD7fSb0Rio+pWpp2BPnW7t1eyyVrcpSteOVyBhhd+f6BKk9/nw+/14gxh8qW7aFJNiJ96ZO/Zs4dbbrmFe+65h9jYWFJTU5v0yBYR6UhtvuIsXryYyZMnM3bsWD755BPuvvtuEhMT+e6773j66ad56aWXOiJOEZH9+sfmAj6Lt2E3Q5jZTK3fr2S2yGHI5/Xy4dOPsfbzJQw8ahy/uOgKJbMPQXM/VuutXr2ac889l0d/9zsGjcgFsxn8fuq2b8M5alSX/bFaP66bee9532w2YbaaqK7whDgyaS/FxcWUlJQQFhZGREQEERERwdLisbGxuFwuBgwYQHx8vMZu+xmoqKljQ1EVuytr6eEKo19CBJEOW6jDal59+btde7+XhnXdIRpEuhN/jZe6gkAvvLqCKvw1XszdoASuHLo8dw1r3DXE2CzERTpZAdjNJr6tcDM0yqlSrYcqPjOQwAYo2wbFtRCXrrLibbR582ZWrV4dSFpbbWACLIFzlOHzYdTWYvb58AFrNm2i32uvMeT000M2lJM93NqoAfCmnYH/d+TtIdpc3qUrXkXHB5Kr5UVF7ImJxu/1EpOUTFyvlBBH1vU1qSbETw2Iv/jiCyDwW2v06NEaI7sdVHh9rK4MjOm+urKaTK9Pja9E9qPNV5ubbrqJu+66i+uuu47IyMjg9OOPP55//vOf7RqciMi+at1uSnZsxV1ejjMqCrc7cNH/uKSSyMRAEhvq/1cyW+RwUp/EXvPpYjCZAv+DktmHoLkfq/vKHjqUbLMJk80WGCN7zJjAGNldsKw4NB7Xrb5Htt9rEB6p8pKHi6qqKiwWC06nk9LSUoBgj+zw8HAGDBhAnz59QhihdJaKmjreWVnAlpIqbBYzdT4/6+IimJyT3HWT2SLSrvw1Xtwri6nZWAZAzcYy3AnFOHPilcz+Gdjt8VLnN7CbzPj3fhewmkzU+Q12e7whju4w4IgKVA8BiB8AA48OJLFVTaRNUlNTGRAfz+qCAryGH6vJHEhmA/h8GD4fPpMZMBgQF0diSQmejRsJP+KIUIYdVL67BgBXDwcxic4uXfHKHh4OQJ9Bg0lKScEZFUVcrxTCuuhv167O4XCQnZ1NQUEBEPgsK4l96Cq8Pt4sKmVZaWB4rGWllZiLSjklIUbJbJEWtPlb/cqVK3n++eebTE9MTKS4uLhdgjpc+Xx+duSV0iszBouldQOii8hPat1u8r74lNLCAsxWKx6vlwfLAuWCbKafktj1lMwWOXw0TGKbbVasNjveOo+S2Z0g4uijiYyKwltSgjUuDntGRsh6B7RGc+O6RceHE5usmxeHi4iICHw+H9HR0bhcLsrLy6muDjRsi4qKUjnxw5jb7WbNmjXBx2sLKvhiYzF9Yp1U13ko2LaFfGcPfEW9GZgc2WjdrKwsnLqJKXLY8RZV4yupwRobBoA1NgxfSQ3eomrsKZEHWFu6ux52KzaziUqfD7NhAOA1DGxmEz3s+m3QLuqrh6SNgT5db1ih7sBisTDxqKPwvv02671evH4/GD/N9wF+DAbGxXF831R8W7fiLSkJWbz7qnUHyst3p4pXCSlp9OuCw2B1Rw6HI1hGPCkpSUnsdvBdhZvPSyvx+P0AJNmtbKquJc9do0oiIi1o87e6mJgYdu7cSXp6eqPpK1asoHfv3u0W2OHG5/PzyfNrWf/NLvqPSGT8bwcqmS3SRiU7tlJaWEBsr96YLRa+9ppYXroVoMUEdZjZTJ3h541dpUxJjGVcnG5miHQ3zSWxgUAyG5TM7mAWp7PL9AZojebGdeuqY7jJwYmPj6eoqIiSkhLCw8Px+XzB5HV6erpurhzG1qxZQ25u7gGXe7SZacuXLz9gBQoR6X58lR5MFhPU/x40mzBZTPgqu26CRdpPptPBsEgnH5WUs6m6FgC/YTAs0kmmU98H2lXhali7KzButnplt1l4//78YsAAWLeOPI8Hny9QMcBnNuMzDLL2JrHNhoG3zoM1Li7EEf8kzBmocuP3G8H/VfFKuhqvt4Iq90bqPLux2XsQ4czAau1694ArvD7eLSpjc3Ut1AbOA3nuGrIMVElEZD/afEfv7LPP5o9//CMLFizAZDLh9/v59NNPmTlzJueff35HxNjt1Sex131ViMlkYt1XhQBKZou0kbu8HLPVitkSKLMyzGIwtK6SHUCd32h2nVq/H8OA05JiOCqm6/YgFJHmtZTErqdktjRn33Hd5PBSX+KuuLiYqqoqIiIigr0EwsLCQhyddKSsrCyWL18efNywR/aOzRv4+59+zzk3/pVTJ4xqtke2iBx+LC47Hp8B9b8H/QaGz8DiUoLl5yDSauGM5DgyIxy8vdXFYuD05FjOSI5Tedb2UhsofcvmpRCeBj4PFKVD9mlKZreBxeUidtIkTu7fn4Wff87OwsC9YQMYGBvLWK830BO7zoMtJQV7RkZoA24gqkegUUjl7hpKd7m7VMWrfYcf9Oyt0iQdo7CwkLVr1xIREUF8fHyXaUDs9Vawa9e7VFdvxWSyYhheqsLXkZg4qcsls/PcNVT4fERbrZhsgevU7jovhZ46VRIR2Y82Hx333HMPV111FSkpKfh8PgYNGoTP5+O3v/0tf/7znzsixm6tYRLbYjVjsZnx1fmVzBY5CM6oKPxeL36fD7PFgtnvY0rlLt4B6oxA0rphefFavx+PH36dFKOy4iLd1LbVP7D28yVgMjVJYtez2ux4vD7Wfr6EgWPGkZozrHODlJCrqwm0XN6Rt4feseXqgf0z4HA4Go2DvWvXrhBGI53F6XQ26lWdWVOHJSEwRra5PDB+4xFDBnPK8WM0RrbIz4Q1IRxLnANvXqA3rndPLZYBDqwJ4SGOTDpLpNXCMbGROHv2YDYwPCrQgP2b8ip2e7z0sFvJdDqU2D5Ye6vgEdMXevQDvxeK8wL/+hy4Sor8xOJy4Ro2jCk5OWz5xz8A6N+/P7+aPh3f5s1dbignT7WXPQVuirZWAJCYGk1iSlSXqXi17/CDfq+XHWUVIY3pcFVbG7jGbt68OVgRq6ioKGTjZTccbsjrraJkzzLKSr8hPLwPhhHN1q27SUzMJynZR6RrQKN1Qz3c0G6PlyS7DbvJxNq6wH2MKq9BpMWiSiIi+9HmK47dbufJJ5/ktttuY+XKlVRWVjJ8+HAyMzM7Ir5urbkkNhD8X8lskbaJ65XC7qQt7NmxPfglNSauBwDHxbn4zA8QSGYriS1yeOiTPYSBR41jzaeLAyXWmklme+s8YBgMPGo8fbKHhCDK/TN8fmrzywhLj8bUBa/3vspKPBs3drkbJ63lqfaybc0eAEoKqsj/voiSHeH0G5EY8psrItKxIh02Jucks6Gois8rAzfax/aPVxL7Z8ZXWUlNXh4ANXl5+AYM6FbXMTk0ZocVZ048jqJoABwZ0Thz4jE79B3g56rK6+PNolI2VddiN5nxGH7WhtdwSkKMktkHozrwPRvT3tfObAWLHaqKQhdTN2exWDjmmGMAOOaYY7BHR0MXG8rJU+1lwze7KCuupqSgCgB3WS29MmO6zG+sfYcf9FS72fXttwAUbd1EbVYWYU5nk17bcb1SCAthIrM7Ki0tBQJDzvbo0QO/309xcTHFxcWNGhd3ltYONwT/bDIl1MMN9bBbMZngiEgn/ohA4rqnw8akhChdo0T2o81XnjvvvJOZM2eSkpJCSkpKcHp1dTX3338/t912W7sG2F21lMSup2S2SNuFOZ1kjh7b6AuotbAYgBmpySS5Yni1sJQ6I1BOXEnsw1d3GftGDp3FauUXF10BBMqHe6FRMttb58Ff5yVr7IQuWVbc8PnZ89oGqlcWEZ6TgO2kZDZVbqKkpoQ4Rxzp0em47KG72e6rrKR84ULqtm7FZLNj1Hmw5eURNXFiyGJqLb+/jtLSL/GUZlFZGuiJGRnnICbZSWmBmz0FbpUXF/kZiHTYGJYSg78o0LgxIqxrXQekY9Vfx9xffgmA+8svKbdaiZo4UclskZ+pLTUetlTXkul0YDGZ8BkGee4a8tw1jIiKCHV43UtNOVSXBv4u3QJ1yYEkts8TGCtbDppl75B59f93Nbs2l7NjfTG2qNVAYOieytKaLvUbq+Hwg3W1tRRu3EBFSeAe4bZVP5IXFUnqEcPZ/P2KRr22dydtIXP0WCWz26B6b8l2kylwf9VsNmOxWKiqqgpJPPXDDVVUrqN0z5dgMlNdnY/NGsO6vHxuufljHnjgt+SOPLXZHtmhlOl0sDa8hk3VtcFRUXJc4QyN1PVJZH/a/Ct/1qxZXH755U1KMLjdbmbNmqVE9l478kpZ/80uTCZTkyR2PYvNjN9nsP6bXfQ/MomUrLhOjlKk+wlzOunZf2Dw8c7ywJcmq9nEvQMCjWve2FXKaUpiH7a609g33VVFTR0biqrYXVlLD1cY/RIiQtq7raVkdrdJYn+3CzDh/nYXu8ry+eyItVisVrx+L+tL13Ni6okhS2Z7Nm6kbutW7OkZmCwWDJ8PT/5GPBs3hiSe1vL761i79nYKd72N03o8WMYF55nNJsxWE9UVnhBGKNKYGmCJdIz665glIZBQ8dfVUb18ObbefXAdNTrE0Uln8Nd4ca8spmZjGQA1G8twJxSrV/bPWFmdF7vJjGVvwsViMmE3mdnt8YY4sm6mphxWvwF78gOPt38NUSXQIwMSsiBelTkPV55qLxu/20mt458YrmVU1AUSgYZBl/qN1XD4wao9JVSV7sHmCAwrEZWQQGlhAcb3Kyhr0Gvb7/OxZ8d2SnZsbXRvUfYvPDzwuhpGIPPq9/vx+XxERIQm+Vo/3FBxcSnl5Wk4HMmUlYfh8eympibQ8GLw4CMYO+bkLvebK9Jq4ZSEGPLcNXzuDMQ6LjZSvbFFDqDN3+oNwwi2vmnou+++Iy5Oidh6vTJj6D8ikXVfFeKr8zebzPbV+TEMg8wRSfTKjOn8IEUOM7a9yewpibEcFeNSEvswUz8GTn2LS4ejFyaTj+rqWjZseI3BQ9wkxOc0u26ox8DpTipq6nhnZWC8UZvFTJ3Pz7q4CCbnJHepZLbH6wPD6LJJ7KqKSr6c+wG160rAbMZkNeGtq6NmSw3lX+zAfnwPrA4ryyqX4dniITM2cCOosz+r3pISTDY7pr09AUwWCyabHW9JCUR1jZb2+womsQv/ByYTlZ738Fi2N5hv4PcahEc2P6a6SGdTAyyRjuMtKcEwwLNhAwC+omI8JXuo+PADwocMVq/sQ9SwHKvNbgdM1Hlqu1RpVm9RNb6SGqyxgZvB1tgwfCU1eIuqsafoHPtzFG2zUmb48RlGsEe2x/DTw961fi90ecV5UJIP8f0Dj3uPBEslxGdB9mng6Fq/FSo9leTtCQwzkbcnjwGeASGtfNVaPreb6u+/71LDPJXsLKMu/J/YnUsxYcYSsQKAmsqaLvUbq+Hwg6W7CqitqsQZFRhmwmy2YLZaKdmxnZqqCrx1dYQ5nUTExmG2WnGXl4c4+p/4a7yBa1mlB4vLjjUhvMs1xIqJiQECJcZ3796Nz+cjLi6O+Pj4kMZls/fAMLyYTDaio4ZSXbMTqy0wHEJs7Jgu+1sr0moJVAiJDcQXoSS2yAG1+qwYGxuLyWTCZDIxYMCARslsn89HZWUll19+eYcE2R1ZLGbG/zbQsqu+fHjDZLavzo/P62fAkUkqKy7SjmxmE+PiuuYXFTk0Bx4D5/kW54R6DJyurL6BQL21BRV8sbGYRKeFHQXbiE/uw2d5PnxF8QxM/unYCkXjgIbJ7LWfL2HgUeO7ZBLb8Pn5cu4HHH/jr1te6InmJ3f2Z9UaF4dR58Hw+YI9so06D9a4OPB2vV4r9UnsgsI3ABsmw44JDz7rVwCUl1RRWuAmOj6c2OTQ31yXn6+G59a2NsBS4yuR1rPGxeEr2oV3TykA5qioQI+ryio8GzcS3sXGHO1Oat1u8r74lNLCAvyGQfHmfDBBQt80TCZzlynN6qv0YLKYoL4Rs9mEyWLCV9l1eg1K5+rrsGMODyPPXRMcIzstPIxMpyPUoXUvVUWBMuL1939j+kKiARHxXTKJ/f7m91lesByA5QXLsW+2h7TyVWtVLVtGhc3WZJinUCWz/f46tuy8GyI+weS3Yfht+P21APgiXiQ8+qSQxNWchsMPbl+zmmKbDYxAQtDv9+Gprqamspw9O3YQHh0NGIQXRxHmdOHsIo2266uK+EpqMFlMeHwGljhHl6sqEhYWaCyWmppKz549iYiIID4+HocjtOfVCGcGVeHrcLvzMZmsYPiIiR4BvIjVqlLdIoeTVp8R58yZg2EYXHjhhcyaNYvo6OjgPLvdTlpaGkcffXSbdv7JJ59w//33s3z5cnbu3Mmrr77KlClTWlz+lVde4bHHHuPbb7+ltraWwYMHc8cddzCxwTiOd9xxB7NmzWq03sCBAxvdpO8sLSWzlcQWOTQNewYUbd0a6nCkk+w7Bo7dHo/bvYF16/K55ZaPufe+kxk2dNzeVpcRTdaV5h24gUDzQtU4oD6ZPXDMOPpkD+mSSew9r20gpSSSdy6dh8n6U8M/r9/Lml0bmPnKbOac9icic2JZ3Oc7cpKP4KieR+G0OTv9s2rPyMCWl4cnf+NPN09SUrBnZMC6dZ0ay4E0TGIbfiv4rRgmPxhW6r/SmiJfIi1nPHE9o7GHd53PRsPrVlfqxSYdp3Xn1uYbYKnxVevsOwxGVW3Xa3wjHc+ekYEpwoXhDgw35NuzB1tWFubY2EB1EWmVfRs2AhRt3cTGb1dQ7vUR4wijYlcBGAblZhsRsXGszf+c7eUVjDtxUkga39T3YKsrqqauuBpvsRsA755a/FE+LK6u02tQOldEg7Ktuz1eetitZDodKtvaVhEJgbGwjb3fqQ0f+Lxdcmzs/LJ8tlVso5erFwC9XL3YVrGN/LJ8chKar9rWVXi2bYPMAfirqjA5ndRu2BCyhlj1v7cqPe+BYcVkCcMw/GAEKrOZI77hx+//zPAj78UREd7p8TWnfvjBuF4p5H3xKds//xyAPTt34oqPpaa8DIfLhd/rxWK1UrJtGymDc4jrldIp8TV3fa1XXV3N+uWrSfbG4kqKCjTI8ht482pxFEWTc2yuGrcegNUaSWLipEZDOO3e3XV624tI+2n1Hb5p06YBkJ6ezpgxY7DZDr28aFVVFUOHDuXCCy/kN7/5zQGX/+STTzjxxBO55557iImJYd68eZx66ql88cUXDB8+PLjc4MGD+eCDD4KPrSG8yb1vMtvvMzAMQ0nsdmT4/NTmlxGWHo1Jr+dhr2HPALPVyra89QB4qquD85UsODzVj4Hj9Waya5eF0tKvcVdX4amLAWDoEWPo399GfEIU0VFDQxtsN1LfQKDed1v38NGaImp2bealB2/iyjvmYOvRh9EZTXtkh4rFaiU1Z1jI9r8/tfllVK8sItwWzhF9sxvNM/DjM3wApMX3YZDRn5K4aky9TRTFFHFi6ok47Z3cy93lImriRDwbN3apcnb7alROHBv4rYHeV0Zg2BvDF/iuV+39nD21D5AYNmv/G+xE+163/F5vl+nFJh2n4bm1cY9sC+vWbeb3v3+IJ568h9wRE5tdtyup8Pq6XDKguWEwvLuKQxqThIbF5SLqF8djXR/4TRCWkUHY4MF4d+4IVBeRVjnYho0QmsY3DXuwGXU+6gqqqMkvBaBuazm++DrMkaEbEkdCL1i2VQ5efCYUpUPep4HHpVtg4NguOTZ2SU0JVrMVwxQYv9dsMmM1Wymp6foNmrw7C6j1+sBqDVbE8mzf3umJ7Ia/tyxmO37Dis9r4PcbwWVMJhtu3wes+vFWho2cjdncdc6z9b2zt5dXAOCpqqTYXUZdTTUWexhh4U569OpDeFQUCX3TOu13WHe7vraktjbQK3/z5s2Eh4fj8/koKioiOzs75L2yrdbIRvcArdZvQhjNwVldWc3u4rIu81tLpCtqc4Z3woQJwb9ramrweBqXa4pqQ2mOyZMnM3ny5FYvP2fOnEaP77nnHl5//XX+97//NUpkW61WkpOTW73djtYwmb3+m11kjlASu73U93yrXllEeE4CsVP6datktq+ysssnD7qakh1bKS0sILZXb8wWC1GlZQCUFRcqWfAzUd/iss5bjt/vISIi0NvCbA7HZLJQ59kd4gi7l/oGAhBIDPxQsx3bnkgKygKNQ7YacZyRO4JThvcJ6RjZ3UVYejThOQlUf7cLw+vHZP3pmmTCjN1i3/u3iV2plfQ7YhBWq41N5ZtC1mPA4nI1e6PE5w70bKr6+muqrdaQXqNKS7+kcNfbYDJhMux7e2ITbCBo7L2/YvihcNfbJCWdTFzc2JDECo1b3hdt3cS2VT8SlZCA2WyhurqadUs/I3dXMSmZA5usq7LSh4eG59b6BliBMbItVFcHzgtDjzimy9ycaqhh4jrcYiKvqpYCT12wPOva8BpOSYgJ6Q2WDUVVbCmpIiPBhcVswuc3+CSvOmTxSGg5hgzBMai+8ZiBd+eOn6qLSKvs27ARAtevT99/n788MpfbZ1yFCx8YBvEpfYmIjaO8qIg+gwaHpPFNcFzshHB8e2owR9kxRwS+p9pSIrFE2PBX1EGMSkmLHDRHVGAs7AITMBdSj4HsU7tcWXGAOEccXr8Xv+EHwG/48fq9xDm6foMmX3kZluxsTFYrhtdLbd46jOrO/07T8PeW2RKGyWTg83rBaLCQ3wqGibKq9ykt/XVIf281J8zpJDo+CYDSokKckRG4y8uwhzupc1cTFu4ktlcfohITOy2m5q6v9VavXs25557LEzf+nUFHDP6pR/aeWhwZ0V2qcWtpaSkQGCu7R48e+P1+iouLKS4upk+fPqENrhur8gY6OiwrrSS9orrL/NYS6YranMh2u93ceOONvPjii+ze3TRZ4PP52iWw1vD7/VRUVBC3T0vrvLw8evXqhcPh4Oijj2b27Nn07du30+JqTn0yu/+RSfTKjFESux0Ek9jf7QJMe/+n2ySzfZWVlC9cSN3WrV1mLJzuwF1ejtlqxWwJXNDN5sD/NZVVTZLcfp+PPTu2U7JjKz37N00WSPdltUYSEzMSb10ZYfbAj1XD8GEYBjZ7jxBH131tKKpiV0UNJ2QnsaJmOx8AMeFW+idGKondSiaLmdgp/QACyWxolMxmb+Xb4p5VWI+3E7Z3VlfrMeCrrKRq2TIAPBs3UuF2h/QaFRMziqTEkygs/B8GgRKHgQS2gclkCg7dBwZR4ScQEzOq02NsqFUt7x+Z2+zkrtTyvruoqamhsDAwjE9hYSE1NTUh7xnQ0L4l72JiE/dO7xo9xRo2vKjy+liyp4IdtXXYTCa2lVewY+tmjs8eiM0ZgcUw+LTGgz/GRbYrPGQNL3ZX1mKzmLHsHRPXYjZh7Qbf/6VjWFwuIvYOc2bPyCDyqKPUQLiNGja+qVeblUVZQQEAfZISiTMbgTGy+/TGZDKT3a9fyBoM14+LbTKb8Nd4MdstWJyB76rWWAfmMIvGyBZpD44oSNrbUCgpu0smsQHSo9NZX7qeZZWB3y87KndwdOTRpEenhziyAzNHReErLg4msi2xsZjCO79sd8PfW35/LRg2TCYTZjOY937FMow6wE50xImd9nurpKSEysrK/S5TW11NaeEOaioq+f777wCorKmlzGbFa7JQtqcUi9XKHnc1Ga5IKn2wZcuWRttwuVxN8gvtwWG2MyQhE1+lB4vLjjUhvMnY19lDBjEkPhOTxYThM7AM6HpjZFfvbVxh2vvD22w2Y7FYqKqqCmVY3d6WmsB3lb4OOxnOMHyGQZ67hjx3jaqKiOyjzWfEG264gY8//pjHHnuM8847j0ceeYTt27fz+OOPc++993ZEjC3629/+RmVlJVOnTg1OGz16NPPnz2fgwIHs3LmTWbNmMW7cOH744QciIyOb3U5tbW2wRAZAeXn7j6UQHMPNqGP3jnL6JUQoKXAIGiWxLWZMVjOG198lktktjX9SXV3Npk2bSEtLIzw8nJq8PNxffomtdx+oqwO/QeqGDThCNBZOd+GMisLv9eL3+QLJan+g8YzDFdE0yW2xYLZacXfAMS2hF+HMoCp8HTU1gR+rNTU7CA8/mghn1+99Y9TV4f76a5wjR2Jqh6E62kt9YiAizErP6MCP58QoB9WezmukdjhoKZlteP3gDzS8WJtVQAKZWLDg8/u6VI8BX2UlFe+9R/W33wJgieuBPT0DT/7GkI3XZjbbGDgwUC68oPANMBv4vda9PbENTKa6QKy+I4kNmxnyMncNW97v2yN7fX4+N901m4cfuJ8xxx7f7LpdUf1YpPu7CRMKNTU1rF69ms2bNwOBcnerV6/uEmXuGmpY8i7S1bXK3bWm4cVn+zx+ZO//oWp40cMVRp3Pj89vBHtke33+To9Dug7L3mRqxMiR+i3VTsKcTvoOCZy3ElLTGT70CMBEnac25EM4WVx2PD4Dw29gdlgxfNUY9ecAvxFIBGiMbJGfDZfdxYmpJ+LZEkgK5SbncmLqibjsXb9Bk61nT8L6Z2LUVGNyhOOvrsbeu3enx9Hw91Zh4f/2Nhi2gtkUrH6FyYu59kQGDf5Lp/zeKikp4Zobb6Gk3N3iMn6fj6qyPXg9HjCZKN1dBMCrX/1IhDMCk8WCr64OMLCFhROxtQzX4hVNthMX5WTOX+9p12R2w2EwTBYTHp/B/7P35/FxXfeZJ/w9d6u9ABRWgiDBfRFJLbRMSZa3RJZlW7bidN6evO504kk7nXQmk0y2Nx3PtJ123Gnn7Z4kk3S6nWWcSccdx4m7Y0eyJMuWbdnaN2ohKS4gsRM7CrXXrbucM3/cqiJIghS1oG7JwaMPBNSCwiFw6yy/5/c8j565nKSO7uokNtTddues1YjVmytU/WKQUuL7PonEBtn6RpB3A6WDVm8Q0IXAEhrLjhfmsDawgbbEa54V77vvPv7qr/6K9773vfz0T/8073rXu9i1axfDw8P89V//NT/xEz+xHuO8DF/60pf4zGc+wz/8wz/Qt8oSZLVV+fXXX88tt9zC8PAwf/d3f8cnPvGJNV/rc5/7HJ/5zPplKa6V4XYmk+CDhwY2yOzXgbVIbKiTBBA6mf1G8k8e/tf/mu5s+yjy2hGZwS0s90+yMnMezTAoLAab1I6e/stIbqdaobAwjxWNMruRl/0Dh4bCrbMrIFk7u47Q1/cBDGPtpqV2gXJd5n77tyk8+A3SH/wAA5/+dNuQ2auJgQaqjs9y2eHbJ+fpTkY2GrGuEavJ7MqLC/heYHU3PxBkdnXEOhkvjGNoBp70GEoNtYVioOEWUn7sMWQ+aAKqjZxB7diBMC28ENeoS8lszQDpGQjNBS2YB8zajxNPhz/Pr1a21fbtYySdasZe5OJBEeDmW257yyivZe3aijBhYGlpiWw2S2dnJxDY3WWz2Q2bu9eA1Y0XT60UOVGyKXoeOc9n9uwIz/2bX+eWf/d7fOTmGzGFYNJ2uG2VIjsM7OxNcCaTYHSx1DxfDXS0Xr20gQ38oMOqF6637D/QJLXbAUZvDD0TxVusglIIRdP+1luqQi+42WrzuWGvVRvYwAbWH0krye6uIL97d9futwSJDWANDYHvocUTKNchsnNHaNEYq89bs3P3opQErGbTsKi9jV07/i3RRGv2XKVSiWyhQvfhu0h09a75HLtUoLi8hBWLI4SgUsjRtf8Mqa5eFKIZO2jF41ixOOmeXqLJi50FyiuLLB99iFKp9KYS2atjMIQmUFLhLVbxFqtYWy7UrbSIcdHtdkTjrJXL5VheXsb3fTKZDD09PeEO7C2ODjPYn8h6g4CvFI6SdFsb+5YNbOBSvOZ3RTabZUd9QU2n02TrBc13vvOd/PzP//ybO7or4Mtf/jI/8zM/w1e+8hXe9773XfW5nZ2d7Nmzh7Nnz17xOZ/85Cf51V/91ebtQqHAli1bXtfY1lLjnp4r8vToEn1xnZm5aXoGhnhixMdf7GHvwIWFaiMT8dVxJRK7gXYgs6+Uf9LIPvlv/+2/sX///osV2ZoGUjLsuRjrYGXzRuBUPVbmKlSLDrGURddAHCsW3oIaicfZfcvtZGemqBQKFM0IEBRZVpPcUimWJsZAQLVY4Nxzz7Q8L3u1ek1YOiiFcmXbdli+FWEYKWLRoFvZc3OUK6Mk4jvalsxukNj5r9+PEIL81+8HaBsyezUxMJcLin/FqsfZ+SKzEWOjEes1Quga5ocGmM2dpWvM4ljPKP9DfBsA13c5MnAE27fJRDNs79jeFsUWZ3QUd2oKa+cuxPkZAPx8AXd2Fnwv9DXqcjLbQymF7r8dGCPVmaBroL32Uldbt94q8Fdq+PqrF2HCQLlcRtf1ps2dEGLD5u414iJL4UKZ0+eXEK7PdRGTlKHzHOBvGWZleBfdlsHtsUjouW2pqMkHDw0EjlelGt3JCMX0hgPPBjbwjwVa1CB+qKd51ors7iLetQSApwmeWihyM+DNVdqm8WoDG9hAeCg5JcbyY2TtbFudvQASt91GKp3Gy2YxMpnQozE0zWT71k+RnS1RFQ+jpIeq52N1xT5O/7bWnwcTXb109G5a8zEhdFB6s86X6OwhEkuDCjK+PdfBsCxiyRSxVJqOvgE04/L14PLw1DeO1TEYAEITCF28JaMvIpHgDDs8PMymTZtIJBL09PS0lQNWu6Lo+YxUbJYdj27LYHc82jxHbY0G7jGTtoNeqeEoybZYhN3xjd/rBjZwKV7zTn7Hjh2MjY2xdetW9u3bx9/93d9x5MgR7rvvvmZ3znrib/7mb/gX/+Jf8OUvf5m77777VZ9fKpU4d+4cP/mTP3nF50QikeaE/EbxRtS4G5mIr47aWJ7qsUVAXEZiNyAMDeUrqscWid/QQ3RXV0vHuFa+mO/7TE9PA7B//34OHz6MMzhIbm4OZ3wCLZlES6WI7NwZWuflajTI68JyleXzZXzPx4zqSE+RnYmx83Bf6GR2I/M6j37R/Q2y4Pypk8Q7Oti0ay9WPN7yvOzVFkIohTtXRgHmQCIosm8UVN4UeF6RlZXA8LRcPsfSYoVy7ExbKrMvIrEtC82ykI7TVmT2amLgqdJUcF/MYN+mdNO6dXSxxLnFMjdu6Qx1rG8VjJfG+fruJ0mlTGqbNYZGAoXoVHGKuBnnlsFbQh7hxfCyWYRpYfT3o3d0AKAqFZzRURLvvL0t1qiLbO8WHiAdex8D3e8H/o6hfV2hrk9Xwlrr1uLUOOfwQ7dnvRKk7eHOBWRwbaYEQ6otizCJRALf95s2d0qpDZu7N4Dd8SgpXWeiWsMUgmIt+Bvvjhj0WSbv60lfVHwJE6moedFadHSx/d77G9jABtYPWvRiBVskHyjs/mJikZcWTD5Q6eLfHN6Kv2y3RePVBlqHk6Uqy0v5ywiDDfzjRMkp8a2JbzFdnG66YZ3NnW0b63E9Hm+7SIz8gotW/AViaYEtvovmvR0YB01nZiTXFiKXBgzLQilZt0IXCCGIxONEEkl0Xcf3fTTdwIpFicaTa5LY64XVMRiNZuC3evRFf38/e/euf031BwVFz+friznGqzUsoeEoyemY3WwKTtTXp9s6k/SlYhvr1huBXYClESgvwvx6tKZsIGy85tn7p3/6p3nppZd4z3vew2/+5m/ykY98hD/+4z/GdV1+//d//zW9VqlUukgpPTY2xosvvkgmk2Hr1q188pOf5Pz58/zVX/0VENiJf/zjH+cP//APueWWW5ibmwOCnIaOerH113/91/nIRz7C8PAwMzMz/NZv/Ra6rvOxj33stf5TXxfWUuM2FNnkZvjDT/0S/9tn/wg6B7llx+WK7A1cHZHtHcQO9QaZo55ck8xWngQUsUN9RLZ3tH6Ql8D3fe6//36++c1vNm/7pRLlxx9H1mpoySSyVETv7iZx++0t7bxcy0GgVrU5/dKjjJ/VySQ3UcsrUt0xeoaSCB1On7OZznZx+x03t6WDQIMsqBQKOHYVqz7GVuZlVyoVjj3yPPZoHqMrgl90cOfL2G6N2TNZtu/agTEiiC52YA5cXGjfcGZ4bShXRrHteQCi0UHi8W1UKmMUCsfQjQSus4xpdYeu0l6LxAYCMhvajsy+cUsncrEbgJilo9fJK10TmLrGcqkW5hDfUsjaWYp+iel0nmglSqEWzEGudMna7RclYWQyKNdBmCaR3YE1n9aRJvmud5J6//tDVQesRoPM7u+/m87OI7hekHO2OFVkfqzQNoWVteBUA7eD6VdOkHJrSM9ruWPIq6HRjGWP5gHwFivU3Dy+7WGkLLSk2TZFmJ6eHhYXFxkZGQECu7u9e/du2Ny9AeyMRzhVrhKRHnfe9z+4D7jta1/h9jvfy+F0ezUIFG23qcpemCuGPZwNbGADIcKrZ2Q/sVIiFlXcPxHssz65rb8tGq8ugu/CxOPY/W9jaSVPuVzeULe9CSh7QdTMk7kS24vVywiDDfzgoqG4fn4uqMcWxhZRN0iErjGWH2O6OM229DZ0TceXPuOFccbyYxzqPRTyyMNBNpulVCpd8fGpM1mWsyVS4p/h+TcgaxrwFU4fGyNmppG+ItUVZfhQN9ZVxBnJZPJNtepeC9F4klqsRK1SRggNpSTRRPKKyutWYnUMhtBFcH7KRNFSJs5UEXssBwQxThv4wcRIxWa8WmN3PIouBL5SjFRsRir2Reeq/ckYh3vC5zDWwurzVlvGDdoFmHkBTj8AtSIkB2BiNnhs+igkFiDRCz27IZq++mttoK3xmmf0X/mVX2l+/b73vY9Tp07x/PPPs2vXLq5/jR1kzz33HD/0Qz/UvN2w9/74xz/OX/7lXzI7O8vk5GTz8T/7sz/D8zx+4Rd+gV/4hV9o3t94PsD09DQf+9jHWF5epre3l3e+85089dRT9PaunaXxZmMtNe5u20XvneOJp58J7ugc5B23HNmwZn0dWJ05Wn1pAQUXkdnKk+BLYjf0hZaRvRoNEvvYsWNNpdBjjz3GftPEnZoiuncfQtdRvo8zNoq/sAADAy0b3w+Cg0CDEJg6eYJN6URT1XZpXrb0faTnEU+v/6J16tQpbr373a/re9vl9/pWgessA0HRqlI5R7kiqVanKZZOYpoZopFeQAtVpX0lEruBdiSzV8OrZ2Y3FNmuL+lOvjkuJv8YENWjLFWXWLaXSZkpspWgqCqEIBNtrygJAGvHDsyRkWBNygZdrLEbb2wrErsBTTPJZG7HqXpMn1oBIDtXZuzlxbZwD7kS8ktB8026t5euTYMtdwy5FjTz3LqC97oSAq/kICd8vIiBFtWJ7OnC6A3fHj0ajbJ///5mg+vw8DD79+/fIAJeBxqKgUnbIY3i7X/yx0SfexaA/uefpfv3/iPq3/5W26xRRdvlwWNzTGbLmLrG2GhgK1zeKAb+o4Y9MkKxUGgLe9YNtA6uL/kvj5wDwECQMHQcX3L/RBZZ9fjsnoMhj3AVfBfu/zXUia9S6n8XZ7Z8HM2M4Ps+i4uLG2vYG8CkHTQsbI1a7IhHrkgYbOAHC6sV13PZIBqp9MAki/4pen9sH1k7i6EZ6FrQzKBrOoZmtGVTcSuQzWb55d/438kWKld8Tq3qUS06GBENgWBlOdhj/f2Tf893z3wfhcKrSWIpi8hVzluZdJz/6z/8+3UlszXDoKNvgEohj10qADpmNPwzClyIwXCmi7izZVCgJU3sk8v4BRdvIahn2mdzyJu8DcfGH0AsOx6W0NDrMVi6EFhCY9l5a5xXGuetswtFirZH0XbZ1pPkJ27ZyqbONnif2QU4eS9MPAHZMYh2gGZBtDN4fOQhSN8MvgOL22H/PRtk9lsYb3iGHB4eZnh4+HV973vf+94mubcWGuR0A4888sirvuaXv/zl1zWW9cZQV6xJWt+wpXODxH4DuBKZ3c4ktq7rmPWi39mzZ3koFuN2w0TowUZa6DrCtPCyrd1Ir3YQkNJjYuJPWFz8HpOTNT73ufP8m399J73i46DpJDujdPTFKC3bDO7uagsHgVqlwuTxlwDInp/i3HM0VW2r87I1w0B6Hp39A2QGt6z7uPbt28dT93//MkX2mfOj/C9//kn+9Fd/j91924juyxDZkr7sezdw7dC0KJXqOAC2PcPS4jiut4JpdgACTTNIpw5SrU5RrozSkb6h5WOsPPcchQe/gRDiMhK7Ac2ykK5L4cFvkP7Qh0jcdluLR3llDHTEGF0sYeoari/Zmkmws3ejEHTNEBAzYsT1OBWvgu3bAHRFu9jesT3kwV0OPZkkfdddOKOjWE89BQS5be1MBKzMVSjlgt+rEALP9Zk5myPVHWNoX2vjRa4Fdimw69bqxbRWOoZcKxp5btTdGHB8jK1x9KiBljBRtofZF2+bYks0GqW/vx8I7O7alQDwvCLlyigrK8/Ub7dXjndDMbDT1Dnyl3/K4BOPcqyuYNMti+oDDzCnibZpuDq3WGYyW2ZHbxJdE7hdgaPB9Eo15JFdAWvZ3dVKMP18cN+GUuANwa8EBfnKM89g79iJch3MkRHSd93V1mvYBt44XF/yqa8d53tnFgEwDQ2kwhIChOLBhQLmM2P8uy3XY7a4RnCZA5rvwWO/T/XktxjL+WxNPQhbVpjf95MoNEZGRpibm6O/v3/Dqet1IO8GxID2FiUMNvD60FRcx7ex9amgCVNIqL28zIp+jsztXXjSw5d+U5HtSa8tm4pbgVKpRLZQofvwXSS61hZ9KV9SzNq4NR+hQapQoXP/nfQPb0PoJtIH35ck0hbJzNpkVnllkeWjD1EqldZdlQ3g2lU810UIjdLKMq5dbQtVNoC3UkNWPIQusI8v4Zddovu70TuD69XPO20XgWHbNktLS03HkFq1fm7xN+bT14Juy8BREl+ppiLbUZJuK/zrci1cum85PVfksZFFSo7HSr5EaXmOY4keps/28WOHh0hELvw7Qtm3LI0EBHakMzhLJXqhNAu5Ot8YSUH3TpBe8NylERh6fYK+DYSPa3rX/NEf/dE1v+Av/dIvve7B/CBitVKgYLsAPDW6zN7pPIeGOjbI7NeJy8hsXwGqbUlsY9XGSROCU/PzOLbNe5UiumkTwjRRroPRgs3dajQcBKR0OX36t+jufpZMdxpUDTjP5m1n2b35MXKnPk5mUwexlEXHrnBUbrVKhezMFJVCoZknmp2ZolhXDKZ7euka3HyRqq2Rl736e1ph2xqPx3n7D99Gpbeekd2tcMwSfiGYA3ZEBjnUv4dIbxeJ6/rbhgh4S0IIUEGhwverSOmgaQaGkSIaGaDmLFBzlhHCqKu3W4/4zTeT/uAHyH/9fqTjrElmS8dBKUXHBz9A/OabQxhlgLUsWm/f1UNqaKB9bYTaHLZnc133dQgEM+UZ5pbneJRH2dmxsy0y2daCnkwSu/56El5wSNXbvIhaLTo0+iJXZiv0RGpUSw5jLy3SN5xqG1V2Yx0rLi4A4NoB+d5Kx5BrRSPPDVn/xQrQNIHZG8fojuItVVGuDHeQbzF4XpGFhW8EjVXlwOpsZeUJPO/GUKMvGih6Ps/kSkwUymz78//M4GPfwzcNfBnYtArTRFhWW7mHLJdqmLrWjL/Q6p/z1TazEIYLaoHsGOgWTIwH9498EyqR4L4NpcAbgnv+PADm5iGs4eGm45UzOtp22aMbePPQILHvfXEGo957hQAMAQoihgFScd/xOYSu8dmPHmwpmX1tDmjfqH9cjA2nrteODjPY81V9n7FqjbzrkfN8buncaML9QUbWzmJJg/1fTXGmFDgfCRXkEVdenKffz7DlhiHGC+PNjOyh1FBbNhW3EomuXjp6N13x8XSPpFb18F2J5/h0D25DSoXvSrDAcyRmJEKqK4m2RuwjQKsqMHalRK1aIRJPIIRAKUWtUsaulIinO1s0irXRdLrqjSE0gW97qGUbp+BwbDkgh4Uu2ioCw7ZtTp48STabRdd1pFtDPfYHwYOP/T4c/iLoGzWha8HueJTTscAZpJGRvS0WYXe8PRuvr9W59fvAH15yXyj7lvJicI6KRyA3TmB7YEA5qLkQqZ+zNSN4XnmxtePbwJuKa6rs/cEf/ME1vZgQYoPIvgQNpcBgZ4wTdlAQHl0s8/cvnGd6pdpeyuzVKgEzBghwK22rDriIzD62iHldF+dvr3Js5lEy0QzbO7aHQhJcjcQG0KREU4pzpok8d5Z3njpFZHgrkZ07sXbsaPl4GyT2/Px9CM1EFxZCNDZQOsS/T88hncHuT5LuSYWSO1qrVBh5+nFy83NNdfVy/yRGNIamX8i6ulTV1sjLDgMNCyFvsYpfctA7LPTZoNtSS5no3VGcyQL4isi2Doze2Aah/Tog/Srx+FYAdCNBJJpBSQ8lg/lWoOO5eXQ9hml1hzJGYZoMfPrTQGAfLuEiMls6Dspx6Pjw3aESA1eyaK04HuFTLG9dZKIZNKGxLb2NXV27OLNyhv/Kf6U33prIk38MiKUs7FLQKBRNGsTSJtKXOLbHylyF/u3h719Wr2N2JShYTJ86zuauNEJoLXMMuVYYvTH0tInzYtDQ4uccxF4dLW2hpGqbfOy3EsqVUarVKeLx7USjAdti2/OhuYWsRsNS/HS+xM2f/0/se+r7uIYJpoVwgj2hVKrtojC6kxHcVfEXst540RFrw2uzoRbo2R0UUjrrjSCLp+GGHwvu21AKvCH4+XzwhRYU08NyvNpAa/HMWJb7X54FpTAbZn++AlmPIBMQ0QVeTXL/sVk+csMgt+/qadn4mg5odSU2Zx8GzeTkMvzzv5njL/9pN/t7NYT0KPbfwpm+uxnevrOpyN7Aa8PWaDD/f2u5gJhfwRSCXstgpGxzQyq+kZP9WtGoEY4/GdyuXTlTOUxktE7e9Y2dREoXk6lCChDgHMvyDvYy/+6dZN2VUOuFDfi+z/T0dGg//1qgGRqxlAVKUq3O49gxqiUPw9KCRqGYERDGVS94XojwHAchNETdjUEIgRAanhM+OdxwuhL1hks9ZlBVis8dn+LeE1MAuJ5sq7PV0tIS2WyWnp4eNCSDR/9Pzs0/DYA88y1Kf/dzGPf8IdHERqXoUqyVP3+D55Ou1si5Hp2mwXYirMyUWQFmZ4MG50vjdV8N65U/v9q5FQJF9teOTuMphVyZ5mt/8El+6Od+m60793DrjgxHtndf9L0tR6I3aAbuGIJkHxRmwM6DCpqxidUd+qQXPC+xUYd7K+OaWJOxsbH1HscPLBpKgaVSjVwlWEA74hadUYPJbJlzi2Vu3NIZ7iDhYpWAkjB/Iri//2CgeGxTdUCDzBbXJfme9hRTM9PNDsuzubPcOXxnyzenExMTnDhxAiHEZSQ2AL6PYZhI6TPR1cWuYom9u3aFkj96KYmtaQHRqte7KTUtgqabSP0RvGSa3uHPoGmtJ1uzM1Pk5ufoGtzczLtemTlPR/8A0vcv/HvaTNWmRY2mNVD5pYWgqAIo16c2WkA5PspTyIqHnokSP9SzQWa/RgTkdFCQiMd3YOg5fOGh6xa12hyuV0A3kiSTe0jEW98o0sCVyOx2IbHhyhatDx6bY8jONK3Fz2QS7dWE1ebY3rGds7mzTQXATCnIbduc3BzyyH5w0DUQx4gE61at6lHJOcTTEaJJk2ox/OIFXLyOdVUCJXY0niSWSrN533Utcwx5NUjbw1us4mareCs1VJ0Y1CwNv+jgLVcRQqBnom2Rj/1Wglt3BxHiQhFdCD00t5DVaFiKv2d8hOFnH0cJgWuaaAoaJWFHKlyl0C0L0SZRGDt7E5zJJJrxF9MrgbX0UFcbXpsNtUBjH924Dnz3wn0bSoE3BL2jI/hCBk0CyvdDcbzaQGtxZHuGD+zs4eun5vEaRLYmAkcRqUAX1DyJAu4+tIkj20NwQLvhENz/a1D5HmxJghEBPdgL7O+LcMOgiZAumnye66NdJN/98Q1y4A2i4kv6dQ1T0+i1DCZtZyMn+7VidY1wqb4ujX0fbrqxreqC0vHo/lINv3SJujHgsOs1GIFzLMtmvZ+DHz3YNg6O3/zmN5u32xZKkss/R7U6iR4dJOLuQzd0NF1gWjqu4wcK7ZBhWBZKSZRSTUW2UhLjCtFurUTD6UpJFSiy4wb/IZfnoWIZWbcV+4uxBY7YLobdHjnZ5XIZXdcDEvuF36Nz+mH8et3NFxrRka+T+1sbfvxPN9arVbiW/PlLkV8JzoK/+3/9Fzq6rl18s1758w3n1gZ2lmvMLp/iiWKxeS7csmM3e647xK3XD4bPafXsDjir7FhAWjtlMGLQEwcehvwULMcCEjuzPXj+Bt6yeEOzYyPfutHxtIHL0VAK2K6PXu8Ol1KSjlsopVgu1UIb20W5B/Mn8cceZaGaJG2WmTw9ybbeBLHKMqQGYORxmBPQvx9ofe7BZdlSdVSrVcbHx/E7fY7njjOYHEQJhVSSJ0tP4kw6fOS2j7R0rMPDwxw4cIBjx47hed7lZLYQeCrY6O3p7ma4uwc9kwkluy2Xe4b5hQdAiCaJ3RgjgKZrGEYU3/eZX3iA/v67yWRub/k4K4UCmmE01dcN5bUViZLKBAt9YWmRlXi07VRtDVLAmSziFwJCRWgaqv6fHjcxemN4i9W2yMS5NAenp6enLbNGGzmjdvVCF3O1cp5KdREQxOO70XWTaHSI3t73kU4fCt269TIy23UDO/E2ILHhyhats7kq766T275UjC6W2qcJ6y2ApJXkzuE7GcuPkbWzZAaCg0bcDJ+0vBr8Ugl7ZAQAe2QEf8+etswYdaqB6joaD94/sbhJ79YUsbRFKWuHrg5o4NJ1DCCR6aZrcCg055BLIW2PyrEgEkOWHNyFCtQLfdF9GYx4BD1hbriIvE6YVjdKeSh1oViplB+aW8hqLDseltBQh9/G3Lvey6ZHvoPpuqjV7iEobKkw3RpmG0RhAKSiJh88NNCMxOhzApXl6qy2tkFDLSC9gLBuXAe6eeG+DaXAG4K5OWgQc89P40QiQUb2li2hOF5toHUwdY1PvW0rfq7GV+fVRY8ppXA8hetL7rlhsOW24k1MPA4nvgpCC0hsQHEhukMIDWFE0HxJ9/yjiPnnYcd7Wz/OHwBM2sF5ezBicjCdQCrFnONief5GTvZrxWonkaV6rahwPri/Z/cFN8cQHRyVL1n+61PI5RoCESSO1cvTovE/RUBmK6i8vED8hh6iu7paPtYGVjs4Nmrqjz32GIcPH0bX28wxoEFiVyZAgCfPI6KSSORGhNBQKtBB6Wa4jQEQNAjXYiVqlXJ9bJJILE40Hv751eiNoWeiQc1PwO+cPM9DpQqWcUFB/kSuzG89cJJP3bqDjht6Qz9nJRIJnGqZ7lN/TMfC9/ExaLQrCN0CXdA5/TD2vf8b/E9/umEzXse15M9fCtexGTjyIToHtmBa11Z7bVX+vO9Ljv79KInnc7xrIMLj9fuTEYNdfSl29rZBc1g0HQgvl0YgNwGaDq4NucCpFT0SrFGdw23pNvxWgXJd/JUV/JUVAPSuTvSuTMvr2K9rZvzCF77AH/zBHzBSL3Du3r2bX/7lX+ZnfuZn3tTB/SCgoRR4dnyZ/CpFdk/SYiZXpTsZeZVXWD9cW+7Bd1Z9/SfNr1qde3CtGQ1rodVj1XWdu+++G4Bjx44BXERm+0ohlWJvJsMPDW3BHx8LTSnQ2XmE/r4PMT9/H1LWLiaz65CyBkrR3/8hOjuPhDBKiKfTSM9D+n5TkS09j3RfH1sPBnacmc1b2HnzkbZRtcHFpICXr6H8YOvnFx2IKrSogTICi6F2yMS5NAfH930W52c5kFjB2v1DbbM5XZ0zqpSPbQeZiJXKCKguhKZTq03T2XkLW7f8z0SjV859ajVWk9mFB79Bxwc/0BYkNlxu0Vp1giJ7yfGYXqnQn44SNXVMXQu1CeutiKSV5FDvIQCOLh4NeTSvDr9UovDQQ1SeeQaAyjPPUDAM0nfd1VZktlP1OHd0gfxSFa+uBCjlHTxXUsradPTE6Bpoj/Vg9TrWgPT9tnEQgYvz21zPR0uaqMV6TIOlY3RFMXpioTdcvVWRiO+gHDtDpTKGbQcWctFof6huIQ10WwaOkvi6xcqv/Qa2VGz//nfwRbBvBTCFwHQdcF3cu9pn7UpFzWYRZaSejV2utSFZ0VALLI0Equtc3Tqwd++F+zaUAq8bfql0ISN70yasHduxNm/G2rGjrdatDawPouko//vuTSzMpxkH3LoLVsNJIlQSG2D4djjwo3DsK+DVUIaF7wX7ASklSkk06SEAceBHg+dv4HUh716Y/6VSaEKgKSh4Pt1WGzY5tTMudRIB0MyAKFg8GZDcjbUrJAfH2lgeZ7zQdGEQqsljX0CDzJYQ2dZBZHtHS8e4GhfFEGqBYwDA2TNn+PrXvsaHP/rR9iGzLyKxtbqjkI/SZ6jWQFcHQWpYUZ1Ii6MH14JmGCQzPajsEo5dxYxE0S2LUi6LYVlE40m0tRwzWzG2euxgda7MZ799hgcXC1imTkQTuPUL1tA0HlwowFOjfLY/TmJbeNcpQCoeZc/In9K78iSeMPDRgGB+lVLhYaIJSezs/YHjyN2/1zb1wnbAq+XPN1DxfZZqLuVUP8LQ2BQxiV/jHLDenl6+L/n+l05z5tl5hBBEZ2xuThp8BXjn7t72cmmMpi/EMs2fwO+9jtlXXqg/qAISeyO26XVDuS7OxDju7By4QaSfu7CAuWkAa3hbS2sCr3kW//SnP83v//7v84u/+IvcVreSe/LJJ/mVX/kVJicn+e3f/u03fZBvZTSUAkOZGIXpgPhPRw1mclW2ZhKhdq80cg98z+OZL/854ydPg9CYzxX44pMv8Fc/fwsHbrolUGTnJmH4nRcpssMY66U4efIk//yf/3M+9/nPkU1lGUwOogkNqSQzpRneNvC2UDIa1iKzG1DALsPgnQj88bFQlQKaZrJ372cAAjJbAQTZogBKOiip6O//CHv3fgZNC2eRygxuYbl/kpWZ882M7M7+AZJd3eRPBDb40WSirUhsuJgUEIZA76hnZEd0tKSBsAyMhBlq3uhqt4P5+XkmJibo7OzEcRzOT01yXe5b+JVX0HbdAe/8VdAvLButdmZooJEzGolsIl94oUkIeH4Z09xBPL4dx13GdXPUnIW2IrLhApmd/tCHiN98c1sQAXCxRatU8PRYkClZdXxens7Tm7I5tLkD15ehNmFtYP3hjI7iTk1hbh4CwNw8hDs1hTM6Suz660Me3QWszFXIL1XpHIjTUQishKNxg2jCZHBXJ10Dcaw2KKzAxetYoW7PmMp0t5WDyOr8Ni1qBBuWRjVwIxf7DcMwUvT1fYBC8Ti6/iAAltUX8qgC7I5HOR2zGanYWEKj9Au/RFQTDH3vO5h+nRRwPTQhmH7vHWi/8uscapO1q2i7PHhsjrMLRc6MBuvW/zg6za79h9jU2UYW46vVAuVFqA4BfwK73w9bEqGr2t7KcObmWPny35J/Nmi+qk1NIbdv3yCx/xHB6I0R7bT4eCrJXwOelJR9CZrgnhtDJrEhKO7f/XvB18e+glQSWXdo0zQNXQVuDOXdHya5QQa8IXSYwb4vbejMOy4CWHQ9bu9KsjveHi5jvi+ZGckxuLsTPWSL66titZNIA9IFpwIrdaV2w01kaST4aDFRENneQez6XqovLgRq7Lry+iLUb+s9Mbp/Yl8otuJ+qUT55Zf5xhNPcKZu2ax7Hqqe3yyk5Pjx4wDtQWavSWLT/Kz0WYSmk4zfRCRuoRnhX8fS8yhll3CqFVCqed6KJVMgoBYr0dE3EBqZ7Zsan31+ggcmlgMS29BQ3gVLdlMXmJrgwYU82rfP8Ln/+W2hrlvOyCNszj8bqNs1E6EU9d5WpAoasFylowkfdeKriIP/ZMNJ5DXAlZJ5x+NUqYotJaYmwIEFx+WGVPyayez1wmoSWzc0dFPDdyX50UDlvKs30T4k9mqUF/Ex+PZDz/K9x14ECCzxN2Kb3hBksYCfDZTYWj3KSVar+NkVZCaDnmmdw9xrnsE///nP8+d//ud87GMfa953zz33cP311/OLv/iLG0T2GkhFTd6xswf/1mH+DNi/Kc2t1w2wM+Q3fjwe54brr+fbX/g8/lKW4b5uDA00EaxOhbzGDckV9KgLhw/D9R8JrbByaUZDAxU3yJ1Ibk6S2Jyg5tdImAk86XFb6jbuHL6TuBUOsanrOh94z3vwsllOTk7i1gIF43BXF+/buxcjHm8LpUCDzJa+ZH7hPpT0kfVsOd936O35aKgkNkAkHmf3LbeTnZmiUigQT6dJdnUz8fILTL8SENnTr5xgJJ1i9y23tw2ZvZoU0NMWesPi1tLRokbQHOxKvMVqaHmj1+528Lf1jwtotdtBw058afE7OM4SUtYolU7iy2AekNLFrs1gmh1owkRJpy2yR9eCMM1Qc0XXwmqL1ufGs3TWCcDhngSgOLdQwvMlb9/W3R4WQhtYN3jZLMK0mt2WaBrCNPGy2XAHdgmqRQfNEGiawHeDvYtfb8RqJxIbLl7Hluui7E279160roXdjKUnLeyKi3e2hl90UDU/cBABvJUa+p6NXOw3A+XSWarVCQCWlx9hdtZg06aPhhp/kTJ0PtzbyUjFZtnx6O5Oset3f4eJf/sZ9K/+PQCa55L74Id44ef+V97fJmQAwLnFMmcXiuSrLoW6Ivvl6Rx//fQkP/eeHe1VZFmtFijX3TkiSRhq3V7qBw1+qUTub/+WyjNPN52PVKlM7dy5tmu+2sBrRzabpVQqverzpO1RmV0im18C4Hphc7KwwB37+/iXh9PMnp++6vcnk8l1teYELiKzxUt/h2jEC0gXIQXzfe+meOOvsXeDxH5D6KkT2QXPJ6OCxLTbu5L81GAPKSN8pWuDHDh7dIFdh/t49z/b275kdmog+AWe/gbM1N240pvBil+s1NaM4HYIRIHQNbo+uhMgILMvfcIqErv/l25EC0GV75dKLH/tazz88suMRqNoSqG5LlIIqJNVum6gKcXxV15BmCZ33313eGT2FUjsBhq3PTlNTerE9HBjZhqolEvM1lwG4gk8u9q0ltd0DTMao1YpY1dKxNOdoYzvmbEs9x+bRQARQwuuzbpTQAOWruFKxUMTy/zoWJbbd/WEMlaAbHo/Ts876F98FCFdlDBpvKGUCj5M4YNSlLfdSXLDSeSa4UrJpO0wWXUoeD4RTUMgMIVg0fE4U7Y5kIw1HRtajbVIbAgiBBqNQA995TR67052DyTb6qzlRzN8+/tnOHVupRnd8MzRSQ5/MEP4O4CrQ/mS2lieyPaOUBqurgZp10BKhK43o2iFpqGkRNq1lv5uX/Mq7rouN6+Rh/a2t70Nz2tDG7c2QiOv7cj27rbIFvU9j29/4fOcevx7aKYZ2F9LH90IJqHxZYNvv1TljiOJtnzDl5wST80+BcD50nl6/B4ieoQdHTsYTA6yvWM7SSs8gtgvlSg//DC3razg6gYz9ffHDY6LPzWFVldit4NSQNNMuuP/P5acEr71XSA4qGje24iI94Q7uDoi8XgzR7RWqXDm6ccZf/F5RD3HN93bS25+juzMVNvkjepJC8dXKKlwJJyoNzPoCZPEkQG0iIFyfPSkFVre6Gq3g6YiO50i973/ws//2RP85f+nk+sGk+jSCzqwd72vqcxupdvBajvxmrNMpXwWqVx8v4KuB6SqrkWQ0sFxsphWF7qWbIvs0bcSUlGTG7d0slyqcSodEBV7+5PEB7s4t1Bk70C6vSyENrAuMDIZlOtQt+kAKVGuE1oMxpUQS1lIT+HYPkuTRQBy81XOn85iGIK9t25qOzK7Z8ceFqcWAJgdOY1YmGk6jSz3T4bbjGVpONMl/FwNoQukJ5sxvtEdHcQP9YSe13Yl2LbN/Pw8EKxltm0TjbYP2dpAoXic5eXv43nB9ep5RZaXv08iuYtMV7jNTSlD53D64ial4X/7Wzx9fgZGzjB/8xHO/dz/ynAq0TaqNoDlUo2C7bFScehKBA2Dg77O+EKRc4vltjhzrYlanZwbfwL6VNsrsdu1uOKMjuKMT2B096DXz1pebgVnYpLiI48AtM15C4JIjJW5CtWiQzQpEPFX6O27NdSG4XZFNpvll3/jfydbqFz1eUpK/JKLLLsUa4FSaOHkt8joScbGo/xvj7z67zaTjvN//Yd/3zIyu1QoIM7/AwDC98gO3sWprf8ze1LhWsleCY1mYtdZxrS6ScR3hNp8dSUUPZ/nC2UAOgwdUxPsjEf4qcEeBqPhO8pcatN65tlg39KWZLZdgNHvgmdDtANqo8H9m2+Gzl6YPxEosRuKbN8JFNwh4FIyG71uJ9QGJDYE69T4KycZi0YRmoZBYNOKUlAXkCBA1zQkcOLECQ4ePMiOkFwba84C1eokCC4jsRsQdRfMSmUC3+7D1PswowaRmBGKOlsqxdGqy3krySKK3V4RUScBfc/DEgIhNDwnvCi/I9sz3H1oE/e+NEPNlVjBwC88wVM4no8CPnRwgCPbwz13J1IdPDfwMQ5Uq2wrPYdENPO8hQBD+OjK32jCeh3Iez5Fz0cIMDWBpQlKvo8uBL5SzDsuKVtna9RqOZl9JRIbginLr0+sxfESj/7NaUZ+eJAP3bCpLeqDvufx7fsf5dS5LJoGZn0uGp/O8+37H+WOnz2MHpIjw6tB+ZKVr52jemyR2KFeuj66s63OW1o0ApqGcl1EvUFASYlmGMFjLcRr/gv+5E/+JJ///Of5/d///Yvu/7M/+zN+4id+4k0b2A8iGnltz4wto/XmQlVkX0xiGxhmfWOva6AFmxXNMDg17UJHhDtSM+ghWAVdDWP5MRbKQTF4U2IT27u2M14YZzA52MwiDRMNa9bYjp28Z3oau1TmvwCGZWFt34EzNtpWSgG7pIj6v4QyDOB+AIQQTC38Jq52d+iq7AZqlQojTz/O+EtHqRQKrKzkAJCej2YYVAqFcAe4CkZvDD0TpXK+xL87Mc3XzwaHVV+ALHtEd3SGTgqsdjuwbZuTJ47R9+zvMiNeAWB/f5ybt8QQCPBqUPkezPa1PAOnYScej28nFhsCJNns40i/BqoxDg3fr1JzFjGtDtLpg22RPfpWRHcygle3ujI1jaGuGI7nc/O2TFtsUjewvrB27MAcGcF94gkA3PPTmO94R2gxGFdC10CchXGL0aOLzI3lABA6KKWYOLFM16YkQ/u6wh1kHbVKhfnzk3x2vsw3RgJlWC67zIH9+9B0Hen7rMycD7UZy50qglKYQ8nAmlEXaMWgYGEOJEJfr64E27Y5efIkExOBynliYoKTJ0+yf//+tiOzS8VX8Pwiphk0WZlmN55fpFR8JXQiey0I00T89E/DX/8Vs//ff84Hezu5IZVoC1VbA93JCEXbRdRzUAE2jVfpSdZYusmG9nHvD2AXYOYFeO4vgtujj4D3HPTsgrf9NHRsDnV4DZScEmP5MbJ2lozZRf/3DdxXcm1XXPGyWbRkEj+bbRIC/soKTtXGyGQofuc7mCMjpO+6K3Qy26l6nDu6QH6pijB8bP2P8K3v07/0IfZf99m2OGe1E0qlEtlChe7Dd5HougJBJiV+roZXcMCTeNKh/9BddG8dRsdAi5to6asX2MoriywffYhSqbT+RDZguz6nd/0sM0dHgCeYiuxlJPJBdnV00dMTnvruSljdTCyEgVIe5dgZ+vo+0HZk9kihROXYywDckE6wraeDkYrNnOOGTmRfyaY1LDL7Vd0OZo/BuRcgsx2im5lNRoGvMzt+mslNW6CWghPPXsjI7twClShMTl70Mi1xOyAgs80PDbBiZ5EzF8jBsElsCNapAcdhuzIYjUTwAU2IYM3y6x2jvo8vJeg6Bw4cYHh4OLTxRqw+YrGtVCsTKPw1yGyFlIESVzoDFAoxdLNKJBYQ2emeWEvJbKkUz+TLnJdBGPqM0pDRNNsqZQSgGwZKKZSSGFZ484Cpa3z2owcB+IcXzgNgGToYwVnLrVt333PDJv7dj10fbhwG0NPTgxVN8GjHjyI0ja35p9HrJKaufHSlyA29j1NDP9W2TVjtippUaEIQ0zTy+LhKIRWYApQQpA2DoueT93x6rNZeBzMjOc4eXUAIcQmJrXAc2XRvFQLMWZvzZ1Y4N5gOvXG4yXE99SSaFcXQNdCCRkjNiHDqqSdBt7jjEz/fdmR2k8R+aQEQ9c+01XlLS6XRM13I2TlkPh/caZromS60VGsbsV/XX+8LX/gC3/zmN7n11lsBePrpp5mcnOSnfuqn+NVf/dXm8y4lu/8xo2i7PH42sLo6u1Ci9MocZzKJ0JRt0yePc/qpR0GICyR2A/W8Jl3XQMDpkxPs3RJleGt7ZQpk7Sy6dmFTpWs6hmaQtdvD+rRhzSp0HVEu0zh+y2oluM+02sqmNZayUJ5Oh/VrdEa2Ar+Grz+HUnFmZ++lWnS47sBniSbCtRXNzkyxND2FFYtRWFxodtqW8yvIzjTxdPuoWbSogXldht96foJv5svIunr8L6aWODiWw8hEie7sDHeQqxA1dQ5N/j9oS48zowfzgm7oAYkNYNQLQce+EnxeJzJ7rYN1NvsKpVKBaDR4zyi1hUJhkGplkpVcsLiXSklyOY10+nrSqffiOvuYmVkBVtb8Oa06WLc7irbLsek8r8wWUEpxYHMHw50x0mNVAOafWkBlimztSbavpbhduJA7+hbJGB1ZGaE4VSQTzYTuIHIp9GSS9F13Ea+r2+JHjrQFCbAWPE9SWrHxnPrexdSJd0TIL1ZZmiq2BZFdq1Q4+fTj/MeKxmOxLrz6AfCr8V5u03Q0QNP10JuxvKyNZmoYHReK/u2QefdqWFpaIpvN0tnZCUBnZyfZbJalpSWGhobCHdwluMzy8lXuDxNFz+frizleKAduMnlgyna5ob14C3b2Jtjem+Sps4v0nCk270/MO+Qem8ff39ceSje7ADMvwun7ITsG8+PB/dlzsGkPjH0/uBBu/6XQ16+SU+JbE99iujiNicGBZwdIT3WjC43KC/Moqcj8k11tUVwxMhm0VAqlJP6ZEQD8fB5r335iN9yAMM1Qm4crlQqnTp0CYPl8iZmRFZLdJsulv2T8/OP091mciX2FU6cXGB7+V2jahfLMvn37iLdJXFIDrlQ8lStxa2cyyHNsARJdvXT0blrzMVly8NwaJEBWXISpkendihYPzidGZwQt+erERSuDiJaWliiUq5Rv/FngCc4N3kOf3x6rwOrrtYFi6Qzzc4+zsKCzdesmIhEd236Szi6fVHJP83lhX6/KdfE/9+859PX7AJitVKmUqhQ8n6mqc5njSCtxNZtWoOVk9jW5HZSXoFYA6xgA+XywP/3dP/87Ou5/PlBhO2Xw3aAeYM2CdvSyl2mV20HJKfHw9MOc3ztN/GQQjVTod0n8qz2hktgQrFO6afKOfB6tr5ezBGSVrhSq4STiuvhKcXD37nBtxQGERmdH4MIakNkXK7OllKAkQg2iqvuIJHR8RyI0gWP71KoesVRrCOMGiT1WraEJgYbCV4pZzcRLZthZzCJ9Sa1SJhKLE42He5ZtkNlersb9o4vgX/AV94TiR3b0tAWJDRCNRtm7dy+1Wo2zm/4VsakY+ty3ARDKY773Dk4N/RRdPX1t2YTVzohoAqkUHYZO3vMoehJPKYSEpKGRMXRqSlGTrd8bDO7uZNfhPs48O4/vSnRTC0jsqo/vSpQXjElKhT1gcd70+c7JYA0LS6x5ZaFmvaZtmmimwanHvwfQVmT2RSS2riEMDeXJtiOzhWliDW9DT3fgrwT1db2rE70rgzBb+zd/zX+548ePN9V7586dA4JOnZ6eHo4fP958XsNyYgMBzi2WmcsHpMBgZ4xtvUlGF0uhWd4N7T/I3lvfxanHv4fnOheT2aJuweJLULD3umGG+mOhWQVdCRmzi/RSUGydr8xTW6pRcAoc7m+PnLmGNatfLuOXSnhLQSODMAyU77edTWvXQJzsTIzl6Ty5/JMAKF9H+THQHFaKD/LCsy43vf13QyOza5UKE8deYvrUCXRNw3Md8gtzAKzMzNB55BYyg+0jvXF9yW994xQPTGex9AtWPE9ky/zOyRn+D13D6I4iiy5+yQnVYhyAiccxTt0Hmo5ezxdrktgNGJHAYvzEV+HgP4Ed731Th3Clg7XrFXCdLJoeQwiBUgrfLwMahZWA3P7iV/J0dveiG+OgvoAQJroeRxNr/z5bZiPYxijaLl99YZpHTi9RsoND/5Nnl7glq+HN1DsYF2yGx2scGsgQN9tHhdeEXYCT9wbEQEMVsLgd9t8TOhnQQMkpcWL5BKeWTzEyEhTan5x5kr3de/Gkx9ncWe4cvjN0MtsvlXBGR/GyWYxMBnNzoAyM7t7dliT2wkSR86eDjXQjZsIuOlRLDqj2IQfnz0/yHysaj8czWKim4+GLkRR/4Eb4FbOGJn2k54XajGVkotTO5VC+ROgaypeoNimuXw3lchld15trbC6Xw3EcSqUSjuMwODgYujK7YcvquQU8r0ipPAtArTaPYfSRSu4PdXxrYaRiM16tsbWuYtsatRiv1hip2KESApciFTX52NuGMJ9f4fxKUBAWuoZmCJZfWeH7Xzodvm1rY52aeCJYqzwbnHrDnpLgVQNl1vSzgVp7R+tjfVYTWCMrIzw/9zyD8UHSz0mOHj/KbGcfSSOG8IGZU0RGT9Dxw1sQuhYqgWXt2EFk5w5q50bR6/On0d9P4h3vQKu/78NsHj516hRve9u1OJqNAv/PRfc8//zzzbpLO8CTit88M8W9Cznu6evkd/dsaRmZfSUoTyIECEtHuRLl+uArpO1hdETQ2ihepIHGmoUejM31FUopxsfHMU0zVDeRa79eL0eY16tyXeZ++7dJPPytpjND919/kdHf/LfMK+ixDH7YS4fiJrIWia1U8H5SSiE0gWZoLSWzr8ntoLIMhRmIpEBouDWbgYO30bntIGY0Hji1GZHgrHUFN4lWuh2M5ceYLk6zrXMbY28fA+DooTGuq05wKBmuW6O1YwfRgwfwH32UI2fP4fd0M5ZKBZLGum2w0jR2aRrv27cvXBK7gSuQ2Ur5QEBiC/cACBXsvwUoqdB0ge/Kq770m4XVJLYuQEeghI6uJELBUixFNJniel1iWhbReBKtDcgrU9f4zB17kCWHBxcK2HUXvHd0JfnMHXvagsRuYHBwkHw+TzabZWT3z1GdWAa+iz30Torv+Qx7Uh309PS0zTnLdZbRtCgIwcrKS/XHyqGObS10GDp5Q6fo+XSbBgoP25f0WCY9poGlCaqeJBLCHkvXNd79zwJ3uDPPzgd1V08FJDYg6m/vmSg8qqoYE1Vu0TQeDlGseVWhZh2GaeF4PqefepS973gXw4dubOkY18JaJDYQkNnQlmS20deH0dcX6jhe8yz+3e9+dz3G8QOP5VItsDaoQ9cEpq6xXKqFMh7dMLjjEz8PEJDZcOENX1c5S99j364e7ri5C71nZ6BwaxMUq0WsbxU58GLQnf3S3Iv0dPSSNJOM5kY50H0gdEJA7+tDOg65r3wF5TjIYqASqY2PY/cPENm5o61sWq2Yweb9KRaK/x4VeQ4AVbds1kQEhaDiP8wrJz7FjTd/ruX2dw1L8ZkzJylns8TSKSLxOOl691/nwKZws0UvgetLPvW149z70gyWrmH6CrfeUWcA36xWEa+c599ENCzTQOgCx1fomWh4OaTDt8OBHw0U1/4V8oO8WlB0PfhjwfPfZFzxYC1d7NoMnl9BIFAoDD2OFemjVs0z9I5xugaGEJqDL2sXPScaGbzsgN1qG8F2xbnFMi9PF9CEYs9ACnyF+VIOZ9mD+sbZMDQWXs7y7WNZjh3q5v0/tY+ORGtzUBpYSynC/EmqZx5hvBJl2+YBYpYBI4/DnID+gBwKs8heckp8ffTrPDr9KGW3zPRMYCtdqBXoj/djaibjhXHG8mOhxmL4pRKFhx7CnZpCmBbKdSi7bmjjeVXLQ+DE8+c5P5fFioAbCawkl1cWGR0ZJ9kVxdHNSx0OL8N6OzO4UvHZ+TKPxbqwUFhAtf6YriTfliZOscrP5M/T0z8QajNWZFcn9mgeb76C0AXKV2gd4bzXXwsSiQS+7+PWr9eJiQl6e3upVCqUSiV2797N9ddfH1qRpWHLWi6fo1QawffLuG5gySVlla7OW0mnw4/EuRTLjoclNFS9QUATAl1oLDteyCO7GL4vOfPAJNFZG6PebGUZGtGIgfTCs229CEsjAYEd7YRkbxAy59bzRp0KrEwEija/BqcfgMGbWt6I9VYisC5tukrcfjvR3buJd3bAF/5vIrt3o6cC64Cwm4f37dvH888/D8DSdI6JyT/FN55lctLjc5+b4pO/uY3tOxIgPJT06O5+d1OZvW/fvlDGfCmcarBqfe6lU7y4U0cTgq/O5wBCJ7OFEZCCQoCeNJE1rUliG5loEJvWZmisWY09TiwWIxqN0tvbG7qbyOrrtYFi6QzPP3cfv/ZrX+KP//hX2bVrE7Y9Q2fXkcsU2WGgQWLnv34/mmUFYgxgxwvPs+ML/4Xn/tUvYUsZWhPWpTatSkHN8/FXKe10IRAKzj6/wK6397NlX2vmq6u5HeB3w0okUGULHeIGPX31CBS3AqYOqgZ6DboGmsq3S9Eqt4OsncXQjMCxsT4labreFm6NejJJ54/8CNGdO7FPnuK9M+dhaZmxVBK/Tqxudhze6fmoXC7cwa7GZWR2IHSyjCFkZX8wvyoXpYLuYaEJlOQiO+L1wlokNgR/eiE0NBFk+U5LMCNxjqQSgZ17myA2kOBTt+6Ap0b5h0CXw788tJnYQPs0ikKgyt6/fz9LS0uUy2VGb/8V4Lsk7/oUe/cfCHt4wMXxFyAplk6BEpRKQe1nZeUJPO/GtorCMDWNrVGLvOdTk4rN9a+rvqSmFFVPkjJ0OkKKcmqQ2UoqTj09h5KXCwSEgKrj0WlZDHRE2RGiWPOqQs06PNcBpdh767sZ2n+wpeNbC1cisRtoVzK7HRB+O9I/EnQnI3irbEN8qXB9SXcyvALhmmS2YeC7AYG1bccgd/zoB9C7t7eVTWuxWmTkb54kc86iMZ3+8PTNWO/soT85wFxlri0IgfLjj+MtLoHvIaJRzM2DAGjRKNFdO0m9//1tpXCT0uXs2c/gmd9FiID0E4J6noxAaBZKKXKlb3D6tLVumdlXIi/mx88xeeI4NWFQUYrcwiJOtcpCOVCN1tCYr6verwXrSV6sJrFNXSOia0jfCzb5gCkEhiZ4qFKFl6b5zF17MQ0dJRXeYhVvsYq1pbUbLdu2gw3q7p9jc7mEmrn38id5taDIeuifrntG9loH67QcwPWKSGmjaVFMI4XQLHy/QrqvA8dZxPc9kpHN6FoEhcLzikSjCSyr+7KfsV4H66LnM1KxWXY8ui2D3fFoUwFwtcfCwHKphuv5mLqOpiB1skRkyUNpFxSunuujFKBg/uVlvvKnx/inP3coFDL7tRXa/6T5VZgqkbH8GCeWTiCVpCvSxYyYAWCuMsdidZEtqS1tEYvhjI5SO3cOLZ5AlsuIeBxnLFA0+L7P6Ogow8PDLVEJXJPlIVBYrlIt1DCS5ymVA7LqG8fvIzX1OMnOKImXLTTt6hv+9XRmcOvqtYc9HVN6WJccPiKGga7rPBbtJNXdw3+8bkeozVhGZ5SO9w9TO5vDy9oYmSjJcvXVvzFk9PT0sLi4yIsvvgiA67p0dXWRyWQol8vMzMwwODgYGjFQroxSrU6h6zEQgnh8N4ZxAgDL6iOVPtBWxRUI1qol1+N0uUrMDppupVL4StIdskXnauTLNb71xVMsHM+CLoKNK3We2JehKN3WRHkxKPIbEciNQ6wbzHqxslYEGQEzDkYUKtmA+B56faTy68VqAmtkaYTi96bYNNfJSHacX/na7/CHH/437OoeDn7FjWqWBpG93ezdveeKr/tmY62mK3PLFtJ33UWibtFqDQ3hjI1e9HhYzcPxeJzDhw8jpctJ61MYyeMomQKCxputwxZ79yURQiBlDSWfJZEYWLdz1mtFcXmJlx/9DgBPuZDwXCK6hqeb4ZLZvkRWPWQtyJeVFQ9RtztpZxIbLqxZK3VrRtu2icfjdHR0UCgUKJfDU481rtfV8Lzd2NUJ4Ets2aKxa5dGLHZbW2RkryaxhWWhWRZ6Y80ydQ4+9j3iusajP/u/hmYvPri7k6EDKR57+Dk0XUNpwfqkCYHr1VguzNEV7cPSLJLdUWbyMRaPtkHEgG5B1zDYeXBt0Aywc1CaC5qyrHjg5Gjng4+Q3Rsz0QwVt8J4PmgOBpgtzzKeH2+LGCc9mSRx660kbr2V4iOP8J5vfwdRqzFbf/xGuwalUls5NgJNMltJRdWewtQ2E7NupOb5uLaPAmplL2jSkKqZk73eWHBcJqq1IAP7UgfBOnQEEsVEtcaAdMlIF6NNlNla1KDjhl4+2x9nT2SZn/8LSO7pCs+Z8SqIRqPNM1RzfdLbZ5yNc1Y8vp2qfR7QAqcWLahV2fY85cooHekbwh3oakiJWFykp7e36crQbcomsR3RBB2GjvkqdYz1hhLBln81ie3X3279FegpG4z3GDieDFWseVWhJuB7LtL12Hf7e9rGVrw2lqd6bBEQl5HYDQhDQ/mK6rFF4jf0EN0VfmReOyD8v94/EuzsTTDQEdgxz+SqyMUSWzOJ0PNGL37DP4Lj2Mg6kX1ku4c+8yykwrUNWA3lSxb+/pWAxDYEsn4F71oZpOfoJnI/bLQRITAKUqLFYujdPTA1BYAsl6k8fxRjcDOxgwfahszO5Z4hX/4WIJpEdgNKKQQCoSyEZjO/8AD9/XeTyby5ityrkReVQp5atQJKBXnYnoeSkmrNBuBP/vZrfP25V9CukWRZT/LimbEs9x+bRQARQ1vT39YSAk8pvl2ucM9SmdsG0ghNIHSBX7qCGnqdYNs2J0+eJJvNous653t+HNUzAzxUV2ZHW0piXwlCsy4jpH2/QqHwMp5XwPdrKOXi+1VisWEMPYZAIKXdsjE2MkXHqzUsoeEoyemYzYd7OwGu+FhYZHZ3MoJp6JRtm+SJGtFZGyloqrEBlM+F46ECe6TAN//qFP/kZw+1nBBYXWj3PY9n/uG/M/HSc2iWx///q4/y3z73S+zfNgC5SRh+50WK7LCQtbNUvEpzXap5weY+Z+fI2lkykQwzpRniZpzMYniFFuf8edyJunzZMMDzcOeC8sojX/0qXjzOdfv38+GPfnTdyexrsjwEqgWbfP4lhLkNz3HpvWmFeGoXqeRNdPYlXrVrdb2dGZ7Klbh3IYcmBBFdIH2/br0XFN6VlFgCfKHxsK14wZG8600fxWuD0RnFuHngwu2jMyGO5sqwbZuZmRnm5gIZQyaToaOjAwga1bq7uzHqjQKe54VKDLjOMkIYOO4KjrNYn0+D5lYpbVwnfLXQajTWsdPlKo5UnKrvyc5Va9xqmVR8n28t5UNvxiraLvc9PE7ueBYpFR4Ku05kVl0Pp245rGsCTQjOHm2t0u0iJHqD/VTHEMT7AiKgOc8rQAZ7LM+Aah5yEy0nslcTWNtPbiKvnUENKJQKrtVd3cNcv2nvhW9o7G3zoM060CI+yxkdxZ2awtq+A6HrKN9vZmD7fjC3WjffzLLvs0nTiPT0YO3YEfp5K5d7hsXlb6BpGkKPoomGs0EF3zcwjBSaFsFX3rqds14rapUKz3/rQb7kBcVgzfcxlET6ClPTEZoWDpntS7ysjaz5QWOFVEilWKr59EZNRKQNbHmvgobC7ZVXXgGC9WtoaAhd1/F9n0SivRR5hpGiq+sdACQSO+npvY1EfEdbkthAM75FGiauaTD8ve+SdT1O/cqv88PdrbcX13WNnhsk//5f/tw1Pf9Tf37x7VAjBnTrwvq1MhGsXZ4N1Sy4VUj0BGptt3Vn7SuhL95HrpbjpcWXyNZjxyYLkyxWF3lk6pGWxTi9mqOUXy5TOn2a8uQkw1IyW1dgz83Nsmn/PlzTRH81OynW31FqNaQPwjmA7vWiyFC2XQxLI5mJEnN9pB9YipvRgMTWrkDKvJnos0yGYxHGqjV81EVktgKUkvhSIYVgwLUxSyWquoFSklqsREffQFuQ2YltHRw5EpDEWmSDnnk9aJyzhNDxvSKaMEEEdUIILPFdp1XeENcAKakcfR53cgpz6xbih98GmoapafRY7dGA14jEOPvcApqp4bkSIQMS268PUQnYVhbEZ32S+4zQxZprkdkNSM9vKxIbILK9g9ihXqovLQQxOWvMm8qTgCJ2qI/I9o7WD/LS8bgufiFPsVgklUqhpztano8NG0R2y5CKmty+K7BA3tWX5NbrBtjZm2h5dsBaaL7hy4ucfuEltu0IVJB6rQCzLwdKgfxk6HmjDesF82QNT1MI/UJ6rxSSwYk08Uc9Rg95ZKLhdjI658/jTIwj8wW8lRx+uUJtOiCy3alpbATe8hLJd7+bjnvuCb24AtDZeYSOxJ2sFB9EqUAlIETgIi0EQU6m5oJQ9Pd9iM7OI2/6GK5GXtilAsXlJVCK4vISnuehIVDCp++mLJnBYfq27SCafPVr9M0iL650UBnQJe8agG+dXEBqGpYQKNfHKwed924pS2llHhd4T0eCPm+FqdlCoMjO2kSTXViTF4ru631QWVpaIpvN0tPTg6ZpSCl5sv/DwENBHrZTCi6EEEnsK6FWm8PzCgihI4QGCHy/TK02gxYbRqGCnJwWoZEp2is9Xjl1ipLn87IvqXSnAcU3zi/iz80ytG0rvYkEjzsesjPJ/uSF3PlWdt7v7E1w/VCa55+pEJmxkYDUBZYmUKvzreqTbUOZvXB8mYe/eIr3/eS+lpLZjUK773l8+wufx58aZag7w9TiIgB7On0O93qw93bY/5G2cBLJRDPYnk3JKbEpuYlKJDhU2b7NVHGK6eI0AkHOzrW00HIpVLWKt5IlsnsPwjDwq1XsF44CMLq8zJBlcfz4cYCWkNnwKpaHSiL1Z0lFHaTqBKWR7utFCIWhLZLq2XFNB5X1PNbe2pnknr5Ovjqfw9MMdLwgd7puJ6mUxHY9fF3jI72d3NoZ/l6gAWl7ONNFykcDJWttqoi8zmsLpYBt27z88suMjIxQqwWNIZFIpPm1aZrNtcz3fSKRSKjEgGl1o5SHkja+X8ay+tDqKgHPK+D57aV6b6xjB5Nx9sRjPJOO8yiwyQrW/kdXSm3RjHVuscycJbGGYjBZCQosdXJVqsD9yjK0elajYM/N/Qzu7mz5OIHA2WpmIMjArpWDPVXPbuBZZv1N+N3b0WNpsBKwfDawGw8RnXv6cQ5mcV7Mosl6XtsVOUpB9eUlojs7W2J552WzCNNC1NcgoesI06K2tMSj9Ua3v//qV8n09XFg/34+/J73tEXeaGfnEfr7PsT8/H0gXJS48L6XsobnEcwLStHfvz7nrNeK+fOT/Ge9k1PdwR/frGegogVNzhFNA2TLyeyGEluL6iAE0pMcnc1z3vUYilrcaEfRV2z0rih6wmxLZXY0GmVH3SXA8zymp6exbZvu7m5SqfZy6AAwjGAN7eo60haqtqLnc+57j2E+8I3Astu6oLxq2Af7SiFNC+X53PD043ztuffw9VQilDXrwIHreOaZZzn6jQnGjy/jo5ACFhcn+KvvfI6f/KFPsql/GFPTUb5k64FuDn9gGF3X2iNiwM4HFuPRTqjUHfCq2eBgaFhghpuPC7BQWaAj0sGtg7fyzMwzAHRFu0hZKQYTgy2JcXo1Rynle3hLS8hKBVUNBETFuijjz5aLdL94Eu2Vz13Tz1pPUcalqFU9HFsSiQ/UHRvBqXjEUoJEZzhOUpoQHOkI5qWxag1EQGYrgoZhTyqUgO5Snk1LM8iOTsyuwP2kViljV0rE052hjP2tiIZ74/j4OEDzzNUOaJ6zlI9upJDKRSjQ9U4AlPIx13BnDAV1EtuZmEQATl1EYN54E3mp2kaN3YjEQAh8oXC1YCvlqUYrNnh1tXZfXuLMVRmVMnSx5qVktlu/Tvu27+S9H/+XbUNiAwhdo+ujO4HAPlzBRWS28iT4ktgNfW1hK65cl9rUFOfzeQpSkl7JsbmjQGTLlpaT2e3zV/xHgES9w+rI9u6WZwa8GnTD4I4P3MLePoeFUp1AS/RCUg82rNmxUGzuGmiQ2JUXF/B1iYOL8DVk3arZEz6e5tN51uAdai/bDmwLZZzN8Var+CsrmNu2IwwDZ24OVQwIT2NggMjBg/jLy9jHjxM7eJDY9deHOl4ATTO57sBneeFZF7gvuFOAZgiEECgchPDo6/nwutvdrUVepLp6sSIJistLxDsU0vfRTJNqqpOtXg1d04mlukj39CE9D7tSwnOcK9oHvVHy4tUOKlIpWCgxU6wR0KvglnMAzL30EFqsgx5T53xXnF9/WjUPBcLU0FMmYtXGZb0PKuVyGV3Xmxa8vu9TrQXNDJXN70CJFxAHf6ytSGwlHVyviG3PIqWLED66HkVKge9X8bwSrrOMFenBbKFiYNnxUAq+//IxvvhjH27e/+WrfM9/vuR2KzvvU1GTH71piJ2ZJC87Y8iJMkKA5oP0VhHZ6oL4SgpAwNnnAoKr1WR2g8Q+9fj30EwDw7TQjIAUeuaVIoc/+FPo/fvagsQG2N6xnYHEABOFCeYr8+RrQTbuQHyADqsDQzO4vud6ElYCX/qh5WWLWAy9qwt/aQlhGNjLyzy+ezc8+SQKMHUD3/c4/sorCNPk7rvvDo8cUJJc/jmq9iRBRV0gEIEqBImvzrOSfZae3lsC28OQYGqC392zBd/z+NpiDsNXgZqtrnD00FC6zu2VLL8aiYeaM9qAtD2c8yUqLy/izZWxJ3IAlJ+aYcUfJXF9L9ZQKlRCe2lpiZmZGTQk+6MLZJP7yJcqTdW1UoqFhSBPKhKJMDg4SE9PT2jjTcR3kI+c5Om5Z9kiwKnN4fv1ApDQrmCIGB4a2di6ENi+T9YNetpfLlZwKlUOp5PoQuArxUjFDi13dLlUwzR1SgdSVIo1erM++ioHHE8qdC9QEKT2pcPNyF4DjXzU742nMFNwx00SvZqDeE9g2RoihK6RvmkTyy+vrH191n/PwtRAQPX4EvEbe1tieWdkMijXQfl+U5HtOTW+PzrK2bNnASjWaqQ9j2Mtbr66GjTNZO/ez6CUZG7+71HyYvclKW2Uchno/ydtYyv+5EqJ57o2IRZzF92vlELUycKIpuEqyb0LOT7a18W7Muu/51Z1twWEQCrF0cUi046HAKaqDsqX3GhZKF+han5b2Yw3o5zKZXJ1Jaau69i2TTQaxfM8RkdH2b9/P9Fo+ORgO+DSxvGS5/Pt5QLnjSgHbriJ/qceB8/DiESQKObqxOCK67FYLqK5PmduvoXnkp2MHD/JUmeKO7rTJC8hs9ezcTwej/P2t9/M4cOHefiLpxh5dh7lK3Q3mEz7ercy1LeXiKmjSYW/KCmdSrTFuqWkpLy8RELXQI9QrngkRAWBDJTaid4gGiNkZO0sCTPBcHqYkcgIAFE9Sskpoaf0lrg2vpqjlMyt4M7NoyWTICXStnFyywzh03PwBsz4tc2f6+0odSl8VyK0Cw1tgW0z9UbB8LAWmS2UapLYvXaZbYUlfBSOXSVSs7FicYTQ8JzWOiC+lbHavXGpHuU4NjbGTTfd1BbrVCK+g3LsDJXKGAHNKlFKoGRw1opG+0nEw4mXuQirSWxNCxoyfR9nYpKC6zN33cGgEVsp8obO1qjVEjJ7LXGWH5Gktnqce2kJj+D9pOr1wGIpqKRXy1nykXm8TVG2Z5Js6tAY7nBYWZhlZY2f0yoXidVk9vTXg7jMhYlRHvmvf95Wimy4MpndbiQ2gF/IByS2kiAIPufzDHekMbpbW2e55r/gpz/9aX7kR37kNWRTbuCtBj09wPCmBMuvLNTvESA9iHUGs1Z5MbSx1cbyVF5ewMPDFR6+7yPxcKVXH6lAMw00V6NvIoF53oNdoQ0XEYthdGVQ+TwiHkczTah3CpubNqFFIijLQrkuXrZ9LCWjiRg3vf13GZ3MAmMIzUfTgsw2hIde+yFE7n/Bqwms2Ku+3JsKzTDo6BtAAZ7r4CvFRGYTi2aUTLXI7koOTdORnkd+YY5atYIQ2rrZB12L9e2wUrw8nWM6W6mPu0bi+vdjdg6wOZnk+s0dGAkTZftNOxER1Zs5KdCag0oikQjeU3X12vT0dDO37Xjn++nfdQ9bbv9xom1EYlftKTyvDEiktBHCQNMsNC2ClC66HsMwO4hFtyA061Vf881Ct2Uw77h4m4f5+P+4H4Al12s23RTGz/H4J3+ND//HP0Rt2UbK0Plwb+dliuxWIhU1uX1vLwf/lzTf+uIp5o8t4/vqouesJrF9I8h4l55i7IUFZm4daJlV61okNoBe7wIcPzfNt7/xNHd84jDh668CJK0k7x9+PyW3FOSML8/zKI+yJ7OHfZl9VLwKCSs4hOtaawota8HavBlreBtaLIZTKXNfOkV5PmhWUIAjfUwRZNAeO3YMIBwyu0FiVyYADaXEJUpBLVAKeNPk8jqdHTeHTmb/asRhsbLC4/EMylc49bnURvBh4fDT1QXcYviNF9L2qBxbwhnP48yUkY7XdGPwKy7uVImKr/BWasQP9YRGZpfLZXzX5sjil9laPMps5hae7vmfmo9ff/31TeJ6YGCAwcHBUAstSovyf4+/zDcnRnh7uoOfGOgBLdhre75LtTqJ5xVDt2ltoNsycJRkqebywHKOc4VAOXqqXKWQq7ArFqPLMtCFwBIay473Kq+4TuNMRnB9SSxq8twmjd2+j6gfVZQCoRTKB3coxnUf2R4uGbA0AsU5GHo7zLyEX7B55vnx+lglp6Y9sBR3vPcAuqxC53B4Y60jurOT+E19MHsKuFDAEqtJbEOAr4gd6m2Z5Z21YwfmyEgzA9tzajymaZyemWF1SV3WB90WzVeXQK0ROdS4v1p0cKoe0UT4e+7bupLcNj/Fd+vnEiUDq3ldN5tRTjUpUQru6W+dq4gwgnVeScXRpRJTFQeNIEbAl6pJat+UMJE1H1n10JKtOwtcCQ0yYHFxEdu2m9bijuOwd+/eppPI3NwcSikSiQSJRIKenp5Q1jDPK1KujOI6yxRLwZp1bPEYh+QhzBY1WqzVOF7wPBZdj4Su8W0FlpnEWF5CiQpoGiUnILL/60qRtGlR6sxQWC7g/MnnSeoaLwBfMQ3Sl9QFWqFw1XWNH/pnexg/tYybc5t7LNPQ603tCt0M3m9nng324GGS2UpKzp9+hfzcDB0pExDkC9CRSLI57SJiXYEi260EjiIhIhPN4EkPX/rE68S6pzySVhJf+niyda6NV3KUcl2Fn1Foq1wX/HSGXstCM5NouoWWSl+Tuq2VRslB/nV9b9UgtCTNa7UV6DJstrijJCuFyx4btBQnnCrnbRdQICVpu8RQKYfyfXzlYUiLuJPD0uO4nk1CdRGtXEy3pd0lisb62+RL28NbrOKXHPSkhayFs4++Vqx2b2wQ2YVCgaWlpWZ2dpgwjBR9fR9orlddXe8AIVhefgmArq53hH/GWovEJnAVUlJizZxnE1C9/gZcNOYdF0dKei1zXdXZVxNnKaWYnSzilt1A2FZfoyrVQJTx3dMPkOjuxj2v030qQvpV3IbXfY21C8F5q7yInujlvT/+47x87Hj9HxMotIH2J7P9wAKznUhspSRT8wsUpI8QdaGjUhSkz9T8Atsymbo7amtwzX+96elpPvjBD2JZFh/5yEe45557uOOOO7Cs8A8FbwUUbZfTc0UAvvnKHDP6DEOZeNvYiwOBxd3idnBeDm6XF/Cj+5leUgylbfTElXMq1xuR7R3U9hjor7gY6PhCoosL1lG6pmP4GrrQiF0ffn6AtXkz5vBWtHgCVamgd3dj5HPBg7oedGA6DkYijtGibJtrRTQRY+91vwj8NxQeUlYQmiJu3ElH4tfIzzuszFXo3976ortmGHT2DSClzwu2ZMGMIoClWApNaKQqgRqrVq0QiSeaE+x62gdd1foWeEfPAN84Nkex5mEBdG8hqWvcNtRFtCd2TQqB9T6o9PT0sLi42FQILC4u0tnZCUBnpodJvZfYSp6hRHsU2V2viOeVMYwUuhbF80r1jzJCaOh6AsvswrJ6W0piA0FeqK4zYUaI79mPrxT7TYOc66ME9FgGjwP6lu2wey87k3Hu2bEptIzRBoq2y0OvLDA5bBFZiSEmqpdFu/tC4esCjUCtLRTEdqRbZtV6JRJ7NTRDb8tN6oGeA8yUZziXO8cSwQFwpZylay7KUmoJX/romt7yQstqWDt2ENm5g/LEBP+9ViN/yYHJUyqwEhUCNI1jL72Ect2WK91qzgLV6iQIEEpDEYwLgqY2hSK4Sn2q1Ulisa1EIgNXf9H1GGelQnZmikqhwMrMeT5RLGAlknxTs3CbG/1g7NLziKdbs6ZeLbvPmSlhj6ygXIlTLCJdyex0kJG9kF9iamWWaCqNOLVE1M9iDV6ZtFjPrutEzOJts19ic/4ZEBqDy09wU81hWgsSxoc2DXD47eHb8gK40uV3nvodvjHxMAKdJ3NZfFljORsoQR5aqXGLZ1OujLbMrvXV8hujnk9yucADywVGyjbmSjBn9ZcLzE5N8WRphbd3JPGVYsGu4XSlmKzkL3ud9e6839mb4EwmwdmFIoah8+14lV2RYC4wFBhKMJ2Gvpu62D0Qsm1/eTHIGi0v4pfm+fYrgvGlYB7QhUITPqdGcyBOc8ePvA+9Z3e44yUorJh3b2Ll2Cr7SKUCMns1id3iQoueTJK+6y6c0dHATnxyktPT08j62FZDAkgZbvNVYyzS5fTp32Jh4X50PYHQVheuFSgLMMmVHuKFZ31uevvvEk20uGv4EvRv3sqvTk/xvO8yB1R1g7hhYkYigT2rlDgSfrS/s6UZ2VrMgIrH8/MFpqoOmqCZj6oLASimHA+KVQ53JuoZg+FjaWmpSWKXy+Xm3mV5eRnf99E0Dd/3yWaz5PN5+vr68H2fxcXFliu0Pa/IwsI3qFanEMJgOTsKwO89938ylhrj/7j1/2gJmb1W4/h8zcXwPJKN97KU8MorpBbmQRd4vk9/cYl0rIviwCArO3eT0jWimkaXaVCVki7DoD9yYfytVLjOjxaQFQ80EJGL582G04FuakhfcfboArve3t+yhuHVaJDYublZQLCSr4EKXORyJQkixuaeHoRbbYuM7O0d2zmbO8t4YZxa3fVGKYXt2YwXxhlKDbG9Y3uoY9SiEXwpg2tW04K80VwOFYmgPA9fSrRkHnOo9VatV0MkZlCL6jgVD6EFJLYV1YnEWnfO/mDXOP9s6V9TP0ZfGy4tFdTqHwCza3/Ll7r2vvbBvQY0mob9rI3QBY6vsBdy6/oz3ygudW8E0DSt6YTVDjCM1GXnqK6uSP2xcJtsrkRiA0jXBT8gB62Z8yhgfu91lKRCKqjJ9VVnv5o4K112WJwoICo+1K3EPcem5+BddAxtITbUQaXm0xE36U9fec+67musXYCT9wZOwrqF79o88uwy82OBO4cZiaCZRlvWCeESMvvYIrFD7UViT05OkrOrBMacwT4l+KzI2VUmJyfZunVry8jsa/7L/cVf/AVSSh5//HHuu+8+fvmXf5nZ2VnuvPNOfuRHfoQPf/jDLbEJeCuiaLs8eGyOx0YCmcA3T8xxzj/P9p4Eu/pSfPDQQHuQ2dF0kIM9VQX+O34kzbePFjg9Ns7e/cPccdv20BRuQteYvt3GKOfZPNGBoWko7cJmX/ODYop5fRfnb69ybOZRMtEM2zu2tzxnFBqEwE7cqSm0RAIdhbVtGzz6KO7UJLVyGS2ZIHrwINaONrA5uQR+3WlHd28GXiGq3UFX9NcD1avhUi2GaMWj64zEu1jSHDQp0VBIBIuxJMe8GgdLBYTQmtdG0DEUjn2Q9HyeH81ScbyA/AM0oOpLXipWeXtfnPCXpgAdHR1Uq9Xm5qJRLBFCoOt6W21UpbQRjf80i3h8G5XKBCgfw0yja3F0s4MVt0avKdFa2B2WMnQ+0Jum6Pt0Ggadpk6PafBcIfj9DceD32tfxCAWi/CB3o6WkdhXIy9OzOR5aWSRrZkE2vUa53N5chOBKrhQybJcnqeGwtQDG1whFQspjeu2xzh/fvqi11ov8mL65HFOP/UoCLEmiQ2gGyYIOP3EI+zdEmX4+sNBk1bINuNJK8k7Bt/BaG6UqdIUAIdGtnLdVA+RTR7H336GeCSOJ73QCi16Mknife/jvi99iVyhUPeNWxWOrhQ+UPM8LE1DAidOnODAvn3sOnCgZeOMWH3EYlupViZQBNm3F9CQCgZF61hsKxGrr2Vja6BWqTDy9OMsTU/hVMvk5+epeQ5e93aUceHatZTPd6WJ1r+LPxpY/472V4vE8MsOyg4Oqn7JRbk+hVIOgP/05NdIH+tET1sITSCiOnriyo1C69Z17bsMPP8fEfmncYWBFAaa8hguPMMtK4FylMd+Hw5/MfQojAaJ/cDYA1iahaVbVD3FE7kC5Xygcn6puMyfjx/lN7reTkcLpqlXuwYa8Oq24UXPRy8FJPXZL3+ZaiLN14XgCdNAoohrGi9ZJsYaAcrr3Xmfipp88NAA5xZT7O5P8bUXphnJNnwvIdttIA+m2DeYbsk566oNAjkfZhbwnSqPP73C2RmXoh1cA4slj6hVQ9PgieOzLETOcHtmCd3IXfYyrbLla2C8NM5LuyeCGw13Bl2iaQI9BBK7AT2ZRB8e5jtHj/LKxERgJHmJzDkorwT3izYgs3O5Z5hfeACEQNej6KuJbGUhRLLegFuh4n+X8xOPsfO6O1s+ztXQojH+W+9OCkbAHPiaRskw6dK00EhsAKkJXihVmKo66EJcOEup4H960OnGVDU4+729oz1qVOVyuUlip1IpEomguG7bNvl8np6eHvL5PJVKhR07dtDd3Y2UkqWlpZYr38qVUarVKeLx7fgK/mH2mwB40uOBsQcAWkZmw8WN467jUrEd4obebFqfPpIhceoVOuaC5ruuZBelTYO4+w+QVpA2DRK6hk5wPs9ELTqsi8feKoXr4O5Odhzu5+xz8ygvmLekUuha4HgFgWWzUordh/tb1jC8GqtJbKEJlJSrmkZBaIJcyYPzy2zuiSDaICM7aSW5c/hOxvJjPDX3FAB377ibbX3bQq0LroaWSqMl88hiETQNWa+v6N3dAXEtJbJYRBYL6Jn2yPSVnqRW9dB0gRXV0XSBGTWIxAw0o3Vr/4Mr25jY/y9Idl7ZwlaiOL+8grY8j+84KBlY4CopsRIJUplezIiFFY03XUVWo5Rb4pUz3+Gd6/jv8Bar+FkbozdWf28p/JFLokYuUWwbvbFQI51Wuzc2xyhlcw3bwNXhLS7iTk4Fc+elJLbngRAIKVGANTOD3tuPmemmw9BJ6hp5zyfv+fRY6/d+u5I4K56R+PEK1ZkyouTTaBnt2rYDcyBGxfFxNBcjHSOeiWNe5TzwZqyxVzxrzR6Dcy9AZju+o/H4I6c5e3qSYi14b+UrVZLRKJ7n8MQ3H2Qhm+X2H//JK5LZrT5vwQUyO35DD5HtHW1FYmez2aAJQ6oLtlKKgLjWBNm6y3CryOzXNBtqmsa73vUu3vWud/Ef/sN/4OTJk9x333386Z/+KT/7sz/LkSNHuOeee/jYxz7G5s2b12vMbwkUbZdzi2WWSzWWyw5n5wpEiz4A27oTIAL7q2fHlynYLjdvy7SHOjuabuZgPzPThb+wCJrOqVPn4a//OtTOlUwiw/cOvwxsYdNEB3KVgZymBNW9OkdvOM3UzDSGZuBJj7O5s9w5fGfLN62r1QJeNouRyZC58Ub44heJHzlCangbkf37iR08gJ4MWSUCOFWPlbkK1aKDYWmceyGwDytNfJgV+X5k6m107hFohkJ6ilgqHCcGqRTP5MuM19x60SKYRDWCjrU5I4KQGvtUqZnfplRgg2e02D1Cej7PnFlmsmSjCS4Q2fVOtomVCozB27d3N50FwsDqzBtd10G6pJZeohIJvPmVUvi+31YbVU2LBipMVBAroEWJRvoxzA40LYLQLI5lxzhfOs5Qaoib+g63lMy+IZVgynYZr9aQCqZrDjemAouz5+sWrJamcWsyyt4TL6NuObLuXdevRl4sFG1yFTcoougC15NUZ4PC5bfOPEBsugOp6sULwLUEdkzj2dEoD6YuLl6sF3kxtP8ge299F6ce/x6e66xJZvuuC77B3p0ZhiJLcPqBwGlk/z2hk9kLlQVytRzdVvB72ZvbihgQ7J4dpPdEL8UfjpBJhFdo8X2fb3zve0zV5wLf9y88KESTFPCFoEbgiLLLMBhw3dYOVGiBXThQqUzUu0G1JmshhAQhEXKQiHY90getxduW7MxUQGKXS1SKBYhE+JvN+zkmosSkj658skBE0zF0nUdEnN+aXuZ396xvTvardV2riouXr6FZGu6yjax6LLg2H77+Ljq7BjEjUUTKQrM0jI4IIr72vPVmdV1fdlj1XfjOv4NT96N0E18YQVFF6RiezcrSHACzLzzE5F/+LPzwv7kmMns9DqtrkdgKhSt9/Pr6BaCj8f2Fc0RfuZfP9Lx33UmBa4lFacCo1hiv2PSsLNF/8weJ9A1R1Az6LINu08TSIKXrGGsoBFqlbktFTW7c0smNWzrZ3Bnj89kJngW0G7vY88NbmS3YDGXWP7vzVRsEpIcqzbM8t0jZDubWSjUgsr87uURsody0auPUP5C4/1G6h7ZcVgxohfXtamTtbLPYWxj2UC4oQaDEvik8tYBfKnHya1/j1Ph4QFZf4XnNdauudjlx4gQHDx5kRwhNxJ2dR+jv+xDz8/fVI5saDbdxoOEi5QAKw383mnuo5WNcDVcqfvPMFPcul4jU/8amAFdB1vWwhBYKiQ2wWKwxtVJFCBGcodbwatcbBGfVZbvnMXCZNK81WJ2JXS6XqVQqwbjrNuIA0WiUxcVFhBAsLi4Sj8fp6Ajc5TRNC6Wh2HWWEcLAV/BHr9zLs8tB/rwr3dDI7AY6DJ28oZP3fLR6TnrKNMgeOEhME1izMzibBlm+7iBDUYuyLyl6PhVfBs81dDpCdMLSdY33/WQQIzX9YF0tpmtE6vbivivxPcmet/eHYiu+Foktfb/pKS0VaFIhNMhlC6BSbO5PEV414wKSVpJDvYdwB4KzyaHeQxzecjjkUV2AME3MoS3IYgG/EHwox8FfWUHv6EBEIgHBbdfaIiJLepLCUhXH9i9TYreSxAZY8aJMmTvoiF/ZCRGg5uXI212ouKJWLiGlRNM0ugeHoefqzc35cpoV74k3c9iXwS85CF0g6uum0ARCv/DuWUuxrWeioUY6rXZvzOeD5tZ0Ot2McdrA1WH09mJu3YIzMRmor3X9IhK7ASElTizOSipNWtOI6Vpzn1OTV8ikWWeYusbW7jj5mMnKRBE37yBSBtUOg4VclZoniRga+aoL2QpbXoXMfiO46lmrvAS1Aso8xvLiCuVSFQTYtcCC4Zunp4nFArJVKQWv/HcS935nzbMWtP681YDQNaK7ulr6M6+GUrFUjxwN9q3BwSqIFBIaILRg3+JLVlZWyHRlSLXAZfANzYT79+9n//79/MZv/AaLi4vce++93HtvEKb+67/+62/KAN+KaCiwJ7NlTF1jZLZA70gF/XQOoLkpOTFTwHZ9HE+SqzicySTaQp3tewHRMn7qLMN9GQzTwvNl6DYM2zu2czxxnK/veYqby9u5cXEXhh/8NvNDLrkf0plamGZbelvTonW8MM5YfoxDva0vBujJJLHrrweCAsr8+DgAnffcQ/fh9tlQO1WPc0cXyC9V0QxBbq7K2MtBv5RjC+yl/VQWKiBypLqidPTE6BpY/6LgpWiQ2GPVGroIsg+lUs2FRwd8JZnTLbRYB3sqebR6RnYkFicabx0xJJXi2dEskyUbXQsaLQKFQABRPwhOLAfFjJu3ZUIjs1dn3mhIDpz7Uzpmv8s3i3sAyOVy7N27t602qqaRwjMSeF4RgUAqSd4XbE5tRgiTFxaOMlU6jwAmi4H6tZVkdiP3eqRis+x4dFsGu+tKbKszyX8GbktFecef/CeyDz2E98EPMPDpT68rmX018sKTksp8CbtUI2JqSKkQvsItl7nu5rvoTW/GcA0ajSMqbkDGwlOKzZ0xOuMXioPrSV7ohsEdn/h5IMi48eAyMlt6Lvt2buaOH7kT3bRAekFWztJIs0krLGTtLJ7rctv4dXwZkELiGh661OkZjbKtY1OoFkITExOcOHECIQSxWIxarXbxE4QIMmcJSIOtqRTvsSKoXK71g72UzJYy2E0LiUIi7X6EvI6i51Cr+KR7Yi0tuFQKBZxqmUqxQKSrm7/t3cGxRDeG7xMxdKjvoXTTJGYa1KTkq/M5gJaQAleMxPAlbrSKLDm4MYeXTJcZJIPdm9lmmGhCICIaZm8cIxO9ajTGG+26vuywqiQsnobSHIgYNDV4WvA+VzHyFQOo8rvPmnS88jB88Tj07n3VjPQ3+7B6JRK75JSo+c5FxV5PeSREnIfPP4X11O+0jBS49BpwpSTv+dSkIqIJOgydnb5P8plnSc3Ns9I3wGj3IH2WwQ2pOPFrULS2Mr8R4MCmFHvra4LKWMwWbLZmEuzsXf9GvGtpEKisLFI7dZJkPWjSdV0yB5ZJdHSjGcHfPCgSBIWC3n0HiHdeuCZbaX3bQCaaoeoFhPsz28+wpTbApskO3P2RUNcrZ3SUvmyWPd3dnMpm0ZRak8xulP10PVBuHjhwgOHhcPLHNc1k797PADA/fx9Kuc37hWqQ2C4x404o/jzxdOvPWg00SOyvzuewNPDra1JK16gJcBQMRw0+u2uo5SQ2QG8qwtZMnPGlMlKpC3anjc6FOrmpgOHuGH2d4Vi0X9osXKvV8H2fSiVY16r1ZpZMJsOuXbtIJBKk02mWlpaargFSylAaik2rG9d3+JNXvsZ35441SXdFsMahJPePfh0pHT71js+0lMw2NY2tUeuiNSuua8zWXGavO0h8YJBKJkPKNOg2DbpNLlvf1itv9FrRILPHp07C3wVGQu1OYguCjmalqJPZIDSNXK4MZ8+yee91iJB+ryWnxFh+jKydJRPNUHGv7joTJoRpIqIxvLPnmkS2qtWQxSLG5s0gJVo0EvYwAahVPRzbx4obzWxsp+JRq3qhCVteDdF4kmosRmFpCenXdwZCw7VtpOehhWwnrCctHF+hpGoqsoNMXPB8yfePznAw7xPrjzcf9xareItVrC2tj/lrNGOZpklXV1czfnD79u0tjbt4S0PTiB8O6lDOxCTK84J4gUvrv5qGaVfZPnKalQMHqfqSqKYhVbB2hQVT1+hJReg+YFHJ1zCTJtMrVSquz0A6SjxioAko2h6Fqkt3cn3mr6uetSrLUJih4hnUphZIEgjIfF/RfWiZRGdP4NxYR7CuXX7WgnDOWwDKl9TG8m2jxgZIppJ0dXWRzWabTUEI7aJaRrA/VHR1ZUimWsO5vGmzeG9vL5/4xCf4xCc+8Wa95FsW5xbLTGbL7OhNoikQz2VRSx6lerdw/FyZ+aSDj6AjZrCzL8VQV4zRxRLnFsvcuKUztLH7nsczf/83AGhCYggFno2h6XhtkD0qhMAwTJ6+4SzW8QhiPngL9d6xm1P500wXp7F0i95YL1EjiqEZZO1sy8e5Gr7vc//99/PNb36zebudsDJXIb9UpXMgjvQk50+vUM7Xc4WkxDA17IqHUIrt1/fSNRDHamEeTgMLjstEtYYgyEGTBLa3qxPxGqrnOWEw3NVLRroYlkU0nmzppnWxWGOyYCMEaI0T32oomh3kk9kKw90J+tPhbASbmTdIBl/4PTpnvoMS0F86DsDwls0tz2V7NQjNIhbdgusV8f0qx1cmmKkssOC4KCTTxfNoQqAJDanCI7MPpy8vOu1PBsW0nr/+ItXnjyKEIP/1+wHWncyGtQms5VINqzNOT1Jiuz4Vx6Na89EjXcieLSxrggFHEK2BSuiI3ggSSEYMBtfoulxP8uJKZLbvBQXhbbu2cse79wQkNgRS3Hou6XrhanayZafMVHGKXC1Hvppj3/f7cceDwuVsZYlkLoEmNKJE0L+/SHRxho67htfcvK63xdHw8DAHDhzg2LFjeJ5HJBJBX12QWpU/2qlpfGj7DpgYxwgrVqZOZiupqNoT9fEp/Go/yt5PJGlgRnUc2295wSWeTlOrq67OxDt4KtGNUApLCyzFLrWzi2garpLcu5Djo31dvCvT+kJFAwLwPcVLvsv5YKXlPBKkxw2GgZWOvCqJ/WbgssNqaR4mU0EVXa+v50oFKm0V7Ktc12fghhU6OzsxIzFQEjexCa//EJFoDG0Naf56HFafn3+eh8YfQiAuIrFtf1UsRv0I6EmJrkcwEKEp3FwpmbQdiqvUbXlN0H/yBF1zsyige2GOlKGTvvltmEKw5LhtRQr4vuTo349iPxesQDu7E9x23UDL3a6u2CQCpLv78T3Byux5lFJEgFRXX9Oyu7FVFEKja9MgAzv3X0YKtLo5oC/eR8kJ1rezxbP8+bZlbu27kY/+8MdaVmRZa40tnTxJrVRmz+AgiysrnMvlkNB8XqlUIldXDWmahqZp7Nu3j0OHDnH+/PnLfkarLAQbZLb0JadOfwUIimwKB5SH4f8QFH+ezp5UKE3DcDmJHdE0KvWL0/d9UoCtCWZsl0+dnQ5Fka3Vm4BlzWeyaINSFzUEy3pjw7aeRPs0C9cV2A1S2nGc5tmqt7eX6667jmg02iS/G2S27/tkMpmWNxRb0a38+fgLPDJ3Al9JPBUQQo3Vy1U+SioeHP8GQChk9qU2q1ujgryhUx7oR0lJ1Zecrdh01QntsNepS6HrGoc/MAyfBulLXNtHKRUaiQ1QzmXJzzfCg0WzgWH1O0g0yWwVZNIryM/P0tk/QDLT+sb3klPiWxPfYrp4wZWxNhvUskZWRihOFdvGWrwBb34ev1hE7+pCVcpIx0WWSnjz81hDm9FS4bqINeC7Muita6S2iKDXznev5IMSPjTDIBJPYloFtHgcwzQxTAunZmNXSsTTnaGOz+iNoWeieItVhC5QvkLvCM6o/+WRczxXWOL9vWl+q38HJqKp2PZLrY9JbKxHi4uL2LaNbdtklwLnzkiIrhZXgytdnp9/nrf1hytguAyaRvzGm/AWF5GF4sWPKRU0uRsGSElmbhapFBN7r8PQdQajZqguIg0IIUh0BvuWqKnRETMvOmNpAmre+nMda561/G5YiZC2C/hSJ5evu/YYJqnugYueKmWwf+kc2LTmWQtaf95SvmTla+fq+di9mB8aYLw03mzOCmv9EkJj69atABeT2XXIeuxIJpNpz4zsDVw7lks1TF3DcX0WH5lDTFXwAVnfAZhLDtsnXEaGDHpTUfrTEXRNYOoay6Xa1V98HeF7Ht/+wucZf/lFoJ41qtUnTOlhGCaeEKGR2WP5MRYrixwZOIIrXaY75jhXOwnAgxMPUqwWydfyFJwCA4kBrstchyc9MtHwcrEaJPaxY8eauUKPPfYYhw8fDiWfbS1Uiw6aIZCeZH68QDFrN7sX7bIbkMUKInGT/u3hbar7LJPhWISxag0fVY9vvbgw0dhSD8cibO1IhFa46E1F2JqOMpGr4qPQGwoBoHEU9KVCCRjOxOlNhdd1m0gkkG6NwaP/J53TD6O0wK6VusVR/5m/JvqBD4Q2vitBaBaG2cWxlTGmKwsIYKIwASh0odUbCGTwGS0UMnstqLrjRenRxxCdnWiWhXSclpLZl6Lm+Zi6IB2LsFyqUap56JrAMjR0TeD6klxCY0d/lHRXDKeuKk3HzHWzDroaLiWzHc9H1jfNR+66A52JQKGpGcFn34HE1S10Xy+uZnHkSY9lexnbC8irRM4kVrPIV3IA/NnzD5COdwRFdqEHc8SjCu1vLMy++GXz23pbHOm6zt133w3QzBK1GpEMq0jstG3zY52dMDGOuWULVggWrU0Ija6ut8MKVO0pVK0ft7gHM6JTq7h4btCM1eqCS2ZwC12bBpk6fowhxjlsJHiuewhPsWaWcE0GFk339Hdya2d4RTa/7OKWXV6s2pxXEo2gQcwHpqUPSnBrT2zdSezVaB5We/pBK0N2FIRer6Q5oBoNCsFC29PTBUJHakExQFCmWh6nmriRdFcGXb983/pmH1bf1v827tp2Fw+MPYDjOyCg5tcuIrAbMHULS7MQQuBKl4fGH+ID2z/ArZtufZNHdWXkPZ+i59PRyBuVEuOlF/FmZxCahqbrKN8nOnMe+aLO5P4DFKW6QHobOlujVmgkge9LHv7iKUafX8Cvq1r8Vwoc+rF0KETAa8JF84G65HP4WKgskLKCxpqdHTvZvXk3Za/KYm2RAQZe5bvfOK60xvrFIn52GRGLo4BstULZdalWAvvlx58/RjSeCFSEQpBMpnhlfI6vPvS9NX9OKy0EvZrAX/xXeLkzwCjSryJVFJl/F+nYL7Npb4a+4XQoTcMAT+VK3LuQQ4iAxFZKNd3alPSRnktU17A1I9TmK00Ibt7ciZrINrOwG3OSDwx3xLipMw5lF2JGS9atS5suzp07x9LS0kVN7MVikUw6wc6V77MSDfZPsViMhYWF5nMaRHelUiGZTJJIJC56vBWNF0f/X/beO0zS66rz/9z7hsqhq3OaIGmkUc6yLCeEZckJ8AKLFwxrE8ySdrG9Boyxzdq7Zg1eg7wsYJacf2DAmLXkgOUk2ZJt5Rwmdk/n7urK9cZ7f3+8VdVdnWYkTXfNhu/zzDMzlfr2W/c995zzPed7Fh/m7oWnCbQi1HrdyaWJ6GxNoBVaab5w8ou8/vzv3tNzaytYUpIzYcUPmHV8fK0BzbwXMBazOC8ROyfJbIB9l/YjinDomt6R2ACpfIHc8Cil+TkgUjtQYdga2Ra9pl10tSbrL8gNj5LK9ybndrx8nFPVblXGu6p3AfDA/AMc7D/Y05GDW0HVa9G8UdNEpNMIz4/UJeJxrInJPc8FbAfDkrREZEBpPDfEawZYtoEK1J7Li58plAqx4gliybWCMOFJAm/vyeCNkHGT5OUD3TOw09Eoia8+u0RyJMdn50uIb5/kA9fvx6RFdqf3vgN+eXm5Q2LX63UkisFn/hKA4KsfhWv++oxGOO0FgqBKufYsH33wE3xl7hFu3X8Lb4y/CYDHlh7jcnX5no/B2LTGlRVU0wEpo45s6JDYwjQj1TspkUoxsLRAMD5OqTUr+1w7u2KmgWoVNLV9L6V7WOBg2NC3H+GUGU81YXqB0spqi3hdN5d8HYndSxWR9eiQ2I8sAoLGw4sslo/zjSuewTB7OzIXtieze0ViQ4+J7K997Wt89KMf5YEHHmBubo5PfepTvOlNb9rxPV/5yld417vexRNPPMHk5CTve9/7eNvb3tb1mt/5nd/hox/9KPPz81x55ZX89m//NjfccMPu/SIb0J+OUW/6nPjGEsl5Fy1Ar6tkM03JeEWTXRHsuyZL3DIIlcYP1a7JMJwJTj31OM/cdzedBErggN+MyAAhQIeYVhwvCHnmvru56KZXsP/yq/ZsfUWniClNfOXz2PJjFJ0iR2PHAfj2/Le5fDiSD19qLFHzagQq4JrhaziYO7hna1yP9SS2wdrNduTIEe644w7e8IY3nBNkdiJjowJNddWlWfExLdnZAm7Dp7rcRJoSzwk58egyiYzdk65sKQQ35KIO1+NNF0k0L6RdIKAQaCE4ELe5oYckdnut159XQLdmZAPr5hxpQh2R7vv7kj3tFAAY6MsRn/4zcqe+SCgttDCRQiLNlrN85Itwx3+EN3zsnHFUAZRWPLT4IFPV6Yi+0GszRxVR137riUgOV3eT2b2A9n2W/+f/BDbqsI4AAQAASURBVCJZMdkiCaVto6BnZHbbEZUCDClQShNqjReqiMEC3ECxqhWTSQvL6L3c2Xoy+5n77ubAVdfCnV/CKByAuIykxA07IroKB2Hg0K6sYyeJo7JbImgsMG6lMOqQmo/k2QeDJkNXvIZs3wiWFSmHdIorwki2zz6YQ6b3RrJ9PTaS2UEreS1adjbreXzXiZPEbz6P9E0vxT7vPIx0jxNCLTLbKI1TqyWwYpGfFYYKp+5hWpJUfm/3bCyZ5MpbXgcaVudm+LHSNLFEkm+k+/HXOfkqDGl4PoEQfM9AtiedbR2ECq/o8FCpwakg6JDYPmtFYtN+gDlT4vrz+vf+3BIS9r00+nfxWCQ1rtvkwLpqMWmghRUlBlqJgoSzQKMyj5NIkkrt/n61pMWv3PgrANx5/E4saREzYjih03lN+7xKmlHRihd6aDS3Hbhtz7sI3BYpLYQApUg++gjW3Cy6RWJDpCQAEExNYfsBuSuvQrRIrnIQUg7CTd1xu42q4/PcfI3HPn2M6rMVtATVcrZOPbXCF//iaW75kcPnDJldLxUpL84jpIFAryMH1nzZ6HoLyovz5EdGe9Ldth5Fp0jcjLovklaSZtCk5teYrc3uydimbc/YwMefm0PVG2hgrFJhyTYpKUXfFd9JIj+EadsIIGPbTJ53/rZ+1V6dr14zYHW+weyREtNPrlI58QbgU6AFtVMvofzcDyLOd0jnHIb2965w+MZ8mu8eyvOphRKuUphKodtdmSKyCW6oUELzpuG+nhZfmUmTa4eysFhhuul15MT3pWJcmYhBIyDQAbIZ7LqSyFZFF47jUG/UsSyrc5/7rktf/Vmk36TkRQbrwx/7bfKFASzbwpCnzw3sReHFBckkeSvOrNMmfjae+dG5G2pNIZbhisErdm0tzwdtEttRiqQhsaXEUZoVP6DfMvf8nDpTHP7OcXQ4RlCweWy2sudqIm0IKRm/6BKAlry4jBTvwrBLYE5KEaUH0eRHxnpKCLRzhO17x5BGZyTGWHqM/dn9PR85uBEylUYvLEZkljQQtgDTxBwYOGdIbIBYwsSNG7h1H7cZEPoKw5K4jYDKcnPPRzedKUzbRmvV5WNprTDtc0MOXcbNjky4Hyo+8f+dAMCUglTMxHUD7jy5gnZD3ntolPhAAnNwd0Zk7KQsd/ToUaanp2k0GmQSJpdP/Skzy1Ej2eIjX2TqT38SvvN9Z5Qj3M0CrCCoMjt/B7c//km+tnQMIeCzJz7HjDMLwMfu/xjHM8f3XPFqI8zBQez2rGzoIrFFiwyWYaSG5o2OkRweJlBR7vhcQzZhUW76VJ0AKaJxE+mYSTbRQ/tl2JAaRKRgPDcBrTEZipbimIrirvzwSM/OrEajwdNPP935f92pU/zSMczjPo72OFWeYyw7THzWou+EYOVaH4Xi3tq9jNvj3LB/73jN9dhIZodhJCfeCxIbekxk1+t1rrzySn7sx36M7/3e7z3t648fP84b3vAGfuqnfoq/+qu/4q677uInfuInGB0d5bbbbgPgb//2b3nXu97FJz7xCV7ykpdw++23c9ttt/HMM88wNDS0278SAAcLCVJPVGHeRa3z+VXL+4vZBrGYgVj0WP7KPMvX9uFrvWdz3LbDxMWXcdE11zB1xx2tBYeg/LXONsMm8D3QmotufCUTF1+2p+srxAsEKmC+Pk/RKdIf7+e4iIhsKSRJO8klqUs4WTlJzIhxQf6CnlWtrCexZRgitYZWNbYIQx595BG01rzxjW/cEzJ7JwfF8wPqQZGZp1aplz3Kiw3K9UiOvVQrslicw7ANOF6nroqoUJPpi7P/8n7seLcJ2e0K8Y1kNkQzW0NAC5iQmusziZ4Sw21I0+CGC/sRR4ucLEfBlCHokNiTcZurs0mk0p3u5z1H6BO/61eIzX0ZZcWjOX1CtORCIgIrxEA9+neoMMD87o+fE2T2ehJba0Wo1abnfcBaVz7Qrho7VT3FZGaSvaZite8z/6EPUbv7nmg9G4LUXpLZ6x3Ruhvgq0gqTkKLYFEoCV6gdnXuzfNFm8y+6KZXsNj04Nc+CrE0XPzdEZFdX4o6sQcOQXx3k8JbSRw1appUQpG2MtQyNTwN/c0sKTT5/snoRQIkEkNKhIq6Nc3+BLED2U1zk/ZK4mg9mT0zFQVaUkomheA1sRjKspCxGIkrep+0VIHqJFb8Rh5pBhgomjUfpaL5YxAVZSXS1p4mWzL9A1z/3d9HcXaaRqXCRzIZftO1+eeVGmFrXV4Q+QcvbxR5m15COSOQ7I2cbNAIeLjSIrFFVHzlabrmzmrg+EqUpO85mb1yNPq/VqwnsTFstFJoNKL9/afG8OMFLD/Ys6VuJLNtI0qetcns9v8FEYntKY/XH3x9T5IsMdmaJ9sise3ZGZSQXdJhWoUIw0ABqblZfCFoXnFllOQWAlftTpZlO/+15vh8+alFlr6xgJhrEmqQpqBSjixlqbrC/V95jNXVOV7ypvN3JLP3SlZ6fadbNzmwRmILKdFK97S7bT3Wz8g+VjpGvVrvyNy9bPxlexZjbXXG6oFRVLVCsLRMGEvR19/PbLVCuX8EU0MgJflYjGGtiMVTGIX+bT9/t89Xrxlw9MFFystNKstNSosN3Eb0vVeO/CCp9C0QRjFVebnJ6nyjZypYlhR85MLIR/nUwipBEK7NR9bgAb6U3GqGvS2+AjAkdn+c6xMmYqbEqZrLvkycK5MxzERUiC+0Rjkhqhl0FQiebWxVdKFUQKVcwXW9ltpFwGDpYWQwDsCoHzB8eRN13nci7QSxmE02l91yDEYbe1V48fDig6x6TQwkIaprlBesFWNJBCtOhUeXHu15R7avFNOOR62Vc6mHCk8JLCFQml07p54vqo7P0aU6KzWXxflIYvbeY0XMoTzWvIM/U+LZQorXXT5ybpDZQiKl7tgBKUCg0VqSH+kdIdBGO0cYqrDTkd2ekd0uFDakcU6MHGzDHB4mWFoiLJdb577CyGQwh4d7vbQuSFOSHUhQWmzQrPlYMQM73rvRTWeKeDKNm6jhNuoIIdFaEUskiSd7342/Hn6oeP8/Pc5Xn41GoAkBCIjFTPBDPrtYxsjH+C+vvBwZP/vUzU7KchAVY62WSqBDBp1jfDJ0KTdiQJWPPBAj/9QX4S8eh8GLophsB+xmAVa59iy3P/5J7l4+jmVY2NLEDRy+NvN1AJQOuePYZ3CcGX7hmp8il74Q0+zBGK8Ns7IRAmEYHRU+oRRaK7yxcZpXXIkWAqVVT+djbwfLkEwWklSaPm7QW7XGrdA5x0Kf0uJCuzeHfDbG+GACoQNg723X008/zbXXvrCC9UN3HOoZkQ3dZPbq6ip9fb0hseEFENlf+9rXuOmmmzA3SEoHQcA3vvENXvnKV57xZ73uda/jda973Rm//hOf+AQHDx7kYx/7GAAXX3wx99xzD7/1W7/VIbJ/8zd/k7e//e386I/+aOc9d9xxB3/8x3/Me97znjP+WS8Glek61qxDIEAokK1EYEutGTdQGKbEDDXGvMOYNtl3SX/PKi/bMEyTV984xnPfiOTNQx1J8kZdL4ogCFBKcPhlr+rJjOyDuYM8vvw4d03dRbFZpOJWuiSCm36T/ng/+VgeU5pcM3zNriZYtkuuhWHIl770JZ5++mkMQAQBwjCpNiIHoVytYhWLfPWrX2VpaYlXv/rVO5LZLzbBdjoHBaLZBo2KR63o4HuKRjOaK/fFZ+8kMZ1DSkHyGZtY0kKjCVxFImMT29CVvRcV4lIIrkvFcOo1ZltEpQb6ayUmGmXKQZO+kbE9nYm9JUIFTsg1Q9EePFlponW01sm4zdWFJDjBridWdsTJr8MTn0IIiWGtVXlqNGFLqjlEorSCJ/6J2aFXUB24hlQqxcDAQE/mZm8ksdUGEnv963wiGV9Ba34HMJmZYCAxSLW+sJfLpnH//VQ++7mOo6p8v1Ml3Ia0bZTvU/ns58i+/vWkXvrSPVnbekdUKY0bKBpegBe0EpdEhVheoKi7wTlDZEN0bu2//CpW7v1a9MCRLwFPgp2E/P49IbG3Q8yMo51IKcBVLlOpefaHo4x5A6BBCR1JdguFVgKhoZkNyU0mNpHYZws7FTUBhPU6/tQUQanEoWyWb7Qk2DK1GpdVqixojVevk15Zod4iubfCXhBDKlBUlpt4ToiQ4DshgR9iJ0ykKTBbhFAyYxN4qifJllgyyegFF3X+/1GlMZ6d5m+eie6tQEpeb/j8h7SgOjdPcXa66/V7icWqwynXRxApWnitGaMb0Saz9w2kGM3tTnfAjthEZrekLYWMKrHbi1QKUJTNAVaMSaxAYVp76xdsJLOlkBgi8lva/quvfIQSPSOxAXKmQdk0cBcWyM3NRsKxhuw6rwgCdFv6TinsuVn88Qn8gQGU1ruSZNnJf600fYpzdaQbEonHEtnQRst/ffpOElM55AOC5D/YFEZTm8Y1tLFXstI7dbpJKTuy7r3ubluPg7mD2DK6r+p+naXmEv3xfmpereedbcKyMAr9KMdFBwHSshhBkPYDklrjJBNkMhl0rYZyXHqpgbU636C83CQ/ksQwJcunqqigVdBUPQ+RtKLqISGQpqBZ7a38qSUFH5zoZ3Vuhru0hd/i/7wgQCnNjeUFfvnQWG9J7DYMiZmxueGiQQ5WXQqhhkbQNdRVCNDB3owY2Vh0kR0IcByHwHPJnPoyMmUAWdbGYUBozVAefRWO62Onc6dVDtmLwsbrRm7g5YNfjLrbNvC/bRLbFBJDSG478JpzYh5pOQjxtcaWglCDJQS+1ig0tjDOCTKg6vh89rF5pop1LENy/NgyANPFBi+7MMFyzcXxQ759YoWJQoKbzu+NKkf3eTUDtGPWSEpca8gP9DF++PKen1UHcwc5UjrCicqJzozs0VR0D7bzBKEKez5ycD1kMkns8GH8qZOE1RpmJo05No52mvilEjIeQ2ay50R3tgqjPGHghYBGhYrAN3syuulMIU2T3NAITqNG4HmYtk08me59fnAd2iT2Pz8yi9myTTUnQDo+2bhFzDYgENxxZAnjc0/zn9902VknCXdSloOoGGtpYYHCyv1YOlI59f2A0Ssr5PsHsCwz4gry+2Dsmm3J7N0swPKVz0cf/ARfWzrWIbG11vh6rQBL6QATzRdn7sfzfo13XPavGRt5Q0/IbB9oXH4lOlBYM6fQSiFaI5xQEYk9f8llSKVRWpExjXNiPrYfKop1j9WGBxr6UjaFlH1O5QU3QuiA8byCqqLcFORSkvEBG+GWwSnv2ujBnXD48GEeeOABdKg49fknMI67UXWYgmOLU/z8Z/4LH3/j+zg4OEEoFCYm1cmAhy84zpWXXrnn692INpld6CuQzqR7QmLDCyCyb775Zubm5jZ1N5fLZW6++eauGUBnG/feey+33HJL12O33XYb73jHOwDwPI8HHniAX/7lX+48L6Xklltu4d577921dW3E2KE8gxf3sfDoylqTCBBrnfNBqPC8EKE1F10/zCtvOXhuyN45FYyZb3HDSBQeKS0JtMAUgkALVBBy+BWv3nUSe7tEe92rszK3gr/iU6vXEJYgKLe6RsshS7NLOLZD2S1zxeAVmGWTqebWifbdJIcbzQZzc/MASHSUTBWCZiP6ne5uz20D7vzqffz1P32O5A4dWC82wXY6B6UNHSrmjpZwGgGh7zJ45W2k+8YQ0kJKSW4ojtWqBPSdgHjSJrlOrnWvKsQBvGad88pLeHaKlXiaAafOgfISWho0K2US2SzJbH5X17AjQkVQdFBuiBBwZTKGcgNOOT6TtslVCRsaAQpQbojsVXHo/pfBpf8KHvskBC6Y0fepQoUIor1thk0EMY4lr+LJRYucP0cYhiwtLXHxxRfvOZm93FziVPVUi5jeOXhSWqGFgdagUOzLTPZsRnbyuuvIvu616L/9OwB0vYYyTWQ6vUYWeB5aa3Kvey3J667b0/VZhuw4ohooNyUrdRfbkAgEmYSJFyiCc6SboQOnArMPwf1/HP3/2c9BVUJyAPrPg8HDUYd2D8jsrJ2hYqUouSVWnVUaQZMnkkcAzZg32PEPhG51NGYVC4USMkhSMM++DT1dUZMOA4LlZbTjdDpdS9UKAF++70EeSKbRWoEGqx5gfuuRbX/WXhBDbjPAc0LspIkQYNoG1RUHpx4gRCR7aMRM7ISJ74XnRLKl3el27OuKfwJukD7vMBxMYSBNk0al0rO1DWXiTCRsppvetiR2Gxo4uVJnOBvvjQKKkITj16N9H6MyjbYTyKAJqiV9qRUCRUkWmDMmCRtNkggsa++LxtaT2Z8/8Xm++/zvBuAfpv4BAE95vHb0ev79Jd+FUA70gMi2pGRf3KY8Nkq4MI41cyqaL24YHRIbiP5WCiEltZFRSrk8YRDuWpJlJ/91Zq6KPdOIqCBBpGmnIQxcCpffSio/imHGWiozMHQoTyK7Odmyl74rbCQHZhHoTgyoVUi+L8P4BRf0nBhoI22nGU5FHWJVr4rpm6yKVQIVMFObOTckWuMxQqXQrb2aqNfRUpLKZBAtpQEZ722irVn1kKZABZrQV5iWgWr5U74bRsUgSYt0XzTu6VzocKvOn+KHTj1FLTnM11r7MRCCK48/yfctHqGcugH3/Au7ZpD2ElIIhrNxVM0j0EE0EqVV6KQ1iB7J3xqGSSoRh+NfAK/aerT73DT8GrnZr+L130iwh8ohOyGXvpB3XPavUY/9HV9afK7zeDsKMFuFWa8/+Fref9MHez57FKKO64SUYMKqF9JszfeOS0m/ZZ4TZMDRpTpHFqskbJO6G2C07q1y0+OxmTLLNRdTSlZqLv/yxAKXj+d61ujSOa/cKuVimXw2BkJSrjrksnHG942cE2dV2k7zmv2v4Xj5eEcxZL45z0f5KLO1WWKVGIEKmMhM9Gzk4EZo3ydcWQYNRjaLDny8I0cQsRjCNAmVQqbLPZ+XrQJFca6B2wjQGsIgUhIM3AC0gWH1/vvfDtI0e5sPPA2+dbzIHY/NIYhIwjZcX1GhRWabkiBU3PHYHN915Rgvu2B3Clu2Ur0BQAXkl76FyMZgnZ7hwEABhAGmHeW6dQmaJ6JC423ybLtRgOUrnw/f92Humn0IS8oOiV0PHDy1xkt5KipysqTF3cvH4fFP8ivpQ/Tn97YAy1eKKcejGoQYF19KXinSc7MdtQt7/z5SV12NqTSuioqEz4X52H6oOLFcY7bs4LeKAheqDmO5BAcGUudMF/YmOGWEV2F8MEY+sEklDITfjFww3znt23cDyWSSa665BufIKgNeAzUaIrTECAUijHzDC/r3c/nQhfgyAAEilAxOTnLJ6CU9WfNGCCHJZHs3AgleAJG9sYOsjZWVFVKp3ZXFnp+fZ3iD3Mrw8DCVSoVms8nq6iphGG75mvU69Bvhui6u63b+X2klEp+cLZOuriXtcwmLyUISxw85sriZaL1sPAfAsaUaqzpkQa4dSFkVdQXKRJaiYTITBvRfkCXx0gGmVpscHEgRKs1Tc5uTmBeNZLAMycmVOlWnO7gZzsYZzMQoN3ymV7uT03FLcsFQVGX0+Ex50+deMJQmbhmcWm1QWi3B8a/CcsAxNYyRLrM/38DRA6yaWUBy4JLDDL/uLTy33ODwSLRxn56vEGwY2nBgIEU6ZjJfdliuuV3P5ZMWE33bX8OxRMg7fvG9zDYNtOjenm5jnnJjHivWR400vvZwqg3s4fM5/jXB0n3ThEAsvY/FxDxfk59YuxZeCYHGNdMoaZFNxXn3z/8suVyO0Vyc/nSMUsPj1Gqz62cmbIPzB9ObruHc/BIzToyxq19BpjBIw9e0c+Q5rTCWlmhUVrGUj1bgyxjJ0Cd17SqxTAHTMrCUBwjM/CC5wcGuQz9lRTNSiivLzDz5TR44vsRoMwq++lI24/kETS/k6FL3NRQCLh2L9uFzC1XcQDG31KRp9xEkB0j0DWObkqYX0vS7i05sQzJ8MMfybI0mmtzQJFJA6GukJcgNpTFMg5oXEkofcjHCZORYp+xobQvS5pmlJhVj7VolbYPzBtMopXlyi/194XAG25RMrTR4prXWsqsJ6x4JyyBhG3iBouau7f161aERwMHGIv12BSvQNLFBC9xAoKsOdkphGpK6G+BuqML3Wl0QDT/cdG8YUnDxaLS/n12odrpS29jfnyQTt1iu+11rBbBNSTpm4td9ilUXGTOig1LCftNk3BYMZuM0lMLXGtwQo+lj1A1SMYOYaeD4IQ1v7bupudG+BXbHRrwhUrh4/OH7IIiBaaGcKidkAmE2EDrgmHEJn7S/j4EghRIZtFSszq4wOLhMfnCEE8v1rs81DfGCbUSj1YUSKk2xvrkjpZDoJ27GqTR90BucJukiZIhWJigbKSRKSRSasfQwVw9dCVpQbHjUXE3T7uvs14tHsxhScHy5Tt3tvoYvxEa00bGzVZ/Vt/0cc998ArsSMp0fZVi5FKpVmvl+5swMWvhkXvNy/J/4SapFhwuHo/vrqblKR4q4jYMDKVIxk7lyk5Va93Xayka07UDF1eRarys3/U2fG7cMcgmLcsPDlBKlo5GCXqAwRGSXQqUpN/1Nv2tfyx64VnqTHRjLJyikbIp1j9lS9zV8Pjai4qz7uW6dofkvM7T4depLU9jD53O0bmPHroBGlVi6wKHicVh+jifkBV2z3QDOH0yTsA1mSk1WN+y1/rTNaC5B3Q04vmF/r7cRx4vOJjuQiZtYhsQPJCk5TE1BENQROgPK4YnkUZSGrD+EiMT5aMZdgj4DIQVu4FBp+l1FAzVXE7S64lZqLnPlbuc7FTNP60fUajXmGoLMFa8nke3rPBc3IWYI3OIqlaVVZDLZSf5my0VGrpomn+qjaadBSEQ6jTU8DIZJ2oquR9PXeC1T2aysMvP0vRyfX6VQKFBzg+dtI8yWDXSCtevaRsyUJAxJteRSrLtYYYBpRdfOtwUpaRD4Ct8SKEtQ9kN8NyCIG9iBis5AP6TphV12QKfq7O9P4YeKZ+arbMTZsBEPHl2gsjTPtUtL3Dl8PlecPEUxb5PP55mvKzzPprnOjgxlYgxl41Qdn5Mr3fbbNiUXDkc+3pnaiLYdaAaRHQhCRaVzZigOZhMsOD7NjTfLFji+3MALFJeM5Tpktm1I0nETpbvtaxuXjGaRUnBsqdZ1zsFmGzG3zh8QTZ9swkJrzWrDR6mAaqWGJ8/DzgxixTMMNJ4jqC7htUKbisyzZIxiI4jbklALlioNEuuayI113VnPLXevFc6OjWj7EW+a/PdclLqFQ7n9DCbmOfnYwzyTmuNQ4jJekbyGbz1zH/HYcQ6MvZKDQ0OnjTWOLtVobriGE30J8kn7tDZio80CyCcsBmI21SuupmrY+HPzUZeFlMSlIKZ8fGnQMOOIZJLg8GVYoSBjCsbjNpaUXWd2+95q+1+nVhuUGt3nxkA6xkguvq2NaNNjYWqAMNktC13Yl0epCmE9wEN32BUDKBQmsBEoNI4AI2ZgDvURthJBhVRkS8tNn2CDLzDZlySXtFiquixUuq9hJm5uayPmlproFjlVdXz8DTYtaRvELQM3CKm7IYmJC6i5PtVSGak1tnLpK2RI5NKslsuQWPNvcgkLQwoa/ub76mzbiDbW+xEnSiH28PlU3T6sWh9NISnGT7LqrHZijfWYLCTJJSwWqw6LlW4fLxu32NefxAsUzy5strMbbcR6OxD3Q2KWgeuH1NftfW0k0HaC+OwsyvMohxJCjSg3MAKQqRT9qeg61JwAL+xeb9CKCaru5pggZkoOta7hE7PlF+xHnHQ8plfq+KcqOHUPT4eIlv1ppOMs2ZBKCcKKQ24gwYV90R5drDgsVruv4ZnmI16MjXhqrsL0yRVOlXxunHmW50ixIA0OLU7xitISS2MHKT01xXL8CUYvuPDs5SM22Ai3Fv0/2CImkALyyeg6lRoene2tFSGaZCPANgSNQOEZAkMraH1GzJSkYiaBUlSawaaYoH0NjyxWcTYUwW1nI+aWmrhmlAtTSlNq+8kqgONfA0+RJyLcqzqBv0EjIOk5DCx9g2rq1k2/qylFZ+5k2d1sB9rXcLrY2OSfvxgbMZK7hXj8HgiW0Kofexi0moBmHG01+c59+3nHVe/imbnuz90qH9F1DZ+HjZjbkBvoS0ZjsDb6ydHPjaxwXhgIFM1A4WoYiUkGhcSSsuNHbIRo3dyzZY/Khn06lI0xlIlTbvpMF7t/1+drI+49sswjp0oIIG6bVJZrCCvOVCXEW6xFRcUiuoZVN+DoUp1kKxeyHu18xIuxEa6V2eQLbMpHKJ9ENgm6QTZpkDF8csk4XjzDahjr3FMQ+RFSiq4zsH1vFRs+++Cs24i2H4GOI7x99Mt94EHJaWAN7OfakWsZ6R+hUk0zbI1xYikEop/xYnKW29mIuaUmYauoox1rrIdlCDJxi6BSplhpIpIZkBJdrxPUqvS1RnvVhYVfamDFVpG5PNDtR2y0A2eaj1hvI9r3ltOKCfxQbcoj+U0f3wmwYpJqoAhDFTVhGIKEJehvzZxveMEmW9m2s22/c73Ner42Yr0dMJyAdNzcMR+xnY1I2caWfgTQ6YrWsCl+gR3yEbwwG5GyDW48WOCupxcjxdbMIMKKoQHHVyjtETMNNHDrJcOkbKPruz3TnOVONsIN1JYxQSFlg1ZUnr2HwNHAWudyCoeY8HGUpOHbaGEiCGFlCSs1R2ZgHKV1173atgPteP9s8RqPLT/GZ558BBmLYRsKz0nS8Gz8NomtPIx0CQDXF6BGAPjSVJOa8zf8hxsOctlYVMR6NnmN+aW1XMD6fFvJD1j0fAYSFpZl0rj0CmpYJFeWsUaGURddTBhoBuJWx4+obihuyyetyI9w/E124AXxGq17q73Gmhts2kuOF7BSj9QkY5bRup4hc+UmtiHZPxD5PqsNb9MZ2M6LNYPNduCF8hplV5No53S24TXScZPQa1JWKQg9sOJ4QTTns094CCu+rY0ACF4kr3E6G6FtODmuiR3XxFAcwAA01tBBpkwTmxCUQANjF8V4yQ3XUm4YnFjqtkvPNx+xHmfCa2xnZ9ux9JbXcAteo20Dpksu+/ZtzWvUqmfe0HHGRHZ7hrUQgre97W3EYmsVOWEY8uijj3LTTTed8Q8+l/Bf/+t/5YMf/OCmx3/g9+9DxtaqjN901Ri3/5urmS87vPG379n0+hMfeQNhqPip3/8WR5vuenvP6+sWGSB5+BV8aXIEcGDOgT9c5Kbz+/nrt99Iwwu2/NwH3ncL/ekY//kzT/LFpxa7nnvfGy7mJ15xHvccWeZn//rBrucuHctyx394BQDf+7vf2BS4f+Gdr+TC4Qy/fdcR/vb+aSKZq++CxHeRueaT4H8Le8DirzJvjt6wCvzuvYxk49z33lcD8LY//jbzGxI9f/P2G3np+f382b0n+L2vHO167s3XTfLr338FU8XGpt/VNiRf/HeXUqw0mD/0fdTC7u15Xq7KiFim4Q9QrEcXNwaMvgRylsuhQh1JjG8tpNjYb/aa/SaWFHxrPmC5Gd1ob/nzxwD40Pdcyr996QG+/Mwi7/zb7g6zq/fl+dTPvAxg83czcSsTGZPc4ChPHF3eEBz2M96XJR6sUgstZmmRBjmoAhYB55slhBQ86/Xx7FwkgdlZ78XD5DIxnlwJOTFxK2//5BHgCAA/cuN+/vObLuPoUm3TmtIxk8c/GMns/8xfPchz7UN14laOzga8IuUw0Zfk2HKNR091G8HJvgQ37uujXHF5oLLOCEoghCFHYRiaR1eqlPwQVtcOmxsOFBgQUElNdK0V4CUHC/ztv3spvlJb7u97f/k7Gc0l+MjnnuLOx+Y7a2V2nismclw6lmOx6nD3c8td70vKNJdRJ+vUeZBxFDLyPB1gxuO2fEAhZfPUXGXtOrRwICtJA8dWHH7mH7vXVEjZPPj+1wDw9j+/f1PQ/2c/dgOvunCQf36iyNF1a4XoMLjp/AHqjs+Xljcn6N6USiICxf3VBsX2/dh0YBZuPK+fgwMppooNHji52vW+9MA1ALtnI97wMb733s/hrSeGs2DmfwZNif9ZfzmfrIzAKQVEv+sbDlpcfkGdabfMD/7BfV2f+2JsxBsuju6VRgB3PzHf9ZwUcMG+RRp+g7BxMTrsLp4yks8h7CLKG0A5+7ue82QcKSROEPL59ueuu7ce+0+3kolbfODTj2/aay/YRgBfefd3cGAgxX/73NN8+tE5uObHGb0Gbgd+6Okv8MNPf4EnZI4PXP/j0RsC4PfuY39/kq/+ws0AvOUPv7kpqfUPP30T1+7v4w/vPs4f3XO867ltbcTErZycC5iciP57z5HlqCBgHV5xaIDJQpKZUrOLaKoCmZhJKmbirr+G6/AD10UzFWcGrt9kBz7yvZfzb27YxxeemOc9//hY1/uet41Yh1+4oM7Pjud5wrqE0be9m1/QwOPRc4dmGvzLK49BfYk3//VyVzEMwGf+/cu5bDzHJ75ylL+472TXcz/+8oO8/42X8PR8le/7vW90PbfeRrz3zpPMbLAD33HhIKP5BEcWqzw+WyGiVqKLbtgrmMlj+BjczTqnu2nACbjiPE3MjHPf0RVWNnznE8moUvqOx+b4wKef6HruFYcG+Isff8mONgJgvv9qnqtloLZ2La6ezHN4MMvxcsi37XS0B1vIZfLccsEhjFSKvz9SXzulFgACXn/ZCLmkzdPHVzi21D43MjBxK//42ArXXnI+j516/jbi498TdUycqCiOTXd/5wf7kxxOJSjXXB6qN2HdcSWA77pwCN8JuWe2RDVYd41Xa7zMHGBfIcnJ5ToPTZdaFzayA7dcXOEP33o9laa/5TV8sTbCbTT43j+4v/XoIUbf9nH+IoChh+9jPGvyT/Z1fO2fTgGnOu/9+Vcf4p2vuZAHp0q89Y+/1fW5L9hGTNyKX1KMjELFCba8l88UMyWHmdLadzfZl+DlhwZxQzi6wXcBeOa/vJaYNPjlf3yMbx7vnlO4pY1o3VtD1SKvvngYpdlivRletS+OCgMeVhcwTUsxSgEBjNouo5aioUwePFYB1oKkbMLi5VFOg3//qWM0/CNdn3w2bMRmP+JRPvLaaV7f38+nr3odjyz9ED/7T2vPvv7SJ/jdHxnaMdYAePcnH+GhqVLXc7/15iv5V1dP7GgjnEBt8l0A/tXV48SlwUOnyszofhhZI44Pr5zgYGWO5XiOh4db0vdHIj+lL2lx4LIoVvqXJ+fpimUnbmW24nEI1sUaa/jp7zifX3rt4W1txN/98CEA7p8PcGa6v/dXXjhAYjTJ8RMlljbo3vaFgslA4Ak4YkfjkZ5s+UZSwJuvj+Z73Xt0mdVG0OUL/M4PXcMbrhjl0w/P8F/ueKrrc2+5eGhHG3FxSzb+/hOrm2zatfv7uHA4w2zJ4b5j7b6Ufkj2kwgbnN84AQi+sNwHyy5tXwvgjVeMkolbPLsaMrvhvtoVG8GaH/HY7CL/6+lLGX3bx1muwPLjIKXL4Wv/kJpX6441WviDf3sdr7lkmE/ef4qPfv6Zrudef/kIv/uWa1mpu1tewy1tRGu/3mA3OX8ozanVJt860W0/BmMxborFIJ3hHn9dZ70PlOB7DkhM4OHpVaY3JPcu7JPYwCOzdd772Se7njs0lOZf3vUqAN78+/e9YD/irf/4UNdzSQQ/nI2SQF/M5ym7ZVgk+gP82aEcr8rH+atvTvHxu57reu+Z5CPghduIbj9iovO8jKV42dMP8qXJ1/Hc6lj04GejRZ/dfMQa3nJ1pMRQdjXfPNltAxKWwZuujmZNf+WZpU2JzJv39zEYszi+UuephTrMrj133mCKlxzsp77+DGzdW7ZxjGc/HI2++/n/72GemO1OrO1kIzL9V3MI8EK14ayKumi+X34FScj96kLm6S7OuVY8w4XhKVaX5vnmand805+yufXS6LD6+my3zYK1WONjX3iGf3p4tuu9L8ZGvP7lT3J/+VuEq68ldF/C6NsgdKF6Ag6MPsqP7tM8O3ucf/PH3bHntvmIFp63jVh3Zv3AdZMYAr59oriJnLl2fx+ZjMXUcp35+TXnsAQUM7Ed/Ai4eTLKO33ivjm+crR7Tb9w20X87M0X8K3jRd7+5/d3PfdibEQhZSFW6liDB5hSBaZOrMX/MVNy7f4CKzWXn//Mk9vmI16MjTg1eAPPbfAFts5H9AF9jDTq3JyZIabKfGb54KazquNHTJWYWV+0PHEr//JsiasOc9ZtxE5+xNAPfJBDfYe4ZvIabvy1u5ivdH/vLyZnuZONmExETVVdsUYL4/kEr7xwELfh8mU3A50tnIJ0itvKR4gl4zws+1mSaTjlwKlo3V1+xAY7cKb5iE02YuJWRFUxPArLVZevtGY1t5G0DK5PJ9FK8JTnRo0YEMWKZY8+L00sZvL0XJVnNhSnHRpKc92BAjV/c0zwgmxEyw5MNlejWGOHfMR2NuKGA4Vt/YihTIzrBkAjN8UvcJp8xIuwEe3sW+7G78Pqn+w87gUaPwj419dN8P3XTvCm330+scaZ2Yilur9lTPCD10/A1L3cV+lnhfO63nujeIKDYp4pPcwDqnsc1siyyc0DEUm16buZuJVaK7d0dnmNn6Bw6HcJzBLlhe+gsXp113sz13wSuAfhTbJ84u2dx79wDO5//Js8+L7oXj6bvIYlBe0rE8Ua3fk2e1+O/pzBStnlpDkEw0NRHvupxY6N2OxHRPj+ayeQhmjFGt124EXxGi0X6tFTpU17aX8hAQj8UDNX7vahl6ou6bhJNmHx5VZRxnq85uJhBjIxjpfVnvIaLz80iCvifL44suka/sDYMkY8x7ef3dpGnDVeYx12shEHkPwFkd838kO/zkdiSVjHaP2l7ucSO8VvfP7Js5iPODNeo+FtnSv6wRuiWPq+Y5tzltvyGhO3cvvds7zsikNb5iyVu/0o3I0QWp9B2wV0Zk7/2Z/9GT/wAz9AYl1Lg23bHDhwgLe//e0MDLwwuQshBJ/61Kd405vetO1rXvnKV3LNNddw++23dx77kz/5E97xjndQLpfxPI9kMsnf//3fd33OW9/6VkqlEp/+9Ke3/NytOrInJye596kp0pm1lvmN1Y3lcplGc+1GvqAQ45v/dJQHH1/GR6BaFT0ayISKleXj/MFXf50feO1/YmBoksxNg6w6PjeeV+DmCwcIlebISrfxTCYS3HDRxO53ZD/5VcLFZ/jWE0W++eBT/Om/fJl33nQhLz8/TnxslJGswth3HVzxg5jJ9K51ZGfDMj/z7vdReMVbSOS791JTrTJXn0LoGGgbW1o4octgYpB8PI2STZp+E9+3SVkpDLlGhK+vXFpdXmL+W3fynnf+DKMjIy+wI3uej/zW73LBy15PYXhsy8olo7jI8sI8K1psqrqWaJJWJNnUCKKOvGw2x8jISCTV0Kpcmp+bZeretbXCC6hcaq115IbXMzIysmPlUtKUVMsOi8tNVKiJJU1SORthSJJEknhNpRC2RK6TEEnZBk5pkWNf/jv+4y/8Qmet8Dw7sk9Md9aa7hvYtiNbhQH1lWVUaQGn6bBkp0l5HrG4TSrfT2ZggHwqvn1HdmWZpa/+Ff/tv36QYMOspDOtXHrwqaP8wof/e2etsK4ju+JSXG50OrKVgmLZ4YBtIg1JxQ0IDYGQAiMXQyat7TuyV5dZvO/T/NFH3sP4xOTuqTacXIC//wkoT6GBp5cVb/6DZ/nGTxYYHCxQ1QlW+q6meOnbMEwb1Shz45WHz35HdmmJD33wQ4zf/BZId9uAVWeFx1e/iUYjwgS+UnQdYa2ObJSFbnVkj6RGuKhwmJhpkFlX3VhbXe6yA7vZkR1D8eAHf53Zr93L0cDnl44d4yOT+7jeq1FwqzTMGDPnX87B3/xvyJZU+9nqpOp0ZLfswOgNr2dyIkpAbtWRnY6Z2Kak6vicWK7T8MJobIuOKioPDWeQQmxbAV1Znue5r/0Tv/iun++yA2etI7tcgtIUNFehPM2QXmYobXL3Pfdwy28/yV++9SAXn3cQTIvY0PkcStbhotfvWkf2Vx9+lg/9t9/psgOdylMvpOTUOVo6yqpTJNSKULlc6k4w7PVTYW1BBgbNpIc1ARPZCRou3R3Zq8usfOOT/M/f+ACpwvAL6siemznFj7/nvzJ045s6a4VoDycsg/qpGVZPnAIBrh0jmUpiuA75fAYRi1EWJiKZRqwbLZJNmJiy2862763feO9/4NpLzn9hHdnNIu9+768y9Kq3YGW77YB2Q8KKj7QFKxWX0FMEvkKakkTSZP++LNKQLJccPC/EMCVW3EAasrO/Ox3Z6+zAhQcmXnBHdkI7WMql4gTMVbvvjYQp2dcXY+HEUb5y3+N8LtHH48UKpd/7Td5y23dy44EB4pZF7oobSY53Jwv6kyYDKYu6F3Kq7JFMJMjlojP+BXVkt+zAvpe+npHRsQ0d2aC05snZchc5vRPG8/EtO7JXF2d57p5u3wWeZ0f2Ot8l3z/Y1ZFdq1ZxHAfTsgh9j5H6k6Tdeao6gYcVdRKjqcs8q9YwyZiFHU9gJbMkEmsFqoYUUFtm6q6/5J3v+RWGh7uD3bPVkd3wPP7osT/iG7P3cEVuhF+67laefuQrfP8P/zU/9xuv4zXXvoJUYgzHmWW0cAFXXnDLrnVbHj9xkp9432922SxY10nV8Kg+/jj+7GynyzkeeF0d2QBIgTE6SubSS8mlouLmro7s1r31iQ+9k0PnHXhhHdleiZ959/vIv+ItpDbEBJm4SaPscuKpIt4G425qsBEgwTOjeEwIGD6YJZGJdXVkl1e6Y4IX3JE9P8/tv/5r7H/1W5CZgS07smOGYHVlBR3PIKREN4osnThKreoRF9HMOTOTYfDAeYjkmm/a7siem51l+r7u+2q3O7K/NnUf7/vb3+abH/smV7/jGnKTWUIdkM1U+J7zv4ebBr93dzuy19mB4eHhbTupWF4iWSsh0hlKLZOm63VkNos5ONi5hlt2ZFeWWfjqX/HrH/4gOtkdEzzfbsv1uYG+hMlQ2qLhh0yXPGafW2Xm2VXcRkAybaFWT/De3/xp3v5vP87E5PkkczaWZZApxLn2mkH6sjGW6z4rje7zZqQvw+Xnje16R3aj0eTDDz7GQ2aKYPoEc//x33LTL7+fN1SKxPsGMEybRCbLhTfexKWXHt6djuzyMr/6nz7I2M1vQWyICbbtyG6h7Yvt1CnY6cjeEBO8oI7s+Xl+42O3c+hV/4pM/8gWHdk18lSRglZHdnfBfhIH247hH/4e6htc7PUd2VOnZrvWuv4anq2O7EAF/NFjf8S9y58mbgkIspSPuTz7eyeZ+HcTJEbj3DBg886LL6Gv8BpKqnsc0lntyF5nA9J9A6fttgwlTNVdSk5A0hAM2CZxaXSu4bYd2fVlTn3pr3jf+z9AutA9yuJsdWSfKjb406+f4MGpIuWmjx9ovMUjzPzlezj09v/O5HmHuGg4Q741e1RrzS2XjOxKR/bU1BQ//su/ztBLvrvLF9iUj6jOR7NErThWUCftLnJq2WfeS5FJ2QyODiJyYyCtrTuyW/fWx973H7jq8Pm71pG90Y+47+H7+Jm3/Tve+fE3c+P1NzJkXEUu1m3fd6Uje36e3/zob3Dw5jdj54e27cj2V5ZZObWASKZASsKVFYKVFfqyCYxEgloo8Gp1rPFxrPGoYGd9R/bC/EKXHXhBHdmte2v/S1/P8OjYth3ZVANCX1FxA7TQhL4mnrbIDyfJpezT2tniwixHvt5ts553R/Y6O1AYGNrUka3CAM9pEvo+hXSMRCpDzdfPuyNb15Y5eddf8o5f+pWu+AXObke21/T53X95jrunilgaqvNHmPnHDzP8Ax/CHpjsZAgkEXH53jdcvCkHdTY6sp87doKf+sBvbYoJCuEyHPsSFZUgEDYovxVbrevI1hYNopjAs/OEo9eQzWSifNvGjuyWHfiD//wuzju4/6zyGoEK+PsT/4MvTN2B8PtxfQsvjH5fb87lxG8+zqFf7KNvfwHlDRGgCbTmZWM38dNX//TudGTPz3P7r3+Yfa/+YUgPdHVkL7Q7ss1oHxabHqMnjiIXlzBHhslceimZpN2t7LLh5+41r+F4AVOrTRqej9aCUCkaXqTW2JeyySYs0jGTTNzsjMtoo1e8RjpuEvou5cVptFuh2XBJWAoRS9M3ej4ilt7WjzgbvMbpbIQ7XaH4mSOEoSKhJedh8Nj8s7zps7/Gf/+uD3DBwAGEIUDAAcNk/EcuYWUg/qIV4tbjTHiNs9aR3bIBH/rFn+NlVxzatiP7pRfvo1wukz2NdPkZd2T/yZ/8CQAHDhzg3e9+967LiG+Fl770pdx5551dj/3Lv/wLL33pS4GIUL/22mu56667OkS2Uoq77rqLn/u5n9v2c2OxWFeHeRuXjOW2vIBxy2AsEfIbv/prXbMmnbrH0nQNrfSaE9tWLdTQbJZRzQrffvgfSCRyqG8K6LN5OGHxydTWs7sK2SQXt+ZM7u/f/prnkha5ZG7b58cS4aa504tzawYsJXy+/tWvM3OyjFkuEdZWqDouD80kuMBocGFWY8zeC6OHYfRyploV3uunYm2cOz2Si0dSP1sgbhkdB3A9pqaiNaVtQS61Nh8sUD6rlSpu6OKr6PewtMVIaoThdIqF+jx1v44QIiK2VIqJ1ATmhnlNmbiFignK3ioXDSbYt24N+aTdCYi3wvr1ZsMyCW+1I0uZjpnrx4YAELoJ3CCgZBoY6wgLgY5obR3dfklTE4YKv7aKrQpd8wYSpiCxxVohOoy2uoZttB2m9lpzMYHdmg+WsCOCeD1UoKgsN/GckHzCQiuw4wbZTBy5bq7YduOcHcBU3pZrhWhe6U7r3defhHqis9b1379tSgrm+u/GppAco55LcX/dZV5ajCif61IxUtkcch3RkoqZbLy9yo3oe0taxpZrbaMd3G+FgZS15VoBrJRFvxtDudG8zgdLdU41PUqBxZWWRRoBrkJYEtMyMONmpN9MdG/ErbXvxmgIykG0543TXMMXbCNCn8se+TA0vw1mJCPmIdGBF8mZmFWEKmKUT3Ly8RmOHf5ZBsfGGBgYIB4zd1xTm6zaClvZiKmw3PldN17XXKJA0R3kVH0eYTSwDQi0Rum1Q1kgkYZiMj/IRGaSoeRQ10xsKQWFlB1d1y3urYMD21/D52Mj2tC+z/yHPkTqjju40LZpOi7ewlHGhtORToMQxIXigpOPU/jT/8HIBz6waRZW2wHZCqO5BKO5xJbPrbcRbTuQja1J6eYS28/cysQtDo9mqTR9Kk2fasupnS42GMnFO87LVoj5tW3tQKGVnNkKp7URqQCmvgSrx8GwwTsJ9WUo3EwqlcZbOMr5yuSysA7ZAxArQuEwDBzi0h1mZI/nE4znt76GqdPs74OFOGNqjkvkcdKilZRvxTmhDjhRO8nBYIV9RogTuBxwRxn0qyhRQQkFCIQAA4lwJOaSjW27RLqCaz+nJpd5UkbOXn861plnvhGnsxGxoL6lzVKNBkxPkV5dZimbpWYLVLHKqGESVjTCNEkrhUzXtpzXtt7Otu+twXT0mvQLsBFtXyBubl5rzXVoSrAsg8FsnHrJxfU1liGJGwa1VZfsQIKhge3ndiasiLzvsgMt+2kZcufvfIONiGaP/+q2s8fbqBSXOeIGVOwEYXUVb+Eo/+uRfp44ksQ0TWL3PEwyu/3Phe1nj5+pjWjbgYQZbS7TkJvux5cfGuTbx4sc2xAMrYcg6oy//rz+LWdkS7G97wJw3uB2nsSajVjvu7QT+EJE9jtGjNWgifLrDFSfJuUtopCkhEdK+JEyvgop6Bq5sMmqeTGZZJJ8XxrD6A59yi3X+NDA1muFF2kjBuN8+L6P8c3VOzHiisecaf74qM8NFAjrq/zLyrcRxTrvuuKH8ZwGA4NRAn07P7mN83e4hqezEdv5LgCJygrhyaNRBd4GmCok60dzqdEaffIoxvgIpKLiqPV7qX1vxVr+40Rfkom+TR8JbG8j2rFG1t56rdm+OP2DSSpLzcj3Xx9HCxBSkFACrSE3mGB0ON01HiuXsGCbmGAwE2Mws/U13MpGZMMyorWAreaZaqWYeeZJygtz5IZHGbvwMLMnFwiqNRIiumcUmqBapbm0wPjhoU2zR5PW9vdVJm7tuF9eiB9R82p8Y+4rmMYc3sJRfJ1DxAaxhcQ2shzuP9yJNbbCUCbOUGbrONA2d7azbRux3g605QxjltH5dxuhG8ethDieS962EVqjRICVjWOs2zvp+ObURzsmyMR2jgnaCbWtMJ5PkFDNTbmB9XCbAZXlJipUCCmo10sAfPkbnySTyWOYEsOSGKbkbz4ZI5bYOk2z/hzYLRtx0WiW9zxb5tHhIezAR8z5oEKOjB7kngMH+Z7Hvk6+r4/hQj+Ttte5V06Xj9hpvVvZiHZMYG4RE6zHet9cac1S1e3E6EnbZDvX3ZRyx5ignTjfChttRDYsEwuic7Mda0Sw4bJbCB77R2Qrb58RTTYiMJJUJ7+TnG0T22FEem4bmwUR+TO5zfvO1Ea0Z45+c/VO4paNZVgoVjCMIt7CUZKmIJlK8mhD8wfHG/zq+L9mIn/6fMRWOBMbsd4GrN8D2S1imEYY8nTdYcULkAYEUmAKTcGEptZ4nk/ONLaMQ9p2YCxnb2sHcgmL3IuwEUtVl1TcJBO3qDg+pilQQqB9Bwsf05AkYwZDmRh+qNhXSHH+YGrHGdlD2ThD2eeXb2sj5le39QXW8hEJCJcgnkC7mpk5TbkpSQiXoOrSFB7j+RwiNdR57/r1tu+tQmv81Nm2EW2s9yMW6gvcN/9J/OWTPFV8irnn5tiffZAfvexHqfv1zhxtxEEg/YJylm1stBHZsIyhoqR/O9bYCmY2RyFbQdWqEZHtN1CGQiiFdhxSSpGywEoY2Bu+n5hp7GgHdlrvehvRvrfirZjA2iImUDGTStDEJSArTFSosdMmhdEkZmztjNrJzrb9zu1igjOxEevtQPscN1p2VgUB5cUistnAEJKqU8Gr18kNjSDNre+drfwIiGICAduuFVo5y23QthHFYnEt9x7C1FTUlZgBlBPwja9P8bUnTqJVNA7Bqq8SVpfwy/MIOzpXYiLKEXz660VeMqy5/kB37EcIxWJAoVDYMWe5k42ImXLrmECPQN9BssVjIFwwZSTPrNcIw7jwieMTWmmW+i8ibcnOvS+F2DImMI1or70YXmOr/X3p+HsxDc2dx+8kZVuYYRNX+SCbhLUVEsYYphniG7MILfjuA7fy/pt+EWsdV/B8c5ad67CFjciGazzL+nxbRploR1INQmQQorTi4LGnic/OIAA9dQxDBHDNtUi5+X5cj73kNfy4SdnxWa17eGGAUhBqTSphMZSNYRmSqhOQS1jbrnkveY02DCnoSyeZWZijXG5AoY/x8y5CWNF3uZUfAWeJ19gGbRuhRzI8+fQx0k8LJGvxnr94nH1BwGHTAlMglCZxxRCxgznGDfmC8xEvlNcwZPe9rLWiVq2hPNC1KgnHRcZjyEx2U35wPa/RtgGT+Vjnczeut1LZnFPaDs97Rvav/uqvPt+3bItarcaRI2ut+sePH+fhhx+mUCiwb98+fvmXf5mZmRn+/M//HICf+qmf4n/8j//BL/7iL/JjP/ZjfOlLX+Lv/u7vuOOOOzqf8a53vYu3vvWtXHfdddxwww3cfvvt1Ov1Tkf52Vx7sdKg/5rbSPW1qja1Jv3sKrVyVCUh6M6phIFL4fJbSeVHMcxo/o3bZzI5lt3S0NRXl1h58PPUarVNScvngyjJ+t5tA2utFSvTU9RLRRDgOFFwddfUKvFFF56ZJ/U1SX9fHHHH70Bq66777RKsZwMVr4oTOIymRnFDFy/08ZVHxs7SDBzqfp2MnenMG616VSpeNXJSewQRT5CSkmyzSSURyXEINFLpqIy8dZ8qFXUO9fUVSGe2TzrsNtxmgOeE2EmzPR4VrxHgNgMSmR0i6l7BMHhCxpg3Aa2ZN2weCQQ3ao087Zt3e20SsxAnaATcP1NiuukhBEy7UWBzuTSQKuosDBYbaDfEGkh0yOw9x8mvwxOfAmlALAuhh7AUsIoATNNEaxMR1NlXfRAtZ5m8+NXE41s7dLsBrTxc5xQXptIEKst8s4IETCEIECitEQikEOzL7uPqoWu6COxeoXH//VQ++zmEEEjbRq5T/wjTaYTvE5gmMceh8tnPkX3960m1irN6DcuQJGyDI4s1Ko6PFIKlmstS1eXyiRxJ+3m7EC8Oy89B8TgMHAJpQnoInvksLDwGscg5m4tdSnjVrRjZQcjvj167A4l9NvC6vhP80PIvwfLpX4tkU3DQhSYblcw6+Ou+i7Z+4izAn50lWF1lqa+PWjwGGqqxOAQBhWSCtB2LiIFqFVWtYBT6T/+huwTDkmgVnVFhoNCAnTBJpC2suLnn59aW/uAGhIHP9NwimDHygPYc/KtvwxgYw5FwKHTJ9g8QT2+/V8+WT3g6SKW5djCD8kJObOhIhch1mTRNrhlIb0li7wXi8TgSTbr8JOlgGS0k0QnQggbRknnOqSKpcAqZf9kmEnu30SYE7jx+J7a0MaXADRp8ef4R7plpz+LT3L18AvHY3/COy/41qeR5p/nUvUVXjXXr+9Zad2KcGdfjPKWw5N6ft4HSuHkL1fAQtQBNy7UWIAyBVqDQ5IcSjF6Q7yKx9xJtErs0PwdAaX6W+moR33UQhhHdR1ohpRF1sSwsgniS8Ysu2URm7yWOl49T9+udWCplpfCVT8yIcf3w9Vzaf2nP1rYRIp1hXgjKlTI5w2DEMDDTaWRmd8//9TjdWRB6AQsnKrjNECEh77pkLriZ3EgG0xyO9qeGWMok05cgmd/sLJzN3MDGAvc2fKX5yLFZPr9cwZKRn+1WStGTK4s8fP5F6Auv57umn8CdmiZ14HysqaktP2tjkftuQ2nN/SeKTBUb7Cskue5AoWfnVBekSXHoZeTn7sbWm0ns0EqzWHgJOXsnB3Fv8MDCA3z+xOcRiIjEDh2UWuvGMQRYQhBqxTdLKzxdq/CyfO/W24avFE/Xm0w1o2I2ATRCzaofULRCsmZkX8umwb643ZMza6Xmkk+YNLwQP9AYUnSGDMVMg3TM5KKRLAcHUvSnY6clsfcE8RzEsuhGkZmZIqV6pHAiDYFSUKo4cOQY41cM7Op5tZPN2ojPHf8cx6aPAdDn9tHv9PPYwmPcvnA7/fF+TGkSqIDR9CivGH8FKXuNVNsrmyUsC2tiElWtoBwXmUwSrK4iYzF0ECBMEx34GD1oHlsPaUqyAwncZtSVbViSWMLsanLpNZxGDbfZIJZMdZqa3EYdp1Ejmc3v+XpOl3tXTkBQ8wjrLstuEE1xbETEZ+ORL9BM5lpz0qMpSUMpmz+a+QJ/ssVZtmv5dyFhXysfVTwWdWMb9iYyO7TSlMdehXJ9TGuP80LrYEmLX7nxVwC48/idCCGRrbwgAAJ8rQm04A3nvZFfufFXukjsvVunZF/cphyEuEFI4snHkLMzCCkRhgFhiHcy8qeS11wLPYwB1iMqcolRaviEyqTmBp0u86aviJkGUoAbhKf5pD1E6KGLJ5g5ORudU0BpZRWee4bxw5f3NL5qo+ZXSbXaBPW6iFsDmEQk9pVD9L3pfESveIJ10FoxNTXFanGVrGUyojVSGoRKIdPlLZtddgvP29osLCzw7ne/m7vuuovFxUU2KpOH4Zlv3vvvv5+bb7658/93vetdQCQF/qd/+qfMzc0xtS4wOnjwIHfccQfvfOc7+fjHP87ExAR/+Id/yG233dZ5zZvf/GaWlpb4wAc+wPz8PFdddRWf+9znGB4efr6/6hkh1TdIbnC08//MwAjHHlzCbQZbvj5TaEmFCGjagsRQnLGJPNY2G3Nly0efH3YMrJVi/thzpFMHSUsRHaS+S/9li6Ry/ZGEsI5SQ7G0zcgFF0JyM5G92wlWN3AQQmBJKzp0LKj6VUIVEKoAIUQnhSkQCCFwgzOTxtwNaN8nWJhHmCbDFQ8dKmrpVJR5R0deiVIoooRgu3hD9JB4C32FkJ08ZRQEyujxcw1Ka75VrnO86SK1iggWBFN+SLhU5GWDBcw9MqLbrlEKHlyuMl1zMaRsjRpXTLs+SoRcaZhIIdBeSFh2kQkTI9ujBMb+l8Gl/woe+2TkoJoxEN33j1QeAlgeejlq3017SmID+EGVIKhjmikuyY6AVsw7VSQCS5gErem95xKJDZC87jqyr3st5c/cgfK8jkMdaAg1aNPC8j2k1mRf91qS1113mk/cW8yXHSqOTz5hdYLDUtNnvuzs2FW5K6gvRUFUe2xELAPDl0Kyn7DlOH/14VWszFFe/W+uxtgDEhvgs6sHOHnxj5HeIH273FxiqblEw28Sd03210ZBQyiitbbPLdk+u4RE6pZE874Mcl31Y620zJPPfomX79LvEKyusphORyQ2Ed+utKZiWVQrFfKxOOOZDEiJcly2rnvdG1h2dG/XVhzCULXGYFhYttHTc2ujP9iG0ppvLK1SGk5ga4VsdY7aQxNoISkJwbTyedlQgVh86yrbNs6GT7gjQkVQdAicAAK1qSATYFIaXGWa0Axg5wbyXYNhmGREg1S4QqRzI1p/a4SGdhZbCBOhA6z6DDSWITu2Z2vcSGLbho1GY0sbx2+w6kSdezFpYRs2dy+fJH3iUd4/9n17tsatYA4NYx/Yj3fi5FpX9vrkWZvEFoLVkTEWc330ByED9t6fuZWmT90LyIynqJ2qR2R2qxOblrtt5e1zhsQWUiCkJPR93EYdIWVLgq9luIjqXBVhh/TuJZlddIoMJAboT0SFS33xPoQtuCB/AT98yQ+TtntXfLseWium52Yp+z4ISVkpRDLJ/vGJPUuorMd2Z0Gz6tE3ksFzQwIvJAxCCgdrWOlltBcjrB/GdzVWzCA/kt22GOvFngM7JdkVmmdqTRa9AEHkCwgh8MvRT63/r39GZPLcLQSPuE32aZ/CQ0eQxtZewW4WuW9ae4vEbstnt/8+V8jsRCrDdOZqJmsPYas1MtuXSZYKLyEWT+55bLUVrh2+ltsO3Madx+/EDZqYQm3KT/g6UhR61fAV3DC2W57p80M5CCl6IQJItWxmXSm8VrI9axporSkHIeVdPLN2Ily9cpmp6RnMZgXZdNBaY9SjTs3G6hKyscK4leFQygTtsrpYYXWbn7NnRSKGjU6PMnNiZo3EFoDWSClRSlBaLcMzu1d8dTpicCNOVk6yvBLN9PzaJx8hnk1T9Ss8KE4wkhzpxLPNoMlfxT9Lxl7rVNtLmyUsC6PQj0GUOwSNqtWjEU5KYWSze1qMtR2kKc/NppYWAs9DCNnx84QQCCEJPO8079wdnK6oTVVcVMNnn4bHlmvMqxDtu2SuvBUrPwJWrBN7jcYsrhjNYm7RDbzrBc5CwuRLoLoAbjkisI0YhC5oRWAkWe6/EeX6xGKxnp9flrT4pet+Hs9b4Usz93Hz8AUsVlZ4jiM4oYuhTV534Laekdhr65QMmNB49GG8U6fWSGzo/L3XZHaf6TDpHyPd2CyX3YasNzGVi+srXK0ItUYEkKgYDIs4jh8yFMbINba2FVl/maq5d/yMbpZaJLYb5dikQClFaWEehNHT+EqHiqV/eJrBEyma0sXWJra2OmS2APAUiWuGzzkSu1gsgoay6yJiccbS6Z40uzxvIvttb3sbU1NTvP/972d0dPRFJQa+4zu+YxMRvh5/+qd/uuV7HnrooR0/9+d+7ud2lBLfTUgpOe+aQY4+uIS3BZndzq+plEGYNdlXSG5LYp9tbBVY14rLBK5PItOHYRrRHCetyeQHWEtfCkINgRAYqUHSAyObPht2N8EaM+NoR6PRhFrR9BtUvSo5L41MWzT8Bn4YYBsWMSOG1pqY2bvDVFUrqFodI5eDIGBwcREnFsO3LJQAAx1J0KMo9Pf3nMSG7i63dke2VtHj5xKU1nxztcoJx0No3VpwNHccBDNact9qlZsG+3Y9cbHdoa/QPDVbIV12uEyIyP9QgFQReQk0teSQNpBKIlyBXLWxzM0yJHty6BsWvOFj0b8f+2TXU6HShF4Tk5Dl0Zt5+sCPcWFm79kLpRxA4/tFlHI5lEqilMOi56OFACHYl5k8p0hsiILUkQ98AE9p6nfeSeBGQZUtBZYQSN9DBgG5N75hS1nxXqPuBkghuoJDKcSmGcF7gtRgVGihgojMVgEISTh+A9/6578BQKuApx99Gkof49Uv349x8Rtg7OpdJbRXgzjT1nnkkt3na1EOckpNU6ZMTddYtnIMuwWU1iihW12jdKhsQxtIBEZ/gthAtos8KtezrAbf2JX1a62Yl5JaMiJR23ePlu2OUVh1o/tvRGuseO86hlSgqK26aK2RpiBsbcNYShKyRNjM49UVlm2gAtXzjoF20dV0oJAtujUirTRoEZ1hQjBv2NxXrPCyQXPXi7B2ClZV0yeoeTzTdMk6HldIUK3aO4AEcJ20MJTA9OKYja2ls/bi3JK5URqrQyTdBWREYbe4y1YQqAW0e536DkJma991N7AVid1GI/QJ1o3DcDXkjRRS+Hzu5BeR0u5tkkVKuOpqyl5AduYUQusoVms5hgJQQrAyOs6piy8lYRi4G4fT7hHqboDjh4RK4+dMjFARcxSoyG55CUF8KE6gNJax90TWRhJbSgOlVCf21UoRBgGGabJ+lkRU3R5QXpgjPzxCurC1EtZuoxAvIIXkvGykEnAge4DsQJY3nvdGhlO7UyD+fLE+sSKEQBoySlTV68i52XMivmqj3c2WTVn4XoATPIK0lwAQ9jwq1KjGIQzD3FZW/GxgpyT7QtPFq9TJad01Fiv0XNJX34Y1MIawYoREO3YwbjDZt7XW716piEA3iS1bcZdS5xaZnUwlyeb6mDOvY6z0AJZqEBhJapPfSS6eIB6P77lqyFZY39l2x7H/hRbdCcNAa0wNLx88wC9d/x96Sgi00QhDjjZcykFIoDWGEMTaBU0d32sthtmtM2snwjVQ0fzhU6tNHD8kUIog1KRqkS9WvfdzPDP7MB+/N455Bsn1vSJctVLMPPsUpYqDkLKVb2kliVrdWAqxq8VXZ6J8tB6yOo0sHmP0hgbJwRGkZWI4K6SsNGOptVit5tfJxXIMJaPzbC9t1kZs6tDeRqr1/2EzTNtG68i3ahcpaK0w7d6S79sWOCc8gpKLtCUvTXo8uFpnRiti/ZO0U4YKmDBNrs4kiI2kkOndKWzbEVrB9DfBq7ViAAWBA0KiMmO4oy8hEYJpmT09v4KgSr1xDKd5ilr9WX5kdITrM9/J4XSOp5xF/oavYWDyugOv5f03fbD3Z5ZSNB58AO/kVBeJ3UYvyOznpS7YRvtgDVjbiNvz4MDuqgsSeuCUwXfQZoyZI0ejc6tFYkN7jBN7Viy8VWGbDhXlz5+k8eQSjvbwhIfQEA9jLNeLACzVVzhVWiCxFFKdsnckss9WUdtO+SGNYn5+nkK5QkEIhFZoFLgCW9sMplIgmshaGTO+9Rl9tnNDz9va3HPPPdx9991cddVVZ20R/6dBSknm/CwLTxYx1zUFtdw+GrbAKtgMJbefFbpXSOUL5IZHKc3PocIAqTd3ManWrM7c8AipwtAWn7L7yNoZKlaKklui5tXwQo/zKmNkmxblZB0n61CjNTtbRrOzs/b2cwB2G8px0YBaLRJUKixnswSmCa1ka0DU/Z6PJ86ZJEssYeLGDbxGgJB0ZmTvZjLl+aKbxFYI1U6wAkK0+rIE04HiW+U6N+RSu5q4OO2hv95P2qmFUQPl1p8tsKuHfhsbyGwVtMgA5SGV5AnzUr7hvowrkmkGBvY+qSplvCVv5yKNBBK4KJ1H1Osseu45SWK3UROSb/zUvydfa6I/+xkg2stm4IPvn1Mkth8qyg2fetkllYsRtwxCpWl6Ae2pCKHSpGI9sAsDh2DpYCQx3pK3CnP7ueuOuznx6MMAWKaFlPD0yTq4j/Nqt4pRnoaLv3tPurPXI2tnyNpZnCA6n55NT2EIgwEnD1qhhEJrgRYKQ0tAI/vjxA50k9i7iTYJUBGAIuoWBkLZ6nJdt4xV1wU7xoF0787W9giMWMqKigJDTXWlQdN7BGHOE3pDhP7FOPXIDmQHEj0lsxc9n5PNaJyA1HTm+LZoLIQQGDLyCWa15FS1xoHCNgMAzxLOOFg9nXtaav3ZBrt9bsUTSeayFxOuhmSDFXRr5nz7j0BFHQPZfZj7XtrpeN0trA9WH1x4kH++/59RWmFaJg4OGk3dq+MqF4EgLEcke6NYp7pQwjLj6FDzqeVPUZ4v83PX/NyWyZbd7sJqhCGP1B2KFxzmYBAysjCHbJHZbRJ7aWSMZw4dJisllmwRBnsMP1SUmj7lpk/ciiRj/aQgqwRJT9O0Bc2MAY7PdLHB5B4WDsPWJDbQ+rdEtRTM2n+vJ7OVih7LDY+SyvduRNLB3EEeX36cu0p3AdD0GrzWexWX9F3SszWtxyYSu5WMiroEVdQ1AOdMnNUuGEZqtPUEhjGPUu2+Z4WZWCDepzGNK/ZkPVsl2Y1yiTEsFswYsr0yFWIgiA3vIyrBjuZ2Dgceh8eGdyy+2nUVEbYmsaGV91XinCGzDcOk0F8gmUrSyN9ConoCY+RS+uzed2FvRJvM9rwVvjB9d6tUPLqwoYYbskl++oJXkktf2NuFEp1ZD1UazLs+bkvtrhwExGXkC0gEyZbt11qjtN61M2s7wjVQitlSk5gbMuYHlBvRPOxcQ2HWHSYuvY1UfpR4IU1+IkMmYe5IZu8l4VovFSmvRH3hskumpzXMQ0ikkHtSfLUdMbgRdl+OZlJQHaqhhCDQmkJ2kqSVIhkvdEYRhl6c/vQYuXWjCPfCZm2H9R3ae4Uz6cR8sdjt4tZ4Mo2bqEVKN0KitSKWSBJPnhuqMRshEyayGaDcECNmcHU6DjWHGa06fMGENLjSsjASJrIXuVitYOreSFZcGihtIrSHQOOJGCvJi+mPJUn1uFggCKosLn6OZnMaz1umXj9CMnU+Lxm9FiEsDBEFu+++/hf5oZt+qPckNhAsLeFPTUfR6jZqNsIwQCn8qWmCyX2Yu6Qu3MZ26oJtlJses2WHlZqLGyikEIRKEzMl6ZhBXyrGRF9ix3NrV9UFQw9WT4JbQSOZmSu3SGw6JHYEjZRmNMZpl8ns7QrbwoaPP1uL/BGhUToaMwuCRj2yw7/z6GfIJHOI+wTWHWmM5Pb79mwVtZ1Rfqi9XddfLg20ufoSe5Ybet5WcXJycscu6v+HCCXXZzYjGKlqbLVGYtcscDKS/Ul7z5MqW0FIyfhFUUKiNDeDIpIJiqraokVrocmPTvRUfsGUFhOZCU5VT1H36hyqTZJtRp1A2XqSC8QEq0NNPLU2O9vs4UEl4zF0s4lyXRazGWpSIpRG6khyWgMmmvGhoXMiuQKbZ+BEyxI0Kt6ezsPZyaFe8X3StTqXao3RSqzqlvR91JQbVWEqw0BWBAmRpH+LBMvZcqi3OvRX6h4PTUUBn7H+4NS02tyjQLrdzXCpadFnSmTMwOxPIBPd691tSeEuGBbOqz9MUK+hpj8FRPv0udiVfCX2OoLWjO9eSAdZZgYhTJSugopIP9NIcGkuxz5tMZa7+JwksQGeazic8EMO/cIv0VhZhueeBcdBSUnfOUZiT600aM7WEfWQaqqJGIi6b1fqkSy60hGJPbBNZfCuIp6NCOnl56C+RBgvcNcdd/P0ffciO912GlNqAiRPzwMP1nl15ijG8nMwce2eLrd9biWtFApF1a0yXyghVyWFZjayCRJkNBCDcrJBYiRGfA8TrbVqjdXVVWh1sqEUIawjsbvFpUu+x/QedbltdRY06h4F5WMFLU9aK5qJZ9EsRRXB5gI6OYv0L0DWJGkzRmwH53+3kisqCHAaNRKex7hhMh2AEiA6tYLrOt1aucAR5dO3B52t2wWrCs2TUyXmqi5Srkne01Y+gdbegCHT4JLhDFZ+67NgL84twzDJ5fsoqosx68+RdBZQrT0rdNTP0IyP4g9cQW4PSOz1wapC0SjFWGouI2lGvgmaQK0pWQS16DwtfimknqkBUQIu1CF/wR18u3CUfCy/6WedjYB1Oz8r0IqjDZcBz2e/NPAviJM0FP0ry8gWgbU6MEh5f4zL1HESnmQYixHfxAy6r/FuJy4rTZ9QabJxCy8IW+cTNHMmyo/OqULORkpB1QmoNH3603unJlEvFSkvREmSNokNkUWVUoLWqJZ0uwrDqNNNGigVopUmPzLa8xnZQJfy2hXP7eeik30cn/o24rUDHCyc1zN58e1I7DbORTI7KhiWNN2H0XIWkK2ku0ZgAAoztYgOn8RtXEciu/e+tvI9LgmaGJbFrI6uV5v0EVKidBTDDntNbsglez/GaRsSu41zkcxOpdKQSkPf3sgvvlBY0uL9N32Ixl1v5Z6lE7itZocrsnl+8vyXk89ejGnuTWHjTrmBU47HcNOhoKL9EEZ9QkggYUiyhkHcFZieRGnNfsPY9TNrI+G6UnPRfpOhfhOlIVF1kMseybiGhCA9sC/yuDTUSgZhMnVa1cazQbieCYmpY4pcf43aahnQUawAaxJ+Miokw4D0wABDsSVEY211eyknGyifilfFDRzG0+M0ggZO4JCyUvQn+llprlD1qp18UcpK9bTx5VzAC+rEfAHYzeJWaZqkCwPo4jKe08ROJEkXBpCm2YnHAs/DtG3iyTTS7HGTjiExC3FUM0AHCiMX47paHLlY4ZQXMGEYXBmzsbIxrKEE7DVfsJ7EFga63eUuLIQOMZVDYuUxlhEMj472VEmk3jhGszlNMnkQP6gihEmt9ixCGGQzVyBE5H9fPnj5OUFiA5iDg1j7JqOO6zDskNlRqjjSFROt+MDeN4k5eHoViheL7dQF25j1GpwSTcqmj4vCEJFimykFOdvG7EuzeJoYazfVBXHK4FYgnqNea1KutpoI2koC7XxWa47qXhRfbaskojXeqRrhapMQhWJNqSvwXQavvJV8foyYncDoS2BPpLdtcDmbRW1b5YfandiVciW6bkQd5R2FhnVi6GnbZnT/fqS59X12tnNDz9vq3H777bznPe/h93//9zlw4MBZWsb/eQiVJtCa43HFuCNIKkHZ0NTTBiPpGHFL9pzEbqNDZjdXKa3WWrJ3kXESQF9aMn7BBT1PppjSwhQm46v9ZJtJtFCoVvVKqh7DLtrUR8LO7OxeQmayaNNkPgyoSgm6LX0NUim0FPhCMFetsK+/0PPkShvtGTgqUFSWG7jeIoICKIkbN/aku+2sOtSl7Z86Gw71Voe+SmhqtVZSRW1OqoAm1FGQPSEN6rEYTtxEWBLDsDGS3YmrXT30N8BxHJ569girA2+maD0OfJuT1iEaIz9EXpqsrKywvLzLkc42ENImnhhHNQKktFqOqSYI6hTiowgdgDg35zeteAG2kBiWxcoP/1v4qz8HKanf8hou3kMS+3QJi1LDY3SmhmiGkfFvaJx5QT5pEEtLwlBjGRLDEIw2lsjpzdd71xMW8SxMXEsYBNz1R78XkdiWidF2mlqJNtMQBBqePl4Cc5pXj8/3ZK6zKS2GkkNk7DTHSseoelVO5ZcJdcig09eSP4ZSosZc3yqTamup5t1COpOmr6+PYrGIlrITRK1h7d+yNXNsr4iBHc+CrccPdsMDzmCc3tlOrqggoLw4j9tsIITkfK0IEzlmkFE3Jm3Za40SESkwTsiFXg0rt/uqN9sFqwsVh7trS2g0pt4YMK3tg0DDkz74oo+x5NYJwL06t5KpJJ6XpWxegtaQdOdBRJ3DXmqcUupCctbunwtbBav7teLxlceZrc1iIBFSEiifsGWjlO+TvapEYqAfadm0g0EFjKXHuKz/sk3319kKWJ+Xn5Vt/VmP4pn9nN1MXLpBiGUIUjGLUgNcXxG3JGnbJNb0EWWXwFHERhNIEb1+L9GleKXCFpmtQYUIrZCsdd4IAVIFKK3RmnOGxD5ePs5SY4nLCpcBsH95EDWiyDwnOeUe5YvfcZRbDr6mJ2R2pwiLDSS2jkhulOqUYa0WVyn0FchkeztrVBqg7SfQag4wovu7fdPT6ngVgDFLrfEQicxLdl1JYiNM2watuBwfRERmCxEV3IU6UmQYCVyujElS2fyerm0rLFVdploktYT1U9E6kBJUCFPFBvv7Uwz3oEDgf1ck7D7ee8Mv8OH7fo27po4D8COH3kQqbhKETZaXv4Rl95NKnrerpPb/7mSbG4RI0fKj0eRqmtCLiGAhO7WCAIhaSGOmTjlhMZDZ3eKr53Vdz0QsKACObn54txV6AuVTdFaZrc0QqIC4GUcgSFkpLipchCktAuWTtFJ4oQcC8rE+CvG+nja+nAs4XSfm2cBuF7eqIKBWXMZrxV1es0GtuEy6MECtuNyJx7RWuIkauaGRc4LMlmkbQoVqBhgxg2tGckw2PAYTFmbKwkhZPSexkVFncEdLTBoIrcgGy8jVx3HyOVLpvfOtNko1F4tPUqtVsO05lleeotE4StTsskw6vcjCQjTSb25ujqmpqTP+ObuqfiVlJBfOmnw4htHJD8tQobXCGxsnddXVezIj+3SImQZ+qLBNiRSCIFTYvsK3wDIE2USP7ajvRPtVSFLpJLm+DKViBaUF0jCifS2MFiEs9lT5akslkUGNc7xEsNIis9c6Hegf2I+BxOxPED+YP61K49lSEdkqP1StVDhacdDkMaRskdhq3Zo0KIUSEhFIauwjk9zaHpzt3NDztuBvfvObaTQanH/++SSTSawNCfh2gvP/dtitQ8cyJTNxTVJp6kIzEjcRQhAze5FS3x5CSsbPPx+efJjVumB9GhuloFGE3FivlhdBa+JzAruRRktNlAcWKKGQCOxyVNVeKfR2PjYApsFiMkFVqU6nMESlS0IKhBFNHiuWSjA1dU50CqyH2/Bo+g+jzTlMMY5tXIHXCHGbAYnM7iaFT+dQ1ytlHqvUKbWk2OS66E+1ZiWPmAZX9GXWOso2YDcdaikE1x2IDsSTKw1YT2aLqFpcAROWydX5JKZtgiXRXojo8TzX5eVlisUiA0MjfH3g9cC3+aZ9ExcFCsuKrrPdQwkh2yoQxuoEfoUgKKNCFyFtXGcBz1vBMnNII45hpKIObnluENv9tomnFaHW0Kq6fOzHf5JDP/aje9qJfcaSMRtz0kHrz3rsQCTuhRT+qace55n77gYhMC17LdmrwuiP0JimgRcInjm6xEXzDfb3UA01YSa5qHBRJ9FyMruAFAb9TpZaymE6u4Qt7T0/u4SQ7Nu3D4DiSjHqqlgP3X4dSKVBhGgh9oQY2OosUKGiXnLxvQAlj6LlElpL2lnr9lRvLRQ6GCCfvZTYDiNcduMscBo13GaDWDLV6fq4sFEmMJMsmHa7EZ8wEr9msFnjQuET77EM3mAmxr7+JCdXGlEgLcRaQ35rH4StSvEJ22Rwh073vYJhmOT78jhOHC95Lc78Q8SdeZrxEUqpC4m15o7uFTYGqzcNjvLQ4oNMVacxkNhS4oUeSkckmzUcFS4IRMc3OJjdv+OYjLMRsG51b5WCgAXXx9OaFT8gLgRNFX3fWin66zWCXI4AgS0FQ7bFSMzE3Gadu524jJIqmrrr4wUhCIHrK7I1DxoKBIQVHxdQeXPv4i7lQ30J4TuMTw6BDiktLKC0RgrdUTjQrdECtqHxw0iiD6HIj473nMSueTWOl4/z1VNfpdgocsHD0T4JhSIwQ4QS7Jvuh6+scDx3jMtH9kYKez3WF2EppVpd7qDDIIpbNS2FBsjaFqkeKAlthOst4rjTrbjQiPZBa0SSaBU1CSVBaHw1g+stEouN7Oka10u0XiQkoZlgwYxmYgOMKZ9r0zFS2XzviQBgMGkxkYwxVXNQQq91W7fPL6LCfIB9hSSDu0wM/p+IQu4q/uOVb2WydAe/wDME/gKen4D6c7hOCq0D6olnGRp67a6R2TvlBk45Hs81HEINtoxyrGNTx+hfXcEfHGTwssvOqCBkN8+smGmgNCil8eabqKrfRWJHXXhtP1Yj6gHFkxX6Lx3oUsU423g+JKauLbJ48ji1RnTetlUvANIDQwztP7hlLmu3fYFA+ZyqnmK5uUzNr2FJGyEkfbE8db9OxauStTORqqNfj2yt0jT8OoX47o7yOR207/d8JvbpOjHPBna7uHWruMtt1KMO7S0edxo1kr0qxGoR1zpQIAW6GaD8Vr5Yw0gmhlmI7z2B3UZ1HlaPA5HaAkT/ZI1nQyMRaNL+EuWVaeKJi/akK3srqWY/qOB7RTQa3y+idAA6BFFFsIDbjHy/j9z+u+SehwrKbqpfdXBxGkcI/Lk5lJCEQmBohTA0/uAQywdj5BrPkN/B19orxYtswiITt1ioONiGJFvTJFzwE5KxfKL3DZpWPPretUIIyfjEIAQepaobxV9y7RqeE8pXQhA/mKcYNLDKck35CIHQUM94FPYN7Nmowe2wKdZqN2JDK0ck0C1/u6/QRzqzd3msF9SR/f9wesRMGcndhQoIcbXGFhKlNOmY2fuqla2QLIARA+FFHcQRJ0ipoeHZpxm/qoCwepQI0Br3RIV4xUCJMDpL2wl2IklMITSximSYPJm+3s5EqVVrlJsOyMgYtW90DIkw2lFLFLgUV4pktSabzfbEgd0Erag1HmrJ3gkCfSrSE5WXEPqbZ6ifbezkUKsgYDWYwa3MMZMuUEznkVpHkpdCoIVkXCguGSwwv8N13G2HeksyG1BKEeqIBLgqEccQEUugvRAZM3Z1Bs7GCsatcPToUZaXlwnDsOO8Lq2sYh+PqvBt28YwjB0rGnezglFIm0R8kianCMIGlt1PEFTxgwpKOXhyCdNIY5oZAitLIj55TpDZh5Jxnkk4PNdwWHSiytDk5VdwKLu3dmq7hIVGszhVpVZ0OnFK22xBNNvXi0vs/hhx26DhhQxlYuQSm6/tXknhT1x8GRfd+Aqe/vpXCJw62q1H6143uy0II8LgoksOMHHDq3d5RWeGhl9HCknMjLFgL7Nor9CM+1jCoi/e1xOJuw6ZrWF5ZYX13bcQJdVk6yHVIjWzcXPXiYHtzoIwEbBa/DaeL9B6BJCdLvK1hJ9CmIplo0Ff4tJtk5i7cRYEnocQsrMW0ZKwulS5KMdnKZ5u9WLCQKPC5YYiNzjUc7m7TecW6+YhrivCmkzaXJ1NdvZEr9GWak2l0oTZV+EVT+HbeXKWTTwe76nknRSSq4euAWCqOo1SCoEgaZg4YdAiNwVhay7eRGp0RxL7bGGre2vG8VgRATEpONn0aIQhrogUZOKmJJM0MFuKAhNxmzBus7hD8L/bflbCNvACxXLNJW4ZmBIGm2A1Q7SglQ6AoOwT05AZ3YN9oAIon4IgBsJA6JDx/gQ4CUqleqtARKM0aC3IJ2Es7TJbtSk3JbmsvWdJle18wrpX5+6Zu5mrzVF2yhx+dAjn6SgJN99YIVvOYAgDGQrEo5pHl77KsVuOceHARaTsVNdn7ao/uL4Iq51ggQ5rqVujfTJByLDjoGtVKPRWyjlmD5FI7KPZOIkmRCvR8Vk6SihCI9Ak4vuI2buv0LER0jTJDY10pFivl5IHmgFzGAz5TQ5Ui9TdZM/Pqw6ckKsycRAwVXPQWq+p7+go966A/f3JnsuKb0QYBjTqDRrN6LxNJpIkU8menllbwTQzjI28gesnAf6SeGIS2wpJpw8jhIHWIY3GceqNY+SyV+7KGnbKDTixkFO6xrIXILXm4mNP01gyaIhhjFNQNmpR99tp7OpunlnZhEW56VMruciK3xrdR7RHW3+kiApaEFHjQ1D2aZRdUtuMcDkbeF4kZuwgWgyzcnKWUqVNnAjyIyPED1/O3DbXd7d9gYpXpe7XsaRNzIgTN2I0wyZu6CKEwA0cKkDdr5OxMx3CoOpVqXhVCvHdOaNOB+37+KemUbU6SEmoFDJdxpqY7H0u8H8zbBV3CSHxnCYIybIw6Ed1lMUCz+vNQkNFUHRQbhjd5m4YSYvn45FSrxsSlCNJ5J6R2ZkR6DsYdWSrKB8YXU8RzfLVAqGjQVMVY4CKTqBWS+T78rt+dm0p1ax8HHeWpjNDENRaqo0SISyU8jDlAAdfGSM/Molln5kt3VP1KwvYt81z82f2c/aigcQyJBcMpUFDuNjEdqPkgO0o/AUHnY3vatHVaRHPQSwbSYwLA6F8xkcyYMYorVai+OtcG98kBEzEqYcV0rVYp5htJVFmJV+jWddMZCZ6qhqyKdZq2YJ28NIuwiv09e15Y+bztjZvfetbd2Md/8ch1SKrTSnwQkUQarwgZLwveU7Mxu5C6KGbJWaOHKNUC6LEpWh34wiUUpQqHjz1GOOXXduTGz6seoTFJojuFHt0u8u1ziElSFRNZENvlkLcQ6QzafoKLblWEXXbCAkIEUkytLox0JpMGBKrVPGrtZ45sIEbUFt1cZseynwiIrG1jCQ4CAn0KYRQGOZ1e7qujXAaNXzXpW9ohMOlVZ5tGqwkMoREe3XSFLy0UOj5zDboJgVOLEcJTK1h0ja5KhkHX6EtiYybGG0Se5fswlYVjFvBcRzqjTqWZVErlwC4+9uPkHjyGFJKTMvki/c+SCaTwZBbdzftRQWj5y3ihyUCr04Q1ACBVi5CWEjDwgwzSNfCDqIu7a2wlzO7ACbjFmU/ZKXl4yWk5LmGw6FknMwedYptlbDQWjN3pERpJRsRVa3Eyno7KwHRAE9LzOE46ZSFnU9S32K/7pWksGGavPotb4Glp3jqiZN4ftQv5IcaLQxCBQrB4Wuv5NU/+Q6MdG8SFevRTrbk7TxjxX5iVUkxXqGUdxjNjPVU4k4Iyb79+1CNBsVWUhXdIrFV5Fq3iYGsFIxo3TNiIAiX8YIZopNVru1VTafLUYioq81xp3G9/Xva1WbaNlqrtS47rdFakc73c2W1wkPNKsvxNP2NKhfUS9iDg+cMKbBlEZaIuohCYDJhc3U+Fe2JHiuIbAXDtDGGzqP3vZdrWE9mn6qeYjTZx8GY5tlamSXP73S5j8bTXDNy3a6T2NshJqOucFtI9idsZl2foheQMiIf21FRLDNgmcSkYNGLSO+caWDtcVzgh4r5soMXhBhSECpFvg6Wo1rFWKJTPyoFqKrP8vEKoxfkdzfR4tXBrUH/UFQ8oxWiOsd4HyCzlIoVwla1WD7uMZ4OEcpjPKPIW4pURiBq85AaAGP3ivB28gmrXpWiUyRhJoivSh5uPEOlUQLgDx78LNlk5FNFxKtAIqj9L4ewXzKYHMRc1/VwNvzBnbApwdI6s9oiElkEw63548pxezJepAtCks9FcVSjcRKtBQjZ6m0CiCT7TGOCvr7r91xWvA1pmp1utUalxKHGMv1aknaiwof6aqS+N7T/vJ6fWzpQGFJwzWAaHSqmGxFBYYiIwA6VZn8+cU6S2MWVIuVKOSoeBqrVKrlsjkJ/4ZwkszPpCwGwzCyG4XVmjwphIISJ750tgcvnh6RhcF02xeOVOv1PPs7g0jxaykhOVKmOdOuZkNm7BcuQTBaSlOMWq67GK3sI1oQDBO28sI4aIAAzZ5HMnUMKAoaNKBxg3E7B0WOUyw1yhT7GD13UUzLADZyWuolFPWh7ABIv9DGlgSFNlptL1FqFxHEzgSmMDsndK6hqBVWrIzOZ1vwDhapWUdUKMpPteaf2lmsOFG4zIPQVhiWJJcxdHzl4JpDSwHeahIGPaVmdOMxKJHkkECwqk1ERjczQWkUjNHoA1QxQboiMRxLHItRoJ0R7AcrXaD+EUPeWzBYS9r00+nfxWHSQSiNSvVGgdYhAU7MGqWQvJpVK47oujuOQSu1Ng8ZG9ausGqFStXDdBUwzg5QxQBAEZeLxCdLpw8/7Z+yW+tVGnHI8ppsOEyePkykuU+ob4Pjkfgoxm7gUDMesHTuyd6uBxA8VlaaPG4TETINswiJhGRSamooTdTyKVsNedanJnGD3Y6ydYNjQt781K7sGzdWomHgwDoFLqeJGylecO+ObALLxLKdGK4QzDTKNOKVEneJAg754oaMo0qtiqzY2xlrIKPZTKlJILvT1se/AgT1XF37eXvLpZgu0f8n/25FNWOQSFjU3IGZKLCOSbTwnSeziCWZa1ZXRHKx10DqSDtJQWi7CM0/25MY3MjayL4ZaaUYPiIgIjtIoIqpk1dFNZfYnMHZZ/vp02HTDi0i+PZK7i256rSETKkb8AJkxkMlUx4E19pAcCNyAxZNVmg0XM/U0prWIVi0iQOnODDctZ6k7D2Enr8foUeKiXXFp2jaZvgKX1Go85QhWEhn2xy1uLOTOqURFmxRQbsh01YlI7FwSQwiUAK101Imd3t39umUF4xZQKqBSruC6HgXfI33xK4hlB0hlM5imhWWaeJ5PNpshkUxten9P5ne+COxFBWM1CPnMUokTTRet4dFqZMO+VqxyYmaZqzJJvn+ksGdk9kY0yi6V5bZdjZL/CN1FZrf/th1FXhoM78E5diYKAkzdx3nJEzziVSk1o+TlasNlbrWOlIILLjnEoTe9jZliDYpbf9auzkDaADdwkAjSCyZ2JUpcF5pZEqtJ+vrzPZ/TJoRkcngIdfIk5ZaksGi1YKtW2Vja9RjNZiO/oEfEQMwewpLj+OF0a2VRFX67u0VIgRCRtGwisfddbeulWdsz2WKJJMlsDo3GKDeipFsiQS5l4TtOb2XuNmAjma1aasgTpsmVwkAVHYgbEVNwDiMMAxzHIfADTMvsaXd2m8yezEyS1DWcxjEuyQ7zdK3IgttkyDK4rHAehuxd0jpnGpRNg3IQIoXABAqWiSFgNQgJlEahWfACKkFIn2USMyR502Bf3N5TMrvS9Km5AfmkDXhkawrDaak1CdFRlNJte6ChvBidc7uaaAn9KAnYDuaF7MxqHp8cA60pr1bJJRTjSQcR+FG3i/JIx4xIlrx4NCLE+/bvGpm9k0+42FjAdsvkgzSpOTNSxAqaDF91G7n8KIZps75cwNAGGs18/yr9g6PkYnng7PmDp0P3eIyVVkeDJguMADoMwTSR8XOEEGqR2b4T4uvpFnklEahoJIY/SiZzTc9IbIjUr9od2V6jjvI9MkGAaVqEYYBG0yiXaFTKpAv9Xa83bXtPC7OEKdE6yl1cnUlAoJj2glaXSKQgcs1g+pyKDWGtcBggkUgA4Hke9UadZCq5Z4TAmSIIqlRrzwKRlGsYqojQaHVkax1g2b1TPMgZkiuPPUuwOI82DIRsdWe2Rrm5J6dwQkXm2muxepTDsAzJQCZG/6U2R55YwSt5rYLRVumViEhsgUClDAr7s73tcNsGwm8wPpQkn7FJJQxEeXpXz6vTIWbG0Y4mbsaJGXEaQRNfeVjSJG33UfUqVNwKzaDRmp/dpC+WR+vejiJUjhsR2G3fqfXvsF4nLJfPuU5tFSgqy008J2zX6eHGDbIDiZ6S2SoIcBt1fM9DNVpqUqZJZmCAZ2JpFghAa2aQhAFc2cMxTjpQrRG9rc7xVhylnDDKuVoSjULGTZQboprBrucIt8R2ZLaIRqHUYkM0CleQtGPIlq0N/I0z6PZyuTbx+ETU3KI1SnktiWkLy+rd+IAzUbx4LGgwH/N49vzz6bNPMD02iSckcQSHkwniiRiNPVa/8kPFdLFBzY0aHJWGUsMnVvKpLjWRLRIb6Mg37kmMdToYNqRaMU2zCIm+SGb8QBpOzFCueuRGxs4ZEhvAlBYT2QmOq2OUysvIjE2fVcAUBo5welpstR4bua2oiRAKhf6ejch93p7cgQMHdtyc7arS/9vRrrzcWMlyTpHYAE6Z+uoK5arbmXeDan2H7bnOQiBRhAjKC3Pkh0dIF04/S+esQggaIwrPdUnXYig0UcoqoljacnLVTJP+yULP5wnA1rJ3LX4o6hQQkuHQQ0iJ9oOOA7vX5EBt1cVpeljpZzDii1GHgG4n4VqJwFZ5g+dPs7Ko6B96SU/I7HaFpQoDnFqV0HM5r1FjVPmM6ASN1RClwj1PpOwEKQTXjecZP1VhwJAtOXGi4NWU0YycPcLGCsatkB1YS/4XxvcTBAGJRBKlFL7voZsOIp4kXRjYkhTYiwpGrX3q9SN4fjHqCmolUYQwQQhMI4PWHvH4BPH4+JafsVcS2M81HE40XQ4l4xxpOJRajv7xpstCqcZj1QYFy+R7hnvjaCdzMbIDCUqLzY6UeDsR3JYUbncL/P/s/WmUZWt+1gf+3mEPZ4w5MnK8U926NejWPEiUJmMLCRuEEIsF7eUGodXYPdSCJX3AknthC9sgMIZV3bZX88GCZnXbCLwavMDqllDLKpBEURJSTZJqvHVvzpEZGcMZ9/QO/eHd52RkZuRUlXH2yep67oqbcXacjNh5Yp/9/t//83+ep0gkUff017EH1GLOhMa+rUBFUNuX+v032L8zYFJKpnkooH/1yj6t21MiBedch5//qZ955M86bcXYcSQyYW2vTTwWOBFIIeGhNdIM3rjNyivbRLrZISzVX+FsFNM5OGAURYyiCGq1WC/LOZNliF4PnGuOGBCSbvv97B8YRHSzLgPCNRlurw68o9V+IajfFlxc32/NOluPUIrPlJ7bSRsB3FIxvyc0r4myOZu7h+A4mX1lf8qFNOa9aYyoHCIS+Mph9nPis6q5HLdHwFrD0eERRVHMVfF5ki/E+u5hkEKy3T5DlhVIlQCGt3f6bMcJq1GEVq1GzmuGSEoupTEDYymcp6MkN4uSifVEQtDSkqExGO8pHIxtyMo6MrBiLJvx4q6DwlikgFasKccVampDYo+4G4s0R01me+8Z3slY2WqdnlWrikJ317u5InvWpBZ4zl/aYXW1Q4cRwregnIb1zVaQ9oMLkkqgGAZVQefhg4fPAifVhDZPKMc3iKMeidbEA0kXz+b2i+HrzuJwtR+GRHjBUWuMXS+J+iusdO9+v0XpM+f7Lec4ODykVxm6RYkzBhFFqPU1ZK9Bm677Ua9hhwOLkDfwPmS6u/wMiXqdpN1cHeCMYXB7lyKbIoSkyjPy8RgdRZTGYKsK5xxeOoZ3bpG0O4wP7syf772jaI1Z2V6MC4psaWRmcLlFOs974xik4FphuNRNeF83RUWNa/GBe4er8iLH2pDlKGb+clLinW+UEDgJxoy4ufs/c/v2/xeA4fBzFOUZ3LDAuhJrh7TbLzdihQ+Ac0x/57cxV64gZkpsQmlovcdJGaJQrl1j33k2PvyhxshsCLbHay/02DVHiImdq7J9TWr7rqJ9rsNKu3kF7gPIB1AMEa0Vuu16jcsHC1mvHoZ+3GMYdZhUExIV+kSdqM1Ga4PD/JCD7IBO1KGt21TOMCqHeO/YbG02Euc0g0wTrHNB5DITuzgHxj5Uqb1Iocv9KDJDmVvitp4PCpZTQ5EZWg0KiIJbY87K5hamKjFVhTGGL0YdrpVhMHNGxt3SCV9JYz6qmlkTZoNXwvsgHogkaAHG1W0Xh4gUMlH40i60R/jgyd5HZtvQd7f9S4zSV0iTdD4x6r1HR832XONojTQ5S1UdhiErGUjsuEEi+0nQ1RKTOd72lS+ycXuXXpbxe6+8RqpUI45XcHdYuJdqpAjK28n1CcXE3kti15g9XgoyG6DKw36q7v8IqTh/doXV7ZTOhWYdRB6GSMXsJwf0RYR1hsxOGZZD2rqNcVXjYhe4l9s6PDxkbW29MRIbvgEi+zOf+cw9j6uq4jOf+Qx/+2//bf7qX/2rz+zEvhUQKclGd0kmwB+GKqfTSVlZ8xwdjHAu5PWFjpCfV9a1EwMrZ87SWW3G3qBwBUdbU2IVEw8EnkAGCB/aKYftMTdXDojNCuu6eRtZeJDMntlGrcYpO0JgJxO8EIhIzwvY0yAHHmXVnE4nrKg9Iv01MB7v5V0Ca541Q33Agt+jve/o9O7dsC7CqnmmdJscHVJMpyitSVst2jiOblxjHCe0ul0QYt5IWQYyW7c1Z3oJbljMrZuFlgglltKedQalNFVlcNYynU4pqxJrLSITHJ1iHs6TTDAWeovp5OtYl2PtFO/KMMUqU7TuIhC0Wi/Sap3sErIoC+z90hCLMMBwNSuZurApOawsWMft0vB/u3KLD690OJcufiMohODs21ZxHgZ7x8lsQsQE4aPqKI5S6EwrzqycLuFyj1psZTVkjhbjWt1m6uJUsHvjJboXHV083lZsHB3Q6m+gdYQQkG6fYeeVdzzUSnBRijEAvKe9K4nHKU64WtEm5pOs6VCSvXVE9MpWo4NYIorQqyv0i5xOloNzjJOE3nTK1miEaLVwkzHRzk5jxIAzDucEZO/EGodKb9VfkVAPuDVFYs9w3JoVQr70bw4mXHcC6X1w5QBueInVLT4SNTvAcBJmZPaFJGI9d2AcohOFFJfK4nLTnFrgPtyvvnbWURQFaXq30ZLn+UKt7+6HdyVldUBR7OFchVIdIhWxHa2CNyj1oMvJohFJOSekK+c4qAyHVYUDCuewPmhHpQAtBHnt2FDMNgkLQqJVnSkKq+spWe4Q40D+3LVorVG73+Chv9U6XavWuANJd57RhrfQrgfy8gFCKLqxhWQHdAJ3vgyVA6lC4wUPrdXwd6tmpvBnpMCoGjFZE+yM+8RG443HyrtN1bD3Eow6GVf7e8QyblTdJoTk0osvsrqyysHtW9yYTFmJNBfO7BCtry+FLetxRIlG2++gNB4R7eLyM7jpO6AvycYlrW7ciMotn44psilJu4MQgiiJKaaTQFQjkFqh4xipFM5YRgd3KI8933tPMZ2QTx/jqPOsoCR6PZ1btupI8X4bc9EaNiONTnWIbmoQsyzso8ER1lqiKKKqKkxVIZXE1+045xxa64URAk/kfAQcHf0brt/4BXZ3jwC4cf0KcXyTOFpHqRStV9D6Fgf7/zMbG9+H1veusaftfGT29qiuXA1Dt8cIKu99EBICKIkwjvjGDYa3brNx/typnc+TYKUdMTjXIbsxQYztXJUdrUasv9Bnpb2E4hd4gCAIDiTNrVdQK9p6FxiWIwqTk+iUSGq+fPBl9vN9rLPkNidRCRutDTKT04/7jWePyl4f2R3gRqM5YS27HdDqRKV20/EYtnLh1z0TYoZkDGzVINnKXbdGqTWx1ghd8XuVZ894pPAoKcPYnQCL562iQgwmfGSls3CnjuODV7N2u+rF+MphxyVSK2Sqgho7N4hYBQK5qXvBcTL78E1Yexlx/sMkR0PyPJ+v+UmShP1WgxAypt1+kcps4FyOlCmR7iFk83vUR+FspHBf+SLrt27ihWDz1k1eB4avvwe72K3VHLNh4dn7w2UGMQlCx/tJ7BkWNjD8ONgSTA75IeDDvkwE16PuxmZj8SLHYVw1X6+U1IzKIZNqgvOO3ckuzjmklKQqZVgOuTa61vh6NcOM21pfW6fb6zZGYsM3QGS/973vfeDYhz70Ic6dO8ff/Jt/kx/90R99Jif2rYyTMgcaK1ajFIHj/IUwRXl0MMJ5N8/HxgfjTo9gtWErhkSnODzjM4bEelqjCOXDJPNhe8Sb/Zu0ZWdpLBhmuGd65eCQfpxwxrsgHYEwyWbDtKXsdk6FHHgiq+aT44hPxuAfwK0HD5+2VfNM6WZNUATErRZVUZCNhnNFm60SWr0+RTZdGrtWJwUHLcmqj6GwcxJbtppvsBzH/So26yzGGEZVSVmWKKVI05ROu7PwPJz7EUdr2HQbUw0xQlGZIUqmRNEKSqb4JSAGRsZypzJ8eZLh8EydY1RnOccCelpjfcV+ZfkXhyP+N2ebmbQWQnD+1VUAhnvZ3PbKO48ETFtRrUX4rJoTr4tAZ22LlbYGk4TMUe9gsofPjrh+pKjUCu1e2KgC9Na25uSGQ1IVltHB4SPXrYUoxryneGuIOyjqDYAAL5BC1HaCtXDv0FK8NSR5sd8oma06HWwUI6YZWwcHdJOEVpYhtAZr0VtbjVncHbe2U5EmO3o7Ud8TtW8T8kUhTS41SmLfD1NV/OvDEVeNmw+GeDez5g0KgS9YyUe9Xzr7UykEO/2Ucjo5PnfRiKPIw3CS+to6W8fieKqywFqLtZayKOk0sCx4VzLN3iLPb+JciXMF1k7Ruk+k++ioT6SbUwadhEhKLqYxQ2MZVnY2eoPFY3xt300gvJOHNDZOC/1WxCCrGOW17d1GjBYCP6pwzs8vVFHbCnkHK9ut01cJSA0rF6DbCo39KIU0ZEqTD+49NqlXH6GAinmIh/eADc9rADNS4GB6AFcztAnW4QKBchIrHQoFHvbSQ65299BCs5auNapum+FoNOQoy0AIBsYgBke8sLa2yNLlsXDGMT4s8F4gqndRTrYop32kDE4X2aikt56yut1eOJk9IwVm7xOpNK3+KgyPAvGqI6RUREmCjmLKPLvn+UKEaKqFuowoiezGyC5gHS4znDUOoWXYYzVICM7Wp9F4RFEUKKWQQtJutamqCmcdWRZUTEopOu3OQgiBB5yPHoEsv0ZZ7jMdhf3L//0f3qDdlQhxnShaQ+s+IHD2XxHFv0Sk7+1jnLbzkd7aIrp0MWRhWzsns+uZ/IC65pqePYtYa17sECnJpY02g1bE4eURZljS22xx/tWGlWyPQ5SGAa3jriO+ufVqBi2je3JE3xq8yagc04t6ZCZDS01mMipX0Y06bLa3GicFRBQRXbg4z8IWScxUStrO4Y4GDyi1G43H8A4n7uBdl1pMHDRPDlTU7F5r5tbovccYw2cKy17aRXqHcB5rHbKOG1C1hf+bWcigXjiZfWzwyhsHUuAzE+5VWoaBrEkV8oej8Ngc5M1kZc8wI7PXXoLeDkpIVtdWlya66bmGc/C5z7J5excrJV4qhLNs3t4l/X1J8sEPNnJas2FhV/clZEvjOwoxsXjnTySzFzYw/CjYEg4vQ34UhC9Hl0FoSHvQ2b67H2sQxlVcG11jUk0QQjCtphS24GznLL24z0G2z538DmvxGuutDZSQjMrRUmRlzyCEpNdv3uXqmd1xXnvtNX7rt37rWX275xaPI6lPyhwYZFVz2dnpCiR9RDHk/FawuTsaOZwQs8QuvPOsbq41nidwXCUw3PR0jGI967OXHvK1znWkl0iToeTyLKS+qnCjIXYyYcc5ev0evW6YXvHGEJ05A3i8scg0Qfb6p0IOPMqq2VaWvatjiqwk6l1Bpwe1vfixRUp6hPD4agNpXqG/2Sa5z/JqUVbNUmvaq2uYqgpka1UhayuxOG1RlSVRVS6+kfIQOO/5N28dcOVgysW1Fh8800fY5Wiw3I88zx9QsU2zaW3fDWkrJYripcnDaaUXqfQIayfo6gi8RQiNxy+MGHiYomFsLL+yP+TrWc5BVvH/riqOKsv0zl74+p3b7F67SiIFsVZ83k35WDV56M85bUXDjMyWIlgDzbSteSrJugpXWSItWVu05eXx6f9yDFXOxMQMpuHau7+Wnm1IpRBYaC4O4xjsqMQehEalmFkc4u9pVnkRiAJ7kGE3UlT/dDYBj3LnmMGRU5ZvYLJ90BbvJbItg3tIEpO2eqjKBe7lBJymO8dxa7sqNyRphM3eidcSEd/CVasY+w6y2JC0dKO5bRBI7N/YO+C6l0iCvfBx8Wp4nzWrEHgoakLAFRYhBS6rM4gX6CjyJNdrlk9JJ2PiOJmvW5N8gvceWQisCc13ayxt0WYj3kDKu7qWRbjJVGZEWR6C9yiVIoTGuQxrM7TuotRy5aHOsBFp1iLN2Dg8IXNWefB4ipoYOBNHrOjF6oROim8yfcftrw+Rk1k8EsGq0cHqIkjsGWR0ssXq7JgtA6k93gVdk9rZUR3r5KEcQf9Cow0X7zzyekUyjnHC4YVHOYWsyWwEHHUmHK5nnE3PspqssZ6uNUoMeO+4cuUKBwcHCO/nUU5HoxH+K1/mxbe/hoxPv355kntWMa1oT0qiVFE5y7jIcf4OSqgww1ZCdKRZjVsP7LVO+351nBSYDQZJJVndOUs2HCK1RkcROo4p85w4bVHW+4TZ87136DimKrJnck5P8preAwnMftXFk/2V03pdZ/sqrTQuCorrqqyI4oh2u42Sat4UbrfatDvthRAC9zgfrT3aEno6fYOiuImrYOP9Hw228bJAygitOuhoBSU7WDtGR6skyV1b+YU4H0lJ+wOh4V9evgIEZXa9BCBqInC0c47r73g3Ly3Jvvt4ZvZ0UNBeSZabxAaI2qHOGlwNeaQqhnR1KQiC45hUE6QQJDql8gZjK5x3jIoRGysbSzF0BdTRFxvIev06PDxkbWWFM53OA0rtxuIxvONo8G/I8iuo+Czl9N0hFtFBnCqShgUZM7fGYjpht6i4099CANLfHQ50ziLxIBWq7m9fzgpebMXsJAvua9SDVwBuXGIrh2wHYYsdldhxhWppVC+uI0cazMrmmOOVa6OnU6IopqrKpSOxvSvJ8qsYM0Eg8HiM7tBKLy6nKruOxCgv15EYUoY9tlI46+jevEH0eQ0f/ODCVcQPDAt76JzrzDOy4V5ltneBL1rIwDCPqAnzQ5js1uvUAYhp2HNVbagyyGzYoz0BnlVNeP+5Dsoh1XSPTtRGeMHADhmWQzbkiLZuM3RDhn5E31X0y9AXnpgp/ckRm+7BWm0RPYxlxVPfdYbDey8Y7z03b97kZ37mZ3j11Vef2Yk9j7ifpK6sR0nBaiuik2j6rejBzAHvGeWGYVY1Y0OuYlh7IVjeVTnnLwi4tsfRyDBLO19tw/k1h/CGuzvDxeN+66Dx+RFf3b/GKJ6SqhSJXJ4GMIHErq5dxQ5H2NEIXxQkSYLpdVH9/kIVbY+zai6iiv1rY/Lpq2j9JVR6K5DZdZtdCI/Nz2An76DVTdhe7T9AECzKqhnuFq2j/TtYYwCPlBKlNdaY2q4t2N81iRmJfXk/TLxf3p/iS8eHzq+ilozEBjCVCcXHMd8oJRVSSZRUJMeIgqbycLwrqcxobhuk6lxR7w3GjPHeoqM+SbJz6oXroxQNQ2PYqwwdJXEehsZyVFmq4REA43/y/2LaXyORgn0hyJOILzxiM7WILOeZzTgEm3HVi6g6gXjVUrLRiVjvLPg9dXz63wbVWkdOWWlpjqaynhZ94B9SE26elZ1zjcVhzKB6MXItwR7keGtrn8PQ8J1hZtOq1lth83pKeCJ3DoAEeJiz/z6PlbKfljvHcWs7Z4P83leCcvgq0UqOjA7Ji8/h9t5DnEYkbT1XDDRBbF8bjbnhw+CPgnooyIVstPpDeo9tsqlyEqzDHOSBxBZhwyqiEDsiIrUwR5Envl6fFEf1x304bTcZayfzD6xECIlzBSAwZkI2fYtcXCNtnSeO1peq6RIJQUfJ0BSqb1mzYYwzccTbO2kjGW6z+KbKOg4mJW/tT8lasCmCakDUCUnRatx8XtsMM7VAMayt74bBSnzlApgiqAj6F8K+TDV0DXhP9tYR6VDihK914gIjDJHXCASlMgy2Cl7qv7wUCgFXllx+602OxuNg0eup77GAh0FRcPmtN3nx1VdP3Qbvqe5Zs9nFqP44DgPcOPmvneb96jgpMMu8TlptuuubSKkosmnIRs3z+fHxwZ0Hnp+2u8+MyH7m68BDcBqv62xfpbTCl/WNUwiMMSip6PV7jTlbQe18tPXwGCeAtJCMxxbncrpbbUDhvUHKBFXHOeloFWMS0vQ8cXyvu9RCnI8eQmZLG2Kc7pzZ4fKr70ADA2PZiFwj69ZJEEI0Z8P6NLAlDG+EoSsVh8e6Bf1zza1XD0En6rA33aOTJYgUcpFjvGGrvbU0Fq0zHB/CAsHB0RGsrnL+3Fl8UZ6q0OXxJ1eT2NPLIMBwg7gtSdR70bFeioHhmVtjPh2TX7nMxnTEfqePQ4Ttdl3FzPZfTgg88GIrYTtu9jrwxs1d8BBhSFhGMgwJy7vxjk25Xz3MqVHrCCUl3nvyJD+1qMGnQWVGGDNB695dIrs6IuMaUibLZTV+H4k9G7ySPtTcQklwYK5cYSoIa9sC16uThoX7rQi9Jbgp7mZhCykWTmLDN1AT5vXHUxYjz6ImfOJzHT3m8SNw2j2MZcVT33FWVx+8QL33XLx4kZ//+Z9/Zif2POI4Se08TMYFw3FFXlnSSHEwKSitZ5wbpBC0YoWWwZ6vMPbxP+C0oOKgFLAlYrLH+c4EKsUg16y04fyGQrgqKAhOUhksEMetg26Ob1CslXREn8pVRDLCeot1i1GJPm5C3A0HmNEeSIGVI8RqjC9KlOrD6Dp6/w6y/+gJ1kVN2STtiJ2XVygyw+jgvZTV5xHRLtTjDCY/Q3n0dpKWorfRWpqi1QPj/T3iVpuqLDFVia0MSmta3R5pu7nmwHESW4pQTDvvuTLK8ZcP+OB2n3ijQaugE6AjXRf6/h7Cut1qU5Zl43k4D0xbeoN1BVJEWDvBuQKpEsKmxZ/6FOajFA23igptDN3a4u7IGNqVJTElex/6Ifz6WYhjlBB0leKFdspmpNAnFKqLzHKekdkrWy2ibsQoN83GYNSuIeSD0DQpQ6P6fN+AhaMieoDMdj7UJaub6407iQAYb9hdPyItoDtOgsOhADlrqLtggas3WqduK/4odw4Ae3hA8dZlvKnweVFb2wlkN6gXZBwTX7zwyLXrNN05VBRUAN6DVAJrHNZa0vWvIqOj8JzkFtb8LqOD18jHEVGq8A6KVNHfXOz6teYqdpxhV8VYPKJ2t6BWt+LB1b/vF5agqTLDTIkt05BHLxIP00Bei7rJsghHkcddrxAU2eNRUGR776lMRVlbuDrvUUqjpEDrCGMq0jSl272rxjltNxnvSqrqqCayS6SM8N7Ol1nnCrwrcb7E2DFpcpZ2+8XGmy2Vc1zJSwbGkDuHJ+Rid1VQDVxIYy6lcaNkQGUdb92ZcP0wY1waCuMYK8H5VJHkDt9VrL3QWxiJ/Vjl6EwtEHch8lANYXwNshSipFa2FVC8eeJfP/U9QR2DIQ9tTWKHWmqWK1AJi3ISbRRnDlfprTfvJODKkre+8mUGRYFAIHzd8BWh9hJCgIej8ZgrV65w6dKlUyWzn+SeVUwrJoOgyJ4OS/JxBc6jE4lUkqp06Eiyca6zcPer46SAKUt0HJO2u9/Q8WeFJ3lNv1mc1us621fFUYzWmrIssdaiVLAXbzpX9GE4PjQMoFWXwmY4VyEEKNVByhhnpzifYMwIrTvNRmTcR2YLF+JcDs+eZfTu19lRilRKJtYyMJbNeHn2388F8kEYwmqt3bUVzwdQTUMO6RLhTOsM0TVLb5pykI54a2XIRrrBy6svLy2JLYQIqkznApkt5amvV48+ueMktkQIBVgqcw0dK3rdZiKcJod7D/2axLJz9cuYlU324xa+KnB714m2ziPjBC8kTggupjHvUG1Gdx5kix71/Z8Gj60HncNOS+ykRFQaGYffvXclwsXIUgU1eeVQNkJOH7xuT7smPO7U6LynHI+ZTCZ0O13SbhcpBHmeNxo1OINzeagBZ4ES3mHMCGOnRLof8oet5/zqO1GyQZt+wOztUV25GgYv6z5hmGc4tldRCu8c1ZWrmIuX0GfOLPQcZ8PC92MueLmdzR07F0liwyNqwvwQRruhd1VloOMwNJyGcyZdge6TvY7Pqia8/1wH5ZA7M0U2AuMNd7I7RDIi1S2sN1S2IlIRSoT6saVbnOlso8SDdfWiHHHvx8x12OVFY8NWT73L+NVf/dV7Hksp2dra4m1vexv6GW5ankcUxiJFsDSdFBVFTWBHStKOFdePwvRKZRzGOdJSsd6JcT5kETSOfBCseaMW51cmrHYEHV0g5GqwaKiWy7Yg0SkCQSfqzCevRuWIRC9mU/jEEzYemK3tEcGvF+B2/fEYLGrKRmpJq1YC7l97V2hdRbew+TbV4O1IqYhTTau7HBsAqTWr2zsIoMimRHGCAOJWi/7mGdr9lWfaSHkaPEBiC8AF1TjeczUr4faQD7c0+hTVl0+LKAo2RkeDI5RSKKVopS3anWB313Qezv3TlpUZYc0dRBTIPx2thqaL0BgzoTKjB5QBp4H7FQ2Vc6i8pMxLSl3b2xmLN5aNNObcpZfZLQwHlSGVgu04IpOCI60eSgwsRNHA3XiMibWYQ4OWYu4oskgSe74BLIaQViArKEsoJ8Fi3Jac63puq5RxqQE/848AoJsKts/2EPlXH/ozFjUoNCxHTMwEea5HecsTDySOwGKJms0s+57ijEF5gxand499rDvH1S9TFRmi08HmR/giw1uLqGLUyirR2g7xxouPLFZP050jaWmKVFHW1vLeWeKVryDj+1xE9E1IHEK8TtzSQUE2NRSZma9zi0AUx7xjcIRsrXCdeuIawu+eYH/vgUtaNmsrXtuI+zpXdKbEPu7OIVRQZasFKoced70C2MRw5I7I8pwiz2u1gEYisd6y2llFqtAQyn3OSmeFzrEht9N2k6nMCLxFyhbOlXhf4b2tm4IKY0ZIGSMIjZi8uImOVkiSxTYt7sfAWA4qQ2E9mXPYWkw4tY6dJOJsEjWuaBtmFXfGBVllUQIiKciN44r0nF2L2dhss9JeXM36DStHLVASFLoNuV3AsRgMESIP5gM39cZFAF6B9ILWSCOnHhqOSRvu3WZQFHOXC1ydKToj4P3MZV5weHjI+tr6qWa7Pck9y8WOockoMkPhKjIbcjpkJYIS20O/18Kt9hpxv5Ja0+6vftPHnxWe5DV9JO5b304awjqt1zVNU/KkthfXej4IvLqyujAb8afFfGi4GmJdgakG9VfCO8l7g/cVzkEUbxLHGyjVWQ612zEyu7pylercecbvfDcbx4YEpRMUxzNevo0nw/GoJwh/CrV0/UF8iMZYzbp44djMV+jGPTo767R0u+mzm+MBEhvw1gUrXyFqhTbNkNknktgghMJDOA6sriyOzO52u6z32+z/zi89tEwyVcnh7g3kNMN1VjgyFflv/Us6H/5eZG8VLwRnkoi1TspVHr7fWu+36Xa/OWL2qerB+99C93Pfj0jVOM2asCxKKlNhJpaqKinLEuccWR7qxE6n23jU4AxSpnUEUh3VZqc4VxAnO0jd5fcP3+Lm9IBb+YQPnv2uu2KCBqC3toguXQzuIdbOyezj8NbigfjSRfRWs0LC4zju3ji8k9HdbBHvtLk5yBYmenloTZiU4C6H6CY/AhdDnIBshdgmeRaSJ3O8elY14f3natKKW+JuRrZxBtqbRDJGSclqskYv7pKZnMLkJDolinvcesgA1iIdcWeYuQ678QSkxDqH7A4W6jYM3wCR/X3f932ncR7fEki0wvlAYlU2bP4rY8mkoLKO0jjW2jEu8uSVZZhVOA/bvYR+awnIwSqvs902Ed7RTTR4DXFd9EXNTg0bV81txROd0tIpqU65Pb2N9Q4lJGvp2sJybx42DeSNwdy+hT08wk2noQ1kHbLXg6pC9fvgHHp767GK7EVO2TjjKDJDlRukUuSD14haO7hilSiRKC3pbaSNq7FncMaQT8cIpYlbbZRSRK3tZ64EeOrzup/EnoUN15gRFVezEnH9iI+8trUUlvjWGkbDIc55lFJYa4l0RK/fnzdalm/a0oCQOFfC/LgAb+oM0sVvrmcqtiNjqDxczkqEgDiwQuyVFQ7I6s1qWmfidJRiZJpVCcziMUIuTkVhHImW9NKIlVbExfX2wsjsJ9oASqBTf5yE3cf/nEUMChUmn9tIT3aCy0U8EOAc3gvG3ZzD9Slu6hlWw8Zt77z3+Ok0uC/EMRQFIopJXnkFvbXVjL1dDakl/c0WRWYwpcHHb2D8LfAhgTrcAxTg0K1beCvBvx8hJEIGa/JFYmbT+vZsQCUSbsctnBRIfCCxBZwxOd+5vdMoiW0OclxmQsPf+mAlLmsl9tydg1PPxP5GoJRmdW0Vf3hEWRb0+n3iOAbvORocMZ6MSWq19mk6iTxMfVGWt6mqo6AMMCOo3URmzRYAUU9Za93D+4qYnLZ98YGf8SyHbx6nOrmdl9zKCibGUpU55s4N9MY5olaLTCtuZwlrj7gXLELVEk1zimLKGR9cQ7yC0liM85xJU15td9HFo6/ZZ/maPlY5mh8Ga1ZrwkCWycEZkDp82AKiTrAbj3vQP3tPnttp7wlUL0att3B3poGyEvOocQCkUEgfyGG7JnHtEJnQJNpCsKIUA3c317meCglWjD78OxCwtrZGt9e8ilwqiLsDyryNEIK4pcPaJEBHYah441xnafZbzzXui8nwHmRm0Oun44h10v3qfGopZIE1FtUKMU1SZk+c330/TnsIszIjTDUMLiFmhHX5fPgqjrZwPkfKNlIokmQbKVOcy6kMS0Vmm4uXqFbXsEV1T4a7857kgUyib+OxOB71NFNke9t4f3AG4yqG+RBxrSQeAkIgpQbnaY8k8noFL/pTdb16UtxPYgsXHPAQs/QhAbIhMvshJHYYYgF8iJqZTt/ClJa19Q+jFtB3W19f5xP/1V9jPB4/8nnjwwPe+vzvcPn3f5//h0/51G/9S9p/7EeJX3kbf2hjhf/zqxeJHvP+73a737QD3qPqQZdV2FGFrPs9rnT40qC6MbIb4SsH1oMSyEQ91Fb6NGtCaw1ZnpFnOQhBVZUoqRBCEEURxhjKsoSGogbvR6R7GN3BmFEtdhkiVYJULX738DI3puG9dG2yi7z9O7x/+wPNkdkPicKYwVuLd474hUsLtxV/EszI7M5Gyh1jORhk8yztQVYttF94D2aRuXEHBlcDt1XVMU46DfFNhzQa33Q8KndaTRgUA6y3eByVtUyrCevpGq10eYau7ocbDXHjSeC2ZBgedqMRbjRErZ++gGyGb+iu88Ybb/CJT3yCL37xiwC8613v4i/+xb/IK6+88kxP7nlDvxXVBECwYh1mFVpKIuUY547COJSESEqs81gHvVQ392afwZa1XdAgTKukK8GKoZyGasqU0N4MxxuCcRXXRtcYlkMqZ6hsSTtqEx3bLFnvmFQTdie7tKMO/bh3qmTAw6aB7ME+VeYQ6y9gxSFuOsXlY6gUqtNB2joje+PxUyuLmrJxxjG8k1HmFiGDZauONNgN4lYgsZOWptVdDvWwM4bB7V2KbPpAJluTJDbA3qjgykHITJ7XHcc7gb5Wjnq4Oi54aVRwpt/8JnBmH9ROE6JiQtVZJy9KqqoMpMASYDZt6bzBuRxjpzhXolR7TgwE+Y3G45Fy8a/rwFhGxrKuNQpLVTdNViOFAq4XFdZDIkWwwCYo3NrKIUWzKoFZPIaWgV5ZbUVMK0tpHDcHwVFkUevVQzeAroL9N2B0M6xdzuG943bWZmxiuils9xxCKth4O3QebkW5qEGhRKf4/O6U8PiMIbaO7iRh3M0pz0m6ojd3FRmWo8ZyR2WvhzcGXxSIKArZTUmCiCJ88Q12XZ8xpJa0upqjwWex3KjXAAnMrHBB1I0WoW9SWEWs3jPPyl7suQbb1WQ65j1Hh3yhyrmdtHF1DtaZMuMjKx10g8MBLjNBrWZdaJ4IcJkNmdjToMT2HmSiTj0T+0lhrXnQIUTKYCfuHFVVEkUxrVYLrTVpkp66k8jzkuP6JKoWgJtFyW5W4gAzPGL46V+l+9Hvp1xZo1IKlWhGj7luF6pqOS4IkcC4/ngCPKuBpoeqBGb7LOJwotUR0AI3ITCtMgwOR3Fossfn6272GWjfVWKc+p5ACJIX+0yqMdGgrlGEPzY8GIqWSbfkzsqYzmTS+OCVSlN2lELoiKOyAHxo+AsBQuDrnO/19fVmbVpnqEmC6fQKQu3Q23wfOEFZWsqJobuWLDwC40kxGyA+LQvx08ADMRne43KLywzyFPa0z8s68Cg4l+N8hbV57SJi6w+DsSOU6qBUCyEkRbGLENHdTFLdOfVYpyeClOgzZ1hxjoF1DIxFCoHznp5WrCyDEyJ3XbAajXB6UsyinrLDsKbZEpKV4NrYMIyruDa8RnozRDhZAUL44IMkBTiw+xkFnHqEEzzeVnoymTA+vMY6oVzxgb1mLhL2HkEYdOfwGqvtAzqde6e2T2ugpShvk2VXQqLIMRI7GJ3cJbMRjrK6xtGds6xtXlrImrW+vv54gvnSJd713vdRTKec/2f/lD8GRBub/HsvnOdvvf4q3e5ibPAf5SRiyxwXVYikXj8T8LlBtiPUU/QET7MmzPMc5xxpmpLl2TwjW0kVBoKcI88zVvorSxGRIWRMK704j8RQukNZHvF7R1e5MT1ACslMW3RldBVgKcnsZSexZxBCYGLJZFrQS/V8fR3lhmFWnWhLvhCoGPrnQv/v8DIMr4XP404YwMoHjcflHo/KHRQDVpPVe9yFm+wDPglcXoTrcnZt1p+7vFjocPNT7z5+6Zd+iR/+4R/mfe97Hx/72McA+I3f+A3e/e5388/+2T/jB37gB575SS4rTpy6bTkm0nDHlezpAi0F2gtyHIW3dCe63lsLCuvYKGPOZ50Tc1EXYntqy/AmL+p/Q5nBdD8oA2YTl3E73BAWNLly0us6KIcU013WrKH0FQKYZhlaaN7R3QEkw+KISTalU7SJZYJ+SJ7Aab+usze3iCL0+jo2SYIyQCnU+jp6cwO1tt6oqu1+FJmhzC1xO1yfPgUhKpK2RscKFQUie1kaK/l0TJFNSdqd+YR1MZ2QT8enam/3JNjqJVxab3N5fzoXhMzhAtFqawLzUjtma4G2l4+CqQx4R7r3WdJsl7y1w7j9Nvb397mzdwepJL1en16v25gVXqR7VDIlz6/X9qzh9TTVsKavxijVBm/QUb+RrLbCeWStvrVASwocgqn1WO8x3iMReIJ159Q5UikpnUcJGlUJzOIxKlsrF4CstBSVQ0mxUDL7kVaS8UtgfwumB0F8awt8T2ELgYkdN40IBatdAdMPircoDU2YY+vYogaF+nGPYdRhVI7mNkJqUzHq5AzjKeuso1FBTywEhWnGps9XFW46uavArarQTKkHWaobN3CTMXJlJQxlNZCHM8Os0RLeLQq4q7IQs//5kOdm3HV8tUOSniFpgIid2a6m7S7vu73L50zBLRVzpsx4rZog6OCMaYwQ8MZBTWKLONifOwDPwjOxnwTWGo4OjyiKYr7+T6MpRVmQZ/lcGaBVidKKbre7EDeRh7r0+IrJ5GsYM0SICOcrnA0KtzB8YQluAhIhNEp1aHdeIo4enGp+FsM3T6pq+ed7A/7R7gE38pLxG19h+OlfZedP/El23vEOXm2n/ImddV7vPbqBfZqqFuMcg8xw5WDMYGqQMuytpBAkSvLSVpeNzuP3Lac+0HR8nyVUUF3Pphtn+ywX6i+kBpXUKu2oGctWIRhul1DkbOQrc6vGmUV30XeUZyU9ekvRcJG9Pro74Mx4jJeSgbVBiacUzju8X0YS+zJ4cPIGeSlI9HtJWhoB6FgtzV7rOE4aIC5aY1a2d5aazPbGPRiTIep17xTwpHnezlmKslZp65lK+8lagKeeky5TnKtqErsMJFsNazOE0EixhrVTPJAk63eJbDNaWKzTkyCSkktpzH5lOKyCi1dnCeoYuOuCNS7McqjZ7sdsAKvK7+6h+ueC6KUsw37K2+Ay0qDCDWCYD2sSOwXhg2Dceyw2DGKJMI61KDL7iV3FTvr8JNw8+fBpDLQk8Tat1iWy6WU8wYkhtFvuDtoL4YJjiz+LzVcXHtv0pMiPggr3z05u8YcuO97K93j1ox8jaTc7fCG0DC5Xfnldr0xlUFKRdlsIIZjWa38rTdFRRJ5n9PsrrK6tLk1EhpDxfO2xruALB5e5kR0Eh8zwjKAqd245yWwXQn2WmcQ+Pnw1zAze+7mj3CxOszC24bMkrEc6hXQN0mNxQksUhzGtJuS2oMqrubuw9a5WZS8vkS3TBOvcXeer+nOZLnZ44anvOj/1Uz/FT/zET/DX//pff+D4f/wf/8f/f0VkP1GRcvx9LIHqvq8PaSz3AqiV2MNQnIaqDwaT2uYuBhVBNQ2WDPFiJtieepr5CRUXx3Gar6vQGjeZBBJAKfx0ircW1e2AtbjJBLW2XDcnWzlmAgYIf0otkEqgIomtHAVmachsU5bBKnZGXAiBEBJTlg2fWVjEP/Ri+P1e3p+C5R7LWAs4AZc6Ce/rtSC30G1+MlxK6B78Lml1G4Qgnd6gm+VcVxfn+Uej0YiN9Q02tzYbKVqFjNFRH1kdImUPIRTGjDFmiJQJQiiUapOk54ijtUYUAUFpHSzCYiEYEWzEAzEUMhsdUDiHRFA5h3GejpKcTaJGVQKzeAwt6+GQKpzbShoysfppxLhoeNISwlq0+XY4eCOsVeUUUU3ouhEgQaWQrobmSn4YLFq9DUqCBpotD7MRGiYFw2IIeNZbGyghg/2xbmay2Y2G+Cwn2jmDvbOPdw6fZchaUem9o7p1G5XnuCRtJA9nhiTeJk0ukuWXw+9WyLuuF/N1LOSkSs7T7Z+l1U0aXb+k1qxt7/Dh4YArh4f0igwdJ4wP96nyrDFCQGiJs4Glmq9UHkSkFp6J/SSYuYekaTpvAA1HQ7z3pK00DGWJ8Lxev7cwpcCjhm8Kvcl0ehkpI7y3VOUhxk7mDVbvTG2DGdFqXaDXffeJ69ezGr55ElXLB1YnvNlb5a2s4HPDkJEqNzZZPXeB7zmzxvfurNNbwHp10ut6nACwLc+eKaisoxUpWrFis5swXu9QPAEZcOoDTffvs/BQXA5rV9IN7lcIcPWGUap6mLg5y1atNFfXDpBHkrWsHgj0cNAe4c8kqCUYvJpBRBHRhYuo0ZALeY4YDjmaTrE2SPTX19aWhsQ+PPwtsvwKOIFH4r3FiuuMJoZYvAeBXLhryJNimQeIH4VFEwZPkudtreFg/4DJdIJ3Icqj0+6wvrH+RHur075nRbqH1j3Kch/vXf3eCfuaWT62ryOcpNR3IzLq/5qIdXocJtbVg8awW1RMrONSGhM1SBTMXLCWSs02w/0DWLM9VNwNe9mVi3ftxZdA4WZHBZ1JAsJTGzThvA8ueMd6RdJL7EGG3UhR/dN7jZ9koMXj2N3dZXg0CEPDQt6dcasV2v3VFXZ2dhAnMN2nNtAiZMi+hprMpo5vmsGBcPjqLNh3Y5xndBDe88vSJwQ4uHGV0UHwHfrgWo+tc+c5vHGdgxtXOfu2048VexRkSyMzg8vvRl4QK25Xhm0fLUXkoI401ll8WSKEQGuNMbX7ofesLBmJfRzOOz679wVu5IdBiV3Xf7M6UEoJbvmU2dWVq8SXLi41iX31YMo4r5CFYyw8hXV0Ek2kJM57nA/9xKXAEsdhGFcxKAYMiiOcdxhn0FIjhSRVCTudnUbdrh4F2esjuwPcaDQnsmU3CFwWiae+83zxi1/kH/2jf/TA8R//8R/nE5/4xLM4p+cGjypSBlnJ7VFBO1bz4vT2sMDj6cQarSRJJCkqS78VsdV98A21ENvTKg8F6uzmPVMFFOO7x/B1dvbGQgiAk17XQTnkyvAtPJ5EpeA942pC5Sq6UReHpbIGLRX9uE9bt5mYKf24x2br3sL6NF9XX1XY4QBXFDAe46sKbwxqaxO9vhFsFxrIEHgcVCTDvb3e5zvrKKaGYmKCzXgcFqQiVUtheafjGO9dvan2VHlGMRkTpWmjyrYZpBB86OIqLrNcGecgggrXeh9I7G7KB7a6iMKemirgqeAd6a3PoKrbeCEJUzeWVX8ADvY7r+IRlGXJaDyi21uMwu3EU/UWpVKEUFTVAGOGBP2gJ4pW6s2gbszWbkUrBloxMBYPWO/J6wlLQb2xBlQ9tQjhz0iIxlUCs3iMQVbhgVEeJq+ch1asaCearDTLMWnZ2YRyEposiQq5N/i64eJgvBtuaGkvTGI23Gw5yUbI1jfdYTkCAalK6dSxGE1g5iaiWh18WWKPBoFkKwpEGtZdmaaIKEb2es2uZUKS6veS2wqvboRDQtWuF4J6ZAjhzyHNu6lyR+sUb1mPsxE8jtyO2LSHRN10TghU45xOtEvaefjv/rTcZGRLI1ONGxZzJbbQEqGCJaMbl0HVtiSqbFOZOgf37uSdryMZOp0uVVVirUVrTSttLUWTJY7WsckEYyZYm2FdAVhcbR+JAKVaRNEqUbTRvC0r8Go75Tt6bSIpsL0Wvwe8u9vif//CNt+12lsIif0w3E8A9FLNnXHJaitiq5fQitXy2LXev8+KOyB0IApUUl/HKhDYzgC1QjtdbSzSqR116CRdhtsl/vaQtazLnXTA17s3WMlXaOkW1jkqVz6wx2oCIopQ6xvIquJsXuCynKFz9KXkjAeMhSYJ4jmJfRlciMLwtaW8x6GSWxTTz6HcdxAtaGj8abHMA8SPwkmEQdMxGdPJlEE9HCSExBvLYDggTVN6/cU2Ak+CkDGdztsoy32q6hBZr0cCgfOOSK+Rti7hfUVR3J7H5szcG5qIdXoUZpFPK1rNa66BsQyMZTNu7r4wc8GSQmCcZ1IY9sclWWkxzrPeiZtbtx4QutR7KFvcu54JuTCF26Pq7E40wLQntKZxGFbB4+rey7xW9OG1VispsT6C6YNk4bOqs59koAXAv/QaB2+9xcHBIcJ7hA/Xpxew2u7QufAKNx8S73aqAy0nkdkzvybhwJzFTd6BEw5bhT7WaD9fmj5hMZ1y/Uu/z/gwENmmLJFKIbVmOnz8Pu3UoSR6PQ2xTsbhleS394ZcPcy4tN7mQy+uN05mR1GMMYYinyCkwDmH1ppup0vaSk81qumbxZ1sj2ujawgESp58jlJKnHNcG13jYu8i2+0zCz7Le06G6H3vJzt7nvHaOlNjWdE0Omh1EoZZxTivSI4Mdlix2tPcSmB/XNBJNM5DN9H0W0tCwM7iMPLBvQNZDcblznCQHzKpJgAUtiRRMcYZ+nE/iF2W2F58NkDsRkNcXiDTpBGXxqe++2xtbfHZz36WV1999Z7jn/3sZ9ne3n5mJ/Y84FFFSpU4brl77YLoQmUcsZYY59FOYITnUrtN1X5wKvBZFigPLf6qPciuQRGHiYp8CGY/ENe6HYq/KoNiAgMZ7BlOwLNssJ70upq0Yte0uD3dIxUJDoeL1oJVq5VoqSl9QUd2uJBeIFYxIzHibOcc5X03gdMs/OaKtnPn8EWOOTiEyQTVakNNrnrA7N1p9I1/P5KWpkgV5dQAnnxSYU1NuEVBqt3qRZS5XQr7oLTdpWiNyccjiukUW5WoKKaYjBnc3mVleweg2Sy33PK+XgoCroxzXJ3eerEV84Gt7jwnu3EbIe/gyqeIRldxQtWWgR5fWweuugN0/nVupa8ghMRaGxRvDSEQ2EfgPZUZ1pP/AikNzlWBDLAToJlBkZmN3cBYCudpK8m1vKR0nlRKRtYytQ7rPUoIEilRAirvG1cJREpycb3NSlYxKQxHWcVgWrLajmknGu9Do0UrSTIumiUHVBzU1fkAxreCfRA+qAUQoQHj6ilMOPVmy+Rw757H1lWMqwmFLUhUQjfqoGTE/vQW49Eeo8Mv093eIYkioqLC5WN67Q5dq5iUD9qR3P/9TwNzqyApw+AVYPYq1MoKaIWfZmFQJNILy8N51L97elSQTbdAH2H8TUxpGe0d0dtcRccKLc8Sqx0MB4wPDUWeknbuXWuf1ev6DeVi3n8pPsQ68DhOxU1GSaLtFhXgchMIbA/eecxBzjwLQ4LsRESbrUbJbB2Fe9F88s4HRRuEJmWShKGLPM+Jk+YJYQjEQJKcBXYpywO8rxCizksGwKP1Sp09uhxEVk8r/sjWKhfTmLIVXsd/b3ulcRIb7iUAIKxd3UTRb4UGylLZtd6vCBASkk5w4/EOkl7409lg29reDErt+6IwFol+3KMf97mT3WG/v8+g3aNsOXr0uJPtE0mNlppIRozKIevp2lIoB9xoiB8NORtFrJiKto7wo2Gzw8O1nXiWX6lzRetrcO7SGmpt3b6NN1+kKj6MTpajOVzlOaODO5R5WPudMXjfmpOB3jv0Q0iWxmHdnCQQiUIlYdBtGQayptkUay2tVmtOAGdZxjSb0u60yfMcUxl0pBsjC5Rq0+2+ndHo9/DeAAIhNJGIaHdeJo438K7E2gnGjOb/Dq07jcQ6zVA5N99/JVLQVpK90jC2DiUELSXRdQRF4fzjv+EpItGKynqOpiX7k5JJYbDOkxtFvms5t5Ly4ma3mXXr/gGs2R4KGlO4PVGd/SS3oynwtYd/+dTdMI9BCMmlF18E4ODwsI5Mg75UnKkqzPVrjTlfHSezp9PLtVrc48sdyuHbQYB3lrQT0e7FIAXl1DTeJyymU7766d/gzpXLTI8OAdi/doXylZdxxtBegmEhAJREdmOc9/z2WwdcOQjxbZf3pwCNk9lVVaJ1RNxPcM4ipQqZ2a20MTHLk2KztcWF3gWujK7inAsK7PvgapHJxd6FxgcyK+e4UhpGvRWkdThjGWjVuGvI/chLi9ovsZMgaHEjw7pVlGvB+aabaHZW0uWIxoB7e4XHIzIajMGAoMa+Mb7O1IT3eri1elKd0o7aaKkbd7t6HGYDxE12A566Mv7zf/7P8x/+h/8hX//61/kDf+APACEj+2/8jb/BT/7kTz7zE3xeMSMEjisCIiX44s0RB8N8rtLupxGt+PQvgadqss7uPcdt0HPg2qP/2mkWflpGvLz6MgDDckjlKkobrE5SlaCkoqVSSlcyKAeNKduO52OLKEIDVVFgx2O8CzZB9vAQ32rhjcE616g96wxSS/qbLYrMMB2U6MgRJQJTOXQkMaXFlBIhmU9eNgmpNSvbO/jbu1R5Tmt9gygNzZViOmE6HASVdoNZbt6EXOEPbIVi7+q44EKseF+SIAqLWwJVwIzE5uDreKnwfuZvVH+5tsHuVns457muLqJUPM8gbQaiPjcDs2xRIZEyDANZO62bLovB45SYt6uKc85QeU9eesbWkjuHFoJEhhzqlpSsCU3LS8aFZcVErB67Tk9LiXkSIiXZ6CZsdBPOHrNvHecVR1lYFNLKcf0oaz7LTcVBXV3lELdDRost781ImDeNT6fZ0u12We+32f+dX2K/PmacYT/fJzf5vOGb6pSNdIPMZFy/fZk3PvUZ3v0930W73yczGevpOoO4x+ARP2u936bbPb3N4/1WQSKK0RsbiDgK+dl5jur3kWnr1PNwTnpd70eRGbJRiY4lxr/JcHCDL/zWDV7/8Dm6vbMk+mXgswBUpSVJ9YnNlWfxup7kJOOsrW3D8znhGqUpOk7IhgOi9JgiO8/prK09UpF9mm4yItbEZzvYSYU5DOS1Nw43qRBKIuJgw2YHBbKlT9WS8XFI05Q8ycnzu++vTjuQv8ePJUmyMFvxx8G7kqK4iTETZg4i4OvswfDYuQytzzRKApyEq3nJ0Ia67zPDjPbeEX9ka3VOZo+M5avTnP3SsBFrXm2np050z2IwXG0betzObunsWk9SBOgWMK5dsGw9PNyC/gXoP1rB9azwuNrlvDZc45ARIzqtjFil5Cbjjr9DSkpf94iVI59+hRUOWYnvbQ4vsm6ZwU4muFHIm2pJiXeT4IwzmTRGZBflbbLsSv1IErr/xwg0Xx8XFqF3yfLbtPqXFn+i96HKc259/asU0wkIibd2PjCkouCKlbTapO0lbGhbhznIccW9Kmy9njbuKAJhSMxaS1GUKCVRdTa2d56jwyOKopivY3mSN2bfGkVrKNXFmFCZel/hhcbaCWW5T6R7tNKLVGaEczlSpkS615ijSOUcV/KSkbFIIai8o3Qe6z1T6zDe07KS9UjjfCC6m0QrVpTGcTApyCsXzilSrLYiKuvYn1Ssdxpatx5mydraDm5YDSjcHmfXbb1hXEwQtyv0GLwIfTfhAS/I2iV6p8PKI851IW6Y92FGZruq4mg0YjVNOdfrIbxv3sXxuDI7u0KkLxDF78G3oMwMxjjavTgMwIanN94nPLhxlaNbu5x9+2u8cSNMCA9u73Lza1/mwjvezfq5i42e33E47/k3bx1weX8a8oXruNllILNDRrYkObaPKuohq2WHFJL3b38AqO3DHfeQ2c45LI5LvYvN2orXWFbXkOPw3lPuZjC2d0tZB2JiUN4jtlPGhWF3kDfbG7wfs17hEmFYjjDOEMkYORsMrZ1tYhU3GjP4POGpq+K//Jf/Mr1ej7/1t/4WP/3TPw3AuXPn+Jmf+Rn+wl/4C8/8BJ9nzAiBGW4Nc5z3tGONFKGA9R6y0tKOT3eDcmLxlx/CaDdY241uQjUJu72ZykUoiFohq03HsPbiQxXZiyj8WrrNa+uvcW10jd3Jbm1tZ2npFpnN6Md9CluSqoSz3XP0497CFQJzRVutakNIfJ5jsww3Gs3JbH32LDJNwbnmi9TZuWtJqxdjK4dzoQg1ZW0hLMCaMNG2LPltUmt0HJN0uiTt9vy4EJJ8PMRUVaNZbrNcNgl8YKvLpW7CuhPotkZEcilUAYx24fBNQCCkQliHqxfS+l9Ri0Y8PXuHFiu0OpuNkgPeG6JoFWsmWJEjZQiUmikGpIwQYnENn29IifkNYJET4jMcH8jaGxUk2rHZjec5OEuT5RalYZ2K0tqeNQw4AIEoyIen1mxZX1/nE//VX2M8Hs+PfWn/S3zqxqe42Ls4t666OrrKd537Li72LvI//Or/wF/91Gf4vj/5frZf3uZs9yzfc/576DzGUrTb7T421/abwUlWQSJt4fMMO5kgkhScxWXZqefhnPS63o8yN1z+wj6jwxyhLF+8+l/z0791gx//Ux/mhZW/yMpWF2dgMigY3M44/+oqL7y+QZzee394Fq/rSU4y0+ERw0qT9O5dh7rdDaruvYNWSbfNytqjB62epZvMw5ToflphBgUyVdhhhZuWGFNwNNhl7cwFNBGqStDbj75WT9NBQCnN6toqeZ5TFiXWOZSUaB3Ua865RpVsJ6EyI4yZoHUPaydYG3KRw3qlcC5H61Va6cWlsBWf4avTnLeygktpOKdLacxbWcFXpzkf6HcYGcv/snfEW1lBLCSld3y5ld9DdJ8GZjEYo/yu6rqbaFqx4sr+lHF9vBVrtAzxHY1FYtyvCJAaDt4AV9R7LgfSQhzBAifwF1G7LLJu8VWFGwywoxGy30fGESCwg0GwFm8ISbxNq3UpKNpwtcoxfDqHCA9svo3Ui7ESfNwgw+DObVR2i6jdng+ElJMJbbFKmnZRUUScGmQ5gYe4izcxzADgMoMrLDJVwV2ksJhBAdA4mW2twViDc46iyGuLdkGkI6RSFEUR9lg1A5/nYWjrNBRw918D3lcYO8W7AiETvLesmdt4HM7mdT62Q7tDpIhRqkWSnEGIutfixmDuvaEs8hq4nwwYGrhjSrbjiEhIprVa2+I5E0esNOwskpUWLQVppCiMQ0uJFGBdPaDlfHPr1sMsWTub4aMBhdsT2XV3gDVP/uYRZj+vyQEYdwvys3Chf4HJI3qDp50//zAIITnf7dIrSnq93t34nAU4Xz3+5AKZ3WpdIom35wPi2ahktJ9T56TNy5mm+4TT4RCpNXGrzepOuFa8EMStNi+85/339AybxEkkNtTRyE4slMw+qR5Yc1PG1ZhYJPP1qKwKuq5La/r0r+Gi64GHkdnLRmIDFPU9/3h0yzK4hsxQGsv1rxyRHRaBwIZQW4U/iDKHPqyId1qMiiXpDUIQtyyZGhtgWk1C/IW3lNbg8JS2RAo5txdvKmbwecJTd3iEEPzET/wEP/ETP8FoNAKg1/v2C/04VNZx7XDKpDCkkaJyHmUEiZYLKVJPLP7Kq+FNLQBhIapvR9aCzcELcAlEXehdgJVXHvrmX1Thp2VEohL6SR+B4LAIljECiXVuTmI3lSlwXNHmAXP7dnDkbAe14LwwNRWQLsye9Wkwy8tWscQD2bgME1dC0F2NSJpUEN+H41nZx63uQDWe5XZ/LtumkMj28igCAOjtwNpLcPD1MHwhqEnseoIZP6eyB3KdTPdJ68ZKUyRByF4TKN1DuQznSry3SBHjsbXCzeBduRBC4HET4sY7dgvDxFoy58mtpaUVqRDkzjMylrVIsRZpvIextZxJ7lVkNzEhPsNsIKswFuPcfMJSiobJgeNIV4Id6/DaXSJAJ9A9C+2NQGafYgG7vr5+DxH6pniTbbHNTn9nfqwYFiQbCe+8+E5+aP+H+Kv8Vd73yvv4zg9/Jy+tvEQ3Xg5V04lWQe12WNuSBHt4BIBaW0OtrZ2qk8j9r+tJuHTxEoe7U7JRSWvjJ4Ff4KPf+ZdY5UUOdicc3cowE8HGSo/Ut6n2Ui59YJt4AevY/Zmi3ntMWTI+OqC7uk6UtnDOLjT64nFKdzMq8dMKpMAXFlcYhtmA3/rir/GRd38fvdYKaiUmWm899medpoOAUoGozrOc8ph6LUmSxtRrj4JzOaL+T6k2QgyO2bVGKKVptc43SmJPp1O+9KUv3XPsXx+OuD0t8FffAuDG176KKCr+dTuBtR5fHGd86mjMd33Hu2m3W1jv+eo0nxPdp4WTXK9asWJ3kHOUVUxLg3GOtLSsd+K5WrsxHFcETPbAZIConUQsmCLEYUzv3B3KOuWGy+NqFwgKt1uT22QmQwhBbjIqV7HZ2kQLjcczqaZstrceUGQ/y7rlcaSrNwZz+xbV6A4+GsP0JpQaEcfQEkRoounD6/9TbbDWJIB3PmRk+zoXOcxggnAI4bH5GSjeiVxZzP7giQYZIu6mHwAkBIe2p3ipmhjC9MYFYx4PdlzhKwvWLwWZnechDqnb7VIUJd6H4fFWu02kNdaYe1yFhBCnpoD7VhvEvZ8MqHzISPbARqxp20Bkry2JZeukMExKg6t7GM55KkLPUEkRYpyaWrceZ8m6ZAq3eyAE6Uur5BxhDnKm3YrRdslKfLIYZ1mg0pQuIGaColN2vnoqCEmS7NxzKGlpslgyPSpwziOlIO023yds9/s4YyizKUe7QZEtvKfMplz+/Gd49aMfa5zMfhiJPcOiyeynWgseZR33GCy6HrifzJ7ZiS8TiQ2QyDAseLyf3YRryElD6JW1XP7qIW5kao2ICGS2LRkd3aCzchatExiCOtKYlYj9IkJnD/YJFhGTN4ct4fBy2FsdH8Zae6FRMtu4ikExYFxOiFWMEJZYxKQqZbO9yZn2mUbEmM8jnnqlefPNNzHG8Oqrr95DYH/1q18liiJerHM+ngb/3X/33/E3/+bfZHd3l/e+9738N//Nf8NHPvKRE5/7/d///fyLf/EvHjj+7/67/y6/8Au/AMCP/diP8ff//t+/5+s/+IM/yC/+4i8+9bk9Kwyzisp6Yi1JolCUZqWprQMaKlJntkHWhsaJqTf5aR9sDNZAdwdWL4bpyyWYYAFIdIrPg1VrolKmJqNyJZHUrCSbjU6wHFe0mb07CKWCFWunE/LFhgN8WeHL2rd9mYrUGlEcMpIHt6d45wmNVohiRXctQTad6XwMURyUwaM7e0itUVqTdrpEaYvx4f4DBPdCs9yURK+ndzPaGlZgP7QRuLUO9joMroVc7FmB4l29/fdk0Ro2ikmrK8TDW7SqlDiK6fV7SHn3/vUsm4EPLXZcRV5MMWaMMSOMHWPKitHeAb2tddLWGsXw64z0bdLkXLDsfJrv/5R4kglx23IUxrJfGg6NYSuKiKSgcp5rRcmuFHSVwnlPL1Wkacz02K6mqQnx43iUlWujmE1b6jTYstoyqLJb642tW+vpOsYZrLMoqbDOYpyZD1i1o7CB/uDOB3l96/WFn9/j4KvqAVW2ubWLG0+Y+Z65yRi11mxTqMzMXRK7F7OyGUjTpJXyyju34XduM9rPufDOdXr12nW0O+Vwd8qZl04/I+34oJU1hvHBHYrplKTdxltL2ukuNO4CHq10d7lh/Js3KS8Pke0Ic1Rgj3K+dOfr/NgXf42Pf+yP846Nl+h8aIfuh3ZO+O734rQdBPI8X6h67ZuBlCm+/k+rLlG0QlUdIYVGyoQ4XiOOmhnAnOFLX/oSH/zgBx/5nP/L/+k/OvH4i//8V3nlPe9FCUEsJPvl6VsPnuR6dXtUBMc7IchKy6S0WOc500/pt5akKTBTZUsdLFpn+dg4GN68G4dxyg2XJ1K3Aab1drJyRGFylNSMyiG3j8VmdDodot6DKrdnWbc8cZO1U3/cj1H98QicaoNVSNbWPgyHhKxsPFJJvLMgPFRnSfTrIAVRupj14HGDDIM7txnt3SJq3VVkV9mU3tYZVja3n+hnLHQI81gmtq8c3np8YfGVRUQSj0OmGldYXGaQ3WZ6GjO71rTbI0lKrLVYa+m028RJTJZld53x6sb2acU5Hb8GKjOgLG6jVCfkNWLJ8hs4m6N1H7BYV+G9IY7XifUaxk6Joj5x/PDrYZHXwP1kQCQEDo8SAi0EXSWx3rMVR42T2ADGeQrj6KdhkHlSWkpjGZeCfqrZ6ETNrltLaMn6xBACdanDfmvIkR4hvGBveptpNeFC78JSkgT3xzudtvPVtyrWz11k/8wVrn3x9xje3gWgu32W3VdfJ711g40bVzn7tsUPWB3H3qjgykEgqR92K5IylIZXDqa8sNHhTP/03BAfVg84ZynKgqo0FEWO86BkqP1O6gM+Ck2JMo6T2ddG17jYu7BUJDbAilYMtGJQx2I47+lptTDXkEcNue8dTJnsZqEuOMarF/mQr33t13nlbd9Nu7VSR+YI7IqmXG9RpCffY087Jm+OfBBI7HTlbjxGPggfDa5rw3KE9ZZ+3KNwBYlKyE3BZmuDV1fftpRr07LiqSvjH/uxH+PHf/zHefXVV+85/ulPf5r//r//7/nkJz/5VN/vH/7Df8hP/uRP8nf+zt/hox/9KJ/4xCf4wR/8Qb785S+zvf1gYfyP//E/pjymqtzf3+e9730vf/JP/sl7nvdDP/RD/L2/9/fmj5OkWaKwMJZWTWAPsxLnQwHbilRzRerMNmi8C7YCV4U3uVGADwT2me9YGgJ7hn7cYxh1mFQTEhUaxZ2ozbnuedbTtcZvADNFm8sL5GiIz4u7m1IdgbH4sgyF6pIVqc44xocFVWHrWCRBFCvaqzG29FSlQy8J5+5qYsA7h9QaZyp0HNNd30RpHTKyp5O71q1NZLkp2Viz5H48thF4f610f31Xz15QctdG8ITv9c02A58kG9d5g7VTnCvwruLw4Da/8xtf4EPf/T5W1rcAgbMZUbxOpB/+3lpUMRVJyWYsWdGKK7kI+W0uFKrnkmBvZ31owqxotRQNlvvxMCvXRpssx6ctAappWK9Wmh2+emnlJb529DXeGr6FlhrjDBd6F3hp5aVGzudp4KuK6trVOWlt6+llnEOtrNwlshuOxCgzwxu/c5vBnQypBc54ru8fzr8et0Ie9vr5DqvbdyfvpRZko8U4c6TtLkVrTD4eMR0OKKfTmtwGayryyZhkgXEXMzxM6V5eHTFdA5tuBYtxXWBEzpENpPfZzTO8+LaXaL9yhs6lJyMyThOmMnddbuDU1WvfDCLdw+gOxoyCKlumqPQ8UbSKUp1Gc0VneMc73sFv//Zv33NsYiy/djji8nDE+Po1uucv8EK/x/es9ehoNVdkn33lbQBY7ym9Y+MU45Iq6+5RYs/WoGuHU0Z5hXWeyrq5esV52FlJF5bX9tghuekABkMoRlTZhKOjI1ZXekSRhkiCP4SoAwdvwqQIriJP+r2fMYyrGNYkdqJT+nGP9XTtgWOnve96rPPNnT3ccIRIEuxwgC9KvDGIJEZvbKC3zyAeMTC0kAZrTWaLgSCb2YxLj7DnUPI7wEnitlqYmu1xgwzV9gvcGt/NyMY7kvZFzmy/yuQJ44UWNoR5fya29YHQNg6sx+MQkUImCl/acLwh6Ejj64HQJEnnA1hxEgeXkSQMYx13GTmtOKfj10CeX6eihdY9PJay3KdQOU4EZxkQYU8rI5Jkiyhax5gRaXqeOH54HbjIQdx2fY+/mpfEUqIE9LXCeM/Q2IWTA4+DlsGZMa8zhZUUJFqx3o5423aP9U68PDmjzyFGZsxRNKIX9xAIPJ5ROWJYjhpzbnwUTop3kr3+qTpffTMoMoMpHe3VZDZ3Qzk1FJmh1Wuunk3abV796MfIJ2Pab70FwC9feAe/p9b5rhXJXx4MefT43uljq5dwab3N5f3pPI3yftQpj1xab7PVO93m6+PqgclkzMAOHhgcXhErdJ6wt9qkKGNGZl/sXWSztbVUJDaEPuGlNGZgLIXzC+8HPmrI/X/81Ftc+5c32Ro5vBQgBdY67hxd5mtf+3V+8N3/HmdWXwQH7mzK1h84w7/1zm26DyGyT3vIfY4qr6N86tdQyPC4WnzczXEUJkdLTS/uz12uYpWwkqw0zmE9b3jq3dJnPvMZPvaxjz1w/Du/8zv5+Mc//tQn8Lf/9t/mz//5P8+f+3N/DoC/83f+Dr/wC7/A3/27f5ef+qmfeuD591/4P//zP0+73X6AyE6ShJ2dxytGFoWgZpvpMerV3oNuskCd2QbpBMppsF+VGmxRKwFeXjoSG4K9+IXehYU3Up4WMk1C8enBZxkIgS9L9PY20dkdvLFLV6QWmaHMLTqWeK/QkcKUFmc8QoKtFrP5f5JmXT4eMtq/g1SK0f4uvY0dysMpQnrSbh8hPUJ6TJWhoxghPaP6+y66GbgMeFwj0NkKf/PzyPFuGKrDMxI9bssdamE+URTR63aJk5SqDEq4bveuA8KzaAY+STbu/fiN3/h/8u//xhf4i//RH+Ud73gBgDy/Srf7GuvrD65XMyysmKrRdKH6zeAkK9d+K2qmyTJTYY9vQXYY1Nf5oLZnHYWvl5PG7IO6cZcfeOEHeHPwJgf5Aevp+lLZhz8KbjTEjSfIXm9OWlc3b4Q16niAV8ORGIe7UwZ3MlZ32kgZLBm//Ma9m5NWL8YZP7e8c87jjF9Yg0Vqzcr2Dv72LtPBEXG7TdIOedlVWeC9Z3p0iCnLhdqLPwx2XCIThd5u44YlIlZ469E6SByj812i7c4T2YovAjNC4Lh6zTpLZQyDo6Oly8lWqoNzYSIsjraJo7XGyevjaLfbfOADH3jg+PuM5avTnP3SsBFrXm2n8/zrV41FzjKypwWld7zYSni1fTrES2UdVw+mjIu7A1WDrKKTaIwN9alxHi1FGA7TEi1DFmn7FMl1eLIhPCDstUY3YXiTwWjEr//eDb773edZ6XWCs0jUhrQX9mWzTNJjWNQAnnEV10bXmFSTMCCSGZRQrCQrtKMOW+2the27Htdktd1NqsENZNSDNYfLMuxwQLR9lujChcfusRbWYK1txgGy7Aqt5BKpfi/WhFinpKWXxvUqSlPOvPwqo4M7lHlGnLborW8SnRKp+s3gnkxsIRDe46YGEStcbpCpRiaKWftFNPgaP4qsVkqzurZKnueYyix0DTvuGmJthjVT6iRMgBDhJAXeW0w1xDlDHK8R6eWIF6yc42ZRYYFYSirnSLXi7e2Uyvul2HNNS8PuIGdSGDqJJokknVjX9uLQihTWeXpp9G0S+xmgqJ1DQmRaiE4TQlCYZomMR+HEeKclha1c4IbuzpIutFf4KCTtNuff8S5aX/hdAH5LxKR4fi3u8wkb8391nmjBts3HIYXgQy+G/tPl/Sm4e+3FnQsOeC9stBeSkf04PE+Dww+DFJLt9pmmT+OhmIlemsLDhtw3Lxs+8x2KS/uC1s0cLwXGeyoT+rRrnU22ejv03t7nXX/0JV7d6dJ7CIm9UMych4MyL/zpbTjeIGbOwkpIulF3PmDVjk4vjutbFd9QRvYsG/s4BoMB1j5dVmZZlvz2b/82P/3TPz0/JqXk3/l3/h0+9alPPdH3+Lmf+zn+9J/+03Q69/7yP/nJT7K9vc3a2hp/8A/+Qf7L//K/ZGOjGeVQZR3GeaalYZgZ2olCKEF/rtCu7rHHWyhUDFEL2usgt++S2c4EhfaSQstoKacpj0P2+qh+Hzschs1zVaG3t0leeSVkZi8hZkWpkpIqr9/PQmCNQ0qJik53gX3iRiAwHQ4osil5kfM7n/51PvDR7yZNUpJWm3Z/5bE/a2HWJkuCRzUCrTUcHR5RtFfolb9Lp9hjoNa5lbyMdQ5jDM55unGXtc4aUghym7PSuXcS81k1A58kG/c43nzrZQC2tvpcuLCF95bpdMDm1rtY6V/6ps/nG0Xl3D2kdVtJptYtRUPlG8H9Vq6N4LgKuxhDNQnHvAPdCmuXKYPTSNyB/rlGTrMbd5/INnxcjpeK8HZ5MSeqAZASEUX4yjAfG1+CSIxsVCK1QNaNCCkFQt270V/baXNwo8XR7nSu2l7ZbLG2s7j1V2qNjmOSTrfOzK7zG50nH48RCKyxeO/IkiFJu7Pw3OwZVDemtB6hBHojRXSjQAIU9bXgQG+30VvLQWTfTwhYZzHGUGRThB0w0avkaavxzGzvSrL8KsZM5qoga8cQLXde4ww9rR6ad93Tij+ytfpQovtZY5hVjAtDL9Vz+71RbiiNI9aSyjiKyqKkwDqPBGKtKMzT7U+/ETzVEF4+gi/+M77wL/4xf+T3bvBTP/IeXt/woDTsfAf0zwdF9it/EM7eu44sagBvWI6YVBN6cQ/rHQfZPvvlAbnNSVXKMOosjUXrSZas0c7OE5HYC0dNZrdal0ji7btqkSVElKasn7swf+y8Z7co2Y6jxhvrxzHPxD7eZFcCURPYrrBBie1BJirEPDWEGVk9nUyZZsFaNo5jrHXk+XhOYHd73YWuW8ddQ6wZY900REyJ2qUPj7UFSrXrNWyKNTHOGxTND2QNjGVkLOtazQcEBsZSec9mvNh7wElRXrmxHNwao0vDWr12JZHibUoyqCoSLcF7olgRlYLVo5SV1sNf12cZ5fWtihlhECREofYKkY7LN4zzPMEZR5EZiqmhyi06VkglgkbLceq9widFb+cC/8v6RQC0d6TWYpTklzLHT33lKn/97ReXi8y23I1ug6UhseHkweHTjL34NpYH7zrb51+9sc9XzjteqCJW9iqkAFVfl9463vGRM3zvv/8aapmGr6I2IODocihhBJCu1cebw8xZeFSO7kY0RZ1Go3GfBPfHDi6DEPOp7z7f+73fy8/+7M/yD/7BP0Cp0Kiw1vKzP/uzfPd3P50O786dO1hrOXPm3umYM2fO8KUvfemxf/83f/M3+d3f/V1+7ud+7p7jP/RDP8SP/uiP8tJLL/HGG2/wn/wn/wl/+A//YT71qU/Nz/k4iqKgKIr54+Fw+MBzvlEcVw/U0QE4BxvdmE6iyUqzkAbLo08yDzmy6TEL3nzYuPXC43CS5d0yNFRmmFkEqSV70z8MzjhMaSkmhqilkFpSlRZrHEoJ0v7pW949TSPw1ltvcOXzn+X2JOP/+Olf53/3x/8w250Wl97zPs68+Mpj//5C1bjHstuazso+CbOs0ThJOey9gzu2z8i3iQCto7kFjxSCqixP3e7uadFKzwOQ5zeYTmO8N7RaF+m0X27snCrnuJKXwUZcCCrvKZ0jloJISJz3DLTiUho/V2R24zieeSNVGLgqp/XnJnwed6AoYHC1UYvxx2FaTfnly7/MtdG1uQX5146+xg+88AONkdkyTbA1UT0jBEQUIdN0qXLbTlJbe+vveU7c0rzyge17crTXdtrEC2piO2PIp2PK6QRnbYi8KAqEgDLPUFrTXllBKo0zhsGdPaJ4QJS28N5RtMYLzdDWWy3UeorZyxBK4K2n9doa6VoYAk1fXqH9+iZyQRmuj8P96rWqJrE3p18lntyg7Jxjn7eT52mjmdmVGWHMBK3v2lsaM6Iyo0dasj4veBTR/axRGEtwtqsHWIRACkAQ7FlFsGedfWgtKYwjWZCV7FMN4V26yJ2JAX6Ls+cvcal/UH+TFMQhvPP98M7vuXdvtkAcV7PlJqNwBalOiGREL+4tlUXrcUtWO5ngiwKX5ZRf/zpqbQ21trZcey8hSZLlcY17Ejjv+c3BhMtZwQuthI+sdJaiwQ5BYe09iHua7KBq0noZ919lWWKNRQjBYDjEHOyjtUZJhfeePMkXOoQlZEwrvUhlRpTlXojBEDYQ185gXVZzGBpZ91rK8g5MFL3uOxp3FylcsGsX9TUphEAKQeH8Y/7ms8djo7xmsA/5HOAJ5qG+2Sivb3U8r4TBMsMZx/BORplbwGMqy2g/J+2Ee0KcLi4e41GonOc/u7bPb8vQp4qkREURkVKU3vNPbh0BNE9mO8/71zvYUcXVvMSJsHZd6qZ86OLq0qyxi469+DaWB69fWOH7X9vk89eG3Hm7Rquc/u0S6p7LpXdvLB+JbUsY3gh/mhJMFsQus+MNOTbC8+MsfBwnxQ7K7oDowsVG91ZPvdL8jb/xN/je7/1eXnvtNb7ne74HgF/7tV9jOBzyv/6v/+szP8FH4ed+7ud4/fXX+chHPnLP8T/9p//0/PPXX3+d97znPbzyyit88pOf5N/+t//tB77Pz/7sz/JX/spfOZVzvFc9EDaCcLf54jwLa7A8FEtqvfAo3G9553O/VOqAGZ4Xi6BZYVpkBuc900GJ0gKpJHEnorfRotWNFmJ596SNwDObm7Ss4ehf/2sAYmd57d3fwasf+ijJMineZ9ltmQm5bdYhU0203UKcstXlk8JUBo9nMp1QlhVTulhnsHmOlIoo0rTbbbrdHpHWS2fZOrO+XV37CP3+NlG8Qaf9MrpBy7uZOmClVgcMjOWOsZxNYvpazdUCA2MbtRI6CSdlkDZhcXdiBMDwRrATTwx4A9McpgdUecbR4RGrZ84SdWIwOUwrcF+5J2P0sd9/gbg+vs7t6DYv9l9ESYV1lreGb/Hm4M0nUnOfBk5Stql+H31mB59nSzOUdb/aOjiIhA3//vUx5TsNcUsTtzRnXlo8EeSMYXB7lyKbggdblVhjEFLirEVIiVSKfDxGxzHOOZwxyHabpN3Ge08xnZAvMENbppr265uYvQw7LlHdGL3VIvn9WwBEO52lIbFnUErPSerB0QHt0ZeJ85sAxJPr9CvDQLwboLE1y7k8WFoet7dE4NxyD4suI0JMU9hLzZUrHnqJ5sZRRmlcyGk0LkQ3eYiUmOdoLx269SB3ugIf/BGIW1Bl0NmCzVcbI7HhrpqtchWDYsC4DMyKlhHWu6WzaBVRhOz1MQcHVDd3oQquYtXt20Rnd4hfeHFpyOyZoi24YAnAz5Vsy2QvPsOMxH4zKxDAm1kY/l8WMlu2NDIzuNzOs1rnymslkd3lGmacDQ/P8kbzPKfIC+J+QpLezc3O83yhQ1hCxsTxBpHuUZb72PIOzhmEACljIAcsUqZhPfMszVBWIsN64L2fEy3OB/erReOkKK+v3R5xMK3oJndrkHFhaEeKJJKkkaI0DmMdpfWcW22x0Xn4dfssory+VXFc6NKOOrSjDtaZ54IwWHbMIgjjtkYIiFJFNqxQWtJeiZdi/aqc56e+cpV/cuuIqF6flNaoeig4EQJwzZPZs/7gqOS9SuO147oxXGzFvK+TQm6huzwd5DiOMTZYiUdRjDGGW7duEccJ/X6fOF6udda7sh4kHmDMGLxDR32SZAellqhPvOTopRF//P0XeM+FCfvjgrWPxex98iY3f/lrAHzgh15YLhIb7opedBII63SlJrOTcDwfhH1WQ3genIWP46TYQTca4UZD1Hpztd9Td3Te9a538fnPf57/9r/9b/nc5z5Hq9Xiz/yZP8PHP/7xp1Y3bm5uopTi1q1b9xy/devWY/OtJ5MJP//zP89//p//54/9OS+//DKbm5t87WtfO5HI/umf/ml+8id/cv54OBxy8eLFJ/xXPBrH1QOtWJOWlmFWMcwqnPd0E918gyVdCTYLo5t3VVjtzXB8SXGQH3Inu0MkY2IVkeqUSTVZGnXA84ZZYZp0IpKWpiwsZWborib0N1uNF6UnIWm3efWjH+P6MEQdXHjXu3n1ox9bLhKbOrutVgN4ExRDblhQAfHZzlIoA3SkqaoKYwxSKrRSOOeow7IxxmCMWcpC9Th63bezuflgxmcTuF8dUDqH9XBYGQTQUrIxtcCj8LAM0ovr7YWR2Y+MGMgHML0T1iwhggo7O2JwdMivf/4tvvt9b2Ol1wYZg4ogXX0gY/Q4mowYGBZDtAwKHAAlFVpqDvKDRs4H7lW2PUBat9tLM5R1XG092s/Yvz7GmpDLduOrh7yxfptXPrC9MPX1/cinY4psStLu4L2nKnLK/C7p453DFEXIx04SvA/2h7omWoIFucSU5ULPW6aa+OJzqFbxjs6dz6PymyAkXkhwlnaxC4cw8O9qzGb8ePbo3N4Sj5TLOyy6rOi3IgZZxSi/uz51E41Wcp4vqmtCw1hPEkkurC1u7Xpi5EP44j+FG78THhdDyO7Aiz/cKHl9HP24xx0Z8dbwLcbVGOssWmqO8kPAk6hkqSxag2LgGtW163jnUHXDxU2n2IND3Pp6ow2XGe5XtOWTChBzRVuRqqXadx0nsZUAhcDil4vMVhK9ni6l8vokmKokqQ4gPQsInLMIKcK+CxrPHxUypt1+EWungEMIDUicLxgawVYM4EGAlNFSDGWtaMVAKwa1C5bznp5WrDQgFjkpymu3M+aN6ZhVGc2J9iMqXux3AM+NQU5V17CRlhy4Fi8mnYeuXc8qyutbDQ8IXZxn1fTY3jpLpJe3f/G84P5cbKkkUapI2ppWbzle3399NOaf3j4KRPtDCOpESirv+Ke3j/iR7TW+Z33x+x6XGVxhQQqEh/fGEeeFZEtpKMLX5BIkIM7jB4sirEsmPA77GIl3I8bjEefOnV+aHuEs0qkqjyjK2zhXIoVGlLcpitv0++/5Npn9FOilEe+7uDp/bP+3K9jeLfgfWT4SG4KrsKhdG4W8++FMcCFectfhZcNJsYNIicuLRvuC31A359y5c/y1v/bXvukfHscxH/zgB/mVX/kVfuRHfgQA5xy/8iu/wsc//vFH/t3/6X/6nyiKgv/gP/gPHvtzrl27xv7+PmfPPpgNC5AkCUlyOlmPx9UDWgrWOzHee1baMVvdpDGl2/MM4ypujK8zrsYkKmViHIlKSVS8VOqA5wn3FKZKkNSTljpWS9NMOQlJu83WxRcB2Lr44tKR2BCy27z1eOOQcbjdO8DlgeBeBqVAUKopiqLAe4PzDq0UQgqUUkipEAiqqlyaInXZcVwdYIGxdWTWEQnBfmVIrSCSshG1wKMwzCoGWYWWgsqGdWuQVaxk1cLysR8ZMZCP4Gu/DEdXw5SlLWH1Il+4MuSP/B/+M37qj7+f19/xSlBhD2+emDF6HAuNGLgP/aTPbXcb6+xckW2caXwY63lxEjmutr5zfczKmZDd3N1IGdzJONydNqLGBuo8bIkQgirPsNag4xjvHDqO7yqxrcVZi60qVJ2nDQRi27v542/jEfAOrnwKPbqKFxKPrAcDBFJIOuUtooluzGb8ePbojMjWukPUoGPI84pISS6utx9wDNkb5bRjjZKCorIIIfHe0k00649QtTWGO18NGdirl8Lj1Uvh8Z2vwoUPNntuxzA1UzKT4b1HCoUUEuMtR/kR57rnlsaidWZ7V+3ewhVFUAtIiex2EEqFxwtsuDzK7SWfVEwOc6KWJh9PuHP1Ct31c1jTQSea8aGhyNM5sf003/tZ4yQSG+o/xfKR2cuwn3os6qErObxKPt3lsPca1jqcc8hZk3AJ8keTeAvbeoGqOsR7iwPenObcLidsV5bXun20SpAiWoqhrEhKLqUxA2OZWIfxHl27Ya1oGo9w2llJ2RsVHGXVnGjvpxHn11qMcsOdcRnOUUAnVowLw3CB+65vFQzLEZNqQi8OUS7tm5J4JMkmR0SvbN1lYL+NbwgqksG4826Kw1LlYgN852qXH95e5Z/cOqJ6iFigcMG954fPrPKdq82wxd64cDn6MNysYsW2EMGlxbgwqbkEuN9BpBwOKcqCTqdDHMU478iznOFwyObmw4UDi8Qs0slj8N6idRfvDUq2sGZEUezSbjB+8HmHUpKdl5ZX8Dh3G5a6dhquP6ReetfhZcRJsYM4h0ybrU8a9wj8yZ/8Sf7sn/2zfOhDH+IjH/kIn/jEJ5hMJvy5P/fnAPgzf+bPcP78eX72Z3/2nr/3cz/3c/zIj/wIGxv3TlePx2P+yl/5K/yJP/En2NnZ4Y033uAv/aW/xNve9jZ+8Ad/cGH/rhlOUg9s9dKFqtsei3wA1RR6Z+9ai+eDxm0XHoZhOcI4QyRjUhXeQFOT4b1bKnXA84TnoTB9UhTTKQc3rjIdDmn3+6yfu9gowS20xFs3c73F1/+TWgSF9hJAKc3qyirWWrzzWBuKayHEnLjWWjemDngaGDNiMv06VbnfqMX4cXXA1DrGxhIJQeEcxgvGxnM2jRpRCzwKk8IwyoMl56zZMju+yIbKIyMGLl0MDf/J3tyC9c5nPgvA2e0NLm2vgs3gfLMZozOMyzFvDt7kID9gPV1nPws6873pHqzBVw6/QjtqY5zhQu8CL6281Oj5HoevqpPV2UuEbFQitUDWQyFSCqQWZKPFqpmPQ8cx3rswyGLCfdM7h5ASW1V4Hz7XSqN0RNxqIaWkzPOahHMkrTZpewnG8ZcZNYnNwddBKISUgQSol1ZfFzTx5Dqr1lF1F08SHs8edS5HypRI9xrPFH1eESnJRjeZR2DsjXLyyuG8Z70Tk1eOyli0WkI1dj4Ma9dXfgkmd8DWTT+hgnp00mzcxXEc5IfsZ8EdREmFINyXpJSkOmUlWVkai9aZ7Z3q9fBZhjMGV5aIIgr3Xa0X0nB5pJtMjWxUUuSGKFYc7O3x+c/8Ku9+/ftZWVsnTjVVaUnSR6vbFuEk8zASe4alJLMBrFteZfaxoSuHJ81u0DUlN6OXggOLMRR5vhT5ozNVdmU2sDbjdw8vc7sM7gG3yxwxUbyr30VH/aUZyoqkZEXfjXaSQnBYGQZacSmNGyWz27Hm9Qsr7A5yJoWhk2h2VlLasebOqAi24s7hPGSlQUu58H3XtwIKk4eotHLC6u2UZJLiAXloKd4akrzYb5TMfh72VI9C0tIUqaKcmnnLeFlysWeIpOCvvz04q/78Vx78euEcpYM/fma10YxsoWXotwoQUoaeoPMgFWgJSyJ0MJUJ7oL1+8YYM3eVAJBCIqSgLIsmT/MezCKdnCuDwxkSD3hCjKoxJwglvo0nxiiv+PJucET98u6IV/OKXrpE97F0BZI+5EfhcXYIOg2K7HR1qV2HlxEnxQ7KbgfZa7a/2viq86f+1J9ib2+P//Q//U/Z3d3lfe97H7/4i7/ImTMhN+zKlSt3J1RrfPnLX+bXf/3X+ef//J8/8P2UUnz+85/n7//9v8/R0RHnzp3jD/2hP8R/8V/8F6emun4UIiXZWUnnhWu3LlyXqrEys1+YWS5UUyjGML4V3uhquZpthclJdYoQksxmCCSVK+lE7aVRBzxveB4K0ydBMZ3y1U//Bke3dpFa44xh/8yVRi3HRRwKPDs2YDwogYgkKIlYIrV7u9OmLEuyPEdXJUVREOkI7z2RjlBKNaoOeBIYM+H27V8ky64ihMZ7w6T1Fba3f2jhZPZMHbBfGb44zhGEPUCwEvdIYGwclfcssvR7nJpncDBhtD+ln2qsKZnuXcd0txn4NQau801//2eCtB9UazNS4PK/gqO6bfzCd8PZjaXIGIVAYv/y5V/m2ugaWmqm1ZQvfumLQMjI3rAbJCrhpZWXON89z0srL9GNl4O8nKnc3HgCUmKdQ3YHRBcuLlXjpdWLccbj6ul15zzO+EZt7tJ2l6I1pphOsMZgyxIVRVR5jncOZx22qpBK0VldBSHorm0gtQp243FM2u4i9XLfcxvHaBcO3yR4nIahICGYG3jjBb4mYNJ8F1keAYt3PJhlj34bzwb3R2BU1lMaxyg3REqglGS7vWRq7Jmd+MGbgcTe+yIM1sLXvAVrlmp4+Kg4xOPQQmN9sBUvbRgOaukW7ejx9cCiMLO9E3EMUQRZhjcGay2y20Wtry2k4fJIN5kae1dGXP3SAf2tlM9/5vP8xGd+lT/24T/GO1/7DnqbKaawvPj6JluXHl6zLsJJ5nZZcbnOxL6fxJ5BIXB4LmcFL7ZidpKG32+zzNHiWFb2pEK0NDjfLLF9bOgqOIcIBI6+2SfSMfudV2m120RaoyNdO2U1u/4LGaN0l8/d+RLXJrtIIVH1/eBWMSWaZnyg/+6lGsqakdgrWs3JloGxDIxlM252z92ONS9vPVjfF8YxzCsiFVx8rPNMy4piSYbdnycoqRnmQy4cbdLNUiwOL0CjsPsZBTRGZj8ve6qHwRlHkRmkEsSpQipBlOqlyMW+HzMy+9bvfYH/AebK7GUhsQFkSyMnFWZahYE7IRGRRHajIHhJlkPooCMdSOta7aR1eDyLznPe4Z0njpdn6EYIjbVTnDM4ZxDChuP1QKbWy9FneR4xyiv+P1/Y5dNfvwPAp79+B7W1yx9+fWd5yGwVw9oLkK8GTsuZoMZOukvJbS07Hhk72CCWokP28Y9//KFW4p/85CcfOPbaa6/Np4DuR6vV4pd+6Zee5el9U5iWhq/dHjOqC1RTL6RLpcie2S/YCqb7gci2JWQSDi+HG8ESveETnSIQ9OM+R4VjUo2x3tKOls9WeoZln8CUWtLfbFFkBls5VCSXsjB9HA5uXOXo1i5r584jlcJZy+GN6xzcuMrZt722+BOyDjsoETKQ166ySK8QsQgF7BINCiilWV1bJc1zWmnKaDzCGouONEopWmmrUXXAkyDLr5NlN2i3X0IIhfeW6fRNJtOvs9J/78LPJ5ISLQSJEuQuqLFdbTWugKGxfGWS8+5u69SVAk+iFAK4PcoZjgqGQDE+Yvdz/5LN7/gexJktzGoL/QTnuZDc6eOkgIrh8lvh+OpFeO17T/dnPwXeHLzJtdE1Xuy/iJKKtwZvcXNyE4CznbO8tPYSbw3f4nz3PK9vPdwCvQnMVG6yzhrFOdxohBsNlyJrdIa1nTYHN1p8+Y0QLTLez1l5W4u1neZqAqk1K9s75NMxZZaj45hyOqF0QaUtlcJ7j7OWMs/prW/Q7q8sJXHtiqAoz988otwaobdayHRJzrO3A2svBUW2syBVbStO3cQGSbCbyVtnSdYvNH3G38YzwDCrGBeGXqrnziFH04qVVkQaybnl+NLss+Cunfjmq2FfhYfLvxm+dnQFXvtY+NqywIMSCq00UzOltCXGGaSQwWLcGYyrlkKVLdMEYyrcdAqASFPIMmS7RfLKK+itrYXtuR7pJgPsbBna4jaDOxm9dnheygotsYbZ96xut3nt3S/TXWu23t6UgvNKcNU4Ku9qBTah2V4/x4ZxIV5sJWzHzV8Hs8xRmapAVFmPOcoRkwqZqLAeZAa9ni6WzL7POYQ6ZAIpwUMrv8kGUPU/yMrq6uLO6zGwruDf3PhX3MgOkIDE471BCgVIrk1vI/e+wPu3P4AUzd1rK+cYGEvhPENj5+s/9Z9SiHqIeDlRWvcgsSpEOP5tPB2858LRJmtZDy8c89+6BLxolMx+XvZUJ8EZx/BORpnbBwQvy9orjKTgL7ywE4hsHyLevF8OEnsGDwglEUrWauywJixTjzBNU/IkJ8/zcD+VkiROqKpq7uSYpAn9frPCgRm8KzFmgHU53leAw5gBUiZAhtZ9kmSn6dN8bvHG3oQrBxMurIUey4W1NlcOJryxN7knR7txqDgMBy/RgPDzjGWMHVyOO+S3KCrr+NrtMbeGOWmkyKtQXC86b/SxmNkvjHehGIU3froaMkaL4dJZjPfjHoc65cb4BtNqivEWJSS3JrdQQvFC/4WlaKzM8LxMYEotG1WwPQtMh0Ok1kgVbrNSKaTWTIfDRs5n3lTpRshOhCssLjfITrT4RsoTQClNp9MlTVPiOGaahaZgu9Wm3Wk3rg54HEx1hBAaIWbKPIUQmqp8FHV7uiicJxGSyhum1tXWRiEnPfKew2oxSoEnUQoB/N6NAb/6xVsoKfj073yO3c/9S179nj/K669/B++9sMb3v7ZF9zETlwvJnT5OCkgNq3XD5+v/EtandxXZs+cesyFfpEr7ID9AS42q1aKZyeafQ7Bs1VJzkB8s7JyeFDOVG7PhhfrzRWaNPgniluaVD2xz7SCoG8+9usYrH9gmbrgJILWm3V+l3Qe3scnBjat4DyqKQtSEB1tVtPt9VrZ3lobEdrnB7GXYcYmIFfmXw7Vpbmdkv7+PWk9pv765HGS2kHDpu8LnB18HF7KxZ8oBiQXvGMfbVNvvpa2f7xrn2wgojEUK5lbGUggiJUgjybnVJR1qneyF/ZXU4ePCh+CNPeDTwUnknX+0cQeR41hN19jL9vDe09ZtChtsI7txl1jF7E1vM60mXOhdaHzPJXt9kAo73EemKSJJEP0+Io6RabJUe63ZenW4O+XN6+F+dPGd6+y81EfHClNaJkdlo0S2M4bR3i1emozJVMpe3MbjUYAXLqhGAY/gopZ8qJMsha34PHN0di6VA+MRqUSkGuE9Lre4zCwuT/t+EluqOq+x/nI9GNDKbxLf+TysfG9Y1xqG847f2f03gcQWIvCA9UkLb5E1tX1ldBVgIWT2SY5PxjluFBVjG6zEJ9ZSWo8XnmzvGt3tC0yVpp9EDB5zH1hk/vxxKCFItSSNFM55pBTklUUtwXvquYL3RNcdrayHFx5Hneo2ex2lAEdjZPbzsqc6CUVmKHNL3NZzt4tyaigys9S9Q12T1f/WepdPEzKxl4XEdpnBVw61EqP68d0eYUsvVY9wJnTJ85yyKLHOkaYpVVXhvCdNU/r9/jyKsGlUZoS1Oa30PNblWDulqoZo3SFJzpAkOyi1pPuE5wD744JISeyxODelJPvj5bGWB4IoMx8E5+Eo/bYS+1sQS9CF+tbFMKsY5RVppGhFCk/IvRGEJszSYGa/YIsQ3t5agagdGi1ChRvAEkHLiF7cR4pdtIro6z6xjMltwWF+yFq6znq6eOvIh+F5mMCc2QU9z2psgHa/H7LxrJ0rsp0xtBuaErynqSLChKUQzK3FlxHWGo4OjyiKYm7LVpYl7c7yF306WsX7G3hv54ps7w1Rg5auiRQU3gVr8Zq9Cp9D7jyRtUysY3MB5/I4pRDA2nbFSK3ym28doDqBGHzt5Ut83wfexY2jjLK1waVlmLg8TgpAcBQBuPppOB+FAvZGPXE72g3PtSXsvQTv/OGFEQbr6TrGGayzKKlo6RbW3V3/rbMYZ5ZqzZpBpgnWuVAX1GsXzi0ka/RpEbc0G+eDC8DG+W7jJPb9kFrTXl3DVBVJu4P3nirPcKZC6uUhWVxumH7hDvYgRyiBOcwp3qoHwQS40mLfGqDXEtK3rTV7sjPUZLb3Lgy3EOzEhQtUyzja5mb0Ir2iwlqz8IEs78pv52M/YyRa4XzI8Z0psp0Px5cWna2wBs0s7lQMrdXwtTPvXCoSG2A9XWOns8Nhfoj1jkhFOO853z1HJCM8nlE5YliOGl+/RBShVlfwRY6IYkSkkWkLl2VLSRLELc2Zl/r0NgJZvXKmNbcSP7o9JRuVTZ4e+XRMkU3RSvG28QGyL7kVtbDeI53DipA3uVVOeMnmjMx0KYaxZpmjoh5k8rU1s1A1aSFEIGEWadl8YvyFwAt/l8xGInDo8bXw/P65xZ3fQ3An2+P69BYAEoHH1TrymcY13Hu9h2uja1zsXWS7feZUzuVRjlJDY9irDB0lEQiM9+yXhr3R/4+9Pw+P67zvu+HPWefMCmAwWIiFJECCiyRKIilro2zZsRV5S+qk7dPUSZPmTZ03m9vUT97GeZPYrZvYT5ImcdLkqdtcSdo+bZYmTXO53vTasmXtlk0pMiVxAQmQxEIsgwFmP3OW+7x/nJkhCIIkuGDmgDwfXjYwB9stYObcy/f3+35XmHnhaXa+/T2ku7oxdI3iBkTLljhKraErrjNf9LPRFdmfz3RVpqsd0Rhb+PDfLVpoBRB4NHqxPTzfBhkJWfJ8Mdv1cHNV3G4DJdW6/czaPZVn24hyGUlVcXNLgXNrXI1rCyT5ou4vSf7S27W3hmvAP9/RT210hIc7E4EQsWFrnREqih93YVZNrPr5IEDUMOjs6gxUo4sQJuD5HdnCRlFiyHIEXc9gGIPtHt6WpzsRwXbree7U49xcEZwGTfDnseVzfkOmpPjOw5FU4FyGQ26ODd11Dh482LxhXYtXX331pgZ0O1FzXDRFplbvxPbnKgnbFcE7bFF0SPSBUwM9QdM3xnP9hWzAcIWDKmvEJAVD9ccnCxnXE9ScYAnvQa/AXM8uqGYopDLRLSdmpweGWeo7z/LsTDMju7Ovn/TAcFvGs/ZQBc/zHwf09+q6DsvLKxQKeQwjiqbryJKEafqWQvF4sDNlosYg0ahCpTLZzMiORoeJx0bbNqYOVUGTJDz8/bPj+bbivt0dOJ6Hc4WojHaQNDTed6CffNVipt6xsrcvQTyiogWp4nKtKFBa8K937YDuXf71yWf9ayPv8D9HOH53dnbcz9huASMdI7yRfYNX5l7Bci0kSaIn6jucXChfQCpIDCWHGOkYacl4rsbaCAzJiCJFDZyFhebBS6uyRq8Hq+qwPFdhdnwZANt02jyi9WnkZpulIrVKBde2UDSdWrlEfmEuEEKAs1jFzZmoPVEkWcI1HYTpF17YCxVEooZbsqm8tYQ+lGxJV/ZGu6SqUh9u8RzVudN0dHWiqQoFuZNcNIEuShStKrJTJRq7NNt3M7uwPGFRNadwnHJTBHDUOFFjOBSzb4JUVCNftSmafka28CARUUlFg3kIDPhuIIsj/vzTKKxKBfNQzRE2BatIRImQiWZQZZWKXcESFlq9+1pCQpKkwOy5lHgcETEuKRr2HAevVsOenQ1UrFNjzios+b87u+bfY4XwEI7X9g43x7KQJBnhOshI7KkWcCybbCyJV++67XNMRks51ESSWrWCWSkRS3W2ddxyVEWu+nOWJIHnClD9aCegPXuwdeIvwLdn9XNHgbpITNeI//kBIBPtYTDWx3R5DuGJNY2rq7JRgeHkEJno5jn3Xc1R6rlcgeMlk+3Ri4fopysm5TOn+L9feJqf/+hP8cThQyQ2eO7WEkepNaTjOgMdUXLlGq4ATZFIxyOkWy1kb/HDfyWp43ZIyCsgeSCkhgcaeJ7A9UD2fAcEJR1FafF9Vk6mkBN5RLGIB7grK/4HajWKU9Mkkkkiw8Fya2ygaLJ/LOwBwsOyXKyyg6YrCEcE9rzQqlYBuHDyLR7sTCCMYYgFozFjq50RmqZJrVbzYwbr4w3i+aAkKdj2CiAhSXK9cM0jEtmcQqs7jV09cU6l47w47rt2Ti9XeHRPnF098Wt85a3lqnv3yhIUZiGSBEnGrpmszLxO52gBrXNjhYLtcmgJ2TgbOoH60Ic+tMnDuD2JqAqa4k9SpuWA5FsF9aWMYB62NCzGzfylC1ijo90ju4yIaqDUM9qoi0DCE2iySkQNlvAe9K62rWoXtB6RWIyxh46Qm52iUigQS6VIDwwTaeWi1RVNS3HPEXi2i2u5/kFKXc0UNRew/PybgFRdNjqxC4U8juNQrVawbYt4PIGHR6lUwrEdVM2vygxS9WUDVY3T2/teypUJbGsJTe8mHhtFVZNtG5MmywwbOiVH4IqL3dgefpdDVPFztINE0tB420g3b37Xv5fqqoIrPOwgVVyuFQWWz/nXE/WNiqyCY118v/FW0f1u7hayuhBQk7WmaD3aMcrDww8z0jFCQm/vJrARgeEWini2hWc7yLEYaP7vrlGMFzSsqsOZV/280dxcGYDpE8scPOi0vCt7I5seSfawaxWKS3PUqgXS23ZgWxLl/BJWrYyRuHKRwGZuqiqVCidOnMCcXMFZqKLk/Ne5m69xauYMAGeWzyPPaYiyhZxXiakX0Prj7Nu3j9gmzLFX68Jaj3K5zNLiHMe+/SKP7utFT3SzrPWhan9HLBrDdhyWIxHi8cs325vVhWU7RRynjKomLwrZThHbKaK30Slkq6MpMsPpGIWqTc1xg5mJvRYj5buBrI66iJXbParLcITNdHGasl1uuvLEtbhvNV5ZwDfvl5qdbkHZc8nJFFI01yy8ulge6CGpWmBinVbPWVbVL7qaHV9hOFNCkiQ6MlG6+tt70K7qOp4nfGcrIXAqZYYqi4ieQZYTnaQrefa4NQRS3QFLxbHa20UOgCKjpg3fstURyEnd349ZAkny81HliNLa7NHL4i9oitmSr7b7FeTpUf/zWmwrfrV1xW5liEp1jvnair/+s22spRX07jSSpiKAgcQgu6RtFLPz1/39r4crOUrt7yxzPpsnHTNQJAnX84iXqnRWigD0ZrMMbdtGKhqM+9R6aIrMzkycdFynXHNwhIcqSxSqdmvnNTPvi9hGx8WGFjMfuIjB9WgUX13oytJhReioxMETeKtaiD0B4KF0x9qSkS1pGtrQMKJYwFnM4kUiyN3dXDCrrNRqpPJ5dnSkULtb4dF2fUSiKjVDoVayqZkXHRxrVYdCthrI5pdapcL5N14HIDczxZnvwFLfecYeOtLac8ErcFnhVTvmp+vAsR1/zlr1mpIkCcdubfF4l2oybE+QqKwfG2nZOarWDCBdvI/hETV1dDe/oZ+RsrMU1dYUadpCkHdcP4pQluioF12tvabJwXh9NRpe3EX/PvXQaIb3HegneY3YwVvFhs4Gyll/LtP913k+X+D5F7/FY0cepWNg14Z/VjscWkI2zobulJ/85Cc3exy3JamoRke9a0CSoGYLUoZGIqK2fnG6ERoW41vAUiilJ+kyupgrz5G3/ElJkzW6jC5SevtEq/VYXYHZELLlRDwwXW1b3S5oLZFYjG2797bnh7sCJ2f6ByemA44ARfarLl0Pqd465JkOTtVBrjqBycG5WGkZpVqpoKoqjuNQM02qZhVVVRGuwPM8zIgZOCuhBqqapCN1X7uHcQndmsqAoWF5gpJTtxmX/G7tpKoQD8Dffy27euL0d0QBmF2pIhZLbE+3vuLyiqwVBYZt4AvQeE4KBxp5uI2ubeH4HQctPJCZzE+yWFnkwf4HUWQFV7g8d+45AFKRVDMbu51iti9iT2PPXvDtmT2QZBl7bg5JVdF37vQP/gMYi7E8VyGfrdLZHyNZ8A8qSysmy3MV+kZaM8der+BaKeRZWpjju6+9wqGHHiOZ6sCu1YhEY8RSVy8c3KxN1YkTJzh8+OouBf/vz/7CutePHj3KoUOHbvmYrtaFtR4XLlzga1/7Gse+/SL/+O9/CK9nH56k0N/fTyqVIpfLsWvXLrZt23bZ125WF5YQpt+5Wi8DabzvW9+F3AyaIgensGqjGKlL3UAWgudiVrCKlO0ySf1i8UXRKhLT4sS1OEWreInAHbQ9F/iFV55t+/utnp5AzV+r56yOgr/GMmIa0bjOtt2ddPXH2h6N0XQPKZcQroNV8W3Gd+bm6DHLxMtF3EQcJAlZUfA8gRqQfEwU+ZL8ayWuNYVtSZXbU0B8JTFbuOC5eF07qXQfwMkXWlYsvNF1S8o1mV8+Ts6q4JZqFF6fIXXfIGrCIG10YCT6mJCf9+1bpfXHvJmHwWMxg5NRk/GKiS7JlFyXZduhWvK7xeTf+z1eP32G+37tU4EXsxtOI6Wa7zSyUrXJV22G07HWnBfapt/I0iimkORARgyuZXXxlenWWEwuMuJtI11N4dVtxmVPQvLA6oB4G0TsBpKmoaS7EWYN4ThNERugIFym5hfYmU77naRrXLLa6SgiqzKpTJRCtoptuRidEfSIArIU2OaX3OwUxZx/d0tleugaGGR5dobc7FT7zglXs6bwqm3z0wZwXQfbcaiaVTxArzs2ep6HqrV2vfK+rrN8OPuLkN3cn/NnXZv/HLGF4LxpUXTcZkzSUv3vX3VF81peVdhu6IESs/f2+2v/vf3JlonYsMGzgQvH4MzXIT0CssKxU2f54Ivf4uMf+T848K4f2PDPaodDS8jGCZ4KcRvR6BjoqNqUaw4rVRtXeFQsh1LNae3idKMo+sVD/gDn5Kiyxo7UDpJ6ipXaMnjQaXSRNrpQ5WB1u6+uwAzCYnQtq+2CGhWBnvCvh1yb1dXmXsXGyfsbEmE6voDtCGRDQZguTs2kUFkgPbAdNRJB5FzUSgQpduXnwmZ24bmug2maOLaDWTPx8IjoERzbxrZtXOFSrpSRZZlEPIGsKIG0EnIcv6tpefkV8gWl7V3Ya9FkmdFohLiiMFkxqQmPmCoTkWU6VaVZfRkkkobGkd1+teXu3gQP39XPrp54Sxer12S1KJCrV1evnIcl2Z+/Bg7611ZbuaZH/G7uFpEzc6iyilLPRlRkBYFfJHR07igj3SM4wuH0ymme2PFES8TsS+4pjo194QLO4iKiVMZzXX+OisdxqyVss0K5cIH08AiqFsEtFJEreeR4AjmiIycSsCbjuZV2TNWihaxKyKsyzyRFamnG6PUKrvNnz/DMF7/Av3rtFX78+59kW1eS/IULDN11gJGDDxCJRq/4tZu1qdq3bx9Hjx5F1BzM0yu4eQtJkfBcj2q1ytlzZ9m5fQexVBw5quIWbYzRjmZH9mZxpS6s9ejt7eXMGb97XBm8n1Qq1fwekiSxf/9+9u/f79vitQhZNvzO1dVdrHjIcnAP1EPubGqOiSStKb6QJFzhMJQcomAVqTkmEdUgpSfbvudqHPY7i1ncfB6lu9uP8VqYx13J4y4vo6bToKqBiHVab86Kdep09sVaVnx1LWRVpaO3n0ilBJKEoqqokQhOzcIQDpYkUatUiMTiCNfFiCcwYsHYD1zGGmG7bawVs12/S8zr2kkusZda/mKBSCuKhTe6bikU32Ix+xx/MX2cr7/6dxReh953a7zjvl38yI4H0BQNz3MxjAG6ux9HVS9/HmzmYXBSVfhgTyfjFZMlyyFrO5zKF7n3f/0V/wmQPEHn01/lnKpwz699KjBnLw1sVzSdRUxbkK/adMa0poBRNB0KVbs1RVuacdEhIOARg6tZXXwV8+LgeYwnphgVA/TUulDqs1kpUUMfap+IvRopojNrWRS8enF7fX24YlY5f/48w9sGcGamEaUyyHIgHEVkVUbVFSIx9ZJiq6A2v1QKBf/cqo6sKMiqSqWwfidvWwjK/HQVGo6NplnFEx7FQgFVVYkYBlHDaOmeCuDLyzs5t///RaJzfecC28lj1RZQlHjTAdF1y+iRXjR1Yy6zpZUsb536Oo/dumGvS95xKTouHarSnP8vWDYA23SteS3vuOQdl4wens3DBs4GejtBX4bcJCg6WdV/zW/bc5Dt27e3ZpAhm86GVshdXV0bzsjO5XI3NaDbjdUdAyvtXJxeL1sgJ0eVNXpjvfTGets9lGvSqMBs5+HJlcQFzxXUKial5YsZ2ZLkkF9YpGtwO5pu+J9jughbIGsyEUNBWlOAcadlSaxXze6WLTzTxRMeni2aQjbCQziCYmWFV958locOvovOnh5wBJKhoFwjB+tWVbOv/hsJ4VDIF6jV/Nxey7ZwHcc//JckaqUSuQtn6ewfQtejVLyL1kG1Wg1hlnBSqSt+/1bhOEWWl1/0f375DNnFCuXoKXp73xs4MXvI0OnT1cDaBa0lHvGXCA+OdHP/cGd7B3MtIvXXx47HYFu3X5DVEKxXW7lmxnwBvEWkjTSOcHCF2+zIXjFXABhIDLAjtQNXuJwtnGUyP8mBngObNpZ171nFIm5uCSQZZznXjOqQIwaeY1MolfjO9BkePvwIyVgCd2kJSdeQIwZ4AskwUDMZpDUHrq2yY4omdYTjIcQqI1m39Rmj1yO49mUyTB4/DkB+4hR6REUzDGoXpih1dbL97e9que1dLBZrdlWLg47PJ1IyAAEAAElEQVSflV2yUBI6clLDHF/BzZlNcVu5yyB2INOSjOyNYhgGIyO+bX9fXx/3338/ALZtE4/HyWQyLT9w0dQkjhrHcYrNg0pVjaMFaG4KCVlNRDXwzDXFF3ULcVXWSBvB6U5oRGKIUhlRLiMqlYsfq5p4tt3MIVW6ugIR6xSUOetayKrazLwuLEpEYnE8z8M2q0hyESORwEik0KMGRiyBrAZnLggsq8Xs5UnoGqXSfYBavohhGAjPw7Is8oU8HtDVAjH7WuuWbPY0sWgfv7zrHkrLBU4zyaHhHn7mvp0M9D6AJCl4nkulMkl3xqYj1fpD4qSqcCjlu0V9dW6J9Of+gMIrLwPgGVGEqqE+9RXmdJX+T3wiMGK27QqmcpVmB3a+auO4XjOFTlN8EaPmuK0Z0HoRg1oMXBtWpgLX1NKgUXzlegLTqaLICoZqcCGdI5KPkKrGKMVrmNsg08L935XwPMFMoUBBCCS8esSAhyTJIEvkcjlErUZfrYaaTDXdHIPgKLKVml9iqRTCvfjaEa6LcBxiqfY/B7YSDcfGWDSGYUSxLAvTrGIYRlvcGZcdgyltlI7Y5e5aAJ6wqKpTOE754r7L2E7UGEaSN3bvypdTLDsv3sphr0tNeMh1i3bwo+gaS8PV12RJorZqzRhyDda6NlaHgM9dPCsMuS3Y0J3ns5/97CYP4/an5rjIEsj1m5IsScgSrVucXi9bOCcn5FI2Yh8mhMCuCYQjkFWZSmWFF7/+ZR57z/tJdnRRKVg4lmiG+6q6TCylI68R4O6kLIn1qtmt2RLm+DLI4CxUkWMqTs7E8/xs5OOzZ/iRN5/lX7zrH3Lg0H0gPIyxLvSBq//Obraafb3ngGmalCtlNM2v+BNCUC6XWVAUFEWhsLLM8Vdf5r6H304kmiAS0ZuVgbZtE4/FWV5HFGj1c6BcmcA0/Ww2wxggFttJpTJJuTIROItx8AXtsKJyE+nbD3vX2BwPXd0yeTMZ6Rjh9MppzhbOosoqjnCIqn7HrVy371NkBVVWmzbjm8V696zSSy9ROzWOmslQfuklrHPnQJaRo1Hkzk7empzkO9Nn+LmD9zCmabj5HuKPHkGORvGEwJ46T+LRRzH277/kZ7XKjineqQMSZ7+7xMK8n4mY6DTanjF6NSKxGNvGfMuy8nKWTF8fwnHIzc5gVSp0Dw6z/Z572zY+2VDRhy8VWmMHMpeI22pPNFAi9nrout4W8Xo1kqwTNYaxnSJCmMiygaYmN3yYEhLSalJ6ksIWsRAXxQKiVEZOJkGREbaNW/KzpqV4HMmy8FwXJ5tFVKto2/rbHuvU1R8jNxtlZa5CMedb9gZ5zmrYjNcqZd/u1vPo6Omlo7c/FK9vhIaY3TUCyX6cfKF+eO1RLpdwHAfXdSkU8kjQ9hgnTe/GdcuYxQu8P5Piz4AnOiQ8J4/n2UiSUv+fim1tJFxl8/Bsm/7f/XfwzNOsNJ+bHq6uo8kS+S98ESAwYnahbiOeNFRkScIVHudzFWxXYGgKwvPwoBnztOmsjhislcAxoboMlawvaEPgmlrAL75yqg656hI1UUNCwhYO3dE02s4kZslFT6bIGKn2O4h4gvPnz5NbWUFSZGTq9cMNN/e6VrVSKuHJMoNS3RtFlgPhKNLIyrYqTvN4WDcUIgHMdU4PDJOsi/6F7CLLMYPOvn7SA8NtHtnWYnU2tixJGIaBBGiqGsiIwa2074rI/tzveV5zvd0w61l9TXh+80vIdbDatbEcvBinkJtnQ3efH/uxH9vscdz2RFQF4YHwvGZHtvD864Fki+bkbAVanXlzvbanAMeOHeODX/8yH//5n6G/YydTJ3KkegxkWUIIj8KiyfC+ND3bLz3YutOyJNZWs4teh4qSxVmo4DhVRMXGS/rZ2Gomygp+t0iaFL2VBLF7uond37fpgsB6z4EzZ86wsLBAV1cX4HetzczM+Asm4W+0jr/6Mv/gA99LX18fhmGg6zqu69LZ2cnu3bvXFQha/RywrSUk6eJ9NCgHKiEhAAk9wRM7nmAyP0nOzJE20qQW/YN04flWbK5wcYTTkk63tfes6soKxQsX0AcGcL/3eym/+CK1M2dQIjq4DssDfsVzRtcYPXAAt2pi7NjR/HqrVsOIREi2warJqjpMvZXDtV30qIJt+q4RfaOptmeMXotKMQ+Apkfp2jaIBJjlIuXCCgtnz7RVyF6P9cTtoJHP53n99dcBOHfuHIlEgp6enpbbia9FknV0PRiZ8rcrq+1ZI6pCKqoFK7ZpC6HKWiAtxNdz/HHm53ELeWRRA8fFtcqYiwvkizm6egdRPf/gz3MdJLuCaijo8Uhb4zD0qMquQ70sz1XImn4X6dC+rsDOWQ2bcbNSwrEsVF0PO7CvwcaeTxLU5imu5JiZOEWyZwBXCBRVxbUdiEZZKhewyytEY/Eb+P63hnhsFFkxqORfQ63vteLGIJ6wqZoXiMdG8DwXz3PQ2jjPebbN3Kc+hfb/ewpb03Dq3WuOB6oESiQCUrDE7LVNLg2xyPXa2Hmn6H4TS3XFF7BrRf+apECs229yCVhTS0pPokgKS1YOQ43g4V0svJIklA6DmmNSsIptn8tKxRLLy8uA1GwGkTzwXLfe5lx/jEfedem0LBKRiN+iHwBHkUZWdq3q4NoCRZOJRFVkNXjrrUgsxvZ7/IaG9OAwux54kPTAcMsdr7Y6qqbied4lbfjtyMa+HrbKvqtDVcirCvlVGdnd9d/r6mvJgMYQhoS0kxu6A505c4Y//dM/5cyZM/ze7/0evb29fPnLX2b79u3cfffdt3qMtwWpqEa+alM0ffsg4UEiopKKtr8idF22aE5O0Fltg9fKzJvrsT0FyGazAGzbto202oe7LUpn78WF34paoTuVYvv29fNJ7lRkQ212rtm5KtgCp2jhZqto2+Iw2SyzQ9JkWrlXXfsckGUZIQSZTAbXdZmenkZRFGKxGJVKhZ4ef5O6e/du0uk0mUyGeDzeNovWK6Hp3XjeRWeLIByohISsJqEnLrEMz3f6IuZsaZZIIYJTzx4d6Rhp+dj00VG08XGsyQkkTUffuQOlowO3WMTYt494pQJ/8zcohoHa24s4fx7PdZEUBc918WzLzx9tA8tzFfLZKt3DCWRZYtntBKBatNsynuvBLJX9d2T/XECSJCRJxrFrQFh1fb2YpsnRo0eZnZ0FoFqtYpomi4uL9PT0MDQ01OYRhmwWa+1ZhedbtA6nY6GYfYMEyUL8aq5SjWgMKRoDScJzXVamp/j25AnetucASVVDiuhg2ciJBLwl/Jin5OVFOXeSo9T1stpmPOTKbMQBbS25pUVe+vpX2HfwIYxoHFmWUVQFI2JgOw65SIR4PH7Z17Xq+aqqSRLxPVi1RfSInzOZSt2Prs9QqZz2rVs9h2h0mHhsdNPHcyUq3/kOhS9/BUmS0A2DiO2vAyOyRET2O1/RdYRtU/jyV0i9//3EH3mkbeOFy5tcXFdgqDId9UIsTZFxhYcrWpw/3HBlVCKgOaAaYFXAqQayqUWVNToiHZiuiSZraLKGoUYp1ApM5M+AB5qio8kqKT3FUHKobWJ2Ipmgq6vLtw8XwhezV3t1S76IjQcpWcEwqwjL8kXsRLztjiLgi9lBi8JYj1qlQj5bd+xLxEMR+wYxDAMzYmKaZrNDOBKJBOYccCujyTLbDf2yyEFgy8QQbilqJZg+2ra4wZBby3UL2d/85jd53/vex5EjR3j22Wf59V//dXp7e3n99df54z/+Y/76r/96M8a55dEUmeF0bOt0DKyXkxNJ+ddDbphLbPAClHlzNVbnuTU6soUTvDy3oNDoXGt0rwnToXIsi3U2j8hbAOgDCYz93YiVGs5itS2dbplMhsXFRbLZLOVymcXFRTKZDKqqIkkSCwsLgC+w6LpOPB5n7969LR/ntYjHRjGMPgBMc5ZKxWv7gUpIyNWI1S36Dvcfpr+7n7SRZqRjhITe+kN0JZEg9eSTWBMTOLkcajqNNTODNTGJvmMH0unTAEiahhSNog0PN0Vvz7bQhofRR9vzWqsWLWRVQq7bbTXe1irBF7KNhH8wLSsqZqmIJMmYlTLxVAe9O1tf0LDVyWazZLNZdN1flwghyGazdHV1US6X2zy6kM1krT2r8DyKpkOhatOdaG/30u2EI+y2dGlfzVXKLZcpffOb2BcuIKkanmNzqnwv//i3T/Cz+0YZU1XkeBylqwtj3z6cbJbInjES6whYrXITsqoOZ15dIJ+tkpvz703TJ5Y5eNAJbFd2yMa4cQe0r/BTP/aP0TSNdDpNMplEURRyuRy7du1i27bLs0Bb6X5lRIfQ9QyxqP/zVDVGLDZCLL6biJ5G07uJx0ZR1fa5tsQeeIDU+95L/gtfRLIs1HqXsypJDddmhGX51vjvey+xBx5o21gbrG1yqbkeuiqTjutoitycy1ru3thwZVQVsIr1bmAbykug6pDoa+14NkBMi2MoBkk9WbcWt8mZORxhk9AT1FwTvAgFq0DBKratUEuSZLbXHawaYrbk4devSr6dsAd0KAqD6W7URLxlDo63E7VKhfFvvcD0W28CMP3Wm4ynkow9dCQUs68TRVHp7OrENE0c20HVVAzDCKSt+FbkSpGDYQzhJjD5LFQ832XEtWBxxM/SDsXsLcl134E+/vGP82u/9mt87GMfI7mqovl7vud7+IM/+INbOrjbgfXt7rbAwcrqnBzb9DuxjY5AZeJsRYRZa+bcAIHJvLkaq/PcZFVCOB4dmWhg89yCRqNL2zMd5IS/AdH6YigRBU+RcEtWW8ZlGAb79+8nm81y6tQpAIaGhigWixQKhebneZ6H67rrdgQEAVVN0tX1KADx+C4yPY+0/UBlI9hChNWWtwKzANlxOPuS/7i28QPEdjPWNcah4UPX/sRNRkkkiN57qZV17eRJ3+qujmfb6IOD6KOjl4je+ugoSpu62BpFVpbpUC3a5C74ooCyBey3Ykm/KFCSJKxqFUmSiKc6GHvwEfpGx9o8uq1HuVxG0zQqFT++wzRNyuUytm1z3333tXl0ITfL1ax0lwpVahUbNeJvaR2rxuLMOdTtO1EznTf1vUN8HGEzXZymbJf9jhzTo6DFW9bZdjVXKXd4+JI5yS0U4Ld/m51vf4ztMzPou3aj9fcjaRpWzSS5fz/RNkRhNGg4iXT2x0gW/I6m0orJ8lyFvpHwMG2rc6MOaA8++CCGYZDL5VAUBdd12b9/f9ujMcAvGC5HT2Ga/jrbNGeJxx+ht/fJwOy1JE2j/xOfACD/hS8i7EsLGoVl4VkWHR/8QCBsxeHyJpeepEyhalOxXGTJbZ97Y8OVUUv4536VLDg1UCNADMwViHcH6kwwpScpaHGKVhFJkqjYFYTn+gK3GgXPo+pWQZKoOe3tKF8rZsNFO3EP6NQj9HkCNRH3HUTaONatSm52ipX5OVJ1d8FUTw8r83PkZqfYtjt4TRlBR1F88drEF7NNTDRNx7atUNwO2ToUZuDux0FWQTj++WF2/GKWdsiW4rrvNseOHePP/uzPLrve29vbXIyH+Gx5uztFD1QGzu2AbER8iyghmh3ZQci8uRqr89yqRYtoUqerPxZ2DlwHsqES2dmB8rr/d5ZUGU94eK6HkmjfRtAwjKbl6smTJ1EUhWQySTwep1qtArCyssLo6CiWZXHy5MnAWYsDqKovsnd1PUhHKviihS0E502L4qr8m7yqsN3QQzH7ejALcPzzkJuEbF2MmHwWDt4fVlfeBProKOobb1D51stUp6cBUFaJ1mtF73bR1R9j4azO2dezWDWX5VlfxCwsVbGqwe1uq1UqXBg/CUDXtkFScQMjmeKut7+Tof0Hwm6BGyAej+O6rp/jVkcIgRrmuG5pNmLVWzBtlko1YroKEpTzyxx/6WscfPv34vZvrHMstJW+OgWrSNkuN7vdPDyKVrGtnW0N1s5JyquvAhB7+GHi8/PYU1M48/NtdxBpsNZJBEBSJKrF9hS13gjCccLM7FtMJBJpFheXy+VA7bVUNUlv73vp7PKLGzu7HqS3972BEbEbrBazvf/xV83rQRSxG2iKfIlzSDqut9+9seHKWGsUtXv+tXgG9ARYpcDlZKuyxlByqOkaokoqHh62sJu23RIStmsRUdv/mlorZnsS4Hl0KEpdxE4EwkZ8q1IpFJBVFVn2ywBkWUFWVSqrGjVCNo7rOqwsr1Cr1ZAkCVcIHMdGVVUUWcHzPMyISWdXZyhmhwQK13GaZy7Imi9ig/9W0X2b8ZAtyXXfaTo7O7lw4QIjI5daH7722msMDg7esoHdDoR2dyFrkZMp5EQeUSw2heygZN6Ab3m3PFdhdnwZANt0AF/MDjsFbg61J4rS4YvWbt1SXEkbqD3RNo/sUptxRVEwDIOBgQGA5tvJyclml8Di4mIgugTWw3GKlCsT2NZSIOzu1iPvuBQdlw5VaeYN5R2XvOMGwkqoaNqcWSzzyqQvHZRrTptHdAWy476InRmDbH1OLcyE1ZW3AM/z8Dz//KfxOGjoUZWubXEWzhfp7FMpq/4cZZasQHe35WanKOb819bAnn3s272L5dkZYh2doYh9g2QyGeLx+CXCdV9fH52dndh2cKzmPWFhO0WEMJFlA0WJ4rrV5mNNTSLJwelyajcbseotmTbPnFzkbLZEqeZy/vQJjr/0Nf7lz/4Ujz+ysXmglTa9QWKj3ejzK+eZmz5JuWcAT5NQJQ2By1LZQYnVbup7bxZKLHZZbEY7HUQarI5rauC5wYtrupJYLRyH/MIctWoFSZLxPEEtWqKjtz8UszeAaZqXiNW12sXXz+ri4qChqkmSiT0AJBN7ArevatAQsxMXLsCpk4hq1bdqDqCIvb5ro9z+M8LVroz5aZBkX7RuCAABzMkGX8xuFFZFzBxlp4Lt2izXllEkBddz6Yv1kdKD8dxdLWYv55bpSMQYTCRQDCO0Eb9JYqkUwnEQwi++EcJFOA6xVDD3hUHHNE1qtZp/7idJ/mOzhp6KEDEM8DxM08/RjsfDotCQYOA6Dk//8X/gm1/8fP1xze/EbnRku1agCrJCro/r3nH80A/9EL/4i7/IX/3VXyFJEkIIXnjhBX7hF36BH/3RH92MMW5Zao6LLIFcz+mRJcnPwHHca3xlyO2KpGloQ8N+VnbAMm/C3LbNRTZUjN2dAKi9UaJ3daP2RJGN9v9uV9uMNw5XGtERmqZRLBbJZDLIstzMHs1ms4E7cHGcMgsLX6FanUKSVDzPoRw9temdA9dzYGuaFd46P4HVtQ0rFsOQZRRJoua65FQVLXL5vaCVB8JF0+bLx+Y4nyszt+CLBy+cznL/QZuk0f771CWUF/0DF3nVa0jWwurKm8SamMBdXCT+8MPEJifhv/0/iOVlrImJwHRjN3AsQTJj0NkbY9GsO14EvLutUiggKxfNAmUl7BS4WQzDYO/evbzxxhsAdHR00N3djed5gYnF8IRF1ZzCccp+V6vn4Ioaihzx5ys8HDVO1BgOxexVbMSqt6t3gP/+ynnKiyUyff0AzFgRunq3BW/eCgDX6nR3hEPVqWILG03WWM5lOfbc8wy8bT/RVAKBAA8GEgOYkc4r/pzN7HR3S6V1RWq3Hi9Q/s53sFQVfXQ0UPPW6rimYs4XgxKdRqDimq4mVpuVErVqhUgs3izErFXKmJUSsVRnu4ceaEzT5Pjx45fYh8/Pz7d7WLcNRcdlvGKyZDlkf/ifwB//MchSYEXsQLs2rnZlXDnvi9kAnqjbjgevkH01UdXAdi1KdglXCDwEST3F9tT2lsRhbJSGmN2VShETAq8W3L3LViI9MMxS33lOTr4MQGFxkf27dpEeGG7zyLYmju0gSRLUNQ0hXCTZ14EA3/FAknDsgDY+hNxxNETsEy98E69eOPrKm3kOjZ1E0QxfxE6P+M0wIVuS61ZQPv3pT/OzP/uzDA8P47oud911F67r8uEPf5hf+ZVf2YwxblkiqoLwQHhesyNbeP71wOJaYS72JiNpWiAzb8Lcts1Hrmc4GiOd6MPBqAhusLYTYGFhAYBqtUo0GkWuW17LsoyiKJTL5baM82pUzRmq1VlisREkScHzXCqVScqViU2xHN+I7elqHM/j3MI8J579KplH3kW2I01ElkioCqYQOJpK7QodLa2yPj2zWOZ8rsxoTwI557sFzOWrnFksc/9w56b//Osi3uPPWWLVxknYYXXlTeLkckiajrRKbJU0Daee5RYktkp322piqRRiVf64cMNOgVtBd3d3c54ql8ucP3+e7u7uZlFWK7ha0ZFtL1OrzaEqCUDCrOZYmjlBZnAfkWga8HDcLJVIDU3ruu7vfyczX6yB5/Hu/X2clf3fUWDnrQBwtU73slXmuZnnuFC6gCqrOMLh/Ph5jj33PIf+3i76dvXhCAcPjx8c+0EO9h284s/ZrE53t1Si8NRT2FNTSJru24aPjxM/coTyS36OrzUxQbFSQRsfJ/Xkk23vxG6wOq4pa/pFNkP7ugJVMHw1sdqxLCRJ9g+1AUmSkCQZxwoFmGuRzWbJ5XKXFAaPj4+3e1i3BUXH5QuLK5yt1tAlmcmiXyTS8X/+Av0/+k8CJWLDFnJtbNiMm3m/E9tz/cdGR1uGs9E10FI1y/LieSrZeSLdGRRdo4bDojiHiGZu+vvfUhwXY2UFu1DAs20820ZOJIns2oUcujTdEJFYjLGHjjBTKAIwdNfdjD10JNiuV65AVB08RyCpMnJUhSAUtQCqpuJ5HsJ1sR0b27JxHIdmQIrn4XkeqhacdUxI62k4Oi6VanQnIuzqibelmHi1iC1rKlrEn1PPnl3g6WN7ePd770FJ9fsidhhFuGW57ruNruv80R/9EZ/4xCc4duwYpVKJgwcPMjYWVjOsJRXVyFdtiubFastERCUVDdZiuolrwfI5PxNn9WK1a0coZt8B3A65bSG3nmg0iuu6CCGaBy+u6wamy201jr2CJKlIki/ASZKCJKnY1kZk5utnI7anqzlWrPA/Xv42J579Ku/40R+Fkd0UHYchQ+dgKs67u1MkrlDo1Crr06VSDeF5TC9XOFPvyPbq1wNHZgwWR3wr8Xz98CE1GFZX3iRqOo1nW3irxFbPtlEDaL27Fbrb1pIeGCaZ7gagkF1kOWbQ2dcfdgrcJMVikUh9s9rT08Po6CiWZVEsFuno2NxD140UNVn2Eo5TQlH8AqHlpTmOPv86hx+7j65uv4vYdauoagJd677izwrznC9nqVRDU2SUVetXVZGDOW8FhCt1uh9bPEatUOPQ4CEUWcEVLqdnTgNw7657Se9Kk9ATmI5JZ28n24e3t3roWBMT2FNT6COjSIqC57pYkxMUv/4NaseP1z9LQt02gD01FTg3kUZc08CyX7CiBcCZaTVXE6tVXcfzBJ7nNUVuzxOoepvPCeoigGu7ZC2Xvu4ocsAaB8rlMoqiXFIY3Hg/5OYYr5icrdYYixkokoRr+M/HubG9gROxYQu5Nq62GW9jk8v1Fo7PlGaYW5xm7jun2PnQfRipOBWngmOkGUxcPQqz1WssUSzgFgp4lo1Xq4Ek4SwsIEkQ2bsvkM/frUAkFqNneCcAPcM7Ay9iOzkTUXORJD/WS646qGkjEGK2YRhUtAor+RVc123GjVUqFZBAQiISiQQycjCkNax2dNQUGdsVnErHed+B/paK2WtFbFW7OFfJqsqJv3sL4j28+yeeRAnjcLY0N/zXGx4eZng4PHS7GpoiM5yOrZt/E0jKWSjNgRIBVQEt4YvaZj7scLsD2IqdbSGbT2dnJ4ZhNPOzXdclnU6TyVy5orldqFonnjeL57nNjmzPc9D0K4sCN8tGbE8bnMzmSff2AXBoZDuxvbsZL5vcm4zxwwPdJANw6BbVFU7MFZEliZWiLwJMZstE9faP7TKMFIy+y1faxZx/bfCBsLryJtFHR9HGx7EmJ3Dm/N+r2t+PPjra5pFdzlbobltLJBZj+z2+Q0R6cJhdDzxIemA42IcsW4CVlZVmHrau63R0dFAoFFriHrKRoqZC8S2Wcy9gGMNIksLfvf4KH3n+dX76nz7Effc9iOe5mOYUXekjpJJ3XfH73Kl5zlejOxHBdgXuqvWr44pgdbZtEXJmDlVWUWR/zldkhZjm35t6oj3s6d6DK1zOFs4280hbzVrXEElR8DwoPfcsdn3Oqk1MYOoaSlc6kG4iVtVhaca/XyzNlLD2ByfG6WpitRFLUIuWqFXKTdvxSDSGEWtjcU1dBHBMh9fyFaarFsNLBg/u6Q6UmB2Pxy8rDG5aswYQxylSrkxgW0toejeOEzwnrgZLloMu+VFNcFEgzgfU6nZLuTauthlvE9dbOP7XJ/+aL774Rea+c4p3/MP76Bnt4WzhLI8OPMo/2PsPrvq1rV5jueUybrGEZ5pI0ShyJIIMuMUSolhASW/eGcadRK1SITc7RaVQIJZKtX/ftaoD27P99+WY6tt0ex7CdP1rifafwyqKimEYlCtlIpEIquqvESvVCqqqkkgkMAwDRQnGGgb8OCfbKSKEiSwbaGoyjG3aRFY7OiqyhCs8JhZLLXXGupqIDaCoGrKmcuKFbwLw7p/46VDM3sJc91/u7//9v8+DDz7IL/7iL15y/Td/8zf59re/zV/91V/dssHdDmiKvDUOUlwL8lNQK4Lq+G+1KKgRvwIz5LZnK3a2rWZx6ixncIOxOF2DMB2cxSrm5Ir/uBbMjfV6RCKRy/KzM5lMIKsuo8Yg0ahCpTLZzMiORoeJx4IhwHXrKna9ijUiywwbOqYQPNgZD4SIDfiiMFLjnTrSpQ+DglmAiW/A8iTUD92Z+Q6Yj4Zi9k2gJBKknnwSa2IC/WU/Xyz+yCOBsWZdS9C72+DyA5QGw/vvZtvuvW0c2e2BaZrMzs4280anp6fJZDLE4/GWuYdcq6jJcbpYWChSrU4hSSqZbr9CvbtbJ50u1+ere+ntPYKqBit6JOjs6olzKh1nYrHE3EoVgP6OKLt6guccE3TSRhpHOLjCbXZkR1XfRWC2NEukEMERDkPJIUY6RtoyxtWuIZKi4JbL1E6exM1mLy5VZBnzxAnUdDfxRx5pyzivhFV1OPPqArPjywDMji9zJr3ArkO9gRCz1xOrtUgE4biUVnJoRhTNiCKE2xS35TYeBoqqg2M6/F3JZKpqIQHnSybSRI63jWWaomY7MU0Ty7KwLIvJyUkSiQSSJBGr71XPnj1Lb29vYPZXjlNkYeErzfnK8xyWl+12D+uKdOsqlidwPQ+lLgwDdATU6nbLuTYGgOspHD+iHeHl0/7+xUk41FI1+pJ9HLn7CNu3td5F5EqISgVnbg53edkXMF0XLAtUFTmiI8xa4KIItyJWtcr4t15gZX4OWVURjsNS3/n22Y2v6cB2qw4Iz7cTl6hnToPnBKfQSQhBRI8QWTU/RdwIRsQgHg/W+YAnLKrmFI5TRkLCw8NR40SN4VDM3iTWOmMpsoTWYmes6eNvcPLl50CSLhOxG6iajuW4nHz5OfY++nZ2HLi/ZeMLubVc9+ru2Wef5V//63992fX3ve99/PZv//atGFNIOzDzvpit6L5tEIBV9u3FtfZvqEI2n63Y2Qb+4hRg+q03Sdq19i9O1yBMh8qxLG7OxFnwx2qeXkEcdJADKLisx9r87KCiqnF6e997SQdBPDYaGFFgLGYwEPEPKWZMC7tisjMaYSwWnHts1XbZ15/E0BROrvhFWCOZGFU7GHZ3lUqFEydO+A/mj8O5F6BzO8dz/sL5+Bt/B13/G/r2X/J1+/btax4YhlwbJZEgeu+9xB2/6EYJf3c3TK1SuewAZTZfbPewbitmZ2eZm5trWuFWq1XOnz/Pvn37AuMeoqrJS+anrnQv8Id+B3aqN3Dz1VZgdR7bUDpKOq7zzSn/2LcvtQWKiAPISMcIp1dOc7ZwtpmRPZT013+H+w/T391P2kgz0jFCQm/P4eVq1xDPg9rJE7jZJdA0RMnvGrXOncONRpGER+3MaaL33B2YYqzluQr5bJVEt7/2S3Qb5LNVlucq9I20vwhPVlU6evubmdiyrFCrlCktLyFJMsJ1kGSZaCIY9yrXdnktX2GqaqFIkt/hKgTn8lWkszke2Jluq5htmibf/e53mZ2dxbIsbNtGURTGxsaYmZkB/PzskydPsri4yP79+9suZpcrE1SrU8RiI02HK9N8rq1juhpjMYOTUZPxiokuyZw3/Vi07UYwRYst59q4xbi7+27u7r6bv+KvKFgFurwuHux/kLu772730PBs27cTL5dxLszh5vNIioKo1QAP4TjIHSkkTUc2grGOEY6gVnVwbYGiyUSiKrK6dZ6r+ew8TrVM18AgsqIgXJfl2Rlys1NtKSYWVQdRc5ENBSQJ2QNRqPnXomo9cxqkAP2OGznZ/sCkQOdi204RxymjqsmLQrZTxHaK6Jvo0ngns9oZq9GRbbfYGWto/z3sffjtnHjhmzi2ta6Y7dgWeB57H34HQ/vvadnYQm49133nKZVK6OvkIGmaRqFQuCWDCmkDtglazM/Gtisgyb6wHUn6WTghdwRbobNtLfms34GV6umha9tA2xena3EWq7g5E7UnipLzJ3M3b+EsVtGHg3EItB61ml9BF7QugWuhqkk6Uve1exjrklQV3t7l/83HYhEeznQwFjOC042NvxCVJRjqiuL0+oe+Uv16EDhx4gSHDx++4sd/5DP/Ez7zPy+7fvToUQ4dOrSZQwsJWZfc7BQr83OXHKCcnHyh3cO6rZibm8NxHHp6fPvLVCpFJBIhGo0Gat5aPT8lE6/W3+4hkwnvTdfL2jy2cs1huWxTKfsCxutTK8SPzbU8n22rk9ATPLHjCSbzk+TMHGkjTd7OAzDWNcah4fY/V1e7hlSOvorTlUYf24M9MYFq+X9/OZlA6+3DOHgQd3ExUDnZ1aKFrErI9c4VWZaQVYlq0WrzyC4iqyqxVCcAlcIKds0kEovjeR6V/DK1crmZmV2Llujo7W9LV7bwPF5dLDFVsVBk6ZLMYYBzSxWAtorZs7OzjI+PI0kSiqIgSRKFQoFCoeBnjAIdHR1kMhmy2SzZbLbtxcO2tYQkqUhS3b5fUprvF0unyGZXAlV8lVQVPtjTyXjFZMly6O9M8IdAPED7q7VsGdfGLYpez/FOG2m6jC4iavt/155tY09PIUplRM3EWVpCUhSUdBqpUECYJmgySDJKKomcbH9hk3AEhWwVy3SRZPAE1AyFVCa6ZcRss1QmGVGR63EksqIgqyqVNmkXniOQ6uZ3Xs0FRwASomJfzMiOKL6oHRAMw8CMmJim2YwcCWouthAmUv0f0HxfiNBldrNY7YzVyMjeno631BlLUVXe/RM/DeCL2XCJmO06NsJ22Hfk8cDaijvCpmAVqTkmEdUgpSdR5XAPux7X/dc7cOAAf/mXf8knPvGJS67/xV/8BXfddeVMt5CA0+i6jnWDUwXHAlmFju1+l3ZISEAx690XshyMxela3JKFpEhI8sUDFEmRcEvBObBai2maTE5OAsHrEtjqNA5VHu5KcigVPNvToFu07tu3j6NHj/oP5o/DueehcztVy+XszBw7YybRPe9atyM7JKQdVAoFZHXNAYoS3MPVrYws+4dqiUQCXddRA7hJDbk1rM1jm1gsc/T8Mjs1/7U11BXjfK7c0ny224WEnuBAz4Hm41enXm3jaNan4Rri5HKISgW1rw+RX0GMn2p+jj46QmTHDpz5+UDlZEeTOsLxEPVMdyE8hOMRTbZ2v11eXtzQ55WWs5RyOczSMtFkF7bl23cLzyESi1NaXsSqlTESl4ouG/3+N4rwPL5zNsf5gumL2OArAHVk2Y/FabeYPTc3R61Wo7e3t5mNvbCwwIULF5pzlj9eGUVRKJfbn0Wt6d14noPnuc2ObCH8PcHK8isUCjvxPIdy9BS9ve8NjJjd2Fe9moi2eTQh7WQyP8my6Uc3HO47zEj/CGcLZ5nMT14yt20m693/xMoy9tw8ciKBWykg7AqiWMOrFcmXl0nqCVRPRY924xoq1ZXsdX3/zaBWdbBMFz2mNkVWq+JQqzotn7NuFCMRR1TLCNdtFhQLx7kk6qmVSKqM53qIouXbh0vgeR6yqiAZKkpDxA6QQ4OiqHR2dWKaJo7toGpq4HKxG8iygVf/1+jI9vCQ5a15hmkLQd5xqQmPiCzRoSpocnCeGwBJQ+N9B/qbLlndiQi7euItLyReT8xuIBw38CL2dHGasl32i0VMj4IWZyg5FIrZ63Ddf8Ff/dVf5Qd/8Ac5c+YM3/M93wPA008/zZ//+Z+H+dhbGaMDIimoFfyubFmBaD/EQ/uNkGBjJPxNqxC+9XG7F6drURI6luvhiYuHK57roSSCu/jPZrNNh42gdQmEbC6rF6Ivl6YAOLI7E5iOtlgsdrGz2twNxz3ITYKic2SwD9IjsP/7wozskMAQS6UQjnPpAYobDKv+24X+/n4mJyebAkC5XKanp4f+/v42jyxks1ibx1a1HDRFxqz6ry1ZllBanM8W0noaedmSphG99z702VkA9OHtRO+9D0nT8GwLdYPZqq2gqz9GbjbKyTN+d1BpyaRjd5Su/tZEeCQSCdKpGEuvPsXSBj6/WioyP32ON4+9xt133080GgUkIrEYaiSCXasRicaIpS53cEunYiQ2ydJ9sVjjfM4XqRvd7azaayE832YcOJ+rsKM7Tl8qOAfZuq4jxMUMVCEErusSj7e/cDQeG6UcPUWlMtnMyJZkv6PVMAaIxXbieS6VyiTlykRgnbBC7kxyZg5FvlgwqsgKqqySMze/oOlq91cnl0OUSkjRKF61ilss4lkWRcvkO+dP88CO3XT1bUMrDSGduvYx/WbeXxu4tkCSfTdp8N9Ksn99q9CR6SNhVVienWlGPHX29ZMeGG7LeOSoCrKEZzp+B74HckxFUmS/EzugZ4SKogYuD3s9NDWJo8ZxnGJTyFbVOFoACq7W42pCtS0E502LouP66xnPI68qbDf0QIrZQSgcVlSVd//wD0N5kROvfhfb9Ivwdt5/uC0i9kaLjvK1FeZWpvCWCyR7t6HoOot2Frlo0hHpvKnvvVk0ojKEWUM2IsjJFJLWmjPj6/4rft/3fR9/+7d/y6c//Wn++q//mmg0yr333svXvvY1Hn/88RsaxB/+4R/yW7/1W8zNzXHffffx7//9v+fBBx9c93P/83/+z/z4j//4JdcikQimedEqwvM8PvnJT/JHf/RHrKyscOTIEf7Df/gPjI2N3dD47ggUHbp2+FnZtul3aBsdYTd2SODpyPQBUFhcZLGzgzfkCI+2cXG6FrUnipI2sC+UsaZL/kUJ5GQwhMH1KJfLge0S2KoUHZfxisnLy342btkJrpDVWIiKRb+QKR4JXtUi4IvV+78fsuNQXoR4D2TGQhE7JFCkB4ZZ6jt/yQFKMh0WCd5KBgYGGBsbY25uDvAt8MbGxhgYGGjzyEI2i7V5bFFdxXYFRr0jWwgP0eJ8tpDWszovW9J0pLoIKCfiOPPzeLaFNjyMPjra5pFeRI+q7DrUy3TOj3EaGOti16Fe9BbZiKbTaT77m5+mVCpd83Nr1SqnX3mB559+mjePvcaTe4fo7UjQt3M3A2N7kVWF/Pwc2++9n76duy77+kQiQXqTigh6khG2p2OcW6ogPGgaX63quhZC4AE7uuP0JNtzL2gUWhUKBVRVxXEcIpEIIyMjLC/7XaP5fJ5sNks6nSaTybRlnKtR1SS9ve+lXJnAtpbQ9G5i0WcA1tiNq9jWRsohQtbDdkWYk70JpI00rri4z3aFiyMc0sbmFzRd7f5qHj9O6cUX0Ya3g21TfetNrMlJTnvw4+dP8/Pf+z089JMfQe/t3dDPulX316uJIWbZprxsokX9jmy7ViM3fY7+3aO47saEwXaLLXo0ytjBg+Rmp6gUCsRSKdIDw0RirSkeuwxF9sVs2wVF9p0bdQXPcv0O7ZCbQpJ1osYwtlNECBNZNtDUJJIcPF3jWkJ13nEpOi4dqtK0dM87LnnHJaOHc8W6mAWU8S/x7gMKFDJMv+Kvsx583/taKmJfT9GoIxzmK/Nks/NMf/tN9j/2MMnOTqpOlYqWuOrc1YqCpvVYHZWBLOMKgZzIow0Nt0TMvqG/5Ac+8AE+8IEP3JIB/OVf/iUf+9jH+NznPsdDDz3EZz/7WZ588klOnjxJ7xUm8VQqxcmTJ5uPpTU2Ub/5m7/J7//+7/Nf/st/YWRkhF/91V/lySef5K233gptca+GovtCQEjIFkKP+vZh/fvv4v/J7OQbjsKpTIrfMoJhKyYbKsZYJ6WciajVN1WOwBxfIXYggxzAHPJ4PB7YLoGtSNFx+cLiCmerNRYqfnfYc8tF7nfcQOVjg5872rAFWpgrtns418ZIwdCVM7NDbk+sqsPyXIVq0SKa1FvWyXYjRGIxxh46cskBijp/ZbvAkOvHMAzuvfdelpb8reJdd93FvffeG675b2PW5rFVLYfR7gSVCxcAmF6u8Oie1uazhbSe1XnZTi5HItMNv//7JB59FKOvDzWdRh8dRWnDIc/V0KMq3YP+mLoHEy0TsRuk0+kNCSAXTp8kqWkceOhh+C9/xu6776bTc0l1dZDQFITj0H/3PYw98FDLRQFZknhgp//fcG6p4ne1rfq48DxcYHvSaGtGdqPQanZ2FsdxiMViDAwMsHPnzqaQnclk2Lt3L5lMJjDzlqomL+m01iPHAfA8t/nW8xw0PSzMuxFsVzCVq1CqOciSbyaQr9oMp2PBErNda8s1uox0jNAb98+RL5QvIBUkhpJDjHSMtOTnX+n+6qbTFEol7KkpJE3HHd6OvHs3rizDl77Inh/9J4zs29eczzZ7/tqI0CKEoFKwcCzfArtUWOb1V7/Oo9/zPrq6N1500y6xpUEkFmPb7r1t+/lrkSMKQlOQDYWGZ7vn+bbjITePJOvoW2BuupZQXas7yzT0LkmSkCWJ2mr3mZBLyY5DbhKldy/v/tB+7GQHv/XlZ1GKsy0dxkaLRstWmedmnuON7Bu8+QZMf/tNDv69UR479BgL1QUeGXiEfd1XjkjczILRqyGKBUSpjJxMgiyDEIhiEVEsoLSgYaPtCsrv/M7v8JGPfKTZZf25z32OL37xi/zJn/wJH//4x9f9GkmSrmgX6Hken/3sZ/mVX/kV/t7f+3sA/Nf/+l/p6+vjb//2b/mhH/qhzfkPuV0J+OLVETYFq0jNMYmoBik9GWYI3KH8uWvwoqshyfD5pRLKqSn+rz3DaHJ7Di5WI4o2EmCM+J2iaiaKmzNxFqvow+23uTFNk2w2S7lcJh6Pk0wmSdWt2YPWJbAVGa+YnK3WGIroZOtPx2OlKq8XyzzWFZzu4aJp8+Vjc5zPldEUmckJX2wr15xrfGXI7YRbKjUPUcz5+XYP5zKsqsOZVxfIZ6vIqoRwPHKzUWwpuM/TtQcoFwqhu8WtxjAM+vp8h5a+vr7AiAEhm8N6eWx9yQjPvLjC/w08NJrhfQf6AxOLEbJ5NPKyAYxX/TxvY2yMZCOGJOAszZQ4q2abRVmtFrWvRqVQQFZVtIh/P+3duYueaIRYMkXXwGDbO9tWi9lns+XmNeF5uJ7HcFTngcHOtonYcLHQamBgoLnPagjWkYjfJb5z587ARzdFjUEATHOWSkXH8xyi0WHiseC4HQSFjXSgrlQsFvMmhuxQWJwh3jPIolBRKgadsauftbWsw9W1YPncxehBz/WjCLt2BOo8cC0JPcHD2x4GYLRjlIeHH2akY4SE3t6CptWFV9bMDF61ihSNotXj3AAKTz3VFLo920IbHyf15JObImZvVGixTIf8QhWzbHN2epwf+Wdf5//7sZ/lwIGN5423S2wJKnJURa46CNNtZo/LjWzskBvCE9aW6MJezbWE6ojsr2c8z2sK3cLzLchDrkB50Z+fZBUF2Dbknw1QXW75UDZSNHps8Ri1Qo23972dWqHG1/k6hUiBWX2WQ8OHeHTHo22fu9ZDmDVfwG64uNbfF2aNVrRptfVOaVkWR48e5Zd+6Zea12RZ5j3veQ8vvfTSFb+uVCqxY8cOhBAcOnSIT3/609x9990ATE5OMjc3x3ve857m53d0dPDQQw/x0ksvrStk12o1arWLGWqFVYuJO5qAL14dYTNdnKZsl/0bu+lR0OIMJYdCMfsOwqlP9N/IlUj2QkSWqQnB/5pfAQiEmO2WLCRFuuh7J0tIioRbsto6LvBF7OPHj5PL5VAUBdd1SafTDA76BxZB7BLYaixZDp4H3y1WGC/7MRgXTJuvLBa4LxkPTFf2mcUy53NlRnsSKLKE3eUfTE4vV9s8spBW4ZZKzUMUz4Pise8CUD12DHfPnkB0tS3PVchnq3T2x5BlCSE8VuYqFGXz2l8cEhJy27BeHtve/mTzbShihwQZ2/SLr2bHl+mQC82irFbajF+LWCqFcBxE3aZXCN/+cnDf/sB0tzXEbM/zfJvxup34cFTncG8KNdb+36VhGIEXqtfiOMVLrMUbdHY9SCrVi6Z3E4+NogY0d7QdXI+V6FK5RtF0cCt5Trz8NDsPvws13kEpptGbvPZ++1Z1uF5VFK8sQWEWIkmQZOyaycrM63SOFtA6Nxbd0i5b6Zjm72EP9x/mQM/GBdfNRkkk0EdHMcfHm4J1ZeIMANaZM9jLy+gjo0iKgue6WJMTWBMTzWKtW81G3TnY47+Jvuo79m3bto3t27dvypjuCBQZNW0gqg6eI5DUut14kNwYthCesKiaUzhOuZmL7ahxosZwoMXsawnVHapCXlXIr7IeT6oKHQE5Owwk8R5fxxIOyKqvYQFEu9o7riuQM3OoskpcjzPW6Uchp400uzt388SOJwIpYgPIRgRXCBCi2ZGNEMhGa2J82rqyz2azuK7b7KBo0NfXx4kTJ9b9mr179/Inf/In3HvvveTzef7dv/t3PProo7z55psMDQ018/HW+56Nj63lM5/5DP/m3/ybW/BfFGw2upC0LZOVuSk6U3E0c6m5eMUTkJuEcg1il9oFtGORWrCKlO0yST3ZnLCKVpGCVWxJBk5I+7GFx++f81/XmuSL2NB4GxwxW0noWK7n+4YBCA/P9VAS7V9YZbNZcrkcmUwGWZYRQpDNZrEsX2TfCl0CQadbV5m3bFZsl7TmT7txVaLo+rnZh1LBsD5dKtXQFBnZA3O6TOMVk6+2v+DidqBklZjMT3J07igAFbvS5hFdjjUxQe3MBCgKteNvYc36Nr3FZ75Jod5J0G4xu1q0kFUJuX5Pl2UJWZWoley2jiukvZimyXzdQWB+fh7TNMPiq5CQkMBSWPKLrxLdBp29sWZR1vJchb6RYLj1pAeGWeo7z8nJlwEoLC6yf9cu0gPDbR7ZpciSxNtG/LOJqVyV4USEBwY7fRE7FAauG8cpsrDwFarVKSRJxfMclpf9NVYysYdMZmu4HbSa68mff3M2zzeOzzNx6gQnXn6afY9/P6SHObQ9zQ8/tJ3ENQqxbrbDdUOieznrN7Toviiczxd4/sVv8diRR+kY2LXhn9VuW+mgYU1MYE9NNQVrrd5QVZucROrpRVLqOfSKgqTpOLlcO4cbslkoMnIAzgJvB2yniOOUUdWLuoDjFLGdYqAtxq8lVGuyzHZDJ++41IQvcHeoCpocrmuuSGYMFkd8i3FFh5Xz/vXOYK1bG6SNNI5wcIWLXm8W7Yv1cajvUGBFbAA5mUJO5BHFYlPIlhNx5GRr9i/tL1G9Th555BEeeeSR5uNHH32U/fv38x//43/k3/7bf3tD3/OXfumX+NjHPtZ8XCgUGB4O5hP9Rrie6lCA/PISz3/tS/4i1ZCai1cArIrflR2/3GK41YvUmmMiSRJSXW6R8G05ak7YlXUnYAuPj5+a4hs5f7O4VqgOkpit9kRR0gbOuL9RcZZrKHsM1J7253iXy2UURUGuL4hkWUZRFKrVsAv3VjEWM0gqCueqNbD9qsBuTaVP11iygmOH3J2IYNsuuefmMCdKFDR/xuiIhpusm6Vklfjqua8yXZxmNu9n9Lx84WXGymMsVBbImTnSRrrt1nfWzAzWubOIQgEnu4Rn+fcsKRLBnpra1K6AjRJN6gjHQwiv2ZEtHI9ILOy+vFNpOIucO3cOgHPnznH8+HH2798fitkhISGBpFbxhcG1RVnVYnCKByOxGGMPHWGmUARg6K67GXvoSNusxK9GQ8zemanRk4y01U58q1OuTFCtThGLjSBJCp7nYprPtXtYW4KNdrh29dq8WdD5zin/kN3o6GbPvt3EDQ0r2s32NW4jmzHOa4ruF47Bma9DegRkhWOnzvLBF7/Fxz/yf3DgXT+w4Z8V2kpfipPLIWl6U7BmlSjl2Rae6zY7sj3bQg3A786qOizPVTj/ln82MPXWEoNdhcDFYYTcmQhh+lrAal0ACSGCrQtsRKjWZJmMHgrXG8ZIwf7v94Xs8iJUh4DPQSSYovBIxwinV05zavkUp5ZONa/3xnrbOKprI2ka2tCwn5Vt1pCNCHIyhaS15jyurbNOJpNBUZRmB0WD+fn5K2Zgr0XTNA4ePMjp06cBml83Pz/Ptm3bLvme999//7rfIxKJNDOKbkeupzoU4NixY3zwa1/yF6na+ebiFeH6Hdm7vge2XW7R0+pFakQ18EwPD69ZeeV5HhE12IeWnm237QV/O/HySonPL6xwtXOKiCxje4LPL6zwod4u3p5uj/2ZbKjEDmQwFjsAMEY7iB3IIBvtX/jH43Fc10UI0ezIdl2XaLT9IvvtQlJVeG9PB0XXpRrx/+ZjMQNJ8ru1g8JIOkrX8TKVM0WQwJzy8wYHOm7f+bFVTOYnmS5OszO1Ey/uOzNMF6f5Hyf/BwCqrOIIh9Mrp9tqI+RVq7jLy8jxOEoyefGQxXUD0xXQ1R8jNxtlZa7SzMjuyESRpGDP/SGbR8NZpLOzE4DOzk5yuRzZbDZ0FAkJCQkkjeIrUXdrahRlRZPBKh6MxGL0DO8EoGd4ZyBF7AayJNGXCtcCN4ttLSFJKpJU7wyVlOb7IbeGpKGxry/Jm2nflWt3b4JD27uYy5sslWrX+OpbwzVF995O0Jf98z9FJ6v68Yvb9hwMbaVvAjWdvkSwRvh23frICFoigTU5cTEje3gYfbS9OfRW1eHMqwvkLpQ596YvZJ99I0cmOk+6Px6oOIyQOxNZNnwtYLUugIcsB389EArVm4CRgqHD/vvlV9s7lmuQ0BM8OvAoEysTzJd9XXShusA3pr7BB0c/GOiubEnTUNLdLcnEXsuGZpzV3crX4nd+53c2/Lm6rnP48GGefvppPvShDwEghODpp5/m537u5zb0PVzX5dixY7z//e8HYGRkhP7+fp5++ummcF0oFPjWt77FT//0T294bLcbG84/wT8QhPoi1ehuLl5xLdh/EPa/3b85tJmUnqSgxSlaxWamRFyLk9KDm9Xk2Tb29BSiVAZZxhUCOZFHGxoOxezr5OHOBN/f28lfnLzy59SEwPPg+/s6ebizvZOAbKho/f5mVeuPB0LEBr+gaHFxkWw2e0lGtmkGu4JxNY7jC67Ly6+QLyiBzGy7LxljyrR4oV5hOW85HIlGGIsFY4HtuoJX/2YC52wJVZORFAlF9cd64uszPPawQAntGa+LSqXSjEk5OneU2fwsXtxj6swUAFNnpliqLnG47zCe5CE8wUullxjUB3lwx4NtGbMUjaJ2pXELBdxiEa9Wvw8oSmC6AvSoyq5DvSzPVagWLaJJna7+GG8cXz8+JuT2p+EsItUr2yRJQlEUyuVym0d2OY3c0eXlV+qPgzfGkE3GLPidAmdf8h/XNlZoHHJ9uKUS1sQETi6Hmk6jj462PRpjNaluf/1XWjJZWag0i7K6+oMrFIfcGWh6N57n4HlusyPba+RMhtwyhtIxOuoC4LaOKJoiY7uC7kRACoi3WGfbelTsCscWjwXG+QpAHx1FGx9vCtb2zDQAkV27SN1/f+DmreW5CvlsFdVQLna8SqDqCvlsNVBxGCF3JpqaxFHjOE6xKWSrahytzeeB1xO/2ox37R9G0zd2PtiOeNeQW8/Zwlkm8hNEVH/urzpVnpt+jpGOER7a9lCbRxdMNqSkvPbaaxv6ZtINWDh97GMf48d+7Md44IEHePDBB/nsZz9LuVzmx3/8xwH40R/9UQYHB/nMZz4DwKc+9Skefvhhdu/ezcrKCr/1W7/FuXPn+Gf/7J81x/DzP//z/Nqv/RpjY2OMjIzwq7/6qwwMDDTF8pANEklcuniN9/iZAy0QsTd6U46ZNivT54hkMiSiKRKuQtnK3pLvvRGud3LKjb9FQqjoXWmQJRAeYjqL5pjInV2bOtbbDU2W+L/2DDP/5jH+O77V+GpqQmAJ+IG+zrZnZAcZwzDYv38/2WyWcrlMPB4nk8k07/tnz56lt7eXTCYTSItWxymyvPwiAOXyGbKLFcrRU/T2vjdQYnZSVfhgTyeiM8EfAo90JvhgTydJtf0dDq4rePbPTnLq2/Moqoyi+YK1Xn97/s0lnv2zk7zjw3tDMfs6OHHiBIcPH77ix//m3/wNAP+N/3bJ9bEvjrVNyNYHB9F2bEdXVMzjbyHN+DboXq0WiK6ABnpUDQ9NQpo0nEU8z18HeJ6H67rE4/E2j+xSVueOlst+/vzy8os4zv2Bmq9CNhGzAMc/7xcJZ+tr/Mln4eD9gSgSvl1wKxUKTz2FPTV1sbNtfJzUk0+2XRRooNULWgfGuugdTjWLssLOtjsD0zSbroTz8/OYphmYfVY8Nko5eopKZRJJUnHdMg0LtGLpFI4zFs5Zt4BdPXH6O3wHtAtn8ojOItszCXb1BGjtsoU628CPcprMT3J07igAz00/R0JLBMb5CkBJJEg9+WRTsI5l/AxfJRZDSSTaHuG0lmrRQlYlbNNFqh8DSAo4lotmKIGKwwi5M5FknagxjO0UEcJElg00NYkkt8fh5nqjXWFVvOt73k9H18ZzvVsd77rlaBQLn30Rer2W6VnXw4mlE5TtMt2G/3fvNrop22VOLJ1oqZC9mYUXt1rX2tBO6Rvf+MYt/aGr+Uf/6B+xuLjIJz7xCebm5rj//vv5yle+Ql9fHwDnz59vZrcCLC8v85GPfIS5uTm6uro4fPgwL774InfddVfzc/7Vv/pXlMtlfvInf5KVlRUee+wxvvKVrwRmc7ClWL14bQE3nOf9nvfjdHWT3+DPudkb/s1MTg/dc4iOzMXMA69aRU4krtjpFk5OV0aTJf75jn5fyPZ88Toiy6GIfZ0YhnGJ/appmkxOTgK+Q8PJkydZXFwMZN5ouTKBafoHQYYxQCy2k0plknJlgo7UfW0e3aUkVYX9Cf/AYn8iGmgRezWyInPq2/7vOBSzN86+ffs4etQ/SKnYFV6+8DIL5QWELVicXiQ9kAYNdqR2IEsywhPMlma57+72PW/10VEiu3ZhT01h3H0Pet3uLvnOxwMlAGxVapUKudkppo6/CYBVrbZ5RLcHDWeR8fFxAFZWVti7dy+ZTKbNI7uURu5oJLINJN9BYCn3MlPTSdLpI4F0Ewm5OVY7cwAwfxzOvQCd2zme89emx9/4O+j639C3/5Kv3bdvH7EA2zgHGXtmBvvCBfSR0WbWqDU5gTUxETiRoHswwc57g3WvCtlcTNPk+PHjnDt3DoBz585x/PjxwOyzVDVJb+97/T1WdZpS6SSe8MWqleVXWFhQAlcwvBUomjbHpvO8daGA53ncPdjB/YN+7Jj6Rp6dGYsn3tFL0ghd+m6EklXiq+e+ynRxmtm8X4j72sJr/MO7/yFxPY4rXM4WzjKZn+RAz+Uxia1ktWBtvBrsAoFoUkc4HmpEwfO3hXiu35EdxDiMkDsTSdbR9Y0LwJvJ9Ua7wqp415//GQ4c2Pj9qdXxrlsKs+AXCwNkT8HJZVgc8Zs1gyRmX0mqaJGEsRFty3MdRKWKZ9tImkaxVuWFbzx1XYUXt1LXCkTJ78/93M9d0Ur8mWeeueTx7/7u7/K7v/u7V/1+kiTxqU99ik996lO3aoghLeJG87x/6MffT89oD52RToaTw8T1q1ey3uwN/2Ymp48+cph7H3wISZbxhMCeOk/i0Ucx9u9f9+vCyenqqHWR+l3pBC8KsD3fTjwUsTfGZYes+J0Bb7zxBgC5XI5UKsX4+Dhzc3PNIiMIxiGrn+N2URD2c9xUbGujJSZ3NrPjK5x+dcG34l1HxAZQVAlJkjj96gK739bH8L7wfrQRYrEYhw4daj6+37qfyfxk096uN9bLi7MvMl2cbnYKPJJ8hLu23XWV77q5rO0SSA5sgz/9U6IHDoQi9k1Sq1QY/9YLrMzPkZvx7eXPv/E6Bw8eDHTm6Fag4SwyN+eLwzt27AiMILAa21rC81yKxTcol/1clEplkuzi13DsAvH4rlAcuM24ljMHwI985n/CZ/7nZdePHj16yRwSsnHcfB5J0/38UUBSFCRNx8nl2jyykBC/SDiXy9HZ2QlAZ2cnuVyObDZ7SWFxO1i7LyyWiqwsTzM97b+WpqYE8BKdXS7JxJ5LvjYI+8KgUjRt/tdr0zxzMkvJtAF46XSWbd+dAECRJJbeWubVv5kIi4ZvkMn8JNPFaXamduLFfYeeqlMlV8sR1+MosoIqq+TMcB64Hrr6Y+Rmo+QulBH1AudqySa/UGXbro62xmE0CoQrhQKxVIr0wHC4pwoJBNcT7Qqr4l23bWP79u2bNaw7i+w4FGb89zuGILPdv5Ydb2mz5rXY17WPb1/4Noum37W8ZC7Ro/Wwr2tfS37+tbQtt1ym9M1vYl+4gKRqeI7NuG3zwjeeuq7Ci1upa21IyP7BH/zBDX/Dv/mbv7nhwdzO2K6gULWpOS4RVSEV1dDCBeq6XM9N//yF8wCc884hGzLzYp6yV+aJ/s23DLrRyWlwbIy+UvGi1d2995I6ciQUCW6Sf76jn75EJ59fWOH7QxF7w1zrkPWXfumXrvixIByy+jluF3Pb/Bw3By0gFZlBZ2Csk92Hejn17XlcW6wrZruOh+d5jB3qY2Css/WDvE1I6InLOgCe2PHEJeJ2ELLbVncJaM8/D0D5O9+hqqqByGrbquRmp1iZn6NrYJBUxc8eL+aWyM1OsW333jaPbutjGEaz0Kqvry9wIjb485VlZbHtZSTJ72DR1BSSrKMoUd9yPIBuIiE3zmpnDqDekf08dG6narmcnZljZ8wkuudd63Zkh9wYSkcH3oULeK7b7Mj2bOuK7lchGycUDm6ecrmMoijNWD5JklAUhXK53OaRXXtf+NGP/t4VPxaEfWFQObNY5rvTBWTJY09/ElwP7fUVyrP+31zVZRR1izlgmYW2RBBeiZyZQ5VVFPlSx7NCrQBJcIWLIxzSRjgPXA96VGXXoV6S5wq8ecJfu8Y6NBRVAs+7xldvHqsLhGVVRTgOS33nGXvoSDgnhQSWouMyXjFZshy6dZWxmBEIl8bbkvIiyKscTmQVFN2/HiDuztzNkcEjPH3uaQCSWpIjg0e4O3N3y8ZwNW2r+t3vUjRN9EOHm3uq3LPfBNpXeLEhIbujo2Ozx3FbY7uCqVyFUs1pxCKTr9oMp2OhmH2TzJT8CpuBxAA7UjsCZRm0FrdSAUAyDPTBQaRoFH1wMBQGbhFqPTP7Q71dPNyZCEXsDXLZISt+R/apU6eoVCoMDg6i6zorKyvs2LHjso7sdhOPjWIY/phMc5ZSqYYs65jV6ebHw+62K6MoMu/4sC+iNQ5P1orZwhXseVvf1jhU2WKsJ24HBbdUovzSSwBYExMUK5XAZYxuJSqFArKqIisXN6uyolApFNo4qpBWEo+NoihxKtVz2PYyAKqaQFNTOG4FVYmHbiK3GWudOTB3w3HPz8hWdI4M9kF6BPZ/X7Bs7rY42uAgmqpiTU5cLBweHkYfHW330LY0VrUaCge3gHg8juu6eHURyPM8XNclHm9/NvLafWGxdIqV5VeAHqanswwNZYBFOrseXLcjO2R9lko1bMdFUxRkD5LHS0SyDqVVxxWN/deWELPNAhz/fHMuw7XabtmaNtI4wsEVFwvcY2qMglXgXOEcjnAYSg4x0jHSlvFtZfSoihZRicR8+WBobxeDe7pYmauwPFehb6T1f/PVBcKyoiBcl+XZmbBAOOQyPGEFIj+76Lh8YXGFs9UauiRjeYKTUZMP9nS2fCx3BPEeEPbFx8Lx56p4T/vGtA4JPcH37fo+5FmZP+aPeWLnE3xg1wfa3tzSwMnl1nG5am8EyoaE7D/90z/d7HHc1hSqNqWaQ9JQkSUJ4XkUTYdC1aY7EWn38LY0hZp/ACxL/iI/qJZBqwUBZ2YGy3WbByqhIHDr0GSJt6dD0fJ6uOyQFT+7rb+/n1wuh6IouK7L3r17A2nV6skGM6ITgGh0CFkRCNekXD5DqXSScvRUaNV6Da4kZruOf8C2/e7uYB+mBJySVQpc1/Vq1osXADDHxzn++usATNgOqvCwX3yRmONgjI0BoY3k9RBLpRCOg3AvHrAJ1yWWCsWrOwVVTdLT8x4ct4yu+/dTXe8BSUZVYqGbyJ2AkfIP+gPUxXY7osRil8RkqOl0uOe6BeSz8zjVcigc3CSZTIbFxUXGx8cBWFlZYe/evWQy7c9KX7svdJwxFhYUqtUpDhzYgec5RKOPBHpvVXb8ddbLy0UolAPR8dadiKCpCmXTJPFmDeOCiZDAky4tvN8yYnZ23BexM2N+l5tw2m7ZOtIxwumV05wtnOVC+QIA9/Xex7t3vRvTMQO5B1uNOT5OsVAI7HxVLVpIysXnqyxLyKpEtWi1ZTxrC4RlRUFW1WAWCLsCUXXwHIGkyshRFYL4ur4N8YRF1ZzCccpISHh4OGqcqDHccjF7vGJytlpjLGagSBKu5zFeMRmvO7WF3GIyY5Aa9N/PT0O25hcPZ8baO651SOgJxrr8cY11jQVqnlLTaTzbusTlyrH9AgF31blWS8d0I1/kOA7PPPMMZ86c4cMf/jDJZJLZ2VlSqdQtC+++nag5LrIEcn2hKksSsuRfDxQNe6CzvuBKbeP5z+0iFfEPfoTn57UE1TLImpjAqWc3qv3b0EdGsCYnsCYmmhauQUEIm3z+79o9jJA20sgbzWazlMtl4vE4mUym7SL2WsHNEQ5/fOyP+fp3vg7AmbNFKpUchjGAJLl4nsA0/Ry3tz3w/aHgdhXWitnC9RCuf1899N4dwTxE2QKUrBJfPffVS3KwT6+c5okdmx9/sVE2kuH6k7/z2+teD20kN056YJilvvMsz85QyPqWVsl0N+mB4TaP7PajVqsxPT0dqPmrQSp1ANOcIaL7dqK12ix4o7hulXh8F/FY2DF622OkApXNdruyOiYj5NZglsokI1tEOAgwjX3WXP1sYMeOHYEsFga/AKu3972UKxPY1hKa3h1ot6ui4/LcchGA8UqNfDbf7Hhrp5i9qyfOvUMpjr5SITJrIgChSKjr7K8UTUa4HqdfXWD32/oY3hescy3AL8RSdF/EhkBYtib0RDOy6eW5lwF4x9A7eGjbQ20b00ZouDZWXnkFc3SX7yASAAcsq+qwPFehWrSIJnVUXcFz/SL3Uq7GwtkCtYrDwK7OtoxvdYFwo7BKOE7wCoRdgZMzETUXqe7GLlcd1LQRitktwHaKOE4ZVU1eFLKdIrZTRG9x8fCS5aBLMkpdF1IkCV2SfZvxlo7kDsFIwcg7/Pcze2DvI2Hx8A2gj46ijY83Xa4cq8bf1Z/Dzz//PIcOHUJRWru+um4h+9y5c7z3ve/l/Pnz1Go1nnjiCZLJJL/xG79BrVbjc5/73GaMc0sTURWEB8Lzmh3ZwvOvB4bV9kD1A1Ymn4WD9wf6hT6Y8CtsZkuzRAqRwFoG+XYMF+0XfDsGHScXrM5xIWxOnvwk46f/uv7YafOIQtqFYRgMDQ21exiXcC3B7ad/8lev+LFQcLs2q8Xs068usP3u7ub1kBtjMj/JdHGanamdKLISyPiL9eIFwO8MyL34IhcUle39/Riqij0zTezBBy/pyA7ZGJFYjLGHjpCbnWKpXse4/Z77QjvWTWBycpJKpdJ0FFlcXAyMSNAQBbrSLvCHZHqeoL//bozoUKDFgaBSNG3OLJZZKtXoTkQo18J1a0jIZmEk4ohqOXjCwRbsdjMMoxnX1NfXF4j56UqoapKO1H3tHsaGGK+YzNb8TqFBQ2dnzGh2vB1Ktc+6PWlo/MDBIXalE3zXmkScKyPLEhH18uepaws8z2PsUB8DY52tH+xGiPf4Fq3CudiRHQDL1kZkk93vPwdiWvDX2PaMH5WIoiIqZSQjSu1MexterKrDmVcXyGeryKqEcDziHRE0w5cP5icLxJ0iekRh+UKZ3h0p9OgN9cjdMKsLhBtRF519/YErEBZVB1FzkQ0FJAnJ8xCmi6g6yInW21vfaQhhItX/Ac33hWh9F3S3rmJ5Atfzmh3Zlifo1lv72rmjiNSLgXY+CkPhWfCNoCQSTZerWjbLc+fPc/70aQBOnz7NF7/4RT7wgQ+0VMy+7lfMv/gX/4IHHniA119/ne7ui3UjP/ADP8BHPvKRWzq424VUVCNftSmaFzOyExGVVLS9vvKXsNoeKFu3Oy/MtNUeaCM0FqeH+w/T390fWMsg347hYj6D57p4toWaDk6FbUPEnp//34BfbXnu3Oc4dOggshyg52rIHUtDcGt0Yj8/8zyarCG7MqW5EnK3zOHeAX5493tRZQ3PczHNWTq7HgwFtw3SELN3v62PueIEfKLdI9ra5MwcqqyiyP7CLojxF+vFCwC4e/ZQUFXsqamLGaOPPtr2DoGtTCQWY9vuvQwX/G5cPRpt84huTwqFAnfffTeyLCOEIJvNks1mA1OcparJZrZob8976OsLN9Y3QtG0+fKxOc7nymiKjO0KnIVsu4cVEnLb0pHpI2FVgiUchN1uIatYshy0VXbdqzve2k3S0Diyt4eHP9bNs3920nfAqsc4NXBtgesI9rytL7i24uCfGS6O+GeFjYzsgFq2Bh17YQEA6+xZ7FoNz3Hw8LBmZtomZC/PVchnq3T2x5BlCSE8VuYqKPXCi66BGMP700STGqVcrS052asLhCuFArFUivTAcOAKhD1HIElA474kSf5c5Yi2jutGcF0H0zRxbAdVUzEMA0UJtggrywZe/V+jI9vDQ5ZbXzw2FjM4GfULqxoZ2TujEcZiBuMtH01IyMZREgn0u+/mq1/8IsdnZpBlfy6QZZljx44BtFTMvu67znPPPceLL76Irl9aPbRz505mGtVkIZegKTLD6RiFqk3NcYmoCqmohhakhelaeyAAWWurPdD1MNY1xqHh4B4E6qOjqP39ADhzF7DwmhnZQaAhYs/Nfx7QwPOLGbJL3+TkyU+yd++/CcXskLYTi8U4cP8Bfv3lX+c1/TW6dnWhK/5c1Lm3k5pb45S7wtf4Fj81+nYUSV6V4xasTU2QURSZ4X1pFl892+6hrE8jBmML5IumjTSOcHCF2+zIDmL8xXqsrr4MM0ZDthKyLF+ywVIUhXK53OZRhdxqziyWOZ8rM9qTQJElXOHx7Hi13cMKCblt0aNRxg4eDJRwEHa7haymW1exvYvicBA73lY7YJ3/4qnm9cCK2OvFDxop2P/9W2Y/FmhqNQCUzk7kjg6cXA5rYpLaqVO4b397W/Zd1aKFrErIcj0as56HbZb9POz0tjgdPX4xrqxabcvJbhQIBxlJlfE8kDyPRrWV5/nXtxKu67CyvEKtVkOSJDzPw4yYdHZ1BlrM1tQkjhrHsVcQno0QNqqaRFFaX0yeVBU+2NPJeMX07cR1lbGY0dbYi5CQjeC6Ll/84hc5duwYiqI0BevG+60Ws6/7jiOEWDfQe3p6mmQytMO7Epoi052ItHsYV2a1PVADYbfdHuh2QUkkiD/yCOCL2smHHw6MILBaxPaECkJFCH9B7QmlLm4TitkhbccWNr/+8q/zpckvoct6U8RuEFH8e+xz2XPoepr/z6GfoiOxJ7RqvZ1YHYPR6ABYHPEPUwJ4eDLSMcLpldOcLZxtZmQHMf7iSoQZoyFbESEEQohmR7brusTj7bMUDdkclko1NEVGqR+0KvL6eaMhISG3jqAJB1ut2800TbLZLOVymfn5+XYP57ZjLGYwEPHPK2ZMC7tiNjvegkRDzD519i0AHCvAIvbV4gcD6Nw4X57n6+e/DsDXz3+dwb2D9MX72jyqqxDxzy+cpSzm0hKiXAZJonb6NIWnnmqLE1Y0qSMcDyG8Zke2cDwSHf7rSAiv+VY4HtFkWDR0JeSoilx1EOYq15CI4kdgbCFM06RWq/kxGPX/ENM0MU2TeLz9Z9rr4QkL2ykCEsKzEMJCliPgudRqF4gaw0hya5+7SVVpa8xFSMj1slbEVtVL712Nx60Us6/77vm93/u9fPazn+U//af/BIAkSZRKJT75yU/y/ve//5YPMKRFrLYHytcXqanB0B7oFqLUq9XjDzwQGGHgUjtxDYSKpEhIjcMATwW0+sdDMTukvRydP8pTZ59CQrpMxG4QUSI4wuGbc2/woarNw52hiH1bsToGo5HJlh0PbAxGQk/wxI4nmMxPkjNzgY2/CAm5nUilUmSz2WZGdjqdJpPJtHtYIbeY7kQE2xW4wmt2ZDtuMMWrkJCQzWErdbuZpsnx48fJ5XIoisK5c+cAqNU7MkNunqSq8PYuf+83FovwcKYjsB1viiJz6L07/BgnmWCJ2PkZOPMNmHoZSgsw+i7oCH784Hx5nj947Q949fyrAHzj/DfIv5bn5w7+XGDFbK23FwClK41UKKDv3g22TWTPXuypqbZkZXf1x8jNRlmZqzQzsjsyUZwOv2i8tGSyslBpXu/qD53vrogio6YNRNXxC69U2Rexg/A6vw4c2/HPiC8pGpNw7PbHNqyHJyyq5hSOU0YIE8cuoGopdL0bkHGcIrZTrD8OCQm5EufOnePNN99EkqTLROwGqqoihODNN9/knnvuYXSTnYevW8j+7d/+bZ588knuuusuTNPkwx/+MOPj42QyGf78z/98M8YY0gpW2wPl67ZBI+8IZIdbyK1jZeUV5he+VLdi0/GkNQeAEkjoILnML3yJvr4PkE4fac9gQ+54Dvcd5smdT/KlyS9huda6YrblWnh4PLnzSQ73BW+DHXKTrI3BkFX/cYBjMBJ6ggM9B9o9jJCQO4aRkRGGh4cpl8vE43EymYzfQRByW7GrJ86pdJyJxVIzI7u/I8ydDwm53SgvX2WNJwRuuYbI+d1utlWjkL9AemwUrboxceWq3/8Wks1myeVyZDIZZFmms7MTgJWVlZb8/DuFeF20frgrGfjOt4Zo/egP7uYdHwqQiP2NX4el02AWoZb37cSVeoxfgOMHX5x9kXOFcwwnhwEYTg5zrnCOF2df5AfGfqDNo1sfbXAQAK9aQY5GkTwPpa8Xbds2nPl5nFyu5WPSoyq7DvWyPFehWrSIJnW6+mNUj88BMDDWRe9wqnld32LdxS1HkQMddbGRObBaKVMsFKlKUMjOksoM4Hgg2Umwrx7r06o5djW2U8RxyqhqEtt2kRUDISxct4qqJJCQEMJs+bi2GkXT5sximaVSje5EhF09wZ5TQ249O3bs4O677+bYsWM4jrOumO04Dp7ncc8997Bjx45NH9N1zzhDQ0O8/vrr/MVf/AXf/e53KZVK/MRP/AQ//MM/TDQaHh5saRr2QAv1KqtI2DF2uxOLHCIVfQ8rpa8ANTxPQ1r9CR54WEieR1/f++nsfLBNIw0JAU3W+OWHfxmAL01+CeASMdtyLSxh8f6R9/PLD/8yWugesCHWW6AmjYD+7lbHYDQ6sl0rjMEICQlpEolEGBoaavcwQjaZpKHxvgP9l8xfxVSh3cMKCQm5RSQSCdKpGEuvPsXSVT7PEwLPEniuoFBc4cXnnuKx97yfjq6Nd1qlUzESm2zfWy6XURQFWfbFyoYDWrV6dREg5Panf6QjGCI2+J3YS6eh5y4wl2FpAopz4J3xPx7g+MHJ/CQ1t4Zp+gKVh4cqq8yUZto8sivTcG2MHTyIMjOLPjqKtm0bkqbh2RZqOt2WcelRlb6R9ZuaugcT7Lw3dDq6KVzR9i7tjc6xAK5wKZfKrOSyHPv2Cxx42xE60xkqiTiKfG3Hi1bMsavxRWoP1y0j3CpCWEiSiidsPMXDw0OWwyLnq1E0bb58bI7zuXKzYPhUOs42J5hd+CGbg6IofOADHwAu2oevxnEcXNflwIEDwc3IBr9t/Ed+5Edu9VhuS2xXUKja1ByXiKqQimpoQVmkhtzRWFWHyb9bppb9CErUxFG+AZ6HcHU8z8+9QXIAmb6+7w9txUMCwZXE7FDEvjGutEB934H+dg9tfVbHYDQystMjYQxGSEhI4HGcIuXKBLa1RLG00O7hXBdFx2W8YrJkOXTramCsWpOGxv3Dnc3Hry6GXUEhWxur6lzWBXendrul02k++5ufplQqbfhrjh07xgefe4qP//zPcODAxt1wEokE6U0WjOLxOK7rIoRAluXmfjtsBgkJFCvn/T2WqoHRAbEuqOYgf97/eEDjB0tWiWVzmaXqErLpn7deKF0g6SYZTAy2eXTXJvbww8Tn57GnpnDm5/FsC214GH2TLVpD2oArcHImorYqN7vqoKaNlorZ1zvHmqbJSy+9xD/9py/wf/7sR3jkkUc27HjVijl2NZKkYNsrUG/TEsLC8yp+RrZTRFXjaGoYQXglKpUKX3jm23xrIstQVwxXlkB4vDheoVf4LhHHjx9f92v37dtHLBbGDdxOrBWzXdcF/PzsVovYcANC9vbt23nnO9/J448/zrve9a5N9z7fytiuYCpXoVRzkCWwXT/DrTOqEY+owRS1zQLM129I88fB3B3ai9+mLM9VyGerdPWnkKSPkzNVKvZXkSTpYqW47NIfitghAWOtmG0LGw8vFLFvgDOLZc7nyoz2JJoZoxOLJc4slts9tPVZHYNRXvQ7AjJj4TwVEhISaBynyMLCV6hWp5AklZXls/XrAb3XrqLouHxhcYWz1Rq6JGN5gpNRkw/2dAZCzA4JuV2wqg5nXl0gn602c0lzs1F2Heptm5hdq1RYnDoLwOLUWWr79hFp4QFlOp2+rsPvbDYLwLZt29i+fftmDeuGyGQyLC4uks1mURSlaSnesBgPCQkEndth8hlwbFAj0DEE5Sxou4EXAxs/OJmfJKpGGUwOMuFOADBXnmN7ZDuPDjza5tFdGyUWI/Xkk1gTEzi5HGo6jT46itLCLtaQ1iCqDqLmIhtKPeLRQ5guouq03IL8eufYhuh94MAB9uzZs1nDugVIF/9fklHkKMJzUNUkkUg/mppEkoNr995uTpw4wYc/8M6rfs6VmluPHj3KoUOHNmFUIe1ktZg9OzsLgBCi5SI23ICQ/elPf5pnn32W3/iN3+AjH/kIg4ODPP744zz++OO8853vZGwseNV5m8nV8h5WKhaLeZO4oeB5sLxSJDd7nu6B7cSiMWIRhYHOKKq8vpjd8iwJswDHPw/nXvAfn3sejnu+aBDAxWrIzVEtWsiqhCxLgEra+AUs08FVnwGpBkCm+/FQxA4JJKvF7KfOPsWTO58MRewbYKlUQ1NkFNlf7CuyhKbILJVq6DXfNuiVySXknpXgWI43YjBCQq5A2NEWEjTKlQmq1SlisREkScEwLACqZnDtLhtd2K+slDlervJAKk5CVXA9j/GKyXjFDHz+aEhIu6lUKpw4ceKy641OltUdLUszJWbHl0l0G8iyxM7tu8hnqyzPVa5o8bqZ1CoVxr/1AtNvvQnA9FtvMp5KMvbQkZaK2bcLhmGwf/9+stks5XK5aSkeiUTaPLKQkFXsehecfxEW37rofjVwP3R9P/BfAxs/mDNzdBldPLnjSZ6de5bneZ5dnbt4YscT9MX72j28DaEkEkTvvbfdwwi5BVztLF8UaoiKjRTx96aOVWN55hxd23cSyWxsrm9H7vRWwvMcNK0TSVIQwkbWNDzPRdM60fWNx47cqezbt48/++IzzY5sWZYQwmN6ucL9gwm0SpadO3eu6yizb9++Now4pBU0xOwzZ/yokd27d7dcxIYbELJ/5Ed+pFl5ceHCBb75zW/yhS98gZ/5mZ9BCNFsMb/d2UiexFK5RtF0iOoKpu2SzWaZe/1ZnMPvIpPJMG85FBIRUlcRBlqaJZEdh9ykX4UJ/tvcpH89FA1uO6JJHeF4COEhyxKep6BXP0pkmwF8HoAdO34qFLFDAktDzH7vyHs53Hc4FLFvgO5EBNsVuMJrdmTbriCqKzx72u9qOb1QovTWXNNyPBBidkjIFbDN4HW0rcWqH15PHX+Tbak46YHhUBS4zbGtJSRJRZL8jV7jrWOvtHFUV2Z1F/ZU1WK2ZqHLEgeTcaKKjC7JLFnBz0grmvYlGdqBKcgKMCWrxGR+kpyZI22kqdiVdg9pS3PixAkOH77yPvpqcW1/81+fYjC9m2rR2oyhXZPc7BQr83Okevw83FRPDyvzc+Rmp9i2e29bxnQ1hOlgz/kuF/ZcGWE6yEYw5v0GhmEwNDQE+JnZISGBo2MQ3vXLflb2ynn/THDXu+DMfLtHdlXSRhpHOCT1JHd13wXAcHKY0c7QQTSkdWxEJxCmg1uykXQZSZLI53O89NJXefTtT9LV37vhn7XZWoFpms3Cq3g8TiazdXLRJUnBdU1kWUOWdRTZwHErYS72BonFYnzwex5F6fEjCBVFRriCR/eE54F3Ooqi8NhjjwHw2GOPtVzEhhvMyK5UKjz//PM888wzfOMb3+C1117jnnvu4Z3vfOctHl5w2UiexJuzeZ4fX2R7Os6ZhSLH3niDr7z+LN/7A/+Ytx26j+nlCnv6kjw21nPF79HSLInyol91KfmdeUiKn9FRDqu9bhS3VGraA5nzwVr8d/XHyM1GWZmrNA/7OzNJRu7+t+TLdwE/gSwHa/MfErIWTdZ4eNvD7R7GlmVXT5xjMZlnX/wWqiLjuIL+jignzRm++4bfgSOWZyBr8OJ4BXcxw95+P08ozL8JaTVX6myDi11tR1/5LnFxodnRJoTHyTMmRtdDjNzV/uz3WqXC+TdeByA3M8WZ78BS3/mww+02R9O78TwHz3ORJAXP8wt/Va2zvQO7AuMVk7PVGmMxA0OWKbuC+ZrNXMRmu6FjeYJuPdhrxKJp8+Vj/gGMpsjYrghWQZZZCFxMRskq8dVzX2W6OI0qqzjCoXah1tYxbXX27dvH0aNHL7terVY5e/bsJR0t63Vk11Y8osn22F9WCgVkVUWW/UMqWVaQVZVKodCW8VwNYTpUjmUxJ/IAmBN5Kj1ZYgcygROzQ0ICT8cgHFpbZBOss6y1jHSMcHrlNGcLZ7lQvgBAb7yXkY6RNo8spNW0MxLjWjqBMB2suTLm+DJYLnJc461zp3jppa/yS//yZ7j38P0b/lmbqRWYpsnx48fJ5XIoioLruiwuLlKrBX9N6AkLxy4ghInrFgGQJA3D2BbmYl8HSUPjfQf6w4LgkMtoiNftELHhBoTsRx99lNdee439+/fzzne+k49//OO84x3voKurazPGF2iulSfR1WtTVDr9CpZUlJruH06MbB8g3TfAilzirj39bB/ubNGIr0G8B6wi1POaWDkPXZ3+9ZDrxi2VKDz1FPbUFJKmU5nw7RfcSjA6G/Soyq5Dvevar3Z03N/u4YWEhLSApKGxU13hRz/6D674Ob/7yx9d93og8m8CKASEbB7X6mwD+Nl/+ZF1r//vwa8FQsjOzU5RzPk1+qlMD10DgyzPzgS2wy3k1hCPjVKOnqJSmUSSVEzTz5aKGoNtHtn6LFkOuiSjSBJ9ukavrjJeqXG6bFITgp3RCGOxYHc1nFkscz5XZrQn0XQcmVgscWaxzP3t3ns14pxykxetWxdH2h7nNJmfZLo4zc7UThRZwRUuz5Wfa9t4bgdisdgV10pHjhy55LG13+FM+qKjSG3FoyMTpau/PUVOsVQK4TgI4Rfe5Bfn0fNLJLsz1CqVQBVfOYtV3JyJ2uXbdKtdEdycibNYRR8OD65DQm6aWl2YO/si9HqB23Ml9ARP7HiCyfwkL8+9DMDD2x4moQfTCn0rIYTNysorCOELWEszJc6q2UDGNwUhEuNKOkGj4MqtyniDCUTRQoooFDv8OXZgxxDbt29vyRivRTabJZfLkclkkGUZIQTZbBbLao9DzPVgO0VcYWIYgwhhIoSFJ2xUtSPMxb5OkobW/j1TSMgarnvGOXHiBPF4nH379rFv3z72799/R4rYG2F1Bct0rkJ1rpNvANmShbpYYns6zq6eAGXLJfuhkoOZV/3HM9+BvkP+9ZDrxpqYwJ6aQh8ZRVIUtHr1mj0TnDxEPaq2JXPtVrG22rK4Yzul5SUqhQKxVCq0aw0J2QCH77vnsm6hk3NFnj8xg1ZZon9oO6qmM71c4aHRSzuy20pAhYCQzeNKnW1wsbstFekhd756SUd2acnk7gN3t3i061MpFJBXVa/KSnA73EJuHaqapLf3vZQrE9jWEp1dvfXrAdoHrKJbV7E8get5RBWZe5MxLM9jf9zgwc4EYzGDpNqeKuyNslSqoSkyiuw7TSmyhKbILJUC0E3SiHPKjIGsgnD8a22Oc8qZOVRZRal34Cqy0nw/ZPO5WpFxO0gPDLPUd563xp8HYG58nPTeMZampxjnhUA5ibglC0mRoP56R5aQFAm3FPxD95CQwGMWYPJZ//3sKTi5HMg9V0JPcKDnAHa/DUBMC8b9aSsjhM3Jk59kfuFLLM7fD8Ds+DIdcqHt8U3rOXUtTp1l+q03yVb8GKdspcq3Xn6ZmUKRnuGdzc9rh7Ncs+CqJ4okS3i9MZzFKrIrA34kRvX4EkpCR+2JttVNpFwuoygKsuyPTZZlFEWhWo/HCjKuW67/r4TngaJE8RDUavNIkoqmJkNB+3an0exy9iX/ce3KTsohW4/rvjMuLS1x7NgxnnnmGZ566il++Zd/GV3Xefzxx3nXu97FRz6yfifMnUqjguX+4U46KkP8HrC7N8HDd/UHz5ahOAfRNAw+AHzLfxvt9K93BLNjJMg4uRySpiM1DqzriwA3n2/jqNantGwy9VaOwlKVVHeUih2AQ75rsLba8tx3X8ebPY+RSKFHowjHCe1aQ0I2wHrdQmOm3czEaViyBi4TJ6BCQMjmcbXONvC726zq5RnZHbujDI4EI9crlkohXLf5WLguwnGIpYJzEBiyOahqko7UfQAkE6+2eTRXZyxmcDJqMl4x0SUZyxM83Jnggz2dgRewG3QnItiuwBVesyPbdgXdiUi7h3YxzqkR4SOr/uM2xzk1MkZd4TY7sl3hXvsLQ24ZQSoyjsRijD10hDdPjQOQ6u0llelBMyJkp6foHg6Ok4iS0LFcD4TnXxAenuuhJMLD6pCQmyY7DoV6M0bHEGS2h3uuDbI6alBNp9FHR1E2Mdf4VtIUsef/N0gSS8u+Q0uiW6OzN4YQHitzFZbnKm2Ztzbi1PXxX/vMutfb4SzXKLiS6gVXUr3gyln2z17NiTy2XsJyPZS00dZojHg8juu6CCGaHdmu6zajUIKKJyxsewnLyiIh4QGW5QIykYiEac7gqHGixnAoZt+urG52ydb3VZPPwsH7A1V4FXLjXPddUZIk7r33Xu69914++tGPcvToUf7gD/6A//7f/zt/+Zd/GQrZVyEe8X/dD450B9OeobwIkSR01l/cndsh4rX9UGWroqbTeLaF57q+mC0EAEpHR5tHdimlZZNvfX6ClQU/K3v65DKLleB0jV+J3OwUK/NzpHp863tV11ienWX7vT10bRtAuG5o1xoScoNsiUycgAoBIe0laB1ta0kPDJNMdwNQyC6yHDPo7OsnPTDc5pGFhFwkqSp8sKeT8YrJkuXQratbogt7Nbt64pxKx5lYLDULsgLjhhXv8V1EhHOxEMu12h7ntDpjtJGR3RvvbeuYQtpLJBZD0/3D3lq5TGk5R6F+MFhYWAjMHkvtiaKkDZxxXxBwlmsoewzUnmAfuoeEbAnKiyCv2gOGe64NsTZq0LMttPFxUk8+GXgxe7WILckashxBwr+/luz/hufdiyyryKpEtdge54v1nLoaHdmRVAcXFhbY1ttLrZBn6K67L+vIbjWNgitPeEiyhFtzcZZN7Lmy//GkhpqJ4gkPZ7Ha1miMTCbD4uIi2Wy2mZGdTqcxTbMt42lQXr76Pce2l6mUZ3DcIo7lUFhcJJ5OoBtxXCOGqsg4bpZKpIamXe4sfK3vH7IFWN3skq0XLxdmwsKr24jrPtV79dVXeeaZZ3jmmWd4/vnnKRaLHDhwgI9+9KM8/vjjmzHGkFbROFTx6k8LzwXXafuhylZFHx1FG///s3fm4VGVd9//nmX2yTbZQ0IWCBIEBEEQUGitivapVWvfWpdqtdqq1datVm3Vx1br1vra1qqttrba+moXa30EQR+rdQFEoEWWAIEkJCFMMpNJMvtyzrnfP05myApEmZxzw+9zXbkw5yTpt/fc+29rQrKlGYLFitS+DgCAZZK5otvbtwfQ1x2Fp8IFURKhqRqa1xu7QTkcosEgRFmGOJDyUE2lIMoyUnF9g03pWgkzElH0yKZ1vSEgGDG1YcD0NXFMagggjMdMEW3DsTmdmDxTj8r1TKrClPkLDC+DocUVPd1dOGmKdHaEOciRJZyYawKj7ziIJBQAwPqWHojFEZxaX4SuUI75HLKK6vW0rP6mA6UxPLX6cwMZXGM0EA/AY/egP2W+TFLExJIaqIlpz8mBK78AqqKgp70NyYR5zouiXYZzVhHsPt1h3F6XZ2g0G0EcVbiKAS114Hs6cx0Ww0sNMlVFsqUZyeZmOGbPNlremIxmxAYAccCZIaasQ2/8p8iz3gJNYXDkGBPZOlqmrsT06WjKzUFflxd1pcXQFAX5MxpMkaUx43Dli4ExhpQ3AgGAGtbHVsoXA0upECyS4aUx7HY7Ghoa4Pf7EYlE4HK5UFRUhO3btxuix+12w5PrRM+m1eg5yM8lUz1IJLrBmIZwMIZ/r2nE7IWTkZOXC4slD7LshqrGIMtuWC2Fo/4NT64TbpM7mhAHYXiwC6A7YpHj1VHDuHf2CxYswNy5c7Fs2TJcffXVWLp0KfJMFmFqFkLx1JBotvTlimlJX6o0faB/39cGHLfE8EsVXpHcbuQuX55JJeQs0hdKyWRproM9evpVURqofyKJEGTBYFWHxpmbC01RoA2kPJQsFmiJGCx2faNN6VoJsxFSVLzXGwIANEUT6Pf3Y6cjzlWqVlNhUkMAQRwK60BatqqG4w2PZtPiCqJb/FADcQiSYIp0dgTxSQjFU/hgtx8AsLs7jPB2LyZ79JIYpnPKsufqtUX9TfrFiqtYX7tMkPIuXWM0zaZ2c6fBJ7KPbNPPVvFwCJG+XmiaCntuLix2c0U7i3YZljLd+cZS5qI1jCCOFEX1QO5AMEZ/B+BP0JnrMBhealCQJAgWK5RA4KC/Z2Q68rGM2ICenRUAwCREUm8iEVVQXHgbCsrMc7+ZLokR6GxHNBiEMzfXcIfhNGmHK8UXQ6K1H5rLAuvkHFji+lqqRZJQg0lIBXYqjTEMj8eDxx7+CcLhg9c6Doa2w7v/H0gp/djbynDtmkZ87cv5OG56FQryF8BmK0M83o4CzxLk5swY9W+43W54PJ5s/N8gJoLBwS5ptBQ5Xh1FjHt3HwgEkEuGoUMSiqfw+pah9UWVbr/Rsg5O+lLFKwB4Cqg+BWg4xxSXKrwiud0Zb0v7JnNeBOUWOtCxsxeaqmUispnCjJZ1SDwVVegpbcPOlnUAACWZQkFFBVLxBHr3d+relyZK16rFlUzaoJQ3Ai2u0AXLMUZTNI7OhO5xO8luRY3TjqaoXoOUt6g3U2BiQwBBpElEoyMuU8yE4otBDcQhFzsgiIIp0tkRxCdhjy8Cb38MAFCR70BNsRvNvjD2+CLmM2THg7R2EdzgLtDTbxaUVyC3uBQWuw2peAJ5xXQpSBDHBPZcoHap/t9F04DjFtG6dRgMLzXIVBUslYR8ECOZ0enI+/rWo6t7JSAIQ4zYAIABQ7Yk2yCIAjTbeyiceiGsjslZ1zUebE6n4Y7CYyHaZVircqCGk2AJFaJNhujSI91ZQkOqOwam6E7FRpbGiMfjaGxsRCAQyKQW9/l8SCQShmnyeDyHNDArSgHy8nzo6fkXenraAADFxTmYNKkIeblOAGE4HLNRUrIEskzn3KOSwcEu/QNR2LmTyPHqKGLcVpRsGLF/9atf4ZFHHoHX68UJJ5yAX/7yl1iwYMGoP/v000/jueeew9atWwEA8+bNw09+8pMhP//1r38df/jDH4b83vLly7Fq1aojrn0s9vgiaAtEUFfshiQKUDWGd5tiE/a//4mx5wKlDfp/lzbQ5vQYoGqGB527+xDojECUBWgKQ47HbrSsQ5L2ttwX1CNcq2efgMWnLEW4t8d03pfpiLd4s56eMd7cj2ixnyLejjF6kgoswoFsB5IgwCqI6EmaPFuHmbHnUq0bwrQkolE0ffgB+rq8EGUZmqKgp7QNSavx61IaNZyEIAkQRH1uEkTB8HR2BPFJ6AknIA9kFwIASRRgkUT0hI27dBuVeBBofFWv35bOJuKr1R2z6NxFmJC8olIAANMYrA4HNEVBUWWV6RyzCILIIrYBI2rNYqDyxIP/LAFgZKlBlkrCUlUFa13dmL9jdDry/PwFKC35PLq6/gealhhpzAYAQYEoWFBa+l8oLjk565qORobUy5b1vauUZ4Vlkhv22jzDyzz5/X4EAgEUFRVBFEVomga/349k0tznQ1nOQXnZeXA5p2BfxyoAb6Ky8lLUVM+GpsVhsRbC5awjI/bRzOBgl/61+rPapXTGOoow3ILy0ksv4eabb8ZTTz2FhQsX4rHHHsPy5cuxc+dOlJSUjPj5d955BxdddBEWL14Mu92Ohx56CGeeeSa2bduGSYNqD5911ll49tlnM9/bbKMswFmkJ5yARRIhDVwMSqIw5HKFIMyCu8COhV+sQ/v2AII9MeQWOtCTSh36F02AzelEcVUNAKC4qgY5hUXIKSwyVtQoZCLeCvR5SC6wQQ3EKeLtGKPQKiPFDmQ7UBlDkmkotBq+FBMEcYSJRqN4781V6Ni+DbnFxRBFCZqmYmfLOsRsuod9Y2PjqL87ffp0OCfICWvIRcpARDalsyN4pNBtg6Jqme9VjSGlaih0T+wZ8JD4m3QjdlG9Xr9NU/Rn/iZyzCJMSbocRuWM41FaVWUqZ+HhaAOl3OItfUgWhww3BhAEcewyvNTg4aQJ/6TpyI8UomjBccfdCwC6MRsYYcxmmoLS0i/huOPuzdTNJg6OFlf0O8FwEpLbCjHHkqmXrfbpDpeWCjfcC8pMsWZFIhFIkgRRHCg/KYqQJAmxmPmD82Q5Bx7PYpSX2wH8GAUFC+DxkPPNMUU62KV7IIjIRjXPjyYMnyEfffRRXH311bjiiisAAE899RRWrFiB3/3ud7j99ttH/Pyf/vSnId8/88wz+Nvf/oa33noLl112Wea5zWZDWVlZdsUfhEK3DSlVg6qxTET24MsV4thDjUYBAJENGxCT5QmtdXMo3AV2NCypyHy/aZPXQDVHH+mINww4toAi3o5J6p12VNj0w96+eBKpaBw1DhvqnebPgEAQxPjYsWMHzjrvgoP+zKWXXjrq840bN+LEEyfmwC0XOzIXKWAMaigJ2CRoCYVKYGQBRQkhEm1Gb+/6ge8jBis6ephS7EJZnm5w6+yLQfOFMdnjwpRik5XuiPj0SGxxYGyJsv59xGesLoIYRro0RnvjNgB6ZPaUE08yWNXYaHEF8d19AAClO4bY9h5IHjtlwDoGCCkqmqJx9CQVdIfNb2ghjh0Glxo8HD5JOvIjzVjGbMb0u6vCwqVkxB4H6eyMaiAOQRKQVPXU4fb6fGihFOSwvne1T803zVrlcrmgqio0TctEZKuqCofDuHTnBEEQgMGG7GQyiY0bN+KOO+7IPBNFEaeffjrWrl17WH8jGo0ilUqNqJXwzjvvoKSkBAUFBTjttNNw3333obCwcNS/kUgkhtR6CAaDn+D/zVCmFLuwy+NCsy+cqZGdvlwhjj3UcBiRgT6dbG5GKBqd0Fo3hLGkI96gDUTjUsTbESVtGEgle0ydLihHlnBqga6r3mnDyUV5qHfakSNLBisjCOJIM336dKx65W8jIrKDPh+K6qYirGioqakZ9UJg+vTpE6ZTtMtwzipCcl8YsW1+/ZlVQmJPP5TeBDcGgHg8jq6uLgBAV1cX4vE47HZzOQkpSgjd3asQi7UjEtkPAOjtXQNFmWPKNYs3cuwWLJmqZ+WZWuLGyTPKMKXYhRy7yS5aXcV6OnFNORCRrSb15wRhEgaXxgjsawcAtG3djLlz55oyEhsYyIDVrxtapHwb5GIHFF+MMmAd5YQUFa/5+tAaS8AqiGjpCwMAIopqsDKCGD+fJB15NhhuzFaZAqbpGS+qq68hI/Y4yGRnLHZksl8pvhi0UArWqhzYffkAANFmnvNWUVERfD4f/H5/pka2x+NBPB43WhpBEMc4454pf/SjH+HWW28dkfIwFovhkUcewd13333Yf8vv90NVVZSWlg55Xlpaih07dhzW3/j+97+PiooKnH766ZlnZ511Fr70pS+htrYWe/bswZ133omzzz4ba9euhSSNNBg88MADuPfeew9b9+GQY7fg7Fll2OOLoCecQKHbhlDupzeQE3ySbG6G4tWjnOWyclhraye01s3RTHIgvU174zaU57pMmeouE/HWpDvMKL0JSNPskIvJueXTMtgwIAgyGFMQcexCSclZpjQMuAaM1icX5ODEXJNFiRFZJZwMo6W/BYF4AB67B7V5tXBbyZFpPCRjCnq9UcRCSThyrCgoc8LqMM+hfzBOpxOfOeMsNOXmDKmR3TBlCuoXLjHVOiXaZYhWCaJNhlyZM+SShQcDQDweR2NjI/bu3QsA2Lt3LxobG9HQ0GAqY3Yk2oxYrB1OZy3sdj1DSzzehUi0GXm5Jxis7ujANXAJuKC2EHOq8o0VMxZF9XpNbH/TgRrZnlr9OUGYhEBnO/wd7bDYbVCS+vmlr7sLgc52lE89zmB1o5PJgDWAQBmwjgmaonG0xhKod9ohCQJUu+4o3hanz53gj0+SjjxbDDFmd69EYeFSAHsgiuY8e5mV9NokDGRn5GFtstvtaGhogN/vRyQSgcvlQlFREbZv3260tKOaxnAMPf5+FFplCngh6P5wDMa9At1777245pprRhiyo9Eo7r333nEZsj8tDz74IF588UW88847Qy6qvvrVr2b+e9asWZg9ezamTJmCd955B5/73OdG/J077rgDN998c+b7YDCIqqqqT60vx24ZcomyyUcL/rGKXuvmgNfi8Fo3ajhsis0qbySiUbRt3QwACOxrx54NQE9pmymNBM5ZRbD78gAA9ro8bqLczM5gw4AgSGBMRTTaQoYBwlSEk2G8ufdNdIQ6IIsyFE3B7r7dOKP6DNqMHibJmII9m7rR749BlAVoCkOg04EpJ5aY1phtczpRv3AJAp3tiAaDpq4rysslSzQaHeHs2tXVhb1796K3txcA0Nvbiw8//BBer3eIs+xE1h4fjVSyB4IgQxAOXEoIgoRUsscwTYdicLpWulQ5QthzgYYv6obsiA+wOAAIwN41elR2Ub3+MwRhIMHubvR5OwEA4YA+R4X8PgS7u01ryJbcVjCVZb5nlAHrmKAnqcAqiJAEff8iDvzbn1KMlEUQn5jxpiPPJmljdmnpf6G52QLgWaMlcUc6OyPTWMZZmIe1yW63o7Ky0mgZxwTpDCJr+8KoDcWQZBp2OuL4QnE+nbuOUXi4P4w3NSEUDE64DWvcN3+MMQiCMOL55s2bR6T3PhRFRUWQJCmTCjBNV1fXIetb//SnP8WDDz6I//3f/8XsQyzydXV1KCoqwu7du0c1ZNtsNthstnFpJ4jxoNe6SWW+H1zrRg2HEVy9Gqn29gPpgyjt+AhGu7z2tbdi65YtAAB/JAp3OIKdLeuwLxhCcVVN5ueMvrwGdGO2pUyPwLWUuciIfYQYbhgQBAmCIJvCMDCaAYA4Nmnpb0FHqAM1uTWQRAmqpqI12IqW/hbMKp5ltDwu6PVG0e+PIb/MCVEUoGkMfd4oer1RlNaa1+hjczpNe+k/GF4uWXbs2IF58+Yd9GfuvPPOUZ9PZO3x0bBYC8GYAsYOpDtlTIXFOnrpI6MZnq6VLlWOIPZcoHIeEA8Cja8CgZYD0dm+Wt3QTcZswkCSiThiwSAKqybDEY4CABKRCJIJ86YVlYsdkPL0NUvtS0DxxSB5KAPW0U6hVUaSaVAZgyQI0JjuzJBnobM2QRwJRNECj2cJWls3GS2FSzLZGX0xCJKgn69obSIGkc4gMtluRZ3TBpUxNEXjaIrGKYvjMYqZ7w/VqH4uiK5fj3jdlAm3YR327q6goACCIEAQBEybNm2IMVtVVYTDYVxzzTXj+h+3Wq2YN28e3nrrLZx33nkAAE3T8NZbb+H6668f8/cefvhh3H///Vi9ejXmz59/yP+djo4O9PT0oLy8fFz6COJIIZWUgGEgjeTWrYgnE7BNmQJrXR2Szc1ItbfDWlsHQZLAVJXSjo/CoS6vb/vR/WO+M/rymsgegw0D6YhsxhTDDQNjGQAqqV7bMUkgHoAsypBE3fgjiRJkUUYgHjBYGT/EQkmIsgBxIGJYFAWIsoBYyFwRw7zCyyXL9OnTsXHjxiHP0hHZDocD+/fvR3l5OWKxGKqrq0dEZBuJy1mHiGMXotEWxON6jWy7vRQu58TWPTxcRqRrpUuVI4+/STdiF9UfqJftb9K/Kg/usEEQ2cRid8Cem4tosA+xkF4ezepywWI315owGNEuwz41HwAglzjgmFEIudhBzsNHOfVOO3Y69LXJKohDDAIEQRBGk87OqPhiUMNJSG7rkLVJS+jZI+ItfUgWh2jdOgZJZxBJZxSRBAFWQURPkjKLHKuY+f4wtW8fAMAyqRLW6uoJt2Ed9uz42GOPgTGGK6+8Evfeey/y8vIy76xWK2pqarBo0aJxC7j55ptx+eWXY/78+ViwYAEee+wxRCIRXHHFFQCAyy67DJMmTcIDDzwAAHjooYdw991344UXXkBNTQ28A3WH3W433G43wuEw7r33XlxwwQUoKyvDnj17cNttt2Hq1KlYvnz5uPUR5iRdK2CjV7/MjKaiBisaGzUcRuSDD8AUPSJbi0Uh2mxwLVkCye0eSDtuhTBQv3142nFCZ/r06Vj7/vvo93chHo7A7nZBSSTR1rgNQUVF5aQKWGUZQZ8PlTOOHxGRTRydDDYMpGtkOxxVhhsGxjIAaFSv7ZjEY/dA0RSomprxqFQ0BR77+DLZHMs4cqzQFAZNY5mIbE1hcOTQReWR4FCXLGbB6XSOcExL18gOBAKorq6GqqrweDymq5EtyzkoKTkLkWgzXK61AICCgsWQZXPWIB+erpWnS5VIQsF/2vvQE06g0G3DlGIXcuyWQ//iRBPx6ZHY6XqToqx/H/EZq4s45skrLkZBWQUsdhu6Y/oZNreoGHnFxQYrOziiTR9L9tp8WKvMObcSR5YcWcIXivMzWbDK8t34FQAXB5lDIgMGrPUtPRCL+8y7VnFKNBXFFt8Wqi9KGI5ol0ddk7S4gvjuPgCA0h1DbHsPJI+dizKE8Xh8RA1tM527eCKdQSSdUURlDEmmodBq7j5gOuLBA6WbXMVAImK0ok+Mme8P1f5+/T9EEcDE27AOe1RcfvnlAIDa2losXrwYFsuR2WBdeOGF8Pl8uPvuu+H1ejFnzhysWrUqE0HR1tYGcaBxAODJJ59EMpnEl7/85SF/55577sF///d/Q5IkfPzxx/jDH/6Avr4+VFRU4Mwzz8SPf/xjSh9+lDC4VkBnv167a93+dZiTnGPKjWk64tpWXQMAcM6bDzAGtbsbKCsbSDueBFPVTER2Ou04cQAJgDsZhRKLIMcmQ4tF4CrwYNLJCxHu7YUoy9AUBfVVlfBMmoxUMmHqeqTEkWGwYSCV7IHFWgiXs85ww8BYBgCq13ZsUptXi919u9EabM3UuKnMqURtXq3R0rihoMyJQKcDfd5opkZ2XpEDBWU0vx8pxrpkMTt2ux0NDQ1cXKbIcg7yck9AQYE68L15I5uHp2vl6VLlg91+yMFcWCQRKVXDLo8LZ88qM5+BwFWspxPXlAMR2WpSf04QBuKpqEJRZRX6urywDNyh5JeUwlNRZbAyghhJjixlMoVscps3a8BgQvEUPtjtBwDs7g4jvN1r3rWKU9btXwebxWba+qIEofhiUPv1QAcp3wa52AHFF4Pii5nmTDbYYJ0uC5tIJDJOxJIkQVVV+Hw+0zkR80I6g0hbPAkpmkCSaahx2Kg04XgYrVxT18iyyLxg5vtDKR3YrGkAMOE2rHHfRCxbtgyapmHXrl3o7u6GNiA8zdKlS8ct4vrrrx8zlfg777wz5PvW1taD/i2Hw4HVq1ePWwPBD4NrBTCX7rHUHek2Ra2A0UhHXCNdI1sUIVgsGW8Va10dLE1NSLY0H6iRXVUFa505U00aRaCzHX1dXhRUTIIoSdBUFb2d+zB55gkonVKPaDAIi9WKnn0daNu6OWPY7iltQ/3CJWTMPopJGwbMxFgGgDKq13ZM4ra6cUb1GWjpb6GogE+I1SFjyokl6PVGEQsl4cixoqDMCauDxhShG7MrKyuNlnFUMTxdK0+XKt7+GJYe74YkClA1hmZfGHt8Ecypyjda2lCK6vWa2P6mA5cunlr9OUEYiM3pRP3CJQh0tqNnoCrO5Jkn0HmKII4Qe3wRePtjAICKfAdqit3mXas4pTvSjVNzTzVdfVGCSKOGkxCkA8Y2QRQgSALUsDmy+KWzXvl8PsTjcWzfvh0A0NnZifz8fBQVFUEURWiaBr/fD7/fT+exwyCkqJksIoMdhBflu1GS40ChVUa9044cDjKLGE00GsWOHTuArkZg7wdA/mRAEAAmo3GrnsG3sbFx1N+dPn06nCbd15r5/tAyaRIAILWvA0mbbcJtWOO+/Vu3bh0uvvhi7N27F2wg7UEaQRCgqlT/k8guw2sFAHq9ADPUChiNdMQ1tIHxomlDvFUktxu5y5cj2dwMJRCA7PHAWlcHyW38BGUmosEgRFmGOJCCXZQkiLKMVDKByTP1Ogz7d+9EpDcwwtgd6GxH+dTjjJRPHGOMZQCopHptxyxuq5suTj4lVoeM0tpco2Uc1WhxxfTpxY8GFEVPddbbux79QckUmUSGMzxdK0+XKrIkQhIHMqKIAiySiJ5wwmBVo2DPBRq+ODQNXlG9/pwgDMbmdKJ86nGoCurzldXBR6Qrr5gxTauihEyX9epooSecgCwdyDxp6rVqMF2NwM5uLtYrSZRMWV+UINJIbiuYesCuwjQGpjJIbnPcGfn9/owROxQKIR6PAwC2bt2KhQsXZrLniqIISZIQifCbynmiCCkqXvP1oTWWyNwTst4QAKDB7cCJRXmH+AvEYHbs2IF58+Yd9GcuvfTSUZ9v3LhxRMkyM2HW+0NpwPjvXLAA9tLSCbdhjftm6pprrsH8+fOxYsUKlJeXQxD4DdUn+GRwrYA0mqqipCsHrEKDMOhAYAbSEdepNWsA6F4rlsWLh3irSG43HLNnGyXxoCRjiiki4Jy5udAUBZqqZozUmqLAmXvg8DSWsTsaDE64XuLYZrgBwCEJAAR8HIoCACIKOX0RBGEutLiC6BY/1EAcgiQgqTJu6rTxhKKE0Nur7wkjkT3w+6KIOHahpOQs0xkIBqdr5QlF1aBqLBORnVI1FLpNWmLKngtUHvwChiCIo5t01JuZ0rQqSgjd3asQi7VDEGQwpph2reKRQrcNinogu6Xp16pEWP937/uAo0bPIOKr1Z2xTGrMVjXVlPVFCSKNXOyAlKcbrZWeGBJ7+iDYJGhJFVpcMfz8FYlEMkbsZDIJRdHL5AWDQezbtw9FRUWw2WzQNA2qqsLl4u/MMNE0ReNojSVQ77RnMje+k0gZLYtbpk+fjo0bNw5EZL8/EJEtAUxFrGsPWsUa1Jz4WThGccacPn26AYqPHuz19cgxwBFg3LNiU1MT/vrXv2Lq1KnZ0EMQh2RwrYD9kf0AgNlN1chpiqF37x4UnDfFVMbsdMS1c2DRdy5YgNzly7mIuE7FFezZ1I1+fyxTkzTQ6cCUE0sm3JjtqahCT2kbejv3ZdKG55eWDanVdjjGboKYKNIGgMFel91R3cv+vd4Q5iiqaSPbQvEU9vgi6AknUOi2YUqxi+q1EcRRjuKLQQ3EIRc7IIgCmMZMV6ftaCASbUY8rteYs9sr4HTWIBptQSTabHiZjOGp7niJwB5OWZ4Dzb5wpkb2ZI8LU4rpcm28hJNhNPU2AQCaepswLTnNFCntCOJow+/3IxAImCpNayTajFisHU5nLQRBAmOqadaqo4EpxS6U5ekX6519MWi+sLnXqr52/d/8yUDhFEBT9Gwi/ibTOmOVuEpMWV+UINKIdhn2qfkAAC2lO7aIVgmJ3X1QAnHDnYldLhfi8TiSySRSqRRkWdficDgQi8Wwbds2FBQUwGazoaSkBEVFRYZp5YWepAKrIEIaCAqVBAEWChD9xDidTj2qOj4VaGQDNbJFQFWA4z6DJSZ2tiI+GeOeERcuXIjdu3eTIZswjMG1AtZ1rgMAePY5IFQIiG3uBgBTGrPt9Xq9O3t9vamN2MmYgp59usdtx45elOUWoLDKDVEUoGkMfd4oer3RCU/vOrhWWzQYhDM3F56KqiG12g7H2E0QE0GmVguAxnAMa/vCmGy3grW3AgA2b9+OVx02NLhH9ww0slZLKJ7C61u8aAtEMkaAXR4Xzp5VZl5jdjxIqVkJ4lOSrtMmDKRkNludtqOFVLIHgnDAOCwIEgRBRirZY6Cq0VPd7XTE8YXifO6M2UumFiGnsoycsT4F4WQYb+59Exu9en25jd6NsO614ozqM8iYTRBHmEgkAkmSTJWmVV+r5Mx6NXitopTjn54cuwVLpupGn6klbpw8o8zca1WsV/83vX8RZUCy6mcvExBOhjO1RL29XgDAyeUnI68qz3T1RQliMKJNN8uIFgm2KfmmciYuKipCYWEhurv1e/ZUSo8cVhQFoihCFEXE43G4XC7U1dUZXg6DBwqtMpJMg8pYJiI7NaxsL/EJoHJNxwzjNmTfcMMNuOWWW+D1ejFr1ixYLEM3WrNNmh6ZOLpwW92Y6Tke/sY9AABBEiHYJDBFM60xmweSMT0Cu7NJP6jsb+6HfVIUBeVOiFYJoihAlAXEQsZcaqdrtR3s/aGM3cTRidkuVA5Vq+XPN9+AP4/xzuhaLXt8EbQFIqgrdmfSsjb7wtjji2BOVb5husYkHgQaXx3wvrRykeqOIMyI5LYiqTIwjWUuUcxUp+1owWItBGMHykswpoIxBRZroYGqRk911xSNoyka5y69uMsmm3O9MjGDHfAAPQJ7o3cj1C69r6pdKtauX4tkWxL1BfVDftdoBzyC4B2XywVVVaFpWiYi2+g0rfpapYAxNRORzZgCUbRTyvEjhGvAgJXnsGZqY5vWmO0o0P9N7180RT9zuYqN0zRA2vGqI9QBWZTR4m3JvDNjfVEi+ySiUe7uBM3oTGy32zF//nyEQiF0dHRk7D+iKCI3NxdVVVXIz8+H3+9HKBRCXh7Vdz4U9U47djr081XacbjCZsI5n0eoXNMxwbgN2RdccAEA4Morr8w8EwQBjDEIggBVpbqfRHYJJ8NoCTRDfKMPsZ1+AIAgDyz4sggGkDH7E9LrjaLfH4O7UPekyymyIx5OIdSbQEGpE5rGoCkMjhzzXmofythNjI94PA6/349IJAKXy4WioiJDPS1HM1gDMN2FSqZWC4ZGZKuJBLxtexEvKcPS0qIxI7KNpCecgEUSIQ0cpCRRgEUSMxcspsPfpBuxi+r16AAOUt0RhBmRix2QPHYovhgESdCN2B475OKR8xTxyXE562C3lwIA4vFORKMMDkdVZj0zitFS3VkFET1JxVBdxMRwKAe8R295dMx3RjvgEQTvFBUVwefzwe/3Z2pkezweQ9O0upx1iDh2IRptyZyvHI4qQBAo5fgRIpLQ19cPm/2otfabOwtW/kCGu742oEfUjdieWv38ZTAt/S3oCHWgJrcGkigh4dbPrPvC+wxWxi/JmIJebxSxUBKOHCsKysxtBB5MIhpF04cfoK/Lm8nS2FPahvqFS0xtzGYmdSbOy8vDZz/7WWzYsAEfffQRAECSJOTl5SEnJ8cUGUR4IkeW8IXi/CGlnCJ+cgIjiMNl3IbslpaWQ/8QQWSJcDKM/215E4XvAFVtBUhBd5xgOJCKg4zZn5xYKAlRFiAOGLBcuTbY41b0dkYgCICmMOQVOQzdyPLoXckr8XgcjY2NCAQCmUsVn8+HhoYGQ4zZihIa1WBtd1Sa7kIlU6sFQL2iQtzvR/ijjxCaNQdlx89GjcNm2nSthW4bUqoGVWOZiOyUqqHQbTNa2uhEfHoktjiwpTFZqjuC4AXRLsM5q0ivlR1OQnJbIRc7DK3NdjQiyzkoKFgMAHC5pqCoeJHhWUSA0VPdJZmGQqu5P/9QPIU9vgh6wgl0e0NGy+GWwQ54wIGI7CK5CL5OH4oriuFX/JhXNm/UiGyCID45drsdDQ0NpnIeluUclJScNcKBuK/vozFTjhPjo6M3BgCoLHCipshl7ixYtoGU3NWnAOWFpkrbGogHIIsyJFHvk6Kg3/0FE0EjZXFLOktjvz8GURagKQyBTgdSAh+OjYHOdvR1eVFQMQmiJEFTVfR27kOgs93UAS9SntW0zsR5eXlYsmQJkkk9Qjw/Px/l5eWwWCymyCByMELhXfD7+0yRtTFNjiwNyXa1yYR3ggRhVsZ9M1FdXZ0NHYSRDK8tmjCvJ1VLfwvizX2o6qiDIAKirG9SVW1oJgBBFsFUhtgWH5wnFME+tcAIudzhyLFCUxg0TXcMECQgx2ODzSGDqboRu2qGB1aHMZeavHpX8orf70cgEEBRUVEmzZ3f74ff70dlZeWE64lEm0c1WCtK0NQXKm6mYfFTv0Rk9WpEPnsapDvuRH2u25RGbEBPabfL40KzL5ypkT3Z48KUYnMeTuAq1qMCNOVARLZJUt0RBG+IdtnQWmzHCrKsz6c5OccDAPr6PjL8gmW0VHc1DhvqneatdxeKp/D6Fi/aAhFYJBEtzXqmpnSUG3H4DHbAA4BpyWmw7rWiI9SBuuPqoGgKFuUsohrZBJEl7Ha7IeergyHLOSOcgsdKOW50eYzhNIZj6PH3o9Aqo95pN+W5qz+mG4VEXrJgAXpktst1wGHYBMZsj90DRVOgaiokUYLGNABArs14IzuPpLM05pc5IYoCNI2hzxtFSIwbLe2wiAaDEGUZojTg2CBJEGUZ0aC5HRvsU/PhqCw0rTOx3W5HaameUaq8vBz9/f2mySAyGoqi2zX6etcjGKwxRdZGgiA+PZ9oVnz++efx1FNPoaWlBWvXrkV1dTUee+wx1NbW4txzzz3SGolsMlpt0S7BaFVjEogH0FcaR/8UBfm7LRD0PSoYY0N+jikaAAbHrBLYaqlOx+FSUOZEoNOBnXv0TWp/dwz5JSokWYTFIaHfHwO2BzDlxBJDjNm8elfySiQSgSRJEEXdYcTotEGpZM+oBmsGmPZChaVS8P7oR4itXAlJEJDzv28iz26F++67AZjvQgUAcuwWnD2rLBPhVui2mbdeG6BfoPhqdYes9DpmklR3BEEQB6O3dw38PospymKMlurOrJf/afb4ImgLRFBX7IYkCkgV6E6N6Sg3UzPckdgExoDBuK1unFF9Blr6WxCIB+Cxe1CbV0tGbII4xhkr5bjR5THSRBQ9wOE1Xx8KunphEQXMyXHiy2Ue061neQ49bXA6iMD0WbAAoOVdIMoOnLl8tUDDFw1dv2rzarG7bzdag62QRRmd4U4AwCT3JMM08czwLI2iKECUBSTCKYOVHR7O3FxoigJNVTN3hpqiwJlrnj3WaIg2fpyJa2trUVVVZZoMIqMRi+ulBez2CjidNabI2kgQxKdn3JaoJ598EnfffTduvPFG3H///Zma2Pn5+XjsscfIkM0bo9UWbfqX0arGxGP3IAUFbYujAJwQ9g/UxhYOGN+ZogGqBscJJZRW/BBEo1Hs2LFjyLOUoCAi6h62PeEOgAF5pQ6IMd0bc+eeODoCBVjyuflwTnAUNK/elbzicrmgqio0TctEZBuZNmisCIAcdwPi8X2mu1BJG7H7X1sBwWqFaLVCSybR/9oKAEDZ3XdDsJjTOJxjt5gvpd1Y2HP1CxQTGwQIgje0uEIpxieAeLwLTueppimLMTzVndnpCSdgkURIgy5bgQNRbqZlNEdiExgDhuO2ujGreJbRMgjiE0HrWHYYK+W4WSLcdkV1R6aQosLFGCIpDW/1BFHvsuGUAvPMr6F4CvGUnj3k444+aIVBiIJg7ixYABDcBxy/7MDdob9J/6qcZ5ikEY5XZR4AgNNizox9ajiMZHMzlEAA8a4uo+WMYHCWxnREtqYw2JzmvLcYjqeiCj2lbejt3JfJ4phfWgZPRZXR0o4a+vr64PF4TGvEBgAl1QcAps3aOJi0A9a/eoLYub8HTlFElcNqqEPx4NJNpg9s4ZxwMoym3iYAemmnaclp5Dh8EMa9k//lL3+Jp59+Gueddx4efPDBzPP58+fj1ltvPaLiiAlgcG3RVAwI7QeC+/V3ibCx2kYh420ZasW+2TLkJj0yVNT0iyuzGrHVcBjxJn1iijc1QZ02DZLb+Ilpx44dmDdv7EPHbT+6Ycx3GzduHJKCcCLg1buSV4qKiuDz+eD3+02RNmisCIDc3FnIzZ1lqguV0YzYAHRjNsCFMZsr7LmGXqAQxOGQiEYR6GxHNBiEMzcXnooqU5bF0OIKolv8UANxCJKA5ECdNuesIjICHGH0SxXzX7CYlUK3DSlVg6oxSAOXrcCBKDfTMpojsQmMAQRxtKAlaB070ihKaORZy8CottEc8gHgg83bAABCeys0qwUCY+iIJbGyww1nuW7gnD59+oQ75A8mXRZjc3sfAIBBQCyp4YzjSzFrUp45jAVjlR8ULfq6Bej/StYDacYNZLDj1SbfJoPVjI0aDiO4ejVS7e0QLFZEm/foz6NRg5UdIJ2lsc8bzdTIzityQBDMZ6wcDZvTifqFS7g4c/FGIqGXPdi7dy8cDgdUVYXP50NDQ4PpjNmyJR8AwJia+dcsWRsHE1JUvNcbAgD8MxAEOntQaJVR67BhmsuOLxTnT7gxe3jpppSqYZfHhbNnlZljfTqKCCfDeHPvm9jo3QgA2OjdCOteK5VyOgjj3sW3tLRg7ty5I57bbDbD0s0Sn4J0bdFECOjcDIS9QKRbf9fyLjB3jukiA04oPgEdoQ7sCTZDbBiIyNYAllABMFMasYOrVyO6fj0AILp+PYKyjNzlyw03Zk+fPh0bN24c8TwWi6G1tRW5tmIE2mJwF9oz3pjhnjgq6gswffr0CddL3pUTi91uR0NDA/x+vynSBh0qAsAsKYLGMmKnIWM2QRx7JKJRNH34Afq6vJn1q6e0DfULl5juYkXxxaAG4pCLHRBEAUxjUHwxKL4YNynveEG/VDFfWQxemFLswi6PC82+MCySiI5e/SK4ssBhsLJDMNiRGDCVMYAgjgbU3gRUidaxI4WihNDdvQqxWLspSmEAh3bIf+173x3y/b8APDDw30Y45A8mXRajcqAcxgmVeYBNgtMqm8NIcLDyg1oKaiqJjg4/KiuLIKlJ/U6ROCySzc1ItbfDWlsHQZJgGTAMpvbtM1jZAawOGVNOLEGvN4pYKAlHjhUFZU5sbfQaLe2wsTmdVHowC/T19QHQM/IWFhZC0zT4/X74/X5UVlYaK24YDrteWiAe70Q0ajVN1sbhNEXj6EzoafsFANNddnSnFDglEa2xBJqi8QnPljW8dJOqMTT7wtjji/CTtZETWvpb0BHqQIW7AgBQ4a5AR6gDLf0tlBVrDMZtyK6trcV//vMfVFdXD3m+atUqNDQ0HDFhxASRri26d42+UbXnAQU1ANbqaYNMFhnQFenCs1ufxd7gXsiijK7AQCqeKU4gDjhmmcuIDRzYrFom6Qu7ZVIlUu3tSDY3wzF7tqHanE7nmIe4JUuWIBlTsGdTN/r9sQPemFMdhtXIJu/Kicdut5tqUyrLOSMM1qNGCBgYjR3dsAHB11dBEIQRRuw0otUKLZVC8PVVyP385+FatGiCVRIEMZEEOtvR1+VFQcWkTEaR3s59CHS2m+6iRQ0nIUgChIE0zYIoQJAEqGGTp2vmELu91HRlMXgix27B2bPKMqnvSpJ6xhiXzeQRl2lHYk05EJFNxgCCOGKosRSEPFrHjhSRaDNisXY4nbWmKYUxlkP+v/b78PTGjyGUlsNmd0BhGmyiiK9PKsKigpzM7xpJuiyGOqgshiSJ6AknDNWV4SDlB1V3Od76x5vY2RzAcXUefO6cz0IqqjdYMD8ogQAEixXCQKk8iPq9pdrfb6CqkVgdMkprzRPQdDQxvOyFllCMljQm8Xh8SFBLKKRHDqdLe4qiCEmSTBnUKMu68Te/YAFyc0tMcU84Gj1JBZaB9tQA9Coq/AkFrWICk+xW9CQnvn8ML90kiQIsZlqjjiIC8QBkUQYT9KxioiBCFmUE4gGDlZmXcZ/yb775Znz7299GPB4HYwzr16/H//t//w8PPPAAnnnmmWxoJLJJurZorA9QkkDxNIDF9XeixXSRAWs612BvcC+m5k+FLMqw5uhGom3TOvCZ2cthq80zlREbOLBZRUr3soIoQrBYoATMPzGN5Y1phBE7DXlXEoMxY4SAc/585J59FvpfWwEtmRzVmK0lk2CMIe/ss+CcP98AlQRBTCTRYBCiLEMcuLgSJQmiLCMaDBqsbCSS24qkysA0lolkYyqD5DZ5umYOKShYjKLiXNM4YvFIjt2SiQ7YFDNJRNPwlKxF9UMzXKUdif1NB6LdPLX6c4IgPhFaXEHKq1+ms5gCza7SOnaESCV7IAiyqUphjOWQH+0N4sNJUxDTGDTGIAoCHKKAE6pLcKJJamSny2JgoByGpjFoqoZCt81gZQOMljVE1CPF12/thbqvD4CIHc19wHofPjfHCWOquPKH7PGApZJgqqobszUNACDl5RmsjJgIRivfFO/uM1rWqMTjcTQ2NiIQCGTKDAYG7rAZS89dGlRVhcs1sRHD4yHHPQ1FRcZl4DgUhVYZqYH27E6mkIgmEGUMUiKFfkXFUs/EZ3EdXrpJ1RhSZlqjjiI8dg8UTYHG9LVAYxoUTYHH7jFYmXkZtzXqqquugsPhwA9/+ENEo1FcfPHFqKiowM9//nN89atfzYbGo4L9fTG8sV1PxfLGdi/K62IozzdJ2jt7LjB5ERDrBfInAx279OeBFiDi1y9jTJJefF94H2RRhjywqZZEfcvcHffBPrXASGljkt6sanHdQSDRvAcptxuOg6TCMhPkjUmYGTNGCAgWC8ruvhuAnj5cA4YYs7VkEiyZRN4X/ovSihPEpyQZU0zlbDUWztxcaIoCTVUzEdmaosCZa771VS52QPLYofhiECRBv/z32CEXm2TfyiHDM4coim5skWWXacpi8EwonsIeXwTrW3SjSsTI6JbRUrL6anXH4fR5Ku1IfDBjN0EQh03aOBBv1qMalZ44VGsK2B+BaJNoHfuUWKyFYEzhohRGTGWYneOEQxIRVFTkyhJiqoaYyoyWliFdFmNNk14Oo6M3isXTXJhSbBJj0ChZQ1RFj8Rr3fIxqsuKIVusUFJJ7Fi3FpCs+Nw3roUkm2//bTasdXWwNDUh2dIMwWJFal8HAMAyaZLByoiJYLTyTWqTOTOF+P1+BAIBFBUVQRRFaJqGxsZGAHqK8Z6eHqiqCo/Hg6KiIoPV8ku9044Km34fGFcZJFVFjkWGSxSgB2oLE65peOmmlKphssdEa9RRRG1eLXb37cba8FoAQGe4E4tyFqE2r9ZgZUNRw2Ekm5uhBAKId3UZquUT7TQuueQSXHLJJYhGowiHwygpKTnSuo4qdneF8MDKHdjycTsA4O+bOrAl9h9csaQWC+sKzVEHJx0Z0LUV2LtBf6bEgJZ/AV1bgOP+C6iYY/gFyyT3JKzfvx6KpkAWZaiaCgAodpo3FZ+1rg7y1q2I/u1vAIDk7t1QJk9GYs9uOGYeb3idbB5JRKNcphdPpw2Kt/QhWRyCXOyAaKcD36fBjBECwEhjdooxaBYrxFQSQioF5czl2PrtG7E/lkS9ICJHJj92ghgvo5W/CHQaV/7iYHgqqtBT2obezn2ZGtn5pWXwVFQZLW0Eol2Gc1bRkLR3tF59ckbLHNLbmzJa1lFDKJ7C61u8aAtE4O0OAwA+2O3HnLkpY85Yo6Vk9TeNLNdkzzVV+SaC4JmMcaBAjxaSixyQXBZYSp0QXRZaxz4lLmcdIo5dXJTCKLTKEASgym6FJAhQGUNTNI5Cq3k++3RZDNWnG38W1hXh7Fll5rgXBEZkDVFTcazfpjuJiLIE2aI7aMsWKxQAOz7Q046TMfvQSG43cpcvzxgEnEW6M4hk8F0WL47BvDNW+SYzEolEIEkSxIH096IowjoQnFFdXY3y8nK4XC4UFRXBbrcbKXUIaefhnp73AAA9Pe+hPyiZNutVjizh1IGyF1OdNkzx5MAqiCi0yoirGmKqNvGahpVuKnTbMKXYZZ416ijCbXXjjOozkGzTHVrmlc3DGdVnwG01j51IDYcRXL0aqfZ2CBYros179OfRqCF6PtXK5HQ64eTAeGQkoXgKv363Gdv298MyED0cS2rY7QvjhfXtCERS5ti0piMDGABxs/7MUQAkQkB/x8C/bUOjCQxgccVibOrahN19u/Ua2SHdE+SEYvNGs0huN2x1dZBy9XRBjtknwD13LpT9naaok80biWgUTR9+gL4ub8YY0FPahvqFS0xtzNbiCuK7+wAASncMse09kDx2OGcV0aXKp8DMEQKCxQLXnT/A7mgC+W+9CZZKgTGGplM/gy1XfAvuWArJaAI7HXF8oTifjNkEMU56vVH0+2PIL3NCFAVoGkOfN4peb9R0mURsTifqFy5BV3MTultbADDkFpcaLWtMRLsMa5X5Dvs8EY1GsWPHDoTCu9DXux52ewUEQQVjGrZt+xgAMpENw5k+fTqdsQ6TPb4I2gIR1BW7IQb0aEtvfwx7fJFMyvEJZbSUrJLVdOWaCOJoIm0cwIBxAKIA0SZBdFngaDD+TMA7spyDkpKzhmQWMatRoN5px05HHE3ROKyCiCTTUOOwod5pHkMLoBsKKgv0Nas/lsQeX8Q8hoJBWUPUoBdvrfoQra3dAABJHqqPjNnjR3K7M3eA9k2bDFbDl2Mw74xVvsmMuFwuqKoKTdMyEdnaQCr80tJSHHec+co9pp2HI5Hd6AnoAXo9Pe+iuysGl2uKoeUHD4Zr4B6wwm7Fwjy3KZywBpduIrKL2+pGfYFeXqq+oN5URmwASDY3I9XeDmttHQRJgiWhZ2hJ7TOmpNe4R0RPTw/uvvtuvP322+ju7s5MZGkCHNT9nUj2+CJo9UfgsklQZf1gJQqARRQhC0BbIGLcRctw7LmAq0iv0QboxuvcGYBsBez5enTB8GiCCabUVYrr516PNZ1r0NLfgqKeIqzESgTiAXRFutAd7UYgHoDH7kFtXq1pJgAtHoelVM9cIJeVQXK5oFqspqyTbXZvzEBnO/q6vCiomJRJz9rbuQ+BznZT185WfDGo/bqXlZRvg1zsgOKLQfHFTGssiMfj8Pv9iEQipvS2BMwfIbA7peKDb12PUwHkvfNPdJz6Gfzhoitxqs2GOqcts0FtisZxYi6l6iGI8RALJSHKAsSBi2tRFCDKAmIhc6ZoA4CgrxvxcAiiLKNt62YEfV2md8QiPhk7duzAvEOUkbn00ktHfb5x48ZRa38SI+kJJ2CRREjigYgWWRLRE04YI2iUlKxQk/pzgiCyQto4kK45DBPXxE6fr1pbWwEAiYRBc9U4keUcU5bCCCkqmqJx9CQVFFpl1Dvt+EJx/ohnZnMYDsVT+GC3HwCwuzuM8HYvdnlc5ghyATJZQzq2/Ac7t+wABHHMH5UtViQVFTvXvYfjFp+K6llzJk4n8anhyTGYd4aXb9ISauZdyhuBFldME+RSVFQEn88Hv9+fqZGda8KSWINJlx0UpUHnagGQJAdisXZDyw8eDhU2i+mdsIhjCzUcRnTjJqT27YNgtUEuKQEGsjSo/f2GaBr3DPm1r30Nu3fvxje+8Q2UlpZCEMyZBsMs9IQTyHdZ0B2KIxTX0wimNA0JRUVhjg0WIy9ahhMP6jWxAy3694wBEPQLGEe+/r0JoglKXaVYXLEYHaEO7I/sBwC82/Eutv57K/JseXBZXFA0Bbv7dhuekiFdRyDZ0gqluzvznKkqWCoJ2eMxTNto8OCNGQ0GIcoyREk/jIqSBFGWEQ0GDVZ2cDKRAgOk0wipYXMaXOLxOBobGxEIBDIbV5/Ph4aGBlMZs80eIdCTVGCxWNF98/cQ/twZ+KhuGuRoEpGBFEGSIMAqiOhJGljPkyA4xZFjhaYwaBrLXLxoCoMjx3wX1wC/jljEJ2P69OnYuHHjsIhsPXNIf/9e9AcrMKNhKRyOkTVbp0+fboBiPil025BSNajagYgWRdVQ6LYZI2hYSlaoSd1JuKj+wM/Eg1Qf+wijhsOINzUBAOJNTVCnTaPyTccQGeNAk36vovQmIE0zX03swecrv183Yra0tGDu3LmmOl/xQkhR8ZqvD62xRObiP53pyuwOwnt8EXj7YwCAinwHaordaPaFzRPkMkBlw0wcd/KpaHv1Ff2BpgBKHBAkQJQAQYSSSgKM4biTl6KyYaaheonxw6NjMK+kyzcl94WRaA1C6U9ADenrVry5H9Fiv2kyNtrtdjQ0NAwJbDF7tqh02UFVjUDAQOlBSFDUKGTJZXj5wUNxakEOXEV5pnbCIo4d0inF47t2IbXfCzUcgVxcBG1gHpDy8gzRNe7Z8b333sP777+PE04wrxeLmSh021DitqEJIfTHdENFKK4gTxQxucCBSFI17qJlMPEg0Pgq4N8BaAN1+3p2AR0xIK8KsOYAUb8pognCyTD+vPPP+Mj7UaZGtj/mR29/L06ZdAqqc6uhaipag61o6W/BrOJZhugcXEeAMUAZ8FZJtrchCQZLVRWsdeaIGk3DgzemMzcXmqJAU9WMIUBTFDhN7h0oua1D0gYxE0cKAIDf70cgEEBRUVEmlZDf74ff70dlZaXR8oZg1ggBQK/TlmQaVMmK6Lz5sEfjSEYScEkDXmyMIck0U9Vt4w4yCByzFJQ5Eeh0oM8bzThf5RU5UFBmzkM2r45YxCfD6XTixBNPhKLUo7tbGqiRLYExBodjqWnT2/HGlGIXdnlcaPaF4e3TjQJleQ5MKTbIiDEoJeuo61L6zBVoOWDo9tUaXr7pYISTYbT0t5gy6xVw4MwVXb8eABBdvx5BWUbu8uWmMmabPesV74guGUzR7wZEtwX2+nxTGAPSZSYAoKurC3v37kV+fn4mk+HWrVtRUFCA0tKR5UaozMTBaYrG0RpLoN5pH5KKlYdMVz3hBGTpQJSzJArmCnIZQJJlfO4b16KppRlY+U+oyTigugCkAFGCwiRoiorpS5ZRWvFPQLrGaGTDBsRkGda6uglft3hzDD4aUAJxqIEYtGASLDmwbuVYoAbipsrYaLfbh9z9dQ8KzjIj6bKDouQEg96uDCpkyWma8oMHwyVLpl+7iGOHdEpxx5w5EC0WpPx+JJtbkLTqWWMskyYZomvcu4zp06cjFotlQ8tRyZRiFwrcNrhsMvIGDlIWSYRVFrGzK4TFU4qNu2gZjL9Jv1ApnQlMAoDVgKYByQgQ9QHNbwP1ZwyNJjCIlv4W7A3uhcfuQcqhG92DiSBytVzEUnrflEQJsigjEDcudffwOgLORBx4+WVIOTnIOe00Qzaph4IHb0xPRRV6StvQ27kvUyM7v7QMnooqo6UdFLnYASlPPwyofQkovhgkj/kiBdJEIhFIkgRxIG2IKIqQJAmRSMRgZXxRZrVAALDa3488WYJNFDDFaUNYVdEcTVDKoE8LhwYB4shhdciYcmIJN4YBXh2xiE+H2TOH8E6O3YKzZ5Vhjy+CdeF2AMCSqUUAgP+096EnnECh2zaxtUcHUrKOSvrMVVR/IPW4v8nw8k2jEU6Gsc2/DW+3v41IKoIiRxFEQTQ869VgwyCgR2BH169Hy4C/aAsDsGYNnIoCe/3Qs6tRhkEesl7xihZXENnUhcSuXijd+l1AsrUf8cYeOOeWGm7MPlSZiQceeAAPPPDAqO+ozMTB6Ygl0ZNUkNRisAoCBAFojyWxvi9i+ki2QrcNsQED1p7uMISiCGJJxfAgl+Hza5rc8gIAwP6+MCBIkCQRqpKCxkTUzD0JBXMXIpFMwkmG7MNGDYcRWbsWgH53GIpGYWlqmnAnrMNxDE7GFPTsCwMAevaFkWxQaO36hCi+GNRAHLBIEEQhUxJDCyUhTDJvxkYzoiihIecrm7UEDkcVIpHdB36IAaqq18g2S/lBguABJRCAYLFCcrlgnz0bcnc3Es3NsLh1G6ZkkKPluFeeJ554ArfffjvuvvtuzJw5ExbL0AsBs9dMmGhy7BZUe5ywWSSkYzFdVgnRlIpYSsWp9UXmqIET8elGAFEGkiH9WfFxwKRqwFkMJPqAommmMAwE4gG4rW74oj70xfsAACmWQjQVhcOiGwVVTYWiKfDYjUvdrQQCYAxI7dsHLRyG1qdHZFsqKuCYPdswXQeDF2/M3OISxCMRAAwlNVNQWjeVi/qiUu5AO1pE2KbmwzrJbfjlyli4XC6oqgpN0zIR2aqqwuUygeMNJ4QUFf/qDSGhMeTJEoKKijqnDZdXFCGsaZQy6EjAiUFgcDSbXbIDAhBX4qaLbEuXw1ACAcgejykdroZjdcimyRhyKHh1xCI+PWbOHHI0kGO3YE5VPjTfgUiL17d40RaIwCKJSKmaeWqPDj5zAfq/ktUU5ZsGE06G8ebeN/Hh/g+xp28PZFFGMBnESSUnoSPUYWjWq0MZBr/5fx8d851RhkEesl7xiuKLIbUvDCYAkmfAMVQAkh1hWCtzDY9sS5eZAIZGZCeTSezbtw9OpxPTpk0bMyLbCIYbB8zofBVSVGyPxLAzEkeBLKMzkQQD4JAEWEQBr/n68IXifNOesUpzbAgNZGxs8oXh3e1DXaEbpTnGGrIPNb8+v2bTyIevvwP85BFyvDgIozkIxJua0Lh5MwCgOaVA1hhSw5ywJsL56lCOwWlHrM6mXgBAZ1Mv9ni6yRHrE6KGk2CMQQ0moAQTUAfmgWRXFFqtatqMjaMRj8eHpB4vKiqasDIZihJCd/eqgYxXMhhT4HBUweM5BS73NBTkSwBehig5oLEULBZzlfXklVA8hT2+iDGOwuOFMjd+KmSPByyVBFMHshtoGqAqht8PjnvVyc/PRzAYxGmnnTbkOWMMgiBAHfg/SByAAYgmFbgGUseW5zuQsEpIqUBXKIHyfBNEZLqK9Ug2TQESAxGXjAFWp177RrQAUXPUk/DYPbCKVoRTYbSF2gAA/fF+5El5CCaC2BvcC0VTUJlTidq8WsN0inY7Ejt3AKIEwWJBvLNTf24zQSr5MTB7mtZENIqmDz9AX5c3YwQI+rpQWjfVaGkHRYsriG7xI9mue7GyuAolEId1knkNREVFRfD5fPD7/Zka2R6PB0VFRUZL44amaBw7IzG4B9IIl9ksCKsqwppm+pRB3GxQOTAIpI0BHaEOaExDU28TGBimFUwzRWRbmsHlMASLFSyVNCQq4HDhMUWrzelE/cIlCHS2IxoMwpmbC3dB4ZDvPRVVhjpmaXFFjxQIJyG5rZCLHaZ1uCKIsejojWGfFEFdsRuSKEDVmHlqjw4+c6lJoH8f4NsJ5E7SL1xMcsHS0t+C5r5m+KI+9Cf7YREs8Mf8UDQF0wqmGZr1arBhEDgQka0Wl6DN58Pk4mJIvm44FywYNSLbCHjIenU4JKJRU61ZwIBRQGEQJFGPbgMgSCK0hIpEa7/h61m6zAQwtEa2JEmYNGkSPB4PGhoaTFMjezTjQMSxy3TlMJqicSQ0hmkuG/ZEk4ipGjQAkx12zM91oTWWMHWK8a5QAjkD+9b6YjemTi1GXyyJ93b7UeiyGnb+Gj6/pom1/Qet61di8tSZ+HhTE/a2dKJ6Ug4WnPtlSOUzM79rJGnH4Y1eXX80FTVUz2AO6YD16M9GfT5RzgFpx+C0wX1f74F3PfvC6GzqRXdIz3rTHWrH+g+BjkABCgfdZ1EphLEZ7MiQ8kYQ3eKDGk0BKkOTtwUAsKulCdYyF1yeEoi+A2uVWdt1+Hqmqip8Pt+ErWeRaDNisXY4nbUDpZtURKMtSCS74XLWQdX0Mg3JZDdCIQWxaBvi8U6Ul59nirUspKhoisbRk1TQHeYj63EonjKvo/BwOMrcGE6G0dTbBABo6m3CtOQ0w+8GAcBaVwdLUxPiO3cgtbcNSm8AUkEBEu36WpAujTHRjHsnf8kll8BiseCFF15AaWkpBEH41CJ+9atf4ZFHHoHX68UJJ5yAX/7yl1iwYMGYP/+Xv/wFd911F1pbW1FfX4+HHnoIn//85zPvGWO455578PTTT6Ovrw9LlizBk08+ifp6Y9Jii9CzdPfG9INqd38cngIBsiiYpwZOUb0+qP1NQGrAkB31Afs2AzYXoMQAdzEQP93wQV/iLEFMicEX9UGA3v/KXGWY4ZmB4zzHwWP3mCLKjQ1oY6kUtEgEap++G1TjccM0HYq0N2b33iB87WEIViCn0ASOFgMEOtvR1+VFQcWkTFrW3s59CHS2o3zqcUbLyzDc4zbljSDe3I89fbrjRVNPK7R/JiFtdsBW4YZUYINo06djozaqo3lTNjQ0ZJ6ls2/s3bt3wr0tD0U6aiAe64CixiBLDtgdlYZHD3TEkmiNJiEIyNRtY0x/fmKua8jm1UyR2fv7YvjTh21o6gohpWqIJBUUu+04b+4kLKzzmGuT6ioGEqGBtSsKWJx6SQxXsdHKMrT0t6Aj1IGa3Bp0RjohCAIECLDLdlS4KtAabDU0si3N8HIYTFUR37kT/a++CpbQ9y+2hgY4Zh5vuGGb5xStNqczs16N5pzVU9qG+oVLDDEMpJ2u1EAcgiQgqTJIHjucs4rImH2EMGOU21hrkVnXqOEMdrzq9upZpfpjSVjyREgDRi1T1R5Nn7m6tgI9zUDUDziLAN8OoBGGX7CkDQFv7n0T/+n+DzpCHUhqSbitbiiagj19e+C2unGa/bRD/7EsMdgwCADqtGkIyjLijTswIx6HsuVj2Gpq4Jk/H/YpUwzTORhHjhWpmIreriiUhArZJkGJq6bLenUwIn29+OjVv6F3/z5YXS7YHC4UVVYZtmalkdxWCLIANaZBDeljPOWPIeWMQpBFsIRqmvXMbrcPOV+Z7UwFjG0ciESbTZVZpCepwC1JqHfaEVdDUBmDVRQwyWqFW5ZgTYroSSpGyxyVUDyFDa0BBCJ6f63yOFCRb8f2/f3oCSVQX5pjmIFg+PyaYcZULKm2AoEWLK6eiY595aicMRvSzPMMvx8EhjoOd/brASTr9q/DnOQcUxgERnMQiDc1IbBmDfZLMiaXlcEuy0jt6xjihDXRzgGHMrgDwG3/fcOozykif2wOp12v/9NdwJ9GPp/odh2rvAAANDY2Zv4dnGFEEAQwxhCPx1FcXDykrna2SCV7IAgyBEE/mwiCBEGQkUr2IKhE0N+vZ4+Q5Tw4nTVIJQMIhbYiN2+24WtZSFHxmq8PrbEEEirD+v1+AMCrXb0oiydRYTfn3nCPL4K2gEkdhYfDUebGN/e+mXHA2ujdCOteq+GBLtFoFDt27YJaWopoSwuifb2wVFZBLirCXq8XALD1/fdHTS+ebZvGuHfxW7duxb///W8cd9yRMRq99NJLuPnmm/HUU09h4cKFeOyxx7B8+XLs3LkTJSUlI35+zZo1uOiii/DAAw/gC1/4Al544QWcd9552LRpE2bO1D0BH374YfziF7/AH/7wB9TW1uKuu+7C8uXLsX379gk/JITiKbT2RKEyhqSiAQASqoqkoiEQScBhNcmFkD1XvzjxNwFdA84J0V5AsQKpIOAqAWJBwwd9OBnGms416I33IqWlEFd0o7BVtKI/2Q+H7MCyqmWG6RsMi8dgra1Fqr0dSjAI0ab3vdjHH0MNhw03BByMUE8ciWgKoiygY2cAoZ6YKQwE0WAQoixDHIhwFSUJoiwjGgwaqms4h9qoXj3G5h8w5gBwMG/KyspKw70tD0Y6aiAS2Y1orBWpZC8slgI4nbVwuaYYGj0Q0TT0pBQ0uOyQRRGKpqExEkdE04ZsXq2CiCTTsNMRNzwFXiiewp/Wt+GD3T7EUyp8oSQgAK09EewPxtHZH8X5cyvNY8zOKQNiAaBj/QFPy8Kp+nOTEIgHIIsyJFFCOBmGRdTbLpwMQ8qRIIuyoZFtadI1cISB+ZWlUkjs2Y3Y+vUQBsZ55MN1cC9dirwvftHQNexoSdFqNuesdM02udgBQRTANAbFF4PiixmemvVowIxRbmOtRcsKcvCv3pDp1qjhDI8MaGnWL4JssoiwqkHVWOaiJaVqhtceBXDgzMUA9HUAdZ8F8ioByWL4BUv6MmVP3x5s9W9FU18TVKbCKloRVaKQICGmxKBqqqFZr4Yjud1wzJmD0JtvItnWDtHhQHz3bvT89rco/s53YC0zfk/gyrciHkmhr6U/44CVX+KEK9+cl5WjsWv9GjgjITjy8hAL9gOMwd/RjsIqYx2K5WIHLKVOJNu9SHXqDvlaJAU1nNJLJOXZIEiCadYzu90+IZf8n5SDGQfMRKFVRpJpsIoCprjsCKkqBAgotMn63RvTUGg1nxNeet3a6Q3CP1ALd2eXXiM7EEnilKnFqClymc9AMOi+UIr4UH2c8WlaBxvcmnqbsNG7ERXuCihe3YFhy9YtWJGzAvUFIzNzTHTgwGgOAmknrCHZsBYvNjQb1mgG93REtiUH6PR2oKKsEqkQUFE/MiKbGJ3h7ZpoDyG+oweCTUJSUNDetx+VOeXIn14CS5lrxO9OJIdjdL/00ktHff7rX/8aU6dOTLZMi7UQjClgTM04XTGmQBTt8Pn+F4lEFwA9IjsWs0GWndC0hCnWsqZoHK2xBIosMlb29aNjIOjxNV8f+lr247baclMas3vCCVgkkzoKD8eEmRtHcxJJr11ql57dWu1SsXb9WiTbkkPWroletw5nHrjyjjuAO+4Y8TzbNo1x7+zmz5+P9vb2I2bIfvTRR3H11VfjiiuuAAA89dRTWLFiBX73u9/h9ttvH/HzP//5z3HWWWfhe9/7HgDgxz/+Md588008/vjjeOqpp8AYw2OPPYYf/vCHOPfccwEAzz33HEpLS/HKK6/gq1/96hHRPRbDO+ZObwgtTT64ghEoPR0AgHBXGxwWGbGIHTu3xmHvLwAw8R1zdE8rAY0D83pjrADoywNkC9CfAvrbgcRaoFswTGt6kCeTScR74rD36pfr4X1h7FH3YGtoK0p6hzpAGKU13tWF0M6dUIIhyIWFaFVSAICm9nbkrFgxItWdEVpHw8wGAmduLjRFgaaqmUt/TVHgzDWX4WL4RjUdka04GVp2N6Nc9MBuscFS6oKUY4XSm4C9Lg+WMpchBwC/349AIICioqJMPWy/3w+/34/KyspDvjeSdNSAKDkBCHC5piGZ8kOSHIjF2g2NHnCKIgqtMrpTCmQIUMBQaJXhFMXM5rXeac9EazdF44anwNvji6DFF4ZVFhGMK7DJIgQRsFskKKqKjzuCmF1pkgsVAAh5AacHyPsskIzq5TBSEf153iSj1QHQy2EomgJVU+G2upHS9LXAbXVD1VQomgKP3fiaTYNr4AiShNT+/VC8XojuHFjr6yEASHm9iG/dCsfMmXDMnm2Y1qMlRavZnLPUcBKCJBxIzSoKECQBapivdjUrZoxyG2stAmDKNQoYen7Z6Q3hw2Y/KgucUEUB6NOjsPbuaYK9OIF3m2KQJRGKqqEsz4Hyk8qNlH4Aey7gKgJKGoDCQRHDBl+wpDOIOC1OOC1OFNgK0B3rhkW0QGEKHFYHHJID0wrMkfJuMPHNm6EGg3CedBIEWQZTFCSadiG6Zg2sX/qS0fIQ6UvC7rag0uOBklQhWyUoSRWRviTcBeaIxh0rCisdgbV96zZMKiqEU5TBNA3Rjk7Y3G70ixacUVFl6D1GojeESJ8Xu8P6fcueZCfEHhlSwgprMAdSng1qXwJy2IE5hScbft42M2MZByzWQqOlDaHeacdOh74uMab7BmmMIaZqaIrGUeOwod5pvrGVXreKc+xwRHVDy/btjejqi8NmEZFw92GPX98Xevti2FdsM8+5y55rqki2Q120P3fXc3gOz414bpbIYcntRu7y5Ug2N0MJBCB7PLDW1RnqLDyawT3ZoGCPR8+ENbVill5+cAYfmbDMwvB21WYoiFYcyIK10CRZQ4CxywsAQCwWQ2trK2pqahAMBkdEZOfk5MDlmphzgstZBz+2YP369zJrld1eCqttF3r8e9DRrgcStu1VwFgbJMkOu60cPYFu5Lg3GWp/WdcbQnc0gcZUCntCMdj36Zk7rR17sUkS8fu2PJxVnJ/5XTPYCQCg0G1DyqyOwsMZXMopHZGtJg3N3Hg4xuFHb3l01OcTvW4NngfSJZwskyoBUUQ8Hkfz1i047owzkD9rZFbJbNs0xj1D3nDDDfjud7+L733ve5g1a1Ym3Wya2eO42Ewmk9i4cSPuGGTBF0URp59+OtauXTvq76xduxY333zzkGfLly/HK6+8AgBoaWmB1+vF6aefnnmfl5eHhQsXYu3ataMashOJBBKJAx4kwU9xgXg4HbP95YfRPvDfKwc9n+iOeSitl/5m8yhPXwRgPq1vPPAGAGDlkBbVMZvWG/7nf4D/+Z9R35lhU21mA4Gnogo9pW3o7dyXScOaX1oGT0WV0dKGMGKjGlcQLdY3qnMc9VD7E5BLnLBVuSFYJCj+GCyT3HA0GHNBEIlEIEkSRFEEoM/DkiQhEokc1nsjSUcNqGoEAiSIoh5BoKhRyJLLUI/LKocVtQ4bnJKImKrBIYmIqhqqHFb0JBVYBRHSQHkOSRBgFYxPgdcTTiDHbkFnXwyqqkGSBDAGpBQNTrcNKUU1l8dlxAdYc4YaAnr2mKpGdm1eLXb37UZrsBUa08AYAwNDXImjNdiKypxKU0S2pWvgJFuaIVisSDY3Q7A7IOXlQUiPfasVLJWCEjA2gtyRY4WmMGgayzhcaQrjKkUrYD7nLMltRVJlYBrLRGQzlUFy89WuZsWMUW5jrUXtsaQp1yjg8M5a995y7ajPz9y4EaUe4y+vAZjygiWdQSSaisIu2VGbV4tQKgRBEOCW3ci15KLYWYwTis2TXjhNsqMDosUKQdavNwRZhmixItnRYbAynVgoCYtdQn7JgYvIvu6oKc5XaQ41th7900tjvtu48Xhznbefu2vMd2Y4b5sZl7MOEccuRKMtmewhDkcVXM46o6UNIUeW8IXi/EwJjKWeHAAMMZWZrhzGofrr+mfvzfz3K8PezVvxDgDjzwlmZPBF+5CI7KSC/e37oeVrOLn65FEjss2C5HYb6hx8OKTLD/Z69TXLkWNFQZmTjNifAtEuwzmrSM+GFU5CclshFzsMN2IDBykvMMCSJUsAjJ7V0ePxoKioaEJ0ynIOenqqcd55Xzvoz91115phT3TnFrPZCQBg7Q9uAQC8B+AHg56bZd8ypdiFXR4Xmn3hTI3syR4XphQb6+Q8KoPL56YzN3pq9ecGMZqTSHrtKpKL4Ov0obiiGH7Fj3ll80ZEZE8kg+eBodlDLGBgWHD++YZlDxn3LHnhhRcCAK688srMs7T3jSAIUFX1sP+W3++HqqooLS0d8ry0tHTMmgxer3fUn/cO5GhP/3uwnxnOAw88gHvvvXfUd+NleMdMe1xKooimzh4g7AfcRaivKISqaVhYV4TjynIyvzuRjOVpFWv7D1rX/QM1BRY41DAgiHrq1ooTgRO+CtjchmlND3JRENHc3wyWYtjXtg/Tp05HrisXiyoWGb5RHdyusS1bEHrnXxDdbqSsVnQkE5gkivAsXjxmRLbRmNlAYHM6Ub9wCQKd7YgGg3Dm5sJTUWVoXbbDYfBGNdHaj5QswDrJBcEimcJQ4HK5oKqqnn5vIOJaVdWMN+Wh3htJOmpAlJxgUKFpegSBLDkNjx6od9oxzWVHaywBlyQhyTRMc9kHogPiSDINKmOZaDczpMArdNuQa5dR6LahKxhHUmUAgLJcO2yyCIssmcvj0oSGgOG4rW6cUX0GWvpbEIgHcHL5yYAAxJU4PHYPavNqTRHZNjwqwFJehvDatdAiUTBNgwBASyYhu5yQPcZGkBeUORHodKDPG82kaM0rcqCgzNxrwXDM5pwlFzsgeexQfDEIkqCvTR475GKHIXqONswY5ZZOzTp8Lapx2NAaS5hujQKG7rMHR2SLooB4LI6tO5tw5smzMbt65Dpghn12BhNesKQziDgsDqhMRaG9ECXOEoiCCEVTUOwsxpJJS3B80fGGaRwLa2UlIuvWgSlKJiJbSyVhNUkKZzOfr9KMeTcQi+HfH64FensgqArikQgEQUA8HEbplKk4fulpht9jpLwRRLf4EA1FsC/SjcrCCsi9ejSWpcwF0SFDyrPCPjXfXPOACZHlHJSUnIVItBmpZA8s1kK4nHWGlcA4GDmyZHiWkMNhrHVLSSXh7WhDylmI+VNK0RVMwNs/NJPIaSfPMVa8iRl80T4tOQ3WvVZ0hDpgE22omVKDypxKw+uMHi1YHbLh2RmPNkS7bHipi0+D3W5HQ0MD/H4/IpEIXC4XioqKJrT04MyZ80bsW0LhXejrXQ/GctHc3ITi4iREMYr8gkUoLloGWdbXDCP3LRFFxXu9IbzfF0JzNIEcVYHQ5UVNTTW8EHFW0ciIbDOQY7fg7Fll2OOLoCecQKHbhinFLvOUHRzM4PK5EZ9+P2hwOYzRnEQGr111x9VB0RQsyllkqrXLbNlDxn0b0dLSkg0dhnLHHXcMifIOBoOoqvpkl4jDO2Z9PAWp2Ivd3SEEXREEokl4XFYUFrowtSQHZ88qM2zQj+lpNWMqllRbAd8OIB4C4v1A4WJg/hWGpWpNa00P8ua+ZkSDUfQl+jD/+PmYnDsZU/KnmGKwj/BccbszdW9OSiVhqaoytO7NoTC7gcDmdBpah+2Tkt6oysUORLfo0dmalDKFoaCoqAg+nw9+v39Ub8pDvTeSdNRAJLIbAEMksgsWSwFUNQaXa4qh0QPDIwUGRwcMToeXrj9qhhR4U4r1tSmhauiNptDeE4EkiZBFAaIoYnZlrrk8Lk1oCBgNt9WNWcUj0+6YjcFRAWo4DDUWQ+S995BsagIAiG4X7DNnwlpnbFTO0RIZYDbnLDNHBxwNmDHKbay1aFmBfrFmtjUKGLrPTp+z2gL6WmVVNZw3a56h56vDxoQXLOkMInv69oAxhr2hvShzliHXlot8Wz4+W/VZHF90vOFnrdFwLl6MyIYNSDTtgmix6kbsmho4Fy82WhoA85+vgINHYc2fOxdNH34Af0c7krEIEtEoCsorcMLpZyOncOLPA6NmvyroQmxnL1hcD6wQykXIZU7YKnMgexy0no0DWc4xrOTF0chY65ZDElFZfhwme1w4e1YZAPBhIDAhwx2HzeQsTBBHK3a73dBSg6PtWxSlHt3dEmKxdsyevTBz3iopOctQh6zhWucoKub3hvF0Rzf8KQW5koQYgBOdNnzdpDWyAd2YbZpyF4fCZOUwRoOXtctM2UMExhgbzy+8++67WLx4MWR56CFAURSsWbMGS5cuPey/lUwm4XQ68de//hXnnXde5vnll1+Ovr4+/OMf/xjxO5MnT8bNN9+MG2+8MfPsnnvuwSuvvILNmzejubkZU6ZMwb///W/MmTMn8zPLli3DnDlz8POf//yQuoLBIPLy8tDf34/cI5DeMRRPYY8vgo5AFNGUCqdFQqXHae6NaTxoqouVwYSTYbT0t2BfeB9iSgxO2YkKd4UpBzugGwPM4rlyuCRjCvcGAjOjxRXTGQri8fhBvSkP9d5IFCWESLQZ8VgHFDUGWXLA7qg0bfRAmpCijmrkNprBa1YgmkQwmoTdKmNGeS5mVeaZb90y8XrFO2o4jPjWrYg36llybA0NcMw83vRrGEGYlfR6ZaYot7HWIrOuUcNJr1l08X9kSJ+zOsOdiCpROGQHJrknmfacNZik14vomjVIdnTAWlkJ5+LFsJaVGS0rA+/nq0Q0ahrHq9HQ4gqSHSGk9kfAGGCd5IZ1ktvwMxZBDIfWLYIgiOxhxvPWWHTGk/hXbwjtsSSqHFYsK8gxrRGbILLFeOyw4zZkS5KE/fv3o6SkZMjznp4elJSUjCu1OAAsXLgQCxYswC9/+UsAgKZpmDx5Mq6//nrcfvvtI37+wgsvRDQaxf8MqjG8ePFizJ49G0899RQYY6ioqMCtt96KW27R6wsEg0GUlJTg97///ag1sodzpA3ZBEEQBEEQBEEQBEEQBEEQBEEQBEEQxzrjscOO2z01XQt7OD09PZ+oXurNN9+Myy+/HPPnz8eCBQvw2GOPIRKJ4IorrgAAXHbZZZg0aRIeeOABAMB3v/tdLFu2DD/72c/wX//1X3jxxRexYcMG/OY3vwGg1+u+8cYbcd9996G+vh61tbW46667UFFRMSTqmyAIgiAIgiAIgiAIgiAIgiAIgiAIgjAnh23I/tKXvgRANxR//etfh81my7xTVRUff/wxFn+CGlQXXnghfD4f7r77bni9XsyZMwerVq1CaWkpAKCtrQ2iKGZ+fvHixXjhhRfwwx/+EHfeeSfq6+vxyiuvYObMmZmfue222xCJRPDNb34TfX19OOWUU7Bq1SrTpMElCIIgCIIgCIIgCIIgCIIgCIIgCIIgxuawU4unI6T/8Ic/4Ctf+QocDkfmndVqRU1NDa6++moUFRVlR+kEQqnFCYIgCIIgCIIgCIIgCIIgCIIgCIIgjixZSS3+7LPPAgBqampw6623fqI04gRBEARBEARBEARBEARBEARBEARBEARxKA47IvtYgiKyCYIgCIIgCIIgCIIgCIIgCIIgCIIgjixZicgezF//+lf8+c9/RltbG5LJ5JB3mzZt+iR/kiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAAAOJ4f+EXv/gFrrjiCpSWluLf//43FixYgMLCQjQ3N+Pss8/OhkaCIAiCIAiCIAiCIAiCIAiCIAiCIAjiGGLcEdlPPPEEfvOb3+Ciiy7C73//e9x2222oq6vD3XffjUAgkA2NE04623owGDRYCUEQBEEQBEEQBEEQBEEQBEEQBEEQxNFB2v56ONWvx23Ibmtrw+LFiwEADocDoVAIAPC1r30NJ598Mh5//PHx/knTkf7/VFVVZbASgiAIgiAIgiAIgiAIgiAIgiAIgiCIo4tQKIS8vLyD/sy4DdllZWUIBAKorq7G5MmTsW7dOpxwwgloaWk5LMs5D1RUVKC9vR05OTkQBOGI/d1gMIiqqiq0t7cfsni5kfCiEyCt2YK0ZgfSmh1Ia3YgrdmBtGYH0podSGt2IK3ZgbQeeXjRCZDWbEFaswNpzQ6kNTuQ1uxAWrMDac0OpDU7kNbsQFqzQza0MsYQCoVQUVFxyJ8dtyH7tNNOw6uvvoq5c+fiiiuuwE033YS//vWv2LBhA770pS99IsFmQxRFVFZWZu3v5+bmmr5jAvzoBEhrtiCt2YG0ZgfSmh1Ia3YgrdmBtGYH0podSGt2IK1HHl50AqQ1W5DW7EBaswNpzQ6kNTuQ1uxAWrMDac0OpDU7kNbscKS1HioSO824Ddm/+c1voGkaAODb3/42CgsLsWbNGnzxi1/Et771rfH+OYIgCIIgCIIgCIIgCIIgCIIgCIIgCIIYwrgN2aIoQhTFzPdf/epX8dWvfvWIiiIIgiAIgiAIgiAIgiAIgiAIgiAIgiCOXcRD/8hI3nvvPVx66aVYtGgR9u3bBwB4/vnn8f777x9RcUcbNpsN99xzD2w2m9FSDgovOgHSmi1Ia3YgrdmBtGYH0podSGt2IK3ZgbRmB9KaHUjrkYcXnQBpzRakNTuQ1uxAWrMDac0OpDU7kNbsQFqzA2nNDqQ1OxitVWCMsfH8wt/+9jd87WtfwyWXXILnn38e27dvR11dHR5//HGsXLkSK1euzJZWgiAIgiAIgiAIgiAIgiAIgiAIgiAI4hhg3BHZ9913H5566ik8/fTTsFgsmedLlizBpk2bjqg4giAIgiAIgiAIgiAIgiAIgiAIgiAI4thj3IbsnTt3YunSpSOe5+Xloa+v70hoIgiCIAiCIAiCIAiCIAiCIAiCIAiCII5hxm3ILisrw+7du0c8f//991FXV3dERBEEQRAEQRAEQRAEQRAEQRAEQRAEQRDHLuM2ZF999dX47ne/iw8//BCCIKCzsxN/+tOfcOutt+Laa6/NhkaCIAiCIAiCIAiCIAiCIAiCIAiCIAjiGEIe7y/cfvvt0DQNn/vc5xCNRrF06VLYbDbceuutuOGGG7KhkSAIgiAIgiAIgiAIgiAIgiAIgiAIgjiGEBhj7HB/WFVVfPDBB5g9ezacTid2796NcDiMGTNmwO12Z1MnQRAEQRAEQRAEQRAEQRAEQRAEQRAEcYwwrtTikiThzDPPRG9vL6xWK2bMmIEFCxaQEXucdHV14Uc/+pHRMg6L9vZ2XHnllUbLOCz27NmD0047zWgZh4XZtO7fvx9//OMfsXLlSiSTySHvIpEIN/3VbO16MHjSytOcRVqzA0/9lSetjY2NqKurM1rGYUH7gexgtnaNxWJ4//33sX379hHv4vE4nnvuOQNUjR9exhYvOgHz9dWDwdMcwJNW2rdkB57GFmnNDjxp5Wls8aSVpz7Ak1ae9lk89VeetPLUX3lq182bN0OSJKNlHBY8tSsvWmluzQ48tatRWsddI3vmzJlobm7OhpZjBq/Xi3vvvddoGYdFIBDAH/7wB6NlHBbhcBj/+te/jJZxWJhJ60cffYQZM2bg29/+Nr785S/j+OOPx7Zt2zLvw+EwN/3VTO16KHjSytOcRVqzA0/9lSetyWQSe/fuNVrGYUH7gexgpnbdtWsXGhoasHTpUsyaNQvLli3D/v37M+/7+/txxRVXGKjw8OFlbPGiEzBXXz0UPM0BPGmlfUt24GlskdbswJNWnsYWT1p56gM8aeVpn8VTf+VJK0/9lad2BYBxJPg1FJ7alRetNLdmB57a1Sit466Rfd999+HWW2/Fj3/8Y8ybNw8ul2vI+9zc3CMmjlc+/vjjg77fuXPnBCk5NK+++upB35vJaeEXv/jFQd/v27dvgpQcGp603nnnnTj//PPxzDPPIBKJ4Pvf/z6WLVuGN998E3PnzjVa3hB4aleetPI0Z5HW7MBTf+VJ680333zQ9z6fb4KUHBraD2QHntr1+9//PmbOnIkNGzagr68PN954I5YsWYJ33nkHkydPNlreEHgZW7zoBPjqqzzNATxppX1LduBpbJHW7MCTVp7GFk9aeeoDPGnlaZ/FU3/lSStP/ZWndv3Sl7500Pf9/f0QBGGC1BwcntqVF600t2YHntrVrFoPu0b2j370I9xyyy3Iyck58MuDJi3GGARBgKqqR14lZ4iiCEEQRvVOSj83S1sdTGsaM2ktLy+H1Wod9X0ymYTX6yWt48Tj8WDdunWYNm1a5tmDDz6Ihx9+GKtXr8bkyZNRUVFhCq08tStvWo+GOYu0fnJ466+8aJUkCXPmzBnTyS8cDmPTpk2m0Er7gezAU7uWlpbif//3fzFr1iwA+t7+uuuuw8qVK/H222/D5XKZZj/Ay9jiRSfAV1/lbQ7gSSvtW448vI0t0nrk4U0rT2OLJ6089QFetPK2z+Kpv/KklZf+ylO7WiwWnHHGGSgtLR31fSAQwGuvvWYKrTy1Ky9aaW7NDjy1q2m1ssNEFEXW1dXF3nnnnYN+EYwVFhay3/72t6y1tXXUrxUrVjBRFI2WyRhjrKKigr3yyitjvv/3v/9tGq01NTXspZdeGvM9af1kFBQUsM2bN494/sgjj7D8/Hz28ssvm0YrT+3Kk1ae5izSmh146q88aZ02bRp7/vnnx3xvJq20H8gOPLVrTk4O2759+4jn3/72t1llZSV79913TaOVl7HFi07G+OqrPM0BPGmlfUt24GlskdbswJNWnsYWT1p56gM8aeVpn8VTf+VJK0/9lad2nTVrFnvmmWfGfG8mrTy1Ky9aaW7NDjy1q1m1HnaNbDbg3bRs2bKDfhHAvHnz0NnZierq6lG/Jk2aZJpaEvPmzcPGjRvHfH8oz7aJhLRmh5kzZ2LNmjUjnt9666244447cNFFFxmganR4alfetPI0Z5HWIw9v/ZUXrfPnz+dGK0/tSlqzw/Tp07Fhw4YRzx9//HGce+65+OIXv2iAqtHhZWzxohPgq6+S1uxA+5bsQFqzA2nNDqQ1O5DW7ED7rOxAWrMDb1o3bdo05nubzWaa0lO8tSsPWmluzQ48tatZtR52anFRFNHV1YXi4uJsa+Kev//974hEIrj00ktHfd/b24tXX30Vl19++QQrG8l7772HSCSCs846a9T3kUgEGzZsMIWTwvbt2xGNRjF//vxR36dSqczli9HwpPWZZ57Bv/71Lzz//POjvn/ooYfw1FNPoaWlZYKVjYSnduVJK09zFmnNDjz1V560er1eJBIJU2g5FLQfyA48tesDDzyA9957DytXrhz1/XXXXYennnoKmqZNsLKR8DK2eNEJ8NVXeZoDeNJK+5bswNPYIq3ZgSetPI0tnrTy1Ad40srTPoun/sqTVp76K0/tmkgkoKoqnE6n0VIOCU/tyotWmluzA0/talat4zJk5+XlDamLPRqBQOCICCMIgiAIgiAIgiAIgiAIgiAIgiAIgiCOTeTx/PC9996LvLy8bGkhTEAikQCgpwkxM/39/fB6vQCAsrIyU/dL0podSCtBZAee+isvWhVFwbZt24ZonTFjBiwWi8HKxob2A9mBl3YF+NDKy9jiRedgePj8Ab7mAJ608gRv7crL2AJIa7bgRStPY4snrQA/fQDgQytv+yye+itPWgE++ivAT7t6vV58+OGHQ7QuXLgQZWVlBisbHV7aFeBDK82t2YGndjWd1sMtpi0IAuvq6hpfBe5jmG3btrFrr72WzZkzh5WVlbGysjI2Z84cdu2117Jt27YZLW8Ib7zxBjv77LNZfn4+E0WRiaLI8vPz2dlnn83efPNNo+UN4emnn2YNDQ0ZnemvhoYG9swzzxgtbwikNTuQ1uzA05xFWrMDT/2VF62qqrIf/OAHLD8/nwmCMOQrPz+f/fCHP2SqqhotMwPtB7IDT+3Ki1ZexhYvOtPw8vkzxtccwJNW2rdkB57GFmnNDjxp5Wls8aSVpz7Ai1be9lk89VeetPLSXxnjp13D4TC75JJLmCRJTJZlVlJSwkpKSpgsy0ySJHbppZeySCRitMwMvLQrY3xopbk1O/DUrmbVetiGbFEUyZB9mKxcuZJZrVZ28skns3vuuYc98cQT7IknnmD33HMPW7x4MbPZbGzVqlVGy2SMMfb73/+eybLMvvrVr7Jnn32WrVy5kq1cuZI9++yz7KKLLmIWi4U999xzRstkjDH28MMPM6fTyW6//Xb29ttvs+3bt7Pt27ezt99+m91xxx3M5XKxRx55xGiZjDHSmi1Ia3bgac4irdmBp/7Kk9bvfe97rLi4mD311FOspaWFRaNRFo1GWUtLC/v1r3/NSkpK2G233Wa0TMYY7QeyBU/typNWXsYWLzoZ4+vz52kO4Ekr7VuyA09ji7RmB5608jS2eNLKUx/gSStP+yye+itPWnnqrzy16ze+8Q1WX1/PVq1axRRFyTxXFIWtXr2aTZs2jV111VUGKjwAT+3Ki1aaW7MDT+1qVq0UkZ0FZs+eze66664x399zzz1s1qxZE6hobOrr69njjz8+5vtf/epXbOrUqROoaGwmT57MXnrppTHfv/jii6yqqmoCFY0Nac0OpDU78DRnkdbswFN/5UlraWnpQS/9V61axUpKSiZQ0djQfiA78NSuPGnlZWzxopMxvj5/nuYAnrTSviU78DS2SGt24EkrT2OLJ6089QGetPK0z+Kpv/Kklaf+ylO75ufnsw8++GDM9++//z7Lz8+fQEVjw1O78qKV5tbswFO7mlWreLgpyDVNQ0lJSTaznB817Nq1C5dccsmY7y+66CI0NTVNoKKxaWtrw+mnnz7m+8997nPo6OiYQEVj093djVmzZo35ftasWfD7/ROoaGxIa3YgrdmBpzmLtGYHnvorT1pDoRAqKirGfF9eXo5IJDKBisaG9gPZgad25UkrL2OLF50AX58/T3MAT1pp35IdeBpbpDU78KSVp7HFk1ae+gBPWnnaZ/HUX3nSylN/5aldNU2D1Wod873VaoWmaROoaGx4aldetNLcmh14alezaj1sQzZx+NTU1GDFihVjvl+xYgWqq6snUNHYHH/88fjtb3875vvf/e53mDFjxgQqGpuTTjoJDz74IBRFGfFOVVU89NBDOOmkkwxQNhLSmh1Ia3bgac4irdmBp/7Kk9bPfOYzuPXWW0fdOPv9fnz/+9/HZz7zmYkXNgq0H8gOPLUrT1p5GVu86AT4+vx5mgN40kr7luzA09girdmBJ608jS2etPLUB3jSytM+i6f+ypNWnvorT+36hS98Ad/85jfx73//e8S7f//737j22mtxzjnnGKBsJDy1Ky9aaW7NDjy1q1m1CowxNuH/q0c5f/nLX3DxxRfj7LPPxumnn47S0lIAQFdXF9566y2sWrUKL7zwAi644AKDlQLvvPMOvvCFL6Curm5Urc3NzVixYgWWLl1qsFLg448/xvLly5FKpbB06dIhWt99911YrVa88cYbmDlzpsFKSWu2IK3Zgac5i7RmB576K09a29vb8fnPfx47duzArFmzhmjdsmULZsyYgddeew1VVVUGK6X9QLbgqV150srL2OJFJ8DX58/THMCTVtq3ZAeexhZpzQ48aeVpbPGklac+wJNWnvZZPPVXnrTy1F95atfe3l5cfPHFWL16NQoKCjIZeru7u9HX14fly5fjhRdeQH5+vrFCwVe78qKV5tbswFO7mlUrGbKzxJo1a/CLX/wCa9euhdfrBQCUlZVh0aJF+O53v4tFixYZrPAAra2tePLJJ7Fu3boRWq+55hrU1NQYK3AQoVAIf/zjH0fVevHFFyM3N9dghQcgrdmBtGYHnuYs0podeOqvPGnVNA2rV68eVeuZZ54JUTRPchzaD2QHntqVJ628jC1edAJ8ff48zQE8aaV9S3bgaWyR1uzAk1aexhZPWnnqAzxp5WmfxVN/5UkrT/2Vp3YFgMbGxlG1Tp8+3WBlQ+GpXXnRSnNrduCpXc2olQzZBEEQBEEQBEEQBEEQBEEQBEEQBEEQhKmQjRZwtNPf3z/EayEvL89gRaOjKAq2bduW0VpeXo6GhgZYLBaDlY3E6/Xiww8/HKJ1wYIFKCsrM1jZSEhrdiCt2YOXOQsgrdmAp/7Kk9b169ePiG5bvHixaWr1DIb2A9mBp3blSSsvY4sXnQBfnz9PcwBPWgHat2QDnsYWac0OPGnlaWzxpJWnPsCTVp72WTz1V5608tRfeWnXZDKJV155ZdSxde6558JqtRqscCi8tCvAj1aaW7MDT+1qOq2MyApPP/00a2hoYKIoMlEUmSAITBRF1tDQwJ555hmj5WVQVZX94Ac/YPn5+UwQhCFf+fn57Ic//CFTVdVomYwxxsLhMLvkkkuYJElMlmVWUlLCSkpKmCzLTJIkdumll7JIJGK0TMYYac0WpDV78DJnMUZaswFP/ZUnrV1dXeyUU05hgiCw6upqtmDBArZgwQJWXV3NBEFgp5xyCuvq6jJaJmOM9gPZgqd25UkrL2OLF52M8fX58zQH8KSVMdq3ZAOexhZpzQ48aeVpbPGklac+wJNWnvZZPPVXnrTy1F95atempiZWV1fH7HY7W7ZsGfvKV77CvvKVr7Bly5Yxu93Opk6dypqamoyWyRjjq1150Upza3bgqV3NqpUM2Vng4YcfZk6nk91+++3s7bffZtu3b2fbt29nb7/9NrvjjjuYy+VijzzyiNEyGWOMfe9732PFxcXsqaeeYi0tLSwajbJoNMpaWlrYr3/9a1ZSUsJuu+02o2Uyxhj7xje+werr69mqVauYoiiZ54qisNWrV7Np06axq666ykCFByCt2YG0Zgee5izSmh146q88ab3gggvYokWL2I4dO0a827FjB1u8eDH78pe/bICykdB+IDvw1K48aeVlbPGikzG+Pn+e5gCetNK+JTvwNLZIa3bgSStPY4snrTz1AZ608rTP4qm/8qSVp/7KU7uefvrp7Nxzz2X9/f0j3vX397Nzzz2XnXnmmQYoGwlP7cqLVppbswNP7WpWrWTIzgKTJ09mL7300pjvX3zxRVZVVTWBisamtLSUrVq1asz3q1atYiUlJROoaGzy8/PZBx98MOb7999/n+Xn50+gorEhrdmBtGYHnuYs0podeOqvPGl1u91s06ZNY77fsGEDc7vdE6hobGg/kB14aleetPIytnjRyRhfnz9PcwBPWmnfkh14GlukNTvwpJWnscWTVp76AE9aedpn8dRfedLKU3/lqV0dDgfbsmXLmO8//vhj5nA4JlDR2PDUrrxopbk1O/DUrmbVKhqT0Pzopru7G7NmzRrz/axZs+D3+ydQ0diEQiFUVFSM+b68vByRSGQCFY2NpmkHrcFhtVqhadoEKhob0podSGt24GnOIq3Zgaf+ypNWm82GYDA45vtQKASbzTaBisaG9gPZgad25UkrL2OLF50AX58/T3MAT1pp35IdeBpbpDU78KSVp7HFk1ae+gBPWnnaZ/HUX3nSylN/5ald8/Pz0draOub71tZW5OfnT5ieg8FTu/KilebW7MBTu5pW64Sbzo8BTj31VHbZZZexVCo14p2iKOyyyy5jS5cuNUDZSD7/+c+zM888k/l8vhHvfD4fO+uss9h//dd/GaBsJBdffDGbO3fuqB4hmzZtYvPmzWOXXHKJAcpGQlqzA2nNDjzNWaQ1O/DUX3nSet1117Hq6mr28ssvD0nL1d/fz15++WVWU1PDrr/+egMVHoD2A9mBp3blSSsvY4sXnYzx9fnzNAfwpJX2LdmBp7FFWrMDT1p5Gls8aeWpD/Cklad9Fk/9lSetPPVXntr1rrvuYgUFBezRRx9lmzdvZl6vl3m9XrZ582b26KOPMo/Hw+655x6jZTLG+GpXXrTS3JodeGpXs2olQ3YW2Lx5MysrK2OFhYXs/PPPZ9dccw275ppr2Pnnn88KCwtZeXn5QVN0TCRtbW1s5syZTJZlNnfuXHbWWWexs846i82dO5fJssxmz57N2trajJbJGGMsEAiws846iwmCwDweD5s+fTqbPn0683g8TBRFdvbZZ7Pe3l6jZTLGSGu2IK3Zgac5i7RmB576K09a4/E4u+aaa5jVamWiKDK73c7sdjsTRZFZrVZ27bXXsng8brRMxhjtB7IFT+3Kk1ZexhYvOhnj6/PnaQ7gSSvtW7IDT2OLtGYHnrTyNLZ40spTH+BJK0/7LJ76K09aeeqvPLUrY4w9+OCDrLy8nAmCwERRZKIoMkEQWHl5OXvooYeMlpeBp3blRSvNrdmBp3Y1q1aBMcYmPg786CcUCuGPf/wj1q1bB6/XCwAoKyvDokWLcPHFFyM3N9dghQfQNA2rV68eVeuZZ54JUTRXBvrGxsZRtU6fPt1gZSMhrdmBtB55eJqzSGv24KW/AnxpDQaD2LBhA7q6ugDoWufNm2e6z5/2A9mBp3blSSvAz9jiRSdvnz8vcwDAj1bat2QHnsYWac0OPGkF+BlbAD9aeeoDPGkF+NlnAfz0V4Afrbz1V17aNU1zc/OQsVVbW2uwotHhqV150Upza3bgqV3NppUM2QRBEARBEARBEARBEARBEARBEARBEISpkI0WcDTj9Xrx4YcfZrxBysvLsWDBApSVlRmsbCTr16/H2rVrh3iuLF68GCeddJLByoaSTCbxyiuvjKr13HPPhdVqNVjhAUhrdiCt2YOnOYu0Hnl46q88afX7/fjd7343qtavf/3rKC4uNljhUGg/kB14aVeAH628jC1edKbh5fPnaQ7gSStA+5ZswcvYAkhrtuBFK09jiyetAD99AOBHK0/7LJ76K09aAX76K0/tun37djz++OMjtC5atAjXX389ZsyYYbDCA/DUrrxopbk1O/DUrmbUShHZWSASieBb3/oWXnzxRQiCAI/HAwAIBAJgjOGiiy7Cr3/9azidToOVAt3d3bjgggvwwQcfYPLkySgtLQUAdHV1oa2tDUuWLMHf/vY3lJSUGKwU2L17N5YvX47Ozk4sXLhwiNYPP/wQlZWVeP311zF16lSDlZLWbEFaswNPcxZpzQ489VeetH700UdYvnw5nE4nTj/99CFa33rrLUSjUaxevRrz5883WCntB7IFT+3Kk1ZexhYvOgG+Pn+e5gCetNK+JTvwNLZIa3bgSStPY4snrTz1AZ608rTP4qm/8qSVp/7KU7u+/vrrOO+883DiiSdi+fLlQ7S++eab2LhxI/7xj39g+fLlBivlq1150Upza3bgqV1Nq3XCq3IfA3zjG99g9fX1bNWqVUxRlMxzRVHY6tWr2bRp09hVV11loMIDXHDBBWzRokVsx44dI97t2LGDLV68mH35y182QNlITj/9dHbuueey/v7+Ee/6+/vZueeey84880wDlI2EtGYH0podeJqzSGt24Km/8qR14cKF7Jvf/CbTNG3EO03T2De/+U128sknG6BsJLQfyA48tStPWnkZW7zoZIyvz5+nOYAnrbRvyQ48jS3Smh140srT2OJJK099gCetPO2zeOqvPGnlqb/y1K6zZ89md91115jv77nnHjZr1qwJVDQ2PLUrL1ppbs0OPLWrWbWSITsL5Ofnsw8++GDM9++//z7Lz8+fQEVj43a72aZNm8Z8v2HDBuZ2uydQ0dg4HA62ZcuWMd9//PHHzOFwTKCisSGt2YG0Zgee5izSmh146q88abXb7ayxsXHM942Njcxut0+gorGh/UB24KldedLKy9jiRSdjfH3+PM0BPGmlfUt24GlskdbswJNWnsYWT1p56gM8aeVpn8VTf+VJK0/9lad2tdvtozoHpNmxYweNrU8AL1ppbs0OPLWrWbWKExv/fWygadpB8+9brVZomjaBisbGZrMhGAyO+T4UCsFms02gorHJz89Ha2vrmO9bW1uRn58/YXoOBmnNDqQ1O/A0Z5HW7MBTf+VJa1lZGdavXz/m+/Xr12dS9BgN7QeyA0/typNWXsYWLzoBvj5/nuYAnrTSviU78DS2SGt24EkrT2OLJ6089QGetPK0z+Kpv/Kklaf+ylO71tTUYMWKFWO+X7FiBaqrqydQ0djw1K68aKW5NTvw1K6m1TrhpvNjgIsvvpjNnTt3VK+wTZs2sXnz5rFLLrnEAGUjue6661h1dTV7+eWXh6Rh6O/vZy+//DKrqalh119/vYEKD3DXXXexgoIC9uijj7LNmzczr9fLvF4v27x5M3v00UeZx+Nh99xzj9EyGWOkNVuQ1uzA05xFWrMDT/2VJ62PP/44s9ls7Dvf+Q77xz/+wdatW8fWrVvH/vGPf7DvfOc7zOFwsF/96ldGy2SM0X4gW/DUrjxp5WVs8aKTMb4+f57mAJ600r4lO/A0tkhrduBJK09jiyetPPUBnrTytM/iqb/ypJWn/spTu/75z39msiyzc845h/385z9nL774InvxxRfZz3/+c/bFL36RWa1W9te//tVomYwxvtqVF600t2YHntrVrFrJkJ0FAoEAO+uss5ggCMzj8bDp06ez6dOnM4/Hw0RRZGeffTbr7e01WiZjjLF4PM6uueYaZrVamSiKzG63M7vdzkRRZFarlV177bUsHo8bLTPDgw8+yMrLy5kgCEwURSaKIhMEgZWXl7OHHnrIaHlDIK3ZgbQeeXias0hr9uClvzLGl9YXX3yRLVy4kMmyzARBYIIgMFmW2cKFC9lLL71ktLwMtB/IDjy1K09aGeNnbPGik7fPn5c5gDF+tNK+JTvwNLZIa3bgSStj/IwtxvjRylMf4EkrY/zssxjjp78yxo9W3vorL+3KGGMffPABu/DCC9nkyZOZ1WplVquVTZ48mV144YVszZo1RssbAk/tyotWmluzA0/takatAmOMTXwc+LHBjh07sHbtWni9XgB6WP6iRYswffp0g5WNJBgMYuPGjUO0zps3D7m5uQYrG52WlpYhWmtraw1WNDakNTuQ1iNPY2Mj1q1bx8WcxdP8ylO7Avz0V4AvralUCn6/HwBQVFQEi8VisKLRCQaD2LBhA7q6ugDQfuBIwdM+iyetAD9jixedvH3+vMwBAD9aad+SHXgaW7xp5WXfwlO7AvyMLYAfrTz1AZ7GFsDPPgvgp78C/GjlaWwB/LQrbzQ3Nw+Zs8zcrrz0AZ7mVp4+f57a1UxayZBNEARBEAQxwTDGIAiC0TII4pBQX80O1K4EQRAEcWxDe4HsoCgKZFk2WgZBEMRRB61b2YHa9fAQjRZwLNLb24vnnnvOaBmjEolE8Oyzz+IHP/gBHn/8cfT09Bgt6bBpb2/HlVdeabSMDG+++Sbuuece/POf/wQAvPvuuzj77LNx2mmn4dlnnzVY3eFjtnY9GGbTun//ftx999047bTT0NDQgOOPPx7nnHMOfvvb30JVVaPlDSGZTOLPf/4zbrrpJlx00UW46KKLcNNNN+Evf/kLksmk0fIy/OxnP8PevXuNlnHY8NKumzZtQktLS+b7559/HkuWLEFVVRVOOeUUvPjiiwaqG8njjz+Oyy67LKPr+eefx4wZMzB9+nTceeedUBTFYIU6iUQCt956K5YuXYqHHnoIAHDffffB7XYjJycHF198MYLBoMEqD+D3+/Hwww/j/PPPx6JFi7Bo0SKcf/75eOSRR+Dz+YyWNwRe+sBweNxn2Ww2NDY2Gi1jXOzZswennXaa0TK4mwNisRjef/99bN++fcS7eDxuqvMLL3usc845B88//zxisZjRUg6bjo4OhMPhEc9TqRTeffddAxSNH7OdCQ5GV1cXfvSjHxktI8Nrr72Gu+++Gx988AEA4J///Cc+//nP46yzzsJvfvMbg9UNhad9S2NjI5599lns2LEDgJ5d6tprr8WVV16ZuS/gAbONLV73g2nMusfiZWytWrUKW7ZsAQBomoYf//jHmDRpEmw2GyorK/Hggw/CLLFbvJ23ebnPvOGGG/Dee+8ZLeOw4HFPOBaNjY2oq6szWkaG7du347rrrsPcuXNRXl6O8vJyzJ07F9ddd92o5xoj4eEMw9sZlpfPn7d2PRiG3bcYktD8GOc///kPE0XRaBmMMcYaGhpYT08PY4yxtrY2Vl1dzfLy8thJJ53EPB4PKykpYc3NzQarPDzM1K7PP/88k2WZnXjiicztdrNnn32W5efns6uuuopdeeWVzGq1sr/85S9GyzwszNSuh8JMWj/66COWl5fH5s2bx0455RQmSRL72te+xi688EKWn5/PFi9ezILBoNEyGWOMNTU1sbq6Oma329myZcvYV77yFfaVr3yFLVu2jNntdjZ16lTW1NRktEzGGGOCIDBJktjpp5/OXnzxRZZIJIyWNCY8tevs2bPZm2++yRhj7Omnn2YOh4N95zvfYU8++SS78cYbmdvtZr/97W8NVqnz4x//mOXk5LALLriAlZWVsQcffJAVFhay++67j/3kJz9hxcXF7O677zZaJmOMsZtuuolVVFSwW265hTU0NLDrrruOTZ48mf3xj39kL7zwAps6dSq74YYbjJbJGGNs/fr1rKCggE2aNIldfvnl7LbbbmO33XYbu/zyy1llZSXzeDzso48+MlomY4yvPsDTPuumm24a9UsURXbZZZdlvucBs+wHeJoDdu7cyaqrqzO1xZYuXcr27duXee/1ek3RpozxtcdK1xLLy8tj11xzDduwYYPRksaks7OTnXTSSUwUxUybhkKhzHsz9YFDYZY54HAwk9annnqKybLM5s2bx3Jzc9nzzz/PcnJy2FVXXcW+9a1vMYfDwR577DGjZTLG+Nq3vP7668xqtTKPx8Psdjt7/fXXWXFxMTv99NPZaaedxiRJYm+99ZbRMg8LM/VXnvaDPO2xeBpbxx13HHv33XcZY4z95Cc/YYWFhezRRx9lr7/+OnvsscdYaWkpe/DBBw1WqcPTeZun+8z0vrW+vp49+OCDbP/+/UZLGhOe9oSHwkxrwcqVK5nVamUnn3wyu+eee9gTTzzBnnjiCXbPPfewxYsXM5vNxlatWmW0TMYYP2cYns6wPH3+PLXroTBqDqDU4lngUN4TH3/8MZYtW2YKTxtRFOH1elFSUoJLL70ULS0tWLlyJfLy8hAOh3H++eejuLgYL7zwgtFS8eqrrx70fXNzM2655RZTtOvcuXNxxRVX4Dvf+Q7eeustnHPOObj//vtx0003AdCjSv/+97/j/fffN1gpX+3Kk9ZTTjkFZ5xxBu655x4AwB//+Ec8/vjjWLduHXp7e3Haaadh6dKl+PnPf26wUuCMM86Ay+XCc889N6KGUDAYxGWXXYZYLIbVq1cbpPAAoijid7/7HV555RWsXLkSubm5uPTSS3HVVVdh5syZRssbAk/t6nQ60djYiOrqapx44om49tprcfXVV2fev/DCC7j//vuxbds2A1XqTJ06FQ8//DC+9KUvYfPmzZg3bx7+8Ic/4JJLLgEA/P3vf8dtt92GpqYmg5UCkydPxu9+9zucfvrpaG5uRn19PV5++WWce+65AHRP96uvvhqtra3GCgVw8skn44QTTsBTTz01IqURYwzXXHMNPv74Y6xdu9YghQfgqQ/wtM8SRREnnHAC8vPzhzz/17/+hfnz58PlckEQBFNEjv3iF7846Pt9+/bhpz/9qeH7AZ7mgPPPPx+pVAq///3v0dfXhxtvvBHbt2/HO++8g8mTJ6OrqwsVFRWGtynA1x5LFEVs3boVb7zxBn73u99h27ZtmDVrFq666ipccsklKCgoMFpihssvvxw7d+7E448/jr6+Ptx+++0QBAFvvPEGCgoK0NXVhfLycmiaZrRUrs4EH3/88UHf79ixAxdddJEptB5//PG48cYbcfXVV+Ptt9/G5z//efzsZz/DddddBwD4/e9/j4cfftgU0S087VsWL16M0047Dffddx9efPFFXHfddbj22mtx//33AwDuuOMObNy4EW+88YbBSvkaW7ztB3nZY/E0tux2O3bt2oXJkydj1qxZuPvuu/F//s//ybxfsWIFbrzxRlP0AZ7O2zzdZ4qiiDfffBP/8z//gz/96U/o7+/H2Wefjauvvhqf//znIYrmSUTL057w5ptvPuh7n8+HF154wRRrwQknnIBzzz13zOw2//3f/42XX375kPuxiYCXMwxPZ1iePn+e2tW09y0Tbjo/Bkh7hI31lX5vBgRBYF1dXYwxxurq6tgbb7wx5P0HH3zAqqqqjJA2gnS7CYIw5pdZ2tXlcg2JsLJYLGzz5s2Z7xsbG1lhYaER0kbAU7vypNXhcLA9e/ZkvldVlVksFub1ehljjL3xxhusoqLCKHlDcDgcbMuWLWO+//jjj5nD4ZhARWMzeM7q6upiDz30EJs+fToTRZGddNJJ7De/+Y0pPBgZ46tdCwsLMx7BJSUl7D//+c+Q97t37zaNVofDwfbu3Zv53mKxsK1bt2a+b21tZU6n0whpIziU1paWFtNotdvtrLGxccz3jY2NzG63T6CiseGpD/C0z3rggQdYbW3tiKgwWZbZtm3bDFI1OoIgsIqKClZTUzPqV0VFhSn2AzzNASUlJezjjz/OfK9pGrvmmmvY5MmT2Z49e0wVjcvTHmvwHMAYYx9++CH75je/yfLy8pjD4WAXXXSRaSIxKyoq2Icffpj5Ph6Ps3POOYfNmTOH9fT0mKoP8HQmOJhWs90NjDZnDd7LmmnO4mnfkpubm8nCpKoqk2WZbdq0KfN+y5YtrLS01Ch5Q+BpbPG0H+Rpj8XT2CovL2dr165ljDFWWlo6ZFwxxtiuXbtMc4bl6bzN231mep+VTCbZSy+9xJYvX84kSWIVFRXszjvvNE0WPJ72hKIoshNPPJF95jOfGfVr/vz5plkL7HY727Fjx5jvd+zYYZo5i5czDE9nWN4+f17a1az3LeZxTTqKyMnJwQMPPIB//vOfo36ZrbZU2ssyHo+jvLx8yLtJkyaZpgZOeXk5Xn75ZWiaNurXpk2bjJaYwWKxDKl/a7PZ4Ha7h3xvlrooPLUrT1pLSkqwf//+zPddXV1QFCUTmVtfX49AIGCUvCHk5+cf1OOrtbV1hPe4GSgpKcFtt92GxsZGvPPOO5gxYwZuuummEfOYUfDUrmeffTaefPJJAMCyZcvw17/+dcj7P//5z5g6daoR0kZQVlaWiQRqamqCqqpDIoO2bduGkpISo+QNYfLkyZlohY8++giCIGD9+vWZ9x9++CEmTZpklLwhlJWVDdE2nPXr16O0tHQCFY0NT30A4Gefdfvtt+Oll17Ctddei1tvvRWpVMpoSWNSXV2N//t//y9aWlpG/VqxYoXREgHwNQfEYjHIspz5XhAEPPnkkzjnnHOwbNky7Nq1y0B1Q+FpjzWcBQsW4Ne//jU6OzvxxBNPoL29HWeccYbRsgAA/f39Q6KBbDYbXn75ZdTU1OCzn/0suru7DVQ3FJ7OBB6PB08//fSoc1VzczNee+01oyVmKCwsxN69ewEAnZ2dUBQFbW1tmfd79+6Fx+MxSt4QeNq3AAf2AqIowm63Iy8vL/MuJycH/f39RkkbAk9ji6f9IE97LJ7G1vnnn4/7778fqqri3HPPxRNPPDGkJvYvf/lLzJkzxziBg+DpvM3TfeZgLBYLvvKVr2DVqlVobm7G1VdfjT/96U847rjjjJY2KmbeE06dOhU33XQT3n777VG/nn76aaMlZqipqTno2W/FihWorq6eQEVjw8sZhqczLE+fP0/tatb7FvnQP0KMlxNPPBGAvjkZjfz8/CGbK6P53Oc+B1mWEQwGsXPnziHpeffu3YvCwkID1R1g3rx52LhxYyblwnAEQTBNu06dOhU7duzIbJj27duHnJyczPs9e/agsrLSKHlD4KldedJ63nnn4ZprrsEjjzwCm82GH//4x1i2bBkcDgcAYOfOnaZZoK666ipcdtlluOuuu/C5z30uczDt6urCW2+9hfvuuw833HCDwSp1hqc3S3Pqqafi1FNPxS9+8Qu89NJLE6xqdHhq14ceFoaBEQAAXSxJREFUeghLlizBsmXLMH/+fPzsZz/DO++8g4aGBuzcuRPr1q3D3//+d6NlAgAuueQSXHbZZTj33HPx1ltv4bbbbsOtt96Knp4eCIKA+++/H1/+8peNlgkAuOaaa/D1r38dzzzzDDZu3Iif/vSnuPPOO7Fjxw6Ioognn3wSt9xyi9EyAQC33norvvnNb2Ljxo2j9tenn34aP/3pTw1WqcNTHwD42WcBwEknnYSNGzfi29/+NubPn48//elPY867RpLeD3zlK18Z9b1Z9gM8zQHTp0/Hhv/f3n2HSVXf/f9/z2wBdukgsIAURQREpSm9qAgC1sRYUMFYokbuFCN2o2LX2BFMojeKJRITcgcRRGkiKoIgoHSkWJAVpIu03ffvD35unOwukHz3Ned84Pm4rr3izGev7JMzM6fMZ+acjz6y5s2bp9w/dOhQMzM788wzo8gqUUj7WKXJycmxSy+91C699NLYfEjgiCOOsPnz59tRRx1VdF9mZqa99tpr9rOf/cxOP/30COtShXRM0LZtW1uzZk2pb6Jt2rQpNq1nnXWWXX755TZw4EAbM2aMDRgwwH73u99ZMpm0RCJhgwcPtl69ekWdaWZh7bc0atTIli1bZkceeaSZmX3wwQfWoEGDovHPP/88Nh/EDem1Fdr+YCj7WCG9tu677z7r2bOnNWvWzDp27Givvfaavf3229a0aVNbvny5bdiwIRaX8TIL63g7pPczS9OgQQO788477Y477rCJEydGnbNPcdwnbNeunc2ePdsuvvjiEsfjtC0YMmSI9e/f36ZOnWo9e/Ysts568803Y3EZL7NwjmFCOoYN6fEPabnG9v2WtH8H/BDwpz/9yZ944olSx9euXet33nlnGotKd+edd6b8vPnmmynj119/vV9wwQUR1aWaNm2ajx8/vtTxbdu2+dSpU9NYVLrRo0f7O++8U+r4/fff77fddlsai0oX0nINqXXr1q1+3nnneWZmpicSCe/UqVPK6ZkmTJjgf/3rXyMsTPXAAw94Xl5eyqUREomE5+Xl+YMPPhh1XpF/Px1T3IWyXN3dN27c6DfeeKO3aNHCy5cv79nZ2d6wYUPv37+/z5o1K+q8IgUFBX7vvff66aef7vfdd58XFhb6X/7yFz/88MO9Ro0afumll/q2bduizizy8ssv+6BBg/yVV15xd/cpU6Z4165dvW3btn7nnXd6QUFBxIX/8uqrr3r79u2L1luJRMIzMzO9ffv2PmrUqKjzioT0HAhpP+vf/eUvf/HatWt7MpmM3WkvFyxYsM/10q5du3zVqlVpLCpdKOuA++67z/v06VPq+DXXXOOJRCKNRaULaR+rR48evnHjxqgzDsgNN9zgvXr1KnFs9+7dfuaZZ8bmNJIhHROMHj3aX3zxxVLHN2zY4M8//3wai0q3bds2v/LKK71ly5b+i1/8wnfu3OkPP/ywZ2dneyKR8B49esRqPzyU/Zbhw4f72LFjSx2/+eab/fLLL09jUelCem2FtD/47+K8j+UezmvLfe8+3/Dhw71v377erFkzb9q0qXfv3t1vueUW/+KLL6LOSxHK8XZI72c2atTI169fH3XGAQlpn/Drr7+OzbHUgXjvvff8/PPP9wYNGnh2drZnZ2d7gwYN/Pzzz/f3338/6rwiJR3D/PhU43E6hgnlGNY9nMffPZzlGtf3WxLuMfkIDQAchHbs2GF79uxJORVTnK1cudLWrl1rZntPK9a4ceOIiw4OLFeEZPfu3bZ+/XozM6tZs6ZlZWVFXISofPnllzZ79mzr2bOn5ebmRp0DpAhtHyvu9uzZY9u3by86vWFJ41999VVsTs+H9NmxY4ft3r075Rt5ccJ+C0IUwj4Wry0AKHscwwD/HSayAQAAAAAAAABAkHbu3Glme6/jDcQdz1fgP5OMOuBg9f3339v06dNt4cKFxcZ27NhhI0eOjKDqP/fZZ5/ZySefHHXGAQmpddGiRXbEEUdEnXFAQlqutGp88cUXdtlll0WdcUBo1Qjp+RpSK9sCDVo1QmoN5bUVSqdZWI9/SK0hPQfYb9EIabmG1BrSc4BWDVo1QmoNaRvLcv3vvf3229a3b1+rVq2a5eTkWE5OjlWrVs369u0b++tj/1jcluu+zJs3zzIyMqLOOCBxW64Hw/M1bst0X2jViKqViWyBpUuXWvPmza1bt2527LHHWvfu3e3rr78uGt+8ebP9/Oc/j7DwwG3bts3eeeedqDMOSEitu3btstWrV0edcUBCWq60amzYsMFeeOGFqDMOCK0aIT1fQ2plW6BBq0ZIraG8tkLpNAvr8Q+pNaTnAPstGiEt15BaQ3oO0KpBq0ZIrSFtY1mu/50XXnjB+vbta1WqVLHHHnvMxo4da2PHjrXHHnvMqlatan379rUXX3wx6swDEqfleiBCOcFvnJbrwfJ8jdMy3R9aNaJqzUz7XzwE3HjjjdayZUv76KOPbNOmTfab3/zGOnfubFOnTrUGDRpEnZfiySef3Of4V199laaS/Qup9brrrtvn+Lp169JUsn8hLVdaNcaMGbPP8RUrVqSpZP9o1Qjp+RpSK9sCDVo1QmoN5bUVSqdZWI9/SK0hPQfYb9EIabmG1BrSc4BWDVo1QmoNaRvLctW499577fHHH7drr7222Nill15qXbp0sSFDhtgll1wSQV2qkJbrT37yk32Ob9682RKJRJpq9i2k5RrK8zWkZUqrRlxbuUa2QO3atW3ixIl27LHHmtneTyn98pe/tHHjxtmUKVMsNzfX6tatawUFBRGXmiWTScvLy7Ps7OwSx3ft2mVr166l9T+UkZFhrVq1ssqVK5c4vm3bNpszZ04sWkNarrRqJJNJSyQS+/xEZSKRoPU/FFprSM/XUFrZFmjQqhFSayivrVA6zcJ6/ENqDe05wH5L2QttuYbUGtJzgNayR6tGSK2hbWNZrmWvfPnyNm/ePDv66KNLHF+yZIm1atXKvv/++zSXFRfScs3KyrJTTz3VateuXeL4hg0bbOzYsbFoDWm5hvJ8DWmZ0qoR21ZHmatUqZIvXLiw2P3XXnut169f36dNm+bJZDKCsuIaNWrko0aNKnX8448/pvW/0LRpU3/xxRdLHY9Ta0jLlVaNunXr+v/93/+VOk7rfyek1pCeryG1si3QoFUjpNZQXluhdLqH9fiH1BrSc4D9Fo2QlmtIrSE9B2jVoFUjpNaQtrEsV402bdr44MGDSx2/4YYbvE2bNmksKl1Iy/XYY4/1Z599ttTxOLWGtFxDeb6GtExp1YhrK9fIFmjWrJl99NFHxe4fOnSonXXWWXbmmWdGUFWytm3b2uzZs0sd398nstMppNZ27doF0xrScqVVg1YNWjVCamVboEGrRkitoby2Quk0C+vxD6mV54AGrRq0atCqQatGSK1sYzVCWq6PPPKIDRs2zI477ji77rrr7MEHH7QHH3zQrrvuOjv++ONt2LBh9thjj0WdaWZhLde2bdvanDlzSh0vV65cbC6dGtJyDeX5GtIypVUjrq2cWlzg/vvvt3fffdfGjRtX4vgvf/lLe+aZZ6ywsDDNZcUtXLjQtm/fbu3atStxfPfu3bZmzRpr2LBhmsuKC6l17dq1tnPnzli07E9Iy5VWjXfffde+++47O+2000oc/+677+yjjz6y7t27p7msOFo1Qnq+htTKtkCDVo2QWkN5bYXSaRbW4x9Sa0jPAfZbNEJariG1hvQcoFWDVo2QWkPaxrJcdVatWmXDhw+3GTNm2Nq1a83MrE6dOtaxY0e7+uqrrVGjRtEG/v9CWq47d+60goICy8nJiTplv0JarmZhPF9DWqa0asS1lYlsAAAAAAAAAAAAAECscGpxlOj555+3zZs3R51xUFm2bJlNmjTJli9fHnVKqTZv3mxLliyxJUuW8PgDsPz8/KJPicYR6yyEyt2toKAg6oyDSgj7WWbhdKLsff755/bhhx/arFmz7Ntvv406Z7927txpO3fujDpjvwoKCiw/P9/WrVsXdcoBCWW5moXVCq2Q9ltCauV9t7LHflbZ+ffX0cyZM23GjBmx3S6Esp+1Z88emzdvnk2YMMEmTJhg8+bNs927d0ed9R/Zs2ePff7551FnHDTy8/ODWJ6hdJqZ3XXXXbZ+/fqoMw5ISK1Rr6uYyBZ59tlnbeDAgTZixAgzMxs1apQ1b97cjjjiCLvjjjsirtu/X/ziF7ZmzZqoM1LMnDkzZUdq7Nix1r17d6tXr561a9fORo4cGWFdqvvvv98mTZpkZmYbN260nj172tFHH22nnnqqHX300danTx/btGlTtJE/8uyzz1qLFi2sevXq1qJFi5T/fu6556LOO2CLFi2yI444IuqMAxK31nnz5tk999xjw4YNK7YB3bJli1122WURlRUXyvr1m2++Sbk9d+5cGzhwoHXu3NnOPfdcmzp1ajRhJdiwYYOde+651qBBA7vmmmusoKDArrjiCsvLy7N69epZp06d7Ouvv446s8jBss6aN2+eZWRkRJ1RZNy4cXbFFVfYDTfcYIsXL04Z27hxo5188skRlRUXSuuePXvstttus+7duxetnx5++GGrWLGi5eTk2MCBA23Xrl0RV+7FflbZC6XTzKxSpUp2+eWX2/vvvx91yn6F1GpmNmzYMGvYsKE1btzYOnXqZB06dLBatWpZly5d9nntsSi8/fbb1rdvX6tWrZrl5ORYTk6OVatWzfr27WsTJ06MOi/FG2+8Yd26dbPc3FyrW7eu1alTx6pWrWqXXHJJ7N5kC2m5htS6L3E71mK/peyF1Foa3nf7f8N+lsbq1autXbt2Vq5cOevTp49t2bLFTj31VOvQoYN16tTJWrRoYUuXLo06s0go+1mFhYV222232WGHHWatW7e2Pn36WJ8+fax169ZWq1Ytu/3222NxCdIDsWDBAmvcuHHUGUWGDRtmPXv2tPPOO69onfCD9evXx2Z/YOvWrXbxxRdbw4YNi7ZR1157reXl5Vnjxo2te/futmXLlqgzg+k02/t+9b//bN682e69915bsWJF0X1xEFLrX//615R9qKFDh1rDhg2tfPnyVrNmTRsyZEg0YY4y99hjj3lubq7/5Cc/8by8PL/nnnu8Ro0afs899/hdd93llStX9j/+8Y9RZ7q7e7Vq1Ur8SSQSXqVKlaLbcZBMJj0/P9/d3ceMGePJZNIHDBjgTz/9tF9xxRWemZnpo0ePjrhyr/r16/ucOXPc3f2KK67w1q1b+5w5c/z777/3uXPneocOHfzyyy+PuHKvhx56yHNycvymm27yKVOm+MKFC33hwoU+ZcoUv/nmmz03N9cffvjhqDMPyNy5cz2ZTEadcUDi1DphwgTPzs72Y445xhs0aOA1atTwyZMnF42vXbs2Nq0hrV9/vM567733PCsry7t37+6DBw/2U0891TMzM/2dd96JuHKvyy67zFu2bOlPPfWUd+/e3c866yw/7rjjfPr06f7+++/7CSec4AMGDIg6090PvnVWIpGIOsPd3V9++WXPyMjwfv36eZcuXbx8+fL+0ksvFY3HaT0QUuttt93mtWvX9uuuu85btGjhV199tR9++OH+0ksv+QsvvOD16tXzBx98MOpMd2c/SyGUTnf3RCLhxxxzjCcSCW/WrJn/4Q9/8G+++SbqrBKF1Prwww973bp1/amnnvI///nP3rx5cx8yZIiPHz/eL7nkEs/JyfFZs2ZFnenu7s8//7xnZmb6BRdc4CNGjPBx48b5uHHjfMSIEX7hhRd6VlaWjxw5MupMd3cfOXKkV6pUyX/3u9/5rbfe6nXq1PGbbrrJhw8f7t27d/eaNWv60qVLo85097CWa0it+xOnYy32WzRCauV9Nw32szR++tOfevfu3f3111/38847zzt37uw9evTwL7/80tesWeO9e/f2s88+O+pMdw9rP2vw4MF+2GGH+TPPPOMrV6707du3+/bt233lypX+xz/+0WvVquU33HBD1JkHJE7b2CeeeMJzcnL82muv9Ysvvtizs7P9vvvuKxqP0zZ20KBB3qxZM3/yySe9R48eftZZZ3nLli19+vTp/s4773iLFi38lltuiTozmE73vduskn4SiUTK/8ZBaK0/7Av87//+r5cvX95///vf+xtvvOH33HOP5+bm+p///Oe0dzGRLdCsWTN/+eWX3d19zpw5npmZ6c8++2zR+LPPPutt27aNKi9FxYoVvV+/fv78888X/YwYMcIzMjL83nvvLbovDhKJRNGLqEuXLn7TTTeljN97773eoUOHKNKKKVeunK9atcrd3Rs1alRssuqjjz7yvLy8KNKKadCggY8aNarU8VdffdUPP/zwNBaV7re//e0+fy6++OLYrPRDau3YsWPRTkhhYaE/+OCDXrFiRR8/fry7x2vHL6T164/XWaeeeqpfdtllKeO//vWv/eSTT44irZi8vDx/77333H3v451IJPytt94qGp8+fbrXq1cvqrwUIa2zzjnnnH3+nHzyybF5bbVq1cqfeOKJotujRo3y3NzcotdXnNYDIbUeccQR/vrrr7u7+7JlyzyZTPqrr75aND5q1Chv2bJlVHkp2M8qe6F0uv/r8Z87d64PGjTIq1ev7tnZ2f6Tn/zEx40b54WFhVEnFgmptVGjRj5u3Lii20uWLPEaNWr47t273d39V7/6lZ966qlR5aU46qijfOjQoaWOP/30096kSZM0FpWuWbNmKevSWbNmef369Yse+/PPP9/POeecqPJShLRcQ2oN6ViL/RaNkFp5302D/SyNww47zD/++GN3d9+0aZMnEgl/9913i8Znz57ttWvXjqguVUj7WbVr1/Y333yz1PE333zTa9Wqlcai0rVu3XqfP82aNYvNdqtFixZF7xG67/0SyWGHHea33367u8drG3v44YcXfWnoq6++8kQiUbQdc3cfO3asH3300VHlFQml0929Xr163q9fP588ebJPnTrVp06d6lOmTPGMjAwfMWJE0X1xEFLrj/cFTjzxRH/ooYdSxocNG+atW7dOexcT2QIVKlTw1atXF90uV66cf/rpp0W3ly1b5lWrVo0irZhly5YVfdNu69atRfdnZmb6ggULIiwr7scvolq1avlHH32UMr548eLYLNemTZv62LFj3d29cePGRRNEP/j444+9cuXKUaQVU758eV+4cGGp4wsWLPAKFSqksah0yWTS27Rp4z169Cjxp127drHZQQmptXLlyr58+fKU+15++WXPzc31119/PVY7fiGtX3+8zsrLy/MPPvggZfzTTz/1mjVrRpFWTE5OTtGbAO7uWVlZ/sknnxTdXrFihefm5kaRVkxI66zMzEzv06ePX3rppSX+nHnmmbF5beXm5vqKFStS7ps8ebJXrFjRhw8fHqv1QEit5cuX988//zzl9qJFi4pur1ixwitVqhRFWjHsZ5W9UDrdUx9/d/cdO3b4K6+84qeccoonk0mvX79+0RtCUQupNScnx1euXFl0u7Cw0DMzM33NmjXuvvcbLRUrVoyoLlW5cuV88eLFpY4vXrzYy5cvn8ai0lWoUCFlubrv3eZ+9dVX7u7+4YcfxmZ9FdJyDak1pGMt9ls0QmrlfTcN9rM0KlWqVLTOKigo8MzMTJ87d27R+LJly2Lz2gppPysnJ8fnz59f6vi8efNi855LuXLlfODAgX7nnXeW+HPVVVfFZrtV0j7hJ5984rVr1/abbropVtvYcuXKpWy3cnJyfMmSJUW3V61a5Tk5OVGkpQil093922+/9bPPPttPOukk//LLL4vuj+P2NaTWRCJRdNaQmjVrpmwD3N2XL18eyXaAiWyBGjVqpLzJXr9+/ZQJgmXLlsVmQ+ruvnv3br/hhhv8yCOP9OnTp7t7fF9EU6ZM8Xnz5nnDhg195syZKeOLFy+OzXJ9+OGHvXnz5r5s2TJ/5JFHvGPHjkUThStWrPAePXr4ueeeG3HlXl27dvUBAwYUfWLxx/bs2eMDBgzwbt26RVBWXNOmTf3FF18sdfzjjz+OzQ5KSK2HHXZYsQNUd/e//OUvnpOT48OHD49Na0jr10Qi4cuXL/fNmzd748aNi0579oPly5fHZufv+OOPL/oGzrhx47xSpUr+yCOPFI0PHz48Nt9oCGmddeyxx6acMeDfxWk9UNKHLdzdp06d6hUrVvRbb72V1v9C7dq1U96w6NSpU8pBy6JFi2L1Bhv7WWUrlE731NOH/buVK1f6bbfdFpuzXYTU2qpVK//Tn/5UdHvSpEmek5NT9M2rxYsXx+bN4DZt2vjgwYNLHb/hhhu8TZs2aSwqXfPmzf21114ruj179mzPzs72PXv2uPve/cG4vBkc0nINqTWkYy32WzRCanXnfTcF9rM0OnTo4Lfddpu77z2l7A8Tgj8YMmRIbM6CF9J+Vt++fb1Xr16+bt26YmPr1q3z0047zfv16xdBWXFt27b1YcOGlToep23s4Ycf7tOmTSt2/4IFC7x27do+YMCA2LTWrVvXZ8+eXXT7wgsvTFkvfPrpp7G4zEQonT82bNgwr1u3rr/yyivuHs/t6w9CaE0kEj5y5Ej/5z//6fXr1/f3338/ZfzTTz+NZB8rM5orcx/cmjVrZvPnz7fmzZubmdkXX3yRMr548WJr1KhRBGUly8zMtAcffNB69+5t/fv3t4suusgSiUTUWSU65ZRTzN3NzOy9996zE044oWjs448/tgYNGkSVluL666+3zz//3Fq0aGFHHnmkrVq1ypo2bWqZmZm2Z88ea9Omjf3lL3+JOtPMzIYOHWq9e/e2OnXqWLdu3ax27dpmZpafn2/Tpk2z7Oxse+uttyKu3Ktdu3Y2e/Zsu/jii0scTyQSRc+PqIXU2qpVK5syZYq1bds25f4LLrjA3N0GDhwYUVlxoa1fmzZtamZm7m4fffSRtW7dumhswYIFVrdu3ajSUgwePNgGDhxojz/+uH3xxRf20ksv2a9//Wv78MMPLZlM2ujRo+3RRx+NOtPMwlpntW3b1ubMmWOXX355iePlypWLzXbrxBNPtPHjx1uHDh1S7u/evbu9/vrrdvrpp0dUVlxIrS1atLA5c+bYsccea2Z7911+7JNPPrGjjjoqirQSsZ91aHaa2T73SRo1amR33323DRkyJI1FpQup9eabb7aLL77YJk6caOXLl7fRo0fbr371q6JjralTp1rLli0jrtzrkUcesdNPP93efPNN69mzZ8r2ddKkSbZixQp74403Iq7c69prr7UrrrjCZs2aZeXLl7dnn33WLrnkEsvIyDAzsw8//LBoHyxqIS3XkFpDOtZiv0UjpFYz3ndTYD9L484777Szzz7bHnroIUsmkzZhwgS78sorbfLkyZZMJm3WrFn2yiuvRJ1pZmHtZz3zzDPWt29fy8vLs2OPPTZlG/vJJ59YixYtbOzYsRFX7tW5c2dbsmRJqeOVKlWybt26pbGodF26dLHRo0db165dU+5v0aKFTZo0yU466aSIyoo77rjjbNasWdamTRszs2Kvo1mzZhW91xmlUDp/7JprrrHu3btb//797fXXX486Z59Caf3xXMDkyZOtY8eORbdnzJhhRx55ZNqbEh6XvfuDyHvvvWe5ubnWqlWrEseHDRtmhYWFNmjQoPSGHYBvv/3WrrzySpsyZYrNmDHDjj766KiTiqxevTrldsWKFa1GjRpFt0eOHGlmZgMGDEhr174sWrTIxo4daytWrLDCwkLLy8uzzp07W8+ePWN10LJ161Z76aWXbMaMGbZ27VozM6tTp4517NjR+vfvb5UrV464cK+1a9fazp07rWHDhlGn7FdIrf/4xz9s2rRp9thjj5U4/sorr9if//xnmzJlSprLigtp/frOO++k3M7Ly0t5U/WJJ56wXbt22eDBg9OdVqL33nvPZsyYYR07drROnTrZwoUL7YEHHrDt27fbGWecEasPNISyztq5c6cVFBRYTk5O1Cn79c4779j7779vN998c4njU6ZMsZEjR9qIESPSXFZcSK1Lly61rKwsa9y4cYnjr7zyimVmZtp5552X5rLi2M/SCaHzrrvussGDBwexvgqp1cxs/Pjx9tJLL9nOnTutd+/eduWVVxaNffvtt2ZmKa+1KK1atcqGDx9e4vb16quvjtWHBYcPH56yXG+//XYrX768mZktW7bMCgoKrFmzZhFX7hXScg2lNaRjLfZbNEJq/Xe871a22M8qe6tWrbLZs2db27ZtrVGjRpafn29PP/20bd++3fr16xerycGQ9rMKCwttwoQJJW5je/XqZclkMuLC8MyfP99mz55tP//5z0sc//TTT+3vf/+73XHHHWkuK27Dhg2WTCatatWqJY6PHz/eKlSoYD169Ehr178LpbMku3btsptuusmmTJlio0ePLnUfIQ5Cav13Y8eOtaysLOvdu3da/y4T2QAAAAAAAAAAAACAWOHU4gDMzKygoKDodHxme0/Jt3PnTuvYsaNlZWVFWFbcnj17bMGCBSmfYGzRokXsOs1oVaFVI6TW0uzevdu+/vrr2Jzybl/27Nlja9asobWM0aoRUmso64FQOs3CevxpRQjy8/Nt586dQTz2tJaNtWvX2ocffpiyn92+fXurU6dOxGXF0arx7615eXl24oknBtEa0nKltWzQml7fffedzZ49Ozan7N4XWjVCaQ2l04xWlcha035V7kPE008/7aeccor/7Gc/84kTJ6aMrVu3zhs3bhxRWapdu3b54MGD/cgjj/QTTjjBn3vuuZTxtWvXejKZjKguFa0aa9as8c6dO3tGRoZ369bNN2zY4P369fNEIuGJRMKbNm3qa9asiTrT3d0LCgr81ltv9apVqxb1/fBTtWpVv+2227ygoCDqTHenVYVWjZBa92fu3LmxWb/uD60atGrQWvZC6XSnVSVuraEcw7qH07plyxa/6KKLvEGDBj5gwADfuXOn//KXv/REIuHJZNK7devmmzdvjjrT3WlV2bZtm1900UWekZHhmZmZXqtWLa9Vq5ZnZmZ6RkaGX3zxxf7dd99FnenutKrQqkGrRoityWQy9q37E7d9wn2hVSOU1lA63WlViaqViy8IPPnkkzZ48GBr1qyZlStXzvr27Wv3339/0XhBQUGx685E5d5777WRI0fa1Vdfbb169bLrrrvOrrrqqpTf8ZicfZ5WjRtvvNHc3f7xj39YXl6enX766bZlyxb74osvbNWqVXbYYYfZvffeG3WmmZnddNNN9qc//ckeeOABW7FihX333Xf23Xff2YoVK+zBBx+0P/3pT6VeeyzdaNWgVSOkVgAAUPZCOoYNqfWWW26x2bNn2/XXX2+ff/65nXfeeTZt2jR79913bcqUKbZ+/Xp78MEHo840M1pVfv3rX9vMmTPtjTfesB07dlh+fr7l5+fbjh07bNy4cTZz5kz79a9/HXWmmdGqQqsGrRohto4bNy72rQCA/zdcI1vgmGOOsVtvvdX69+9vZmbvv/++nX322Xb11VfbkCFDLD8/3+rWrWsFBQURl5odddRR9thjj9npp59uZmbLly+3Pn36WJcuXex///d/7ZtvvqH1vxBSa926dW306NHWoUMH27Bhg9WsWdPefvttO+WUU8zMbPLkyXbllVfaZ599FnHp3tMDvfDCC9a7d+8SxydMmGADBgyw/Pz8NJcVR6sGrRohtbZp02af499//70tXbo0FutXWjVo1aC17IXSaUarSkitIR3DhtTaoEEDe+GFF+ykk06yNWvWWP369W3MmDFFx4lvvPGG/e53v7PFixdHXEqrSrVq1eyNN96wTp06lTj+3nvv2emnn24bN25Mc1lxtGrQqkGrBq0a1atX3+d4QUGBbdu2LRb7LrRqhNIaSqcZrSpxbeUa2QIrV65M2Yh26tTJJk+ebD179rTdu3fbb37zm+ji/s1XX31lLVu2LLrdpEkTmzp1qp188sl2ySWX2EMPPRRhXSpaNTZu3Gj16tUzs70rqpycHGvYsGHReJMmTezrr7+OKi/F1q1brW7duqWO5+Xl2XfffZfGotLRqkGrRkitCxcutAsuuMAaN25c4vjXX39tS5cuTXNVyWjVoFWD1rIXSqcZrSohtYZ0DBtS6zfffGNNmjQxs70fIK5QoYI1bdq0aLxly5b2xRdfRJWXglaNwsJCy87OLnU8OzvbCgsL01hUOlo1aNWgVYNWjZ07d9o111xjxx57bInjq1evtrvuuivNVSWjVSOU1lA6zWhViW1r2k9mfgg4/PDDfdq0acXuX7BggdeuXdsHDBgQm3PeN27cuNg1xdzdv/rqK2/atKmfeuqptP4XQmpt0KCBf/jhh0W3b7zxRv/222+Lbs+dO9dr1qwZRVoxffv29V69evm6deuKja1bt85PO+0079evXwRlxdGqQatGSK1t27b1YcOGlTr+8ccfx2b9SqsGrRq0lr1QOt1pVQmpNaRj2JBa69at67Nnzy66feGFF3p+fn7R7U8//dSrVasWRVoxtGr079/fW7du7XPmzCk2NmfOHG/btq1fdNFFEZQVR6sGrRq0atCq0alTJ3/88cdLHY/T9XFp1QilNZROd1pV4trKNbIFunTpYqNHjy52f4sWLWzSpEk2fvz4CKpKdvLJJ9srr7xS7P66deva5MmTbeXKlRFUlYxWjVatWtkHH3x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+      "text/plain": [
+       "<Figure size 1980x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# order plates by acquisition name, or sort by median treated effect if preferred\n",
+    "plate_order = sorted(treated_norm[\"Metadata_Plate\"].unique())\n",
+    "\n",
+    "# collect data per plate\n",
+    "treated_data = [\n",
+    "    treated_norm.loc[treated_norm[\"Metadata_Plate\"] == plate, \"relative_to_control\"].values\n",
+    "    for plate in plate_order\n",
+    "]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(max(12, 0.45 * len(plate_order)), 6))\n",
+    "\n",
+    "# boxplots for treated wells\n",
+    "bp = ax.boxplot(\n",
+    "    treated_data,\n",
+    "    positions=np.arange(len(plate_order)),\n",
+    "    widths=0.6,\n",
+    "    patch_artist=True,\n",
+    "    showfliers=False,\n",
+    ")\n",
+    "\n",
+    "# style boxplots\n",
+    "for box in bp[\"boxes\"]:\n",
+    "    box.set_alpha(0.6)\n",
+    "\n",
+    "# jittered treated points\n",
+    "rng = np.random.default_rng(42)\n",
+    "for i, plate in enumerate(plate_order):\n",
+    "    vals = treated_norm.loc[\n",
+    "        treated_norm[\"Metadata_Plate\"] == plate, \"relative_to_control\"\n",
+    "    ].values\n",
+    "    x = rng.normal(i, 0.06, size=len(vals))\n",
+    "    ax.scatter(x, vals, alpha=0.35, s=14, label=\"Treated\" if i == 0 else None)\n",
+    "\n",
+    "# overlay control wells in red\n",
+    "for i, plate in enumerate(plate_order):\n",
+    "    vals = control_norm.loc[\n",
+    "        control_norm[\"Metadata_Plate\"] == plate, \"relative_to_control\"\n",
+    "    ].values\n",
+    "    x = rng.normal(i, 0.04, size=len(vals))\n",
+    "    ax.scatter(x, vals, alpha=0.9, s=28, marker=\"D\", label=\"Control wells\" if i == 0 else None)\n",
+    "\n",
+    "# horizontal reference line\n",
+    "ax.axhline(1.0, linestyle=\"--\", linewidth=1)\n",
+    "\n",
+    "ax.set_xticks(np.arange(len(plate_order)))\n",
+    "ax.set_xticklabels(plate_order, rotation=90)\n",
+    "ax.set_ylabel(\"Treatment well cell count / plate control median\")\n",
+    "ax.set_xlabel(\"Plate\")\n",
+    "ax.set_title(\"Per-plate distribution of treated well cell counts relative to plate-level controls\")\n",
+    "ax.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12e2f6e8",
+   "metadata": {},
+   "source": [
+    "## Read inference scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "43d3a86c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Metadata_treatment",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "fold_0_score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fold_1_score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fold_2_score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fold_3_score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "mean_score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "plate",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "platemap",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "Pathway",
+         "rawType": "str",
+         "type": "string"
+        }
+       ],
+       "ref": "d881d34a-48a3-4f66-b83c-7e75e9c2cbbc",
+       "rows": [
+        [
+         "0",
+         "UCD-0000253",
+         "1.405144898043835e-05",
+         "3.255575500088818e-07",
+         "3.6096343148901472e-06",
+         "4.423902129767936e-06",
+         "5.602635743776329e-06",
+         "CARD-CelIns-CX7_251210180001_sc_feature_selected",
+         "platemap_3",
+         "Neuronal Signaling"
+        ],
+        [
+         "1",
+         "UCD-0000253",
+         "0.10630568541382204",
+         "0.999926697246038",
+         "0.9234447051934638",
+         "0.9952903744311449",
+         "0.7562418655711173",
+         "CARD-CelIns-CX7_251210180001_sc_feature_selected",
+         "platemap_3",
+         "Neuronal Signaling"
+        ],
+        [
+         "2",
+         "UCD-0000253",
+         "0.00019862336552110477",
+         "0.10104494406971701",
+         "0.8204221581684658",
+         "0.009254381210928728",
+         "0.23273002670365814",
+         "CARD-CelIns-CX7_251210180001_sc_feature_selected",
+         "platemap_3",
+         "Neuronal Signaling"
+        ],
+        [
+         "3",
+         "UCD-0000253",
+         "0.00010897232190528963",
+         "0.003034445604985777",
+         "0.0011685837332062038",
+         "0.0003438957279956733",
+         "0.001163974347023236",
+         "CARD-CelIns-CX7_251210180001_sc_feature_selected",
+         "platemap_3",
+         "Neuronal Signaling"
+        ],
+        [
+         "4",
+         "UCD-0000253",
+         "0.9827479875444656",
+         "0.9973340765490463",
+         "0.9999463052826586",
+         "0.9986012231214456",
+         "0.994657398124404",
+         "CARD-CelIns-CX7_251210180001_sc_feature_selected",
+         "platemap_3",
+         "Neuronal Signaling"
+        ]
+       ],
+       "shape": {
+        "columns": 9,
+        "rows": 5
+       }
+      },
+      "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>Metadata_treatment</th>\n",
+       "      <th>fold_0_score</th>\n",
+       "      <th>fold_1_score</th>\n",
+       "      <th>fold_2_score</th>\n",
+       "      <th>fold_3_score</th>\n",
+       "      <th>mean_score</th>\n",
+       "      <th>plate</th>\n",
+       "      <th>platemap</th>\n",
+       "      <th>Pathway</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>0.000014</td>\n",
+       "      <td>3.255576e-07</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.000006</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001_sc_feature_selected</td>\n",
+       "      <td>platemap_3</td>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>0.106306</td>\n",
+       "      <td>9.999267e-01</td>\n",
+       "      <td>0.923445</td>\n",
+       "      <td>0.995290</td>\n",
+       "      <td>0.756242</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001_sc_feature_selected</td>\n",
+       "      <td>platemap_3</td>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>0.000199</td>\n",
+       "      <td>1.010449e-01</td>\n",
+       "      <td>0.820422</td>\n",
+       "      <td>0.009254</td>\n",
+       "      <td>0.232730</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001_sc_feature_selected</td>\n",
+       "      <td>platemap_3</td>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>0.000109</td>\n",
+       "      <td>3.034446e-03</td>\n",
+       "      <td>0.001169</td>\n",
+       "      <td>0.000344</td>\n",
+       "      <td>0.001164</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001_sc_feature_selected</td>\n",
+       "      <td>platemap_3</td>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>UCD-0000253</td>\n",
+       "      <td>0.982748</td>\n",
+       "      <td>9.973341e-01</td>\n",
+       "      <td>0.999946</td>\n",
+       "      <td>0.998601</td>\n",
+       "      <td>0.994657</td>\n",
+       "      <td>CARD-CelIns-CX7_251210180001_sc_feature_selected</td>\n",
+       "      <td>platemap_3</td>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Metadata_treatment  fold_0_score  fold_1_score  fold_2_score  fold_3_score  \\\n",
+       "0        UCD-0000253      0.000014  3.255576e-07      0.000004      0.000004   \n",
+       "1        UCD-0000253      0.106306  9.999267e-01      0.923445      0.995290   \n",
+       "2        UCD-0000253      0.000199  1.010449e-01      0.820422      0.009254   \n",
+       "3        UCD-0000253      0.000109  3.034446e-03      0.001169      0.000344   \n",
+       "4        UCD-0000253      0.982748  9.973341e-01      0.999946      0.998601   \n",
+       "\n",
+       "   mean_score                                             plate    platemap  \\\n",
+       "0    0.000006  CARD-CelIns-CX7_251210180001_sc_feature_selected  platemap_3   \n",
+       "1    0.756242  CARD-CelIns-CX7_251210180001_sc_feature_selected  platemap_3   \n",
+       "2    0.232730  CARD-CelIns-CX7_251210180001_sc_feature_selected  platemap_3   \n",
+       "3    0.001164  CARD-CelIns-CX7_251210180001_sc_feature_selected  platemap_3   \n",
+       "4    0.994657  CARD-CelIns-CX7_251210180001_sc_feature_selected  platemap_3   \n",
+       "\n",
+       "              Pathway  \n",
+       "0  Neuronal Signaling  \n",
+       "1  Neuronal Signaling  \n",
+       "2  Neuronal Signaling  \n",
+       "3  Neuronal Signaling  \n",
+       "4  Neuronal Signaling  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "logit_score_df = pd.read_parquet(logit_score_file)\n",
+    "logit_score_df = pd.merge(\n",
+    "    logit_score_df,\n",
+    "    treatment_well_df.loc[\n",
+    "        :, [\"treatment\", \"Pathway\"]\n",
+    "    ].rename(columns={\"treatment\": \"Metadata_treatment\"}).drop_duplicates(),\n",
+    "    on=\"Metadata_treatment\",\n",
+    "    how=\"left\",\n",
+    "    validate=\"many_to_one\",\n",
+    ")\n",
+    "logit_score_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6dea5bfb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_treatment_response_summary(\n",
+    "    score_df: pd.DataFrame,\n",
+    "    control_counts_df: pd.DataFrame,\n",
+    "    treated_counts_df: pd.DataFrame,\n",
+    ") -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n",
+    "    \"\"\"\n",
+    "    Returns:\n",
+    "        score_df_aug: single-cell score dataframe with standardized Metadata_Plate\n",
+    "        treated_counts_aug: treated well count dataframe with relative cell count\n",
+    "        summary_df: treatment-level merged summary of score + viability\n",
+    "    \"\"\"\n",
+    "    score_df_aug = score_df.copy()\n",
+    "    control_df = control_counts_df.copy()\n",
+    "    treated_df = treated_counts_df.copy()\n",
+    "\n",
+    "    # ------------------------------------------------------------\n",
+    "    # 1) Standardize plate column in score dataframe\n",
+    "    # ------------------------------------------------------------\n",
+    "    if \"Metadata_Plate\" not in score_df_aug.columns:\n",
+    "        if \"plate\" in score_df_aug.columns:\n",
+    "            score_df_aug[\"Metadata_Plate\"] = (\n",
+    "                score_df_aug[\"plate\"]\n",
+    "                .str.replace(\"_sc_feature_selected\", \"\", regex=False)\n",
+    "            )\n",
+    "        else:\n",
+    "            raise ValueError(\"score_df must contain either 'Metadata_Plate' or 'plate'.\")\n",
+    "\n",
+    "    # ------------------------------------------------------------\n",
+    "    # 2) Ensure mean_score exists\n",
+    "    # ------------------------------------------------------------\n",
+    "    if \"mean_score\" not in score_df_aug.columns:\n",
+    "        fold_cols = [c for c in score_df_aug.columns if c.startswith(\"fold_\") and c.endswith(\"_score\")]\n",
+    "        if len(fold_cols) == 0:\n",
+    "            raise ValueError(\"No 'mean_score' column found and no fold_*_score columns available.\")\n",
+    "        score_df_aug[\"mean_score\"] = score_df_aug[fold_cols].mean(axis=1)\n",
+    "\n",
+    "    # ------------------------------------------------------------\n",
+    "    # 3) Pathway map from score dataframe\n",
+    "    # ------------------------------------------------------------\n",
+    "    pathway_map = (\n",
+    "        score_df_aug[[\"Metadata_treatment\", \"Pathway\"]]\n",
+    "        .drop_duplicates()\n",
+    "        .copy()\n",
+    "    )\n",
+    "\n",
+    "    # ------------------------------------------------------------\n",
+    "    # 4) Plate-level control summary\n",
+    "    #    Use median DMSO count as robust plate control baseline\n",
+    "    # ------------------------------------------------------------\n",
+    "    control_summary = (\n",
+    "        control_df.groupby(\"Metadata_Plate\", as_index=False)\n",
+    "        .agg(\n",
+    "            control_mean=(\"row_count\", \"mean\"),\n",
+    "            control_median=(\"row_count\", \"median\"),\n",
+    "            control_std=(\"row_count\", \"std\"),\n",
+    "            control_n=(\"row_count\", \"size\"),\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # ------------------------------------------------------------\n",
+    "    # 5) Add relative cell count to treated wells\n",
+    "    # ------------------------------------------------------------\n",
+    "    treated_counts_aug = (\n",
+    "        treated_df.merge(control_summary, on=\"Metadata_Plate\", how=\"left\")\n",
+    "                  .merge(pathway_map, on=\"Metadata_treatment\", how=\"left\")\n",
+    "    )\n",
+    "\n",
+    "    treated_counts_aug[\"relative_cell_count\"] = (\n",
+    "        treated_counts_aug[\"row_count\"] / treated_counts_aug[\"control_median\"]\n",
+    "    )\n",
+    "\n",
+    "    # ------------------------------------------------------------\n",
+    "    # 6) Score summary per treatment\n",
+    "    # ------------------------------------------------------------\n",
+    "    score_summary = (\n",
+    "        score_df_aug.groupby([\"Pathway\", \"Metadata_treatment\"], as_index=False)\n",
+    "        .agg(\n",
+    "            mean_score=(\"mean_score\", \"mean\"),\n",
+    "            median_score=(\"mean_score\", \"median\"),\n",
+    "            score_std=(\"mean_score\", \"std\"),\n",
+    "            n_scored_cells=(\"mean_score\", \"size\"),\n",
+    "            n_score_plates=(\"Metadata_Plate\", \"nunique\"),\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # ------------------------------------------------------------\n",
+    "    # 7) Viability summary per treatment\n",
+    "    # ------------------------------------------------------------\n",
+    "    viability_summary = (\n",
+    "        treated_counts_aug.groupby([\"Pathway\", \"Metadata_treatment\"], as_index=False)\n",
+    "        .agg(\n",
+    "            mean_cell_count=(\"row_count\", \"mean\"),\n",
+    "            median_cell_count=(\"row_count\", \"median\"),\n",
+    "            mean_relative_cell_count=(\"relative_cell_count\", \"mean\"),\n",
+    "            median_relative_cell_count=(\"relative_cell_count\", \"median\"),\n",
+    "            viability_std=(\"relative_cell_count\", \"std\"),\n",
+    "            n_treatment_wells=(\"row_count\", \"size\"),\n",
+    "            n_viability_plates=(\"Metadata_Plate\", \"nunique\"),\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # ------------------------------------------------------------\n",
+    "    # 8) Merge summaries\n",
+    "    # ------------------------------------------------------------\n",
+    "    summary_df = score_summary.merge(\n",
+    "        viability_summary,\n",
+    "        on=[\"Pathway\", \"Metadata_treatment\"],\n",
+    "        how=\"left\",\n",
+    "    )\n",
+    "\n",
+    "    return score_df_aug, treated_counts_aug, summary_df\n",
+    "\n",
+    "\n",
+    "def plot_treatment_score_distributions_by_pathway(\n",
+    "    score_df_aug: pd.DataFrame,\n",
+    "    summary_df: pd.DataFrame,\n",
+    "    pathway_order: list[str] | None = None,\n",
+    "    max_treatments_per_pathway: int | None = None,\n",
+    "    min_scored_cells: int = 0,\n",
+    "    annotate_relative_count: bool = True,\n",
+    "    annotate_raw_count: bool = True,\n",
+    "    score_col: str = \"mean_score\",\n",
+    "    figsize_width: float = 13,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Plot single-cell score distributions per treatment, paneled by pathway.\n",
+    "    Treatments are ranked by mean score within each pathway.\n",
+    "    Right-side annotations display viability information.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Filter on minimum scored cells if desired\n",
+    "    summary_plot = summary_df.copy()\n",
+    "    summary_plot = summary_plot[summary_plot[\"n_scored_cells\"] >= min_scored_cells].copy()\n",
+    "\n",
+    "    # Order pathways\n",
+    "    if pathway_order is None:\n",
+    "        pathway_order = (\n",
+    "            summary_plot[\"Pathway\"]\n",
+    "            .dropna()\n",
+    "            .sort_values()\n",
+    "            .unique()\n",
+    "            .tolist()\n",
+    "        )\n",
+    "\n",
+    "    # Optionally keep only top treatments per pathway\n",
+    "    if max_treatments_per_pathway is not None:\n",
+    "        keep_rows = []\n",
+    "        for pathway in pathway_order:\n",
+    "            sub = (\n",
+    "                summary_plot.loc[summary_plot[\"Pathway\"] == pathway]\n",
+    "                .sort_values(\"mean_score\", ascending=False)\n",
+    "                .head(max_treatments_per_pathway)\n",
+    "            )\n",
+    "            keep_rows.append(sub)\n",
+    "        summary_plot = pd.concat(keep_rows, axis=0, ignore_index=True)\n",
+    "\n",
+    "    # Keep only matching scores\n",
+    "    keep_keys = summary_plot[[\"Pathway\", \"Metadata_treatment\"]].drop_duplicates()\n",
+    "    score_plot = score_df_aug.merge(\n",
+    "        keep_keys,\n",
+    "        on=[\"Pathway\", \"Metadata_treatment\"],\n",
+    "        how=\"inner\",\n",
+    "    )\n",
+    "\n",
+    "    # Dynamic axis heights based on number of treatments in each pathway\n",
+    "    n_treatments_per_pathway = []\n",
+    "    valid_pathways = []\n",
+    "    for pathway in pathway_order:\n",
+    "        n_treat = summary_plot.loc[summary_plot[\"Pathway\"] == pathway, \"Metadata_treatment\"].nunique()\n",
+    "        if n_treat > 0:\n",
+    "            n_treatments_per_pathway.append(n_treat)\n",
+    "            valid_pathways.append(pathway)\n",
+    "\n",
+    "    if len(valid_pathways) == 0:\n",
+    "        raise ValueError(\"No pathways/treatments remain after filtering.\")\n",
+    "\n",
+    "    total_height = max(4, 0.42 * sum(n_treatments_per_pathway) + 0.6 * len(valid_pathways))\n",
+    "    fig, axes = plt.subplots(\n",
+    "        nrows=len(valid_pathways),\n",
+    "        ncols=1,\n",
+    "        figsize=(figsize_width, total_height),\n",
+    "        gridspec_kw={\"height_ratios\": n_treatments_per_pathway},\n",
+    "        sharex=True,\n",
+    "    )\n",
+    "\n",
+    "    if len(valid_pathways) == 1:\n",
+    "        axes = [axes]\n",
+    "\n",
+    "    for ax, pathway in zip(axes, valid_pathways):\n",
+    "        sum_sub = (\n",
+    "            summary_plot.loc[summary_plot[\"Pathway\"] == pathway]\n",
+    "            .sort_values(\"mean_score\", ascending=False)\n",
+    "            .copy()\n",
+    "        )\n",
+    "\n",
+    "        order = sum_sub[\"Metadata_treatment\"].tolist()\n",
+    "\n",
+    "        sc_sub = score_plot.loc[\n",
+    "            (score_plot[\"Pathway\"] == pathway)\n",
+    "            & (score_plot[\"Metadata_treatment\"].isin(order))\n",
+    "        ].copy()\n",
+    "\n",
+    "        # Horizontal violin plot of single-cell scores\n",
+    "        sns.violinplot(\n",
+    "            data=sc_sub,\n",
+    "            y=\"Metadata_treatment\",\n",
+    "            x=score_col,\n",
+    "            order=order,\n",
+    "            orient=\"h\",\n",
+    "            cut=0,\n",
+    "            inner=None,\n",
+    "            linewidth=0.8,\n",
+    "            ax=ax,\n",
+    "        )\n",
+    "\n",
+    "        # Overlay treatment mean score\n",
+    "        sum_sub_indexed = sum_sub.set_index(\"Metadata_treatment\").loc[order].reset_index()\n",
+    "        y_positions = np.arange(len(order))\n",
+    "        ax.scatter(\n",
+    "            sum_sub_indexed[\"mean_score\"],\n",
+    "            y_positions,\n",
+    "            color=\"black\",\n",
+    "            s=28,\n",
+    "            zorder=3,\n",
+    "            label=\"Treatment mean\" if ax is axes[0] else None,\n",
+    "        )\n",
+    "\n",
+    "        # Reference line at 0.5\n",
+    "        ax.axvline(0.5, linestyle=\"--\", linewidth=1)\n",
+    "\n",
+    "        # Right-side annotations\n",
+    "        trans = blended_transform_factory(ax.transAxes, ax.transData)\n",
+    "\n",
+    "        header_parts = []\n",
+    "        if annotate_relative_count:\n",
+    "            header_parts.append(\"Mean rel. count\")\n",
+    "        if annotate_raw_count:\n",
+    "            header_parts.append(\"Mean raw count\")\n",
+    "        header_text = \" | \".join(header_parts)\n",
+    "\n",
+    "        ax.text(\n",
+    "            1.01,\n",
+    "            1.02,\n",
+    "            header_text,\n",
+    "            transform=ax.transAxes,\n",
+    "            ha=\"left\",\n",
+    "            va=\"bottom\",\n",
+    "            fontsize=10,\n",
+    "            fontweight=\"bold\",\n",
+    "        )\n",
+    "\n",
+    "        for y, row in zip(y_positions, sum_sub_indexed.itertuples(index=False)):\n",
+    "            parts = []\n",
+    "            if annotate_relative_count:\n",
+    "                if pd.notna(row.mean_relative_cell_count):\n",
+    "                    parts.append(f\"{row.mean_relative_cell_count:.2f}x\")\n",
+    "                else:\n",
+    "                    parts.append(\"NA\")\n",
+    "            if annotate_raw_count:\n",
+    "                if pd.notna(row.mean_cell_count):\n",
+    "                    parts.append(f\"{row.mean_cell_count:.0f}\")\n",
+    "                else:\n",
+    "                    parts.append(\"NA\")\n",
+    "\n",
+    "            label = \" | \".join(parts)\n",
+    "\n",
+    "            ax.text(\n",
+    "                1.01,\n",
+    "                y,\n",
+    "                label,\n",
+    "                transform=trans,\n",
+    "                ha=\"left\",\n",
+    "                va=\"center\",\n",
+    "                fontsize=9,\n",
+    "            )\n",
+    "\n",
+    "        ax.set_title(f\"{pathway}  (n={len(order)} treatments)\", loc=\"left\")\n",
+    "        ax.set_ylabel(\"\")\n",
+    "        ax.set_xlabel(\"\")\n",
+    "        ax.set_xlim(0, 1)\n",
+    "\n",
+    "    axes[-1].set_xlabel(\"Single-cell morphology rescue score (0 = diseased, 1 = healthy)\")\n",
+    "    fig.suptitle(\n",
+    "        \"Single-cell morphology score distributions by treatment, ranked within pathway\\n\"\n",
+    "        \"Right-side annotation shows treatment-level viability / cell count\",\n",
+    "        y=0.995,\n",
+    "        fontsize=13,\n",
+    "    )\n",
+    "\n",
+    "    # Leave room on right for annotations\n",
+    "    fig.subplots_adjust(right=0.80, hspace=0.35, top=0.96)\n",
+    "\n",
+    "    # Single legend\n",
+    "    handles, labels = axes[0].get_legend_handles_labels()\n",
+    "    if len(handles) > 0:\n",
+    "        fig.legend(handles, labels, loc=\"upper right\", bbox_to_anchor=(0.98, 0.985))\n",
+    "\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0d7078fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x21288 with 23 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "score_df_aug, treated_counts_aug, summary_df = build_treatment_response_summary(\n",
+    "    score_df=logit_score_df,\n",
+    "    control_counts_df=control_cell_count_df,\n",
+    "    treated_counts_df=cell_count_df,\n",
+    ")\n",
+    "\n",
+    "plot_treatment_score_distributions_by_pathway(\n",
+    "    score_df_aug=score_df_aug,\n",
+    "    summary_df=summary_df,\n",
+    "    max_treatments_per_pathway=None,\n",
+    "    min_scored_cells=0,\n",
+    "    annotate_relative_count=True,\n",
+    "    annotate_raw_count=True,\n",
+    "    figsize_width=8\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "b0f06850",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summary_df.to_parquet(output_dir / \"logit_inference_summary.parquet\", index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cfret_ml",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/3a.train_dmso_logistic_regression_models/train_platemapwise_model/5.hit_calling.ipynb b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/5.hit_calling.ipynb
new file mode 100644
index 0000000..310966f
--- /dev/null
+++ b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/5.hit_calling.ipynb
@@ -0,0 +1,1973 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3c7330e8",
+   "metadata": {},
+   "source": [
+    "## Hit calling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "146dd69b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pathlib\n",
+    "\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "267e4b59",
+   "metadata": {},
+   "source": [
+    "## Pathing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1dc7f71d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(551, 17)\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>Pathway</th>\n",
+       "      <th>mean_logit_score</th>\n",
+       "      <th>median_score</th>\n",
+       "      <th>score_std</th>\n",
+       "      <th>n_scored_cells</th>\n",
+       "      <th>n_score_plates</th>\n",
+       "      <th>mean_cell_count</th>\n",
+       "      <th>median_cell_count</th>\n",
+       "      <th>mean_relative_cell_count</th>\n",
+       "      <th>median_relative_cell_count</th>\n",
+       "      <th>viability_std</th>\n",
+       "      <th>n_treatment_wells</th>\n",
+       "      <th>n_viability_plates</th>\n",
+       "      <th>mean_average_precision</th>\n",
+       "      <th>mean_normalized_average_precision</th>\n",
+       "      <th>corrected_p_value</th>\n",
+       "      <th>-log10(p-value)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metadata_treatment</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>UCD-0000253</th>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "      <td>0.594624</td>\n",
+       "      <td>0.671355</td>\n",
+       "      <td>0.363558</td>\n",
+       "      <td>715.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>178.75</td>\n",
+       "      <td>176.0</td>\n",
+       "      <td>0.859845</td>\n",
+       "      <td>0.825110</td>\n",
+       "      <td>0.179958</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.002764</td>\n",
+       "      <td>2.558445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0000872</th>\n",
+       "      <td>Metabolism</td>\n",
+       "      <td>0.999833</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000698</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.044441</td>\n",
+       "      <td>0.041355</td>\n",
+       "      <td>0.041632</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.002764</td>\n",
+       "      <td>2.558445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0159319</th>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "      <td>0.837786</td>\n",
+       "      <td>0.988373</td>\n",
+       "      <td>0.249785</td>\n",
+       "      <td>597.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>149.25</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>0.715468</td>\n",
+       "      <td>0.747094</td>\n",
+       "      <td>0.116341</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.002764</td>\n",
+       "      <td>2.558445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0000730</th>\n",
+       "      <td>Metabolism</td>\n",
+       "      <td>0.984177</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.113969</td>\n",
+       "      <td>316.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>79.00</td>\n",
+       "      <td>88.5</td>\n",
+       "      <td>0.389627</td>\n",
+       "      <td>0.412376</td>\n",
+       "      <td>0.280501</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.002764</td>\n",
+       "      <td>2.558445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0018569</th>\n",
+       "      <td>Endocrinology &amp; Hormones</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     Pathway  mean_logit_score  median_score  \\\n",
+       "Metadata_treatment                                                             \n",
+       "UCD-0000253               Neuronal Signaling          0.594624      0.671355   \n",
+       "UCD-0000872                       Metabolism          0.999833      1.000000   \n",
+       "UCD-0159319               Neuronal Signaling          0.837786      0.988373   \n",
+       "UCD-0000730                       Metabolism          0.984177      1.000000   \n",
+       "UCD-0018569         Endocrinology & Hormones               NaN           NaN   \n",
+       "\n",
+       "                    score_std  n_scored_cells  n_score_plates  \\\n",
+       "Metadata_treatment                                              \n",
+       "UCD-0000253          0.363558           715.0             4.0   \n",
+       "UCD-0000872          0.000698            36.0             4.0   \n",
+       "UCD-0159319          0.249785           597.0             4.0   \n",
+       "UCD-0000730          0.113969           316.0             4.0   \n",
+       "UCD-0018569               NaN             NaN             NaN   \n",
+       "\n",
+       "                    mean_cell_count  median_cell_count  \\\n",
+       "Metadata_treatment                                       \n",
+       "UCD-0000253                  178.75              176.0   \n",
+       "UCD-0000872                    9.00                9.0   \n",
+       "UCD-0159319                  149.25              153.0   \n",
+       "UCD-0000730                   79.00               88.5   \n",
+       "UCD-0018569                     NaN                NaN   \n",
+       "\n",
+       "                    mean_relative_cell_count  median_relative_cell_count  \\\n",
+       "Metadata_treatment                                                         \n",
+       "UCD-0000253                         0.859845                    0.825110   \n",
+       "UCD-0000872                         0.044441                    0.041355   \n",
+       "UCD-0159319                         0.715468                    0.747094   \n",
+       "UCD-0000730                         0.389627                    0.412376   \n",
+       "UCD-0018569                              NaN                         NaN   \n",
+       "\n",
+       "                    viability_std  n_treatment_wells  n_viability_plates  \\\n",
+       "Metadata_treatment                                                         \n",
+       "UCD-0000253              0.179958                4.0                 4.0   \n",
+       "UCD-0000872              0.041632                4.0                 4.0   \n",
+       "UCD-0159319              0.116341                4.0                 4.0   \n",
+       "UCD-0000730              0.280501                4.0                 4.0   \n",
+       "UCD-0018569                   NaN                NaN                 NaN   \n",
+       "\n",
+       "                    mean_average_precision  mean_normalized_average_precision  \\\n",
+       "Metadata_treatment                                                              \n",
+       "UCD-0000253                            1.0                                1.0   \n",
+       "UCD-0000872                            1.0                                1.0   \n",
+       "UCD-0159319                            1.0                                1.0   \n",
+       "UCD-0000730                            1.0                                1.0   \n",
+       "UCD-0018569                            NaN                                NaN   \n",
+       "\n",
+       "                    corrected_p_value  -log10(p-value)  \n",
+       "Metadata_treatment                                      \n",
+       "UCD-0000253                  0.002764         2.558445  \n",
+       "UCD-0000872                  0.002764         2.558445  \n",
+       "UCD-0159319                  0.002764         2.558445  \n",
+       "UCD-0000730                  0.002764         2.558445  \n",
+       "UCD-0018569                       NaN              NaN  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input_dir = pathlib.Path(\"inference_results\")\n",
+    "\n",
+    "output_file = input_dir / \"hit_call_table.csv\"\n",
+    "\n",
+    "# Load summary\n",
+    "hit_summary_df = pd.read_csv(\n",
+    "    input_dir / \"hit_summary.csv\"\n",
+    "    ).rename(\n",
+    "    columns={\n",
+    "        \"mean_score\": \"mean_logit_score\",\n",
+    "    }\n",
+    ").set_index(\"Metadata_treatment\")\n",
+    "\n",
+    "print(hit_summary_df.shape)\n",
+    "hit_summary_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e6a68d72",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of treatments screened: 551\n"
+     ]
+    }
+   ],
+   "source": [
+    "treatment_well_file = input_dir / \"treatment_well.csv\"\n",
+    "\n",
+    "treatment_well_df = pd.read_csv(treatment_well_file)\n",
+    "\n",
+    "all_treatments = treatment_well_df['treatment'].unique()\n",
+    "\n",
+    "print(f\"Number of treatments screened: {len(all_treatments)}\")\n",
+    "\n",
+    "treatment_pathway_df = treatment_well_df.loc[\n",
+    "    :,[\"treatment\", \"Pathway\"]\n",
+    "].copy().drop_duplicates().rename(columns={\"treatment\": \"Metadata_treatment\"}).set_index(\"Metadata_treatment\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "61a4cb96",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of treatments with profiles: 450\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>Pathway</th>\n",
+       "      <th>mean_logit_score</th>\n",
+       "      <th>mean_relative_cell_count</th>\n",
+       "      <th>mean_average_precision</th>\n",
+       "      <th>corrected_p_value</th>\n",
+       "      <th>-log10(p-value)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metadata_treatment</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>UCD-0000253</th>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "      <td>0.594624</td>\n",
+       "      <td>0.859845</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.002764</td>\n",
+       "      <td>2.558445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0000872</th>\n",
+       "      <td>Metabolism</td>\n",
+       "      <td>0.999833</td>\n",
+       "      <td>0.044441</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.002764</td>\n",
+       "      <td>2.558445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0159319</th>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "      <td>0.837786</td>\n",
+       "      <td>0.715468</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.002764</td>\n",
+       "      <td>2.558445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0000730</th>\n",
+       "      <td>Metabolism</td>\n",
+       "      <td>0.984177</td>\n",
+       "      <td>0.389627</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.002764</td>\n",
+       "      <td>2.558445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0018569</th>\n",
+       "      <td>Endocrinology &amp; Hormones</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     Pathway  mean_logit_score  \\\n",
+       "Metadata_treatment                                               \n",
+       "UCD-0000253               Neuronal Signaling          0.594624   \n",
+       "UCD-0000872                       Metabolism          0.999833   \n",
+       "UCD-0159319               Neuronal Signaling          0.837786   \n",
+       "UCD-0000730                       Metabolism          0.984177   \n",
+       "UCD-0018569         Endocrinology & Hormones               NaN   \n",
+       "\n",
+       "                    mean_relative_cell_count  mean_average_precision  \\\n",
+       "Metadata_treatment                                                     \n",
+       "UCD-0000253                         0.859845                     1.0   \n",
+       "UCD-0000872                         0.044441                     1.0   \n",
+       "UCD-0159319                         0.715468                     1.0   \n",
+       "UCD-0000730                         0.389627                     1.0   \n",
+       "UCD-0018569                              NaN                     NaN   \n",
+       "\n",
+       "                    corrected_p_value  -log10(p-value)  \n",
+       "Metadata_treatment                                      \n",
+       "UCD-0000253                  0.002764         2.558445  \n",
+       "UCD-0000872                  0.002764         2.558445  \n",
+       "UCD-0159319                  0.002764         2.558445  \n",
+       "UCD-0000730                  0.002764         2.558445  \n",
+       "UCD-0018569                       NaN              NaN  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for_hit_calling_df = (\n",
+    "    hit_summary_df\n",
+    "    .loc[:, [\"Pathway\", \"mean_logit_score\",\t\"mean_relative_cell_count\",\t\"mean_average_precision\", \"corrected_p_value\", \"-log10(p-value)\"]]\n",
+    ")\n",
+    "\n",
+    "print(f\"Number of treatments with profiles: {for_hit_calling_df.dropna().shape[0]}\")\n",
+    "for_hit_calling_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4cab15ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = for_hit_calling_df.plot.scatter(\n",
+    "    x=\"mean_average_precision\",\n",
+    "    y=\"mean_logit_score\",\n",
+    "    alpha=0.7,\n",
+    "    figsize=(8, 6)\n",
+    ")\n",
+    "\n",
+    "ax.set_xlabel(\"Mean Average Precision\")\n",
+    "ax.set_ylabel(\"Mean Logit Score\")\n",
+    "ax.set_title(\"Mean Logit Score vs Mean Average Precision\")\n",
+    "ax.grid(alpha=0.3)\n",
+    "ax.figure.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b6f400c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of hits identified: 9\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>Pathway</th>\n",
+       "      <th>mean_logit_score</th>\n",
+       "      <th>mean_relative_cell_count</th>\n",
+       "      <th>mean_average_precision</th>\n",
+       "      <th>corrected_p_value</th>\n",
+       "      <th>-log10(p-value)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metadata_treatment</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>UCD-0001921</th>\n",
+       "      <td>Epigenetics</td>\n",
+       "      <td>0.969737</td>\n",
+       "      <td>0.818493</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.002764</td>\n",
+       "      <td>2.558445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0001812</th>\n",
+       "      <td>Others</td>\n",
+       "      <td>0.977959</td>\n",
+       "      <td>0.811553</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.002764</td>\n",
+       "      <td>2.558445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0159268</th>\n",
+       "      <td>Others</td>\n",
+       "      <td>0.961089</td>\n",
+       "      <td>0.996066</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.002764</td>\n",
+       "      <td>2.558445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0159406</th>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "      <td>0.908588</td>\n",
+       "      <td>0.911870</td>\n",
+       "      <td>0.889881</td>\n",
+       "      <td>0.010298</td>\n",
+       "      <td>1.987236</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0000841</th>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "      <td>0.912086</td>\n",
+       "      <td>0.944885</td>\n",
+       "      <td>0.895833</td>\n",
+       "      <td>0.010298</td>\n",
+       "      <td>1.987236</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0159442</th>\n",
+       "      <td>Epigenetics</td>\n",
+       "      <td>0.909725</td>\n",
+       "      <td>1.018165</td>\n",
+       "      <td>0.768353</td>\n",
+       "      <td>0.025948</td>\n",
+       "      <td>1.585898</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0001419</th>\n",
+       "      <td>Transmembrane Transporters</td>\n",
+       "      <td>0.991753</td>\n",
+       "      <td>0.896534</td>\n",
+       "      <td>0.723653</td>\n",
+       "      <td>0.041758</td>\n",
+       "      <td>1.379258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0159486</th>\n",
+       "      <td>Others</td>\n",
+       "      <td>0.944601</td>\n",
+       "      <td>0.904964</td>\n",
+       "      <td>0.745833</td>\n",
+       "      <td>0.040348</td>\n",
+       "      <td>1.394181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0159487</th>\n",
+       "      <td>Others</td>\n",
+       "      <td>0.959437</td>\n",
+       "      <td>0.816326</td>\n",
+       "      <td>0.680556</td>\n",
+       "      <td>0.048928</td>\n",
+       "      <td>1.310439</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                       Pathway  mean_logit_score  \\\n",
+       "Metadata_treatment                                                 \n",
+       "UCD-0001921                        Epigenetics          0.969737   \n",
+       "UCD-0001812                             Others          0.977959   \n",
+       "UCD-0159268                             Others          0.961089   \n",
+       "UCD-0159406                 Neuronal Signaling          0.908588   \n",
+       "UCD-0000841                 Neuronal Signaling          0.912086   \n",
+       "UCD-0159442                        Epigenetics          0.909725   \n",
+       "UCD-0001419         Transmembrane Transporters          0.991753   \n",
+       "UCD-0159486                             Others          0.944601   \n",
+       "UCD-0159487                             Others          0.959437   \n",
+       "\n",
+       "                    mean_relative_cell_count  mean_average_precision  \\\n",
+       "Metadata_treatment                                                     \n",
+       "UCD-0001921                         0.818493                1.000000   \n",
+       "UCD-0001812                         0.811553                1.000000   \n",
+       "UCD-0159268                         0.996066                1.000000   \n",
+       "UCD-0159406                         0.911870                0.889881   \n",
+       "UCD-0000841                         0.944885                0.895833   \n",
+       "UCD-0159442                         1.018165                0.768353   \n",
+       "UCD-0001419                         0.896534                0.723653   \n",
+       "UCD-0159486                         0.904964                0.745833   \n",
+       "UCD-0159487                         0.816326                0.680556   \n",
+       "\n",
+       "                    corrected_p_value  -log10(p-value)  \n",
+       "Metadata_treatment                                      \n",
+       "UCD-0001921                  0.002764         2.558445  \n",
+       "UCD-0001812                  0.002764         2.558445  \n",
+       "UCD-0159268                  0.002764         2.558445  \n",
+       "UCD-0159406                  0.010298         1.987236  \n",
+       "UCD-0000841                  0.010298         1.987236  \n",
+       "UCD-0159442                  0.025948         1.585898  \n",
+       "UCD-0001419                  0.041758         1.379258  \n",
+       "UCD-0159486                  0.040348         1.394181  \n",
+       "UCD-0159487                  0.048928         1.310439  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RELATIVE_COUNT_THRESHOLD = 0.8\n",
+    "LOGIT_THRESHOLD = 0.9\n",
+    "MAP_THRESHOLD = 0.05\n",
+    "\n",
+    "hits_df = for_hit_calling_df.loc[\n",
+    "    (for_hit_calling_df[\"mean_relative_cell_count\"] >= RELATIVE_COUNT_THRESHOLD)\n",
+    "    & (for_hit_calling_df[\"mean_logit_score\"] >= LOGIT_THRESHOLD)\n",
+    "    & (for_hit_calling_df[\"corrected_p_value\"] <= MAP_THRESHOLD)\n",
+    "].copy()\n",
+    "print(f\"Number of hits identified: {hits_df.shape[0]}\")\n",
+    "hits_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7be82285",
+   "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>Pathway</th>\n",
+       "      <th>mean_logit_score</th>\n",
+       "      <th>mean_relative_cell_count</th>\n",
+       "      <th>mean_average_precision</th>\n",
+       "      <th>corrected_p_value</th>\n",
+       "      <th>-log10(p-value)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metadata_treatment</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>UCD-0000253</th>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "      <td>0.594624</td>\n",
+       "      <td>0.859845</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.002764</td>\n",
+       "      <td>2.558445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0000872</th>\n",
+       "      <td>Metabolism</td>\n",
+       "      <td>0.999833</td>\n",
+       "      <td>0.044441</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.002764</td>\n",
+       "      <td>2.558445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0159319</th>\n",
+       "      <td>Neuronal Signaling</td>\n",
+       "      <td>0.837786</td>\n",
+       "      <td>0.715468</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.002764</td>\n",
+       "      <td>2.558445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0000730</th>\n",
+       "      <td>Metabolism</td>\n",
+       "      <td>0.984177</td>\n",
+       "      <td>0.389627</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.002764</td>\n",
+       "      <td>2.558445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UCD-0018569</th>\n",
+       "      <td>Endocrinology &amp; Hormones</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     Pathway  mean_logit_score  \\\n",
+       "Metadata_treatment                                               \n",
+       "UCD-0000253               Neuronal Signaling          0.594624   \n",
+       "UCD-0000872                       Metabolism          0.999833   \n",
+       "UCD-0159319               Neuronal Signaling          0.837786   \n",
+       "UCD-0000730                       Metabolism          0.984177   \n",
+       "UCD-0018569         Endocrinology & Hormones               NaN   \n",
+       "\n",
+       "                    mean_relative_cell_count  mean_average_precision  \\\n",
+       "Metadata_treatment                                                     \n",
+       "UCD-0000253                         0.859845                     1.0   \n",
+       "UCD-0000872                         0.044441                     1.0   \n",
+       "UCD-0159319                         0.715468                     1.0   \n",
+       "UCD-0000730                         0.389627                     1.0   \n",
+       "UCD-0018569                              NaN                     NaN   \n",
+       "\n",
+       "                    corrected_p_value  -log10(p-value)  \n",
+       "Metadata_treatment                                      \n",
+       "UCD-0000253                  0.002764         2.558445  \n",
+       "UCD-0000872                  0.002764         2.558445  \n",
+       "UCD-0159319                  0.002764         2.558445  \n",
+       "UCD-0000730                  0.002764         2.558445  \n",
+       "UCD-0018569                       NaN              NaN  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for_hit_calling_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2ab6ef83",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.path import Path\n",
+    "from matplotlib.patches import PathPatch, Rectangle, FancyArrowPatch\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "def plot_pathway_screening_flow(\n",
+    "    treatment_pathway_df,\n",
+    "    for_hit_calling_df,\n",
+    "    hits_df,\n",
+    "    pathway_col=\"Pathway\",\n",
+    "    stage_names=(\"All screened\", \"Profile obtained\", \"Final hits\"),\n",
+    "    min_count=1,\n",
+    "    top_n=None,\n",
+    "    figsize=(10, 7),\n",
+    "    cmap=\"tab20\",\n",
+    "    alpha=0.75,\n",
+    "    label_fontsize=10,\n",
+    "    title=\"Drug screening flow by pathway\",\n",
+    "    process_labels=(\"Image acquisition\", \"Hit calling\"),\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Plot an alluvial/Sankey-like flow map showing pathway composition across:\n",
+    "    all screened compounds -> profile obtained/hit-calling set -> final hits.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    treatment_pathway_df : pd.DataFrame\n",
+    "        Indexed by Metadata_treatment, contains pathway_col.\n",
+    "    for_hit_calling_df : pd.DataFrame\n",
+    "        Indexed by Metadata_treatment, contains pathway_col.\n",
+    "    hits_df : pd.DataFrame\n",
+    "        Indexed by Metadata_treatment, contains pathway_col.\n",
+    "    pathway_col : str\n",
+    "        Column containing pathway labels.\n",
+    "    stage_names : tuple[str]\n",
+    "        Names for the three stages.\n",
+    "    min_count : int\n",
+    "        Drop pathways whose total count across stages is below this.\n",
+    "    top_n : int or None\n",
+    "        If set, keep top_n pathways by total abundance and group the rest as \"Other\".\n",
+    "    process_labels : tuple[str, str]\n",
+    "        Labels for process arrows between stage 1->2 and 2->3.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def pathway_counts(df, stage):\n",
+    "        out = (\n",
+    "            df[[pathway_col]]\n",
+    "            .dropna()\n",
+    "            .assign(stage=stage)\n",
+    "            .groupby([\"stage\", pathway_col])\n",
+    "            .size()\n",
+    "            .rename(\"count\")\n",
+    "            .reset_index()\n",
+    "        )\n",
+    "        return out\n",
+    "\n",
+    "    def format_pathway_label(pathway):\n",
+    "        pathway = str(pathway)\n",
+    "        if \" \" in pathway:\n",
+    "            return pathway.replace(\" \", \"\\n\", 1)\n",
+    "        return pathway\n",
+    "\n",
+    "    counts = pd.concat(\n",
+    "        [\n",
+    "            pathway_counts(treatment_pathway_df, stage_names[0]),\n",
+    "            pathway_counts(for_hit_calling_df, stage_names[1]),\n",
+    "            pathway_counts(hits_df, stage_names[2]),\n",
+    "        ],\n",
+    "        ignore_index=True,\n",
+    "    )\n",
+    "\n",
+    "    total_by_pathway = counts.groupby(pathway_col)[\"count\"].sum().sort_values(ascending=False)\n",
+    "\n",
+    "    if top_n is not None and len(total_by_pathway) > top_n:\n",
+    "        keep = set(total_by_pathway.head(top_n).index)\n",
+    "        counts[pathway_col] = counts[pathway_col].where(\n",
+    "            counts[pathway_col].isin(keep),\n",
+    "            \"Other\"\n",
+    "        )\n",
+    "        counts = (\n",
+    "            counts.groupby([\"stage\", pathway_col], as_index=False)[\"count\"]\n",
+    "            .sum()\n",
+    "        )\n",
+    "\n",
+    "    total_by_pathway = counts.groupby(pathway_col)[\"count\"].sum()\n",
+    "    keep_pathways = total_by_pathway[total_by_pathway >= min_count].index\n",
+    "    counts = counts[counts[pathway_col].isin(keep_pathways)].copy()\n",
+    "\n",
+    "    pathways = (\n",
+    "        counts.groupby(pathway_col)[\"count\"]\n",
+    "        .sum()\n",
+    "        .sort_values(ascending=False)\n",
+    "        .index\n",
+    "        .tolist()\n",
+    "    )\n",
+    "\n",
+    "    full_index = pd.MultiIndex.from_product(\n",
+    "        [stage_names, pathways],\n",
+    "        names=[\"stage\", pathway_col]\n",
+    "    )\n",
+    "\n",
+    "    counts = (\n",
+    "        counts.set_index([\"stage\", pathway_col])\n",
+    "        .reindex(full_index, fill_value=0)\n",
+    "        .reset_index()\n",
+    "    )\n",
+    "\n",
+    "    stage_totals = counts.groupby(\"stage\")[\"count\"].transform(\"sum\")\n",
+    "    counts[\"prop\"] = counts[\"count\"] / stage_totals.replace(0, np.nan)\n",
+    "    counts[\"prop\"] = counts[\"prop\"].fillna(0)\n",
+    "\n",
+    "    gap = 0.01\n",
+    "    block_positions = {\n",
+    "        (stage, pathway): {\n",
+    "            \"y0\": 1.0,\n",
+    "            \"y1\": 1.0,\n",
+    "            \"ymid\": 1.0,\n",
+    "            \"height\": 0.0,\n",
+    "            \"count\": 0,\n",
+    "            \"prop\": 0.0,\n",
+    "        }\n",
+    "        for stage in stage_names\n",
+    "        for pathway in pathways\n",
+    "    }\n",
+    "\n",
+    "    for stage in stage_names:\n",
+    "        y_top = 1.0\n",
+    "        stage_df = counts[counts[\"stage\"] == stage].copy()\n",
+    "        stage_df_nonzero = stage_df[stage_df[\"count\"] > 0].copy()\n",
+    "        n_pathways = len(stage_df_nonzero)\n",
+    "        total_gap = gap * max(n_pathways - 1, 0)\n",
+    "        usable_height = max(1.0 - total_gap, 0.0)\n",
+    "\n",
+    "        for _, row in stage_df_nonzero.iterrows():\n",
+    "            pathway = row[pathway_col]\n",
+    "            h = row[\"prop\"] * usable_height\n",
+    "\n",
+    "            y1 = y_top\n",
+    "            y0 = y_top - h\n",
+    "\n",
+    "            block_positions[(stage, pathway)] = {\n",
+    "                \"y0\": y0,\n",
+    "                \"y1\": y1,\n",
+    "                \"ymid\": (y0 + y1) / 2,\n",
+    "                \"height\": h,\n",
+    "                \"count\": row[\"count\"],\n",
+    "                \"prop\": row[\"prop\"],\n",
+    "            }\n",
+    "\n",
+    "            y_top = y0 - gap\n",
+    "\n",
+    "    palette = sns.color_palette(cmap, n_colors=max(len(pathways), 3))\n",
+    "    color_map = {p: palette[i % len(palette)] for i, p in enumerate(pathways)}\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=figsize)\n",
+    "\n",
+    "    x_positions = {\n",
+    "        stage_names[0]: 0.0,\n",
+    "        stage_names[1]: 1.0,\n",
+    "        stage_names[2]: 2.0,\n",
+    "    }\n",
+    "\n",
+    "    node_width = 0.08\n",
+    "\n",
+    "    def draw_ribbon(x0, y0_low, y0_high, x1, y1_low, y1_high, color):\n",
+    "        dx = x1 - x0\n",
+    "        c0 = x0 + dx * 0.45\n",
+    "        c1 = x1 - dx * 0.45\n",
+    "\n",
+    "        verts = [\n",
+    "            (x0, y0_high),\n",
+    "            (c0, y0_high),\n",
+    "            (c1, y1_high),\n",
+    "            (x1, y1_high),\n",
+    "            (x1, y1_low),\n",
+    "            (c1, y1_low),\n",
+    "            (c0, y0_low),\n",
+    "            (x0, y0_low),\n",
+    "            (x0, y0_high),\n",
+    "        ]\n",
+    "\n",
+    "        codes = [\n",
+    "            Path.MOVETO,\n",
+    "            Path.CURVE4,\n",
+    "            Path.CURVE4,\n",
+    "            Path.CURVE4,\n",
+    "            Path.LINETO,\n",
+    "            Path.CURVE4,\n",
+    "            Path.CURVE4,\n",
+    "            Path.CURVE4,\n",
+    "            Path.CLOSEPOLY,\n",
+    "        ]\n",
+    "\n",
+    "        patch = PathPatch(\n",
+    "            Path(verts, codes),\n",
+    "            facecolor=color,\n",
+    "            edgecolor=\"none\",\n",
+    "            alpha=alpha,\n",
+    "        )\n",
+    "        ax.add_patch(patch)\n",
+    "\n",
+    "    for pathway in pathways:\n",
+    "        color = color_map[pathway]\n",
+    "\n",
+    "        for s0, s1 in zip(stage_names[:-1], stage_names[1:]):\n",
+    "            p0 = block_positions[(s0, pathway)]\n",
+    "            p1 = block_positions[(s1, pathway)]\n",
+    "\n",
+    "            if p0[\"height\"] <= 0 or p1[\"height\"] <= 0:\n",
+    "                continue\n",
+    "\n",
+    "            x0 = x_positions[s0] + node_width / 2\n",
+    "            x1 = x_positions[s1] - node_width / 2\n",
+    "\n",
+    "            draw_ribbon(\n",
+    "                x0,\n",
+    "                p0[\"y0\"],\n",
+    "                p0[\"y1\"],\n",
+    "                x1,\n",
+    "                p1[\"y0\"],\n",
+    "                p1[\"y1\"],\n",
+    "                color,\n",
+    "            )\n",
+    "\n",
+    "    for stage in stage_names:\n",
+    "        x = x_positions[stage]\n",
+    "        stage_total = counts.loc[counts[\"stage\"] == stage, \"count\"].sum()\n",
+    "\n",
+    "        for pathway in pathways:\n",
+    "            p = block_positions[(stage, pathway)]\n",
+    "            if p[\"height\"] <= 0:\n",
+    "                continue\n",
+    "\n",
+    "            rect = Rectangle(\n",
+    "                (x - node_width / 2, p[\"y0\"]),\n",
+    "                node_width,\n",
+    "                p[\"height\"],\n",
+    "                facecolor=color_map[pathway],\n",
+    "                edgecolor=\"white\",\n",
+    "                linewidth=0.5,\n",
+    "                alpha=0.95,\n",
+    "            )\n",
+    "            ax.add_patch(rect)\n",
+    "\n",
+    "            label_threshold = 0.0 if stage == stage_names[0] else 0.035\n",
+    "            if p[\"height\"] > label_threshold:\n",
+    "                pathway_text = format_pathway_label(pathway)\n",
+    "\n",
+    "                if stage == stage_names[0] and p[\"prop\"] < 0.045:\n",
+    "                    label = pathway_text\n",
+    "                    fontsize = max(label_fontsize - 1, 8)\n",
+    "                else:\n",
+    "                    label = f\"{pathway_text}\\n{int(p['count'])} ({p['prop']:.1%})\"\n",
+    "                    fontsize = label_fontsize\n",
+    "\n",
+    "                x_text = x - 0.14\n",
+    "\n",
+    "                ax.text(\n",
+    "                    x_text,\n",
+    "                    p[\"ymid\"],\n",
+    "                    label,\n",
+    "                    va=\"center\",\n",
+    "                    ha=\"right\",\n",
+    "                    fontsize=fontsize,\n",
+    "                    clip_on=False,\n",
+    "                )\n",
+    "\n",
+    "        ax.text(\n",
+    "            x,\n",
+    "            1.035,\n",
+    "            f\"{stage}\\nN={int(stage_total)}\",\n",
+    "            ha=\"center\",\n",
+    "            va=\"bottom\",\n",
+    "            fontsize=12,\n",
+    "            fontweight=\"bold\",\n",
+    "        )\n",
+    "\n",
+    "    process_y = 1.01\n",
+    "    process_text_y = 1.06\n",
+    "    stage_pairs = list(zip(stage_names[:-1], stage_names[1:]))\n",
+    "\n",
+    "    for i, (s0, s1) in enumerate(stage_pairs):\n",
+    "        x_start = x_positions[s0] + node_width / 2\n",
+    "        x_end = x_positions[s1] - node_width / 2\n",
+    "        process_arrow = FancyArrowPatch(\n",
+    "            (x_start, process_y),\n",
+    "            (x_end, process_y),\n",
+    "            arrowstyle=\"<->\",\n",
+    "            mutation_scale=12,\n",
+    "            linewidth=1.4,\n",
+    "            linestyle=\"--\",\n",
+    "            color=\"dimgray\",\n",
+    "        )\n",
+    "        ax.add_patch(process_arrow)\n",
+    "\n",
+    "        if i < len(process_labels):\n",
+    "            ax.text(\n",
+    "                (x_start + x_end) / 2,\n",
+    "                process_text_y,\n",
+    "                process_labels[i],\n",
+    "                ha=\"center\",\n",
+    "                va=\"bottom\",\n",
+    "                fontsize=10,\n",
+    "                color=\"dimgray\",\n",
+    "                fontweight=\"bold\",\n",
+    "            )\n",
+    "\n",
+    "    ax.set_xlim(-0.2, 2.12)\n",
+    "    ax.set_ylim(-0.01, 1.095)\n",
+    "    ax.axis(\"off\")\n",
+    "    ax.set_title(title, fontsize=14, fontweight=\"bold\", pad=6)\n",
+    "\n",
+    "    fig.subplots_adjust(left=0.03, right=0.99, bottom=0.015, top=0.965)\n",
+    "    return fig, ax, counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "417d93ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax, flow_counts = plot_pathway_screening_flow(\n",
+    "    treatment_pathway_df=treatment_pathway_df,\n",
+    "    for_hit_calling_df=for_hit_calling_df,\n",
+    "    hits_df=hits_df,\n",
+    "    pathway_col=\"Pathway\",\n",
+    "    top_n=50,\n",
+    "    min_count=1,\n",
+    "    figsize=(15, 15),\n",
+    "    process_labels=(\"Image acquisition & Segmentation\", \"Hit calling\"),\n",
+    "    title=\"Drug screening flow from screened compounds to final hits\"\n",
+    ")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "97cf174c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for_hit_calling_df['hit'] = False\n",
+    "for_hit_calling_df.loc[hits_df.index, 'hit'] = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "65ef8870",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.lines import Line2D\n",
+    "\n",
+    "def plot_treatment_hit_scatter(\n",
+    "    df,\n",
+    "    x_col=\"mean_average_precision\",\n",
+    "    y_col=\"mean_logit_score\",\n",
+    "    size_col=\"mean_relative_cell_count\",\n",
+    "    hit_col=\"hit\",\n",
+    "    color_col=\"Pathway\",\n",
+    "    label_col=None,\n",
+    "    ap_threshold=0.6,\n",
+    "    logit_threshold=0.9,\n",
+    "    figsize=(9, 6),\n",
+    "    size_min=30,\n",
+    "    size_max=450,\n",
+    "    log_size=True,\n",
+    "    color_by=True,\n",
+    "    cmap=\"tab20\",\n",
+    "    save_path=None,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Scatter plot for treatment-level hit prioritization.\n",
+    "\n",
+    "    x-axis: mean average precision\n",
+    "    y-axis: mean logit score\n",
+    "    dot size: log-scaled mean relative cell count\n",
+    "    marker/highlight: hit status\n",
+    "    optional color: categorical/continuous column, default Pathway\n",
+    "\n",
+    "    Target region:\n",
+    "        mean_average_precision <= ap_threshold\n",
+    "        mean_logit_score >= logit_threshold\n",
+    "    \"\"\"\n",
+    "\n",
+    "    plot_df = df.copy()\n",
+    "\n",
+    "    # Preserve treatment name from index\n",
+    "    if plot_df.index.name is not None:\n",
+    "        plot_df = plot_df.reset_index()\n",
+    "        treatment_col = df.index.name\n",
+    "    else:\n",
+    "        plot_df = plot_df.reset_index(names=\"Metadata_treatment\")\n",
+    "        treatment_col = \"Metadata_treatment\"\n",
+    "\n",
+    "    if label_col is None:\n",
+    "        label_col = treatment_col\n",
+    "\n",
+    "    required_cols = [x_col, y_col, size_col, hit_col]\n",
+    "    if color_by and color_col is not None and color_col in plot_df.columns:\n",
+    "        required_cols.append(color_col)\n",
+    "\n",
+    "    plot_df = plot_df.dropna(subset=required_cols).copy()\n",
+    "    plot_df[hit_col] = plot_df[hit_col].astype(bool)\n",
+    "\n",
+    "    # ------------------------------------------------------------------\n",
+    "    # Log-scale dot size using the full observed range\n",
+    "    # ------------------------------------------------------------------\n",
+    "    raw_size = plot_df[size_col].astype(float)\n",
+    "\n",
+    "    positive_min = raw_size[raw_size > 0].min()\n",
+    "    positive_max = raw_size[raw_size > 0].max()\n",
+    "\n",
+    "    if pd.isna(positive_min):\n",
+    "        raise ValueError(\n",
+    "            f\"`{size_col}` must contain at least one positive value for log-scaled sizing.\"\n",
+    "        )\n",
+    "\n",
+    "    # Clip zeros/negatives to smallest positive value only for visualization\n",
+    "    safe_size = raw_size.clip(lower=positive_min)\n",
+    "\n",
+    "    # Use log1p after rescaling by min to make very small values more separable\n",
+    "    # 0.004 -> log1p(1), max -> log1p(max / min)\n",
+    "    if log_size:\n",
+    "        size_transformed = np.log1p(safe_size / positive_min)\n",
+    "    else:\n",
+    "        size_transformed = safe_size\n",
+    "\n",
+    "    if size_transformed.max() > size_transformed.min():\n",
+    "        plot_df[\"_point_size\"] = (\n",
+    "            size_min\n",
+    "            + (size_transformed - size_transformed.min())\n",
+    "            / (size_transformed.max() - size_transformed.min())\n",
+    "            * (size_max - size_min)\n",
+    "        )\n",
+    "    else:\n",
+    "        plot_df[\"_point_size\"] = (size_min + size_max) / 2\n",
+    "\n",
+    "    # ------------------------------------------------------------------\n",
+    "    # Color handling\n",
+    "    # ------------------------------------------------------------------\n",
+    "    use_color = color_by and color_col is not None and color_col in plot_df.columns\n",
+    "\n",
+    "    if use_color:\n",
+    "        if pd.api.types.is_numeric_dtype(plot_df[color_col]):\n",
+    "            color_is_numeric = True\n",
+    "            color_values = plot_df[color_col]\n",
+    "        else:\n",
+    "            color_is_numeric = False\n",
+    "            categories = sorted(plot_df[color_col].dropna().unique())\n",
+    "            palette = plt.get_cmap(cmap, len(categories))\n",
+    "            color_map = {\n",
+    "                cat: palette(i)\n",
+    "                for i, cat in enumerate(categories)\n",
+    "            }\n",
+    "            color_values = plot_df[color_col].map(color_map)\n",
+    "    else:\n",
+    "        color_is_numeric = False\n",
+    "        color_values = \"gray\"\n",
+    "\n",
+    "    plot_df[\"_color\"] = list(color_values) if use_color else \"gray\"\n",
+    "\n",
+    "    nonhit_df = plot_df[~plot_df[hit_col]]\n",
+    "    hit_df = plot_df[plot_df[hit_col]]\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=figsize)\n",
+    "\n",
+    "    # ------------------------------------------------------------------\n",
+    "    # Non-hit points: subtle\n",
+    "    # ------------------------------------------------------------------\n",
+    "    if use_color and color_is_numeric:\n",
+    "        nonhit_scatter = ax.scatter(\n",
+    "            nonhit_df[x_col],\n",
+    "            nonhit_df[y_col],\n",
+    "            s=nonhit_df[\"_point_size\"],\n",
+    "            c=nonhit_df[color_col],\n",
+    "            cmap=cmap,\n",
+    "            marker=\"o\",\n",
+    "            alpha=0.25,\n",
+    "            edgecolor=\"none\",\n",
+    "            label=\"Not hit\",\n",
+    "        )\n",
+    "    else:\n",
+    "        nonhit_scatter = ax.scatter(\n",
+    "            nonhit_df[x_col],\n",
+    "            nonhit_df[y_col],\n",
+    "            s=nonhit_df[\"_point_size\"],\n",
+    "            c=nonhit_df[\"_color\"],\n",
+    "            marker=\"o\",\n",
+    "            alpha=0.25,\n",
+    "            edgecolor=\"none\",\n",
+    "            label=\"Not hit\",\n",
+    "        )\n",
+    "\n",
+    "    # ------------------------------------------------------------------\n",
+    "    # Hit points: obvious highlighted marker\n",
+    "    # ------------------------------------------------------------------\n",
+    "    if use_color and color_is_numeric:\n",
+    "        hit_scatter = ax.scatter(\n",
+    "            hit_df[x_col],\n",
+    "            hit_df[y_col],\n",
+    "            s=hit_df[\"_point_size\"] * 1.5,\n",
+    "            c=hit_df[color_col],\n",
+    "            cmap=cmap,\n",
+    "            marker=\"*\",\n",
+    "            alpha=0.98,\n",
+    "            edgecolor=\"black\",\n",
+    "            linewidth=1.0,\n",
+    "            label=\"Hit\",\n",
+    "        )\n",
+    "    else:\n",
+    "        hit_scatter = ax.scatter(\n",
+    "            hit_df[x_col],\n",
+    "            hit_df[y_col],\n",
+    "            s=hit_df[\"_point_size\"] * 1.5,\n",
+    "            c=hit_df[\"_color\"],\n",
+    "            marker=\"*\",\n",
+    "            alpha=0.98,\n",
+    "            edgecolor=\"black\",\n",
+    "            linewidth=1.0,\n",
+    "            label=\"Hit\",\n",
+    "        )\n",
+    "\n",
+    "    # ------------------------------------------------------------------\n",
+    "    # Threshold lines\n",
+    "    # ------------------------------------------------------------------\n",
+    "    ax.axvline(\n",
+    "        ap_threshold,\n",
+    "        linestyle=\"--\",\n",
+    "        linewidth=1.2,\n",
+    "        color=\"black\",\n",
+    "        alpha=0.7,\n",
+    "    )\n",
+    "    ax.axhline(\n",
+    "        logit_threshold,\n",
+    "        linestyle=\"--\",\n",
+    "        linewidth=1.2,\n",
+    "        color=\"black\",\n",
+    "        alpha=0.7,\n",
+    "    )\n",
+    "\n",
+    "    # Shade target region: AP <= threshold and logit >= threshold\n",
+    "    x_min, x_max = ax.get_xlim()\n",
+    "    y_min, y_max = ax.get_ylim()\n",
+    "\n",
+    "    if y_max > y_min:\n",
+    "        ax.axvspan(\n",
+    "            x_min,\n",
+    "            ap_threshold,\n",
+    "            ymin=(logit_threshold + y_min) / (y_max + y_min),\n",
+    "            ymax=1,\n",
+    "            alpha=0.08,\n",
+    "            color=\"gray\",\n",
+    "        )\n",
+    "\n",
+    "    ax.set_xlim(x_min, x_max)\n",
+    "    ax.set_ylim(y_min, y_max)\n",
+    "\n",
+    "    ax.text(\n",
+    "        ap_threshold,\n",
+    "        logit_threshold,\n",
+    "        f\"  AP ≤ {ap_threshold}\\n  logit ≥ {logit_threshold}\",\n",
+    "        ha=\"left\",\n",
+    "        va=\"bottom\",\n",
+    "        fontsize=9,\n",
+    "    )\n",
+    "\n",
+    "    ax.set_xlabel(\"Mean Average Precision\")\n",
+    "    ax.set_ylabel(\"Mean Logit Score\")\n",
+    "    ax.set_title(\"Treatment-Level Hit Prioritization\")\n",
+    "\n",
+    "    ax.grid(alpha=0.2)\n",
+    "\n",
+    "    # ------------------------------------------------------------------\n",
+    "    # Hit-status legend\n",
+    "    # ------------------------------------------------------------------\n",
+    "    marker_handles = [\n",
+    "        Line2D(\n",
+    "            [0], [0],\n",
+    "            marker=\"o\",\n",
+    "            color=\"none\",\n",
+    "            label=\"Not hit\",\n",
+    "            markerfacecolor=\"gray\",\n",
+    "            markersize=8,\n",
+    "            alpha=0.35,\n",
+    "        ),\n",
+    "        Line2D(\n",
+    "            [0], [0],\n",
+    "            marker=\"*\",\n",
+    "            color=\"black\",\n",
+    "            label=\"Hit\",\n",
+    "            markerfacecolor=\"gray\",\n",
+    "            markersize=14,\n",
+    "        ),\n",
+    "    ]\n",
+    "\n",
+    "    marker_legend = ax.legend(\n",
+    "        handles=marker_handles,\n",
+    "        title=\"Hit status\",\n",
+    "        loc=\"upper right\",\n",
+    "        frameon=True,\n",
+    "    )\n",
+    "    ax.add_artist(marker_legend)\n",
+    "\n",
+    "    # ------------------------------------------------------------------\n",
+    "    # Size legend: span full observed range, not just quartiles\n",
+    "    # ------------------------------------------------------------------\n",
+    "    size_legend_values = np.array([\n",
+    "        positive_min,\n",
+    "        0.01,\n",
+    "        0.05,\n",
+    "        0.1,\n",
+    "        0.5,\n",
+    "        1.0,\n",
+    "        positive_max,\n",
+    "    ])\n",
+    "\n",
+    "    # Keep only values inside observed range, then ensure min/max are included\n",
+    "    size_legend_values = size_legend_values[\n",
+    "        (size_legend_values >= positive_min) & \n",
+    "        (size_legend_values <= positive_max)\n",
+    "    ]\n",
+    "\n",
+    "    size_legend_values = np.unique(\n",
+    "        np.concatenate([[positive_min], size_legend_values, [positive_max]])\n",
+    "    )\n",
+    "\n",
+    "    size_handles = []\n",
+    "\n",
+    "    for val in size_legend_values:\n",
+    "        safe_val = max(val, positive_min)\n",
+    "        transformed_val = np.log1p(safe_val / positive_min)\n",
+    "\n",
+    "        if size_transformed.max() > size_transformed.min():\n",
+    "            point_size = (\n",
+    "                size_min\n",
+    "                + (transformed_val - size_transformed.min())\n",
+    "                / (size_transformed.max() - size_transformed.min())\n",
+    "                * (size_max - size_min)\n",
+    "            )\n",
+    "        else:\n",
+    "            point_size = (size_min + size_max) / 2\n",
+    "\n",
+    "        size_handles.append(\n",
+    "            plt.scatter(\n",
+    "                [],\n",
+    "                [],\n",
+    "                s=point_size,\n",
+    "                color=\"gray\",\n",
+    "                alpha=0.35,\n",
+    "                edgecolor=\"none\",\n",
+    "                label=f\"{val:.3g}\",\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "    size_legend = ax.legend(\n",
+    "        handles=size_handles,\n",
+    "        title=f\"{size_col}\" if not log_size else f\"{size_col}\\n (log scale)\",\n",
+    "        loc=\"lower right\",\n",
+    "        frameon=True,\n",
+    "    )\n",
+    "    ax.add_artist(size_legend)\n",
+    "\n",
+    "    # ------------------------------------------------------------------\n",
+    "    # Color legend / colorbar\n",
+    "    # ------------------------------------------------------------------\n",
+    "    if use_color:\n",
+    "        if color_is_numeric:\n",
+    "            cbar = fig.colorbar(nonhit_scatter, ax=ax, pad=0.02)\n",
+    "            cbar.set_label(color_col)\n",
+    "        else:\n",
+    "            color_handles = [\n",
+    "                Line2D(\n",
+    "                    [0], [0],\n",
+    "                    marker=\"o\",\n",
+    "                    color=\"none\",\n",
+    "                    label=str(cat),\n",
+    "                    markerfacecolor=color_map[cat],\n",
+    "                    markeredgecolor=\"none\",\n",
+    "                    markersize=8,\n",
+    "                    alpha=0.8,\n",
+    "                )\n",
+    "                for cat in categories\n",
+    "            ]\n",
+    "\n",
+    "            ax.legend(\n",
+    "                handles=color_handles,\n",
+    "                title=color_col,\n",
+    "                bbox_to_anchor=(1.02, 1),\n",
+    "                loc=\"upper left\",\n",
+    "                frameon=True,\n",
+    "            )\n",
+    "\n",
+    "    fig.tight_layout()\n",
+    "\n",
+    "    if save_path is not None:\n",
+    "        fig.savefig(save_path, dpi=300, bbox_inches=\"tight\")\n",
+    "\n",
+    "    return fig, ax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b68570e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plot_treatment_hit_scatter(\n",
+    "    for_hit_calling_df,\n",
+    "    color_col=\"Pathway\",\n",
+    "    figsize=(10, 7),\n",
+    "    ap_threshold=0.6,\n",
+    "    logit_threshold=0.9,\n",
+    "    size_min=10,\n",
+    "    size_max=200,\n",
+    "    log_size=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "eefa1c48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.lines import Line2D\n",
+    "from matplotlib.colors import LinearSegmentedColormap, TwoSlopeNorm\n",
+    "\n",
+    "def plot_hit_threshold_metric_panels(\n",
+    "    df,\n",
+    "    x_col=\"mean_average_precision\",\n",
+    "    y_col=\"mean_logit_score\",\n",
+    "    count_col=\"mean_relative_cell_count\",\n",
+    "    pathway_col=\"Pathway\",\n",
+    "    hit_col=\"hit\",\n",
+    "    ap_threshold=0.6,\n",
+    "    logit_threshold=0.9,\n",
+    "    relative_count_threshold=0.8,\n",
+    "    figsize=(13, 5.5),\n",
+    "    cmap_pathway=\"tab20\",\n",
+    "    point_size=45,\n",
+    "    hit_size=120,\n",
+    "    alpha_nonhit=0.45,\n",
+    "    alpha_hit=0.95,\n",
+    "    save_path=None,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Two-panel hit-threshold visualization.\n",
+    "\n",
+    "    Left:\n",
+    "        MAP vs logit, colored by pathway.\n",
+    "\n",
+    "    Right:\n",
+    "        MAP vs logit, colored by relative cell count.\n",
+    "\n",
+    "    In both panels:\n",
+    "        - x-axis = mean average precision\n",
+    "        - y-axis = mean logit score\n",
+    "        - non-hits = circles\n",
+    "        - hits = stars with black outline\n",
+    "        - threshold region = AP <= ap_threshold and logit >= logit_threshold\n",
+    "        - relative cell count threshold = relative_count_threshold\n",
+    "    \"\"\"\n",
+    "\n",
+    "    plot_df = df.copy()\n",
+    "\n",
+    "    # Preserve treatment name from index\n",
+    "    if plot_df.index.name is not None:\n",
+    "        plot_df = plot_df.reset_index()\n",
+    "        treatment_col = df.index.name\n",
+    "    else:\n",
+    "        plot_df = plot_df.reset_index(names=\"Metadata_treatment\")\n",
+    "        treatment_col = \"Metadata_treatment\"\n",
+    "\n",
+    "    required_cols = [\n",
+    "        x_col,\n",
+    "        y_col,\n",
+    "        count_col,\n",
+    "        pathway_col,\n",
+    "        hit_col,\n",
+    "    ]\n",
+    "\n",
+    "    plot_df = plot_df.dropna(subset=required_cols).copy()\n",
+    "    plot_df[hit_col] = plot_df[hit_col].astype(bool)\n",
+    "\n",
+    "    nonhit_df = plot_df.loc[~plot_df[hit_col]].copy()\n",
+    "    hit_df = plot_df.loc[plot_df[hit_col]].copy()\n",
+    "\n",
+    "    # Shared limits with a little padding\n",
+    "    x_min = plot_df[x_col].min()\n",
+    "    x_max = plot_df[x_col].max()\n",
+    "    y_min = plot_df[y_col].min()\n",
+    "    y_max = plot_df[y_col].max()\n",
+    "\n",
+    "    x_pad = 0.04 * (x_max - x_min) if x_max > x_min else 0.05\n",
+    "    y_pad = 0.04 * (y_max - y_min) if y_max > y_min else 0.05\n",
+    "\n",
+    "    x_lim = (x_min - x_pad, x_max + x_pad)\n",
+    "    y_lim = (y_min - y_pad, y_max + y_pad)\n",
+    "\n",
+    "    fig, axes = plt.subplots(\n",
+    "        1,\n",
+    "        2,\n",
+    "        figsize=figsize,\n",
+    "        sharex=True,\n",
+    "        sharey=True,\n",
+    "    )\n",
+    "\n",
+    "    ax_pathway, ax_count = axes\n",
+    "\n",
+    "    # ------------------------------------------------------------------\n",
+    "    # Helper: thresholds and target-region shading\n",
+    "    # ------------------------------------------------------------------\n",
+    "    def add_threshold_guides(ax):\n",
+    "        ax.axvline(\n",
+    "            ap_threshold,\n",
+    "            linestyle=\"--\",\n",
+    "            linewidth=1.2,\n",
+    "            color=\"black\",\n",
+    "            alpha=0.75,\n",
+    "        )\n",
+    "        ax.axhline(\n",
+    "            logit_threshold,\n",
+    "            linestyle=\"--\",\n",
+    "            linewidth=1.2,\n",
+    "            color=\"black\",\n",
+    "            alpha=0.75,\n",
+    "        )\n",
+    "\n",
+    "        ax.set_xlim(*x_lim)\n",
+    "        ax.set_ylim(*y_lim)\n",
+    "\n",
+    "        # Shade AP <= threshold and logit >= threshold\n",
+    "        ymin, ymax = ax.get_ylim()\n",
+    "        if ymax > ymin:\n",
+    "            ax.axvspan(\n",
+    "                x_lim[0],\n",
+    "                ap_threshold,\n",
+    "                ymin=(logit_threshold - ymin) / (ymax - ymin),\n",
+    "                ymax=1,\n",
+    "                color=\"gray\",\n",
+    "                alpha=0.10,\n",
+    "                zorder=0,\n",
+    "            )\n",
+    "\n",
+    "        ax.text(\n",
+    "            ap_threshold,\n",
+    "            logit_threshold,\n",
+    "            f\"  MAP ≤ {ap_threshold}\\n  logit ≥ {logit_threshold}\",\n",
+    "            ha=\"left\",\n",
+    "            va=\"bottom\",\n",
+    "            fontsize=9,\n",
+    "        )\n",
+    "\n",
+    "        ax.grid(alpha=0.25)\n",
+    "        ax.set_xlabel(\"Mean Average Precision\")\n",
+    "\n",
+    "    # ------------------------------------------------------------------\n",
+    "    # Panel 1: color by pathway\n",
+    "    # ------------------------------------------------------------------\n",
+    "    pathways = sorted(plot_df[pathway_col].dropna().unique())\n",
+    "    pathway_palette = sns.color_palette(cmap_pathway, n_colors=len(pathways))\n",
+    "    pathway_color_map = {\n",
+    "        pathway: pathway_palette[i]\n",
+    "        for i, pathway in enumerate(pathways)\n",
+    "    }\n",
+    "\n",
+    "    nonhit_colors = nonhit_df[pathway_col].map(pathway_color_map)\n",
+    "    hit_colors = hit_df[pathway_col].map(pathway_color_map)\n",
+    "\n",
+    "    ax_pathway.scatter(\n",
+    "        nonhit_df[x_col],\n",
+    "        nonhit_df[y_col],\n",
+    "        c=nonhit_colors,\n",
+    "        s=point_size,\n",
+    "        marker=\"o\",\n",
+    "        alpha=alpha_nonhit,\n",
+    "        edgecolor=\"black\",\n",
+    "        label=\"Not hit\",\n",
+    "    )\n",
+    "\n",
+    "    ax_pathway.scatter(\n",
+    "        hit_df[x_col],\n",
+    "        hit_df[y_col],\n",
+    "        c=hit_colors,\n",
+    "        s=hit_size,\n",
+    "        marker=\"*\",\n",
+    "        alpha=alpha_hit,\n",
+    "        edgecolor=\"black\",\n",
+    "        linewidth=0.8,\n",
+    "        label=\"Hit\",\n",
+    "    )\n",
+    "\n",
+    "    add_threshold_guides(ax_pathway)\n",
+    "\n",
+    "    ax_pathway.set_title(\"Colored by pathway\")\n",
+    "    ax_pathway.set_ylabel(\"Mean Logit Score\")\n",
+    "\n",
+    "    # Pathway legend\n",
+    "    pathway_handles = [\n",
+    "        Line2D(\n",
+    "            [0],\n",
+    "            [0],\n",
+    "            marker=\"o\",\n",
+    "            color=\"none\",\n",
+    "            label=str(pathway),\n",
+    "            markerfacecolor=pathway_color_map[pathway],\n",
+    "            markeredgecolor=\"none\",\n",
+    "            markersize=7,\n",
+    "            alpha=0.85,\n",
+    "        )\n",
+    "        for pathway in pathways\n",
+    "    ]\n",
+    "\n",
+    "    pathway_legend = ax_pathway.legend(\n",
+    "        handles=pathway_handles,\n",
+    "        title=pathway_col,\n",
+    "        bbox_to_anchor=(1.02, 1),\n",
+    "        loc=\"upper left\",\n",
+    "        frameon=True,\n",
+    "        fontsize=8,\n",
+    "        title_fontsize=9,\n",
+    "    )\n",
+    "\n",
+    "    # ------------------------------------------------------------------\n",
+    "    # Panel 2: color by relative cell count\n",
+    "    # ------------------------------------------------------------------\n",
+    "\n",
+    "    # Example interpretation:\n",
+    "    # low relative count  -> red\n",
+    "    # threshold           -> white\n",
+    "    # high relative count -> blue\n",
+    "    relative_count_threshold = 0.8  # change this as needed\n",
+    "\n",
+    "    count_vmin = plot_df[count_col].min()\n",
+    "    count_vmax = plot_df[count_col].max()\n",
+    "\n",
+    "    count_cmap = LinearSegmentedColormap.from_list(\n",
+    "        \"relative_count_red_white_blue\",\n",
+    "        [\"red\", \"white\", \"blue\"]\n",
+    "    )\n",
+    "\n",
+    "    count_norm = TwoSlopeNorm(\n",
+    "        vmin=count_vmin,\n",
+    "        vcenter=relative_count_threshold,\n",
+    "        vmax=count_vmax,\n",
+    "    )\n",
+    "\n",
+    "    count_scatter_nonhit = ax_count.scatter(\n",
+    "        nonhit_df[x_col],\n",
+    "        nonhit_df[y_col],\n",
+    "        c=nonhit_df[count_col],\n",
+    "        cmap=count_cmap,\n",
+    "        norm=count_norm,\n",
+    "        s=point_size,\n",
+    "        marker=\"o\",\n",
+    "        alpha=alpha_nonhit,\n",
+    "        edgecolor=\"black\",\n",
+    "        label=\"Not hit\",\n",
+    "    )\n",
+    "\n",
+    "    ax_count.scatter(\n",
+    "        hit_df[x_col],\n",
+    "        hit_df[y_col],\n",
+    "        c=hit_df[count_col],\n",
+    "        cmap=count_cmap,\n",
+    "        norm=count_norm,\n",
+    "        s=hit_size,\n",
+    "        marker=\"*\",\n",
+    "        alpha=alpha_hit,\n",
+    "        edgecolor=\"black\",\n",
+    "        linewidth=0.8,\n",
+    "        label=\"Hit\",\n",
+    "    )\n",
+    "\n",
+    "    add_threshold_guides(ax_count)\n",
+    "\n",
+    "    ax_count.set_title(\n",
+    "        f\"Colored by relative cell count\\nwhite centered at {relative_count_threshold:g}\"\n",
+    "    )\n",
+    "\n",
+    "    cbar = fig.colorbar(\n",
+    "        count_scatter_nonhit,\n",
+    "        ax=ax_count,\n",
+    "        pad=0.02,\n",
+    "    )\n",
+    "\n",
+    "    cbar.set_label(count_col)\n",
+    "\n",
+    "    # Optional: emphasize the threshold on the colorbar\n",
+    "    cbar.ax.axhline(\n",
+    "        relative_count_threshold,\n",
+    "        color=\"black\",\n",
+    "        linewidth=1.2,\n",
+    "    )\n",
+    "\n",
+    "    # ------------------------------------------------------------------\n",
+    "    # Shared hit-status legend\n",
+    "    # ------------------------------------------------------------------\n",
+    "    hit_status_handles = [\n",
+    "        Line2D(\n",
+    "            [0],\n",
+    "            [0],\n",
+    "            marker=\"o\",\n",
+    "            color=\"none\",\n",
+    "            label=\"Not hit\",\n",
+    "            markerfacecolor=\"gray\",\n",
+    "            markeredgecolor=\"none\",\n",
+    "            markersize=7,\n",
+    "            alpha=alpha_nonhit,\n",
+    "        ),\n",
+    "        Line2D(\n",
+    "            [0],\n",
+    "            [0],\n",
+    "            marker=\"*\",\n",
+    "            color=\"black\",\n",
+    "            label=\"Hit\",\n",
+    "            markerfacecolor=\"gray\",\n",
+    "            markeredgecolor=\"black\",\n",
+    "            markersize=13,\n",
+    "            alpha=alpha_hit,\n",
+    "        ),\n",
+    "    ]\n",
+    "\n",
+    "    ax_count.legend(\n",
+    "        handles=hit_status_handles,\n",
+    "        title=\"Hit status\",\n",
+    "        loc=\"lower right\",\n",
+    "        frameon=True,\n",
+    "    )\n",
+    "\n",
+    "    fig.suptitle(\n",
+    "        \"Treatment-Level Hit Thresholding\",\n",
+    "        fontsize=14,\n",
+    "        fontweight=\"bold\",\n",
+    "        y=1.03,\n",
+    "    )\n",
+    "\n",
+    "    fig.tight_layout()\n",
+    "\n",
+    "    if save_path is not None:\n",
+    "        fig.savefig(save_path, dpi=300, bbox_inches=\"tight\")\n",
+    "\n",
+    "    return fig, axes, plot_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5a59eeb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x550 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes, plot_df = plot_hit_threshold_metric_panels(\n",
+    "    for_hit_calling_df,\n",
+    "    x_col=\"mean_average_precision\",\n",
+    "    y_col=\"mean_logit_score\",\n",
+    "    count_col=\"mean_relative_cell_count\",\n",
+    "    pathway_col=\"Pathway\",\n",
+    "    hit_col=\"hit\",\n",
+    "    ap_threshold=0.6,\n",
+    "    logit_threshold=0.9,\n",
+    "    relative_count_threshold=0.8,\n",
+    "    figsize=(14, 5.5),\n",
+    "    save_path=\"hit_threshold_metric_panels.png\",\n",
+    ")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5c6e220a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for_hit_calling_df.sort_values(by='hit', ascending=False).to_csv(output_file)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "targeted-fibrosis",
+   "language": "python",
+   "name": "targeted-fibrosis"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/3a.train_dmso_logistic_regression_models/train_platemapwise_model/6.hit_visualization_summary.ipynb b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/6.hit_visualization_summary.ipynb
new file mode 100644
index 0000000..1907bca
--- /dev/null
+++ b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/6.hit_visualization_summary.ipynb
@@ -0,0 +1,554 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5061e5c1",
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Attaching package: ‘plotly’\n",
+      "\n",
+      "\n",
+      "The following object is masked from ‘package:ggplot2’:\n",
+      "\n",
+      "    last_plot\n",
+      "\n",
+      "\n",
+      "The following object is masked from ‘package:stats’:\n",
+      "\n",
+      "    filter\n",
+      "\n",
+      "\n",
+      "The following object is masked from ‘package:graphics’:\n",
+      "\n",
+      "    layout\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "library(ggplot2)\n",
+    "library(dplyr)\n",
+    "library(plotly)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7a33024a",
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Updating HTML index of packages in '.Library'\n",
+      "\n",
+      "Making 'packages.html' ...\n",
+      " done\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "install.packages(\"ggrepel\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c10b17cb",
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mRows: \u001b[22m\u001b[34m551\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m8\u001b[39m\n",
+      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
+      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
+      "\u001b[31mchr\u001b[39m (2): Metadata_treatment, Pathway\n",
+      "\u001b[32mdbl\u001b[39m (5): mean_logit_score, mean_relative_cell_count, mean_average_precision,...\n",
+      "\u001b[33mlgl\u001b[39m (1): hit\n",
+      "\n",
+      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
+      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 551   8\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 6 × 8</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>Metadata_treatment</th><th scope=col>Pathway</th><th scope=col>mean_logit_score</th><th scope=col>mean_relative_cell_count</th><th scope=col>mean_average_precision</th><th scope=col>corrected_p_value</th><th scope=col>-log10(p-value)</th><th scope=col>hit</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>UCD-0001812</td><td>Others                    </td><td>0.9779592</td><td>0.8115532</td><td>1.0000000</td><td>0.002764107</td><td>2.558445</td><td>TRUE</td></tr>\n",
+       "\t<tr><td>UCD-0159487</td><td>Others                    </td><td>0.9594374</td><td>0.8163263</td><td>0.6805556</td><td>0.048928447</td><td>1.310439</td><td>TRUE</td></tr>\n",
+       "\t<tr><td>UCD-0159486</td><td>Others                    </td><td>0.9446008</td><td>0.9049643</td><td>0.7458333</td><td>0.040347712</td><td>1.394181</td><td>TRUE</td></tr>\n",
+       "\t<tr><td>UCD-0159268</td><td>Others                    </td><td>0.9610885</td><td>0.9960657</td><td>1.0000000</td><td>0.002764107</td><td>2.558445</td><td>TRUE</td></tr>\n",
+       "\t<tr><td>UCD-0001419</td><td>Transmembrane Transporters</td><td>0.9917530</td><td>0.8965338</td><td>0.7236532</td><td>0.041758242</td><td>1.379258</td><td>TRUE</td></tr>\n",
+       "\t<tr><td>UCD-0001921</td><td>Epigenetics               </td><td>0.9697368</td><td>0.8184931</td><td>1.0000000</td><td>0.002764107</td><td>2.558445</td><td>TRUE</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A tibble: 6 × 8\n",
+       "\\begin{tabular}{llllllll}\n",
+       " Metadata\\_treatment & Pathway & mean\\_logit\\_score & mean\\_relative\\_cell\\_count & mean\\_average\\_precision & corrected\\_p\\_value & -log10(p-value) & hit\\\\\n",
+       " <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <lgl>\\\\\n",
+       "\\hline\n",
+       "\t UCD-0001812 & Others                     & 0.9779592 & 0.8115532 & 1.0000000 & 0.002764107 & 2.558445 & TRUE\\\\\n",
+       "\t UCD-0159487 & Others                     & 0.9594374 & 0.8163263 & 0.6805556 & 0.048928447 & 1.310439 & TRUE\\\\\n",
+       "\t UCD-0159486 & Others                     & 0.9446008 & 0.9049643 & 0.7458333 & 0.040347712 & 1.394181 & TRUE\\\\\n",
+       "\t UCD-0159268 & Others                     & 0.9610885 & 0.9960657 & 1.0000000 & 0.002764107 & 2.558445 & TRUE\\\\\n",
+       "\t UCD-0001419 & Transmembrane Transporters & 0.9917530 & 0.8965338 & 0.7236532 & 0.041758242 & 1.379258 & TRUE\\\\\n",
+       "\t UCD-0001921 & Epigenetics                & 0.9697368 & 0.8184931 & 1.0000000 & 0.002764107 & 2.558445 & TRUE\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 6 × 8\n",
+       "\n",
+       "| Metadata_treatment &lt;chr&gt; | Pathway &lt;chr&gt; | mean_logit_score &lt;dbl&gt; | mean_relative_cell_count &lt;dbl&gt; | mean_average_precision &lt;dbl&gt; | corrected_p_value &lt;dbl&gt; | -log10(p-value) &lt;dbl&gt; | hit &lt;lgl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|\n",
+       "| UCD-0001812 | Others                     | 0.9779592 | 0.8115532 | 1.0000000 | 0.002764107 | 2.558445 | TRUE |\n",
+       "| UCD-0159487 | Others                     | 0.9594374 | 0.8163263 | 0.6805556 | 0.048928447 | 1.310439 | TRUE |\n",
+       "| UCD-0159486 | Others                     | 0.9446008 | 0.9049643 | 0.7458333 | 0.040347712 | 1.394181 | TRUE |\n",
+       "| UCD-0159268 | Others                     | 0.9610885 | 0.9960657 | 1.0000000 | 0.002764107 | 2.558445 | TRUE |\n",
+       "| UCD-0001419 | Transmembrane Transporters | 0.9917530 | 0.8965338 | 0.7236532 | 0.041758242 | 1.379258 | TRUE |\n",
+       "| UCD-0001921 | Epigenetics                | 0.9697368 | 0.8184931 | 1.0000000 | 0.002764107 | 2.558445 | TRUE |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  Metadata_treatment Pathway                    mean_logit_score\n",
+       "1 UCD-0001812        Others                     0.9779592       \n",
+       "2 UCD-0159487        Others                     0.9594374       \n",
+       "3 UCD-0159486        Others                     0.9446008       \n",
+       "4 UCD-0159268        Others                     0.9610885       \n",
+       "5 UCD-0001419        Transmembrane Transporters 0.9917530       \n",
+       "6 UCD-0001921        Epigenetics                0.9697368       \n",
+       "  mean_relative_cell_count mean_average_precision corrected_p_value\n",
+       "1 0.8115532                1.0000000              0.002764107      \n",
+       "2 0.8163263                0.6805556              0.048928447      \n",
+       "3 0.9049643                0.7458333              0.040347712      \n",
+       "4 0.9960657                1.0000000              0.002764107      \n",
+       "5 0.8965338                0.7236532              0.041758242      \n",
+       "6 0.8184931                1.0000000              0.002764107      \n",
+       "  -log10(p-value) hit \n",
+       "1 2.558445        TRUE\n",
+       "2 1.310439        TRUE\n",
+       "3 1.394181        TRUE\n",
+       "4 2.558445        TRUE\n",
+       "5 1.379258        TRUE\n",
+       "6 2.558445        TRUE"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load hit calling summary\n",
+    "hit_df <- readr::read_csv(file.path(\"inference_results\", \"hit_call_table.csv\"))\n",
+    "\n",
+    "print(dim(hit_df))\n",
+    "\n",
+    "head(hit_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "403df0fe",
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Setting hit tresholds\n",
+    "map_threshold <- -log10(0.05)\n",
+    "logit_threshold <- 0.9\n",
+    "cell_count_threshold <- 0.8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "42f7fb70",
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“\u001b[1m\u001b[22mRemoved 101 rows containing missing values or values outside the scale range\n",
+      "(`geom_point()`).”\n",
+      "Warning message:\n",
+      "“\u001b[1m\u001b[22mRemoved 101 rows containing missing values or values outside the scale range\n",
+      "(`geom_point()`).”\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_hits <- sum(hit_df$`-log10(p-value)` >= map_threshold & hit_df$mean_logit_score >= logit_threshold & hit_df$mean_relative_cell_count >= 0.8, na.rm = TRUE)\n",
+    "hit_rate <- round(n_hits / nrow(hit_df) * 100, 1)\n",
+    "\n",
+    "n_total <- nrow(hit_df)\n",
+    "n_ll <- sum(hit_df$`-log10(p-value)` < map_threshold & hit_df$mean_logit_score < logit_threshold, na.rm = TRUE)\n",
+    "n_lr <- sum(hit_df$`-log10(p-value)` >= map_threshold & hit_df$mean_logit_score < logit_threshold, na.rm = TRUE)\n",
+    "n_tl <- sum(hit_df$`-log10(p-value)` < map_threshold & hit_df$mean_logit_score >= logit_threshold, na.rm = TRUE)\n",
+    "\n",
+    "pct <- function(n) paste0(n, \" (\", round(n / n_total * 100, 1), \"%)\")\n",
+    "\n",
+    "(\n",
+    "    ggplot(\n",
+    "        hit_df |> dplyr::mutate(significant = `-log10(p-value)` >= map_threshold & mean_logit_score >= logit_threshold & mean_relative_cell_count >= 0.8),\n",
+    "        aes(x = `-log10(p-value)`, y = mean_logit_score,\n",
+    "            color = significant, fill = significant, alpha = significant,\n",
+    "            shape = significant, size = significant)\n",
+    "    )\n",
+    "    + annotate(\"rect\", xmin = -Inf, xmax = map_threshold, ymin = -Inf, ymax = logit_threshold,\n",
+    "               fill = \"grey80\", alpha = 0.3)\n",
+    "    + annotate(\"rect\", xmin = map_threshold, xmax = Inf, ymin = -Inf, ymax = logit_threshold,\n",
+    "               fill = \"steelblue\", alpha = 0.15)\n",
+    "    + annotate(\"rect\", xmin = -Inf, xmax = map_threshold, ymin = logit_threshold, ymax = Inf,\n",
+    "               fill = \"orange\", alpha = 0.15)\n",
+    "    + annotate(\"text\", x = -Inf, y = -Inf,\n",
+    "               label = paste0(\"Not reproducible & no healthy shift\\n\", pct(n_ll)),\n",
+    "               hjust = -0.05, vjust = -0.3, size = 3.5, color = \"grey40\")\n",
+    "    + annotate(\"text\", x = Inf, y = -Inf,\n",
+    "               label = paste0(\"Reproducible, no healthy shift\\n\", pct(n_lr)),\n",
+    "               hjust = 1.05, vjust = -0.3, size = 3.5, color = \"steelblue\")\n",
+    "    + annotate(\"text\", x = -Inf, y = Inf,\n",
+    "               label = paste0(\"Low reproducibility, healthy shift\\n\", pct(n_tl)),\n",
+    "               hjust = -0.05, vjust = 1.3, size = 3.5, color = \"darkorange\")\n",
+    "    + geom_point(stroke = 0.8)\n",
+    "    + theme_bw()\n",
+    "    + scale_color_manual(values = c(\"TRUE\" = \"black\", \"FALSE\" = \"grey70\"), guide = \"none\")\n",
+    "    + scale_fill_manual(values = c(\"TRUE\" = \"magenta\", \"FALSE\" = \"grey70\"), guide = \"none\")\n",
+    "    + scale_alpha_manual(values = c(\"TRUE\" = 1, \"FALSE\" = 0.6), guide = \"none\")\n",
+    "    + scale_shape_manual(values = c(\"TRUE\" = 23, \"FALSE\" = 16), guide = \"none\")\n",
+    "    + scale_size_manual(values = c(\"TRUE\" = 5, \"FALSE\" = 2), guide = \"none\")\n",
+    "    + xlab(\"Mean Average Precision (mAP)\\n(-log10 permuted mAP p value; higher is reproducible\\nperturbations different from DMSO control\")\n",
+    "    + ylab(\"Mean logit score\\n(Probability of healthy population\\n closer to 1 is healthy)\")\n",
+    "    + geom_vline(xintercept = map_threshold, linetype = \"dashed\", color = \"red\", lwd = 1)\n",
+    "    + geom_hline(yintercept = logit_threshold, linetype = \"dashed\", color = \"blue\", lwd = 1)\n",
+    "    + annotate(\"text\", x = map_threshold, y = -Inf,\n",
+    "               label = \"Adjusted mAP p val <= 0.05\",\n",
+    "               color = \"red\", hjust = -0.25, vjust = -0.5, angle = 90, size = 5)\n",
+    "    + annotate(\"text\", x = Inf, y = logit_threshold, label = paste0(\"logit >= \", logit_threshold),\n",
+    "               color = \"blue\", hjust = 5, vjust = 1.5, size = 5)\n",
+    "    + annotate(\"text\", x = Inf, y = 0.75, label = \"relative toxicity <= 0.2\",\n",
+    "               color = \"magenta\", hjust = 1.4, vjust = -8, size = 5)\n",
+    "    + annotate(\"text\", x = Inf, y = Inf, label = paste0(\"Number of hits = \", n_hits, \" (Hit rate = \", hit_rate, \"%)\"),\n",
+    "               hjust = 1, vjust = 8, size = 5)\n",
+    "    + theme(legend.position = \"none\")\n",
+    ")\n",
+    "\n",
+    "ggsave(\"hit_calling_2d.png\", dpi = 300, width = 8, height = 6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e4b2d90a",
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“\u001b[1m\u001b[22mRemoved 101 rows containing missing values or values outside the scale range\n",
+      "(`geom_point()`).”\n",
+      "Warning message:\n",
+      "“\u001b[1m\u001b[22mRemoved 1 row containing missing values or values outside the scale range\n",
+      "(`geom_point()`).”\n",
+      "Warning message:\n",
+      "“\u001b[1m\u001b[22mRemoved 1 row containing missing values or values outside the scale range\n",
+      "(`geom_label_repel()`).”\n",
+      "Warning message:\n",
+      "“\u001b[1m\u001b[22mRemoved 101 rows containing missing values or values outside the scale range\n",
+      "(`geom_point()`).”\n",
+      "Warning message:\n",
+      "“\u001b[1m\u001b[22mRemoved 1 row containing missing values or values outside the scale range\n",
+      "(`geom_point()`).”\n",
+      "Warning message:\n",
+      "“\u001b[1m\u001b[22mRemoved 1 row containing missing values or values outside the scale range\n",
+      "(`geom_label_repel()`).”\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "highlight_compounds <- c(\n",
+    "    \"UCD-0159285\", \"UCD-0159379\", \"UCD-0159304\", \"UCD-0159655\",\n",
+    "    \"UCD-0159618\", \"UCD-0159514\", \"UCD-0159365\", \"UCD-0159607\",\n",
+    "    \"UCD-0159372\"\n",
+    ")\n",
+    "\n",
+    "n_hits <- sum(hit_df$`-log10(p-value)` >= map_threshold & hit_df$mean_logit_score >= logit_threshold & hit_df$mean_relative_cell_count >= 0.8, na.rm = TRUE)\n",
+    "hit_rate <- round(n_hits / nrow(hit_df) * 100, 1)\n",
+    "\n",
+    "n_total <- nrow(hit_df)\n",
+    "n_ll <- sum(hit_df$`-log10(p-value)` < map_threshold & hit_df$mean_logit_score < logit_threshold, na.rm = TRUE)\n",
+    "n_lr <- sum(hit_df$`-log10(p-value)` >= map_threshold & hit_df$mean_logit_score < logit_threshold, na.rm = TRUE)\n",
+    "n_tl <- sum(hit_df$`-log10(p-value)` < map_threshold & hit_df$mean_logit_score >= logit_threshold, na.rm = TRUE)\n",
+    "\n",
+    "pct <- function(n) paste0(n, \" (\", round(n / n_total * 100, 1), \"%)\")\n",
+    "\n",
+    "plot_df <- hit_df |> dplyr::mutate(\n",
+    "    significant = `-log10(p-value)` >= map_threshold & mean_logit_score >= logit_threshold & mean_relative_cell_count >= 0.8,\n",
+    "    highlighted = Metadata_treatment %in% highlight_compounds\n",
+    ")\n",
+    "\n",
+    "highlight_df <- plot_df |> dplyr::filter(highlighted)\n",
+    "\n",
+    "(\n",
+    "    ggplot(plot_df, aes(x = `-log10(p-value)`, y = mean_logit_score))\n",
+    "    + annotate(\"rect\", xmin = -Inf, xmax = map_threshold, ymin = -Inf, ymax = logit_threshold,\n",
+    "               fill = \"grey80\", alpha = 0.3)\n",
+    "    + annotate(\"rect\", xmin = map_threshold, xmax = Inf, ymin = -Inf, ymax = logit_threshold,\n",
+    "               fill = \"steelblue\", alpha = 0.15)\n",
+    "    + annotate(\"rect\", xmin = -Inf, xmax = map_threshold, ymin = logit_threshold, ymax = Inf,\n",
+    "               fill = \"orange\", alpha = 0.15)\n",
+    "    + annotate(\"text\", x = -Inf, y = -Inf,\n",
+    "               label = paste0(\"Not reproducible & no healthy shift\\n\", pct(n_ll)),\n",
+    "               hjust = -0.05, vjust = -0.3, size = 3.5, color = \"grey40\")\n",
+    "    + annotate(\"text\", x = Inf, y = -Inf,\n",
+    "               label = paste0(\"Reproducible, no healthy shift\\n\", pct(n_lr)),\n",
+    "               hjust = 1.05, vjust = -0.3, size = 3.5, color = \"steelblue\")\n",
+    "    + annotate(\"text\", x = -Inf, y = Inf,\n",
+    "               label = paste0(\"Low reproducibility, healthy shift\\n\", pct(n_tl)),\n",
+    "               hjust = -0.05, vjust = 1.3, size = 3.5, color = \"darkorange\")\n",
+    "    + geom_point(aes(color = significant, fill = significant, alpha = significant,\n",
+    "                     shape = significant, size = significant), stroke = 0.8)\n",
+    "    + geom_point(data = highlight_df, color = \"black\", fill = \"dodgerblue\",\n",
+    "                 shape = 23, size = 5, stroke = 1)\n",
+    "    + theme_bw()\n",
+    "    + scale_color_manual(values = c(\"TRUE\" = \"black\", \"FALSE\" = \"grey70\"), guide = \"none\")\n",
+    "    + scale_fill_manual(values = c(\"TRUE\" = \"magenta\", \"FALSE\" = \"grey70\"), guide = \"none\")\n",
+    "    + scale_alpha_manual(values = c(\"TRUE\" = 1, \"FALSE\" = 0.6), guide = \"none\")\n",
+    "    + scale_shape_manual(values = c(\"TRUE\" = 23, \"FALSE\" = 16), guide = \"none\")\n",
+    "    + scale_size_manual(values = c(\"TRUE\" = 5, \"FALSE\" = 2), guide = \"none\")\n",
+    "    + xlab(\"Mean Average Precision (mAP)\\n(-log10 permuted mAP p value; higher is reproducible\\nperturbations different from DMSO control\")\n",
+    "    + ylab(\"Mean logit score\\n(Probability of healthy population\\n closer to 1 is healthy)\")\n",
+    "    + geom_vline(xintercept = map_threshold, linetype = \"dashed\", color = \"red\", lwd = 1)\n",
+    "    + geom_hline(yintercept = logit_threshold, linetype = \"dashed\", color = \"blue\", lwd = 1)\n",
+    "    + annotate(\"text\", x = map_threshold, y = -Inf,\n",
+    "               label = \"Adjusted mAP p val <= 0.05\",\n",
+    "               color = \"red\", hjust = -0.25, vjust = -0.5, angle = 90, size = 5)\n",
+    "    + annotate(\"text\", x = Inf, y = logit_threshold, label = paste0(\"logit >= \", logit_threshold),\n",
+    "               color = \"blue\", hjust = 5, vjust = 1.5, size = 5)\n",
+    "    + annotate(\"text\", x = Inf, y = 0.75, label = \"relative toxicity <= 0.2\",\n",
+    "               color = \"magenta\", hjust = 1.4, vjust = -8, size = 5)\n",
+    "    + annotate(\"text\", x = Inf, y = Inf, label = paste0(\"Number of hits = \", n_hits, \" (Hit rate = \", hit_rate, \"%)\"),\n",
+    "               hjust = 1, vjust = 8, size = 5)\n",
+    "    + annotate(\"text\", x = Inf, y = Inf, label = paste0(\"Hit rate = \", hit_rate, \"%\"),\n",
+    "               hjust = 1.1, vjust = 3.5, size = 5)\n",
+    "    + theme(legend.position = \"none\")\n",
+    "    + ggrepel::geom_label_repel(data = highlight_df, aes(label = Metadata_treatment),\n",
+    "                            size = 3, color = \"black\", fill = \"white\",\n",
+    "                            box.padding = 1, max.overlaps = 20)\n",
+    ")\n",
+    "\n",
+    "ggsave(\"hit_calling_2d_highlighted.png\", dpi = 300, width = 8, height = 6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f1f10a1",
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plot_hit_df <- hit_df |> dplyr::mutate(significant = `-log10(p-value)` >= map_threshold & mean_logit_score >= logit_threshold & mean_relative_cell_count >= 0.8)\n",
+    "\n",
+    "x_range <- range(hit_df$`-log10(p-value)`, na.rm = TRUE)\n",
+    "y_range <- range(hit_df$mean_relative_cell_count, na.rm = TRUE)\n",
+    "z_range <- range(hit_df$mean_logit_score, na.rm = TRUE)\n",
+    "\n",
+    "hit_df <- hit_df |> dplyr::mutate(significant = `-log10(p-value)` >= map_threshold & mean_logit_score >= logit_threshold & mean_relative_cell_count >= 0.8)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bff76b75",
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "p <- plot_ly() |>\n",
+    "    add_trace(\n",
+    "        data = plot_hit_df |> dplyr::filter(!significant),\n",
+    "        x = ~`-log10(p-value)`,\n",
+    "        y = ~mean_logit_score,\n",
+    "        z = ~mean_relative_cell_count,\n",
+    "        type = \"scatter3d\",\n",
+    "        mode = \"markers\",\n",
+    "        marker = list(size = 5, color = \"grey70\", opacity = 0.6, line = list(color = \"black\", width = 1)),\n",
+    "        text = ~paste0(\"Compound: \", Metadata_treatment, \"<br>Pathway: \", Pathway),\n",
+    "        hoverinfo = \"text+x+y+z\",\n",
+    "        name = \"non-hit\"\n",
+    "    ) |>\n",
+    "    add_trace(\n",
+    "        data = plot_hit_df |> dplyr::filter(significant),\n",
+    "        x = ~`-log10(p-value)`,\n",
+    "        y = ~mean_logit_score,\n",
+    "        z = ~mean_relative_cell_count,\n",
+    "        type = \"scatter3d\",\n",
+    "        mode = \"markers\",\n",
+    "        marker = list(size = 8, color = \"magenta\", symbol = \"diamond\", line = list(color = \"black\", width = 1)),\n",
+    "        text = ~paste0(\"Compound: \", Metadata_treatment, \"<br>Pathway: \", Pathway),\n",
+    "        hoverinfo = \"text+x+y+z\",\n",
+    "        name = \"hit\"\n",
+    "    ) |>\n",
+    "    add_trace(\n",
+    "        type = \"mesh3d\",\n",
+    "        x = c(map_threshold, map_threshold, map_threshold, map_threshold),\n",
+    "        z = c(y_range[1], y_range[2], y_range[2], y_range[1]),\n",
+    "        y = c(z_range[1], z_range[1], z_range[2], z_range[2]),\n",
+    "        i = c(0, 0), j = c(1, 2), k = c(2, 3),\n",
+    "        facecolor = c(\"red\", \"red\"),\n",
+    "        opacity = 0.2, showlegend = TRUE,\n",
+    "        name = \"Adjusted mAP p val <= 0.05\"\n",
+    "    ) |>\n",
+    "    add_trace(\n",
+    "        type = \"mesh3d\",\n",
+    "        x = c(x_range[1], x_range[2], x_range[2], x_range[1]),\n",
+    "        z = c(y_range[1], y_range[1], y_range[2], y_range[2]),\n",
+    "        y = c(logit_threshold, logit_threshold, logit_threshold, logit_threshold),\n",
+    "        i = c(0, 0), j = c(1, 2), k = c(2, 3),\n",
+    "        facecolor = c(\"blue\", \"blue\"),\n",
+    "        opacity = 0.2, showlegend = TRUE,\n",
+    "        name = paste0(\"logit >= \", logit_threshold)\n",
+    "    ) |>\n",
+    "    add_trace(\n",
+    "        type = \"mesh3d\",\n",
+    "        x = c(x_range[1], x_range[2], x_range[2], x_range[1]),\n",
+    "        z = c(0.8, 0.8, 0.8, 0.8),\n",
+    "        y = c(z_range[1], z_range[1], z_range[2], z_range[2]),\n",
+    "        i = c(0, 0), j = c(1, 2), k = c(2, 3),\n",
+    "        facecolor = c(\"magenta\", \"magenta\"),\n",
+    "        opacity = 0.2, showlegend = TRUE,\n",
+    "        name = \"relative toxicity >= 0.8\"\n",
+    "    ) |>\n",
+    "    layout(\n",
+    "        scene = list(\n",
+    "xaxis = list(title = \"Mean Average Precision (mAP)<br>(-log10 permuted mAP p value; higher is reproducible<br>perturbations different from DMSO control\"),\n",
+    "zaxis = list(title = \"Relative cell count<br>(Lower values higher toxicity)\"),\n",
+    "yaxis = list(title = \"Mean logit score<br>(Probability of healthy population<br>closer to 1 is healthy)\")\n",
+    "        )\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "09e57ce9",
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "htmlwidgets::saveWidget(p, \"hit_calling_3d.html\", selfcontained = TRUE)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "4.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/3a.train_dmso_logistic_regression_models/train_platemapwise_model/final_merged_mean_buscar_map_scores.csv b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/final_merged_mean_buscar_map_scores.csv
new file mode 100644
index 0000000..47ae90d
--- /dev/null
+++ b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/final_merged_mean_buscar_map_scores.csv
@@ -0,0 +1,477 @@
+perturbation,Pathway,mean_on_buscar_scores,mean_off_buscar_scores,mean_average_precision
+DMSO_healthy,,,,0.3908649640519617
+UCD-0000083,Neuronal Signaling,1.9725994015460893,0.5553575404350793,0.1033687983046005
+UCD-0000101,Endocrinology & Hormones,1.5963392224662716,0.2311620136639584,0.0593124072470423
+UCD-0000201,GPCR & G Protein,1.250051386505903,0.500450977399115,0.2543771043771043
+UCD-0000243,Neuronal Signaling,1.1711835673738835,0.1547215107365483,0.0979404137495406
+UCD-0000253,Neuronal Signaling,1.038427967664889,0.1122487962275559,0.0642967429480587
+UCD-0000295,Metabolism,1.905625427623463,0.3253093098254039,0.0399579330907688
+UCD-0000324,Metabolism,0.9880746940658348,0.1168451386227974,0.057966492695779
+UCD-0000377,Transmembrane Transporters,1.0118465688629972,0.2152789253353162,0.1532632343376884
+UCD-0000421,Neuronal Signaling,1.169039481116562,0.2301153411312453,0.0489585142132994
+UCD-0000450,Neuronal Signaling,1.0402589552555324,0.0891616194136162,0.0245558444213023
+UCD-0000463,Others,1.4021847182595704,0.6073343653700797,0.5333333333333333
+UCD-0000469,Neuronal Signaling,1.134677163309343,0.2957069472051424,0.1151059355754028
+UCD-0000490,Neuronal Signaling,0.9473724075515866,0.0732976102541305,0.0358477321684785
+UCD-0000533,GPCR & G Protein,1.009689871917749,0.350404404477585,0.2397856444757972
+UCD-0000576,Neuronal Signaling,1.0347720681800276,0.1031765385736038,0.0240277830975804
+UCD-0000684,Others,1.0482950239110185,0.1509003125905046,0.0429034218961416
+UCD-0000721,Neuronal Signaling,2.0014484391598453,0.1679197994987468,0.0098716203073332
+UCD-0000730,Metabolism,4.252958939967666,0.504658060523149,0.1019579181340015
+UCD-0000737,GPCR & G Protein,1.02785140777917,0.2325726719193266,0.0885966328792796
+UCD-0000757,Metabolism,1.1905502940271016,0.5588230834447584,0.123551030252807
+UCD-0000786,GPCR & G Protein,1.0502889521104584,0.1495049615379855,0.0313312539589651
+UCD-0000841,Neuronal Signaling,1.7071276160842226,0.4834809599365507,0.4745039682539682
+UCD-0000849,Endocrinology & Hormones,1.5818534175993066,0.2982423300123984,0.1790670027954166
+UCD-0000872,Metabolism,15.24394666294896,0.2560220880581183,0.0901546938219732
+UCD-0000878,Transmembrane Transporters,2.8283618053115007,0.2874616316991925,0.6747474747474748
+UCD-0000904,GPCR & G Protein,1.1665180748752568,0.2674063057521704,0.0953665821500286
+UCD-0000918,Proteases,1.1117642782875787,0.1591235605803274,0.1308167013771738
+UCD-0000936,Neuronal Signaling,1.3910800988457046,0.3003161790298663,0.1075251817898876
+UCD-0000972,Neuronal Signaling,1.0706765350903922,0.1688232114140501,0.0536108667392406
+UCD-0001002,Neuronal Signaling,1.2046142443944767,0.1883918243410724,0.093791113155009
+UCD-0001014,Neuronal Signaling,1.0931636738848405,0.0435671757724186,0.0163311116598785
+UCD-0001016,Neuronal Signaling,1.0975411715451178,0.3882097989459699,0.1636766363668537
+UCD-0001024,Neuronal Signaling,1.0018048575993022,0.0178400775500052,0.0232870989426068
+UCD-0001040,Endocrinology & Hormones,1.3092110957310157,0.2211091237738302,0.1190626273675637
+UCD-0001059,Epigenetics,1.0075197816612136,0.1439267238609343,0.0354080003315808
+UCD-0001078,Metabolism,2.9049336554922327,0.0118518518518518,0.0818879538725261
+UCD-0001086,Endocrinology & Hormones,1.073384571836109,0.3629370592792418,0.1284613812586958
+UCD-0001098,Neuronal Signaling,1.607902684648433,0.6441011143548738,0.6855392156862745
+UCD-0001101,Neuronal Signaling,11.148175560083551,0.426842584819309,0.2246692625537336
+UCD-0001162,Endocrinology & Hormones,1.2506470957803726,0.1428571428571428,
+UCD-0001186,Neuronal Signaling,1.0820135897067933,0.1390846483652238,0.0216838436355873
+UCD-0001305,Neuronal Signaling,1.0668934910869856,0.209404918129504,0.1116146475726188
+UCD-0001391,Metabolism,1.1122973120320796,0.3177965195889669,0.4535898583539249
+UCD-0001419,Transmembrane Transporters,1.0791039043362505,0.108920146819631,0.0900513877271409
+UCD-0001435,Others,1.051478996712996,0.0819706232625064,0.0229378025934092
+UCD-0001441,Neuronal Signaling,1.0422159134391835,0.2054022126038504,0.0545366536317279
+UCD-0001463,Neuronal Signaling,1.28420143270814,0.3793233829845341,0.0784887411016152
+UCD-0001567,Transmembrane Transporters,1.7618845638699092,0.3221486110065497,0.0219990435198469
+UCD-0001580,Transmembrane Transporters,1.921364067283426,0.0,
+UCD-0001613,Endocrinology & Hormones,1.0399908355439589,0.144622669771175,0.0306486419047773
+UCD-0001616,Metabolism,1.0644374784601247,0.254549152038907,0.0426631339509679
+UCD-0001616B,Others,1.0516513604161302,0.1324272689310332,0.0385274729520894
+UCD-0001653,Transmembrane Transporters,1.0461120702261604,0.1888887932590939,0.0535946475997057
+UCD-0001657,GPCR & G Protein,0.9778624946149826,0.2296432090605022,0.1482597006107068
+UCD-0001733,Protein Tyrosine Kinase,1.1871799262037308,0.4294777797551708,0.1117859886178668
+UCD-0001759,Angiogenesis,7.762690833051959,0.7213960274046058,0.5855429292929293
+UCD-0001766,Angiogenesis,2.7218511135941705,0.4387166761957404,0.087794317765204
+UCD-0001775,Others,1.0101506556682718,0.1626902561056926,0.0265398142948903
+UCD-0001784,Cell Cycle,10.361087830776125,0.0,
+UCD-0001790,Cell Cycle,3.28431111382342,0.7892376681614349,
+UCD-0001800,Others,4.482730410998864,0.0323886639676113,0.0067117117117117
+UCD-0001801,Endocrinology & Hormones,1.6506582891597312,0.3243383709984595,0.0817866155224871
+UCD-0001802,Others,1.3090049904220007,0.4556323851018826,0.0845032066483903
+UCD-0001804,MAPK,1.0767884519160549,0.3591746012858359,0.2152331850248516
+UCD-0001808,MAPK,2.3492334114622224,0.4418019071750826,0.2519207929340518
+UCD-0001810,DNA Damage,1.0317505288352675,0.276060152292483,0.1123703900580831
+UCD-0001812,Others,1.861457535810687,0.6001547909376274,0.2306860393101811
+UCD-0001816,Metabolism,2.0544868462165398,0.6413993393571276,0.2028450153450153
+UCD-0001818,MAPK,1.0308683020994378,0.2846275913973988,0.1020592198076439
+UCD-0001822,MAPK,1.20566063076941,0.4410057938070002,0.1139284861180717
+UCD-0001829,PI3K/Akt/mTOR,0.9578250774948198,0.2104830685748645,0.2277399428882479
+UCD-0001835,Others,7.805743331255739,0.0594245112317401,0.012864354339684
+UCD-0001842,Neuronal Signaling,2.357022894149978,0.3866982348418122,0.1274308857272414
+UCD-0001843,DNA Damage,1.1147631600220864,0.5194936219767862,0.2039674322845237
+UCD-0001844,Others,5.374114270003944,0.7479885540288587,0.65
+UCD-0001850,Others,1.0289627079277612,0.3762982076406035,0.1611914544550725
+UCD-0001856,Angiogenesis,1.2996562897787225,0.6270751129984805,0.756547619047619
+UCD-0001861,Angiogenesis,2.1820655952547234,0.6142212468849387,0.1872267936623275
+UCD-0001863,Angiogenesis,1.8570402596847584,0.6142326308721422,0.6418542568542569
+UCD-0001864,Angiogenesis,4.089984848161967,0.192200557103064,
+UCD-0001869,Protein Tyrosine Kinase,1.1469033344261077,0.3914535227962145,0.4020887690879202
+UCD-0001870,Angiogenesis,22.37804742598358,0.2996515679442508,
+UCD-0001885,PI3K/Akt/mTOR,2.5750593089780907,0.6081438147702605,0.2127828328742134
+UCD-0001886,Cell Cycle,1.5556707180995637,0.1954064327541242,0.0460847643328331
+UCD-0001888,PI3K/Akt/mTOR,1.4173877706704323,0.5788423325532217,0.1518592026549265
+UCD-0001900,PI3K/Akt/mTOR,1.0444145786691972,0.2333163859281699,0.0346098694821248
+UCD-0001915,Others,1.7483493986995144,0.1098139012567554,0.1201189576386195
+UCD-0001921,Epigenetics,1.1458544633315195,0.5696074257009052,0.1366837120841697
+UCD-0001924,Epigenetics,1.88606202539412,0.6524565381708238,1.0
+UCD-0001925,Epigenetics,4.77369382363814,0.5562968267228918,0.0458828846759881
+UCD-0001927,Epigenetics,1.0928655315665607,0.2320715360859522,0.5292748917748917
+UCD-0001944,Epigenetics,1.1030989328830187,0.3503956727857316,0.0806041112361006
+UCD-0001952,Epigenetics,1.0311081992891638,0.1391139060527988,0.0391880434818404
+UCD-0001953,Epigenetics,1.6106841730057138,0.522561204239714,0.0376084878841494
+UCD-0001957,Epigenetics,1.110319747543166,0.2044667550567128,0.2249659373329586
+UCD-0011098,Neuronal Signaling,1.1049633097896294,0.4052062772927434,0.1843587908635327
+UCD-0011250,Endocrinology & Hormones,1.0862713835118605,0.270448213023401,0.048042700425195
+UCD-0011696,JAK/STAT,1.1423563710259492,0.2633970111686166,0.0191336023440518
+UCD-0013217,Epigenetics,1.073660987571016,0.2509052537788301,0.0886014120118939
+UCD-0017582,Neuronal Signaling,1.1594450618484715,0.2783401228238974,0.0240708365868571
+UCD-0017947,Apoptosis,1.2253459312827355,0.3269585018007122,0.0484103878062618
+UCD-0017999,Endocrinology & Hormones,1.1201807276858815,0.150956774836054,0.0586908315595004
+UCD-0018012,Apoptosis,1.0090104522053638,0.1981390935921537,0.0659248307466015
+UCD-0018051,Metabolism,1.015744001835256,0.0385282986932251,0.0434629178783021
+UCD-0018083,JAK/STAT,1.0372830519811418,0.444252461148839,0.2782978133066576
+UCD-0018089,Endocrinology & Hormones,1.0882765081755497,0.2826016935910536,0.1895956571689957
+UCD-0018131,Angiogenesis,1.282001539502423,0.5515942941924896,0.8
+UCD-0018179,MAPK,1.12505411732588,0.5926534830574401,0.3214540701517414
+UCD-0018183,Proteases,1.1353140125377894,0.2517452820084399,0.1996733408379518
+UCD-0018207,Neuronal Signaling,1.751547557500702,0.5166676061894888,0.3776611399456226
+UCD-0018240,PI3K/Akt/mTOR,1.0375563032514803,0.0910493778076797,0.019804014086564
+UCD-0018304,Endocrinology & Hormones,0.998622800156173,0.0589010518464871,0.0357460201321606
+UCD-0018311,Neuronal Signaling,1.0192381098392056,0.1001719937744595,0.0162298139841772
+UCD-0018410,Metabolism,1.0101035199263793,0.1720851082223262,0.0317675317842132
+UCD-0018439,Microbiology,1.233199826776473,0.464699486896829,0.0606324821519599
+UCD-0018448,GPCR & G Protein,1.0622153119910251,0.3605581743333313,0.1324863146620361
+UCD-0018458,Proteases,1.112296222381822,0.1487982287492571,0.140693112786766
+UCD-0018483,Microbiology,1.012428010374168,0.209531612965269,0.0915824954309178
+UCD-0018516,Neuronal Signaling,1.1047494901404191,0.1229855556590385,0.0971660049880852
+UCD-0018527,Microbiology,1.0362930120155112,0.1547506868275787,0.0263093132675852
+UCD-0018540,MAPK,1.3931390704443116,0.5333242546442619,0.286031944214333
+UCD-0018550,Neuronal Signaling,1.0221394038727485,0.3452871890971719,0.24644777265745
+UCD-0022022,Others,1.15631087441718,0.2104071861973802,0.053737847302877
+UCD-0159253,Epigenetics,0.9264274596674732,0.2119220262881512,0.2843822843822844
+UCD-0159256,Apoptosis,1.5691441755982247,0.4282993419288422,0.861111111111111
+UCD-0159257,DNA Damage,1.077257954938324,0.2921325390053263,0.1355070269366923
+UCD-0159258,Angiogenesis,0.9622554819442328,0.3201312825862475,0.400297619047619
+UCD-0159259,PI3K/Akt/mTOR,1.0391121881614211,0.2851588735696325,0.694047619047619
+UCD-0159261,Others,3.763401564063219,0.8275792455992131,0.363523976023976
+UCD-0159262,Others,4.2779431153895455,0.2419392613494486,0.1082672041258813
+UCD-0159263,Others,1.261774193351284,0.5719043272452566,0.2556029984585362
+UCD-0159264,Neuronal Signaling,4.423646763496777,0.2045498361287834,0.0601851851851851
+UCD-0159265,Neuronal Signaling,3.433504513128049,0.3086121651144072,0.0597402597402597
+UCD-0159266,Others,1.2003561075642162,0.2433808717720097,0.1132431594032043
+UCD-0159268,Others,1.2907907062912871,0.6206264044827954,0.2071435874067453
+UCD-0159269,Others,2.2400449854595026,0.7237741396301528,0.7458333333333333
+UCD-0159270,Others,1.1105064097190571,0.1003315302098823,0.0349656690728668
+UCD-0159271,Others,1.0637889017449085,0.3107223260818524,0.1854822597416288
+UCD-0159273,Others,1.028350429430398,0.1932369788100001,0.029004423905733
+UCD-0159274,Others,0.9297517092341272,0.3476075467972724,0.3418062612877414
+UCD-0159275,Others,1.736706485371848,0.4346713377824842,0.6594962785752259
+UCD-0159276,Others,2.2080756738158773,0.3541666666666667,
+UCD-0159277,Others,23.240289450776164,0.1166666666666666,
+UCD-0159278,Others,1.100191871660055,0.3295058259916508,0.068105426369609
+UCD-0159279,Others,1.739211086949583,0.0647015376777,0.053857279779493
+UCD-0159280,Others,1.0657516855792075,0.0484761122103979,0.0294379086782088
+UCD-0159282,Others,3.6676543163727455,0.475301037746028,0.1439273463712107
+UCD-0159283,Endocrinology & Hormones,1.244182705424831,0.3483319376419745,0.3003527110669967
+UCD-0159284,Stem Cells & Wnt,1.145325565079222,0.2190605968576002,0.0782504458542417
+UCD-0159285,DNA Damage,1.0915572087543313,0.4358262113180772,0.2775736014891667
+UCD-0159286,Others,3.108799997668501,0.0,
+UCD-0159287,Endocrinology & Hormones,2.0495309525629453,0.4756433978969365,0.1837852823948582
+UCD-0159288,Metabolism,1.0335443450274595,0.3096981104190778,0.0900293819657842
+UCD-0159289,Metabolism,0.9960039438993832,0.0719685054034488,0.0292564489232399
+UCD-0159290,PI3K/Akt/mTOR,1.7676416067810516,0.0403587443946188,
+UCD-0159291,GPCR & G Protein,1.320429992454646,0.3738910413617808,0.0294544240600105
+UCD-0159292,DNA Damage,1.4303661380041637,0.2734479271076821,0.0632741657036553
+UCD-0159293,Others,1.0186566942303403,0.2971138744261918,0.5171143671143671
+UCD-0159294,Cell Cycle,2.5973749477095613,0.7431801991634668,0.2807230681550101
+UCD-0159295,Microbiology,1.13969658502708,0.4443738537271128,0.3447405994307522
+UCD-0159296,Metabolism,1.1102617275098492,0.4182368773464129,0.3592981798832089
+UCD-0159297,Microbiology,1.0369314443159316,0.2032920542121946,0.1458325673166526
+UCD-0159299,Metabolism,1.32587032548452,0.1318038993446168,0.0198510706946935
+UCD-0159300,GPCR & G Protein,0.9600511632212752,0.0984527155414992,0.0675013399260041
+UCD-0159301,Cell Cycle,1.2529781705059957,0.4527894052433638,0.1165105166374141
+UCD-0159302,PI3K/Akt/mTOR,1.1950548851675455,0.3528072064255108,0.115512199507728
+UCD-0159303,Metabolism,1.0386783707346194,0.2187055303650509,0.1667910940966572
+UCD-0159304,Cell Cycle,1.0525044136316966,0.3694838711830023,0.5058694083694084
+UCD-0159306,Cell Cycle,1.204549832175784,0.3249461975708518,0.2292678554396379
+UCD-0159307,GPCR & G Protein,2.368338773809602,0.3978550070089811,0.1641672402382121
+UCD-0159308,Protein Tyrosine Kinase,6.92295191897226,0.3625895813251749,0.0200396825396825
+UCD-0159309,Angiogenesis,1.0102815217899968,0.1592338660690586,0.0633310019730546
+UCD-0159310,GPCR & G Protein,1.6965918061630203,0.6211389164127157,0.4067351493822082
+UCD-0159311,GPCR & G Protein,0.990201754347802,0.2309312516339673,0.106255703123552
+UCD-0159312,Transmembrane Transporters,1.0751297039299117,0.2179032085437275,0.1299034814875447
+UCD-0159313,Protein Tyrosine Kinase,2.260804747038091,0.8021288178083983,0.1516890753167927
+UCD-0159314,PI3K/Akt/mTOR,2.452234893683218,0.6583755919701605,0.0577956989247311
+UCD-0159315,PI3K/Akt/mTOR,1.4507415268323498,0.6125706659701725,0.2820872842799379
+UCD-0159316,Proteases,1.5143107394572428,0.3326379942677672,0.0419439746522272
+UCD-0159317,Angiogenesis,1.136185674077342,0.4067302432920783,0.0652850905837702
+UCD-0159319,Neuronal Signaling,1.0759971754161624,0.1404722739437336,0.0736687371690717
+UCD-0159320,Metabolism,1.0392346665797827,0.1770646035147011,0.0938616319699323
+UCD-0159321,Neuronal Signaling,1.0140209516329468,0.0892730416838794,0.0513094148317634
+UCD-0159322,Endocrinology & Hormones,1.0359809025170097,0.2046055724398849,0.0461331809917085
+UCD-0159323,Stem Cells & Wnt,2.627067652326214,0.4666076551869857,0.0549158413288023
+UCD-0159324,Angiogenesis,1.0327390644601286,0.1562054135833328,0.0323207275860523
+UCD-0159325,PI3K/Akt/mTOR,1.854988504624773,0.02803738317757,0.0288621465885552
+UCD-0159326,Neuronal Signaling,1.1972050292099816,0.2584629925518733,0.1049607833362745
+UCD-0159327,DNA Damage,1.0033085166435909,0.2403744509070643,0.1556295079618501
+UCD-0159328,Proteases,11.027527432568624,0.0,
+UCD-0159329,JAK/STAT,3.019737730307287,0.2047619047619047,
+UCD-0159330,Protein Tyrosine Kinase,1.032845256370294,0.0843023056027867,0.0539446778156749
+UCD-0159331,Transmembrane Transporters,1.3671444510044153,0.3212646128118307,0.1447187936112353
+UCD-0159332,Apoptosis,1.3533673830216375,0.238650224902843,0.0823829916902672
+UCD-0159333,Transmembrane Transporters,1.016927808941352,0.2985170408419657,0.0565186541328182
+UCD-0159334,Metabolism,3.5358116344443444,0.5645596250272509,0.2951981246218995
+UCD-0159335,JAK/STAT,1.6538635681182647,0.2760900140646976,0.0364450127877237
+UCD-0159337,Neuronal Signaling,1.0479930705916738,0.2822020020996184,0.0547334678556406
+UCD-0159338,Neuronal Signaling,1.0552941753671825,0.1403429208478839,0.0158073636783076
+UCD-0159339,Neuronal Signaling,1.8507336536180492,0.5516056348512408,0.0730963738939614
+UCD-0159341,Metabolism,1.0447828699807955,0.1481410757027731,0.0374259768912273
+UCD-0159342,Others,1.0501830776409875,0.1885146920084539,0.0736963380441134
+UCD-0159344,Endocrinology & Hormones,1.1877998164749335,0.2838985545209932,0.1030192350173249
+UCD-0159345,Neuronal Signaling,1.0816952619936795,0.2151841069412328,0.0382800854456568
+UCD-0159346,Stem Cells & Wnt,1.585522412118775,0.6757474059700633,0.1431032912416023
+UCD-0159347,Metabolism,1.0928938425976362,0.4419691443446111,0.556447862015566
+UCD-0159349,Metabolism,1.3782637546254846,0.3709541199713593,0.2604481188459516
+UCD-0159350,Neuronal Signaling,1.0139604339806174,0.3704564382485336,0.1065400343747229
+UCD-0159351,Others,1.0464646259536796,0.2229514619693109,0.0221398867307954
+UCD-0159352,Proteases,1.6479134119440797,0.4854778388495718,0.825
+UCD-0159353,Proteases,1.1087340986122205,0.2979703635614437,0.0854545186376641
+UCD-0159354,Metabolism,1.0701731562195391,0.1989595490473613,0.3272606440751602
+UCD-0159355,PI3K/Akt/mTOR,1.0717998284457069,0.4358166500348451,0.4487767542914602
+UCD-0159356,Cytoskeletal Signaling,1.1009947103399176,0.2550715819307022,0.0505066104360373
+UCD-0159357,Apoptosis,2.5810855886418973,0.0907692411471666,0.0666655108845778
+UCD-0159358,Metabolism,1.0616042955322509,0.3124900288674079,0.1092958501006723
+UCD-0159359,Others,1.075291588829673,0.1355288445766187,0.1000159919573703
+UCD-0159360,Metabolism,1.288613525199479,0.3857144151668555,0.1180038711288711
+UCD-0159361,Others,1.151292003360579,0.4407324847389504,0.0605914722202151
+UCD-0159362,Stem Cells & Wnt,1.048347170578994,0.2142004901876498,0.0180223250162821
+UCD-0159364,Others,1.0567452316219386,0.2506679627529276,0.0726396407312102
+UCD-0159365,TGF-beta/Smad,1.3044899051106842,0.5734828104605938,0.6407407407407407
+UCD-0159366,Metabolism,1.1387229366556737,0.3779042902785257,0.1890874686397074
+UCD-0159367,Neuronal Signaling,1.0427912830968071,0.1453123928059685,0.209779065782454
+UCD-0159369,Epigenetics,1.6278563981440792,0.7582619484654818,0.1726785469986811
+UCD-0159370,PI3K/Akt/mTOR,1.116723283928574,0.4617696859596504,0.077535717196651
+UCD-0159371,Apoptosis,2.5350559382997906,0.7869298892124682,0.5847162728159729
+UCD-0159372,Protein Tyrosine Kinase,2.070356578143633,0.6270270270270271,
+UCD-0159373,Angiogenesis,1.4973578226020952,0.489871574333791,0.1514410770853674
+UCD-0159374,Endocrinology & Hormones,1.2540262941721194,0.3084933366780266,0.0860722858413082
+UCD-0159375,NF-κB,1.3314479881742285,0.527353528702222,0.0316636043200875
+UCD-0159376,Transmembrane Transporters,1.1736398005467583,0.4825337322423197,0.2469418837294408
+UCD-0159377,Metabolism,1.023888523802616,0.3228620506268261,0.1223314660091654
+UCD-0159378,Protein Tyrosine Kinase,1.1008207784734356,0.2632605297544738,0.0656411336671636
+UCD-0159379,PI3K/Akt/mTOR,1.1343631723972407,0.4124640501477777,0.6201118326118326
+UCD-0159380,GPCR & G Protein,0.9835706629274746,0.0839121148653352,0.0789550843705595
+UCD-0159381,Neuronal Signaling,2.2719724008165296,0.4951374668486481,1.0
+UCD-0159382,Neuronal Signaling,1.1041792190678643,0.0888584480771192,0.0457134920865583
+UCD-0159383,NF-κB,1.1432528811361315,0.422934252049542,0.555028305028305
+UCD-0159385,Metabolism,1.0670385257678916,0.1982007110961668,0.0304575485624523
+UCD-0159386,DNA Damage,1.429485224240917,0.5787785952525046,0.7081569664902998
+UCD-0159387,Neuronal Signaling,1.0513728832634872,0.1265736615184074,0.1226417372521603
+UCD-0159389,Protein Tyrosine Kinase,3.5495713719403086,0.6540329768413794,0.4377960854095155
+UCD-0159390,Neuronal Signaling,1.3553913222449707,0.4851121526401065,0.7708333333333333
+UCD-0159391,Protein Tyrosine Kinase,1.191211827005802,0.5301363634051707,0.0681564200417923
+UCD-0159392,Angiogenesis,2.1033438119553254,0.5658352229780801,0.0681818181818181
+UCD-0159393,GPCR & G Protein,1.1089530931961813,0.2733446865025812,0.1074271845878283
+UCD-0159394,TGF-beta/Smad,2.8237297739727607,0.8041007394860777,0.3808621933621933
+UCD-0159395,GPCR & G Protein,1.0794961662254694,0.3061452088707727,0.0838407211750371
+UCD-0159396,DNA Damage,1.087046309878771,0.3080790919606708,0.26053046605468
+UCD-0159397,Others,1.1907121465602315,0.2841018143649722,0.0586773582798127
+UCD-0159398,Protein Tyrosine Kinase,1.1821199995343443,0.3128320271504331,0.2673016798016798
+UCD-0159399,Apoptosis,3.798995906677746,0.5203120234448555,0.0829836658010032
+UCD-0159401,Angiogenesis,1.5039159417347616,0.6022985832509642,0.2478395061728394
+UCD-0159402,GPCR & G Protein,1.2550520740911904,0.2823919762079396,0.0876685895464282
+UCD-0159403,GPCR & G Protein,0.9895337725983931,0.2098559126754615,0.1040291149849029
+UCD-0159404,Metabolism,1.2303894874976806,0.2441339918783527,0.0839018352900198
+UCD-0159405,Metabolism,1.1330398416086802,0.2115789894455307,0.0184580993384005
+UCD-0159406,Neuronal Signaling,0.9219938605170922,0.1646000057577032,0.0905857159369879
+UCD-0159407,NF-κB,1.20046144335765,0.2078313193138194,0.1434286462005498
+UCD-0159408,Neuronal Signaling,1.156807185965839,0.4464252970692865,0.1054459991523192
+UCD-0159409,Others,0.9683274401791092,0.0187338719382902,0.0775452016598376
+UCD-0159410,Neuronal Signaling,1.1889104419652223,0.2102305877261158,0.1247514538566866
+UCD-0159411,Neuronal Signaling,0.9858807789179452,0.1278594838745214,0.0335682793827327
+UCD-0159411B,Neuronal Signaling,1.252961666883134,0.1375634599318809,0.0780133367942404
+UCD-0159412,Apoptosis,3.7746308739042886,0.0274725274725274,0.0267673301304049
+UCD-0159413,NF-κB,0.951828558288948,0.0402865103314816,0.1280002045240048
+UCD-0159414,DNA Damage,1.3844999936401745,0.5906088343943171,0.1265802397321122
+UCD-0159416,Metabolism,1.0622723256453963,0.2378426224290885,0.8555555555555555
+UCD-0159417,GPCR & G Protein,4.710699338961838,0.3895844107623556,0.5984848484848485
+UCD-0159418,Transmembrane Transporters,1.0658944817795806,0.1476987072381809,0.0468092561547663
+UCD-0159419,Protein Tyrosine Kinase,1.9341869838691903,0.0428571428571428,0.0817307692307692
+UCD-0159420,Angiogenesis,1.546373664649558,0.5381103667287878,0.2418925056639246
+UCD-0159421,Apoptosis,1.245851634119698,0.2050354528251656,0.1496659454380042
+UCD-0159422,Apoptosis,1.2233388083934384,0.3941590122894684,0.168544267613534
+UCD-0159423,PI3K/Akt/mTOR,1.7428470223987889,0.6099712681157733,0.2872043415521676
+UCD-0159425,Apoptosis,1.946032455518432,0.1447215923406399,0.1485690235690235
+UCD-0159426,DNA Damage,1.0846269677180325,0.1462610854058222,0.0354264616864104
+UCD-0159428,Apoptosis,1.1711457359708262,0.2504296072905095,0.048525739022212
+UCD-0159429,Stem Cells & Wnt,1.2838727672403087,0.2229007694457994,0.3184193121693122
+UCD-0159431,PI3K/Akt/mTOR,1.5243476662917967,0.3818835804308423,0.1697237253487253
+UCD-0159432,Angiogenesis,1.167410510101738,0.5433161849985159,0.4105121594092182
+UCD-0159433,Protein Tyrosine Kinase,1.0299684608660444,0.3606998456152591,0.0871698289461447
+UCD-0159434,Angiogenesis,1.1277543805004966,0.0789495353284818,0.0586099490504357
+UCD-0159435,Protein Tyrosine Kinase,3.271080880727406,0.2522095912068057,0.0416666666666666
+UCD-0159436,Stem Cells & Wnt,1.0864451145980722,0.1433546338177573,0.0732457157786306
+UCD-0159437,Cytoskeletal Signaling,3.2396552113879853,0.1216931216931216,0.0240995762711864
+UCD-0159438,Cytoskeletal Signaling,1.861681030184985,0.1793774921124544,0.1983360954102547
+UCD-0159439,PI3K/Akt/mTOR,5.050547611524856,0.7078505750028306,0.464015210863037
+UCD-0159441,Metabolism,1.1232350072473485,0.3267938810701968,0.055815253216569
+UCD-0159442,Epigenetics,0.933344627478466,0.0610825734119381,0.0550776092103351
+UCD-0159443,Others,0.9101969612683944,0.085561030700811,0.0582230557815835
+UCD-0159444,MAPK,1.0445381785481915,0.3054525567021686,0.2046964468703599
+UCD-0159445,Others,3.14471860272696,0.1165413533834586,
+UCD-0159447,Ubiquitin,1.4152923677736169,0.4420787558593065,0.9513888888888888
+UCD-0159449,Ubiquitin,1.100580037497064,0.1823198081298281,0.2671353276353276
+UCD-0159451,Ubiquitin,1.5507291715707725,0.2913533834586466,0.0456043956043956
+UCD-0159453,TGF-beta/Smad,1.0698101478803057,0.0547748695826925,0.1721792874200994
+UCD-0159455,TGF-beta/Smad,1.7771316492848126,0.556393258106742,1.0
+UCD-0159456,Apoptosis,1.2036894582628208,0.3713936755508265,0.7555555555555555
+UCD-0159457,Proteases,1.0527495147612247,0.2252205901435133,0.3603300913645741
+UCD-0159458,Others,1.0238401353405362,0.2284614645395393,0.1001038364251083
+UCD-0159459,Cytoskeletal Signaling,1.1357055442969766,0.3222322305045252,0.5306216931216932
+UCD-0159460,Angiogenesis,0.9868110176410352,0.1380933425367801,0.1962856770251136
+UCD-0159461,Angiogenesis,1.83501678573949,0.0245098039215686,
+UCD-0159462,GPCR & G Protein,1.1457934281456208,0.5593687743722983,0.7444444444444445
+UCD-0159464,Metabolism,1.0775876780517828,0.2051555376776615,0.1680431020504549
+UCD-0159465,Metabolism,1.0733469939255766,0.243038813052016,0.131620405057905
+UCD-0159466,Cell Cycle,1.2725039761710786,0.4068119795343611,0.3651027077497665
+UCD-0159467,Ubiquitin,22.68394406372662,0.4068627450980392,
+UCD-0159468,Apoptosis,5.430109262826612,0.5010074205120645,0.6216666666666667
+UCD-0159470,MAPK,1.108884962944803,0.2243155990743814,0.2660395622895622
+UCD-0159471,Proteases,1.0139037791416057,0.1520336464848311,0.1646271502249763
+UCD-0159472,TGF-beta/Smad,1.246130943064349,0.2690323337572124,0.3551434676434676
+UCD-0159473,Epigenetics,1.2196461970012158,0.2272602920440488,0.1747152103130363
+UCD-0159474,DNA Damage,1.0829667436686474,0.4050023704469029,0.4412698412698412
+UCD-0159476,Metabolism,1.1471153211021774,0.3901184687412422,0.6194444444444445
+UCD-0159477,Metabolism,1.5441023592761554,0.2842039900795259,0.3825221755368814
+UCD-0159478,Cell Cycle,1.559818012356044,0.239026865861008,0.0266055137202004
+UCD-0159479,Transmembrane Transporters,1.011831861854395,0.2151757368143323,0.2373390752395329
+UCD-0159480,Transmembrane Transporters,2.096654626193729,0.2762869921020077,0.0186820652173913
+UCD-0159481,Cell Cycle,1.0805690866644408,0.2504053343887057,0.1495140480434598
+UCD-0159483,Endocrinology & Hormones,1.0037308145550312,0.078804688248776,0.080880649563153
+UCD-0159484,Epigenetics,1.1458902543260818,0.2026328935193785,0.1792678894593688
+UCD-0159486,Others,0.9461621636164677,0.0622594053581777,0.0690476904744598
+UCD-0159487,Others,1.0771365455928616,0.2933458010017388,0.4629960317460317
+UCD-0159488,Epigenetics,2.588917914075448,0.2978025328938831,0.252462091666903
+UCD-0159492,Cell Cycle,1.1108841585033196,0.1710889945607238,0.3261083687554276
+UCD-0159493,Epigenetics,1.449816397652459,0.5182165470666943,0.4947268009768009
+UCD-0159495,Apoptosis,1.0736558459129617,0.0835699270754936,0.1968921333003026
+UCD-0159496,Others,1.046635276224776,0.0965915038192371,0.0982318812565304
+UCD-0159497,PI3K/Akt/mTOR,1.0376471308696054,0.1794186152129343,0.2487446868154964
+UCD-0159500,Epigenetics,1.7738551791944903,0.473134942498212,0.5015773442432785
+UCD-0159501,Cell Cycle,1.3374414818475877,0.2112304857916795,0.1300221768872785
+UCD-0159503,Cytoskeletal Signaling,1.018673977451173,0.0437690313064991,0.1016278323070118
+UCD-0159504,Others,2.443775354895785,0.3062985694801281,0.3992374727668845
+UCD-0159505,Others,1.0898371488223777,0.1995469792126229,0.1045758175399137
+UCD-0159506,NF-κB,1.070995904785053,0.3273937068924147,0.2029237984638596
+UCD-0159508,Epigenetics,1.573139674169759,0.0,
+UCD-0159509,Others,1.1982269571594428,0.1025425376413739,0.130950675008052
+UCD-0159510,Autophagy,1.055012538461218,0.0305496803605566,0.219023569023569
+UCD-0159511,Autophagy,0.9971464473119096,0.024818229396448,0.0688358015535073
+UCD-0159513,Others,1.1931743500819316,0.2583797221429251,0.1657466325944586
+UCD-0159514,Others,1.5357674737364175,0.6762612361225255,0.9513888888888888
+UCD-0159514B,Others,1.480556974004155,0.681100979637588,0.7875
+UCD-0159515,Epigenetics,1.1809636198766027,0.1447751444830614,0.2499503968253968
+UCD-0159516,Proteases,1.3615765271738212,0.1830338567383085,0.1979634838041016
+UCD-0159517,Proteases,1.1034525858265845,0.1391917370919904,0.3482508912655971
+UCD-0159518,Proteases,1.2415064524489514,0.0775621437036652,0.3833333333333333
+UCD-0159519,Stem Cells & Wnt,1.6507976739476553,0.084274193548387,0.1666666666666666
+UCD-0159520,NF-κB,2.109990249069565,0.079646017699115,
+UCD-0159521,Others,1.31133774885286,0.3504235006358186,0.4175983738483738
+UCD-0159522,Proteases,1.2760129890200802,0.1317241606761304,0.1273928083683657
+UCD-0159523,Proteases,1.065700963366306,0.159339613664478,0.5833333333333334
+UCD-0159524,Others,3.4132841750436618,0.7336051610990564,1.0
+UCD-0159526,DNA Damage,1.0983037123645951,0.0616722207980859,0.0206967213114754
+UCD-0159527,DNA Damage,1.0967394793368033,0.1874709810694532,0.0265549262254393
+UCD-0159528,Transmembrane Transporters,1.0087087375185049,0.0716775263977738,0.230787037037037
+UCD-0159529,Cell Cycle,2.03612831045227,0.1031441304278871,0.0679380341880342
+UCD-0159530,Proteases,1.252783002724213,0.1320556406197285,0.0337630666634988
+UCD-0159531,Metabolism,3.2186222241766984,0.0035460992907801,
+UCD-0159533,Cytoskeletal Signaling,1.166686165058154,0.2020141394117954,0.1373811866869533
+UCD-0159535,Others,1.0377628949077802,0.1444821668215982,0.0473771255451533
+UCD-0159538,MAPK,1.2944352848106488,0.4106315798579572,0.5366181789701526
+UCD-0159539,Proteases,1.0706693293915968,0.1493561974519439,0.1296116995988712
+UCD-0159540,Others,1.0530160564778188,0.2436962430992973,0.1056888743864432
+UCD-0159542,MAPK,1.184050680855628,0.3579408793462254,0.2864087301587301
+UCD-0159543,MAPK,1.3425192052711696,0.167286170379359,0.1548278663767794
+UCD-0159544,Angiogenesis,2.001993841278913,0.3400968050647821,0.5833333333333334
+UCD-0159545,Autophagy,1.1312480121256956,0.1226836953606864,0.0429566026002574
+UCD-0159546,Ubiquitin,1.1136593245043078,0.2691453321395514,0.1845995274566703
+UCD-0159547,Apoptosis,1.0569975614489828,0.2017107955158309,0.0528646664573128
+UCD-0159549,JAK/STAT,1.309776366694967,0.2152113166494333,0.5777777777777777
+UCD-0159551,Epigenetics,5.863199741066537,0.0298819689831173,0.2364646464646464
+UCD-0159552,Protein Tyrosine Kinase,1.075014956760058,0.0855266373411534,0.0309362391736581
+UCD-0159553,PI3K/Akt/mTOR,6.885843417518831,0.3688460215918823,0.4128685183464123
+UCD-0159554,Angiogenesis,1.1317395264017815,0.3873402247845812,0.1411076538458994
+UCD-0159555,Cytoskeletal Signaling,1.067666389915339,0.1571793061606045,0.0819734463950705
+UCD-0159556,GPCR & G Protein,1.124019060565085,0.1378507695396273,0.0612692448079259
+UCD-0159557,Others,1.3875897387410028,0.1471633229336396,0.0325089369327637
+UCD-0159558,Cytoskeletal Signaling,1.1110576064713524,0.1096230584445363,0.0384709409927585
+UCD-0159559,DNA Damage,0.9899766824061268,0.175971296027409,0.1945820247290835
+UCD-0159560,DNA Damage,1.2604348940103014,0.0717603525982058,0.1787811128808282
+UCD-0159561,JAK/STAT,1.145377100733569,0.1379626986392615,0.1782967032967032
+UCD-0159562,Epigenetics,1.0665912172390557,0.1591992402977231,0.0605881261398133
+UCD-0159563,Angiogenesis,1.35461249148499,0.3376780790481375,0.2226208976208976
+UCD-0159565,TGF-beta/Smad,1.289683142557846,0.2141214797944518,0.0987531873451191
+UCD-0159566,DNA Damage,1.43428605966416,0.5634515310028824,0.1906171679197995
+UCD-0159567,Others,1.3593560986970508,0.5426792338945509,0.4378153402969579
+UCD-0159568,Others,1.0619677034235029,0.2351668267026531,0.1878809064686544
+UCD-0159569,Protein Tyrosine Kinase,1.167718146452961,0.3819578182433014,0.4788986354775828
+UCD-0159571,Others,1.113638084737086,0.336273924301105,0.1356490448569022
+UCD-0159572,Epigenetics,1.1214620036213754,0.2635387007279535,0.0467042174376167
+UCD-0159573,Protein Tyrosine Kinase,1.915319691894001,0.5505974204615198,0.5354166666666667
+UCD-0159574,Epigenetics,1.7177633417737874,0.388429939697569,1.0
+UCD-0159575,Autophagy,1.1467577390525383,0.4198523774025485,0.6682539682539682
+UCD-0159576,Others,1.130197096528834,0.2935969926210434,0.1348779861385179
+UCD-0159578,PI3K/Akt/mTOR,2.897413657177486,0.4615226864906434,0.0505050505050505
+UCD-0159579,PI3K/Akt/mTOR,1.1764321541788991,0.2828324663858146,0.498067996137849
+UCD-0159580,NF-κB,1.7923903351146264,0.2730496453900709,
+UCD-0159581,PI3K/Akt/mTOR,2.654119765392818,0.688696194426947,1.0
+UCD-0159582,Neuronal Signaling,1.1118243455921917,0.0956264475128287,0.0565224554404662
+UCD-0159584,Cell Cycle,1.043257020379377,0.3144502763864739,0.094708210623231
+UCD-0159585,Protein Tyrosine Kinase,1.0047258591908692,0.1287024093429688,0.1811846386342157
+UCD-0159586,Angiogenesis,1.1223031678688928,0.2808987559441238,0.0965040164708036
+UCD-0159588,MAPK,1.1732088387256383,0.4588442538368356,0.9097222222222222
+UCD-0159589,DNA Damage,1.0924480285318867,0.3054199939205518,0.0928872350571431
+UCD-0159590,Others,1.944701348685673,0.3120666604583693,0.8791666666666667
+UCD-0159591,Epigenetics,1.0780263869491309,0.2508590039706225,0.2599718517206202
+UCD-0159592,Epigenetics,1.3957076815720804,0.5372760809457953,0.1140054241884905
+UCD-0159593,Epigenetics,1.1915131927313811,0.3557319194516886,0.1377544665373613
+UCD-0159594,Others,1.645871904667375,0.2725223650755706,0.1628826532483181
+UCD-0159596,Cytoskeletal Signaling,1.642586406068982,0.0714726095827023,0.0591997676838069
+UCD-0159597,Epigenetics,1.37828384648942,0.2793635895170789,0.1238095238095238
+UCD-0159598,Endocrinology & Hormones,2.5204132572515032,0.0,
+UCD-0159600,Epigenetics,2.394242434113276,0.1097590211094618,0.0402909817125503
+UCD-0159602,Angiogenesis,1.399494001611892,0.2557118211916673,0.1124670561806301
+UCD-0159603,GPCR & G Protein,1.1977186611968496,0.2514878493279662,0.038123488788087
+UCD-0159604,Autophagy,1.3053983448892603,0.2293835150923774,0.0420617086251505
+UCD-0159605,PI3K/Akt/mTOR,1.5213552387497524,0.1422927533793257,0.1916666666666666
+UCD-0159606,Autophagy,1.4448449183758896,0.2732978279716789,0.2108842424631898
+UCD-0159607,Metabolism,1.2648524695849126,0.1068536513545347,0.75
+UCD-0159608,Metabolism,1.2272652730986486,0.0848726504448706,0.0476298396230941
+UCD-0159609,Others,1.6234711628110308,0.3506497663401831,0.0550595238095238
+UCD-0159610,Transmembrane Transporters,1.0628391848209595,0.2337426199490236,0.0541104016277956
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+UCD-0159612,Cell Cycle,1.402920381917362,0.1899904855302886,0.1283807423193612
+UCD-0159613,PI3K/Akt/mTOR,1.4237099162273217,0.0720792844522968,0.1851851851851851
+UCD-0159614,Metabolism,1.179718414817915,0.246030227839819,0.0634491756974107
+UCD-0159615,Angiogenesis,1.0556671354162708,0.137546468401487,
+UCD-0159617,Endocrinology & Hormones,1.118089239595429,0.2300682352312313,0.1827094491398615
+UCD-0159618,MAPK,1.2789403769702394,0.378725892879094,0.0595334950441385
+UCD-0159619,GPCR & G Protein,3.102501055842723,0.6815906732588659,0.0124137931034482
+UCD-0159620,DNA Damage,1.0349366057798786,0.1477093103191596,0.0673090267220816
+UCD-0159622,JAK/STAT,1.1587777041854044,0.3053185921849265,0.5751984126984127
+UCD-0159623,DNA Damage,2.5800193029262246,0.6990236362740021,0.3636904761904762
+UCD-0159624,GPCR & G Protein,1.5216638466524963,0.2299514537932226,0.2213921563940324
+UCD-0159625,Endocrinology & Hormones,0.9668346895330064,0.2403619684201176,0.0849281830915495
+UCD-0159626,Neuronal Signaling,1.2198103391846389,0.4693494779403283,0.0532991411807365
+UCD-0159627,Metabolism,1.081386331595864,0.3503461670240737,0.3360623781676413
+UCD-0159628,GPCR & G Protein,1.1048934090398137,0.2730489927311316,0.0344280155518796
+UCD-0159629,Angiogenesis,3.6719114303906935,0.5186012613360068,0.453556166056166
+UCD-0159630,Transmembrane Transporters,1.115912785892497,0.1659915338841672,0.103258686330031
+UCD-0159631,Cell Cycle,1.1446976198971388,0.2068357966355719,0.0941424948432392
+UCD-0159632,Angiogenesis,2.121123850698978,0.639459701683631,0.1217948717948717
+UCD-0159633,Apoptosis,0.9886803882010984,0.1735491713332272,0.0379699752241458
+UCD-0159634,Cytoskeletal Signaling,2.473926752336852,0.1110471656159258,0.1979166666666666
+UCD-0159635,MAPK,1.2994804092712184,0.1286089238845144,
+UCD-0159636,Protein Tyrosine Kinase,1.042417723145029,0.2246671416326852,0.2032644669086316
+UCD-0159637,Others,1.2731680308546682,0.4141517980292331,0.6105248917748918
+UCD-0159639,Protein Tyrosine Kinase,1.3049178159850698,0.4564191213679897,0.1495649133046674
+UCD-0159640,Epigenetics,0.9354881986385624,0.0942134009605741,0.0492001098739473
+UCD-0159641,JAK/STAT,1.2256716033885002,0.3924204703710015,0.0865719748054982
+UCD-0159642,Proteases,1.2164811644067697,0.0908149562700011,0.0362868233943388
+UCD-0159643,Metabolism,1.0639560481155432,0.225171553465301,0.4254900687529891
+UCD-0159644,Transmembrane Transporters,1.7215298097418186,0.4346727943238543,0.2267603231079979
+UCD-0159646,GPCR & G Protein,1.2505636479288396,0.2140817113179292,0.5198593073593074
+UCD-0159647,GPCR & G Protein,1.153124554194101,0.1354491204625478,0.0734226904367787
+UCD-0159648,GPCR & G Protein,1.0802740138925868,0.1740131347130624,0.0344456774320868
+UCD-0159649,Angiogenesis,1.145147728155213,0.2042113999490448,0.0417443124479189
+UCD-0159650,MAPK,1.1223145756370685,0.1489663830084689,0.146584003385786
+UCD-0159651,Angiogenesis,1.0662597466973986,0.1168599883723439,0.2421030420153842
+UCD-0159652,Proteases,1.1181717312107993,0.1741742348860018,0.0475370858642335
+UCD-0159653,Protein Tyrosine Kinase,1.093083816321906,0.1510683417223536,0.0433285277559257
+UCD-0159654,Epigenetics,1.001319303524704,0.1293831100415197,0.2269296607531901
+UCD-0159655,Epigenetics,1.1126111253698476,0.3232153560910748,0.0750343857162642
+UCD-0159657,Others,1.145189698063649,0.134490253741925,0.0482516237496793
+UCD-0159658,Autophagy,1.137644691822194,0.2300111226454602,0.3310515873015872
+UCD-0159659,Metabolism,1.026770251174604,0.2416128904190178,0.4968133718133718
+UCD-0159661,Transmembrane Transporters,4.169865592593308,0.3455671975464559,0.0856011416370903
+UCD-0159662,GPCR & G Protein,3.207150807696536,0.0,
+UCD-0159663,Protein Tyrosine Kinase,1.1091191269054832,0.3064682683274012,0.1278995517099926
+UCD-0159664,Epigenetics,1.140929677644607,0.4159975258659469,0.2504528289856034
+UCD-0159665,Others,1.135261635505837,0.2438217851236261,0.0451957468890368
+failing_DMSO,failing_DMSO,1.0,0.0,
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diff --git a/3a.train_dmso_logistic_regression_models/train_platemapwise_model/hit_calling_3d.html b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/hit_calling_3d.html
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+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8" />
+<title>plotly</title>
+<style type="text/css">@layer htmltools {
+.html-fill-container {
+display: flex;
+flex-direction: column;
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+min-height: 0;
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+
+flex: 1 1 auto;
+min-height: 0;
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+.html-fill-container > :not(.html-fill-item) {
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+flex: 0 0 auto;
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+<script>(function() {
+  // If window.HTMLWidgets is already defined, then use it; otherwise create a
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+  window.HTMLWidgets = window.HTMLWidgets || {};
+
+  // See if we're running in a viewer pane. If not, we're in a web browser.
+  var viewerMode = window.HTMLWidgets.viewerMode =
+      /\bviewer_pane=1\b/.test(window.location);
+
+  // See if we're running in Shiny mode. If not, it's a static document.
+  // Note that static widgets can appear in both Shiny and static modes, but
+  // obviously, Shiny widgets can only appear in Shiny apps/documents.
+  var shinyMode = window.HTMLWidgets.shinyMode =
+      typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings;
+
+  // We can't count on jQuery being available, so we implement our own
+  // version if necessary.
+  function querySelectorAll(scope, selector) {
+    if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) {
+      return scope.find(selector);
+    }
+    if (scope.querySelectorAll) {
+      return scope.querySelectorAll(selector);
+    }
+  }
+
+  function asArray(value) {
+    if (value === null)
+      return [];
+    if ($.isArray(value))
+      return value;
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+  // Implement jQuery's extend
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+  // IE8 doesn't support Array.forEach.
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+    if (values.forEach) {
+      values.forEach(callback, thisArg);
+    } else {
+      for (var i = 0; i < values.length; i++) {
+        callback.call(thisArg, values[i], i, values);
+      }
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+  }
+
+  // Replaces the specified method with the return value of funcSource.
+  //
+  // Note that funcSource should not BE the new method, it should be a function
+  // that RETURNS the new method. funcSource receives a single argument that is
+  // the overridden method, it can be called from the new method. The overridden
+  // method can be called like a regular function, it has the target permanently
+  // bound to it so "this" will work correctly.
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+    var superFunc = target[methodName] || function() {};
+    var superFuncBound = function() {
+      return superFunc.apply(target, arguments);
+    };
+    target[methodName] = funcSource(superFuncBound);
+  }
+
+  // Add a method to delegator that, when invoked, calls
+  // delegatee.methodName. If there is no such method on
+  // the delegatee, but there was one on delegator before
+  // delegateMethod was called, then the original version
+  // is invoked instead.
+  // For example:
+  //
+  // var a = {
+  //   method1: function() { console.log('a1'); }
+  //   method2: function() { console.log('a2'); }
+  // };
+  // var b = {
+  //   method1: function() { console.log('b1'); }
+  // };
+  // delegateMethod(a, b, "method1");
+  // delegateMethod(a, b, "method2");
+  // a.method1();
+  // a.method2();
+  //
+  // The output would be "b1", "a2".
+  function delegateMethod(delegator, delegatee, methodName) {
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+    delegator[methodName] = function() {
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+
+      // The method doesn't exist on the delegatee. Instead,
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+      if (!method) {
+        target = delegator;
+        method = inherited;
+      }
+
+      if (method) {
+        return method.apply(target, arguments);
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+  }
+
+  // Implement a vague facsimilie of jQuery's data method
+  function elementData(el, name, value) {
+    if (arguments.length == 2) {
+      return el["htmlwidget_data_" + name];
+    } else if (arguments.length == 3) {
+      el["htmlwidget_data_" + name] = value;
+      return el;
+    } else {
+      throw new Error("Wrong number of arguments for elementData: " +
+        arguments.length);
+    }
+  }
+
+  // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex
+  function escapeRegExp(str) {
+    return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&");
+  }
+
+  function hasClass(el, className) {
+    var re = new RegExp("\\b" + escapeRegExp(className) + "\\b");
+    return re.test(el.className);
+  }
+
+  // elements - array (or array-like object) of HTML elements
+  // className - class name to test for
+  // include - if true, only return elements with given className;
+  //   if false, only return elements *without* given className
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+    var results = [];
+    for (var i = 0; i < elements.length; i++) {
+      if (hasClass(elements[i], className) == include)
+        results.push(elements[i]);
+    }
+    return results;
+  }
+
+  function on(obj, eventName, func) {
+    if (obj.addEventListener) {
+      obj.addEventListener(eventName, func, false);
+    } else if (obj.attachEvent) {
+      obj.attachEvent(eventName, func);
+    }
+  }
+
+  function off(obj, eventName, func) {
+    if (obj.removeEventListener)
+      obj.removeEventListener(eventName, func, false);
+    else if (obj.detachEvent) {
+      obj.detachEvent(eventName, func);
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+  // Translate array of values to top/right/bottom/left, as usual with
+  // the "padding" CSS property
+  // https://developer.mozilla.org/en-US/docs/Web/CSS/padding
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+  }
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+  function paddingToCss(paddingObj) {
+    return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px";
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+
+  // Makes a number suitable for CSS
+  function px(x) {
+    if (typeof(x) === "number")
+      return x + "px";
+    else
+      return x;
+  }
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+  // Retrieves runtime widget sizing information for an element.
+  // The return value is either null, or an object with fill, padding,
+  // defaultWidth, defaultHeight fields.
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+    var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']");
+    if (!sizingEl)
+      return null;
+    var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}");
+    if (viewerMode) {
+      return sp.viewer;
+    } else {
+      return sp.browser;
+    }
+  }
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+  // @param tasks Array of strings (or falsy value, in which case no-op).
+  //   Each element must be a valid JavaScript expression that yields a
+  //   function. Or, can be an array of objects with "code" and "data"
+  //   properties; in this case, the "code" property should be a string
+  //   of JS that's an expr that yields a function, and "data" should be
+  //   an object that will be added as an additional argument when that
+  //   function is called.
+  // @param target The object that will be "this" for each function
+  //   execution.
+  // @param args Array of arguments to be passed to the functions. (The
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+          task = task.code;
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+        if (typeof(taskFunc) !== "function") {
+          throw new Error("Task must be a function! Source:\n" + task);
+        }
+        taskFunc.apply(target, theseArgs);
+      });
+    }
+  }
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+  // Attempt eval() both with and without enclosing in parentheses.
+  // Note that enclosing coerces a function declaration into
+  // an expression that eval() can parse
+  // (otherwise, a SyntaxError is thrown)
+  function tryEval(code) {
+    var result = null;
+    try {
+      result = eval("(" + code + ")");
+    } catch(error) {
+      if (!(error instanceof SyntaxError)) {
+        throw error;
+      }
+      try {
+        result = eval(code);
+      } catch(e) {
+        if (e instanceof SyntaxError) {
+          throw error;
+        } else {
+          throw e;
+        }
+      }
+    }
+    return result;
+  }
+
+  function initSizing(el) {
+    var sizing = sizingPolicy(el);
+    if (!sizing)
+      return;
+
+    var cel = document.getElementById("htmlwidget_container");
+    if (!cel)
+      return;
+
+    if (typeof(sizing.padding) !== "undefined") {
+      document.body.style.margin = "0";
+      document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));
+    }
+
+    if (sizing.fill) {
+      document.body.style.overflow = "hidden";
+      document.body.style.width = "100%";
+      document.body.style.height = "100%";
+      document.documentElement.style.width = "100%";
+      document.documentElement.style.height = "100%";
+      cel.style.position = "absolute";
+      var pad = unpackPadding(sizing.padding);
+      cel.style.top = pad.top + "px";
+      cel.style.right = pad.right + "px";
+      cel.style.bottom = pad.bottom + "px";
+      cel.style.left = pad.left + "px";
+      el.style.width = "100%";
+      el.style.height = "100%";
+
+      return {
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+    } else {
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+      el.style.height = px(sizing.height);
+
+      return {
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+  }
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+  // Default implementations for methods
+  var defaults = {
+    find: function(scope) {
+      return querySelectorAll(scope, "." + this.name);
+    },
+    renderError: function(el, err) {
+      var $el = $(el);
+
+      this.clearError(el);
+
+      // Add all these error classes, as Shiny does
+      var errClass = "shiny-output-error";
+      if (err.type !== null) {
+        // use the classes of the error condition as CSS class names
+        errClass = errClass + " " + $.map(asArray(err.type), function(type) {
+          return errClass + "-" + type;
+        }).join(" ");
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+      errClass = errClass + " htmlwidgets-error";
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+      // Is el inline or block? If inline or inline-block, just display:none it
+      // and add an inline error.
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+      $el.data("restore-display-mode", display);
+
+      if (display === "inline" || display === "inline-block") {
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+        if (err.message !== "") {
+          var errorSpan = $("<span>").addClass(errClass);
+          errorSpan.text(err.message);
+          $el.after(errorSpan);
+        }
+      } else if (display === "block") {
+        // If block, add an error just after the el, set visibility:none on the
+        // el, and position the error to be on top of the el.
+        // Mark it with a unique ID and CSS class so we can remove it later.
+        $el.css("visibility", "hidden");
+        if (err.message !== "") {
+          var errorDiv = $("<div>").addClass(errClass).css("position", "absolute")
+            .css("top", el.offsetTop)
+            .css("left", el.offsetLeft)
+            // setting width can push out the page size, forcing otherwise
+            // unnecessary scrollbars to appear and making it impossible for
+            // the element to shrink; so use max-width instead
+            .css("maxWidth", el.offsetWidth)
+            .css("height", el.offsetHeight);
+          errorDiv.text(err.message);
+          $el.after(errorDiv);
+
+          // Really dumb way to keep the size/position of the error in sync with
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+          var intId = setInterval(function() {
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+              return;
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+            errorDiv
+              .css("top", el.offsetTop)
+              .css("left", el.offsetLeft)
+              .css("maxWidth", el.offsetWidth)
+              .css("height", el.offsetHeight);
+          }, 500);
+        }
+      }
+    },
+    clearError: function(el) {
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+      var display = $el.data("restore-display-mode");
+      $el.data("restore-display-mode", null);
+
+      if (display === "inline" || display === "inline-block") {
+        if (display)
+          $el.css("display", display);
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      } else if (display === "block"){
+        $el.css("visibility", "inherit");
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      }
+    },
+    sizing: {}
+  };
+
+  // Called by widget bindings to register a new type of widget. The definition
+  // object can contain the following properties:
+  // - name (required) - A string indicating the binding name, which will be
+  //   used by default as the CSS classname to look for.
+  // - initialize (optional) - A function(el) that will be called once per
+  //   widget element; if a value is returned, it will be passed as the third
+  //   value to renderValue.
+  // - renderValue (required) - A function(el, data, initValue) that will be
+  //   called with data. Static contexts will cause this to be called once per
+  //   element; Shiny apps will cause this to be called multiple times per
+  //   element, as the data changes.
+  window.HTMLWidgets.widget = function(definition) {
+    if (!definition.name) {
+      throw new Error("Widget must have a name");
+    }
+    if (!definition.type) {
+      throw new Error("Widget must have a type");
+    }
+    // Currently we only support output widgets
+    if (definition.type !== "output") {
+      throw new Error("Unrecognized widget type '" + definition.type + "'");
+    }
+    // TODO: Verify that .name is a valid CSS classname
+
+    // Support new-style instance-bound definitions. Old-style class-bound
+    // definitions have one widget "object" per widget per type/class of
+    // widget; the renderValue and resize methods on such widget objects
+    // take el and instance arguments, because the widget object can't
+    // store them. New-style instance-bound definitions have one widget
+    // object per widget instance; the definition that's passed in doesn't
+    // provide renderValue or resize methods at all, just the single method
+    //   factory(el, width, height)
+    // which returns an object that has renderValue(x) and resize(w, h).
+    // This enables a far more natural programming style for the widget
+    // author, who can store per-instance state using either OO-style
+    // instance fields or functional-style closure variables (I guess this
+    // is in contrast to what can only be called C-style pseudo-OO which is
+    // what we required before).
+    if (definition.factory) {
+      definition = createLegacyDefinitionAdapter(definition);
+    }
+
+    if (!definition.renderValue) {
+      throw new Error("Widget must have a renderValue function");
+    }
+
+    // For static rendering (non-Shiny), use a simple widget registration
+    // scheme. We also use this scheme for Shiny apps/documents that also
+    // contain static widgets.
+    window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
+    // Merge defaults into the definition; don't mutate the original definition.
+    var staticBinding = extend({}, defaults, definition);
+    overrideMethod(staticBinding, "find", function(superfunc) {
+      return function(scope) {
+        var results = superfunc(scope);
+        // Filter out Shiny outputs, we only want the static kind
+        return filterByClass(results, "html-widget-output", false);
+      };
+    });
+    window.HTMLWidgets.widgets.push(staticBinding);
+
+    if (shinyMode) {
+      // Shiny is running. Register the definition with an output binding.
+      // The definition itself will not be the output binding, instead
+      // we will make an output binding object that delegates to the
+      // definition. This is because we foolishly used the same method
+      // name (renderValue) for htmlwidgets definition and Shiny bindings
+      // but they actually have quite different semantics (the Shiny
+      // bindings receive data that includes lots of metadata that it
+      // strips off before calling htmlwidgets renderValue). We can't
+      // just ignore the difference because in some widgets it's helpful
+      // to call this.renderValue() from inside of resize(), and if
+      // we're not delegating, then that call will go to the Shiny
+      // version instead of the htmlwidgets version.
+
+      // Merge defaults with definition, without mutating either.
+      var bindingDef = extend({}, defaults, definition);
+
+      // This object will be our actual Shiny binding.
+      var shinyBinding = new Shiny.OutputBinding();
+
+      // With a few exceptions, we'll want to simply use the bindingDef's
+      // version of methods if they are available, otherwise fall back to
+      // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
+      // methods in the future, and we want them to be overrideable by
+      // HTMLWidget binding definitions, then we'll need to add them to this
+      // list.
+      delegateMethod(shinyBinding, bindingDef, "getId");
+      delegateMethod(shinyBinding, bindingDef, "onValueChange");
+      delegateMethod(shinyBinding, bindingDef, "onValueError");
+      delegateMethod(shinyBinding, bindingDef, "renderError");
+      delegateMethod(shinyBinding, bindingDef, "clearError");
+      delegateMethod(shinyBinding, bindingDef, "showProgress");
+
+      // The find, renderValue, and resize are handled differently, because we
+      // want to actually decorate the behavior of the bindingDef methods.
+
+      shinyBinding.find = function(scope) {
+        var results = bindingDef.find(scope);
+
+        // Only return elements that are Shiny outputs, not static ones
+        var dynamicResults = results.filter(".html-widget-output");
+
+        // It's possible that whatever caused Shiny to think there might be
+        // new dynamic outputs, also caused there to be new static outputs.
+        // Since there might be lots of different htmlwidgets bindings, we
+        // schedule execution for later--no need to staticRender multiple
+        // times.
+        if (results.length !== dynamicResults.length)
+          scheduleStaticRender();
+
+        return dynamicResults;
+      };
+
+      // Wrap renderValue to handle initialization, which unfortunately isn't
+      // supported natively by Shiny at the time of this writing.
+
+      shinyBinding.renderValue = function(el, data) {
+        Shiny.renderDependencies(data.deps);
+        // Resolve strings marked as javascript literals to objects
+        if (!(data.evals instanceof Array)) data.evals = [data.evals];
+        for (var i = 0; data.evals && i < data.evals.length; i++) {
+          window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
+        }
+        if (!bindingDef.renderOnNullValue) {
+          if (data.x === null) {
+            el.style.visibility = "hidden";
+            return;
+          } else {
+            el.style.visibility = "inherit";
+          }
+        }
+        if (!elementData(el, "initialized")) {
+          initSizing(el);
+
+          elementData(el, "initialized", true);
+          if (bindingDef.initialize) {
+            var rect = el.getBoundingClientRect();
+            var result = bindingDef.initialize(el, rect.width, rect.height);
+            elementData(el, "init_result", result);
+          }
+        }
+        bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
+        evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
+      };
+
+      // Only override resize if bindingDef implements it
+      if (bindingDef.resize) {
+        shinyBinding.resize = function(el, width, height) {
+          // Shiny can call resize before initialize/renderValue have been
+          // called, which doesn't make sense for widgets.
+          if (elementData(el, "initialized")) {
+            bindingDef.resize(el, width, height, elementData(el, "init_result"));
+          }
+        };
+      }
+
+      Shiny.outputBindings.register(shinyBinding, bindingDef.name);
+    }
+  };
+
+  var scheduleStaticRenderTimerId = null;
+  function scheduleStaticRender() {
+    if (!scheduleStaticRenderTimerId) {
+      scheduleStaticRenderTimerId = setTimeout(function() {
+        scheduleStaticRenderTimerId = null;
+        window.HTMLWidgets.staticRender();
+      }, 1);
+    }
+  }
+
+  // Render static widgets after the document finishes loading
+  // Statically render all elements that are of this widget's class
+  window.HTMLWidgets.staticRender = function() {
+    var bindings = window.HTMLWidgets.widgets || [];
+    forEach(bindings, function(binding) {
+      var matches = binding.find(document.documentElement);
+      forEach(matches, function(el) {
+        var sizeObj = initSizing(el, binding);
+
+        var getSize = function(el) {
+          if (sizeObj) {
+            return {w: sizeObj.getWidth(), h: sizeObj.getHeight()}
+          } else {
+            var rect = el.getBoundingClientRect();
+            return {w: rect.width, h: rect.height}
+          }
+        };
+
+        if (hasClass(el, "html-widget-static-bound"))
+          return;
+        el.className = el.className + " html-widget-static-bound";
+
+        var initResult;
+        if (binding.initialize) {
+          var size = getSize(el);
+          initResult = binding.initialize(el, size.w, size.h);
+          elementData(el, "init_result", initResult);
+        }
+
+        if (binding.resize) {
+          var lastSize = getSize(el);
+          var resizeHandler = function(e) {
+            var size = getSize(el);
+            if (size.w === 0 && size.h === 0)
+              return;
+            if (size.w === lastSize.w && size.h === lastSize.h)
+              return;
+            lastSize = size;
+            binding.resize(el, size.w, size.h, initResult);
+          };
+
+          on(window, "resize", resizeHandler);
+
+          // This is needed for cases where we're running in a Shiny
+          // app, but the widget itself is not a Shiny output, but
+          // rather a simple static widget. One example of this is
+          // an rmarkdown document that has runtime:shiny and widget
+          // that isn't in a render function. Shiny only knows to
+          // call resize handlers for Shiny outputs, not for static
+          // widgets, so we do it ourselves.
+          if (window.jQuery) {
+            window.jQuery(document).on(
+              "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+            window.jQuery(document).on(
+              "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+          }
+
+          // This is needed for the specific case of ioslides, which
+          // flips slides between display:none and display:block.
+          // Ideally we would not have to have ioslide-specific code
+          // here, but rather have ioslides raise a generic event,
+          // but the rmarkdown package just went to CRAN so the
+          // window to getting that fixed may be long.
+          if (window.addEventListener) {
+            // It's OK to limit this to window.addEventListener
+            // browsers because ioslides itself only supports
+            // such browsers.
+            on(document, "slideenter", resizeHandler);
+            on(document, "slideleave", resizeHandler);
+          }
+        }
+
+        var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
+        if (scriptData) {
+          var data = JSON.parse(scriptData.textContent || scriptData.text);
+          // Resolve strings marked as javascript literals to objects
+          if (!(data.evals instanceof Array)) data.evals = [data.evals];
+          for (var k = 0; data.evals && k < data.evals.length; k++) {
+            window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
+          }
+          binding.renderValue(el, data.x, initResult);
+          evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
+        }
+      });
+    });
+
+    invokePostRenderHandlers();
+  }
+
+
+  function has_jQuery3() {
+    if (!window.jQuery) {
+      return false;
+    }
+    var $version = window.jQuery.fn.jquery;
+    var $major_version = parseInt($version.split(".")[0]);
+    return $major_version >= 3;
+  }
+
+  /*
+  / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
+  / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
+  / really means $(setTimeout(fn)).
+  / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
+  /
+  / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
+  / one tick later than it did before, which means staticRender() is
+  / called renderValue() earlier than (advanced) widget authors might be expecting.
+  / https://github.com/rstudio/shiny/issues/2630
+  /
+  / For a concrete example, leaflet has some methods (e.g., updateBounds)
+  / which reference Shiny methods registered in initShiny (e.g., setInputValue).
+  / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
+  / delay execution of those methods (until Shiny methods are ready)
+  / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
+  /
+  / Ideally widget authors wouldn't need to use this setTimeout() hack that
+  / leaflet uses to call Shiny methods on a staticRender(). In the long run,
+  / the logic initShiny should be broken up so that method registration happens
+  / right away, but binding happens later.
+  */
+  function maybeStaticRenderLater() {
+    if (shinyMode && has_jQuery3()) {
+      window.jQuery(window.HTMLWidgets.staticRender);
+    } else {
+      window.HTMLWidgets.staticRender();
+    }
+  }
+
+  if (document.addEventListener) {
+    document.addEventListener("DOMContentLoaded", function() {
+      document.removeEventListener("DOMContentLoaded", arguments.callee, false);
+      maybeStaticRenderLater();
+    }, false);
+  } else if (document.attachEvent) {
+    document.attachEvent("onreadystatechange", function() {
+      if (document.readyState === "complete") {
+        document.detachEvent("onreadystatechange", arguments.callee);
+        maybeStaticRenderLater();
+      }
+    });
+  }
+
+
+  window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
+    // If no key, default to the first item
+    if (typeof(key) === "undefined")
+      key = 1;
+
+    var link = document.getElementById(depname + "-" + key + "-attachment");
+    if (!link) {
+      throw new Error("Attachment " + depname + "/" + key + " not found in document");
+    }
+    return link.getAttribute("href");
+  };
+
+  window.HTMLWidgets.dataframeToD3 = function(df) {
+    var names = [];
+    var length;
+    for (var name in df) {
+        if (df.hasOwnProperty(name))
+            names.push(name);
+        if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
+            throw new Error("All fields must be arrays");
+        } else if (typeof(length) !== "undefined" && length !== df[name].length) {
+            throw new Error("All fields must be arrays of the same length");
+        }
+        length = df[name].length;
+    }
+    var results = [];
+    var item;
+    for (var row = 0; row < length; row++) {
+        item = {};
+        for (var col = 0; col < names.length; col++) {
+            item[names[col]] = df[names[col]][row];
+        }
+        results.push(item);
+    }
+    return results;
+  };
+
+  window.HTMLWidgets.transposeArray2D = function(array) {
+      if (array.length === 0) return array;
+      var newArray = array[0].map(function(col, i) {
+          return array.map(function(row) {
+              return row[i]
+          })
+      });
+      return newArray;
+  };
+  // Split value at splitChar, but allow splitChar to be escaped
+  // using escapeChar. Any other characters escaped by escapeChar
+  // will be included as usual (including escapeChar itself).
+  function splitWithEscape(value, splitChar, escapeChar) {
+    var results = [];
+    var escapeMode = false;
+    var currentResult = "";
+    for (var pos = 0; pos < value.length; pos++) {
+      if (!escapeMode) {
+        if (value[pos] === splitChar) {
+          results.push(currentResult);
+          currentResult = "";
+        } else if (value[pos] === escapeChar) {
+          escapeMode = true;
+        } else {
+          currentResult += value[pos];
+        }
+      } else {
+        currentResult += value[pos];
+        escapeMode = false;
+      }
+    }
+    if (currentResult !== "") {
+      results.push(currentResult);
+    }
+    return results;
+  }
+  // Function authored by Yihui/JJ Allaire
+  window.HTMLWidgets.evaluateStringMember = function(o, member) {
+    var parts = splitWithEscape(member, '.', '\\');
+    for (var i = 0, l = parts.length; i < l; i++) {
+      var part = parts[i];
+      // part may be a character or 'numeric' member name
+      if (o !== null && typeof o === "object" && part in o) {
+        if (i == (l - 1)) { // if we are at the end of the line then evalulate
+          if (typeof o[part] === "string")
+            o[part] = tryEval(o[part]);
+        } else { // otherwise continue to next embedded object
+          o = o[part];
+        }
+      }
+    }
+  };
+
+  // Retrieve the HTMLWidget instance (i.e. the return value of an
+  // HTMLWidget binding's initialize() or factory() function)
+  // associated with an element, or null if none.
+  window.HTMLWidgets.getInstance = function(el) {
+    return elementData(el, "init_result");
+  };
+
+  // Finds the first element in the scope that matches the selector,
+  // and returns the HTMLWidget instance (i.e. the return value of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with that element, if any. If no element matches the
+  // selector, or the first matching element has no HTMLWidget
+  // instance associated with it, then null is returned.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.find = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var el = scope.querySelector(selector);
+    if (el === null) {
+      return null;
+    } else {
+      return window.HTMLWidgets.getInstance(el);
+    }
+  };
+
+  // Finds all elements in the scope that match the selector, and
+  // returns the HTMLWidget instances (i.e. the return values of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with the elements, in an array. If elements that
+  // match the selector don't have an associated HTMLWidget
+  // instance, the returned array will contain nulls.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.findAll = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var nodes = scope.querySelectorAll(selector);
+    var results = [];
+    for (var i = 0; i < nodes.length; i++) {
+      results.push(window.HTMLWidgets.getInstance(nodes[i]));
+    }
+    return results;
+  };
+
+  var postRenderHandlers = [];
+  function invokePostRenderHandlers() {
+    while (postRenderHandlers.length) {
+      var handler = postRenderHandlers.shift();
+      if (handler) {
+        handler();
+      }
+    }
+  }
+
+  // Register the given callback function to be invoked after the
+  // next time static widgets are rendered.
+  window.HTMLWidgets.addPostRenderHandler = function(callback) {
+    postRenderHandlers.push(callback);
+  };
+
+  // Takes a new-style instance-bound definition, and returns an
+  // old-style class-bound definition. This saves us from having
+  // to rewrite all the logic in this file to accomodate both
+  // types of definitions.
+  function createLegacyDefinitionAdapter(defn) {
+    var result = {
+      name: defn.name,
+      type: defn.type,
+      initialize: function(el, width, height) {
+        return defn.factory(el, width, height);
+      },
+      renderValue: function(el, x, instance) {
+        return instance.renderValue(x);
+      },
+      resize: function(el, width, height, instance) {
+        return instance.resize(width, height);
+      }
+    };
+
+    if (defn.find)
+      result.find = defn.find;
+    if (defn.renderError)
+      result.renderError = defn.renderError;
+    if (defn.clearError)
+      result.clearError = defn.clearError;
+
+    return result;
+  }
+})();
+</script>
+<script>
+HTMLWidgets.widget({
+  name: "plotly",
+  type: "output",
+
+  initialize: function(el, width, height) {
+    return {};
+  },
+
+  resize: function(el, width, height, instance) {
+    if (instance.autosize) {
+      var width = instance.width || width;
+      var height = instance.height || height;
+      Plotly.relayout(el.id, {width: width, height: height});
+    }
+  },  
+  
+  renderValue: function(el, x, instance) {
+    
+    // Plotly.relayout() mutates the plot input object, so make sure to 
+    // keep a reference to the user-supplied width/height *before*
+    // we call Plotly.plot();
+    var lay = x.layout || {};
+    instance.width = lay.width;
+    instance.height = lay.height;
+    instance.autosize = lay.autosize || true;
+    
+    /* 
+    / 'inform the world' about highlighting options this is so other
+    / crosstalk libraries have a chance to respond to special settings 
+    / such as persistent selection. 
+    / AFAIK, leaflet is the only library with such intergration
+    / https://github.com/rstudio/leaflet/pull/346/files#diff-ad0c2d51ce5fdf8c90c7395b102f4265R154
+    */
+    var ctConfig = crosstalk.var('plotlyCrosstalkOpts').set(x.highlight);
+      
+    if (typeof(window) !== "undefined") {
+      // make sure plots don't get created outside the network (for on-prem)
+      window.PLOTLYENV = window.PLOTLYENV || {};
+      window.PLOTLYENV.BASE_URL = x.base_url;
+      
+      // Enable persistent selection when shift key is down
+      // https://stackoverflow.com/questions/1828613/check-if-a-key-is-down
+      var persistOnShift = function(e) {
+        if (!e) window.event;
+        if (e.shiftKey) { 
+          x.highlight.persistent = true; 
+          x.highlight.persistentShift = true;
+        } else {
+          x.highlight.persistent = false; 
+          x.highlight.persistentShift = false;
+        }
+      };
+      
+      // Only relevant if we haven't forced persistent mode at command line
+      if (!x.highlight.persistent) {
+        window.onmousemove = persistOnShift;
+      }
+    }
+
+    var graphDiv = document.getElementById(el.id);
+    
+    // TODO: move the control panel injection strategy inside here...
+    HTMLWidgets.addPostRenderHandler(function() {
+      
+      // lower the z-index of the modebar to prevent it from highjacking hover
+      // (TODO: do this via CSS?)
+      // https://github.com/ropensci/plotly/issues/956
+      // https://www.w3schools.com/jsref/prop_style_zindex.asp
+      var modebars = document.querySelectorAll(".js-plotly-plot .plotly .modebar");
+      for (var i = 0; i < modebars.length; i++) {
+        modebars[i].style.zIndex = 1;
+      }
+    });
+      
+      // inject a "control panel" holding selectize/dynamic color widget(s)
+    if ((x.selectize || x.highlight.dynamic) && !instance.plotly) {
+      var flex = document.createElement("div");
+      flex.class = "plotly-crosstalk-control-panel";
+      flex.style = "display: flex; flex-wrap: wrap";
+      
+      // inject the colourpicker HTML container into the flexbox
+      if (x.highlight.dynamic) {
+        var pickerDiv = document.createElement("div");
+        
+        var pickerInput = document.createElement("input");
+        pickerInput.id = el.id + "-colourpicker";
+        pickerInput.placeholder = "asdasd";
+        
+        var pickerLabel = document.createElement("label");
+        pickerLabel.for = pickerInput.id;
+        pickerLabel.innerHTML = "Brush color&nbsp;&nbsp;";
+        
+        pickerDiv.appendChild(pickerLabel);
+        pickerDiv.appendChild(pickerInput);
+        flex.appendChild(pickerDiv);
+      }
+      
+      // inject selectize HTML containers (one for every crosstalk group)
+      if (x.selectize) {
+        var ids = Object.keys(x.selectize);
+        
+        for (var i = 0; i < ids.length; i++) {
+          var container = document.createElement("div");
+          container.id = ids[i];
+          container.style = "width: 80%; height: 10%";
+          container.class = "form-group crosstalk-input-plotly-highlight";
+          
+          var label = document.createElement("label");
+          label.for = ids[i];
+          label.innerHTML = x.selectize[ids[i]].group;
+          label.class = "control-label";
+          
+          var selectDiv = document.createElement("div");
+          var select = document.createElement("select");
+          select.multiple = true;
+          
+          selectDiv.appendChild(select);
+          container.appendChild(label);
+          container.appendChild(selectDiv);
+          flex.appendChild(container);
+        }
+      }
+      
+      // finally, insert the flexbox inside the htmlwidget container,
+      // but before the plotly graph div
+      graphDiv.parentElement.insertBefore(flex, graphDiv);
+      
+      if (x.highlight.dynamic) {
+        var picker = $("#" + pickerInput.id);
+        var colors = x.highlight.color || [];
+        // TODO: let users specify options?
+        var opts = {
+          value: colors[0],
+          showColour: "both",
+          palette: "limited",
+          allowedCols: colors.join(" "),
+          width: "20%",
+          height: "10%"
+        };
+        picker.colourpicker({changeDelay: 0});
+        picker.colourpicker("settings", opts);
+        picker.colourpicker("value", opts.value);
+        // inform crosstalk about a change in the current selection colour
+        var grps = x.highlight.ctGroups || [];
+        for (var i = 0; i < grps.length; i++) {
+          crosstalk.group(grps[i]).var('plotlySelectionColour')
+            .set(picker.colourpicker('value'));
+        }
+        picker.on("change", function() {
+          for (var i = 0; i < grps.length; i++) {
+            crosstalk.group(grps[i]).var('plotlySelectionColour')
+              .set(picker.colourpicker('value'));
+          }
+        });
+      }
+    }
+    
+    // if no plot exists yet, create one with a particular configuration
+    if (!instance.plotly) {
+      
+      var plot = Plotly.newPlot(graphDiv, x);
+      instance.plotly = true;
+      
+    } else if (x.layout.transition) {
+      
+      var plot = Plotly.react(graphDiv, x);
+    
+    } else {
+      
+      // this is essentially equivalent to Plotly.newPlot(), but avoids creating 
+      // a new webgl context
+      // https://github.com/plotly/plotly.js/blob/2b24f9def901831e61282076cf3f835598d56f0e/src/plot_api/plot_api.js#L531-L532
+
+      // TODO: restore crosstalk selections?
+      Plotly.purge(graphDiv);
+      // TODO: why is this necessary to get crosstalk working?
+      graphDiv.data = undefined;
+      graphDiv.layout = undefined;
+      var plot = Plotly.newPlot(graphDiv, x);
+    }
+    
+    // Trigger plotly.js calls defined via `plotlyProxy()`
+    plot.then(function() {
+      if (HTMLWidgets.shinyMode) {
+        Shiny.addCustomMessageHandler("plotly-calls", function(msg) {
+          var gd = document.getElementById(msg.id);
+          if (!gd) {
+            throw new Error("Couldn't find plotly graph with id: " + msg.id);
+          }
+          // This isn't an official plotly.js method, but it's the only current way to 
+          // change just the configuration of a plot 
+          // https://community.plot.ly/t/update-config-function/9057
+          if (msg.method == "reconfig") {
+            Plotly.react(gd, gd.data, gd.layout, msg.args);
+            return;
+          }
+          if (!Plotly[msg.method]) {
+            throw new Error("Unknown method " + msg.method);
+          }
+          var args = [gd].concat(msg.args);
+          Plotly[msg.method].apply(null, args);
+        });
+      }
+      
+      // plotly's mapbox API doesn't currently support setting bounding boxes
+      // https://www.mapbox.com/mapbox-gl-js/example/fitbounds/
+      // so we do this manually...
+      // TODO: make sure this triggers on a redraw and relayout as well as on initial draw
+      var mapboxIDs = graphDiv._fullLayout._subplots.mapbox || [];
+      for (var i = 0; i < mapboxIDs.length; i++) {
+        var id = mapboxIDs[i];
+        var mapOpts = x.layout[id] || {};
+        var args = mapOpts._fitBounds || {};
+        if (!args) {
+          continue;
+        }
+        var mapObj = graphDiv._fullLayout[id]._subplot.map;
+        mapObj.fitBounds(args.bounds, args.options);
+      }
+      
+    });
+    
+    // Attach attributes (e.g., "key", "z") to plotly event data
+    function eventDataWithKey(eventData) {
+      if (eventData === undefined || !eventData.hasOwnProperty("points")) {
+        return null;
+      }
+      return eventData.points.map(function(pt) {
+        var obj = {
+          curveNumber: pt.curveNumber, 
+          pointNumber: pt.pointNumber, 
+          x: pt.x,
+          y: pt.y
+        };
+        
+        // If 'z' is reported with the event data, then use it!
+        if (pt.hasOwnProperty("z")) {
+          obj.z = pt.z;
+        }
+        
+        if (pt.hasOwnProperty("customdata")) {
+          obj.customdata = pt.customdata;
+        }
+        
+        /* 
+          TL;DR: (I think) we have to select the graph div (again) to attach keys...
+          
+          Why? Remember that crosstalk will dynamically add/delete traces 
+          (see traceManager.prototype.updateSelection() below)
+          For this reason, we can't simply grab keys from x.data (like we did previously)
+          Moreover, we can't use _fullData, since that doesn't include 
+          unofficial attributes. It's true that click/hover events fire with 
+          pt.data, but drag events don't...
+        */
+        var gd = document.getElementById(el.id);
+        var trace = gd.data[pt.curveNumber];
+        
+        if (!trace._isSimpleKey) {
+          var attrsToAttach = ["key"];
+        } else {
+          // simple keys fire the whole key
+          obj.key = trace.key;
+          var attrsToAttach = [];
+        }
+        
+        for (var i = 0; i < attrsToAttach.length; i++) {
+          var attr = trace[attrsToAttach[i]];
+          if (Array.isArray(attr)) {
+            if (typeof pt.pointNumber === "number") {
+              obj[attrsToAttach[i]] = attr[pt.pointNumber];
+            } else if (Array.isArray(pt.pointNumber)) {
+              obj[attrsToAttach[i]] = attr[pt.pointNumber[0]][pt.pointNumber[1]];
+            } else if (Array.isArray(pt.pointNumbers)) {
+              obj[attrsToAttach[i]] = pt.pointNumbers.map(function(idx) { return attr[idx]; });
+            }
+          }
+        }
+        return obj;
+      });
+    }
+    
+    
+    var legendEventData = function(d) {
+      // if legendgroup is not relevant just return the trace
+      var trace = d.data[d.curveNumber];
+      if (!trace.legendgroup) return trace;
+      
+      // if legendgroup was specified, return all traces that match the group
+      var legendgrps = d.data.map(function(trace){ return trace.legendgroup; });
+      var traces = [];
+      for (i = 0; i < legendgrps.length; i++) {
+        if (legendgrps[i] == trace.legendgroup) {
+          traces.push(d.data[i]);
+        }
+      }
+      
+      return traces;
+    };
+
+    
+    // send user input event data to shiny
+    if (HTMLWidgets.shinyMode && Shiny.setInputValue) {
+      
+      // Some events clear other input values
+      // TODO: always register these?
+      var eventClearMap = {
+        plotly_deselect: ["plotly_selected", "plotly_selecting", "plotly_brushed", "plotly_brushing", "plotly_click"],
+        plotly_unhover: ["plotly_hover"],
+        plotly_doubleclick: ["plotly_click"]
+      };
+    
+      Object.keys(eventClearMap).map(function(evt) {
+        graphDiv.on(evt, function() {
+          var inputsToClear = eventClearMap[evt];
+          inputsToClear.map(function(input) {
+            Shiny.setInputValue(input + "-" + x.source, null, {priority: "event"});
+          });
+        });
+      });
+      
+      var eventDataFunctionMap = {
+        plotly_click: eventDataWithKey,
+        plotly_sunburstclick: eventDataWithKey,
+        plotly_hover: eventDataWithKey,
+        plotly_unhover: eventDataWithKey,
+        // If 'plotly_selected' has already been fired, and you click
+        // on the plot afterwards, this event fires `undefined`?!?
+        // That might be considered a plotly.js bug, but it doesn't make 
+        // sense for this input change to occur if `d` is falsy because,
+        // even in the empty selection case, `d` is truthy (an object),
+        // and the 'plotly_deselect' event will reset this input
+        plotly_selected: function(d) { if (d) { return eventDataWithKey(d); } },
+        plotly_selecting: function(d) { if (d) { return eventDataWithKey(d); } },
+        plotly_brushed: function(d) {
+          if (d) { return d.range ? d.range : d.lassoPoints; }
+        },
+        plotly_brushing: function(d) {
+          if (d) { return d.range ? d.range : d.lassoPoints; }
+        },
+        plotly_legendclick: legendEventData,
+        plotly_legenddoubleclick: legendEventData,
+        plotly_clickannotation: function(d) { return d.fullAnnotation }
+      };
+      
+      var registerShinyValue = function(event) {
+        var eventDataPreProcessor = eventDataFunctionMap[event] || function(d) { return d ? d : el.id };
+        // some events are unique to the R package
+        var plotlyJSevent = (event == "plotly_brushed") ? "plotly_selected" : (event == "plotly_brushing") ? "plotly_selecting" : event;
+        // register the event
+        graphDiv.on(plotlyJSevent, function(d) {
+          Shiny.setInputValue(
+            event + "-" + x.source,
+            JSON.stringify(eventDataPreProcessor(d)),
+            {priority: "event"}
+          );
+        });
+      }
+    
+      var shinyEvents = x.shinyEvents || [];
+      shinyEvents.map(registerShinyValue);
+    }
+    
+    // Given an array of {curveNumber: x, pointNumber: y} objects,
+    // return a hash of {
+    //   set1: {value: [key1, key2, ...], _isSimpleKey: false}, 
+    //   set2: {value: [key3, key4, ...], _isSimpleKey: false}
+    // }
+    function pointsToKeys(points) {
+      var keysBySet = {};
+      for (var i = 0; i < points.length; i++) {
+        
+        var trace = graphDiv.data[points[i].curveNumber];
+        if (!trace.key || !trace.set) {
+          continue;
+        }
+        
+        // set defaults for this keySet
+        // note that we don't track the nested property (yet) since we always 
+        // emit the union -- http://cpsievert.github.io/talks/20161212b/#21
+        keysBySet[trace.set] = keysBySet[trace.set] || {
+          value: [],
+          _isSimpleKey: trace._isSimpleKey
+        };
+        
+        // Use pointNumber by default, but aggregated traces should emit pointNumbers
+        var ptNum = points[i].pointNumber;
+        var hasPtNum = typeof ptNum === "number";
+        var ptNum = hasPtNum ? ptNum : points[i].pointNumbers;
+        
+        // selecting a point of a "simple" trace means: select the 
+        // entire key attached to this trace, which is useful for,
+        // say clicking on a fitted line to select corresponding observations 
+        var key = trace._isSimpleKey ? trace.key : Array.isArray(ptNum) ? ptNum.map(function(idx) { return trace.key[idx]; }) : trace.key[ptNum];
+        // http://stackoverflow.com/questions/10865025/merge-flatten-an-array-of-arrays-in-javascript
+        var keyFlat = trace._isNestedKey ? [].concat.apply([], key) : key;
+        
+        // TODO: better to only add new values?
+        keysBySet[trace.set].value = keysBySet[trace.set].value.concat(keyFlat);
+      }
+      
+      return keysBySet;
+    }
+    
+    
+    x.highlight.color = x.highlight.color || [];
+    // make sure highlight color is an array
+    if (!Array.isArray(x.highlight.color)) {
+      x.highlight.color = [x.highlight.color];
+    }
+
+    var traceManager = new TraceManager(graphDiv, x.highlight);
+
+    // Gather all *unique* sets.
+    var allSets = [];
+    for (var curveIdx = 0; curveIdx < x.data.length; curveIdx++) {
+      var newSet = x.data[curveIdx].set;
+      if (newSet) {
+        if (allSets.indexOf(newSet) === -1) {
+          allSets.push(newSet);
+        }
+      }
+    }
+
+    // register event listeners for all sets
+    for (var i = 0; i < allSets.length; i++) {
+      
+      var set = allSets[i];
+      var selection = new crosstalk.SelectionHandle(set);
+      var filter = new crosstalk.FilterHandle(set);
+      
+      var filterChange = function(e) {
+        removeBrush(el);
+        traceManager.updateFilter(set, e.value);
+      };
+      filter.on("change", filterChange);
+      
+      
+      var selectionChange = function(e) {
+        
+        // Workaround for 'plotly_selected' now firing previously selected
+        // points (in addition to new ones) when holding shift key. In our case,
+        // we just want the new keys 
+        if (x.highlight.on === "plotly_selected" && x.highlight.persistentShift) {
+          // https://stackoverflow.com/questions/1187518/how-to-get-the-difference-between-two-arrays-in-javascript
+          Array.prototype.diff = function(a) {
+              return this.filter(function(i) {return a.indexOf(i) < 0;});
+          };
+          e.value = e.value.diff(e.oldValue);
+        }
+        
+        // array of "event objects" tracking the selection history
+        // this is used to avoid adding redundant selections
+        var selectionHistory = crosstalk.var("plotlySelectionHistory").get() || [];
+        
+        // Construct an event object "defining" the current event. 
+        var event = {
+          receiverID: traceManager.gd.id,
+          plotlySelectionColour: crosstalk.group(set).var("plotlySelectionColour").get()
+        };
+        event[set] = e.value;
+        // TODO: is there a smarter way to check object equality?
+        if (selectionHistory.length > 0) {
+          var ev = JSON.stringify(event);
+          for (var i = 0; i < selectionHistory.length; i++) {
+            var sel = JSON.stringify(selectionHistory[i]);
+            if (sel == ev) {
+              return;
+            }
+          }
+        }
+        
+        // accumulate history for persistent selection
+        if (!x.highlight.persistent) {
+          selectionHistory = [event];
+        } else {
+          selectionHistory.push(event);
+        }
+        crosstalk.var("plotlySelectionHistory").set(selectionHistory);
+        
+        // do the actual updating of traces, frames, and the selectize widget
+        traceManager.updateSelection(set, e.value);
+        // https://github.com/selectize/selectize.js/blob/master/docs/api.md#methods_items
+        if (x.selectize) {
+          if (!x.highlight.persistent || e.value === null) {
+            selectize.clear(true);
+          }
+          selectize.addItems(e.value, true);
+          selectize.close();
+        }
+      }
+      selection.on("change", selectionChange);
+      
+      // Set a crosstalk variable selection value, triggering an update
+      var turnOn = function(e) {
+        if (e) {
+          var selectedKeys = pointsToKeys(e.points);
+          // Keys are group names, values are array of selected keys from group.
+          for (var set in selectedKeys) {
+            if (selectedKeys.hasOwnProperty(set)) {
+              selection.set(selectedKeys[set].value, {sender: el});
+            }
+          }
+        }
+      };
+      if (x.highlight.debounce > 0) {
+        turnOn = debounce(turnOn, x.highlight.debounce);
+      }
+      graphDiv.on(x.highlight.on, turnOn);
+      
+      graphDiv.on(x.highlight.off, function turnOff(e) {
+        // remove any visual clues
+        removeBrush(el);
+        // remove any selection history
+        crosstalk.var("plotlySelectionHistory").set(null);
+        // trigger the actual removal of selection traces
+        selection.set(null, {sender: el});
+      });
+          
+      // register a callback for selectize so that there is bi-directional
+      // communication between the widget and direct manipulation events
+      if (x.selectize) {
+        var selectizeID = Object.keys(x.selectize)[i];
+        var options = x.selectize[selectizeID];
+        var first = [{value: "", label: "(All)"}];
+        var opts = $.extend({
+            options: first.concat(options.items),
+            searchField: "label",
+            valueField: "value",
+            labelField: "label",
+            maxItems: 50
+          }, 
+          options
+        );
+        var select = $("#" + selectizeID).find("select")[0];
+        var selectize = $(select).selectize(opts)[0].selectize;
+        // NOTE: this callback is triggered when *directly* altering 
+        // dropdown items
+        selectize.on("change", function() {
+          var currentItems = traceManager.groupSelections[set] || [];
+          if (!x.highlight.persistent) {
+            removeBrush(el);
+            for (var i = 0; i < currentItems.length; i++) {
+              selectize.removeItem(currentItems[i], true);
+            }
+          }
+          var newItems = selectize.items.filter(function(idx) { 
+            return currentItems.indexOf(idx) < 0;
+          });
+          if (newItems.length > 0) {
+            traceManager.updateSelection(set, newItems);
+          } else {
+            // Item has been removed...
+            // TODO: this logic won't work for dynamically changing palette 
+            traceManager.updateSelection(set, null);
+            traceManager.updateSelection(set, selectize.items);
+          }
+        });
+      }
+    } // end of selectionChange
+    
+  } // end of renderValue
+}); // end of widget definition
+
+/**
+ * @param graphDiv The Plotly graph div
+ * @param highlight An object with options for updating selection(s)
+ */
+function TraceManager(graphDiv, highlight) {
+  // The Plotly graph div
+  this.gd = graphDiv;
+
+  // Preserve the original data.
+  // TODO: try using Lib.extendFlat() as done in  
+  // https://github.com/plotly/plotly.js/pull/1136 
+  this.origData = JSON.parse(JSON.stringify(graphDiv.data));
+  
+  // avoid doing this over and over
+  this.origOpacity = [];
+  for (var i = 0; i < this.origData.length; i++) {
+    this.origOpacity[i] = this.origData[i].opacity === 0 ? 0 : (this.origData[i].opacity || 1);
+  }
+
+  // key: group name, value: null or array of keys representing the
+  // most recently received selection for that group.
+  this.groupSelections = {};
+  
+  // selection parameters (e.g., transient versus persistent selection)
+  this.highlight = highlight;
+}
+
+TraceManager.prototype.close = function() {
+  // TODO: Unhook all event handlers
+};
+
+TraceManager.prototype.updateFilter = function(group, keys) {
+
+  if (typeof(keys) === "undefined" || keys === null) {
+    
+    this.gd.data = JSON.parse(JSON.stringify(this.origData));
+    
+  } else {
+  
+    var traces = [];
+    for (var i = 0; i < this.origData.length; i++) {
+      var trace = this.origData[i];
+      if (!trace.key || trace.set !== group) {
+        continue;
+      }
+      var matchFunc = getMatchFunc(trace);
+      var matches = matchFunc(trace.key, keys);
+      
+      if (matches.length > 0) {
+        if (!trace._isSimpleKey) {
+          // subsetArrayAttrs doesn't mutate trace (it makes a modified clone)
+          trace = subsetArrayAttrs(trace, matches);
+        }
+        traces.push(trace);
+      }
+    }
+    this.gd.data = traces;
+  }
+  
+  Plotly.redraw(this.gd);
+  
+  // NOTE: we purposely do _not_ restore selection(s), since on filter,
+  // axis likely will update, changing the pixel -> data mapping, leading 
+  // to a likely mismatch in the brush outline and highlighted marks
+  
+};
+
+TraceManager.prototype.updateSelection = function(group, keys) {
+  
+  if (keys !== null && !Array.isArray(keys)) {
+    throw new Error("Invalid keys argument; null or array expected");
+  }
+  
+  // if selection has been cleared, or if this is transient
+  // selection, delete the "selection traces"
+  var nNewTraces = this.gd.data.length - this.origData.length;
+  if (keys === null || !this.highlight.persistent && nNewTraces > 0) {
+    var tracesToRemove = [];
+    for (var i = 0; i < this.gd.data.length; i++) {
+      if (this.gd.data[i]._isCrosstalkTrace) tracesToRemove.push(i);
+    }
+    Plotly.deleteTraces(this.gd, tracesToRemove);
+    this.groupSelections[group] = keys;
+  } else {
+    // add to the groupSelection, rather than overwriting it
+    // TODO: can this be removed?
+    this.groupSelections[group] = this.groupSelections[group] || [];
+    for (var i = 0; i < keys.length; i++) {
+      var k = keys[i];
+      if (this.groupSelections[group].indexOf(k) < 0) {
+        this.groupSelections[group].push(k);
+      }
+    }
+  }
+  
+  if (keys === null) {
+    
+    Plotly.restyle(this.gd, {"opacity": this.origOpacity});
+    
+  } else if (keys.length >= 1) {
+    
+    // placeholder for new "selection traces"
+    var traces = [];
+    // this variable is set in R/highlight.R
+    var selectionColour = crosstalk.group(group).var("plotlySelectionColour").get() || 
+      this.highlight.color[0];
+
+    for (var i = 0; i < this.origData.length; i++) {
+      // TODO: try using Lib.extendFlat() as done in  
+      // https://github.com/plotly/plotly.js/pull/1136 
+      var trace = JSON.parse(JSON.stringify(this.gd.data[i]));
+      if (!trace.key || trace.set !== group) {
+        continue;
+      }
+      // Get sorted array of matching indices in trace.key
+      var matchFunc = getMatchFunc(trace);
+      var matches = matchFunc(trace.key, keys);
+      
+      if (matches.length > 0) {
+        // If this is a "simple" key, that means select the entire trace
+        if (!trace._isSimpleKey) {
+          trace = subsetArrayAttrs(trace, matches);
+        }
+        // reach into the full trace object so we can properly reflect the 
+        // selection attributes in every view
+        var d = this.gd._fullData[i];
+        
+        /* 
+        / Recursively inherit selection attributes from various sources, 
+        / in order of preference:
+        /  (1) official plotly.js selected attribute
+        /  (2) highlight(selected = attrs_selected(...))
+        */
+        // TODO: it would be neat to have a dropdown to dynamically specify these!
+        $.extend(true, trace, this.highlight.selected);
+        
+        // if it is defined, override color with the "dynamic brush color""
+        if (d.marker) {
+          trace.marker = trace.marker || {};
+          trace.marker.color =  selectionColour || trace.marker.color || d.marker.color;
+        }
+        if (d.line) {
+          trace.line = trace.line || {};
+          trace.line.color =  selectionColour || trace.line.color || d.line.color;
+        }
+        if (d.textfont) {
+          trace.textfont = trace.textfont || {};
+          trace.textfont.color =  selectionColour || trace.textfont.color || d.textfont.color;
+        }
+        if (d.fillcolor) {
+          // TODO: should selectionColour inherit alpha from the existing fillcolor?
+          trace.fillcolor = selectionColour || trace.fillcolor || d.fillcolor;
+        }
+        // attach a sensible name/legendgroup
+        trace.name = trace.name || keys.join("<br />");
+        trace.legendgroup = trace.legendgroup || keys.join("<br />");
+        
+        // keep track of mapping between this new trace and the trace it targets
+        // (necessary for updating frames to reflect the selection traces)
+        trace._originalIndex = i;
+        trace._newIndex = this.gd._fullData.length + traces.length;
+        trace._isCrosstalkTrace = true;
+        traces.push(trace);
+      }
+    }
+    
+    if (traces.length > 0) {
+      
+      Plotly.addTraces(this.gd, traces).then(function(gd) {
+        // incrementally add selection traces to frames
+        // (this is heavily inspired by Plotly.Plots.modifyFrames() 
+        // in src/plots/plots.js)
+        var _hash = gd._transitionData._frameHash;
+        var _frames = gd._transitionData._frames || [];
+        
+        for (var i = 0; i < _frames.length; i++) {
+          
+          // add to _frames[i].traces *if* this frame references selected trace(s)
+          var newIndices = [];
+          for (var j = 0; j < traces.length; j++) {
+            var tr = traces[j];
+            if (_frames[i].traces.indexOf(tr._originalIndex) > -1) {
+              newIndices.push(tr._newIndex);
+              _frames[i].traces.push(tr._newIndex);
+            }
+          }
+          
+          // nothing to do...
+          if (newIndices.length === 0) {
+            continue;
+          }
+          
+          var ctr = 0;
+          var nFrameTraces = _frames[i].data.length;
+          
+          for (var j = 0; j < nFrameTraces; j++) {
+            var frameTrace = _frames[i].data[j];
+            if (!frameTrace.key || frameTrace.set !== group) {
+              continue;
+            }
+            
+            var matchFunc = getMatchFunc(frameTrace);
+            var matches = matchFunc(frameTrace.key, keys);
+            
+            if (matches.length > 0) {
+              if (!trace._isSimpleKey) {
+                frameTrace = subsetArrayAttrs(frameTrace, matches);
+              }
+              var d = gd._fullData[newIndices[ctr]];
+              if (d.marker) {
+                frameTrace.marker = d.marker;
+              }
+              if (d.line) {
+                frameTrace.line = d.line;
+              }
+              if (d.textfont) {
+                frameTrace.textfont = d.textfont;
+              }
+              ctr = ctr + 1;
+              _frames[i].data.push(frameTrace);
+            }
+          }
+          
+          // update gd._transitionData._frameHash
+          _hash[_frames[i].name] = _frames[i];
+        }
+      
+      });
+      
+      // dim traces that have a set matching the set of selection sets
+      var tracesToDim = [],
+          opacities = [],
+          sets = Object.keys(this.groupSelections),
+          n = this.origData.length;
+          
+      for (var i = 0; i < n; i++) {
+        var opacity = this.origOpacity[i] || 1;
+        // have we already dimmed this trace? Or is this even worth doing?
+        if (opacity !== this.gd._fullData[i].opacity || this.highlight.opacityDim === 1) {
+          continue;
+        }
+        // is this set an element of the set of selection sets?
+        var matches = findMatches(sets, [this.gd.data[i].set]);
+        if (matches.length) {
+          tracesToDim.push(i);
+          opacities.push(opacity * this.highlight.opacityDim);
+        }
+      }
+      
+      if (tracesToDim.length > 0) {
+        Plotly.restyle(this.gd, {"opacity": opacities}, tracesToDim);
+        // turn off the selected/unselected API
+        Plotly.restyle(this.gd, {"selectedpoints": null});
+      }
+      
+    }
+    
+  }
+};
+
+/* 
+Note: in all of these match functions, we assume needleSet (i.e. the selected keys)
+is a 1D (or flat) array. The real difference is the meaning of haystack.
+findMatches() does the usual thing you'd expect for 
+linked brushing on a scatterplot matrix. findSimpleMatches() returns a match iff 
+haystack is a subset of the needleSet. findNestedMatches() returns 
+*/
+
+function getMatchFunc(trace) {
+  return (trace._isNestedKey) ? findNestedMatches : 
+    (trace._isSimpleKey) ? findSimpleMatches : findMatches;
+}
+
+// find matches for "flat" keys
+function findMatches(haystack, needleSet) {
+  var matches = [];
+  haystack.forEach(function(obj, i) {
+    if (obj === null || needleSet.indexOf(obj) >= 0) {
+      matches.push(i);
+    }
+  });
+  return matches;
+}
+
+// find matches for "simple" keys
+function findSimpleMatches(haystack, needleSet) {
+  var match = haystack.every(function(val) {
+    return val === null || needleSet.indexOf(val) >= 0;
+  });
+  // yes, this doesn't make much sense other than conforming 
+  // to the output type of the other match functions
+  return (match) ? [0] : []
+}
+
+// find matches for a "nested" haystack (2D arrays)
+function findNestedMatches(haystack, needleSet) {
+  var matches = [];
+  for (var i = 0; i < haystack.length; i++) {
+    var hay = haystack[i];
+    var match = hay.every(function(val) { 
+      return val === null || needleSet.indexOf(val) >= 0; 
+    });
+    if (match) {
+      matches.push(i);
+    }
+  }
+  return matches;
+}
+
+function isPlainObject(obj) {
+  return (
+    Object.prototype.toString.call(obj) === '[object Object]' &&
+    Object.getPrototypeOf(obj) === Object.prototype
+  );
+}
+
+function subsetArrayAttrs(obj, indices) {
+  var newObj = {};
+  Object.keys(obj).forEach(function(k) {
+    var val = obj[k];
+
+    if (k.charAt(0) === "_") {
+      newObj[k] = val;
+    } else if (k === "transforms" && Array.isArray(val)) {
+      newObj[k] = val.map(function(transform) {
+        return subsetArrayAttrs(transform, indices);
+      });
+    } else if (k === "colorscale" && Array.isArray(val)) {
+      newObj[k] = val;
+    } else if (isPlainObject(val)) {
+      newObj[k] = subsetArrayAttrs(val, indices);
+    } else if (Array.isArray(val)) {
+      newObj[k] = subsetArray(val, indices);
+    } else {
+      newObj[k] = val;
+    }
+  });
+  return newObj;
+}
+
+function subsetArray(arr, indices) {
+  var result = [];
+  for (var i = 0; i < indices.length; i++) {
+    result.push(arr[indices[i]]);
+  }
+  return result;
+}
+
+// Convenience function for removing plotly's brush 
+function removeBrush(el) {
+  var outlines = el.querySelectorAll(".select-outline");
+  for (var i = 0; i < outlines.length; i++) {
+    outlines[i].remove();
+  }
+}
+
+
+// https://davidwalsh.name/javascript-debounce-function
+
+// Returns a function, that, as long as it continues to be invoked, will not
+// be triggered. The function will be called after it stops being called for
+// N milliseconds. If `immediate` is passed, trigger the function on the
+// leading edge, instead of the trailing.
+function debounce(func, wait, immediate) {
+	var timeout;
+	return function() {
+		var context = this, args = arguments;
+		var later = function() {
+			timeout = null;
+			if (!immediate) func.apply(context, args);
+		};
+		var callNow = immediate && !timeout;
+		clearTimeout(timeout);
+		timeout = setTimeout(later, wait);
+		if (callNow) func.apply(context, args);
+	};
+};
+</script>
+<script>(function(global){"use strict";var undefined=void 0;var MAX_ARRAY_LENGTH=1e5;function Type(v){switch(typeof v){case"undefined":return"undefined";case"boolean":return"boolean";case"number":return"number";case"string":return"string";default:return v===null?"null":"object"}}function Class(v){return Object.prototype.toString.call(v).replace(/^\[object *|\]$/g,"")}function IsCallable(o){return typeof o==="function"}function ToObject(v){if(v===null||v===undefined)throw TypeError();return Object(v)}function ToInt32(v){return v>>0}function ToUint32(v){return v>>>0}var LN2=Math.LN2,abs=Math.abs,floor=Math.floor,log=Math.log,max=Math.max,min=Math.min,pow=Math.pow,round=Math.round;(function(){var orig=Object.defineProperty;var dom_only=!function(){try{return Object.defineProperty({},"x",{})}catch(_){return false}}();if(!orig||dom_only){Object.defineProperty=function(o,prop,desc){if(orig)try{return orig(o,prop,desc)}catch(_){}if(o!==Object(o))throw TypeError("Object.defineProperty called 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A=v>w,k=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(A?[0]:[]);L.exit().on(".drag",null).remove(),L.enter().append("rect").classed("scrollbar-horizontal",!0).call(i.fill,s.barColor),A?(this.hbar=L.attr({rx:s.barRadius,ry:s.barRadius,x:S,y:E,width:k,height:M}),this._hbarXMin=S+k/2,this._hbarTranslateMax=w-k):(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=A?p+M+.5:p+.5,U=o._topdefs.selectAll("#"+R).data(A||C?[0]:[]);if(U.exit().remove(),U.enter().append("clipPath").attr("id",R).append("rect"),A||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),A||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));A&&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 t=this.translateX,e=this.translateY;this.hbar&&(t+=n.event.deltaY),this.vbar&&(e+=n.event.deltaY),this.setTranslate(t,e)},s.prototype._onBarDrag=function(){var t=this.translateX,e=this.translateY;if(this.hbar){var r=t+this._hbarXMin,i=r+this._hbarTranslateMax;t=(o.constrain(n.event.x,r,i)-r)/(i-r)*(this.position.w-this._box.w)}if(this.vbar){var a=e+this._vbarYMin,s=a+this._vbarTranslateMax;e=(o.constrain(n.event.y,a,s)-a)/(s-a)*(this.position.h-this._box.h)}this.setTranslate(t,e)},s.prototype.setTranslate=function(t,e){var r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=o.constrain(t||0,0,r),e=o.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(a.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var i=t/r;this.hbar.call(a.setTranslate,t+i*this._hbarTranslateMax,e)}if(this.vbar){var s=e/n;this.vbar.call(a.setTranslate,t,e+s*this._vbarTranslateMax)}}},1798:function(t,e,r){"use strict";var n=r(20042),i=r(23276),a=r(79335),o=r(99012).axisHoverFormat,s=r(8357).extendFlat;t.exports=s({x:n.x,y:n.y,z:n.z,marker:n.marker,histnorm:n.histnorm,histfunc:n.histfunc,nbinsx:n.nbinsx,xbins:n.xbins,nbinsy:n.nbinsy,ybins:n.ybins,autobinx:n.autobinx,autobiny:n.autobiny,bingroup:n.bingroup,xbingroup:n.xbingroup,ybingroup:n.ybingroup,autocontour:i.autocontour,ncontours:i.ncontours,contours:i.contours,line:{color:i.line.color,width:s({},i.line.width,{dflt:.5}),dash:i.line.dash,smoothing:i.line.smoothing,editType:"plot"},xhoverformat:o("x"),yhoverformat:o("y"),zhoverformat:o("z",1),hovertemplate:n.hovertemplate,texttemplate:i.texttemplate,textfont:i.textfont},a("",{cLetter:"z",editTypeOverride:"calc"}))},1810:function(t,e,r){"use strict";var n=r(62290),i=r(37101).Dj,a=r(16534),o=r(14714),s=r(54485),l=r(43481),u=r(25623),c=r(69562),f=r(88191),h=r(15181),p=h.strTranslate,d=h.strRotate,v=r(51257),g=v.keyFun,y=v.repeat,m=v.unwrap,x=r(3071),b=r(46982),_=r(5126),w=_.CAP_SHIFT,T=_.LINE_SPACING;function A(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,A=n();for(var k 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[k]);for(_=0;_<A.nodes.length;_++)if(A.nodes[_].pointNumber===S){M=A.nodes[_];break}if(M){var E={pointNumber:parseInt(k),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};A.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<A.nodes.length;_++){var t,e,r=A.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,A.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*v,a.node.y[_]*g];A.nodes[_].x0=L[0]-d/2,A.nodes[_].x1=L[0]+d/2;var C=A.nodes[_].y1-A.nodes[_].y0;A.nodes[_].y0=L[1]-C/2,A.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=A.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(A)}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:A,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function k(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 C(t,e){t.call(L),e.attr("d",M())}function P(t){t.attr("width",function(t){return t.node.x1-t.node.x0}).attr("height",function(t){return t.visibleHeight})}function O(t){return t.link.width>1||t.linkLineWidth>0}function I(t){return p(t.translateX,t.translateY)+(t.horizontal?"matrix(1 0 0 1 0 0)":"matrix(0 1 1 0 0 0)")}function D(t,e,r){t.on(".basic",null).on("mouseover.basic",function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.hover(this,t,e),t.interactionState.hovered=[this,t])}).on("mousemove.basic",function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.follow(this,t),t.interactionState.hovered=[this,t])}).on("mouseout.basic",function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.unhover(this,t,e),t.interactionState.hovered=!1)}).on("click.basic",function(t){t.interactionState.hovered&&(r.unhover(this,t,e),t.interactionState.hovered=!1),t.interactionState.dragInProgress||t.partOfGroup||r.select(this,t,e)})}function z(t,e,r,i){var o=a.behavior.drag().origin(function(t){return{x:t.node.x0+t.visibleWidth/2,y:t.node.y0+t.visibleHeight/2}}).on("dragstart",function(a){if("fixed"!==a.arrangement&&(h.ensureSingle(i._fullLayout._infolayer,"g","dragcover",function(t){i._fullLayout._dragCover=t}),h.raiseToTop(this),a.interactionState.dragInProgress=a.node,F(a.node),a.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,a.interactionState.hovered),a.interactionState.hovered=!1),"snap"===a.arrangement)){var o=a.traceId+"|"+a.key;a.forceLayouts[o]?a.forceLayouts[o].alpha(1):function(t,e,r){!function(t){for(var e=0;e<t.length;e++)t[e].y=(t[e].y0+t[e].y1)/2,t[e].x=(t[e].x0+t[e].x1)/2}(r.graph.nodes);var i=r.graph.nodes.filter(function(t){return t.originalX===r.node.originalX}).filter(function(t){return!t.partOfGroup});r.forceLayouts[e]=n.forceSimulation(i).alphaDecay(0).force("collide",n.forceCollide().radius(function(t){return t.dy/2+r.nodePad/2}).strength(1).iterations(l.forceIterations)).force("constrain",function(t,e,r,n){return function(){for(var t=0,i=0;i<r.length;i++){var a=r[i];a===n.interactionState.dragInProgress?(a.x=a.lastDraggedX,a.y=a.lastDraggedY):(a.vx=(a.originalX-a.x)/l.forceTicksPerFrame,a.y=Math.min(n.size-a.dy/2,Math.max(a.dy/2,a.y))),t=Math.max(t,Math.abs(a.vx),Math.abs(a.vy))}!n.interactionState.dragInProgress&&t<.1&&n.forceLayouts[e].alpha()>0&&n.forceLayouts[e].alpha(0)}}(0,e,i,r)).stop()}(0,o,a),function(t,e,r,n,i){window.requestAnimationFrame(function a(){var o;for(o=0;o<l.forceTicksPerFrame;o++)r.forceLayouts[n].tick();if(function(t){for(var e=0;e<t.length;e++)t[e].y0=t[e].y-t[e].dy/2,t[e].y1=t[e].y0+t[e].dy,t[e].x0=t[e].x-t[e].dx/2,t[e].x1=t[e].x0+t[e].dx}(r.graph.nodes),r.sankey.update(r.graph),C(t.filter(B(r)),e),r.forceLayouts[n].alpha()>0)window.requestAnimationFrame(a);else{var s=r.node.originalX;r.node.x0=s-r.visibleWidth/2,r.node.x1=s+r.visibleWidth/2,R(r,i)}})}(t,e,a,o,i)}}).on("drag",function(r){if("fixed"!==r.arrangement){var n=a.event.x,i=a.event.y;"snap"===r.arrangement?(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2,r.node.y0=i-r.visibleHeight/2,r.node.y1=i+r.visibleHeight/2):("freeform"===r.arrangement&&(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2),i=Math.max(0,Math.min(r.size-r.visibleHeight/2,i)),r.node.y0=i-r.visibleHeight/2,r.node.y1=i+r.visibleHeight/2),F(r.node),"snap"!==r.arrangement&&(r.sankey.update(r.graph),C(t.filter(B(r)),e))}}).on("dragend",function(t){if("fixed"!==t.arrangement){t.interactionState.dragInProgress=!1;for(var e=0;e<t.node.childrenNodes.length;e++)t.node.childrenNodes[e].x=t.node.x,t.node.childrenNodes[e].y=t.node.y;"snap"!==t.arrangement&&R(t,i)}});t.on(".drag",null).call(o)}function R(t,e){for(var r=[],n=[],i=0;i<t.graph.nodes.length;i++){var 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r=this,n=e[r.id],i=e._size;r._hasClipOnAxisFalse=!1;for(var a=0;a<t.length;a++)if(!1===t[a][0].trace.cliponaxis){r._hasClipOnAxisFalse=!0;break}r.updateLayers(n),r.adjustLayout(n,i),p.generalUpdatePerTraceModule(r.graphDiv,r,t,n),r.layers.plotbg.select("path").call(u.fill,n.bgcolor)},S.makeFramework=function(t){var e=this,r=e.graphDiv,n=t[e.id],i=e.clipId="clip"+e.layoutId+e.id,a=e.clipIdRelative="clip-relative"+e.layoutId+e.id;e.clipDef=o.ensureSingleById(t._clips,"clipPath",i,function(t){t.append("path").attr("d","M0,0Z")}),e.clipDefRelative=o.ensureSingleById(t._clips,"clipPath",a,function(t){t.append("path").attr("d","M0,0Z")}),e.plotContainer=o.ensureSingle(e.container,"g",e.id),e.updateLayers(n),c.setClipUrl(e.layers.backplot,i,r),c.setClipUrl(e.layers.grids,i,r)},S.updateLayers=function(t){var e=this.layers,r=["draglayer","plotbg","backplot","grids"];"below traces"===t.aaxis.layer&&r.push("aaxis","aline"),"below traces"===t.baxis.layer&&r.push("baxis","bline"),"below 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r,n,i,a,o,l,p=this,d=t.domain,v=(d.x[0]+d.x[1])/2,g=(d.y[0]+d.y[1])/2,y=d.x[1]-d.x[0],m=d.y[1]-d.y[0],x=y*e.w,b=m*e.h,_=t.sum,w=t.aaxis.min,T=t.baxis.min,A=t.caxis.min;x>E*b?i=(a=b)*E:a=(i=x)/E,o=y*i/x,l=m*a/b,r=e.l+e.w*v-i/2,n=e.t+e.h*(1-g)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=_,p.xaxis={type:"linear",range:[w+2*A-_,_-w-2*T],domain:[v-o/2,v+o/2],_id:"x"},f(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:"linear",range:[w,_-T-A],domain:[g-l/2,g+l/2],_id:"y"},f(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var k=p.yaxis.domain[0],M=p.aaxis=h({},t.aaxis,{range:[w,_-T-A],side:"left",tickangle:(+t.aaxis.tickangle||0)-30,domain:[k,k+l*E],anchor:"free",position:0,_id:"y",_length:i});f(M,p.graphDiv._fullLayout),M.setScale();var 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L=k.MINZOOM/2+.87,C="m-0.87,.5h"+L+"v3h-"+(L+5.2)+"l"+(L/2+2.6)+",-"+(.87*L+4.5)+"l2.6,1.5l-"+L/2+","+.87*L+"Z",P="m0.87,.5h-"+L+"v3h"+(L+5.2)+"l-"+(L/2+2.6)+",-"+(.87*L+4.5)+"l-2.6,1.5l"+L/2+","+.87*L+"Z",O="m0,1l"+L/2+","+.87*L+"l2.6,-1.5l-"+(L/2+2.6)+",-"+(.87*L+4.5)+"l-"+(L/2+2.6)+","+(.87*L+4.5)+"l2.6,1.5l"+L/2+",-"+.87*L+"Z",I=!0;function D(t){n.select(t).selectAll(".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners").remove()}S.clearOutline=function(){A(this.dragOptions),T(this.dragOptions.gd)},S.initInteractions=function(){var t,e,r,n,f,h,p,d,y,b,T,A,M=this,S=M.layers.plotbg.select("path").node(),L=M.graphDiv,z=L._fullLayout._zoomlayer;function R(t){var e={};return e[M.id+".aaxis.min"]=t.a,e[M.id+".baxis.min"]=t.b,e[M.id+".caxis.min"]=t.c,e}function F(t,e){var 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k=0;k<m.length;k++){_=m[k],n.isPlainObject(t[_])||(t[_]={});var M=t[_],S=a.newContainer(e,_);S._id=S._name=_,S._attr=o.id+"."+_,S._traceIndices=w.map(function(t){return t._expandedIndex});var E=y.axisName2dataArray[_],L=b(M,S,A,w,E,o);h(M,S,A,{axData:w,dataAttr:E});var C=A("visible");switch(g(S,e,T),A("uirevision",e.uirevision),S._m=1,_){case"radialaxis":var P=A("autorange",!S.isValidRange(M.range));M.autorange=P,!P||"linear"!==L&&"-"!==L||A("rangemode"),"reversed"===P&&(S._m=-1),A("range"),S.cleanRange("range",{dfltRange:[0,1]});break;case"angularaxis":if("date"===L){n.log("Polar plots do not support date angular axes yet.");for(var O=0;O<w.length;O++)w[O].visible=!1;L=M.type=S.type="linear"}A("linear"===L?"thetaunit":"period");var I=A("direction");A("rotation",{counterclockwise:0,clockwise:90}[I])}if(f(M,S,A,S.type,{tickSuffixDflt:"degrees"===S.thetaunit?"°":void 0}),C){var 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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},10017:function(t,e,r){"use strict";var n,i=r(78030),a=r(22138)(),o=r(16423),s=r(94693);if(a){var l=i("RegExp.prototype.exec"),u={},c=function(){throw u},f={toString:c,valueOf:c};"symbol"==typeof Symbol.toPrimitive&&(f[Symbol.toPrimitive]=c),n=function(t){if(!t||"object"!=typeof t)return!1;var e=s(t,"lastIndex");if(!e||!o(e,"value"))return!1;try{l(t,f)}catch(t){return t===u}}}else{var h=i("Object.prototype.toString");n=function(t){return!(!t||"object"!=typeof t&&"function"!=typeof t)&&"[object RegExp]"===h(t)}}t.exports=n},10088:function(t,e,r){"use strict";var n=r(15181);function 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100.;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragBorderRadius, fragWidth;\n\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\n\nbool isDirect = (paletteSize.x < 1.);\n\nvec4 getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = (size + borderSize) * pointSizeScale;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\n  fragColor = color;\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\n  fragWidth = 1. / gl_PointSize;\n}\n"]),g&&(l.frag=l.frag.replace("smoothstep","smoothStep"),s.frag=s.frag.replace("smoothstep","smoothStep")),this.drawCircle=t(l)}b.defaults={color:"black",borderColor:"transparent",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach(function(e,r){t.drawItem(r)});return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if("number"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,u=e.count;if(this.regl,!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var c=[],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];c.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return c},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,u=this.maxColors,h=this.palette;this.groups=i=r.map(function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:"function"==typeof e?e={ondraw:e}:"number"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:"positions data points",snap:"snap cluster lod tree",size:"sizes size radius",borderSize:"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline",color:"colors color fill fill-color fillColor",borderColor:"borderColors borderColor stroke stroke-color strokeColor",marker:"markers marker shape",range:"range dataBox databox",viewport:"viewport viewPort viewBox viewbox",opacity:"opacity alpha transparency",bounds:"bound bounds boundaries limits",tooManyColors:"tooManyColors palette paletteMode optimizePalette enablePalette"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:"stream",type:"uint8"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"})},e=f({},b.defaults,e)),e.positions&&!("marker"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!("positions"in e)&&(e.positions=n.positions,delete n.positions);var g=0,x=0;if(d(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=b.defaults.size),g+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=b.defaults.borderSize),g+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=b.defaults.color),e=t.updateColor(e),x++,e},borderColor:function(e,r){return null==e&&(e=b.defaults.borderColor),e=t.updateColor(e),x++,e},bounds:function(t,e,r){return"range"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,s=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=v(t,"float64");var u=e.count=Math.floor(t.length/2),f=e.bounds=u?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||u>n)?e.tree=c(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=y.float32(t);return i({data:p,usage:"dynamic"}),a({data:y.fract32(t,p),usage:"dynamic"}),s({data:new Uint8Array(u),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 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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 v(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function g(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(v(t,n))}return e}function y(t){for(var e=g(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function m(t,e){if(!(t<1||e<1)){for(var r=g(t),n=g(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var u=0;u<s;u++)r(i,[l,u],n),t.set(l,u,a(e,i[0],i[1]))}(t,e,w,r),t}function A(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=m(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=y(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,u=1+a+1,c=i(new Float32Array(l*u),[l,u]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(c,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,c,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],A(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=u(t),v=l.dataScale,g=t.z[0].length,y=t._ylength,m=l.contourLevels;this.data=t;var x=[];for(e=0;e<3;e++)for(x[e]=[],r=0;r<g;r++)x[e][r]=[];for(r=0;r<g;r++)for(n=0;n<y;n++)x[0][r][n]=this.getXat(r,n,t.xcalendar,h.xaxis),x[1][r][n]=this.getYat(r,n,t.ycalendar,h.yaxis),x[2][r][n]=this.getZat(r,n,t.zcalendar,h.zaxis);if(t.connectgaps)for(t._emptypoints=s(x[2]),o(x[2],t._emptypoints),t._interpolatedZ=[],r=0;r<g;r++)for(t._interpolatedZ[r]=[],n=0;n<y;n++)t._interpolatedZ[r][n]=x[2][r][n];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<y;n++)null==(a=x[e][r][n])?x[e][r][n]=NaN:a=x[e][r][n]*=v[e];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<y;n++)null!=(a=x[e][r][n])&&(this.minValues[e]>a&&(this.minValues[e]=a),this.maxValues[e]<a&&(this.maxValues[e]=a));for(e=0;e<3;e++)this.objectOffset[e]=.5*(this.minValues[e]+this.maxValues[e]);for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<y;n++)null!=(a=x[e][r][n])&&(x[e][r][n]-=this.objectOffset[e]);var b=[i(new Float32Array(g*y),[g,y]),i(new Float32Array(g*y),[g,y]),i(new Float32Array(g*y),[g,y])];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<y;n++)b[e].set(r,n,x[e][r][n]);x=[];var w={colormap:d,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!t.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:t.opacityscale,opacity:t.opacity},T=f(t);if(w.intensityBounds=[T.min,T.max],t.surfacecolor){var A=i(new Float32Array(g*y),[g,y]);for(r=0;r<g;r++)for(n=0;n<y;n++)A.set(r,n,t.surfacecolor[n][r]);b.push(A)}else w.intensityBounds[0]*=v[2],w.intensityBounds[1]*=v[2];(_<b[0].shape[0]||_<b[0].shape[1])&&(this.refineData=!1),!0===this.refineData&&(this.dataScaleX=this.estimateScale(b[0].shape[0],0),this.dataScaleY=this.estimateScale(b[0].shape[1],1),1===this.dataScaleX&&1===this.dataScaleY||this.refineCoords(b)),t.surfacecolor&&(w.intensity=b.pop());var k=[!0,!0,!0],M=["x","y","z"];for(e=0;e<3;++e){var S=t.contours[M[e]];k[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=k,p.enableHighlight=k,p.snapToData=!0,"lighting"in t&&(p.ambientLight=t.lighting.ambient,p.diffuseLight=t.lighting.diffuse,p.specularLight=t.lighting.specular,p.roughness=t.lighting.roughness,p.fresnel=t.lighting.fresnel),"lightposition"in t&&(p.lightPosition=[t.lightposition.x,t.lightposition.y,t.lightposition.z])},p.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()},t.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new h(t,i,e.uid);return i._trace=a,a.update(e),t.glplot.add(i),a}},22745:function(t,e,r){"use strict";var n=r(79758),i=r(78945).BADNUM,a=r(54899).smith;t.exports=function(t,e,r){for(var o=e.layers.frontplot.select("g.scatterlayer"),s=e.xaxis,l=e.yaxis,u={xaxis:s,yaxis:l,plot:e.framework,layerClipId:e._hasClipOnAxisFalse?e.clipIds.forTraces:null},c=0;c<r.length;c++)for(var f=r[c],h=0;h<f.length;h++){0===h&&(f[0].trace._xA=s,f[0].trace._yA=l);var p=f[h],d=p.real;if(d===i)p.x=p.y=i;else{var v=a([d,p.imag]);p.x=v[0],p.y=v[1]}}n(t,u,r,o)}},22809:function(t,e,r){"use strict";var n=r(3438).bc,i=r(3438).VG,a=r(3438).rU,o=r(93639),s=i("Exif\0\0");t.exports=function(t){if(!(t.length<2)&&255===t[0]&&216===t[1]&&255===t[2])for(var e=2;;){for(;;){if(t.length-e<2)return;if(255===t[e++])break}for(var r,i,l=t[e++];255===l;)l=t[e++];if(208<=l&&l<=217||1===l)r=0;else{if(!(192<=l&&l<=254))return;if(t.length-e<2)return;r=n(t,e)-2,e+=2}if(217===l||218===l)return;if(225===l&&r>=10&&a(t,e,s)&&(i=o.get_orientation(t.slice(e+6,e+r))),r>=5&&192<=l&&l<=207&&196!==l&&200!==l&&204!==l){if(t.length-e<r)return;var u={width:n(t,e+3),height:n(t,e+1),type:"jpg",mime:"image/jpeg",wUnits:"px",hUnits:"px"};return i>0&&(u.orientation=i),u}e+=r}}},22886:function(t,e,r){"use strict";var n=r(75671),i=r(46329).findBin,a=r(95663),o=r(74176),s=r(77828),l=r(32443);t.exports=function(t){var e=t._a,r=t._b,u=e.length,c=r.length,f=t.aaxis,h=t.baxis,p=e[0],d=e[u-1],v=r[0],g=r[c-1],y=e[e.length-1]-e[0],m=r[r.length-1]-r[0],x=y*n.RELATIVE_CULL_TOLERANCE,b=m*n.RELATIVE_CULL_TOLERANCE;p-=x,d+=x,v-=b,g+=b,t.isVisible=function(t,e){return t>p&&t<d&&e>v&&e<g},t.isOccluded=function(t,e){return t<p||t>d||e<v||e>g},t.setScale=function(){var e=t._x,r=t._y,n=a(t._xctrl,t._yctrl,e,r,f.smoothing,h.smoothing);t._xctrl=n[0],t._yctrl=n[1],t.evalxy=o([t._xctrl,t._yctrl],u,c,f.smoothing,h.smoothing),t.dxydi=s([t._xctrl,t._yctrl],f.smoothing,h.smoothing),t.dxydj=l([t._xctrl,t._yctrl],f.smoothing,h.smoothing)},t.i2a=function(t){var r=Math.max(0,Math.floor(t[0]),u-2),n=t[0]-r;return(1-n)*e[r]+n*e[r+1]},t.j2b=function(t){var e=Math.max(0,Math.floor(t[1]),u-2),n=t[1]-e;return(1-n)*r[e]+n*r[e+1]},t.ij2ab=function(e){return[t.i2a(e[0]),t.j2b(e[1])]},t.a2i=function(t){var r=Math.max(0,Math.min(i(t,e),u-2)),n=e[r],a=e[r+1];return Math.max(0,Math.min(u-1,r+(t-n)/(a-n)))},t.b2j=function(t){var e=Math.max(0,Math.min(i(t,r),c-2)),n=r[e],a=r[e+1];return Math.max(0,Math.min(c-1,e+(t-n)/(a-n)))},t.ab2ij=function(e){return[t.a2i(e[0]),t.b2j(e[1])]},t.i2c=function(e,r){return t.evalxy([],e,r)},t.ab2xy=function(n,i,a){if(!a&&(n<e[0]||n>e[u-1]|i<r[0]||i>r[c-1]))return[!1,!1];var o=t.a2i(n),s=t.b2j(i),l=t.evalxy([],o,s);if(a){var f,h,p,d,v=0,g=0,y=[];n<e[0]?(f=0,h=0,v=(n-e[0])/(e[1]-e[0])):n>e[u-1]?(f=u-2,h=1,v=(n-e[u-1])/(e[u-1]-e[u-2])):h=o-(f=Math.max(0,Math.min(u-2,Math.floor(o)))),i<r[0]?(p=0,d=0,g=(i-r[0])/(r[1]-r[0])):i>r[c-1]?(p=c-2,d=1,g=(i-r[c-1])/(r[c-1]-r[c-2])):d=s-(p=Math.max(0,Math.min(c-2,Math.floor(s)))),v&&(t.dxydi(y,f,p,h,d),l[0]+=y[0]*v,l[1]+=y[1]*v),g&&(t.dxydj(y,f,p,h,d),l[0]+=y[0]*g,l[1]+=y[1]*g)}return l},t.c2p=function(t,e,r){return[e.c2p(t[0]),r.c2p(t[1])]},t.p2x=function(t,e,r){return[e.p2c(t[0]),r.p2c(t[1])]},t.dadi=function(t){var r=Math.max(0,Math.min(e.length-2,t));return e[r+1]-e[r]},t.dbdj=function(t){var e=Math.max(0,Math.min(r.length-2,t));return 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i=e&&e.length,a=i?e[0]*r:t.length,o=Math.abs(M(t,0,a,r));if(i)for(var s=0,l=e.length;s<l;s++){var u=e[s]*r,c=s<l-1?e[s+1]*r:t.length;o-=Math.abs(M(t,u,c,r))}var f=0;for(s=0;s<n.length;s+=3){var h=n[s]*r,p=n[s+1]*r,d=n[s+2]*r;f+=Math.abs((t[h]-t[d])*(t[p+1]-t[h+1])-(t[h]-t[p])*(t[d+1]-t[h+1]))}return 0===o&&0===f?0:Math.abs((f-o)/o)},e.flatten=function(t){for(var e=t[0][0].length,r={vertices:[],holes:[],dimensions:e},n=0,i=0;i<t.length;i++){for(var a=0;a<t[i].length;a++)for(var o=0;o<e;o++)r.vertices.push(t[i][a][o]);i>0&&(n+=t[i-1].length,r.holes.push(n))}return r}},38793:function(t,e,r){"use strict";var n=r(93441),i=r(15181),a=r(26446),o=r(44236).extractOpts;t.exports=function(t,e,r,s,l){l||(l={});var u,c,f,h,p=l.isContour,d=t.cd[0],v=d.trace,g=t.xa,y=t.ya,m=d.x,x=d.y,b=d.z,_=d.xCenter,w=d.yCenter,T=d.zmask,A=v.zhoverformat,k=m,M=x;if(!1!==t.index){try{f=Math.round(t.index[1]),h=Math.round(t.index[0])}catch(e){return void i.error("Error hovering on heatmap, pointNumber must be [row,col], found:",t.index)}if(f<0||f>=b[0].length||h<0||h>b.length)return}else{if(n.inbox(e-m[0],e-m[m.length-1],0)>0||n.inbox(r-x[0],r-x[x.length-1],0)>0)return;if(p){var S;for(k=[2*m[0]-m[1]],S=1;S<m.length;S++)k.push((m[S]+m[S-1])/2);for(k.push([2*m[m.length-1]-m[m.length-2]]),M=[2*x[0]-x[1]],S=1;S<x.length;S++)M.push((x[S]+x[S-1])/2);M.push([2*x[x.length-1]-x[x.length-2]])}f=Math.max(0,Math.min(k.length-2,i.findBin(e,k))),h=Math.max(0,Math.min(M.length-2,i.findBin(r,M)))}var E,L,C=g.c2p(m[f]),P=g.c2p(m[f+1]),O=y.c2p(x[h]),I=y.c2p(x[h+1]);p?(E=d.orig_x||m,L=d.orig_y||x,P=C,u=E[f],I=O,c=L[h]):(E=d.orig_x||_||m,L=d.orig_y||w||x,u=_?E[f]:(E[f]+E[f+1])/2,c=w?L[h]:(L[h]+L[h+1])/2,g&&"category"===g.type&&(u=m[f]),y&&"category"===y.type&&(c=x[h]),v.zsmooth&&(C=P=g.c2p(u),O=I=y.c2p(c)));var D=b[h][f];if(T&&!T[h][f]&&(D=void 0),void 0!==D||v.hoverongaps){var z;Array.isArray(d.hovertext)&&Array.isArray(d.hovertext[h])?z=d.hovertext[h][f]:Array.isArray(d.text)&&Array.isArray(d.text[h])&&(z=d.text[h][f]);var R=o(v),F={type:"linear",range:[R.min,R.max],hoverformat:A,_separators:g._separators,_numFormat:g._numFormat},B=a.tickText(F,D,"hover").text;return[i.extendFlat(t,{index:v._after2before?v._after2before[h][f]:[h,f],distance:t.maxHoverDistance,spikeDistance:t.maxSpikeDistance,x0:C,x1:P,y0:O,y1:I,xLabelVal:u,yLabelVal:c,zLabelVal:D,zLabel:B,text:z})]}}},39028:function(t,e,r){"use strict";var n=r(24761);t.exports={bgcolor:{valType:"color",dflt:n.background,editType:"plot"},bordercolor:{valType:"color",dflt:n.defaultLine,editType:"plot"},borderwidth:{valType:"integer",dflt:0,min:0,editType:"plot"},autorange:{valType:"boolean",dflt:!0,editType:"calc",impliedEdits:{"range[0]":void 0,"range[1]":void 0}},range:{valType:"info_array",items:[{valType:"any",editType:"calc",impliedEdits:{"^autorange":!1}},{valType:"any",editType:"calc",impliedEdits:{"^autorange":!1}}],editType:"calc",impliedEdits:{autorange:!1}},thickness:{valType:"number",dflt:.15,min:0,max:1,editType:"plot"},visible:{valType:"boolean",dflt:!0,editType:"calc"},editType:"calc"}},39420:function(t,e,r){"use strict";var n,i,a=r(46982),o=r(26446),s=r(93441),l=r(93950).gl_plot2d,u=r(93950).gl_spikes2d,c=r(93950).gl_select_box,f=r(8034),h=r(13353),p=r(51051),d=r(25420),v=r(3115),g=v.enforce,y=v.clean,m=r(40243).doAutoRange,x=r(7514),b=x.drawMode,_=x.selectMode,w=["xaxis","yaxis"],T=r(29278).SUBPLOT_PATTERN;function A(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=A;var k=A.prototype;k.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})},k.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},k.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},k.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},k.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]},k.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)},k.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())},k.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)}},k.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}},k.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()},k.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])]},k.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])]},k.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})},k.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},k.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]})},k.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()}},k.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit("plotly_unhover"),s.loneUnhover(this.svgContainer))},k.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),"hover").text}}},39442:function(t,e,r){"use strict";var n=r(619);function i(t,e,r,n){var i=r.radialAxis,a=r.angularAxis;i._hovertitle="real",a._hovertitle="imag";var o={};o[e.subplot]={_subplot:r};var s=e._module.formatLabels(t,e,o);n.realLabel=s.realLabel,n.imagLabel=s.imagLabel;var l=t.hi||e.hoverinfo,u=[];function c(t,e){u.push(t._hovertitle+": "+e)}if(!e.hovertemplate){var f=l.split("+");-1!==f.indexOf("all")&&(f=["real","imag","text"]),-1!==f.indexOf("real")&&c(i,n.realLabel),-1!==f.indexOf("imag")&&c(a,n.imagLabel),-1!==f.indexOf("text")&&n.text&&(u.push(n.text),delete n.text),n.extraText=u.join("<br>")}}t.exports={hoverPoints:function(t,e,r,a){var o=n(t,e,r,a);if(o&&!1!==o[0].index){var s=o[0];if(void 0===s.index)return o;var l=t.subplot,u=s.cd[s.index],c=s.trace;if(l.isPtInside(u))return s.xLabelVal=void 0,s.yLabelVal=void 0,i(u,c,l,s),s.hovertemplate=c.hovertemplate,o}},makeHoverPointText:i}},39452:function(t,e,r){var n=r(9915),i=r(18610);function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar;var o=n.instance("gregorian");i(a.prototype,{name:"Nanakshahi",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Nanakshahi",epochs:["BN","AN"],monthNames:["Chet","Vaisakh","Jeth","Harh","Sawan","Bhadon","Assu","Katak","Maghar","Poh","Magh","Phagun"],monthNamesShort:["Che","Vai","Jet","Har","Saw","Bha","Ass","Kat","Mgr","Poh","Mgh","Pha"],dayNames:["Somvaar","Mangalvar","Budhvaar","Veervaar","Shukarvaar","Sanicharvaar","Etvaar"],dayNamesShort:["Som","Mangal","Budh","Veer","Shukar","Sanichar","Et"],dayNamesMin:["So","Ma","Bu","Ve","Sh","Sa","Et"],digits:null,dateFormat:"dd-mm-yyyy",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[""].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s<i.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},39555:function(t,e,r){var n=r(49149);t.exports=function(t,e,r){if(!t)throw new TypeError("must specify data as first parameter");if(r=0|+(r||0),Array.isArray(t)&&t[0]&&"number"==typeof t[0][0]){var i,a,o,s,l=t[0].length,u=t.length*l;e&&"string"!=typeof e||(e=new(n(e||"float32"))(u+r));var c=e.length-r;if(u!==c)throw new Error("source length "+u+" ("+l+"x"+t.length+") does not match destination length "+c);for(i=0,o=r;i<t.length;i++)for(a=0;a<l;a++)e[o++]=null===t[i][a]?NaN:t[i][a]}else if(e&&"string"!=typeof e)e.set(t,r);else{var f=n(e||"float32");if(Array.isArray(t)||"array"===e)for(i=0,o=r,s=(e=new f(t.length+r)).length;o<s;o++,i++)e[o]=null===t[i]?NaN:t[i];else 0===r?e=new f(t):(e=new f(t.length+r)).set(t,r)}return e}},39591:function(t,e,r){"use strict";var n,i,a=r(93950).gl_plot3d,o=a.createCamera,s=a.createScene,l=r(8034),u=r(21757),c=r(46982),f=r(15181),h=f.preserveDrawingBuffer(),p=r(26446),d=r(93441),v=r(98054),g=r(25420),y=r(77494),m=r(31489),x=r(36875),b=r(39616),_=!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 A=!0;T.tryCreatePlot=function(){var t=this,e=t.prepareOptions(),r=!0;try{t.glplot=s(e)}catch(n){if(t.staticMode||!A||h)r=!1;else{f.warn(["webgl setup failed possibly due to","false preserveDrawingBuffer config.","The mobile/tablet device may not be detected by is-mobile module.","Enabling preserveDrawingBuffer in second attempt to create webgl scene..."].join(" "));try{h=e.glOptions.preserveDrawingBuffer=!0,t.glplot=s(e)}catch(t){h=e.glOptions.preserveDrawingBuffer=!1,r=!1}}}return A=!1,r},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,A={xLabel:g("x",h.traceCoordinate[0],t.xhoverformat),yLabel:g("y",h.traceCoordinate[1],t.yhoverformat),zLabel:g("z",h.traceCoordinate[2],t.zhoverformat)},k=d.castHoverinfo(w,e.fullLayout,T),M=(k||"").split("+"),S=k&&"all"===k;w.hovertemplate||S||(-1===M.indexOf("x")&&(A.xLabel=void 0),-1===M.indexOf("y")&&(A.yLabel=void 0),-1===M.indexOf("z")&&(A.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?(A.uLabel=g("x",h.traceCoordinate[3],t.uhoverformat),(S||-1!==M.indexOf("u"))&&E.push("u: "+A.uLabel),A.vLabel=g("y",h.traceCoordinate[4],t.vhoverformat),(S||-1!==M.indexOf("v"))&&E.push("v: "+A.vLabel),A.wLabel=g("z",h.traceCoordinate[5],t.whoverformat),(S||-1!==M.indexOf("w"))&&E.push("w: "+A.wLabel),A.normLabel=h.traceCoordinate[6].toPrecision(3),(S||-1!==M.indexOf("norm"))&&E.push("norm: "+A.normLabel),"streamtube"===t.type&&(A.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(S||-1!==M.indexOf("divergence"))&&E.push("divergence: "+A.divergenceLabel)),h.textLabel&&E.push(h.textLabel),x=E.join("<br>")):"isosurface"===t.type||"volume"===t.type?(A.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),E.push("value: "+A.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:A.xLabel,yLabel:A.yLabel,zLabel:A.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,A),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. Context lost.")};requestAnimationFrame(e)};var k=["xaxis","yaxis","zaxis"];function M(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],u=e[o+"calendar"],c=e["_"+o+"length"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(c||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,u),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,u),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],c-1)}}T.plot=function(t,e,r){var n=this;if(n.plotArgs=[t,e,r],!n.glplot.contextLost){var i,a,o,s,l,u,c=e[n.id],f=r[n.id];n.fullLayout=e,n.fullSceneLayout=c,n.axesOptions.merge(e,c),n.spikeOptions.merge(c),n.setViewport(c),n.updateFx(c.dragmode,c.hovermode),n.camera.enableWheel=n.graphDiv._context._scrollZoom.gl3d,n.glplot.setClearColor(v(c.bgcolor)),n.setConvert(l),t?Array.isArray(t)||(t=[t]):t=[];var h=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(o=0;o<t.length;++o)!0===(i=t[o]).visible&&0!==i._length&&M(this,i,h);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var u=n[l];if(u.visible){var c=s.r2l(u[o]);!isNaN(c)&&isFinite(c)&&(e[0][i]=Math.min(e[0][i],c),e[1][i]=Math.max(e[1][i],c))}}}(this,h);var p=[1,1,1];for(s=0;s<3;++s)h[1][s]===h[0][s]?p[s]=1:p[s]=1/(h[1][s]-h[0][s]);for(n.dataScale=p,n.convertAnnotations(this),o=0;o<t.length;++o)!0===(i=t[o]).visible&&0!==i._length&&((a=n.traces[i.uid])?a.data.type===i.type?a.update(i):(a.dispose(),a=i._module.plot(this,i),n.traces[i.uid]=a):(a=i._module.plot(this,i),n.traces[i.uid]=a),a.name=i.name);var d=Object.keys(n.traces);t:for(o=0;o<d.length;++o){for(s=0;s<t.length;++s)if(t[s].uid===d[o]&&!0===t[s].visible&&0!==t[s]._length)continue t;(a=n.traces[d[o]]).dispose(),delete n.traces[d[o]]}n.glplot.objects.sort(function(t,e){return t._trace.data.index-e._trace.data.index});var g,y=[[0,0,0],[0,0,0]],m=[],x={};for(o=0;o<3;++o){if((u=(l=c[k[o]]).type)in x?(x[u].acc*=p[o],x[u].count+=1):x[u]={acc:p[o],count:1},l.autorange){y[0][o]=1/0,y[1][o]=-1/0;var b=n.glplot.objects,_=n.fullSceneLayout.annotations||[],w=l._name.charAt(0);for(s=0;s<b.length;s++){var T=b[s],A=T.bounds,S=T._trace.data._pad||0;"ErrorBars"===T.constructor.name&&l._lowerLogErrorBound?y[0][o]=Math.min(y[0][o],l._lowerLogErrorBound):y[0][o]=Math.min(y[0][o],A[0][o]/p[o]-S),y[1][o]=Math.max(y[1][o],A[1][o]/p[o]+S)}for(s=0;s<_.length;s++){var E=_[s];if(E.visible){var L=l.r2l(E[w]);y[0][o]=Math.min(y[0][o],L),y[1][o]=Math.max(y[1][o],L)}}if("rangemode"in l&&"tozero"===l.rangemode&&(y[0][o]=Math.min(y[0][o],0),y[1][o]=Math.max(y[1][o],0)),y[0][o]>y[1][o])y[0][o]=-1,y[1][o]=1;else{var C=y[1][o]-y[0][o];y[0][o]-=C/32,y[1][o]+=C/32}if("reversed"===l.autorange){var P=y[0][o];y[0][o]=y[1][o],y[1][o]=P}}else{var O=l.range;y[0][o]=l.r2l(O[0]),y[1][o]=l.r2l(O[1])}y[0][o]===y[1][o]&&(y[0][o]-=1,y[1][o]+=1),m[o]=y[1][o]-y[0][o],n.glplot.setBounds(o,{min:y[0][o]*p[o],max:y[1][o]*p[o]})}var I=c.aspectmode;if("cube"===I)g=[1,1,1];else if("manual"===I){var D=c.aspectratio;g=[D.x,D.y,D.z]}else{if("auto"!==I&&"data"!==I)throw new Error("scene.js aspectRatio was not one of the enumerated 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t=this;t.glplot&&(t.camera.mouseListener.enabled=!1,t.container.removeEventListener("wheel",t.camera.wheelListener),t.camera=null,t.glplot.dispose(),t.container.parentNode.removeChild(t.container),t.glplot=null)},T.getCamera=function(){var t,e=this;return e.camera.view.recalcMatrix(e.camera.view.lastT()),{up:{x:(t=e.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?"orthographic":"perspective"}}},T.setViewport=function(t){var e,r=this,n=t.camera;r.camera.lookAt.apply(this,[[(e=n).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),r.glplot.setAspectratio(t.aspectratio),"orthographic"===n.projection.type!==r.camera._ortho&&(r.glplot.redraw(),r.glplot.clearRGBA(),r.glplot.dispose(),r.initializeGLPlot())},T.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+".camera").get();function n(t,e,r,n){var 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this.color=l(t.color,"uint8");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var K=0;K<this.batch.length;K++)this.batch[K]={count:this.counts.length>1?this.counts[K]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[K]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*K,4*K+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[K]:this.opacity,baseline:null!=this.baselineOffset[K]?this.baselineOffset[K]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[K]?this.alignOffset[K]:this.alignOffset[0]:0,atlas:this.fontAtlas[K]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*K,2*K+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text="",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new Uint8Array([0,0,0,255]),T.prototype.alignOffset=[0,0],T.maxAtlasSize=1024,T.atlasCanvas=document.createElement("canvas"),T.atlasContext=T.atlasCanvas.getContext("2d",{alpha:!1}),T.baseFontSize=64,T.fonts={},t.exports=T},41499:function(t,e,r){"use strict";var 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t.y}).attr("cursor",function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"perpendicular"===t.parcatsViewModel.arrangement?"ns-resize":"move"}),A(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(),k.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"]),A=t.model.count,k=A/t.parcatsViewModel.model.count,M={countLabel:A,probabilityLabel:k.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:A,probability:k}]},{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 A(t){t.attr("stroke","black").attr("stroke-width",.2).attr("stroke-opacity",1).attr("fill-opacity",1)}function k(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter(function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color})}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll("rect.bandrect"),l=[];s.each(function(t){k(t).each(function(t){Array.prototype.push.apply(l,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=k(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=k(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=k(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,A={countLabel:y,categoryLabel:g,probabilityLabel:_.toFixed(3)},k=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf("count")&&k.push(["Count:",A.countLabel].join(" ")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf("probability")&&(k.push("P(color ∩ "+g+"): "+A.probabilityLabel),k.push("P("+g+" | color): "+w.toFixed(3)),k.push("P(color | "+g+"): "+T.toFixed(3)));var M=k.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:A,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")),A(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}),A(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 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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 Y(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=A(t,l);m.push(x),y[x._id]=x}var b={},_=[];for(u in h){var w=A(t,u);_.push(w),b[w._id]=w}return{xaHash:y,yaHash:b,xaxes:m,yaxes:_,xLinks:f,yLinks:h,isSubplotConstrained:c}}function W(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 Z(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=Z(V),K=Z(X),it=z(J,y),at=z(K,v),ot=!at&&!it,nt=Y(t,t._fullLayout._axisMatchGroups,V,X);var o=(rt=Y(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,At,kt,Mt,St,Et,Lt,Ct,Pt={element:bt,gd:t,plotinfo:e};function Ot(){Pt.plotinfo.selection=!1,M(t)}function It(t,r){var i=Pt.gd;if(i._fullLayout._activeShapeIndex>=0)i._fullLayout._deactivateShape(i);else{var o=i._fullLayout.clickmode;if(H(i),2!==t||yt||qt(),gt)o.indexOf("select")>-1&&S(r,i,J,K,e.id,Pt),o.indexOf("event")>-1&&p.click(i,r,e.id);else if(1===t&&yt){var s=v?I:O,u="s"===v||"w"===y?0:1,f=s._name+".range["+u+"]",h=function(t,e){var r,n=t.range[e],i=Math.abs(n-t.range[1-e]);return"date"===t.type?n:"log"===t.type?(r=Math.ceil(Math.max(0,-Math.log(i)/Math.LN10))+3,a("."+r+"g")(Math.pow(10,n))):(r=Math.floor(Math.log(Math.abs(n))/Math.LN10)-Math.floor(Math.log(i)/Math.LN10)+4,a("."+String(r)+"g")(n))}(s,u),d="left",g="middle";if(s.fixedrange)return;v?(g="n"===v?"top":"bottom","right"===s.side&&(d="right")):"e"===y&&(d="right"),i._context.showAxisRangeEntryBoxes&&n.select(bt).call(c.makeEditable,{gd:i,immediate:!0,background:i._fullLayout.paper_bgcolor,text:String(h),fill:s.tickfont?s.tickfont.color:"#444",horizontalAlign:d,verticalAlign:g}).on("edit",function(t){var e=s.d2r(t);void 0!==e&&l.call("_guiRelayout",i,f,e)})}}}function Dt(e,r){if(t._transitioningWithDuration)return!1;var n=Math.max(0,Math.min(tt,pt*e+_t)),i=Math.max(0,Math.min(et,dt*r+wt)),a=Math.abs(n-_t),o=Math.abs(i-wt);function s(){St="",Tt.r=Tt.l,Tt.t=Tt.b,Lt.attr("d","M0,0Z")}if(Tt.l=Math.min(_t,n),Tt.r=Math.max(_t,n),Tt.t=Math.min(wt,i),Tt.b=Math.max(wt,i),rt.isSubplotConstrained)a>P||o>P?(St="xy",a/tt>o/et?(o=a*et/tt,wt>i?Tt.t=wt-o:Tt.b=wt+o):(a=o*tt/et,_t>n?Tt.l=_t-a:Tt.r=_t+a),Lt.attr("d",G(Tt))):s();else if(nt.isSubplotConstrained)if(a>P||o>P){St="xy";var l=Math.min(Tt.l/tt,(et-Tt.b)/et),u=Math.max(Tt.r/tt,(et-Tt.t)/et);Tt.l=l*tt,Tt.r=u*tt,Tt.b=(1-l)*et,Tt.t=(1-u)*et,Lt.attr("d",G(Tt))}else 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a=Pt.dragmode,s=t._fullLayout.dragmode;s!==a&&(Pt.dragmode=s),mt(),pt=t._fullLayout._invScaleX,dt=t._fullLayout._invScaleY,ot||(gt?e.shiftKey?"pan"===s?s="zoom":m(s)||(s="pan"):e.ctrlKey&&(s="pan"):s="pan"),x(s)?Pt.minDrag=1:Pt.minDrag=void 0,m(s)?(Pt.xaxes=J,Pt.yaxes=K,k(e,r,n,Pt,s)):(Pt.clickFn=It,m(a)&&Ot(),ot||("zoom"===s?(Pt.moveFn=Dt,Pt.doneFn=Rt,Pt.minDrag=1,function(e,r,n){var a=bt.getBoundingClientRect();_t=r-a.left,wt=n-a.top,t._fullLayout._calcInverseTransform(t);var s=i.apply3DTransform(t._fullLayout._invTransform)(_t,wt);_t=s[0],wt=s[1],Tt={l:_t,r:_t,w:0,t:wt,b:wt,h:0},At=t._hmpixcount?t._hmlumcount/t._hmpixcount:o(t._fullLayout.plot_bgcolor).getLuminance(),Mt=!1,St="xy",Ct=!1,Et=N(vt,At,$,Q,kt="M0,0H"+tt+"V"+et+"H0V0"),Lt=j(vt,$,Q)}(0,r,n)):"pan"===s&&(Pt.moveFn=Ut,Pt.doneFn=Gt))),t._fullLayout._redrag=function(){var e=t._dragdata;if(e&&e.element===bt){var r=t._fullLayout.dragmode;m(r)||(mt(),Yt([0,0,tt,et]),Pt.moveFn(e.dx,e.dy))}}},g.init(Pt);var Ft=[0,0,tt,et],Bt=null,Nt=L.REDRAWDELAY,jt=e.mainplot?t._fullLayout._plots[e.mainplot]:e;function Ut(e,r){if(e*=pt,r*=dt,!t._transitioningWithDuration){if(t._fullLayout._replotting=!0,"ew"===it||"ns"===at){var n=it?-e:0,i=at?-r:0;if(nt.isSubplotConstrained){if(it&&at){var a=(e/tt-r/et)/2;n=-(e=a*tt),i=-(r=-a*et)}at?n=-i*tt/et:i=-n*et/tt}return it&&(F(J,e),Vt("x")),at&&(F(K,r),Vt("y")),Yt([n,i,tt,et]),Ht(),void t.emit("plotly_relayouting",ht)}var o,s,l="w"===it==("n"===at)?1:-1;if(it&&at&&(rt.isSubplotConstrained||nt.isSubplotConstrained)){var u=(e/tt+l*r/et)/2;e=u*tt,r=l*u*et}if("w"===it?e=p(J,0,e):"e"===it?e=p(J,1,-e):it||(e=0),"n"===at?r=p(K,1,r):"s"===at?r=p(K,0,-r):at||(r=0),o="w"===it?e:0,s="n"===at?r:0,rt.isSubplotConstrained&&!nt.isSubplotConstrained||nt.isSubplotConstrained&&it&&at&&l>0){var c;if(nt.isSubplotConstrained||!it&&1===at.length){for(c=0;c<J.length;c++)J[c].range=J[c]._r.slice(),E(J[c],1-r/et);o=(e=r*tt/et)/2}if(nt.isSubplotConstrained||!at&&1===it.length){for(c=0;c<K.length;c++)K[c].range=K[c]._r.slice(),E(K[c],1-e/tt);s=(r=e*et/tt)/2}}nt.isSubplotConstrained&&at||Vt("x"),nt.isSubplotConstrained&&it||Vt("y");var f=tt-e,h=et-r;!nt.isSubplotConstrained||it&&at||(it?(s=o?0:e*et/tt,h=f*et/tt):(o=s?0:r*tt/et,f=h*tt/et)),Yt([o,s,f,h]),Ht(),t.emit("plotly_relayouting",ht)}function p(t,e,r){for(var n,i,a=1-e,o=0;o<t.length;o++){var s=t[o];if(!s.fixedrange){n=s,i=s._rl[a]+(s._rl[e]-s._rl[a])/B(r/s._length);var l=s.l2r(i);!1!==l&&void 0!==l&&(s.range[e]=l)}}return n._length*(n._rl[e]-i)/(n._rl[e]-n._rl[a])}}function Vt(t,e){for(var r=nt.isSubplotConstrained?{x:K,y:J}[t]:nt[t+"axes"],n=nt.isSubplotConstrained?{x:J,y:K}[t]:[],i=0;i<r.length;i++){var a=r[i],o=a._id,s=nt.xLinks[o]||nt.yLinks[o],l=n[0]||V[s]||X[s];l&&(e?(e[a._name+".range[0]"]=e[l._name+".range[0]"],e[a._name+".range[1]"]=e[l._name+".range[1]"]):a.range=l.range.slice())}}function Ht(){var r,n=[];function i(t){for(r=0;r<t.length;r++)t[r].fixedrange||n.push(t[r]._id)}function a(t,e){for(r=0;r<t.length;r++){var i=t[r],a=i[e];i.fixedrange||"sync"!==a.tickmode||n.push(a._id)}}for(st&&(i(J),i(rt.xaxes),i(nt.xaxes),a(e.overlays,"xaxis")),lt&&(i(K),i(rt.yaxes),i(nt.yaxes),a(e.overlays,"yaxis")),ht={},r=0;r<n.length;r++){var o=n[r],s=A(t,o);d.drawOne(t,s,{skipTitle:!0}),ht[s._name+".range[0]"]=s.range[0],ht[s._name+".range[1]"]=s.range[1]}d.redrawComponents(t,n)}function qt(){if(!t._transitioningWithDuration){var e=t._context.doubleClick,r=[];it&&(r=r.concat(J)),at&&(r=r.concat(K)),nt.xaxes&&(r=r.concat(nt.xaxes)),nt.yaxes&&(r=r.concat(nt.yaxes));var n,i,a,o={};if("reset+autosize"===e)for(e="autosize",i=0;i<r.length;i++)if((n=r[i])._rangeInitial&&(n.range[0]!==n._rangeInitial[0]||n.range[1]!==n._rangeInitial[1])||!n._rangeInitial&&!n.autorange){e="reset";break}if("autosize"===e)for(i=0;i<r.length;i++)(n=r[i]).fixedrange||(o[n._name+".autorange"]=!0);else if("reset"===e)for((it||rt.isSubplotConstrained)&&(r=r.concat(rt.xaxes)),at&&!rt.isSubplotConstrained&&(r=r.concat(rt.yaxes)),rt.isSubplotConstrained&&(it?at||(r=r.concat(K)):r=r.concat(J)),i=0;i<r.length;i++)(n=r[i]).fixedrange||(n._rangeInitial?(a=n._rangeInitial,o[n._name+".range[0]"]=a[0],o[n._name+".range[1]"]=a[1]):o[n._name+".autorange"]=!0);t.emit("plotly_doubleclick",null),l.call("_guiRelayout",t,o)}}function Gt(){Yt([0,0,tt,et]),i.syncOrAsync([T.previousPromises,function(){t._fullLayout._replotting=!1,l.call("_guiRelayout",t,ht)}],t)}function Yt(e){var r,n,a,o,s=t._fullLayout,u=s._plots,c=s._subplots.cartesian;if(ct&&l.subplotsRegistry.splom.drag(t),ut)for(r=0;r<c.length;r++)if(a=(n=u[c[r]]).xaxis,o=n.yaxis,n._scene){var f=i.simpleMap(a.range,a.r2l),p=i.simpleMap(o.range,o.r2l);n._scene.update({range:[f[0],p[0],f[1],p[1]]})}if((ct||ut)&&(_(t),w(t)),ft){var d=e[2]/O._length,g=e[3]/I._length;for(r=0;r<c.length;r++){a=(n=u[c[r]]).xaxis,o=n.yaxis;var m,x,b,T,A=(st||nt.isSubplotConstrained)&&!a.fixedrange&&V[a._id],k=(lt||nt.isSubplotConstrained)&&!o.fixedrange&&X[o._id];if(A?(m=d,b=y||nt.isSubplotConstrained?e[0]:Xt(a,m)):nt.xaHash[a._id]?(m=d,b=e[0]*a._length/O._length):nt.yaHash[a._id]?(m=g,b="ns"===at?-e[1]*a._length/I._length:Xt(a,m,{n:"top",s:"bottom"}[at])):b=Zt(a,m=Wt(a,d,g)),k?(x=g,T=v||nt.isSubplotConstrained?e[1]:Xt(o,x)):nt.yaHash[o._id]?(x=g,T=e[1]*o._length/I._length):nt.xaHash[o._id]?(x=d,T="ew"===it?-e[0]*o._length/O._length:Xt(o,x,{e:"right",w:"left"}[it])):T=Zt(o,x=Wt(o,d,g)),m||x){m||(m=1),x||(x=1);var M=a._offset-b/m,S=o._offset-T/x;n.clipRect.call(h.setTranslate,b,T).call(h.setScale,m,x),n.plot.call(h.setTranslate,M,S).call(h.setScale,1/m,1/x),m===n.xScaleFactor&&x===n.yScaleFactor||(h.setPointGroupScale(n.zoomScalePts,m,x),h.setTextPointsScale(n.zoomScaleTxt,m,x)),h.hideOutsideRangePoints(n.clipOnAxisFalseTraces,n),n.xScaleFactor=m,n.yScaleFactor=x}}}}function Wt(t,e,r){return t.fixedrange?0:st&&rt.xaHash[t._id]?e:lt&&(rt.isSubplotConstrained?rt.xaHash:rt.yaHash)[t._id]?r:0}function Zt(t,e){return e?(t.range=t._r.slice(),E(t,e),Xt(t,e)):0}function Xt(t,e,r){return t._length*(1-e)*b[r||t.constraintoward||"middle"]}return v.length*y.length!==1&&W(bt,function(e){if(t._context._scrollZoom.cartesian||t._fullLayout._enablescrollzoom){if(Ot(),t._transitioningWithDuration)return e.preventDefault(),void e.stopPropagation();mt(),clearTimeout(Bt);var r=-e.deltaY;if(isFinite(r)||(r=e.wheelDelta/10),isFinite(r)){var 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r=x(m,e,t.label),n=r.node()&&o.bBox(r.node())||{width:0,height:0},i=u.lineCount(r);c.currentValueMaxWidth=Math.max(c.currentValueMaxWidth,Math.ceil(n.width)),c.currentValueHeight=Math.max(c.currentValueHeight,Math.ceil(n.height)),c.currentValueMaxLines=Math.max(c.currentValueMaxLines,i)}),c.currentValueTotalHeight=c.currentValueHeight+e.currentvalue.offset,m.remove()}c.height=c.currentValueTotalHeight+f.tickOffset+e.ticklen+f.labelOffset+c.labelHeight+e.pad.t+e.pad.b;var b="left";s.isRightAnchor(e)&&(c.lx-=c.outerLength,b="right"),s.isCenterAnchor(e)&&(c.lx-=c.outerLength/2,b="center");var w="top";s.isBottomAnchor(e)&&(c.ly-=c.height,w="bottom"),s.isMiddleAnchor(e)&&(c.ly-=c.height/2,w="middle"),c.outerLength=Math.ceil(c.outerLength),c.height=Math.ceil(c.height),c.lx=Math.round(c.lx),c.ly=Math.round(c.ly);var T={y:e.y,b:c.height*v[w],t:c.height*d[w]};"fraction"===e.lenmode?(T.l=0,T.xl=e.x-e.len*d[b],T.r=0,T.xr=e.x+e.len*v[b]):(T.x=e.x,T.l=c.outerLength*d[b],T.r=c.outerLength*v[b]),i.autoMargin(t,g(e),T)}function x(t,e,r){if(e.currentvalue.visible){var n,i,a=e._dims;switch(e.currentvalue.xanchor){case"right":n=a.inputAreaLength-f.currentValueInset-a.currentValueMaxWidth,i="left";break;case"center":n=.5*a.inputAreaLength,i="middle";break;default:n=f.currentValueInset,i="left"}var l=s.ensureSingle(t,"text",f.labelClass,function(t){t.attr({"text-anchor":i,"data-notex":1})}),c=e.currentvalue.prefix?e.currentvalue.prefix:"";if("string"==typeof r)c+=r;else{var h=e.steps[e.active].label,d=e._gd._fullLayout._meta;d&&(h=s.templateString(h,d)),c+=h}e.currentvalue.suffix&&(c+=e.currentvalue.suffix),l.call(o.font,e.currentvalue.font).text(c).call(u.convertToTspans,e._gd);var v=u.lineCount(l),g=(a.currentValueMaxLines+1-v)*e.currentvalue.font.size*p;return u.positionText(l,n,g),l}}function 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l=r.select("."+f.gripRectClass);n.event.stopPropagation(),n.event.preventDefault(),l.call(a.fill,t.activebgcolor);var u=L(t,n.mouse(i)[0]);function c(){var t=s(),a=L(t,n.mouse(i)[0]);T(e,r,t,a,!1)}function h(){var t=s();t._dragging=!1,l.call(a.fill,t.bgcolor),o.on("mouseup",null),o.on("mousemove",null),o.on("touchend",null),o.on("touchmove",null),e.emit("plotly_sliderend",{slider:t,step:t.steps[t.active]})}T(e,r,t,u,!0),t._dragging=!0,o.on("mousemove",c),o.on("touchmove",c),o.on("mouseup",h),o.on("touchend",h)}}function M(t,e){var r=t.selectAll("rect."+f.tickRectClass).data(e._visibleSteps),i=e._dims;r.enter().append("rect").classed(f.tickRectClass,!0),r.exit().remove(),r.attr({width:e.tickwidth+"px","shape-rendering":"crispEdges"}),r.each(function(t,r){var s=r%i.labelStride===0,l=n.select(this);l.attr({height:s?e.ticklen:e.minorticklen}).call(a.fill,e.tickcolor),o.setTranslate(l,E(e,r/(e._stepCount-1))-.5*e.tickwidth,(s?f.tickOffset:f.minorTickOffset)+i.currentValueTotalHeight)})}function S(t,e,r){for(var n=t.select("rect."+f.gripRectClass),i=0,a=0;a<e._stepCount;a++)if(e._visibleSteps[a]._index===e.active){i=a;break}var o=E(e,i/(e._stepCount-1));if(!e._invokingCommand){var s=n;r&&e.transition.duration>0&&(s=s.transition().duration(e.transition.duration).ease(e.transition.easing)),s.attr("transform",l(o-.5*f.gripWidth,e._dims.currentValueTotalHeight))}}function E(t,e){var r=t._dims;return r.inputAreaStart+f.stepInset+(r.inputAreaLength-2*f.stepInset)*Math.min(1,Math.max(0,e))}function L(t,e){var r=t._dims;return Math.min(1,Math.max(0,(e-f.stepInset-r.inputAreaStart)/(r.inputAreaLength-2*f.stepInset-2*r.inputAreaStart)))}function C(t,e,r){var 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r=e.parse(t[1],1,Ht);if(!r)return null;var n=[],i=null;e.expectedType&&"value"!==e.expectedType.kind&&(i=e.expectedType);for(var a=1;a<t.length;a+=2){var o=1===a?-1/0:t[a],s=t[a+1],l=a,u=a+1;if("number"!=typeof o)return e.error('Input/output pairs for "step" expressions must be defined using literal numeric values (not computed expressions) for the input values.',l);if(n.length&&n[n.length-1][0]>=o)return e.error('Input/output pairs for "step" expressions must be arranged with input values in strictly ascending order.',l);var c=e.parse(s,u,i);if(!c)return null;i=i||c.type,n.push([o,c])}return new Qe(i,r,n)},Qe.prototype.evaluate=function(t){var e=this.labels,r=this.outputs;if(1===e.length)return r[0].evaluate(t);var n=this.input.evaluate(t);if(n<=e[0])return r[0].evaluate(t);var i=e.length;return n>=e[i-1]?r[i-1].evaluate(t):r[$e(e,n)].evaluate(t)},Qe.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1)t(r[e])},Qe.prototype.outputDefined=function(){return this.outputs.every(function(t){return t.outputDefined()})},Qe.prototype.serialize=function(){for(var t=["step",this.input.serialize()],e=0;e<this.labels.length;e++)e>0&&t.push(this.labels[e]),t.push(this.outputs[e].serialize());return t};var er=Object.freeze({__proto__:null,number:tr,color:function(t,e,r){return new oe(tr(t.r,e.r,r),tr(t.g,e.g,r),tr(t.b,e.b,r),tr(t.a,e.a,r))},array:function(t,e,r){return t.map(function(t,n){return tr(t,e[n],r)})}}),rr=.95047,nr=1.08883,ir=4/29,ar=6/29,or=3*ar*ar,sr=ar*ar*ar,lr=Math.PI/180,ur=180/Math.PI;function cr(t){return t>sr?Math.pow(t,1/3):t/or+ir}function fr(t){return t>ar?t*t*t:or*(t-ir)}function hr(t){return 255*(t<=.0031308?12.92*t:1.055*Math.pow(t,1/2.4)-.055)}function pr(t){return(t/=255)<=.04045?t/12.92:Math.pow((t+.055)/1.055,2.4)}function dr(t){var e=pr(t.r),r=pr(t.g),n=pr(t.b),i=cr((.4124564*e+.3575761*r+.1804375*n)/rr),a=cr((.2126729*e+.7151522*r+.072175*n)/1);return{l:116*a-16,a:500*(i-a),b:200*(a-cr((.0193339*e+.119192*r+.9503041*n)/nr)),alpha:t.a}}function vr(t){var e=(t.l+16)/116,r=isNaN(t.a)?e:e+t.a/500,n=isNaN(t.b)?e:e-t.b/200;return e=1*fr(e),r=rr*fr(r),n=nr*fr(n),new oe(hr(3.2404542*r-1.5371385*e-.4985314*n),hr(-.969266*r+1.8760108*e+.041556*n),hr(.0556434*r-.2040259*e+1.0572252*n),t.alpha)}function gr(t,e,r){var n=e-t;return t+r*(n>180||n<-180?n-360*Math.round(n/360):n)}var yr={forward:dr,reverse:vr,interpolate:function(t,e,r){return{l:tr(t.l,e.l,r),a:tr(t.a,e.a,r),b:tr(t.b,e.b,r),alpha:tr(t.alpha,e.alpha,r)}}},mr={forward:function(t){var e=dr(t),r=e.l,n=e.a,i=e.b,a=Math.atan2(i,n)*ur;return{h:a<0?a+360:a,c:Math.sqrt(n*n+i*i),l:r,alpha:t.a}},reverse:function(t){var e=t.h*lr,r=t.c;return vr({l:t.l,a:Math.cos(e)*r,b:Math.sin(e)*r,alpha:t.alpha})},interpolate:function(t,e,r){return{h:gr(t.h,e.h,r),c:tr(t.c,e.c,r),l:tr(t.l,e.l,r),alpha:tr(t.alpha,e.alpha,r)}}},xr=Object.freeze({__proto__:null,lab:yr,hcl:mr}),br=function(t,e,r,n,i){this.type=t,this.operator=e,this.interpolation=r,this.input=n,this.labels=[],this.outputs=[];for(var a=0,o=i;a<o.length;a+=1){var s=o[a],l=s[0],u=s[1];this.labels.push(l),this.outputs.push(u)}};function _r(t,e,r,n){var i=n-r,a=t-r;return 0===i?0:1===e?a/i:(Math.pow(e,a)-1)/(Math.pow(e,i)-1)}br.interpolationFactor=function(t,e,r,i){var a=0;if("exponential"===t.name)a=_r(e,t.base,r,i);else if("linear"===t.name)a=_r(e,1,r,i);else if("cubic-bezier"===t.name){var o=t.controlPoints;a=new n(o[0],o[1],o[2],o[3]).solve(_r(e,1,r,i))}return a},br.parse=function(t,e){var r=t[0],n=t[1],i=t[2],a=t.slice(3);if(!Array.isArray(n)||0===n.length)return e.error("Expected an interpolation type expression.",1);if("linear"===n[0])n={name:"linear"};else 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a=$e(e,n),o=e[a],s=e[a+1],l=br.interpolationFactor(this.interpolation,n,o,s),u=r[a].evaluate(t),c=r[a+1].evaluate(t);return"interpolate"===this.operator?er[this.type.kind.toLowerCase()](u,c,l):"interpolate-hcl"===this.operator?mr.reverse(mr.interpolate(mr.forward(u),mr.forward(c),l)):yr.reverse(yr.interpolate(yr.forward(u),yr.forward(c),l))},br.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1)t(r[e])},br.prototype.outputDefined=function(){return this.outputs.every(function(t){return t.outputDefined()})},br.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 wr=function(t,e){this.type=t,this.args=e};wr.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 wr(u?Zt:r,i)},wr.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},wr.prototype.eachChild=function(t){this.args.forEach(t)},wr.prototype.outputDefined=function(){return this.args.every(function(t){return t.outputDefined()})},wr.prototype.serialize=function(){var t=["coalesce"];return this.eachChild(function(e){t.push(e.serialize())}),t};var Tr=function(t,e){this.type=e.type,this.bindings=[].concat(t),this.result=e};Tr.prototype.evaluate=function(t){return this.result.evaluate(t)},Tr.prototype.eachChild=function(t){for(var e=0,r=this.bindings;e<r.length;e+=1)t(r[e][1]);t(this.result)},Tr.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 Tr(r,o):null},Tr.prototype.outputDefined=function(){return this.result.outputDefined()},Tr.prototype.serialize=function(){for(var t=["let"],e=0,r=this.bindings;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t.push(i,a.serialize())}return t.push(this.result.serialize()),t};var Ar=function(t,e,r){this.type=t,this.index=e,this.input=r};Ar.parse=function(t,e){if(3!==t.length)return e.error("Expected 2 arguments, but found "+(t.length-1)+" instead.");var r=e.parse(t[1],1,Ht),n=e.parse(t[2],2,$t(e.expectedType||Zt));if(!r||!n)return null;var i=n.type;return new Ar(i.itemType,r,n)},Ar.prototype.evaluate=function(t){var e=this.index.evaluate(t),r=this.input.evaluate(t);if(e<0)throw new ge("Array index out of bounds: "+e+" < 0.");if(e>=r.length)throw new ge("Array index out of bounds: "+e+" > "+(r.length-1)+".");if(e!==Math.floor(e))throw new ge("Array index must be an integer, but found "+e+" instead.");return r[e]},Ar.prototype.eachChild=function(t){t(this.index),t(this.input)},Ar.prototype.outputDefined=function(){return!1},Ar.prototype.serialize=function(){return["at",this.index.serialize(),this.input.serialize()]};var 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,Zt),n=e.parse(t[2],2,Zt);return r&&n?re(r.type,[Gt,qt,Ht,Vt,Zt])?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 Mr=function(t,e,r){this.type=Ht,this.needle=t,this.haystack=e,this.fromIndex=r};Mr.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,Zt),n=e.parse(t[2],2,Zt);if(!r||!n)return null;if(!re(r.type,[Gt,qt,Ht,Vt,Zt]))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 Mr(r,n,i):null}return new Mr(r,n)},Mr.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 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.");if(this.fromIndex){var n=this.fromIndex.evaluate(t);return r.indexOf(e,n)}return r.indexOf(e)},Mr.prototype.eachChild=function(t){t(this.needle),t(this.haystack),this.fromIndex&&t(this.fromIndex)},Mr.prototype.outputDefined=function(){return!1},Mr.prototype.serialize=function(){if(null!=this.fromIndex&&void 0!==this.fromIndex){var t=this.fromIndex.serialize();return["index-of",this.needle.serialize(),this.haystack.serialize(),t]}return["index-of",this.needle.serialize(),this.haystack.serialize()]};var Sr=function(t,e,r,n,i,a){this.inputType=t,this.type=e,this.input=r,this.cases=n,this.outputs=i,this.otherwise=a};Sr.parse=function(t,e){if(t.length<5)return e.error("Expected at least 4 arguments, but found only "+(t.length-1)+".");if(t.length%2!=1)return e.error("Expected an even number of arguments.");var r,n;e.expectedType&&"value"!==e.expectedType.kind&&(n=e.expectedType);for(var i={},a=[],o=2;o<t.length-1;o+=2){var s=t[o],l=t[o+1];Array.isArray(s)||(s=[s]);var u=e.concat(o);if(0===s.length)return u.error("Expected at least one branch label.");for(var 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pn=function(t,e){this._parameters=t,this._specification=e,Ft(this,tn(this._parameters,this._specification))};function dn(t){var e=null;if(t instanceof Tr)e=dn(t.result);else if(t instanceof wr)for(var r=0,n=t.args;r<n.length;r+=1){var i=n[r];if(e=dn(i))break}else(t instanceof Qe||t instanceof br)&&t.input instanceof ke&&"zoom"===t.input.name&&(e=t);return e instanceof jt||t.eachChild(function(t){var r=dn(t);r instanceof jt?e=r:!e&&r?e=new jt("",'"zoom" expression may only be used as input to a top-level "step" or "interpolate" expression.'):e&&r&&e!==r&&(e=new jt("",'Only one zoom-based "step" or "interpolate" subexpression may be used in an expression.'))}),e}function vn(t){var e=t.key,r=t.value,n=t.valueSpec||{},i=t.objectElementValidators||{},a=t.style,o=t.styleSpec,s=[],l=Kr(r);if("object"!==l)return[new zt(e,r,"object expected, "+l+" found")];for(var u in r){var c=u.split(".")[0],f=n[c]||n["*"],h=void 0;if(i[c])h=i[c];else if(n[c])h=Vn;else 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e=[],a=t.value,s=t.key;if("array"!==Kr(a))return[new zt(s,a,"array expected, "+Kr(a)+" found")];if(2!==a.length)return[new zt(s,a,"array length 2 expected, length "+a.length+" found")];if(u){if("object"!==Kr(a[0]))return[new zt(s,a,"object expected, "+Kr(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(vn({key:s+"[0]",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:yn,value:h}}))}else e=e.concat(h({key:s+"[0]",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return ln(Nt(a[1]))?e.concat([new zt(s+"[1]",a[1],"expressions are not allowed in function 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e=("property"===t.expressionContext?hn:un)(Nt(t.value),t.valueSpec);if("error"===e.result)return e.value.map(function(e){return new zt(""+t.key+e.key,t.value,e.message)});var r=e.value.expression||e.value._styleExpression.expression;if("property"===t.expressionContext&&"text-font"===t.propertyKey&&!r.outputDefined())return[new zt(t.key,t.value,'Invalid data expression for "'+t.propertyKey+'". 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0===this.fadeDuration?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)},zi.prototype.getCrossfadeParameters=function(){var t=this.zoom,e=t-Math.floor(t),r=this.crossFadingFactor();return t>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:e+(1-e)*r}:{fromScale:.5,toScale:1,t:1-(1-r)*e}};var Ri=function(t,e){this.property=t,this.value=e,this.expression=function(t,e){if($r(t))return new pn(t,e);if(ln(t)){var r=hn(t,e);if("error"===r.result)throw new Error(r.value.map(function(t){return t.key+": "+t.message}).join(", "));return r.value}var n=t;return"string"==typeof t&&"color"===e.type&&(n=oe.parse(t)),{kind:"constant",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};Ri.prototype.isDataDriven=function(){return"source"===this.expression.kind||"composite"===this.expression.kind},Ri.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var Fi=function(t){this.property=t,this.value=new Ri(t,void 0)};Fi.prototype.transitioned=function(t,e){return new Ni(this.property,this.value,e,f({},t.transition,this.transition),t.now)},Fi.prototype.untransitioned=function(){return new Ni(this.property,this.value,null,{},0)};var Bi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};Bi.prototype.getValue=function(t){return b(this._values[t].value.value)},Bi.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new Fi(this._values[t].property)),this._values[t].value=new Ri(this._values[t].property,null===e?void 0:b(e))},Bi.prototype.getTransition=function(t){return b(this._values[t].transition)},Bi.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new Fi(this._values[t].property)),this._values[t].transition=b(e)||void 0},Bi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var 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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}(ta);fa.prototype.bytesPerElement=20,ii("StructArrayLayout6i1ul2ui20",fa);var ha=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,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 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this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(ta);va.prototype.bytesPerElement=6,ii("StructArrayLayout3ui6",va);var ga=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.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){var y=this.length;return this.resize(y+1),this.emplace(y,t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y){var m=24*t,x=12*t,b=48*t;return 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}(ta);ga.prototype.bytesPerElement=48,ii("StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48",ga);var ya=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,A,k,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,A,k,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,A,k,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]=A,this.float32[C+13]=k,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(ta);ya.prototype.bytesPerElement=68,ii("StructArrayLayout8i15ui1ul4f68",ya);var ma=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.float32[r+0]=e,t},e}(ta);ma.prototype.bytesPerElement=4,ii("StructArrayLayout1f4",ma);var xa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(ta);xa.prototype.bytesPerElement=6,ii("StructArrayLayout3i6",xa);var ba=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(ta);ba.prototype.bytesPerElement=8,ii("StructArrayLayout1ul2ui8",ba);var _a=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(ta);_a.prototype.bytesPerElement=4,ii("StructArrayLayout2ui4",_a);var wa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(ta);wa.prototype.bytesPerElement=2,ii("StructArrayLayout1ui2",wa);var Ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(ta);Ta.prototype.bytesPerElement=8,ii("StructArrayLayout2f8",Ta);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,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(ta);Aa.prototype.bytesPerElement=16,ii("StructArrayLayout4f16",Aa);var ka=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new a(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Qi);ka.prototype.size=20;var Ma=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 ka(this,t)},e}(fa);ii("CollisionBoxArray",Ma);var Sa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},placedOrientation:{configurable:!0},hidden:{configurable:!0},crossTileID:{configurable:!0},associatedIconIndex:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},r.placedOrientation.set=function(t){this._structArray.uint8[this._pos1+37]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Qi);Sa.prototype.size=48;var Ea=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new Sa(this,t)},e}(ga);ii("PlacedSymbolArray",Ea);var La=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 this._structArray.uint16[this._pos2+22]},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+12]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+12]=t},r.textBoxScale.get=function(){return this._structArray.float32[this._pos4+13]},r.textOffset0.get=function(){return this._structArray.float32[this._pos4+14]},r.textOffset1.get=function(){return this._structArray.float32[this._pos4+15]},r.collisionCircleDiameter.get=function(){return this._structArray.float32[this._pos4+16]},Object.defineProperties(e.prototype,r),e}(Qi);La.prototype.size=68;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.get=function(t){return new La(this,t)},e}(ya);ii("SymbolInstanceArray",Ca);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.getoffsetX=function(t){return this.float32[1*t+0]},e}(ma);ii("GlyphOffsetArray",Pa);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.getx=function(t){return this.int16[3*t+0]},e.prototype.gety=function(t){return this.int16[3*t+1]},e.prototype.gettileUnitDistanceFromAnchor=function(t){return this.int16[3*t+2]},e}(xa);ii("SymbolLineVertexArray",Oa);var Ia=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0}};return r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+0]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+3]},Object.defineProperties(e.prototype,r),e}(Qi);Ia.prototype.size=8;var Da=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new Ia(this,t)},e}(ba);ii("FeatureIndexArray",Da);var za=ea([{name:"a_pos",components:2,type:"Int16"}],4).members,Ra=function(t){void 0===t&&(t=[]),this.segments=t};function Fa(t,e){return 256*(t=u(Math.floor(t),0,255))+u(Math.floor(e),0,255)}Ra.prototype.prepareSegment=function(t,e,r,n){var i=this.segments[this.segments.length-1];return t>Ra.MAX_VERTEX_ARRAY_LENGTH&&w("Max vertices per segment is "+Ra.MAX_VERTEX_ARRAY_LENGTH+": bucket requested "+t),(!i||i.vertexLength+t>Ra.MAX_VERTEX_ARRAY_LENGTH||i.sortKey!==n)&&(i={vertexOffset:e.length,primitiveOffset:r.length,vertexLength:0,primitiveLength:0},void 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r,n,i,a,o,s,l,u;for(r=3&t.length,n=t.length-r,i=e,o=3432918353,s=461845907,u=0;u<n;)l=255&t.charCodeAt(u)|(255&t.charCodeAt(++u))<<8|(255&t.charCodeAt(++u))<<16|(255&t.charCodeAt(++u))<<24,++u,i=27492+(65535&(a=5*(65535&(i=(i^=l=(65535&(l=(l=(65535&l)*o+(((l>>>16)*o&65535)<<16)&4294967295)<<15|l>>>17))*s+(((l>>>16)*s&65535)<<16)&4294967295)<<13|i>>>19))+((5*(i>>>16)&65535)<<16)&4294967295))+(((a>>>16)+58964&65535)<<16);switch(l=0,r){case 3:l^=(255&t.charCodeAt(u+2))<<16;case 2:l^=(255&t.charCodeAt(u+1))<<8;case 1:i^=l=(65535&(l=(l=(65535&(l^=255&t.charCodeAt(u)))*o+(((l>>>16)*o&65535)<<16)&4294967295)<<15|l>>>17))*s+(((l>>>16)*s&65535)<<16)&4294967295}return i^=t.length,i=2246822507*(65535&(i^=i>>>16))+((2246822507*(i>>>16)&65535)<<16)&4294967295,i=3266489909*(65535&(i^=i>>>13))+((3266489909*(i>>>16)&65535)<<16)&4294967295,(i^=i>>>16)>>>0}}),ja=e(function(t){t.exports=function(t,e){for(var r,n=t.length,i=e^n,a=0;n>=4;)r=1540483477*(65535&(r=255&t.charCodeAt(a)|(255&t.charCodeAt(++a))<<8|(255&t.charCodeAt(++a))<<16|(255&t.charCodeAt(++a))<<24))+((1540483477*(r>>>16)&65535)<<16),i=1540483477*(65535&i)+((1540483477*(i>>>16)&65535)<<16)^(r=1540483477*(65535&(r^=r>>>24))+((1540483477*(r>>>16)&65535)<<16)),n-=4,++a;switch(n){case 3:i^=(255&t.charCodeAt(a+2))<<16;case 2:i^=(255&t.charCodeAt(a+1))<<8;case 1:i=1540483477*(65535&(i^=255&t.charCodeAt(a)))+((1540483477*(i>>>16)&65535)<<16)}return i=1540483477*(65535&(i^=i>>>13))+((1540483477*(i>>>16)&65535)<<16),(i^=i>>>15)>>>0}}),Ua=Na,Va=Na,Ha=ja;Ua.murmur3=Va,Ua.murmur2=Ha;var qa=function(){this.ids=[],this.positions=[],this.indexed=!1};qa.prototype.add=function(t,e,r,n){this.ids.push(Ya(t)),this.positions.push(e,r,n)},qa.prototype.getPositions=function(t){for(var e=Ya(t),r=0,n=this.ids.length-1;r<n;){var i=r+n>>1;this.ids[i]>=e?n=i:r=i+1}for(var a=[];this.ids[r]===e;){var 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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}(Xa),Ka=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}(Xa),$a=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}(Xa),Qa=function(t){function e(e,r){t.call(this,e,r),this.current=[0,0,0]}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]&&t[2]===this.current[2]||(this.current=t,this.gl.uniform3f(this.location,t[0],t[1],t[2]))},e}(Xa),to=function(t){function e(e,r){t.call(this,e,r),this.current=[0,0,0,0]}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]&&t[2]===this.current[2]&&t[3]===this.current[3]||(this.current=t,this.gl.uniform4f(this.location,t[0],t[1],t[2],t[3]))},e}(Xa),eo=function(t){function e(e,r){t.call(this,e,r),this.current=oe.transparent}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){t.r===this.current.r&&t.g===this.current.g&&t.b===this.current.b&&t.a===this.current.a||(this.current=t,this.gl.uniform4f(this.location,t.r,t.g,t.b,t.a))},e}(Xa),ro=new Float32Array(16),no=function(t){function e(e,r){t.call(this,e,r),this.current=ro}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}(Xa);function io(t){return[Fa(255*t.r,255*t.g),Fa(255*t.b,255*t.a)]}var ao=function(t,e,r){this.value=t,this.uniformNames=e.map(function(t){return"u_"+t}),this.type=r};ao.prototype.setUniform=function(t,e,r){t.set(r.constantOr(this.value))},ao.prototype.getBinding=function(t,e,r){return"color"===this.type?new eo(t,e):new Ka(t,e)};var oo=function(t,e){this.uniformNames=e.map(function(t){return"u_"+t}),this.patternFrom=null,this.patternTo=null,this.pixelRatioFrom=1,this.pixelRatioTo=1};oo.prototype.setConstantPatternPositions=function(t,e){this.pixelRatioFrom=e.pixelRatio,this.pixelRatioTo=t.pixelRatio,this.patternFrom=e.tlbr,this.patternTo=t.tlbr},oo.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)},oo.prototype.getBinding=function(t,e,r){return"u_pattern"===r.substr(0,9)?new to(t,e):new Ka(t,e)};var so=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};so.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.paintVertexArray.length,o=this.expression.evaluate(new zi(0),e,{},n,[],i);this.paintVertexArray.resize(t),this._setPaintValue(a,t,o)},so.prototype.updatePaintArray=function(t,e,r,n){var i=this.expression.evaluate({zoom:0},r,n);this._setPaintValue(t,e,i)},so.prototype._setPaintValue=function(t,e,r){if("color"===this.type)for(var n=io(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))}},so.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))},so.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()};var lo=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};lo.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.expression.evaluate(new zi(this.zoom),e,{},n,[],i),o=this.expression.evaluate(new zi(this.zoom+1),e,{},n,[],i),s=this.paintVertexArray.length;this.paintVertexArray.resize(t),this._setPaintValue(s,t,a,o)},lo.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)},lo.prototype._setPaintValue=function(t,e,r,n){if("color"===this.type)for(var i=io(r),a=io(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))}},lo.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))},lo.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()},lo.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)},lo.prototype.getBinding=function(t,e,r){return new Ka(t,e)};var uo=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};uo.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)},uo.prototype.updatePaintArray=function(t,e,r,n,i){this._setPaintValues(t,e,r.patterns&&r.patterns[this.layerId],i)},uo.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)}},uo.prototype.upload=function(t){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=t.createVertexBuffer(this.zoomInPaintVertexArray,Ba.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=t.createVertexBuffer(this.zoomOutPaintVertexArray,Ba.members,this.expression.isStateDependent))},uo.prototype.destroy=function(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()};var co=function(t,e,r,n){this.binders={},this.layoutAttributes=n,this._buffers=[];var i=[];for(var a in 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To(Do(t,this.paint.get("fill-translate"),this.paint.get("fill-translate-anchor"),a.angle,o),n)},e.prototype.isTileClipped=function(){return!0},e}(Ki),Gs=ea([{name:"a_pos",components:2,type:"Int16"},{name:"a_normal_ed",components:4,type:"Int16"}],4).members,Ys=Ws;function Ws(t,e,r,n,i){this.properties={},this.extent=r,this.type=0,this._pbf=t,this._geometry=-1,this._keys=n,this._values=i,t.readFields(Zs,this,e)}function Zs(t,e,r){1==t?e.id=r.readVarint():2==t?function(t,e){for(var r=t.readVarint()+t.pos;t.pos<r;){var n=e._keys[t.readVarint()],i=e._values[t.readVarint()];e.properties[n]=i}}(r,e):3==t?e.type=r.readVarint():4==t&&(e._geometry=r.pos)}function Xs(t){for(var e,r,n=0,i=0,a=t.length,o=a-1;i<a;o=i++)e=t[i],n+=((r=t[o]).x-e.x)*(e.y+r.y);return n}Ws.types=["Unknown","Point","LineString","Polygon"],Ws.prototype.loadGeometry=function(){var t=this._pbf;t.pos=this._geometry;for(var e,r=t.readVarint()+t.pos,n=1,i=0,o=0,s=0,l=[];t.pos<r;){if(i<=0){var u=t.readVarint();n=7&u,i=u>>3}if(i--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new a(o,s));else{if(7!==n)throw new Error("unknown command "+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},Ws.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,u=-1/0;t.pos<e;){if(n<=0){var c=t.readVarint();r=7&c,n=c>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>u&&(u=a);else if(7!==r)throw new Error("unknown command "+r)}return[o,l,s,u]},Ws.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),u=Ws.types[this.type];function c(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];c(l=f);break;case 2:for(n=0;n<l.length;n++)c(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=Xs(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}return r&&i.push(r),i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)c(l[n][i])}1===l.length?l=l[0]:u="Multi"+u;var h={type:"Feature",geometry:{type:u,coordinates:l},properties:this.properties};return"id"in this&&(h.id=this.id),h};var Js=Ks;function Ks(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields($s,this,e),this.length=this._features.length}function $s(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){for(var e=null,r=t.readVarint()+t.pos;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Qs(t,e,r){if(3===t){var n=new Js(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ks.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 Ys(this._pbf,e,this.extent,this._keys,this._values)};var tl={VectorTile:function(t,e){this.layers=t.readFields(Qs,{},e)},VectorTileFeature:Ys,VectorTileLayer:Js},el=tl.VectorTileFeature.types,rl=Math.pow(2,13);function nl(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*rl)+o,i*rl*2,a*rl*2,Math.round(s))}var il=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 aa,this.indexArray=new va,this.programConfigurations=new fo(Gs,t.layers,t.zoom),this.segments=new Ra,this.stateDependentLayerIds=this.layers.filter(function(t){return t.isStateDependent()}).map(function(t){return t.id})};function al(t,e){return t.x===e.x&&(t.x<0||t.x>vo)||t.y===e.y&&(t.y<0||t.y>vo)}function ol(t){return t.every(function(t){return t.x<0})||t.every(function(t){return t.x>vo})||t.every(function(t){return t.y<0})||t.every(function(t){return t.y>vo})}il.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=Ns("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?mo(o):[]};if(this.layers[0]._featureFilter.filter(new zi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:u,index:l,geometry:c?f.geometry:mo(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(js("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)}}},il.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)}},il.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},il.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},il.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},il.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Gs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},il.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},il.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=Fs(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&&!ol(d))for(var v=0,g=0;g<d.length;g++){var y=d[g];if(g>=1){var m=d[g-1];if(!al(y,m)){f.vertexLength+4>Ra.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),nl(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,v),nl(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,v),v+=b,nl(this.layoutVertexArray,m.x,m.y,x.x,x.y,0,0,v),nl(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>Ra.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),"Polygon"===el[t.type]){for(var w=[],T=[],A=f.vertexLength,k=0,M=s;k<M.length;k+=1){var S=M[k];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];nl(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=as(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(A+C[P],A+C[P+2],A+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},ii("FillExtrusionBucket",il,{omit:["layers","features"]});var sl={paint:new Xi({"fill-extrusion-opacity":new qi(Dt["paint_fill-extrusion"]["fill-extrusion-opacity"]),"fill-extrusion-color":new Gi(Dt["paint_fill-extrusion"]["fill-extrusion-color"]),"fill-extrusion-translate":new qi(Dt["paint_fill-extrusion"]["fill-extrusion-translate"]),"fill-extrusion-translate-anchor":new qi(Dt["paint_fill-extrusion"]["fill-extrusion-translate-anchor"]),"fill-extrusion-pattern":new Yi(Dt["paint_fill-extrusion"]["fill-extrusion-pattern"]),"fill-extrusion-height":new Gi(Dt["paint_fill-extrusion"]["fill-extrusion-height"]),"fill-extrusion-base":new Gi(Dt["paint_fill-extrusion"]["fill-extrusion-base"]),"fill-extrusion-vertical-gradient":new qi(Dt["paint_fill-extrusion"]["fill-extrusion-vertical-gradient"])})},ll=function(t){function e(e){t.call(this,e,sl)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new il(t)},e.prototype.queryRadius=function(){return Io(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=Do(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];Ho(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,A=n[0]*w+n[4]*T+n[12],k=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=A+f,P=k+h,O=M+p,I=S+d,D=new a((A+s)/L,(k+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;To(r,e)&&(n=cl(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];_o(r,f)&&(n=Math.min(n,cl(r,f)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ki);function ul(t,e){return t.x*e.x+t.y*e.y}function cl(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=ul(s,s),f=ul(s,l),h=ul(l,l),p=ul(u,s),d=ul(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 fl=ea([{name:"a_pos_normal",components:2,type:"Int16"},{name:"a_data",components:4,type:"Uint8"}],4).members,hl=tl.VectorTileFeature.types,pl=Math.cos(Math.PI/180*37.5),dl=Math.pow(2,14)/.5,vl=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 oa,this.indexArray=new va,this.programConfigurations=new fo(fl,t.layers,t.zoom),this.segments=new Ra,this.stateDependentLayerIds=this.layers.filter(function(t){return t.isStateDependent()}).map(function(t){return t.id})};vl.prototype.populate=function(t,e,r){this.hasPattern=Ns("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?mo(l):[]};if(this.layers[0]._featureFilter.filter(new zi(this.zoom),p,r)){h||(p.geometry=mo(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=js("line",this.layers,m,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(m,b,_,r,{});var A=t[_].feature;e.featureIndex.insert(A,b,_,w,this.index)}},vl.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},vl.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)}},vl.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},vl.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},vl.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,fl),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},vl.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},vl.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)},vl.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"===hl[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?122880/(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 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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(A?-1:1);this.addHalfVertex(c,F.x,F.y,!1,A,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}}}}},vl.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var 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this.length=this.pos,this.pos=0,this.buf.subarray(0,this.length)},writeFixed32:function(t){this.realloc(4),Jl(this.buf,t,this.pos),this.pos+=4},writeSFixed32:function(t){this.realloc(4),Jl(this.buf,t,this.pos),this.pos+=4},writeFixed64:function(t){this.realloc(8),Jl(this.buf,-1&t,this.pos),Jl(this.buf,Math.floor(t*zl),this.pos+4),this.pos+=8},writeSFixed64:function(t){this.realloc(8),Jl(this.buf,-1&t,this.pos),Jl(this.buf,Math.floor(t*zl),this.pos+4),this.pos+=8},writeVarint:function(t){(t=+t||0)>268435455||t<0?function(t,e){var r,n;if(t>=0?(r=t%4294967296|0,n=t/4294967296|0):(n=~(-t/4294967296),4294967295^(r=~(-t%4294967296))?r=r+1|0:(r=0,n=n+1|0)),t>=0x10000000000000000||t<-0x10000000000000000)throw new Error("Given varint doesn't fit into 10 bytes");e.realloc(10),function(t,e,r){r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos]=127&t}(r,0,e),function(t,e){var r=(7&t)<<4;e.buf[e.pos++]|=r|((t>>>=3)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t)))))}(n,e)}(t,this):(this.realloc(4),this.buf[this.pos++]=127&t|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=t>>>7&127))))},writeSVarint:function(t){this.writeVarint(t<0?2*-t-1:2*t)},writeBoolean:function(t){this.writeVarint(Boolean(t))},writeString:function(t){t=String(t),this.realloc(4*t.length),this.pos++;var e=this.pos;this.pos=function(t,e,r){for(var n,i,a=0;a<e.length;a++){if((n=e.charCodeAt(a))>55295&&n<57344){if(!i){n>56319||a+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):i=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,i=n;continue}n=i-55296<<10|n-56320|65536,i=null}else i&&(t[r++]=239,t[r++]=191,t[r++]=189,i=null);n<128?t[r++]=n:(n<2048?t[r++]=n>>6|192:(n<65536?t[r++]=n>>12|224:(t[r++]=n>>18|240,t[r++]=n>>12&63|128),t[r++]=n>>6&63|128),t[r++]=63&n|128)}return r}(this.buf,t,this.pos);var r=this.pos-e;r>=128&&Nl(e,r,this),this.pos=e-1,this.writeVarint(r),this.pos+=r},writeFloat:function(t){this.realloc(4),Pl(this.buf,t,this.pos,!0,23,4),this.pos+=4},writeDouble:function(t){this.realloc(8),Pl(this.buf,t,this.pos,!0,52,8),this.pos+=8},writeBytes:function(t){var e=t.length;this.writeVarint(e),this.realloc(e);for(var r=0;r<e;r++)this.buf[this.pos++]=t[r]},writeRawMessage:function(t,e){this.pos++;var r=this.pos;t(e,this);var n=this.pos-r;n>=128&&Nl(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,Il.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,jl,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,Ul,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,ql,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,Vl,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,Hl,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,Gl,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,Yl,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,Wl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,Zl,e)},writeBytesField:function(t,e){this.writeTag(t,Il.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,Il.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,Il.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,Il.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,Il.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,Il.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,Il.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,Il.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,Il.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,Il.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};function $l(t,e,r){1===t&&r.readMessage(Ql,e)}function Ql(t,e,r){if(3===t){var n=r.readMessage(tu,{}),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 Ko({width:o+6,height:s+6},a),metrics:{width:o,height:s,left:l,top:u,advance:c}})}}function tu(t,e,r){1===t?e.id=r.readVarint():2===t?e.bitmap=r.readBytes():3===t?e.width=r.readVarint():4===t?e.height=r.readVarint():5===t?e.left=r.readSVarint():6===t?e.top=r.readSVarint():7===t&&(e.advance=r.readVarint())}function eu(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 f.h===p.h?(p.x+=f.w,p.w-=f.w):f.w===p.w?(p.y+=f.h,p.h-=f.h):(o.push({x:p.x+f.w,y:p.y,w:p.w-f.w,h:f.h}),p.y+=f.h,p.h-=f.h);break}}return{w:s,h:l,fill:e/(s*l)||0}}var ru=function(t,e){var r=e.pixelRatio,n=e.version,i=e.stretchX,a=e.stretchY,o=e.content;this.paddedRect=t,this.pixelRatio=r,this.stretchX=i,this.stretchY=a,this.content=o,this.version=n},nu={tl:{configurable:!0},br:{configurable:!0},tlbr:{configurable:!0},displaySize:{configurable:!0}};nu.tl.get=function(){return[this.paddedRect.x+1,this.paddedRect.y+1]},nu.br.get=function(){return[this.paddedRect.x+this.paddedRect.w-1,this.paddedRect.y+this.paddedRect.h-1]},nu.tlbr.get=function(){return this.tl.concat(this.br)},nu.displaySize.get=function(){return[(this.paddedRect.w-2)/this.pixelRatio,(this.paddedRect.h-2)/this.pixelRatio]},Object.defineProperties(ru.prototype,nu);var iu=function(t,e){var r={},n={};this.haveRenderCallbacks=[];var i=[];this.addImages(t,r,i),this.addImages(e,n,i);var a=eu(i),o=a.w,s=a.h,l=new $o({width:o||1,height:s||1});for(var u in t){var c=t[u],f=r[u].paddedRect;$o.copy(c.data,l,{x:0,y:0},{x:f.x+1,y:f.y+1},c.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,v=d.x+1,g=d.y+1,y=p.data.width,m=p.data.height;$o.copy(p.data,l,{x:0,y:0},{x:v,y:g},p.data),$o.copy(p.data,l,{x:0,y:m-1},{x:v,y:g-1},{width:y,height:1}),$o.copy(p.data,l,{x:0,y:0},{x:v,y:g+m},{width:y,height:1}),$o.copy(p.data,l,{x:y-1,y:0},{x:v-1,y:g},{width:1,height:m}),$o.copy(p.data,l,{x:0,y:0},{x:v+y,y:g},{width:1,height:m})}this.image=l,this.iconPositions=r,this.patternPositions=n};iu.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new ru(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},iu.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},iu.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})}},ii("ImagePosition",ru),ii("ImageAtlas",iu);var au={horizontal:1,vertical:2,horizontalOnly:3},ou=-17;var su=function(){this.scale=1,this.fontStack="",this.imageName=null};su.forText=function(t,e){var r=new su;return r.scale=t||1,r.fontStack=e,r},su.forImage=function(t){var e=new su;return e.imageName=t,e};var lu=function(){this.text="",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function uu(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v){var g,y=lu.fromFeature(t,i);f===au.vertical&&y.verticalizePunctuation();var m=Ii.processBidirectionalText,x=Ii.processStyledBidirectionalText;if(m&&1===y.sections.length){g=[];for(var b=0,_=m(y.toString(),yu(y,u,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new lu;T.text=w,T.sections=y.sections;for(var A=0;A<w.length;A++)T.sectionIndex.push(0);g.push(T)}}else if(x){g=[];for(var k=0,M=x(y.text,y.sectionIndex,yu(y,u,a,e,n,p,d));k<M.length;k+=1){var S=M[k],E=new lu;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,yu(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=ou,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)*Ll,T={positionedGlyphs:[],lineOffset:0};t.positionedLines[y]=T;var A=T.positionedGlyphs,k=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=Ll,z=!(l===au.horizontal||!c&&!vi(L)||c&&(cu[L]||yi(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*Ll/f,P={width:F[0],height:F[1],left:1,top:-3,advance:z?F[1]:F[0]},C=w+(Ll-F[1]*S.scale),D=P.advance;var B=z?F[0]*S.scale-Ll*_:F[1]*S.scale-Ll*_;B>0&&B>k&&(k=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)*Ll}z?(t.verticalizable=!0,A.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):(A.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!==A.length){var H=h-u;d=Math.max(H,d),xu(A,0,A.length-1,g,k)}h=0;var q=a*_+k;T.lineOffset=Math.max(k,w),p+=q,v=Math.max(q,v),++y}else p+=a,++y}var G=p-ou,Y=mu(o),W=Y.horizontalAlign,Z=Y.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var u=(e-r)*i,c=0;c=a!==o?-s*n-ou:(-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,W,Z,d,v,a,G,i.length),t.top+=-Z*G,t.bottom=t.top+G,t.left+=-W*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}lu.fromFeature=function(t,e){for(var r=new lu,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},lu.prototype.length=function(){return this.text.length},lu.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},lu.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},lu.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},lu.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&&gi(n)&&!El[t[r+1]]||i&&gi(i)&&!El[t[r-1]]||!El[t[r]]?e+=t[r]:e+=El[t[r]]}return e}(this.text)},lu.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&cu[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&cu[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},lu.prototype.substring=function(t,e){var r=new lu;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},lu.prototype.toString=function(){return this.text},lu.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce(function(e,r){return Math.max(e,t.sections[r].scale)},0)},lu.prototype.addTextSection=function(t,e){this.text+=t.text,this.sections.push(su.forText(t.scale,t.fontStack||e));for(var r=this.sections.length-1,n=0;n<t.text.length;++n)this.sectionIndex.push(r)},lu.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(su.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.")},lu.prototype.getNextImageSectionCharCode=function(){return this.imageSectionID?this.imageSectionID>=63743?null:++this.imageSectionID:(this.imageSectionID=57344,this.imageSectionID)};var cu={9:!0,10:!0,11:!0,12:!0,13:!0,32:!0},fu={};function hu(t,e,r,n,i,a){if(e.imageName){var o=n[e.imageName];return o?o.displaySize[0]*e.scale*Ll/a+i:0}var s=r[e.fontStack],l=s&&s[t];return 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Reduce your "text-size".'),t.addSymbols(t.text,g,m,l,o,s,c,e,u.lineStartIndex,u.lineLength,p,v);for(var x=0,b=f;x<b.length;x+=1)h[b[x]]=t.text.placedSymbolArray.length-1;return 4*g.length}function $u(t){for(var e in t)return t[e];return null}function Qu(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var tc=tl.VectorTileFeature.types,ec=[{name:"a_fade_opacity",components:1,type:"Uint8",offset:0}];function rc(t,e,r,n,i,a,o,s,l,u,c,f,h){var p=s?Math.min(Ju,Math.round(s[0])):0,d=s?Math.min(Ju,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*u,16*c,256*f,256*h)}function nc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function ic(t){for(var e=0,r=t.sections;e<r.length;e+=1)if(bi(r[e].text))return!0;return!1}var ac=function(t){this.layoutVertexArray=new la,this.indexArray=new va,this.programConfigurations=t,this.segments=new Ra,this.dynamicLayoutVertexArray=new ua,this.opacityVertexArray=new ca,this.placedSymbolArray=new Ea};ac.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},ac.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,wl.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Tl.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,ec,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},ac.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},ii("SymbolBuffers",ac);var oc=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new Ra,this.collisionVertexArray=new da};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,Al.members,!0)},oc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},ii("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=Bo([]),this.placementViewportMatrix=Bo([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Tu(this.zoom,e["text-size"]),this.iconSizeData=Tu(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 au[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 fo(wl.members,this.layers,this.zoom,function(t){return/^text/.test(t)})),this.icon=new ac(new fo(wl.members,this.layers,this.zoom,function(t){return/^icon/.test(t)})),this.glyphOffsetArray=new Pa,this.lineVertexArray=new Oa,this.symbolInstances=new Ca},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=El[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 zi(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?mo(m):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=mo(m));var A=void 0;if(l){var k=n.getValueAndResolveTokens("text-field",T,r,p),M=ue.factory(k);ic(M)&&(this.hasRTLText=!0),(!this.hasRTLText||"unavailable"===Pi()||this.hasRTLText&&Ii.isParsed())&&(A=Sl(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(A||S){var L=this.sortFeaturesByKey?c.evaluate(T,{},r):void 0,C={id:x,text:A,icon:S,index:b,sourceLayerIndex:_,geometry:mo(m),properties:m.properties,type:tc[m.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),A){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(au.vertical)>=0;for(var I=0,D=A.sections;I<D.length;I+=1){var z=D[I];if(z.image)f[z.image.name]=!0;else{var R=hi(A.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===au.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,A=b.br,k=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,k.x,k.y,r,P,M.x,M.y,E,L),rc(p,s.x,s.y,w.x,D+w.y,k.x+k.w,k.y,r,P,S.x,M.y,E,L),rc(p,s.x,s.y,T.x,D+T.y,k.x,k.y+k.h,r,P,M.x,S.y,E,L),rc(p,s.x,s.y,A.x,D+A.y,k.x+k.w,k.y+k.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(ha,kl.members,_a),this.iconCollisionBox=new oc(ha,kl.members,_a);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)}},ii("SymbolBucket",sc,{omit:["layers","collisionBoxArray","features","compareText"]}),sc.MAX_GLYPHS=65535,sc.addDynamicAttributes=nc;var lc=new Xi({"symbol-placement":new qi(Dt.layout_symbol["symbol-placement"]),"symbol-spacing":new qi(Dt.layout_symbol["symbol-spacing"]),"symbol-avoid-edges":new qi(Dt.layout_symbol["symbol-avoid-edges"]),"symbol-sort-key":new Gi(Dt.layout_symbol["symbol-sort-key"]),"symbol-z-order":new qi(Dt.layout_symbol["symbol-z-order"]),"icon-allow-overlap":new qi(Dt.layout_symbol["icon-allow-overlap"]),"icon-ignore-placement":new qi(Dt.layout_symbol["icon-ignore-placement"]),"icon-optional":new qi(Dt.layout_symbol["icon-optional"]),"icon-rotation-alignment":new qi(Dt.layout_symbol["icon-rotation-alignment"]),"icon-size":new Gi(Dt.layout_symbol["icon-size"]),"icon-text-fit":new qi(Dt.layout_symbol["icon-text-fit"]),"icon-text-fit-padding":new qi(Dt.layout_symbol["icon-text-fit-padding"]),"icon-image":new Gi(Dt.layout_symbol["icon-image"]),"icon-rotate":new Gi(Dt.layout_symbol["icon-rotate"]),"icon-padding":new qi(Dt.layout_symbol["icon-padding"]),"icon-keep-upright":new qi(Dt.layout_symbol["icon-keep-upright"]),"icon-offset":new Gi(Dt.layout_symbol["icon-offset"]),"icon-anchor":new Gi(Dt.layout_symbol["icon-anchor"]),"icon-pitch-alignment":new qi(Dt.layout_symbol["icon-pitch-alignment"]),"text-pitch-alignment":new qi(Dt.layout_symbol["text-pitch-alignment"]),"text-rotation-alignment":new qi(Dt.layout_symbol["text-rotation-alignment"]),"text-field":new Gi(Dt.layout_symbol["text-field"]),"text-font":new Gi(Dt.layout_symbol["text-font"]),"text-size":new Gi(Dt.layout_symbol["text-size"]),"text-max-width":new Gi(Dt.layout_symbol["text-max-width"]),"text-line-height":new qi(Dt.layout_symbol["text-line-height"]),"text-letter-spacing":new Gi(Dt.layout_symbol["text-letter-spacing"]),"text-justify":new Gi(Dt.layout_symbol["text-justify"]),"text-radial-offset":new Gi(Dt.layout_symbol["text-radial-offset"]),"text-variable-anchor":new qi(Dt.layout_symbol["text-variable-anchor"]),"text-anchor":new Gi(Dt.layout_symbol["text-anchor"]),"text-max-angle":new qi(Dt.layout_symbol["text-max-angle"]),"text-writing-mode":new qi(Dt.layout_symbol["text-writing-mode"]),"text-rotate":new Gi(Dt.layout_symbol["text-rotate"]),"text-padding":new qi(Dt.layout_symbol["text-padding"]),"text-keep-upright":new qi(Dt.layout_symbol["text-keep-upright"]),"text-transform":new Gi(Dt.layout_symbol["text-transform"]),"text-offset":new Gi(Dt.layout_symbol["text-offset"]),"text-allow-overlap":new qi(Dt.layout_symbol["text-allow-overlap"]),"text-ignore-placement":new qi(Dt.layout_symbol["text-ignore-placement"]),"text-optional":new qi(Dt.layout_symbol["text-optional"])}),uc={paint:new Xi({"icon-opacity":new Gi(Dt.paint_symbol["icon-opacity"]),"icon-color":new Gi(Dt.paint_symbol["icon-color"]),"icon-halo-color":new Gi(Dt.paint_symbol["icon-halo-color"]),"icon-halo-width":new Gi(Dt.paint_symbol["icon-halo-width"]),"icon-halo-blur":new Gi(Dt.paint_symbol["icon-halo-blur"]),"icon-translate":new qi(Dt.paint_symbol["icon-translate"]),"icon-translate-anchor":new qi(Dt.paint_symbol["icon-translate-anchor"]),"text-opacity":new Gi(Dt.paint_symbol["text-opacity"]),"text-color":new Gi(Dt.paint_symbol["text-color"],{runtimeType:Yt,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),"text-halo-color":new Gi(Dt.paint_symbol["text-halo-color"]),"text-halo-width":new Gi(Dt.paint_symbol["text-halo-width"]),"text-halo-blur":new Gi(Dt.paint_symbol["text-halo-blur"]),"text-translate":new qi(Dt.paint_symbol["text-translate"]),"text-translate-anchor":new qi(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},ii("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()||ln(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 sn(o,a.property.specification);i="constant"===a.value.kind||"source"===a.value.kind?new cn("source",s):new fn("composite",s,a.value.zoomStops,a.value._interpolationType),this.paint._values[n]=new Vi(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}(Ki),hc={paint:new Xi({"background-color":new qi(Dt.paint_background["background-color"]),"background-pattern":new Wi(Dt.paint_background["background-pattern"]),"background-opacity":new qi(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}(Ki),dc={paint:new Xi({"raster-opacity":new qi(Dt.paint_raster["raster-opacity"]),"raster-hue-rotate":new qi(Dt.paint_raster["raster-hue-rotate"]),"raster-brightness-min":new qi(Dt.paint_raster["raster-brightness-min"]),"raster-brightness-max":new qi(Dt.paint_raster["raster-brightness-max"]),"raster-saturation":new qi(Dt.paint_raster["raster-saturation"]),"raster-contrast":new qi(Dt.paint_raster["raster-contrast"]),"raster-resampling":new qi(Dt.paint_raster["raster-resampling"]),"raster-fade-duration":new qi(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}(Ki);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}(Ki),yc={circle:Go,heatmap:es,hillshade:ns,fill:qs,"fill-extrusion":ll,line:bl,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 Ac=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()})};Ac.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))},Ac.prototype.remove=function(){delete this._channel,this._callback=function(){}};var kc=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 Ac(this.process),this.target.addEventListener("message",this.receive,!1),this.globalScope=k()?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]}kc.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:li(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:"<cancel>",targetMapId:n,sourceMapId:a.mapId})}}},kc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if("<cancel>"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},kc.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)}},kc.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(ui(e.error)):n(null,ui(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?li(e):null,data:li(n,a)},a)}:function(t){i=!0},s=null,l=ui(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)}},kc.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)*vo,(t.y*e-this.y)*vo)},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))},ii("CanonicalTileID",Fc),ii("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 $o({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)]},ii("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 Yc=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new $n(vo,16,0),this.grid3D=new $n(vo,16,0),this.featureIndexArray=new Da,this.promoteId=e};function Wc(t,e,r,n,i){return m(t,function(t,a){var o=e instanceof Hi?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o})}function Zc(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}Yc.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]<vo&&c[1]<vo&&c[2]>=0&&c[3]>=0&&s.insert(o,c[0],c[1],c[2],c[3])}},Yc.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new tl.VectorTile(new Ol(this.rawTileData)).layers,this.sourceLayerCoder=new Vc(this.vtLayers?Object.keys(this.vtLayers).sort():["_geojsonTileLayer"])),this.vtLayers},Yc.prototype.query=function(t,e,r,n){var i=this;this.loadVTLayers();for(var o=t.params||{},s=vo/t.tileSize/t.scale,l=Tn(o.filter),u=t.queryGeometry,c=t.queryPadding*s,f=Zc(u),h=this.grid.query(f.minX-c,f.minY-c,f.maxX+c,f.maxY+c),p=Zc(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(Co(t,f[c]))return!0;for(var h=0;h<t.length-1;h++)if(Po(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=mo(e)),r.queryIntersectsFeature(u,e,n,p,i.z,t.transform,s,t.pixelPosMatrix)})}},b=0;b<h.length;b++)x(b);return m},Yc.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 zi(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=Wc(x.paint,y.paint,p,m,o),x.layout=Wc(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})}}}}}},Yc.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var u=Tn(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},Yc.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},Yc.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},ii("FeatureIndex",Yc,{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,Di();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 Ma},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=Tn(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 zi(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=kc,t.AlphaImage=Ko,t.CanonicalTileID=Fc,t.CollisionBoxArray=Ma,t.Color=oe,t.DEMData=Uc,t.DataConstantProperty=qi,t.DictionaryCoder=Vc,t.EXTENT=vo,t.ErrorEvent=Ot,t.EvaluationParameters=zi,t.Event=Pt,t.Evented=It,t.FeatureIndex=Yc,t.FillBucket=Us,t.FillExtrusionBucket=il,t.ImageAtlas=iu,t.ImagePosition=ru,t.LineBucket=vl,t.LngLat=Lc,t.LngLatBounds=Sc,t.MercatorCoordinate=Rc,t.ONE_EM=Ll,t.OverscaledTileID=Nc,t.Point=a,t.Point$1=a,t.Properties=Xi,t.Protobuf=Ol,t.RGBAImage=$o,t.RequestManager=q,t.RequestPerformance=$c,t.ResourceType=mt,t.SegmentVector=Ra,t.SourceFeatureState=Gc,t.StructArrayLayout1ui2=wa,t.StructArrayLayout2f1f2i16=pa,t.StructArrayLayout2i4=na,t.StructArrayLayout3ui6=va,t.StructArrayLayout4i8=ia,t.SymbolBucket=sc,t.Texture=Tc,t.Tile=Jc,t.Transitionable=Bi,t.Uniform1f=Ka,t.Uniform1i=Ja,t.Uniform2f=$a,t.Uniform3f=Qa,t.Uniform4f=to,t.UniformColor=eo,t.UniformMatrix4f=no,t.UnwrappedTileID=Bc,t.ValidationError=zt,t.WritingMode=au,t.ZoomHistory=ci,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){++gt>ct&&(t.getActor().send("enforceCacheSizeLimit",ut),gt=0)},t.clamp=u,t.clearTileCache=function(t){var e=self.caches.delete(lt);t&&e.catch(t).then(function(){return t()})},t.clipLine=Du,t.clone=function(t){var e=new Fo(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 Fo(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=Ml,t.config=B,t.create=function(){var t=new Fo(16);return Fo!=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 Fo(9);return Fo!=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 Fo(4);return Fo!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t},t.createCommonjsModule=e,t.createExpression=un,t.createLayout=ea,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 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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(W(r),Z(a),W(i),Z(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?Y(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=At(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+_,A=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+A,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+A,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+A,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=At(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[At(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,kt),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 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Object support":"insufficient Function support":"insufficent Array support":"not a browser";var r}t.exports?t.exports=e:window&&(window.mapboxgl=window.mapboxgl||{},window.mapboxgl.supported=e,window.mapboxgl.notSupportedReason=r);var n={};e.webGLContextAttributes={antialias:!1,alpha:!0,stencil:!0,depth:!0}}),r={create:function(e,r,n){var i=t.window.document.createElement(e);return void 0!==r&&(i.className=r),n&&n.appendChild(i),i},createNS:function(e,r){return t.window.document.createElementNS(e,r)}},n=t.window.document.documentElement.style;function i(t){if(!n)return t[0];for(var e=0;e<t.length;e++)if(t[e]in n)return t[e];return t[0]}var a,o=i(["userSelect","MozUserSelect","WebkitUserSelect","msUserSelect"]);r.disableDrag=function(){n&&o&&(a=n[o],n[o]="none")},r.enableDrag=function(){n&&o&&(n[o]=a)};var s=i(["transform","WebkitTransform"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var 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 t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var 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r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event("styleimagemissing",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image "'+o+'" could not be loaded. Please make sure you have added the image with map.addImage() or a "sprite" property in your style. You can provide missing images by listening for the "styleimagemissing" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,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),A=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};A.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]},A.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},A.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))}},A.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))}},A.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},A.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name="Worker "+a,this.actors.push(s)}};function M(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),["tiles","minzoom","maxzoom","attribution","mapbox_logo","bounds","scheme","tileSize","encoding"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map(function(t){return t.id})),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame(function(){return i(null,e)})}k.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,function(t,n){t.send(e,r,n)},n)},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach(function(t){t.remove()}),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var 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 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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 kt(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(ea.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 Yt(t,e,r,n){Gt(t,r,n),qt(t,e,r)}function Wt(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 Zt(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&&Wt(e,r,a)?n.push({command:Ht.setGeoJSONSourceData,args:[a,r[a].data]}):Yt(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 Zt(s.layout,l.layout,n,o,null,Ht.setLayoutProperty),Zt(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.")?Zt(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.")?Zt(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*_,A=new t.Point(m.anchorX,m.anchorY),k=re(A,a).point,M={},S=le(m,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,k,A,M,v);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&le(m,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,k,A,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,A=re(w.last.point,s).point;if(i&&!n){var k=se(e.writingMode,T,A,d);if(k)return k}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)),A=re(T,f);if(A.signedDistanceFromCamera>0)y=h[g]=A.point;else{var k=g-d;y=ue(0===x?o:new t.Point(c.getx(k),c.gety(k)),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 A=.5*h*y+p,k=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*A;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>=k.x&&F.x<=M.x&&R.y>=k.y&&F.y<=M.y?[C]:F.x<k.x||R.x>M.x||F.y<k.y||R.y>M.y?[]:t.clipLine([C],k.x,k.y,M.x,M.y)}for(var N=0,j=z;N<j.length;N+=1){var U=j[N];S.reset(U,.25*A);var V;V=S.length<=.5*A?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),Y=G.x+de,W=G.y+de;d.push(Y,W,A,0);var Z=Y-A,X=W-A,J=Y+A,K=W+A;if(T=T&&this.isOffscreen(Z,X,J,K),w=w||this.isInsideGrid(Z,X,J,K),!e&&this.grid.hitTestCircle(Y,W,A,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 Ae(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 ke=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)}ke.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,A=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:A,parameters:m})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:m})}},ke.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(Ae(e,y.x,y.y,a,o,this.transform.angle),c,s,l,u.predicate);if(!d||0!==this.collisionIndex.placeCollisionBox(Ae(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},ke.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"),A=m&&(x||!o.hasIconData()||y),k=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,A=i.attemptAnchorPlacement(g,t,a,s,u,b,_,f,l,v,y,e,o,n,c);if(A&&(h=A.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=A.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),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get("text-padding"),Z=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(m,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,u,c,n,_,v.predicate,Z,W),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?Ae(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||A,M||k,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},ke.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)}},ke.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)},ke.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)},ke.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)}},ke.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),A=_?ze:T;d(e.text,s,A);var k=w?ze:T;d(e.text,l,k);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]}},ke.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},ke.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},ke.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},ke.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},ke.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 ke(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"]),Ye=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}(),We=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(jt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new A(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(Ye,!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);We.getSourceType=function(t){return R[t]},We.setSourceType=function(t,e){R[t]=e},We.registerForPluginStateChange=t.registerForPluginStateChange;var Ze=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 Ar=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 kr(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]}}Ar.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),kr(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),kr(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}};var Br=function(e,r,n){var i=e.transform;return{u_matrix:Hr(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]]}},Nr=function(e,r,n){return t.extend(Br(e,r,n),{u_image:0})},jr=function(e,r,n,i){var a=e.transform,o=Vr(r,a);return{u_matrix:Hr(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]]}},Ur=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=Vr(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(Br(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 Vr(t,e){return 1/ge(t,1,e.tileZoom)}function Hr(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get("line-translate"),r.paint.get("line-translate-anchor"))}var qr=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:Gr(i.paint.get("raster-hue-rotate"))};var a,o};function Gr(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 Yr,Wr=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}},Zr=function(e,r,n,i,a,o,s,l,u,c,f){var h=a.transform;return t.extend(Wr(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})},Xr=function(e,r,n,i,a,o,s,l,u,c){return t.extend(Zr(e,r,n,i,a,o,s,l,!0,u,!0),{u_texsize_icon:c,u_texture_icon:1})},Jr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Kr=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})},$r={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 Qr(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"),A=new t.StructArrayLayout2f1f2i16;A.resize(4*h),A._trim();for(var k=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];A.emplace(k++,P,O,I,D,0),A.emplace(k++,P,O,I,D,1),A.emplace(k++,P,O,I,D,2),A.emplace(k++,P,O,I,D,3)}(!Yr||Yr.length<2*h)&&(Yr=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(Yr,!0),R=l.createVertexBuffer(A,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 tn=t.identity(new Float32Array(16));function en(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 rn(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 A=x.width,k=x.height,M=en(x.anchor,A,k,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 nn(t,e,r){return r.iconsInText&&e?"symbolTextAndIcon":t?"symbolSDF":"symbolIcon"}function an(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=[],A=0,k=i;A<k.length;A+=1){var M=k[A],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(nn(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 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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,on(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}on(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function on(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 sn(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 A=x.imageAtlas,k=A.patternPositions[T.to.toString()],M=A.patternPositions[T.from.toString()];k&&M&&_.setConstantPatternPositions(k,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 ln(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 A=t.translatePosMatrix(v.posMatrix,g,r.paint.get("fill-extrusion-translate"),r.paint.get("fill-extrusion-translate-anchor")),k=r.paint.get("fill-extrusion-vertical-gradient"),M=c?Sr(A,t,k,h,v,f,g):Mr(A,t,k,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 un(e,r,n,i,a,o){var s=e.context,l=s.gl,u=r.fbo;if(u){var c=e.useProgram("hillshade");s.activeTexture.set(l.TEXTURE0),l.bindTexture(l.TEXTURE_2D,u.colorAttachment.get());var f=function(e,r,n){var i=n.paint.get("hillshade-shadow-color"),a=n.paint.get("hillshade-highlight-color"),o=n.paint.get("hillshade-accent-color"),s=n.paint.get("hillshade-illumination-direction")*(Math.PI/180);"viewport"===n.paint.get("hillshade-illumination-anchor")&&(s-=e.transform.angle);var l,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}}(e,r,n);c.draw(s,l.TRIANGLES,i,a,o,Ct.disabled,f,n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments)}}function cn(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,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,c,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function fn(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 hn=new t.Color(1,0,0,1),pn=new t.Color(0,1,0,1),dn=new t.Color(0,0,1,1),vn=new t.Color(1,0,1,1),gn=new t.Color(0,1,1,1);function yn(t){var e=t.transform.padding;mn(t,t.transform.height-(e.top||0),3,hn),mn(t,e.bottom||0,3,pn),xn(t,e.left||0,3,dn),xn(t,t.transform.width-(e.right||0),3,vn);var r=t.transform.centerPoint;!function(t,e,r,n){var i=20,a=2;bn(t,e-a/2,r-i/2,a,i,n),bn(t,e-i/2,r-a/2,i,a,n)}(t,r.x,t.transform.height-r.y,gn)}function mn(t,e,r,n){bn(t,0,e+r/2,t.transform.width,r,n)}function xn(t,e,r,n){bn(t,e-r/2,0,r,t.transform.height,n)}function bn(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 _n(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+" 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_=Math.pow(2,l.zoom-d.tileID.overscaledZ);rn(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)&&an(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)&&an(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&&(Qr(e,r,n,i,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),!0),Qr(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 A=0,k=d;A<k.length;A+=1){var M=k[A],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,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get("heatmap-opacity")}}(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}}(e,n))},line:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("line-opacity"),o=n.paint.get("line-width");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,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 A=T.programConfigurations.get(n.id),k=e.context.program.get(),M=e.useProgram(d,A),S=y||M.program!==k,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&&A.setConstantPatternPositions(C,P)}var O=f?jr(e,w,n,p):u?Ur(e,w,n,u,p):h?Nr(e,w,n):Br(e,w,n);f?(v.activeTexture.set(g.TEXTURE0),w.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),A.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,A),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);sn(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);sn(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))ln(t,e,r,n,a,Et.disabled,Lt.disabled),ln(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();ln(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?cn(t,p,r,a,o,Et.disabled,s):"translucent"===t.renderPass&&un(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),_=fn(m,b,e,r,t.transform),w=void 0,T=void 0,A="nearest"===r.paint.get("raster-resampling")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),m.texture.bind(A,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(A,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(A,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var k=qr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,y,Et.disabled,l,Ct.disabled,k,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,y,c[g.overscaledZ],l,Ct.disabled,k,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?Kr(b,i,t,u,{tileID:x,tileSize:l},g):Jr(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++)_n(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)}}},Tn=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={}};Tn.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()}},Tn.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,Ze.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,Ze.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,Ze.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)},Tn.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)},Tn.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)}}},Tn.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)},Tn.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)},Tn.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]},Tn.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},Tn.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])},Tn.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},Tn.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],A=c[w.source];this._renderTileClippingMasks(w,A),this.renderLayer(this,T,w,A)}for(this.renderPass="translucent",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var k=this.style._layers[i[this.currentLayer]],M=a[k.source],S=("symbol"===k.type?h:f)[k.source];this._renderTileClippingMasks(k,c[k.source]),this.renderLayer(this,M,k,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&&wn.debug(this,l,l.getVisibleCoordinates())),this.options.showPadding&&yn(this),this.context.setDefault()},Tn.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),wn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},Tn.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)}},Tn.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},Tn.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},Tn.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},Tn.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},Tn.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},Tn.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},Tn.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},Tn.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 Ar(this.context,wr[t],e,$r[t],this._showOverdrawInspector)),this.cache[r]},Tn.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()},Tn.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)},Tn.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)}},Tn.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var An=function(t,e){this.points=t,this.planes=e};An.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 An(a,o)};var kn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};kn.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 kn(n,i)},kn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},kn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},kn.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 Mn=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};Mn.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},Mn.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)},Mn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},Mn.prototype.clone=function(){return new Mn(this.top,this.bottom,this.left,this.right)},Mn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var Sn=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 Mn,this._posMatrixCache={},this._alignedPosMatrixCache={}},En={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}};Sn.prototype.clone=function(){var t=new Sn(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},En.minZoom.get=function(){return this._minZoom},En.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},En.maxZoom.get=function(){return this._maxZoom},En.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},En.minPitch.get=function(){return this._minPitch},En.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},En.maxPitch.get=function(){return this._maxPitch},En.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},En.renderWorldCopies.get=function(){return this._renderWorldCopies},En.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},En.worldSize.get=function(){return this.tileSize*this.scale},En.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},En.size.get=function(){return new t.Point(this.width,this.height)},En.bearing.get=function(){return-this.angle/Math.PI*180},En.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))},En.pitch.get=function(){return this._pitch/Math.PI*180},En.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())},En.fov.get=function(){return this._fov/Math.PI*180},En.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())},En.zoom.get=function(){return this._zoom},En.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())},En.center.get=function(){return this._center},En.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},En.padding.get=function(){return this._edgeInsets.toJSON()},En.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},En.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},Sn.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},Sn.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},Sn.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)},Sn.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},Sn.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=An.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 kn([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 A=0;A<4;A++){var k=(g<<1)+A%2,M=(y<<1)+(A>>1);c.push({aabb:v.aabb.quadrant(A),zoom:v.zoom+1,x:k,y:M,wrap:v.wrap,fullyVisible:m})}}return f.sort(function(t,e){return t.distanceSq-e.distanceSq}).map(function(t){return t.tileID})},Sn.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},En.unmodified.get=function(){return this._unmodified},Sn.prototype.zoomScale=function(t){return Math.pow(2,t)},Sn.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},Sn.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)},Sn.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},En.point.get=function(){return this.project(this.center)},Sn.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())},Sn.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},Sn.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},Sn.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},Sn.prototype.coordinateLocation=function(t){return t.toLngLat()},Sn.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)},Sn.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])},Sn.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)))},Sn.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},Sn.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])},Sn.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]},Sn.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},Sn.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}},Sn.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={}}},Sn.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},Sn.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},Sn.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(Sn.prototype,En);var Ln=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})};Ln.prototype.addTo=function(e){return this._map=e,t.window.addEventListener("hashchange",this._onHashChange,!1),this._map.on("moveend",this._updateHash),this},Ln.prototype.remove=function(){return t.window.removeEventListener("hashchange",this._onHashChange,!1),this._map.off("moveend",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Ln.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},Ln.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("/")},Ln.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},Ln.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,"",e)}catch(t){}};var Cn={linearity:.3,easing:t.bezier(0,0,.3,1)},Pn=t.extend({deceleration:2500,maxSpeed:1400},Cn),On=t.extend({deceleration:20,maxSpeed:1400},Cn),In=t.extend({deceleration:1e3,maxSpeed:360},Cn),Dn=t.extend({deceleration:1e3,maxSpeed:90},Cn),zn=function(t){this._map=t,this.clear()};function Rn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Fn(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)}}zn.prototype.clear=function(){this._inertiaBuffer=[]},zn.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},zn.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},zn.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=Fn(r.pan.mag(),o,t.extend({},Pn,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,Rn(s,l)}if(r.zoom){var u=Fn(r.zoom,o,On);s.zoom=this._map.transform.zoom+u.amount,Rn(s,u)}if(r.bearing){var c=Fn(r.bearing,o,In);s.bearing=this._map.transform.bearing+t.clamp(c.amount,-179,179),Rn(s,c)}if(r.pitch){var f=Fn(r.pitch,o,Dn);s.pitch=this._map.transform.pitch+f.amount,Rn(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 Bn=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),Nn=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),jn=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),Un=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Un.prototype.reset=function(){delete this._mousedownPos},Un.prototype.wheel=function(t){return this._firePreventable(new jn(t.type,this._map,t))},Un.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Bn(t.type,this._map,t))},Un.prototype.mouseup=function(t){this._map.fire(new Bn(t.type,this._map,t))},Un.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Bn(t.type,this._map,t))},Un.prototype.dblclick=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Un.prototype.mouseover=function(t){this._map.fire(new Bn(t.type,this._map,t))},Un.prototype.mouseout=function(t){this._map.fire(new Bn(t.type,this._map,t))},Un.prototype.touchstart=function(t){return this._firePreventable(new Nn(t.type,this._map,t))},Un.prototype.touchmove=function(t){this._map.fire(new Nn(t.type,this._map,t))},Un.prototype.touchend=function(t){this._map.fire(new Nn(t.type,this._map,t))},Un.prototype.touchcancel=function(t){this._map.fire(new Nn(t.type,this._map,t))},Un.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Un.prototype.isEnabled=function(){return!0},Un.prototype.isActive=function(){return!1},Un.prototype.enable=function(){},Un.prototype.disable=function(){};var Vn=function(t){this._map=t};Vn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Vn.prototype.mousemove=function(t){this._map.fire(new Bn(t.type,this._map,t))},Vn.prototype.mousedown=function(){this._delayContextMenu=!0},Vn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Bn("contextmenu",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Vn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Bn(t.type,this._map,t)),this._map.listens("contextmenu")&&t.preventDefault()},Vn.prototype.isEnabled=function(){return!0},Vn.prototype.isActive=function(){return!1},Vn.prototype.enable=function(){},Vn.prototype.disable=function(){};var Hn=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function qn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Hn.prototype.isEnabled=function(){return!!this._enabled},Hn.prototype.isActive=function(){return!!this._active},Hn.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Hn.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Hn.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Hn.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"}}},Hn.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)}},Hn.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent("boxzoomcancel",t))},Hn.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},Hn.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Gn=function(t){this.reset(),this.numTouches=t.numTouches};Gn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Gn.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=qn(n,r)))},Gn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=qn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Gn.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 Yn=function(t){this.singleTap=new Gn(t),this.numTaps=t.numTaps,this.reset()};Yn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},Yn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},Yn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},Yn.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 Wn=function(){this._zoomIn=new Yn({numTouches:1,numTaps:2}),this._zoomOut=new Yn({numTouches:2,numTaps:1}),this.reset()};Wn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Wn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Wn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Wn.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},Wn.prototype.touchcancel=function(){this.reset()},Wn.prototype.enable=function(){this._enabled=!0},Wn.prototype.disable=function(){this._enabled=!1,this.reset()},Wn.prototype.isEnabled=function(){return this._enabled},Wn.prototype.isActive=function(){return this._active};var Zn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Zn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Zn.prototype._correctButton=function(t,e){return!1},Zn.prototype._move=function(t,e){return{}},Zn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Zn.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)},Zn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Zn.prototype.enable=function(){this._enabled=!0},Zn.prototype.disable=function(){this._enabled=!1,this.reset()},Zn.prototype.isEnabled=function(){return this._enabled},Zn.prototype.isActive=function(){return this._active};var Xn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.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}(Zn),Jn=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}(Zn),Kn=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}(Zn),$n=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};$n.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},$n.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},$n.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},$n.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},$n.prototype.touchcancel=function(){this.reset()},$n.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=qn(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}}},$n.prototype.enable=function(){this._enabled=!0},$n.prototype.disable=function(){this._enabled=!1,this.reset()},$n.prototype.isEnabled=function(){return this._enabled},$n.prototype.isActive=function(){return this._active};var Qn=function(){this.reset()};function ti(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}Qn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Qn.prototype._start=function(t){},Qn.prototype._move=function(t,e,r){return{}},Qn.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]]))},Qn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=ti(r,e,i),s=ti(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},Qn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=ti(n,e,a),l=ti(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},Qn.prototype.touchcancel=function(){this.reset()},Qn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&"center"===t.around},Qn.prototype.disable=function(){this._enabled=!1,this.reset()},Qn.prototype.isEnabled=function(){return this._enabled},Qn.prototype.isActive=function(){return this._active};function ei(t,e){return Math.log(t/e)/Math.LN2}var ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._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(ei(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:ei(this._distance,r),pinchAround:e}},e}(Qn);function ni(t,e){return 180*t.angleWith(e)/Math.PI}var ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),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:ni(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=ni(t,this._startVector);return Math.abs(r)<e},e}(Qn);function ai(t){return Math.abs(t.y)>Math.abs(t.x)}var oi=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,ai(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 ai(t)&&ai(e)&&a}},e}(Qn),si={panStep:100,bearingStep:15,pitchStep:10},li=function(){var t=si;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function ui(t){return t*(2-t)}li.prototype.reset=function(){this._active=!1},li.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:ui,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})}}}},li.prototype.enable=function(){this._enabled=!0},li.prototype.disable=function(){this._enabled=!1,this.reset()},li.prototype.isEnabled=function(){return this._enabled},li.prototype.isActive=function(){return this._active};var ci=4.000244140625,fi=1/450,hi=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=fi,t.bindAll(["_onWheel","_onTimeout","_onScrollFrame","_onScrollFinished"],this)};hi.prototype.setZoomRate=function(t){this._defaultZoomRate=t},hi.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},hi.prototype.isEnabled=function(){return!!this._enabled},hi.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},hi.prototype.isZooming=function(){return!!this._zooming},hi.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&"center"===t.around)},hi.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},hi.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%ci===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()}},hi.prototype._onTimeout=function(t){this._type="wheel",this._delta-=this._lastValue,this._active||this._start(t)},hi.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())}},hi.prototype.renderFrame=function(){return this._onScrollFrame()},hi.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)>ci?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}}},hi.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},hi.prototype.reset=function(){this._active=!1};var pi=function(t,e){this._clickZoom=t,this._tapZoom=e};pi.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},pi.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},pi.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},pi.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var di=function(){this.reset()};di.prototype.reset=function(){this._active=!1},di.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})}}},di.prototype.enable=function(){this._enabled=!0},di.prototype.disable=function(){this._enabled=!1,this.reset()},di.prototype.isEnabled=function(){return this._enabled},di.prototype.isActive=function(){return this._active};var vi=function(){this._tap=new Yn({numTouches:1,numTaps:1}),this.reset()};vi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},vi.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))},vi.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)},vi.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)},vi.prototype.touchcancel=function(){this.reset()},vi.prototype.enable=function(){this._enabled=!0},vi.prototype.disable=function(){this._enabled=!1,this.reset()},vi.prototype.isEnabled=function(){return this._enabled},vi.prototype.isActive=function(){return this._active};var gi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};gi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add("mapboxgl-touch-drag-pan")},gi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove("mapboxgl-touch-drag-pan")},gi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},gi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var yi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};yi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},yi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},yi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},yi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var mi=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};mi.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")},mi.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove("mapboxgl-touch-zoom-rotate")},mi.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},mi.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},mi.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},mi.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var xi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function _i(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var wi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new zn(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)}};wi.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)}},wi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add("mapEvent",new Un(e,t));var n=e.boxZoom=new Hn(e,t);this._add("boxZoom",n);var i=new Wn,a=new di;e.doubleClickZoom=new pi(a,i),this._add("tapZoom",i),this._add("clickZoom",a);var o=new vi;this._add("tapDragZoom",o);var s=e.touchPitch=new oi;this._add("touchPitch",s);var l=new Jn(t),u=new Kn(t);e.dragRotate=new yi(t,l,u),this._add("mouseRotate",l,["mousePitch"]),this._add("mousePitch",u,["mouseRotate"]);var c=new Xn(t),f=new $n(t);e.dragPan=new gi(r,c,f),this._add("mousePan",c),this._add("touchPan",f,["touchZoom","touchRotate"]);var h=new ii,p=new ri;e.touchZoomRotate=new mi(r,p,h,o),this._add("touchRotate",h,["touchPan","touchZoom"]),this._add("touchZoom",p,["touchPan","touchRotate"]);var d=e.scrollZoom=new hi(e,this);this._add("scrollZoom",d,["mousePan"]);var v=e.keyboard=new li;this._add("keyboard",v),this._add("blockableMapEvent",new Vn(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])}},wi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},wi.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=[]}},wi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1)if(e[t].handler.isActive())return!0;return!1},wi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},wi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},wi.prototype.isMoving=function(){return Boolean(xi(this._eventsInProgress))||this.isZooming()},wi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},wi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+"Window")},wi.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},wi.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||_i(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||_i(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()},wi.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)}},wi.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=[]},wi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!_i(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)},wi.prototype._fireEvents=function(e,r){var n=this,i=xi(this._eventsInProgress),a=xi(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=xi(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}},wi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},wi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame(function(e){delete t._frameId,t.handleEvent(new bi("renderFrame",{timeStamp:e})),t._applyChanges()}))};var Ti=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,A=b.mag();if("minZoom"in e){var k=t.clamp(Math.min(e.minZoom,o,c),a.minZoom,a.maxZoom),M=w/a.zoomScale(k-o);_=Math.sqrt(M/A*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*A*A)/(2*(t?T:w)*S*A);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)/A;var e},D=(E(1)-P)/_;if(Math.abs(A)<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),Ai=function(e){void 0===e&&(e={}),this.options=e,t.bindAll(["_updateEditLink","_updateData","_updateCompact"],this)};Ai.prototype.getDefaultPosition=function(){return"bottom-right"},Ai.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},Ai.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},Ai.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"}},Ai.prototype._updateData=function(t){!t||"metadata"!==t.sourceDataType&&"style"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},Ai.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)}},Ai.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add("mapboxgl-compact"):this._container.classList.remove("mapboxgl-compact")};var ki=function(){t.bindAll(["_updateLogo"],this),t.bindAll(["_updateCompact"],this)};ki.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},ki.prototype.onRemove=function(){r.remove(this._container),this._map.off("sourcedata",this._updateLogo),this._map.off("resize",this._updateCompact)},ki.prototype.getDefaultPosition=function(){return"bottom-left"},ki.prototype._updateLogo=function(t){t&&"metadata"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?"block":"none")},ki.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}},ki.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 Mi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};Mi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},Mi.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)}},Mi.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},Mi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Si={"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"},Ei=t.window.HTMLImageElement,Li=t.window.HTMLElement,Ci=t.window.ImageBitmap,Pi=60,Oi={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:Pi,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},Ii=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Oi,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>Pi)throw new Error("maxPitch must be less than or equal to 60");var i=new Sn(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 Mi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Si,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 Li))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 wi(this,e);var a="string"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Ln(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 Ai({customAttribution:e.customAttribution})),this.addControl(new ki,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?Pi:t)>Pi)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 Bn(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 Bn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Bn(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 We(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 We(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 Ei||Ci&&r instanceof Ci){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 Ei||Ci&&r instanceof Ci?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 Ei||Ci&&r instanceof Ci);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 Tn(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(),Di(this._canvasContainer),Di(this._controlContainer),Di(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}(Ti);function Di(t){t.parentNode&&t.parentNode.removeChild(t)}var zi={showCompass:!0,showZoom:!0,visualizePitch:!1},Ri=function(e){var n=this;this.options=t.extend({},zi,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))};Ri.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Ri.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},Ri.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 Fi(this._map,this._compass,this.options.visualizePitch)),this._container},Ri.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},Ri.prototype._createButton=function(t,e){var n=r.create("button",t,this._container);return n.type="button",n.addEventListener("click",e),n},Ri.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString("NavigationControl."+e);t.title=r,t.setAttribute("aria-label",r)};var Fi=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Jn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Kn({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 Bi(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}Fi.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Fi.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)}},Fi.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()},Fi.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,"mousemove",this.mousemove),r.removeEventListener(t.window,"mouseup",this.mouseup)},Fi.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)},Fi.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Fi.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Fi.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))},Fi.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))},Fi.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Fi.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var Ni={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 ji(t,e,r){var n=t.classList;for(var i in Ni)n.remove("mapboxgl-"+r+"-anchor-"+i);n.add("mapboxgl-"+r+"-anchor-"+e)}var Ui,Vi=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}),ji(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=Bi(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,Ni[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),Hi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var qi=0,Gi=!1,Yi=function(e){function n(r){e.call(this),this.options=t.extend({},Hi,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!==Ui?n(Ui):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:"geolocation"}).then(function(t){Ui="denied"!==t.state,n(Ui)}):(Ui=!!t.window.navigator.geolocation,n(Ui)),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,qi=0,Gi=!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&&Gi)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 Vi(this._dotElement),this._circleElement=r.create("div","mapboxgl-user-location-accuracy-circle"),this._accuracyCircleMarker=new Vi({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":qi--,Gi=!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"),++qi>1?(e={maximumAge:6e5,timeout:0},Gi=!0):(e=this.options.positionOptions,Gi=!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),Wi={maxWidth:100,unit:"metric"},Zi=function(e){this.options=t.extend({},Wi,e),t.bindAll(["_onMove","setUnit"],this)};function Xi(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?Ji(e,n,l/5280,t._getUIString("ScaleControl.Miles")):Ji(e,n,l,t._getUIString("ScaleControl.Feet"))}else r&&"nautical"===r.unit?Ji(e,n,s/1852,t._getUIString("ScaleControl.NauticalMiles")):s>=1e3?Ji(e,n,s/1e3,t._getUIString("ScaleControl.Kilometers")):Ji(e,n,s,t._getUIString("ScaleControl.Meters"))}function Ji(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}Zi.prototype.getDefaultPosition=function(){return"bottom-left"},Zi.prototype._onMove=function(){Xi(this._map,this._container,this.options)},Zi.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},Zi.prototype.onRemove=function(){r.remove(this._container),this._map.off("move",this._onMove),this._map=void 0},Zi.prototype.setUnit=function(t){this.options.unit=t,Xi(this._map,this._container,this.options)};var Ki=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")};Ki.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},Ki.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Ki.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Ki.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)},Ki.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute("aria-label",t),this._fullscreenButton.title=t},Ki.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?"FullscreenControl.Exit":"FullscreenControl.Enter")},Ki.prototype._isFullscreen=function(){return 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ea={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Ii,NavigationControl:Ri,GeolocateControl:Yi,AttributionControl:Ai,ScaleControl:Zi,FullscreenControl:Ki,Popup:Qi,Marker:Vi,Style:We,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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n=r(79758),i=r(26446),a=r(88191);t.exports=function(t,e,r,o){var s,l,u,c=r[0][0].carpet,f=i.getFromId(t,c.xaxis||"x"),h=i.getFromId(t,c.yaxis||"y"),p={xaxis:f,yaxis:h,plot:e.plot};for(s=0;s<r.length;s++)(l=r[s][0].trace)._xA=f,l._yA=h;for(n(t,p,r,o),s=0;s<r.length;s++)l=r[s][0].trace,u=o.selectAll("g.trace"+l.uid+" .js-line"),a.setClipUrl(u,r[s][0].carpet._clipPathId,t)}},55974:function(t,e,r){"use strict";var n=r(69562),i=r(15181);t.exports=function(t,e,r){if(r("newshape.visible"),r("newshape.name"),r("newshape.showlegend"),r("newshape.legend"),r("newshape.legendwidth"),r("newshape.legendgroup"),r("newshape.legendgrouptitle.text"),i.coerceFont(r,"newshape.legendgrouptitle.font"),r("newshape.legendrank"),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 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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:"\nprecision highp float;\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:"\nprecision highp float;\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:"\nprecision highp float;\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\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:"\nprecision highp float;\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 * dash;\n}\n",attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop("colorBuffer"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:"triangle",elements:function(t,e){return e.triangles},offset:0,vert:"\nprecision highp float;\n\nattribute vec2 position, positionFract;\n\nuniform vec4 color;\nuniform vec2 scale, scaleFract, translate, translateFract;\nuniform float pixelRatio, id;\nuniform vec4 viewport;\nuniform float opacity;\n\nvarying vec4 fragColor;\n\nconst float MAX_LINES = 256.;\n\nvoid main() {\n\tfloat depth = (MAX_LINES - 4. - id) / 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2===this.length?t+=67108864*this.words[1]:3===this.length&&1===this.words[2]?t+=4503599627370496+67108864*this.words[1]:this.length>2&&n(!1,"Number can only safely store up to 53 bits"),0!==this.negative?-t:t},a.prototype.toJSON=function(){return this.toString(16)},a.prototype.toBuffer=function(t,e){return n(void 0!==o),this.toArrayLike(o,t,e)},a.prototype.toArray=function(t,e){return this.toArrayLike(Array,t,e)},a.prototype.toArrayLike=function(t,e,r){var i=this.byteLength(),a=r||Math.max(1,i);n(i<=a,"byte array longer than desired length"),n(a>0,"Requested array length <= 0"),this.strip();var o,s,l="le"===e,u=new t(a),c=this.clone();if(l){for(s=0;!c.isZero();s++)o=c.andln(255),c.iushrn(8),u[s]=o;for(;s<a;s++)u[s]=0}else{for(s=0;s<a-i;s++)u[s]=0;for(s=0;!c.isZero();s++)o=c.andln(255),c.iushrn(8),u[a-s-1]=o}return u},Math.clz32?a.prototype._countBits=function(t){return 32-Math.clz32(t)}:a.prototype._countBits=function(t){var e=t,r=0;return 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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 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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,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,Z)|0)+Math.imul(h,W)|0))<<13)|0;u=((a=a+Math.imul(h,Z)|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,W)|0,i=(i=i+Math.imul(d,Z)|0)+Math.imul(v,W)|0,a=a+Math.imul(v,Z)|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(A,U)|0,a=Math.imul(A,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,W)|0,i=(i=i+Math.imul(y,Z)|0)+Math.imul(m,W)|0,a=a+Math.imul(m,Z)|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(A,q)|0,a=a+Math.imul(A,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,Z)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,Z)|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,W)|0,i=(i=i+Math.imul(T,Z)|0)+Math.imul(A,W)|0,a=a+Math.imul(A,Z)|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,W)|0,i=(i=i+Math.imul(M,Z)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,Z)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(A,J)|0,a=a+Math.imul(A,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 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At=(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)+(At>>>26)|0,At&=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,W)|0,i=(i=i+Math.imul(O,Z)|0)+Math.imul(I,W)|0,a=a+Math.imul(I,Z)|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(A,rt)|0,a=a+Math.imul(A,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 kt=(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)+(kt>>>26)|0,kt&=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,W)|0,i=(i=i+Math.imul(z,Z)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,Z)|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(A,at)|0,a=a+Math.imul(A,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,W),i=(i=Math.imul(B,Z))+Math.imul(N,W)|0,a=Math.imul(N,Z),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(A,lt)|0,a=a+Math.imul(A,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(A,ft)|0,a=a+Math.imul(A,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(A,dt)|0))<<13)|0;u=((a=a+Math.imul(A,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]=At,l[9]=kt,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(!(-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!(1&this.words[0])},a.prototype.isOdd=function(){return!(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 A(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 A(t)},i(A,T),A.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},A.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},A.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)},A.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var 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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 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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 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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],A=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,k=0;k<3;++k)this.lastCubeProps.cubeEdges[k]=f[k],this.lastCubeProps.axis[k]=h[k];var M=p;for(k=0;k<3;++k)d(p[k],k,this.bounds,f,h);e=this.gl;var S,E,L,C=v;for(k=0;k<3;++k)this.backgroundEnable[k]?C[k]=h[k]:C[k]=0;for(this._background.draw(r,n,i,a,C,this.backgroundColor),this._lines.bind(r,n,i,this),k=0;k<3;++k){var P=[0,0,0];h[k]>0?P[k]=a[1][k]:P[k]=a[0][k];for(var O=0;O<2;++O){var 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B=A/r[5*O];z[O]*=F[O]*B,R[O]*=F[O]*B}this.lineTickEnable[k]&&this._lines.drawAxisTicks(k,M[k].primalOffset,z,this.lineTickColor[k],this.lineTickWidth[k]*this.pixelRatio),this.lineTickMirror[k]&&this._lines.drawAxisTicks(k,M[k].mirrorOffset,R,this.lineTickColor[k],this.lineTickWidth[k]*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),k=0;k<3;++k){var U=M[k].primalMinor,V=M[k].mirrorMinor,H=u(x,M[k].primalOffset);for(O=0;O<3;++O)this.lineTickEnable[k]&&(H[O]+=A*U[O]*Math.max(this.lineTickLength[O],0)/r[5*O]);var q=[0,0,0];if(q[k]=1,this.tickEnable[k]){for(-3600===this.tickAngle[k]?(this.tickAngle[k]=0,this.tickAlign[k]="auto"):this.tickAlign[k]=-1,E=1,"auto"===(S=[this.tickAlign[k],.5,E])[0]?S[0]=0:S[0]=parseInt(""+S[0]),L=[0,0,0],j(k,U,V),O=0;O<3;++O)H[O]+=A*U[O]*this.tickPad[O]/r[5*O];this._text.drawTicks(k,this.tickSize[k],this.tickAngle[k],H,this.tickColor[k],q,L,S)}if(this.labelEnable[k]){for(E=0,L=[0,0,0],this.labels[k].length>4&&(N(k),E=1),"auto"===(S=[this.labelAlign[k],.5,E])[0]?S[0]=0:S[0]=parseInt(""+S[0]),O=0;O<3;++O)H[O]+=A*U[O]*this.labelPad[O]/r[5*O];H[k]+=.5*(a[0][k]+a[1][k]),this._text.drawLabel(k,this.labelSize[k],this.labelAngle[k],H,this.labelColor[k],[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],A=0;A<3;++A)u[x][A]=l[x][A]/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 k=0;k<3;++k){for(var M=(k+2)%3,S=(k+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(I=C<<k)+(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<<k);else{for(C=0;C<2;++C){P=(I=C<<k)+(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<<k)}}}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(k=0;k<3;++k)V[k]=H&1<<k?-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 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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 A=0;A<3;++A){var k=(A+1)%3,M=(A+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[A]<0!=!!w){S[A]=x[w][A];for(var L=0;L<2;++L){S[k]=x[L^w][k];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!==A&&(_[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 A=1/o;isFinite(g)||(g=1),a.vectorScale=g;var k=t.coneSize||.5;t.absoluteConeSize&&(k=t.absoluteConeSize*A),a.coneScale=k,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)*A,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 A=i[T];o.push(A[0],A[1],A[2],A[3]||0);var k,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),k=p?[(p[T]-d)/(v-d),0]:[(m[2]-d)/(v-d),0],l.push(k[0],k[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var u={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};u.inverseModel=l(u.inverseModel,u.model),e.disable(e.CULL_FACE),this.texture.bind(0);var c=new Array(16);for(s(c,u.view,u.model),s(c,u.projection,c),l(c,c),o=0;o<3;++o)u.eyePosition[o]=c[12+o]/c[15];var h=c[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*c[4*o+3];for(o=0;o<3;++o){for(var p=c[12+o],d=0;d<3;++d)p+=c[4*d+o]*this.lightPosition[d];u.lightPosition[o]=p/h}if(this.triangleCount>0){var v=this.triShader;v.bind(),v.uniforms=u,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return"cone"===this.traceType?i.index=Math.floor(r[1]/48):"streamtube"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},t.exports=function(t,e,r){var s=r.shaders;1===arguments.length&&(t=(e=t).gl);var l=function(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}(t,s),c=function(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}(t,s),f=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));f.generateMipmap(),f.minFilter=t.LINEAR_MIPMAP_LINEAR,f.magFilter=t.LINEAR;var p=i(t),d=i(t),v=i(t),g=i(t),y=i(t),m=new h(t,f,l,c,p,d,y,v,g,a(t,[{buffer:p,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:v,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:4}]),r.traceType||"cone");return m.update(e),m}},7234:function(t,e,r){var n=r(6832),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec3 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, coneScale, coneOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * conePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\n  vec4 t_position  = view * conePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = conePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float vectorScale, coneScale, coneOffset;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n  gl_Position = projection * view * conePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec3"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec3"}]}},1950:function(t){t.exports={0:"NONE",1:"ONE",2:"LINE_LOOP",3:"LINE_STRIP",4:"TRIANGLES",5:"TRIANGLE_STRIP",6:"TRIANGLE_FAN",256:"DEPTH_BUFFER_BIT",512:"NEVER",513:"LESS",514:"EQUAL",515:"LEQUAL",516:"GREATER",517:"NOTEQUAL",518:"GEQUAL",519:"ALWAYS",768:"SRC_COLOR",769:"ONE_MINUS_SRC_COLOR",770:"SRC_ALPHA",771:"ONE_MINUS_SRC_ALPHA",772:"DST_ALPHA",773:"ONE_MINUS_DST_ALPHA",774:"DST_COLOR",775:"ONE_MINUS_DST_COLOR",776:"SRC_ALPHA_SATURATE",1024:"STENCIL_BUFFER_BIT",1028:"FRONT",1029:"BACK",1032:"FRONT_AND_BACK",1280:"INVALID_ENUM",1281:"INVALID_VALUE",1282:"INVALID_OPERATION",1285:"OUT_OF_MEMORY",1286:"INVALID_FRAMEBUFFER_OPERATION",2304:"CW",2305:"CCW",2849:"LINE_WIDTH",2884:"CULL_FACE",2885:"CULL_FACE_MODE",2886:"FRONT_FACE",2928:"DEPTH_RANGE",2929:"DEPTH_TEST",2930:"DEPTH_WRITEMASK",2931:"DEPTH_CLEAR_VALUE",2932:"DEPTH_FUNC",2960:"STENCIL_TEST",2961:"STENCIL_CLEAR_VALUE",2962:"STENCIL_FUNC",2963:"STENCIL_VALUE_MASK",2964:"STENCIL_FAIL",2965:"STENCIL_PASS_DEPTH_FAIL",2966:"STENCIL_PASS_DEPTH_PASS",2967:"STENCIL_REF",2968:"STENCIL_WRITEMASK",2978:"VIEWPORT",3024:"DITHER",3042:"BLEND",3088:"SCISSOR_BOX",3089:"SCISSOR_TEST",3106:"COLOR_CLEAR_VALUE",3107:"COLOR_WRITEMASK",3317:"UNPACK_ALIGNMENT",3333:"PACK_ALIGNMENT",3379:"MAX_TEXTURE_SIZE",3386:"MAX_VIEWPORT_DIMS",3408:"SUBPIXEL_BITS",3410:"RED_BITS",3411:"GREEN_BITS",3412:"BLUE_BITS",3413:"ALPHA_BITS",3414:"DEPTH_BITS",3415:"STENCIL_BITS",3553:"TEXTURE_2D",4352:"DONT_CARE",4353:"FASTEST",4354:"NICEST",5120:"BYTE",5121:"UNSIGNED_BYTE",5122:"SHORT",5123:"UNSIGNED_SHORT",5124:"INT",5125:"UNSIGNED_INT",5126:"FLOAT",5386:"INVERT",5890:"TEXTURE",6401:"STENCIL_INDEX",6402:"DEPTH_COMPONENT",6406:"ALPHA",6407:"RGB",6408:"RGBA",6409:"LUMINANCE",6410:"LUMINANCE_ALPHA",7680:"KEEP",7681:"REPLACE",7682:"INCR",7683:"DECR",7936:"VENDOR",7937:"RENDERER",7938:"VERSION",9728:"NEAREST",9729:"LINEAR",9984:"NEAREST_MIPMAP_NEAREST",9985:"LINEAR_MIPMAP_NEAREST",9986:"NEAREST_MIPMAP_LINEAR",9987:"LINEAR_MIPMAP_LINEAR",10240:"TEXTURE_MAG_FILTER",10241:"TEXTURE_MIN_FILTER",10242:"TEXTURE_WRAP_S",10243:"TEXTURE_WRAP_T",10497:"REPEAT",10752:"POLYGON_OFFSET_UNITS",16384:"COLOR_BUFFER_BIT",32769:"CONSTANT_COLOR",32770:"ONE_MINUS_CONSTANT_COLOR",32771:"CONSTANT_ALPHA",32772:"ONE_MINUS_CONSTANT_ALPHA",32773:"BLEND_COLOR",32774:"FUNC_ADD",32777:"BLEND_EQUATION_RGB",32778:"FUNC_SUBTRACT",32779:"FUNC_REVERSE_SUBTRACT",32819:"UNSIGNED_SHORT_4_4_4_4",32820:"UNSIGNED_SHORT_5_5_5_1",32823:"POLYGON_OFFSET_FILL",32824:"POLYGON_OFFSET_FACTOR",32854:"RGBA4",32855:"RGB5_A1",32873:"TEXTURE_BINDING_2D",32926:"SAMPLE_ALPHA_TO_COVERAGE",32928:"SAMPLE_COVERAGE",32936:"SAMPLE_BUFFERS",32937:"SAMPLES",32938:"SAMPLE_COVERAGE_VALUE",32939:"SAMPLE_COVERAGE_INVERT",32968:"BLEND_DST_RGB",32969:"BLEND_SRC_RGB",32970:"BLEND_DST_ALPHA",32971:"BLEND_SRC_ALPHA",33071:"CLAMP_TO_EDGE",33170:"GENERATE_MIPMAP_HINT",33189:"DEPTH_COMPONENT16",33306:"DEPTH_STENCIL_ATTACHMENT",33635:"UNSIGNED_SHORT_5_6_5",33648:"MIRRORED_REPEAT",33901:"ALIASED_POINT_SIZE_RANGE",33902:"ALIASED_LINE_WIDTH_RANGE",33984:"TEXTURE0",33985:"TEXTURE1",33986:"TEXTURE2",33987:"TEXTURE3",33988:"TEXTURE4",33989:"TEXTURE5",33990:"TEXTURE6",33991:"TEXTURE7",33992:"TEXTURE8",33993:"TEXTURE9",33994:"TEXTURE10",33995:"TEXTURE11",33996:"TEXTURE12",33997:"TEXTURE13",33998:"TEXTURE14",33999:"TEXTURE15",34e3:"TEXTURE16",34001:"TEXTURE17",34002:"TEXTURE18",34003:"TEXTURE19",34004:"TEXTURE20",34005:"TEXTURE21",34006:"TEXTURE22",34007:"TEXTURE23",34008:"TEXTURE24",34009:"TEXTURE25",34010:"TEXTURE26",34011:"TEXTURE27",34012:"TEXTURE28",34013:"TEXTURE29",34014:"TEXTURE30",34015:"TEXTURE31",34016:"ACTIVE_TEXTURE",34024:"MAX_RENDERBUFFER_SIZE",34041:"DEPTH_STENCIL",34055:"INCR_WRAP",34056:"DECR_WRAP",34067:"TEXTURE_CUBE_MAP",34068:"TEXTURE_BINDING_CUBE_MAP",34069:"TEXTURE_CUBE_MAP_POSITIVE_X",34070:"TEXTURE_CUBE_MAP_NEGATIVE_X",34071:"TEXTURE_CUBE_MAP_POSITIVE_Y",34072:"TEXTURE_CUBE_MAP_NEGATIVE_Y",34073:"TEXTURE_CUBE_MAP_POSITIVE_Z",34074:"TEXTURE_CUBE_MAP_NEGATIVE_Z",34076:"MAX_CUBE_MAP_TEXTURE_SIZE",34338:"VERTEX_ATTRIB_ARRAY_ENABLED",34339:"VERTEX_ATTRIB_ARRAY_SIZE",34340:"VERTEX_ATTRIB_ARRAY_STRIDE",34341:"VERTEX_ATTRIB_ARRAY_TYPE",34342:"CURRENT_VERTEX_ATTRIB",34373:"VERTEX_ATTRIB_ARRAY_POINTER",34466:"NUM_COMPRESSED_TEXTURE_FORMATS",34467:"COMPRESSED_TEXTURE_FORMATS",34660:"BUFFER_SIZE",34661:"BUFFER_USAGE",34816:"STENCIL_BACK_FUNC",34817:"STENCIL_BACK_FAIL",34818:"STENCIL_BACK_PASS_DEPTH_FAIL",34819:"STENCIL_BACK_PASS_DEPTH_PASS",34877:"BLEND_EQUATION_ALPHA",34921:"MAX_VERTEX_ATTRIBS",34922:"VERTEX_ATTRIB_ARRAY_NORMALIZED",34930:"MAX_TEXTURE_IMAGE_UNITS",34962:"ARRAY_BUFFER",34963:"ELEMENT_ARRAY_BUFFER",34964:"ARRAY_BUFFER_BINDING",34965:"ELEMENT_ARRAY_BUFFER_BINDING",34975:"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING",35040:"STREAM_DRAW",35044:"STATIC_DRAW",35048:"DYNAMIC_DRAW",35632:"FRAGMENT_SHADER",35633:"VERTEX_SHADER",35660:"MAX_VERTEX_TEXTURE_IMAGE_UNITS",35661:"MAX_COMBINED_TEXTURE_IMAGE_UNITS",35663:"SHADER_TYPE",35664:"FLOAT_VEC2",35665:"FLOAT_VEC3",35666:"FLOAT_VEC4",35667:"INT_VEC2",35668:"INT_VEC3",35669:"INT_VEC4",35670:"BOOL",35671:"BOOL_VEC2",35672:"BOOL_VEC3",35673:"BOOL_VEC4",35674:"FLOAT_MAT2",35675:"FLOAT_MAT3",35676:"FLOAT_MAT4",35678:"SAMPLER_2D",35680:"SAMPLER_CUBE",35712:"DELETE_STATUS",35713:"COMPILE_STATUS",35714:"LINK_STATUS",35715:"VALIDATE_STATUS",35716:"INFO_LOG_LENGTH",35717:"ATTACHED_SHADERS",35718:"ACTIVE_UNIFORMS",35719:"ACTIVE_UNIFORM_MAX_LENGTH",35720:"SHADER_SOURCE_LENGTH",35721:"ACTIVE_ATTRIBUTES",35722:"ACTIVE_ATTRIBUTE_MAX_LENGTH",35724:"SHADING_LANGUAGE_VERSION",35725:"CURRENT_PROGRAM",36003:"STENCIL_BACK_REF",36004:"STENCIL_BACK_VALUE_MASK",36005:"STENCIL_BACK_WRITEMASK",36006:"FRAMEBUFFER_BINDING",36007:"RENDERBUFFER_BINDING",36048:"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE",36049:"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME",36050:"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL",36051:"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE",36053:"FRAMEBUFFER_COMPLETE",36054:"FRAMEBUFFER_INCOMPLETE_ATTACHMENT",36055:"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT",36057:"FRAMEBUFFER_INCOMPLETE_DIMENSIONS",36061:"FRAMEBUFFER_UNSUPPORTED",36064:"COLOR_ATTACHMENT0",36096:"DEPTH_ATTACHMENT",36128:"STENCIL_ATTACHMENT",36160:"FRAMEBUFFER",36161:"RENDERBUFFER",36162:"RENDERBUFFER_WIDTH",36163:"RENDERBUFFER_HEIGHT",36164:"RENDERBUFFER_INTERNAL_FORMAT",36168:"STENCIL_INDEX8",36176:"RENDERBUFFER_RED_SIZE",36177:"RENDERBUFFER_GREEN_SIZE",36178:"RENDERBUFFER_BLUE_SIZE",36179:"RENDERBUFFER_ALPHA_SIZE",36180:"RENDERBUFFER_DEPTH_SIZE",36181:"RENDERBUFFER_STENCIL_SIZE",36194:"RGB565",36336:"LOW_FLOAT",36337:"MEDIUM_FLOAT",36338:"HIGH_FLOAT",36339:"LOW_INT",36340:"MEDIUM_INT",36341:"HIGH_INT",36346:"SHADER_COMPILER",36347:"MAX_VERTEX_UNIFORM_VECTORS",36348:"MAX_VARYING_VECTORS",36349:"MAX_FRAGMENT_UNIFORM_VECTORS",37440:"UNPACK_FLIP_Y_WEBGL",37441:"UNPACK_PREMULTIPLY_ALPHA_WEBGL",37442:"CONTEXT_LOST_WEBGL",37443:"UNPACK_COLORSPACE_CONVERSION_WEBGL",37444:"BROWSER_DEFAULT_WEBGL"}},6603:function(t,e,r){var n=r(1950);t.exports=function(t){return n[t]}},3110:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var u=new s(e,r,o,l);return u.update(t),u};var n=r(5827),i=r(2944),a=r(7667),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function u(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],u=n[15],c=(t._ortho?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*u)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]*this.pixelRatio),r.capSize=this.capSize[f]*c,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var c=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=c[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){"lineWidth"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),"capSize"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var c=r[l],h=0;h<3;++h)if(isNaN(c[h])||!isFinite(c[h]))continue t;var p,d=n[l],v=e[s];Array.isArray(v[0])&&(v=e[l]),3===v.length?v=[v[0],v[1],v[2],1]:4===v.length&&(v=[v[0],v[1],v[2],v[3]],!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0)),isNaN(d[0][s])||isNaN(d[1][s])||(d[0][s]<0&&((p=c.slice())[s]+=d[0][s],i.push(c[0],c[1],c[2],v[0],v[1],v[2],v[3],0,0,0,p[0],p[1],p[2],v[0],v[1],v[2],v[3],0,0,0),u(this.bounds,p),o+=2+f(i,p,v,s)),d[1][s]>0&&((p=c.slice())[s]+=d[1][s],i.push(c[0],c[1],c[2],v[0],v[1],v[2],v[3],0,0,0,p[0],p[1],p[2],v[0],v[1],v[2],v[3],0,0,0),u(this.bounds,p),o+=2+f(i,p,v,s)))}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},7667:function(t,e,r){"use strict";var n=r(6832),i=r(5158),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, offset;\nattribute vec4 color;\nuniform mat4 model, view, projection;\nuniform float capSize;\nvarying vec4 fragColor;\nvarying vec3 fragPosition;\n\nvoid main() {\n  vec4 worldPosition  = model * vec4(position, 1.0);\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\n  gl_Position         = projection * view * worldPosition;\n  fragColor           = color;\n  fragPosition        = position;\n}"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float opacity;\nvarying vec3 fragPosition;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  gl_FragColor = opacity * fragColor;\n}"]);t.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"offset",type:"vec3"}])}},4234:function(t,e,r){"use strict";var n=r(8931);t.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var u=t.getExtension("WEBGL_draw_buffers");if(!l&&u&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,u),Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]),"number"!=typeof e)throw new Error("gl-fbo: Missing shape parameter");var c=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>c||r<0||r>c)throw new Error("gl-fbo: Parameters are too large for FBO");var f=1;if("color"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error("gl-fbo: Must specify a nonnegative number of colors");if(f>1){if(!u)throw new Error("gl-fbo: Multiple draw buffer extension not supported");if(f>t.getParameter(u.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error("gl-fbo: Context does not support "+f+" draw buffers")}}var h=t.UNSIGNED_BYTE,p=t.getExtension("OES_texture_float");if(n.float&&f>0){if(!p)throw new Error("gl-fbo: Context does not support floating point textures");h=t.FLOAT}else n.preferFloat&&f>0&&p&&(h=t.FLOAT);var v=!0;"depth"in n&&(v=!!n.depth);var g=!1;return"stencil"in n&&(g=!!n.stencil),new d(t,e,r,h,f,v,g,u)};var i,a,o,s,l=null;function u(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function c(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error("gl-fbo: Framebuffer unsupported");case a:throw new Error("gl-fbo: Framebuffer incomplete attachment");case o:throw new Error("gl-fbo: Framebuffer incomplete dimensions");case s:throw new Error("gl-fbo: Framebuffer incomplete missing attachment");default:throw new Error("gl-fbo: Framebuffer failed for unspecified reason")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var v=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return v._shape[0]},set:function(t){return v.width=t}},1:{get:function(){return v._shape[1]},set:function(t){return v.height=t}}}),this._shapeVector=g,function(t){var e=u(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,v=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var y=0;y<o;++y)t.color[y]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+y);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var m=r.getExtension("WEBGL_depth_texture");m?d?t.depth=h(r,i,a,m.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):v&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):v&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):v?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){for(t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null),y=0;y<t.color.length;++y)t.color[y].dispose(),t.color[y]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),c(r,e),f(x)}c(r,e)}(this)}var v=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error("gl-fbo: Can't resize destroyed FBO");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-fbo: Can't resize FBO, invalid dimensions");t._shape[0]=e,t._shape[1]=r;for(var a=u(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),c(n,a),f(s)),c(n,a)}}Object.defineProperties(v,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error("gl-fbo: Shape vector must be length 2");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),v.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},v.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},3530:function(t,e,r){var n=r(8974).sprintf,i=r(6603),a=r(9365),o=r(8008);t.exports=function(t,e,r){"use strict";var s=a(e)||"of unknown name (see npm glsl-shader-name)",l="unknown type";void 0!==r&&(l=r===i.FRAGMENT_SHADER?"fragment":"vertex");for(var u=n("Error compiling %s shader %s:\n",l,s),c=n("%s%s",u,t),f=t.split("\n"),h={},p=0;p<f.length;p++){var d=f[p];if(""!==d&&"\0"!==d){var v=parseInt(d.split(":")[2]);if(isNaN(v))throw new Error(n("Could not parse error: %s",d));h[v]=d}}var g=o(e).split("\n");for(p=0;p<g.length;p++)if((h[p+3]||h[p+2]||h[p+1])&&(u+=g[p]+"\n",h[p+1])){var y=h[p+1];y=y.substr(y.split(":",3).join(":").length+1).trim(),u+=n("^^^ %s\n\n",y)}return{long:u.trim(),short:c.trim()}}},6386:function(t,e,r){"use strict";t.exports=function(t,e){var r=t.gl,n=new u(t,o(r,l.vertex,l.fragment),o(r,l.pickVertex,l.pickFragment),s(r),s(r),s(r),s(r));return n.update(e),t.addObject(n),n};var n=r(5070),i=r(9560),a=r(5306),o=r(5158),s=r(5827),l=r(1292);function u(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var c,f=u.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(c=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],u=a[3]-a[1];c[0]=2*o/l,c[4]=2*s/u,c[6]=2*(r[0]-a[0])/l-1,c[7]=2*(r[1]-a[1])/u-1,e.bind();var f=e.uniforms;f.viewTransform=c,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,u=a[2]-a[0],c=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*u/f,t[4]=2*c/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var v=i.attributes;return this.positionBuffer.bind(),v.position.pointer(),this.weightBuffer.bind(),v.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),v.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var u,c,f,p,d=t.colorLevels||[0],v=t.colorValues||[0,0,0,1],g=d.length,y=this.bounds;l?(u=y[0]=r[0],c=y[1]=o[0],f=y[2]=r[r.length-1],p=y[3]=o[o.length-1]):(u=y[0]=r[0]+(r[1]-r[0])/2,c=y[1]=o[0]+(o[1]-o[0])/2,f=y[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=y[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var m=1/(f-u),x=1/(p-c),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),A=a.mallocFloat32(2*w),k=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]),Y=1-G,W=4*q,Z=4*(q+1);F=Y*v[W]+G*v[Z],B=Y*v[W+1]+G*v[Z+1],N=Y*v[W+2]+G*v[Z+2],j=Y*v[W+3]+G*v[Z+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,A[2*S]=.5*D+.5*z,A[2*S+1]=.5*P+.5*O,k[2*S]=U,k[2*S+1]=V,M[S]=C*b+I,S+=1}}}this.positionBuffer.update(A),this.weightBuffer.update(k),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(A),a.free(T),a.free(k),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},1292:function(t,e,r){"use strict";var n=r(6832);t.exports={fragment:n(["precision lowp float;\n#define GLSLIFY 1\nvarying vec4 fragColor;\nvoid main() {\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\n}\n"]),vertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 color;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  fragColor = color;\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"]),pickFragment:n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nuniform vec2 shape;\nuniform vec4 pickOffset;\n\nvoid main() {\n  vec2 d = step(.5, vWeight);\n  vec4 id = fragId + pickOffset;\n  id.x += d.x + d.y*shape.x;\n\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  gl_FragColor = id/255.;\n}\n"]),pickVertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nvoid main() {\n  vWeight = weight;\n\n  fragId = pickId;\n\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"])}},248:function(t,e,r){var n=r(6832),i=r(5158),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, nextPosition;\nattribute float arcLength, lineWidth;\nattribute vec4 color;\n\nuniform vec2 screenShape;\nuniform float pixelRatio;\nuniform mat4 model, view, projection;\n\nvarying vec4 fragColor;\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  vec4 startPoint = project(position);\n  vec4 endPoint   = project(nextPosition);\n\n  vec2 A = startPoint.xy / startPoint.w;\n  vec2 B =   endPoint.xy /   endPoint.w;\n\n  float clipAngle = atan(\n    (B.y - A.y) * screenShape.y,\n    (B.x - A.x) * screenShape.x\n  );\n\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\n    sin(clipAngle),\n    -cos(clipAngle)\n  ) / screenShape;\n\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\n\n  worldPosition = position;\n  pixelArcLength = arcLength;\n  fragColor = color;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3      clipBounds[2];\nuniform sampler2D dashTexture;\nuniform float     dashScale;\nuniform float     opacity;\n\nvarying vec3    worldPosition;\nvarying float   pixelArcLength;\nvarying vec4    fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\n  if(dashWeight < 0.5) {\n    discard;\n  }\n  gl_FragColor = fragColor * opacity;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\n#define FLOAT_MAX  1.70141184e38\n#define FLOAT_MIN  1.17549435e-38\n\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\nvec4 packFloat(float v) {\n  float av = abs(v);\n\n  //Handle special cases\n  if(av < FLOAT_MIN) {\n    return vec4(0.0, 0.0, 0.0, 0.0);\n  } else if(v > FLOAT_MAX) {\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\n  } else if(v < -FLOAT_MAX) {\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\n  }\n\n  vec4 c = vec4(0,0,0,0);\n\n  //Compute exponent and mantissa\n  float e = floor(log2(av));\n  float m = av * pow(2.0, -e) - 1.0;\n\n  //Unpack mantissa\n  c[1] = floor(128.0 * m);\n  m -= c[1] / 128.0;\n  c[2] = floor(32768.0 * m);\n  m -= c[2] / 32768.0;\n  c[3] = floor(8388608.0 * m);\n\n  //Unpack exponent\n  float ebias = e + 127.0;\n  c[0] = floor(ebias / 2.0);\n  ebias -= c[0] * 2.0;\n  c[1] += floor(ebias) * 128.0;\n\n  //Unpack sign bit\n  c[0] += 128.0 * step(0.0, -v);\n\n  //Scale back to range\n  return c / 255.0;\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform float pickId;\nuniform vec3 clipBounds[2];\n\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\n\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\n}"]),l=[{name:"position",type:"vec3"},{name:"nextPosition",type:"vec3"},{name:"arcLength",type:"float"},{name:"lineWidth",type:"float"},{name:"color",type:"vec4"}];e.createShader=function(t){return i(t,a,o,null,l)},e.createPickShader=function(t){return i(t,a,s,null,l)}},6086:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),c=u(new Array(1024),[256,1,4]),p=0;p<1024;++p)c.data[p]=255;var d=a(e,c);d.wrap=e.REPEAT;var v=new y(e,r,o,s,l,d);return v.update(t),v};var n=r(5827),i=r(2944),a=r(8931),o=new Uint8Array(4),s=new Float32Array(o.buffer),l=r(5070),u=r(5050),c=r(248),f=c.createShader,h=c.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function v(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function y(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var m=y.prototype;m.isTransparent=function(){return this.hasAlpha},m.isOpaque=function(){return!this.hasAlpha},m.pickSlots=1,m.setPickBase=function(t){this.pickId=t},m.drawTransparent=m.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:v(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:v(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;"dashScale"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,c=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],v=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var y,m,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);c+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(y=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],m=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):y=m=p,3===y.length&&(y=[y[0],y[1],y[2],1]),3===m.length&&(m=[m[0],m[1],m[2],1]),!this.hasAlpha&&y[3]<1&&(this.hasAlpha=!0),x=Array.isArray(v)?v.length>e-1?v[e-1]:v.length>0?v[v.length-1]:[0,0,0,1]:v;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,y[0],y[1],y[2],y[3]);c+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,y[0],y[1],y[2],y[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,m[0],m[1],m[2],m[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,m[0],m[1],m[2],m[3]),c+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=c,this.points=o,this.arcLength=a,"dashes"in t){var A=t.dashes.slice();for(A.unshift(0),e=1;e<A.length;++e)A[e]=A[e-1]+A[e];var k=u(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)k.set(e,0,r,0);1&l.le(A,A[A.length-1]*e/255)?k.set(e,0,0,0):k.set(e,0,0,255)}this.texture.setPixels(k)}},m.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},m.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r){return o[0]=0,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2]),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),u=1-a,c=[0,0,0],f=0;f<3;++f)c[f]=u*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,c,h,this.points[h])}},7332:function(t){t.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},9823:function(t){t.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},7787:function(t){t.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],u=t[8],c=t[9],f=t[10],h=t[11],p=t[12],d=t[13],v=t[14],g=t[15];return(e*o-r*a)*(f*g-h*v)-(e*s-n*a)*(c*g-h*d)+(e*l-i*a)*(c*v-f*d)+(r*s-n*o)*(u*g-h*p)-(r*l-i*o)*(u*v-f*p)+(n*l-i*s)*(u*d-c*p)}},5950:function(t){t.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,u=r*o,c=n*o,f=n*s,h=i*o,p=i*s,d=i*l,v=a*o,g=a*s,y=a*l;return t[0]=1-f-d,t[1]=c+y,t[2]=h-g,t[3]=0,t[4]=c-y,t[5]=1-u-d,t[6]=p+v,t[7]=0,t[8]=h+g,t[9]=p-v,t[10]=1-u-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},7280:function(t){t.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,u=a+a,c=n*s,f=n*l,h=n*u,p=i*l,d=i*u,v=a*u,g=o*s,y=o*l,m=o*u;return t[0]=1-(p+v),t[1]=f+m,t[2]=h-y,t[3]=0,t[4]=f-m,t[5]=1-(c+v),t[6]=d+g,t[7]=0,t[8]=h+y,t[9]=d-g,t[10]=1-(c+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},9947:function(t){t.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},7437:function(t){t.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],f=e[9],h=e[10],p=e[11],d=e[12],v=e[13],g=e[14],y=e[15],m=r*s-n*o,x=r*l-i*o,b=r*u-a*o,_=n*l-i*s,w=n*u-a*s,T=i*u-a*l,A=c*v-f*d,k=c*g-h*d,M=c*y-p*d,S=f*g-h*v,E=f*y-p*v,L=h*y-p*g,C=m*L-x*E+b*S+_*M-w*k+T*A;return C?(C=1/C,t[0]=(s*L-l*E+u*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(v*T-g*w+y*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-u*k)*C,t[5]=(r*L-i*M+a*k)*C,t[6]=(g*b-d*T-y*x)*C,t[7]=(c*T-h*b+p*x)*C,t[8]=(o*E-s*M+u*A)*C,t[9]=(n*M-r*E-a*A)*C,t[10]=(d*w-v*b+y*m)*C,t[11]=(f*b-c*w-p*m)*C,t[12]=(s*k-o*S-l*A)*C,t[13]=(r*S-n*k+i*A)*C,t[14]=(v*x-d*_-g*m)*C,t[15]=(c*_-f*x+h*m)*C,t):null}},3012:function(t,e,r){var n=r(9947);t.exports=function(t,e,r,i){var a,o,s,l,u,c,f,h,p,d,v=e[0],g=e[1],y=e[2],m=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];return Math.abs(v-_)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(y-T)<1e-6?n(t):(f=v-_,h=g-w,p=y-T,a=x*(p*=d=1/Math.sqrt(f*f+h*h+p*p))-b*(h*=d),o=b*(f*=d)-m*p,s=m*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0),l=h*s-p*o,u=p*a-f*s,c=f*o-h*a,(d=Math.sqrt(l*l+u*u+c*c))?(l*=d=1/d,u*=d,c*=d):(l=0,u=0,c=0),t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=u,t[6]=h,t[7]=0,t[8]=s,t[9]=c,t[10]=p,t[11]=0,t[12]=-(a*v+o*g+s*y),t[13]=-(l*v+u*g+c*y),t[14]=-(f*v+h*g+p*y),t[15]=1,t)}},104:function(t){t.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],f=e[8],h=e[9],p=e[10],d=e[11],v=e[12],g=e[13],y=e[14],m=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*v,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*u+_*p+w*y,t[3]=x*o+b*c+_*d+w*m,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*v,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*u+_*p+w*y,t[7]=x*o+b*c+_*d+w*m,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*v,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*u+_*p+w*y,t[11]=x*o+b*c+_*d+w*m,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*v,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*u+_*p+w*y,t[15]=x*o+b*c+_*d+w*m,t}},5268:function(t){t.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),u=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*u,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*u,t[15]=1,t}},1120:function(t){t.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},4422:function(t){t.exports=function(t,e,r,n){var 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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,A,k,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=A,this.pointIds=b,this.pointVAO=k,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 A=T.prototype;function k(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}A.isOpaque=function(){return!this.hasAlpha},A.isTransparent=function(){return this.hasAlpha},A.pickSlots=1,A.setPickBase=function(t){this.pickId=t},A.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},A.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*k(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,A=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,A));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[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;y.push(j[0],j[1],j[2]),Z=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(Z[0],Z[1],Z[2],this.opacity*k((P[W]-D)/(z-D),this.opacityscale)):3===Z.length?m.push(Z[0],Z[1],Z[2],this.opacity):(m.push(Z[0],Z[1],Z[2],Z[3]*this.opacity),Z[3]<1&&(this.hasAlpha=!0)),X=C?C[W]:P?[(P[W]-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[W]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U)j=n[W=G[U]],p.push(j[0],j[1],j[2]),Z=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(Z[0],Z[1],Z[2],this.opacity*k((P[W]-D)/(z-D),this.opacityscale)):3===Z.length?d.push(Z[0],Z[1],Z[2],this.opacity):(d.push(Z[0],Z[1],Z[2],Z[3]*this.opacity),Z[3]<1&&(this.hasAlpha=!0)),X=C?C[W]:P?[(P[W]-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[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,Z,X,J;j=n[W=G[2-U]],i.push(j[0],j[1],j[2]),(Z=S?S[W]:E?E[R]:L)?this.opacityscale&&P?a.push(Z[0],Z[1],Z[2],this.opacity*k((P[W]-D)/(z-D),this.opacityscale)):3===Z.length?a.push(Z[0],Z[1],Z[2],this.opacity):(a.push(Z[0],Z[1],Z[2],Z[3]*this.opacity),Z[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),X=C?C[W]:P?[(P[W]-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[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(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))}},A.drawTransparent=A.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())},A.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())},A.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]]]}},A.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),A=i(t),k=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:A,type:t.FLOAT,size:4},{buffer:k,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,A,k,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],A=p[w+2]-T,k=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*A/M,t[w]=2*(T-k)/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],A=o.lt(n,l[t]),k=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],A<k&&i[k]>i[A]&&(r.uniforms.dataAxis=u,r.uniforms.screenOffset=c,r.uniforms.color=g[t],r.uniforms.angle=y[t],a.drawArrays(a.TRIANGLES,i[A],i[k]-i[A]))),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],A<k&&i[k]>i[A]&&(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[A],i[k]-i[A]))),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,A=Math.PI*d.rotateSpeed,k=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!v||g&&!h&&!p&&v)&&u.rotate(k,w*A*b,-T*A*_,0),(l&&g&&!h&&!p&&!v||y||g&&h&&!p&&!v)&&u.pan(k,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),c&&g&&!h&&!p&&!v||m||g&&!h&&p&&!v)){var M=-d.zoomSpeed*_/window.innerHeight*(k-u.lastT())*100;u.pan(k,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},A=t.axes||{},k=i(r,A);k.enable=!A.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:k,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=k,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)){k.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,k=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,k.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);k.autoTicks?k.update({bounds:d,tickSpacing:m}):k.update({bounds:d})}var T=r.drawingBufferWidth,A=r.drawingBufferHeight;for(z[0]=T,z[1]=A,B[0]=0|Math.max(T/F.pixelRatio,1),B[1]=0|Math.max(A/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,A),F.onrender&&F.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,A),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(k.enable&&(M=M||k.isTransparent(),k.draw(D)),S.axes=k,x.object&&S.draw(D),r.disable(r.CULL_FACE),o=0;o<e;++o)(C=E[o]).axes=k,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),k.enable&&k.isTransparent()&&k.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),k.isTransparent()&&k.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(),A=[0,0,0],k=[[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=k,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(A,v[0],g,-1e8),l.fragClipBounds[1]=E(A,v[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}(e,r,n,i),a===r.hasAlpha||o){t.bind();var u=t.uniforms;u.model=n.model||f,u.view=n.view||f,u.projection=n.projection||f,m[0]=2/l.drawingBufferWidth,m[1]=2/l.drawingBufferHeight,u.screenSize=m,u.highlightId=r.highlightId,u.highlightScale=r.highlightScale,u.fragClipBounds=L,u.clipBounds=r.axes.bounds,u.opacity=r.opacity,u.pickGroup=r.pickId/255,u.pixelRatio=i,r.vao.bind(),r.vao.draw(l.TRIANGLES,r.vertexCount),r.lineWidth>0&&(l.lineWidth(r.lineWidth*i),r.vao.draw(l.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function P(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=c(a);var o=!0;n(a)&&(a="▼",o=!1);var s=u(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}y.draw=function(t){C(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},y.drawTransparent=function(t){C(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},y.drawPick=function(t){C(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},y.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},y.update=function(t){if("perspective"in(t=t||{})&&(this.useOrtho=!t.perspective),"orthographic"in t&&(this.useOrtho=!!t.orthographic),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"project"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if("projectScale"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,"projectOpacity"in t){Array.isArray(t.projectOpacity)?this.projectOpacity=t.projectOpacity.slice():(r=+t.projectOpacity,this.projectOpacity=[r,r,r]);for(var n=0;n<3;++n)this.projectOpacity[n]=v(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,"opacity"in t&&(this.opacity=v(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||"normal",u=t.alignment||[0,0];if(2===u.length)i=u[0],a=u[1];else for(i=[],a=[],n=0;n<u.length;++n)i[n]=u[n][0],a[n]=u[n][1];var c=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,g=t.angle,y=t.lineColor,m=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var A=(N=P(h,n,l,this.pixelRatio)).mesh,k=N.lines,M=N.bounds;x+=3*A.cells.length,b+=2*k.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),O=o.mallocUint32(S);if(S>0){var I=0,D=x,z=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(y)&&Array.isArray(y[0]);t:for(n=0;n<_;++n){for(m+=1,w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),c[T]=Math.min(c[T],w[T])}A=(N=P(h,n,l,this.pixelRatio)).mesh,k=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)z[T]=U[T];z[3]=1}else if(4===U.length){for(T=0;T<4;++T)z[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else z[0]=z[1]=z[2]=0,z[3]=1;else 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tt=J[K[$]];C[2*I]=V*(q*tt[0]-G*tt[1]+Z[0]),C[2*I+1]=V*(G*tt[0]+q*tt[1]+Z[1]),I+=1}for(X=k.edges,J=k.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]+Z[0]),C[2*D+1]=V*(G*tt[0]+q*tt[1]+Z[1]),D+=1}}}this.bounds=[c,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(O),o.free(E),o.free(L),o.free(C),o.free(O)},y.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},1884:function(t,e,r){"use strict";var n=r(6832);e.boxVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 vertex;\n\nuniform vec2 cornerA, cornerB;\n\nvoid main() {\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\n}\n"]),e.boxFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color;\n\nvoid main() {\n  gl_FragColor = color;\n}\n"])},6623:function(t,e,r){"use strict";var n=r(5158),i=r(5827),a=r(1884);function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}t.exports=function(t,e){var r=t.gl,s=new o(t,i(r,[0,0,0,1,1,0,1,1]),n(r,a.boxVertex,a.boxFragment));return s.update(e),t.addOverlay(s),s};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,u=t.viewBox,c=t.pixelRatio,f=(e[0]-l[0])*(u[2]-u[0])/(l[2]-l[0])+u[0],h=(e[1]-l[1])*(u[3]-u[1])/(l[3]-l[1])+u[1],p=(e[2]-l[0])*(u[2]-u[0])/(l[2]-l[0])+u[0],d=(e[3]-l[1])*(u[3]-u[1])/(l[3]-l[1])+u[1];if(f=Math.max(f,u[0]),h=Math.max(h,u[1]),p=Math.min(p,u[2]),d=Math.min(d,u[3]),!(p<f||d<h)){o.bind();var v=s[2]-s[0],g=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,v,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,v,g,i),o.drawBox(p,h,v,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var y=r*c;o.drawBox(f-y,h-y,p+y,h+y,a),o.drawBox(f-y,d-y,p+y,d+y,a),o.drawBox(f-y,h-y,f+y,d+y,a),o.drawBox(p-y,h-y,p+y,d+y,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},2611:function(t,e,r){"use strict";t.exports=function(t,e){var r=e[0],a=e[1];return new l(t,n(t,r,a,{}),i.mallocUint8(r*a*4))};var n=r(4234),i=r(5306),a=r(5050),o=r(2288).nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var 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i=0|Math.min(Math.max(t-r,0),n[0]),o=0|Math.min(Math.max(t+r,0),n[0]),l=0|Math.min(Math.max(e-r,0),n[1]),u=0|Math.min(Math.max(e+r,0),n[1]);if(o<=i||u<=l)return null;var c=[o-i,u-l],f=a(this.buffer,[c[0],c[1],4],[4,4*n[0],1],4*(i+n[0]*l)),h=function(t,e,r){for(var n=1e8,i=-1,a=-1,o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var u=0;u<s;u++){var c=t.get(l,u,0),f=t.get(l,u,1),h=t.get(l,u,2),p=t.get(l,u,3);if(c<255||f<255||h<255||p<255){var d=e-l,v=r-u,g=d*d+v*v;g<n&&(n=g,i=l,a=u)}}return[i,a,n]}(f.hi(c[0],c[1],1),r,r),p=h[0],d=h[1];return p<0||Math.pow(this.radius,2)<h[2]?null:new s(p+i|0,d+l|0,f.get(p,d,0),[f.get(p,d,1),f.get(p,d,2),f.get(p,d,3)],Math.sqrt(h[2]))},u.dispose=function(){this.gl&&(this.fbo.dispose(),i.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},5158:function(t,e,r){"use strict";var n=r(9016),i=r(4280),a=r(3984),o=r(1628),s=r(2631),l=r(9068);function 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with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * tubePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\n  vec4 t_position  = view * tubePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = tubePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float tubeScale;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  gl_Position = projection * view * tubePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec4"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec4"}]}},7307:function(t,e,r){"use strict";var n=r(2858),i=r(4020),a=["xyz","xzy","yxz","yzx","zxy","zyx"],o=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},s=function(t,e,r){return t<e?e:t>r?r:t},l=function(t){var e=1/0;t.sort(function(t,e){return t-e});for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};t.exports=function(t,e){var r=t.startingPositions,u=t.maxLength||1e3,c=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||"+x+y+z",p={};-1!==h.indexOf("-x")&&(p.reversedX=!0),-1!==h.indexOf("-y")&&(p.reversedY=!0),-1!==h.indexOf("-z")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,"").replace(/\+/g,""));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,l=t[0],u=t[1],c=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=o(a[0],l),v=o(a[1],u),g=o(a[2],c),y=d+1,m=v+1,x=g+1;if(d=s(d,0,f-1),y=s(y,0,f-1),v=s(v,0,h-1),m=s(m,0,h-1),g=s(g,0,p-1),x=s(x,0,p-1),d<0||v<0||g<0||y>f-1||m>h-1||x>p-1)return n.create();var b,_,w,T,A,k,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:A=g,k=x,w=v*p,T=m*p,b=d*p*h,_=y*p*h;break;case 4:A=g,k=x,b=d*p,_=y*p,w=v*p*f,T=m*p*f;break;case 3:w=v,T=m,A=g*h,k=x*h,b=d*h*p,_=y*h*p;break;case 2:w=v,T=m,b=d*h,_=y*h,A=g*h*f,k=x*h*f;break;case 1:b=d,_=y,A=g*f,k=x*f,w=v*f*p,T=m*f*p;break;default:b=d,_=y,w=v*f,T=m*f,A=g*f*h,k=x*f*h}var D=i[b+w+A],z=i[b+w+k],R=i[b+T+A],F=i[b+T+k],B=i[_+w+A],N=i[_+w+k],j=i[_+T+A],U=i[_+T+k],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 Y=n.create(),W=n.create();n.lerp(Y,V,q,O),n.lerp(W,H,G,O);var Z=n.create();return n.lerp(Z,Y,W,I),Z}(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)},A=10*n.distance(e[0],e[1])/u,k=A*A,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>k&&n.scale(N,N,A/Math.sqrt(j)),n.add(N,N,C),I=d(N),n.squaredDistance(D,N)-k>-1e-4*k&&(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 A=(T+1)%8;u.push(h[T],p[T],p[A],p[A],h[A],h[T]),f.push(m,y,y,y,m,m),d.push(v,g,g,g,v,v);var k=u.length;c.push([k-6,k-5,k-4],[k-3,k-2,k-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],A=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function M(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();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=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=c.projections[i],f.uniforms.clipBounds=c.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var 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=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=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=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var 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),k=x.get(1,o,y,1),M=x.get(2,o,y,0),S=x.get(2,o,y,1),E=T*S-k*M,L=M*w-S*_,C=_*k-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 Y=o+A[q][0],W=y+A[q][1],Z=this._field[0].get(Y+1,W+1),X=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),L=b.get(Y+1,W+1,1),C=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];F[N++]=Y,F[N++]=W,F[N++]=Z,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],Z+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],Z+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(Y=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var yt=H?ht:1-ht;if(W=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[vt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var 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-_,A=1-T,k=+x[1],M=0|k,S=0|Math.min(M+1,i[1]),E=k-M,L=1-E,C=A*L,P=A*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 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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 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UCD-0159476<br>Pathway: Metabolism","Compound: UCD-0159484<br>Pathway: Epigenetics","Compound: UCD-0159468<br>Pathway: Apoptosis","Compound: UCD-0159460<br>Pathway: Angiogenesis","Compound: UCD-0159515<br>Pathway: Epigenetics","Compound: UCD-0159483<br>Pathway: Endocrinology & Hormones","Compound: UCD-0159453<br>Pathway: TGF-beta/Smad","Compound: UCD-0159459<br>Pathway: Cytoskeletal Signaling","Compound: UCD-0159500<br>Pathway: Epigenetics","Compound: UCD-0159444<br>Pathway: MAPK","Compound: UCD-0159514<br>Pathway: Others","Compound: UCD-0159506<br>Pathway: NF-κB","Compound: UCD-0159474<br>Pathway: DNA Damage","Compound: UCD-0159466<br>Pathway: Cell Cycle","Compound: UCD-0159458<br>Pathway: Others","Compound: UCD-0159451<br>Pathway: Ubiquitin","Compound: UCD-0159443<br>Pathway: Others","Compound: UCD-0159492<br>Pathway: Cell Cycle","Compound: UCD-0000253<br>Pathway: Neuronal Signaling","Compound: UCD-0159429<br>Pathway: Stem Cells & Wnt","Compound: UCD-0001863<br>Pathway: Angiogenesis","Compound: UCD-0159353<br>Pathway: Proteases","Compound: UCD-0159359<br>Pathway: Others","Compound: UCD-0159365<br>Pathway: TGF-beta/Smad","Compound: UCD-0159380<br>Pathway: GPCR & G Protein","Compound: UCD-0159337<br>Pathway: Neuronal Signaling","Compound: UCD-0001391<br>Pathway: Metabolism","Compound: UCD-0001856<br>Pathway: Angiogenesis","Compound: UCD-0159354<br>Pathway: Metabolism","Compound: UCD-0159387<br>Pathway: Neuronal Signaling","Compound: UCD-0159379<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159470<br>Pathway: MAPK","Compound: UCD-0159462<br>Pathway: GPCR & G Protein","Compound: UCD-0159455<br>Pathway: TGF-beta/Smad","Compound: UCD-0159447<br>Pathway: Ubiquitin","Compound: UCD-0159517<br>Pathway: Proteases","Compound: UCD-0159509<br>Pathway: Others","Compound: UCD-0159501<br>Pathway: Cell Cycle","Compound: UCD-0159493<br>Pathway: Epigenetics","Compound: UCD-0159477<br>Pathway: Metabolism","Compound: UCD-0159516<br>Pathway: Proteases","Compound: UCD-0159434<br>Pathway: Angiogenesis","Compound: UCD-0159409<br>Pathway: Others","Compound: UCD-0159421<br>Pathway: Apoptosis","Compound: UCD-0000936<br>Pathway: Neuronal Signaling","Compound: UCD-0001886<br>Pathway: Cell Cycle","Compound: UCD-0159344<br>Pathway: Endocrinology & Hormones","Compound: UCD-0159350<br>Pathway: Neuronal Signaling","Compound: UCD-0159355<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159361<br>Pathway: Others","Compound: UCD-0159375<br>Pathway: NF-κB","Compound: UCD-0159413<br>Pathway: NF-κB","Compound: UCD-0159383<br>Pathway: NF-κB","Compound: UCD-0159333<br>Pathway: Transmembrane Transporters","Compound: UCD-0001616<br>Pathway: Metabolism","Compound: UCD-0159339<br>Pathway: Neuronal Signaling","Compound: UCD-0159345<br>Pathway: Neuronal Signaling","Compound: UCD-0159351<br>Pathway: Others","Compound: UCD-0159332<br>Pathway: Apoptosis","Compound: UCD-0159331<br>Pathway: Transmembrane Transporters","Compound: UCD-0159326<br>Pathway: Neuronal Signaling","Compound: UCD-0000972<br>Pathway: Neuronal Signaling","Compound: UCD-0001078<br>Pathway: Metabolism","Compound: UCD-0159624<br>Pathway: GPCR & G Protein","Compound: UCD-0159608<br>Pathway: Metabolism","Compound: UCD-0159661<br>Pathway: Transmembrane Transporters","Compound: UCD-0159653<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0159647<br>Pathway: GPCR & G Protein","Compound: UCD-0159639<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0159631<br>Pathway: Cell Cycle","Compound: UCD-0159623<br>Pathway: DNA Damage","Compound: UCD-0001957<br>Pathway: Epigenetics","Compound: UCD-0159362<br>Pathway: Stem Cells & Wnt","Compound: UCD-0159369<br>Pathway: Epigenetics","Compound: UCD-0001861<br>Pathway: Angiogenesis","Compound: UCD-0159366<br>Pathway: Metabolism","Compound: UCD-0159371<br>Pathway: Apoptosis","Compound: UCD-0159364<br>Pathway: Others","Compound: UCD-0159358<br>Pathway: Metabolism","Compound: UCD-0159352<br>Pathway: Proteases","Compound: UCD-0001885<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159342<br>Pathway: Others","Compound: UCD-0018448<br>Pathway: GPCR & G Protein","Compound: UCD-0159335<br>Pathway: JAK/STAT","Compound: UCD-0159386<br>Pathway: DNA Damage","Compound: UCD-0159378<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0159370<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0011696<br>Pathway: JAK/STAT","Compound: UCD-0159357<br>Pathway: Apoptosis","Compound: UCD-0159347<br>Pathway: Metabolism","Compound: UCD-0159376<br>Pathway: Transmembrane Transporters","Compound: UCD-0159341<br>Pathway: Metabolism","Compound: UCD-0001186<br>Pathway: Neuronal Signaling","Compound: UCD-0000684<br>Pathway: Others","Compound: UCD-0159385<br>Pathway: Metabolism","Compound: UCD-0159377<br>Pathway: Metabolism","Compound: UCD-0018540<br>Pathway: MAPK","Compound: UCD-0159356<br>Pathway: Cytoskeletal Signaling","Compound: UCD-0001900<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159346<br>Pathway: Stem Cells & Wnt","Compound: UCD-0159338<br>Pathway: Neuronal Signaling","Compound: UCD-0159334<br>Pathway: Metabolism","Compound: UCD-0000201<br>Pathway: GPCR & G Protein","Compound: UCD-0159373<br>Pathway: Angiogenesis","Compound: UCD-0159478<br>Pathway: Cell Cycle","Compound: UCD-0159449<br>Pathway: Ubiquitin","Compound: UCD-0159519<br>Pathway: Stem Cells & Wnt","Compound: UCD-0159511<br>Pathway: Autophagy","Compound: UCD-0159503<br>Pathway: Cytoskeletal Signaling","Compound: UCD-0159495<br>Pathway: Apoptosis","Compound: UCD-0159479<br>Pathway: Transmembrane Transporters","Compound: UCD-0159471<br>Pathway: Proteases","Compound: UCD-0159456<br>Pathway: Apoptosis","Compound: UCD-0159518<br>Pathway: Proteases","Compound: UCD-0159510<br>Pathway: Autophagy","Compound: UCD-0000918<br>Pathway: Proteases","Compound: UCD-0159464<br>Pathway: Metabolism","Compound: UCD-0159397<br>Pathway: Others","Compound: UCD-0159441<br>Pathway: Metabolism","Compound: UCD-0001925<br>Pathway: Epigenetics","Compound: UCD-0159428<br>Pathway: Apoptosis","Compound: UCD-0159420<br>Pathway: Angiogenesis","Compound: UCD-0159412<br>Pathway: Apoptosis","Compound: UCD-0001098<br>Pathway: Neuronal Signaling","Compound: UCD-0159664<br>Pathway: Epigenetics","Compound: UCD-0159405<br>Pathway: Metabolism","Compound: UCD-0159401<br>Pathway: Angiogenesis","Compound: UCD-0159396<br>Pathway: DNA Damage","Compound: UCD-0001927<br>Pathway: Epigenetics","Compound: UCD-0159472<br>Pathway: TGF-beta/Smad","Compound: UCD-0018183<br>Pathway: Proteases","Compound: UCD-0159522<br>Pathway: Proteases","Compound: UCD-0159553<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159546<br>Pathway: Ubiquitin","Compound: UCD-0159538<br>Pathway: MAPK","Compound: UCD-0159530<br>Pathway: Proteases","Compound: UCD-0159523<br>Pathway: Proteases","Compound: UCD-0159591<br>Pathway: Epigenetics","Compound: UCD-0159584<br>Pathway: Cell Cycle","Compound: UCD-0159576<br>Pathway: Others","Compound: UCD-0159568<br>Pathway: Others","Compound: UCD-0159560<br>Pathway: DNA Damage","Compound: UCD-0159552<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0159545<br>Pathway: Autophagy","Compound: UCD-0159529<br>Pathway: Cell Cycle","Compound: UCD-0159521<br>Pathway: Others","Compound: UCD-0159480<br>Pathway: Transmembrane Transporters","Compound: UCD-0159513<br>Pathway: Others","Compound: UCD-0159505<br>Pathway: Others","Compound: UCD-0159497<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159481<br>Pathway: Cell Cycle","Compound: UCD-0159473<br>Pathway: Epigenetics","Compound: UCD-0159465<br>Pathway: Metabolism","Compound: UCD-0159457<br>Pathway: Proteases","Compound: UCD-0159504<br>Pathway: Others","Compound: UCD-0159496<br>Pathway: Others","Compound: UCD-0159488<br>Pathway: Epigenetics","Compound: UCD-0159419<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0011250<br>Pathway: Endocrinology & Hormones","Compound: UCD-0159381<br>Pathway: Neuronal Signaling","Compound: UCD-0159408<br>Pathway: Neuronal Signaling","Compound: UCD-0159431<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159423<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159410<br>Pathway: Neuronal Signaling","Compound: UCD-0159407<br>Pathway: NF-κB","Compound: UCD-0159402<br>Pathway: GPCR & G Protein","Compound: UCD-0159398<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0159391<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0159435<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0159422<br>Pathway: Apoptosis","Compound: UCD-0159414<br>Pathway: DNA Damage","Compound: UCD-0017582<br>Pathway: Neuronal Signaling","Compound: UCD-0159392<br>Pathway: Angiogenesis","Compound: UCD-0018458<br>Pathway: Proteases","Compound: UCD-0022022<br>Pathway: Others","Compound: UCD-0159390<br>Pathway: Neuronal Signaling","Compound: UCD-0159389<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0159382<br>Pathway: Neuronal Signaling","Compound: UCD-0159374<br>Pathway: Endocrinology & Hormones","Compound: UCD-0159367<br>Pathway: Neuronal Signaling","Compound: UCD-0159360<br>Pathway: Metabolism","Compound: UCD-0001567<br>Pathway: Transmembrane Transporters","Compound: UCD-0159349<br>Pathway: Metabolism","Compound: UCD-0001888<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0000737<br>Pathway: GPCR & G Protein","Compound: UCD-0159436<br>Pathway: Stem Cells & Wnt","Compound: UCD-0159399<br>Pathway: Apoptosis","Compound: UCD-0011098<br>Pathway: Neuronal Signaling","Compound: UCD-0159433<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0159404<br>Pathway: Metabolism","Compound: UCD-0159395<br>Pathway: GPCR & G Protein","Compound: UCD-0159439<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0001924<br>Pathway: Epigenetics","Compound: UCD-0159426<br>Pathway: DNA Damage","Compound: UCD-0159418<br>Pathway: Transmembrane Transporters","Compound: UCD-0001657<br>Pathway: GPCR & G Protein","Compound: UCD-0001002<br>Pathway: Neuronal Signaling","Compound: UCD-0000377<br>Pathway: Transmembrane Transporters","Compound: UCD-0001653<br>Pathway: Transmembrane Transporters","Compound: UCD-0018410<br>Pathway: Metabolism","Compound: UCD-0159394<br>Pathway: TGF-beta/Smad","Compound: UCD-0159438<br>Pathway: Cytoskeletal Signaling","Compound: UCD-0159425<br>Pathway: Apoptosis","Compound: UCD-0159403<br>Pathway: GPCR & G Protein","Compound: UCD-0159417<br>Pathway: GPCR & G Protein","Compound: UCD-0159411<br>Pathway: Neuronal Signaling","Compound: UCD-0000463<br>Pathway: Others","Compound: UCD-0001059<br>Pathway: Epigenetics","Compound: UCD-0159393<br>Pathway: GPCR & G Protein","Compound: UCD-0159437<br>Pathway: Cytoskeletal Signaling","Compound: UCD-0159432<br>Pathway: Angiogenesis","Compound: UCD-0159416<br>Pathway: Metabolism","Compound: UCD-0159411B<br>Pathway: Neuronal Signaling","Compound: UCD-0000243<br>Pathway: Neuronal Signaling","Compound: UCD-0000904<br>Pathway: GPCR & G Protein","Compound: UCD-0159600<br>Pathway: Epigenetics","Compound: UCD-0159646<br>Pathway: GPCR & G Protein","Compound: UCD-0159652<br>Pathway: Proteases","Compound: UCD-0159265<br>Pathway: Neuronal Signaling","Compound: UCD-0001800<br>Pathway: Others","Compound: UCD-0001613<br>Pathway: Endocrinology & Hormones","Compound: UCD-0159293<br>Pathway: Others","Compound: UCD-0001808<br>Pathway: MAPK","Compound: UCD-0001801<br>Pathway: Endocrinology & Hormones","Compound: UCD-0159271<br>Pathway: Others","Compound: UCD-0018516<br>Pathway: Neuronal Signaling","Compound: UCD-0001835<br>Pathway: Others","Compound: UCD-0159280<br>Pathway: Others","Compound: UCD-0018131<br>Pathway: Angiogenesis","Compound: UCD-0001024<br>Pathway: Neuronal Signaling","Compound: UCD-0001829<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159275<br>Pathway: Others","Compound: UCD-0159270<br>Pathway: Others","Compound: UCD-0001844<br>Pathway: Others","Compound: UCD-0018207<br>Pathway: Neuronal Signaling","Compound: UCD-0001804<br>Pathway: MAPK","Compound: UCD-0001016<br>Pathway: Neuronal Signaling","Compound: UCD-0018179<br>Pathway: MAPK","Compound: UCD-0159289<br>Pathway: Metabolism","Compound: UCD-0159285<br>Pathway: DNA Damage","Compound: UCD-0159274<br>Pathway: Others","Compound: UCD-0001775<br>Pathway: Others","Compound: UCD-0159264<br>Pathway: Neuronal Signaling","Compound: UCD-0159261<br>Pathway: Others","Compound: UCD-0159258<br>Pathway: Angiogenesis","Compound: UCD-0001014<br>Pathway: Neuronal Signaling","Compound: UCD-0000721<br>Pathway: Neuronal Signaling","Compound: UCD-0159259<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0001842<br>Pathway: Neuronal Signaling","Compound: UCD-0159266<br>Pathway: Others","Compound: UCD-0000295<br>Pathway: Metabolism","Compound: UCD-0000533<br>Pathway: GPCR & G Protein","Compound: UCD-0001759<br>Pathway: Angiogenesis","Compound: UCD-0001822<br>Pathway: MAPK","Compound: UCD-0018083<br>Pathway: JAK/STAT","Compound: UCD-0159295<br>Pathway: Microbiology","Compound: UCD-0001101<br>Pathway: Neuronal Signaling","Compound: UCD-0000786<br>Pathway: GPCR & G Protein","Compound: UCD-0001086<br>Pathway: Endocrinology & Hormones","Compound: UCD-0001733<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0159278<br>Pathway: Others","Compound: UCD-0001850<br>Pathway: Others","Compound: UCD-0159294<br>Pathway: Cell Cycle","Compound: UCD-0159292<br>Pathway: DNA Damage","Compound: UCD-0018089<br>Pathway: Endocrinology & Hormones","Compound: UCD-0159288<br>Pathway: Metabolism","Compound: UCD-0159282<br>Pathway: Others","Compound: UCD-0001802<br>Pathway: Others","Compound: UCD-0001816<br>Pathway: Metabolism","Compound: UCD-0017947<br>Pathway: Apoptosis","Compound: UCD-0000421<br>Pathway: Neuronal Signaling","Compound: UCD-0159291<br>Pathway: GPCR & G Protein","Compound: UCD-0000849<br>Pathway: Endocrinology & Hormones","Compound: UCD-0159287<br>Pathway: Endocrinology & Hormones","Compound: UCD-0017999<br>Pathway: Endocrinology & Hormones","Compound: UCD-0159284<br>Pathway: Stem Cells & Wnt","Compound: UCD-0018483<br>Pathway: Microbiology","Compound: UCD-0018240<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159313<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0013217<br>Pathway: Epigenetics","Compound: UCD-0159300<br>Pathway: GPCR & G Protein","Compound: UCD-0159323<br>Pathway: Stem Cells & Wnt","Compound: UCD-0018550<br>Pathway: Neuronal Signaling","Compound: UCD-0001435<br>Pathway: Others","Compound: UCD-0018439<br>Pathway: Microbiology","Compound: UCD-0000490<br>Pathway: Neuronal Signaling","Compound: UCD-0001843<br>Pathway: DNA Damage","Compound: UCD-0159309<br>Pathway: Angiogenesis","Compound: UCD-0159299<br>Pathway: Metabolism","Compound: UCD-0159320<br>Pathway: Metabolism","Compound: UCD-0159322<br>Pathway: Endocrinology & Hormones","Compound: UCD-0001441<br>Pathway: Neuronal Signaling","Compound: UCD-0018311<br>Pathway: Neuronal Signaling","Compound: UCD-0001305<br>Pathway: Neuronal Signaling","Compound: UCD-0018012<br>Pathway: Apoptosis","Compound: UCD-0159308<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0159304<br>Pathway: Cell Cycle","Compound: UCD-0000730<br>Pathway: Metabolism","Compound: UCD-0159319<br>Pathway: Neuronal Signaling","Compound: UCD-0000101<br>Pathway: Endocrinology & Hormones","Compound: UCD-0159324<br>Pathway: Angiogenesis","Compound: UCD-0159273<br>Pathway: Others","Compound: UCD-0000324<br>Pathway: Metabolism","Compound: UCD-0159269<br>Pathway: Others","Compound: UCD-0159263<br>Pathway: Others","Compound: UCD-0001810<br>Pathway: DNA Damage","Compound: UCD-0159257<br>Pathway: DNA Damage","Compound: UCD-0000450<br>Pathway: Neuronal Signaling","Compound: UCD-0001040<br>Pathway: Endocrinology & Hormones","Compound: UCD-0159283<br>Pathway: Endocrinology & Hormones","Compound: UCD-0159279<br>Pathway: Others","Compound: UCD-0001915<br>Pathway: Others","Compound: UCD-0159262<br>Pathway: Others","Compound: UCD-0001766<br>Pathway: Angiogenesis","Compound: UCD-0159256<br>Pathway: Apoptosis","Compound: UCD-0000576<br>Pathway: Neuronal Signaling","Compound: UCD-0001869<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0159315<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159311<br>Pathway: GPCR & G Protein","Compound: UCD-0159306<br>Pathway: Cell Cycle","Compound: UCD-0159301<br>Pathway: Cell Cycle","Compound: UCD-0159330<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0159325<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159321<br>Pathway: Neuronal Signaling","Compound: UCD-0000469<br>Pathway: Neuronal Signaling","Compound: UCD-0018527<br>Pathway: Microbiology","Compound: UCD-0000083<br>Pathway: Neuronal Signaling","Compound: UCD-0159314<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159310<br>Pathway: GPCR & G Protein","Compound: UCD-0018051<br>Pathway: Metabolism","Compound: UCD-0001818<br>Pathway: MAPK","Compound: UCD-0159317<br>Pathway: Angiogenesis","Compound: UCD-0018304<br>Pathway: Endocrinology & Hormones","Compound: UCD-0000872<br>Pathway: Metabolism","Compound: UCD-0159627<br>Pathway: Metabolism","Compound: UCD-0159642<br>Pathway: Proteases","Compound: UCD-0159648<br>Pathway: GPCR & G Protein","Compound: UCD-0159604<br>Pathway: Autophagy","Compound: UCD-0159611<br>Pathway: Others","Compound: UCD-0159619<br>Pathway: GPCR & G Protein","Compound: UCD-0159327<br>Pathway: DNA Damage","Compound: UCD-0159643<br>Pathway: Metabolism","Compound: UCD-0159649<br>Pathway: Angiogenesis","Compound: UCD-0159657<br>Pathway: Others","Compound: UCD-0159253<br>Pathway: Epigenetics","Compound: UCD-0159596<br>Pathway: Cytoskeletal Signaling","Compound: UCD-0159634<br>Pathway: Cytoskeletal Signaling","Compound: UCD-0159626<br>Pathway: Neuronal Signaling","Compound: UCD-0159618<br>Pathway: MAPK","Compound: UCD-0159610<br>Pathway: Transmembrane Transporters","Compound: UCD-0159603<br>Pathway: GPCR & G Protein","Compound: UCD-0159663<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0159655<br>Pathway: Epigenetics","Compound: UCD-0001953<br>Pathway: Epigenetics","Compound: UCD-0159641<br>Pathway: JAK/STAT","Compound: UCD-0159633<br>Pathway: Apoptosis","Compound: UCD-0159625<br>Pathway: Endocrinology & Hormones","Compound: UCD-0159617<br>Pathway: Endocrinology & Hormones","Compound: UCD-0159609<br>Pathway: Others","Compound: UCD-0159602<br>Pathway: Angiogenesis","Compound: UCD-0159654<br>Pathway: Epigenetics","Compound: UCD-0001952<br>Pathway: Epigenetics","Compound: UCD-0159528<br>Pathway: Transmembrane Transporters","Compound: UCD-0159544<br>Pathway: Angiogenesis","Compound: UCD-0159630<br>Pathway: Transmembrane Transporters","Compound: UCD-0159622<br>Pathway: JAK/STAT","Compound: UCD-0159614<br>Pathway: Metabolism","Compound: UCD-0159607<br>Pathway: Metabolism","Compound: UCD-0159659<br>Pathway: Metabolism","Compound: UCD-0159651<br>Pathway: Angiogenesis","Compound: UCD-0159637<br>Pathway: Others","Compound: UCD-0159629<br>Pathway: Angiogenesis","Compound: UCD-0159613<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159606<br>Pathway: Autophagy","Compound: UCD-0159658<br>Pathway: Autophagy","Compound: UCD-0159551<br>Pathway: Epigenetics","Compound: UCD-0159650<br>Pathway: MAPK","Compound: UCD-0159644<br>Pathway: Transmembrane Transporters","Compound: UCD-0159636<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0159628<br>Pathway: GPCR & G Protein","Compound: UCD-0159620<br>Pathway: DNA Damage","Compound: UCD-0159612<br>Pathway: Cell Cycle","Compound: UCD-0159605<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159597<br>Pathway: Epigenetics","Compound: UCD-0159590<br>Pathway: Others","Compound: UCD-0159575<br>Pathway: Autophagy","Compound: UCD-0159567<br>Pathway: Others","Compound: UCD-0159559<br>Pathway: DNA Damage","Compound: UCD-0159640<br>Pathway: Epigenetics","Compound: UCD-0159632<br>Pathway: Angiogenesis","Compound: UCD-0159582<br>Pathway: Neuronal Signaling","Compound: UCD-0159593<br>Pathway: Epigenetics","Compound: UCD-0159586<br>Pathway: Angiogenesis","Compound: UCD-0159578<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159562<br>Pathway: Epigenetics","Compound: UCD-0159554<br>Pathway: Angiogenesis","Compound: UCD-0159547<br>Pathway: Apoptosis","Compound: UCD-0159539<br>Pathway: Proteases","Compound: UCD-0159524<br>Pathway: Others","Compound: UCD-0159592<br>Pathway: Epigenetics","Compound: UCD-0159585<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0159569<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0159312<br>Pathway: Transmembrane Transporters","Compound: UCD-0159307<br>Pathway: GPCR & G Protein","Compound: UCD-0159303<br>Pathway: Metabolism","Compound: UCD-0159297<br>Pathway: Microbiology","Compound: UCD-0001463<br>Pathway: Neuronal Signaling","Compound: UCD-0001616B<br>Pathway: Others","Compound: UCD-0000878<br>Pathway: Transmembrane Transporters","Compound: UCD-0159316<br>Pathway: Proteases","Compound: UCD-0000757<br>Pathway: Metabolism","Compound: UCD-0159302<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159296<br>Pathway: Metabolism","Compound: UCD-0159540<br>Pathway: Others","Compound: UCD-0159555<br>Pathway: Cytoskeletal Signaling","Compound: UCD-0159574<br>Pathway: Epigenetics","Compound: UCD-0159566<br>Pathway: DNA Damage","Compound: UCD-0159558<br>Pathway: Cytoskeletal Signaling","Compound: UCD-0159543<br>Pathway: MAPK","Compound: UCD-0159535<br>Pathway: Others","Compound: UCD-0159665<br>Pathway: Others","Compound: UCD-0001944<br>Pathway: Epigenetics","Compound: UCD-0159589<br>Pathway: DNA Damage","Compound: UCD-0159581<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159573<br>Pathway: Protein Tyrosine Kinase","Compound: UCD-0159565<br>Pathway: TGF-beta/Smad","Compound: UCD-0159557<br>Pathway: Others","Compound: UCD-0159549<br>Pathway: JAK/STAT","Compound: UCD-0159542<br>Pathway: MAPK","Compound: UCD-0159527<br>Pathway: DNA Damage","Compound: UCD-0159563<br>Pathway: Angiogenesis","Compound: UCD-0159588<br>Pathway: MAPK","Compound: UCD-0159572<br>Pathway: Epigenetics","Compound: UCD-0159556<br>Pathway: GPCR & G Protein","Compound: UCD-0159514B<br>Pathway: Others","Compound: UCD-0159533<br>Pathway: Cytoskeletal Signaling","Compound: UCD-0159526<br>Pathway: DNA Damage","Compound: UCD-0159594<br>Pathway: Others","Compound: UCD-0159579<br>Pathway: PI3K/Akt/mTOR","Compound: UCD-0159571<br>Pathway: Others","Compound: UCD-0159561<br>Pathway: 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+UCD-0000841,Neuronal Signaling,0.9120864131180358,0.9448852951147372,0.8958333333333334,0.010298273155416,1.98723559295969,True
+UCD-0159442,Epigenetics,0.9097251349105212,1.0181653087831728,0.7683531746031746,0.0259478741154133,1.5858982176908607,True
+UCD-0159476,Metabolism,0.0877858396880912,0.8908245977348397,0.6265873015873016,0.0547226053298927,1.2618332341413614,False
+UCD-0159484,Epigenetics,0.0733764747558531,1.0091534711518184,0.8301587301587301,0.0125152625152625,1.9025600363686452,False
+UCD-0159468,Apoptosis,0.7254765055040252,0.4724457844486683,0.5134259259259258,0.1624708624708624,0.7892245140867148,False
+UCD-0159460,Angiogenesis,0.4971101724767224,1.1696653152884449,0.3691942195231669,0.2538628883806549,0.5954007829762935,False
+UCD-0159475,Metabolism,,,,,,False
+UCD-0159445,Others,0.4422649218533427,0.016,,,,False
+UCD-0159515,Epigenetics,0.2402009341234867,0.5084054073294533,0.569047619047619,0.0780735509977387,1.1074960672902194,False
+UCD-0159507,Cytoskeletal Signaling,,,,,,False
+UCD-0159499,Cell Cycle,,,,,,False
+UCD-0159491,Others,,,,,,False
+UCD-0159483,Endocrinology & Hormones,0.6616583153816692,1.1266135539430664,0.4667522170811645,0.1661318032999449,0.7795472207963838,False
+UCD-0159453,TGF-beta/Smad,0.1526538803318474,0.994444092832442,0.2415865384615384,0.4546268211426582,0.3423449462383081,False
+UCD-0159452,Ubiquitin,,,,,,False
+UCD-0159467,Ubiquitin,8.587363474179593e-35,0.0047846889952153,,,,False
+UCD-0159459,Cytoskeletal Signaling,0.115730971056896,0.9565389954239708,0.6199074074074074,0.0547226053298927,1.2618332341413614,False
+UCD-0159500,Epigenetics,0.1114971014860605,0.8199504759772576,0.4602843915343915,0.1747028481722359,0.7577000146770427,False
+UCD-0159444,MAPK,0.2405286434093204,1.284090715896602,0.4112193362193362,0.2433448904037139,0.6137777683552145,False
+UCD-0159514,Others,0.046115240081593,0.9602461970547564,0.5564213564213565,0.0897892143443745,1.0467758285002375,False
+UCD-0159506,NF-κB,0.2848224827994561,0.7579140975137832,0.7822420634920635,0.0216841981547863,1.663856632698532,False
+UCD-0159498,PI3K/Akt/mTOR,,,,,,False
+UCD-0159490,Ubiquitin,,,,,,False
+UCD-0159482,Angiogenesis,,,,,,False
+UCD-0159474,DNA Damage,0.7071047643793658,1.0066459887387604,0.5204545454545454,0.1082488939631796,0.9655765323708264,False
+UCD-0159466,Cell Cycle,0.7838160160404646,0.7384918331832211,0.5588624338624338,0.0807436291307259,1.0928917345389368,False
+UCD-0159458,Others,0.6462563925226518,0.7613172059046268,0.5949735449735449,0.0677517970751053,1.1690791809044627,False
+UCD-0159451,Ubiquitin,0.060476359900646,0.1180913242009132,0.1964285714285714,0.3234714003944773,0.490164111285638,False
+UCD-0159443,Others,0.8877708811184992,0.8466576810447909,0.8291666666666666,0.0125152625152625,1.9025600363686452,False
+UCD-0159492,Cell Cycle,0.1644356913793432,0.9384449424455258,0.6777777777777778,0.0496199835406883,1.3043433841224432,False
+UCD-0000253,Neuronal Signaling,0.5946240388937591,0.8598445034002572,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159429,Stem Cells & Wnt,0.995343909338972,0.6582825377140081,0.6076388888888888,0.0677517970751053,1.1690791809044627,False
+UCD-0159372,Protein Tyrosine Kinase,0.3692744515747218,0.2785388127853881,,,,False
+UCD-0159336,Ubiquitin,,,,,,False
+UCD-0159343,GPCR & G Protein,,,,,,False
+UCD-0001863,Angiogenesis,0.9838102318424772,0.8028991346331802,0.5004629629629629,0.1228771228771228,0.9105289660399208,False
+UCD-0159353,Proteases,0.9382881009770744,0.7494209995633342,0.5996212121212121,0.0677517970751053,1.1690791809044627,False
+UCD-0159359,Others,0.8584731066902558,0.9032274853092804,0.600297619047619,0.0677517970751053,1.1690791809044627,False
+UCD-0159365,TGF-beta/Smad,0.4976365377659735,0.8459904998946741,0.7232142857142857,0.0417582417582417,1.3792577957042835,False
+UCD-0159380,GPCR & G Protein,0.8586081318562958,1.027931759144666,0.7958333333333333,0.0194832194832194,1.7103392770124284,False
+UCD-0159508,Epigenetics,0.0030796422187744,0.0053333333333333,,,,False
+UCD-0159388,NF-κB,,,,,,False
+UCD-0159337,Neuronal Signaling,0.5776538213078914,1.001412702903011,0.39375,0.2466165413533834,0.6079777972554067,False
+UCD-0001391,Metabolism,0.8553415739097523,0.9468343197122396,0.6583333333333333,0.0502759043531575,1.298640108656559,False
+UCD-0001856,Angiogenesis,0.3079330289733557,1.233266174550982,0.5184523809523809,0.1090038993264799,0.9625579660439308,False
+UCD-0159348,Cell Cycle,,,,,,False
+UCD-0159354,Metabolism,0.5316557091347441,0.9721254758261764,0.6801587301587301,0.0496199835406883,1.3043433841224432,False
+UCD-0159387,Neuronal Signaling,0.9888601510936008,1.003556840129724,0.5594907407407407,0.0807436291307259,1.0928917345389368,False
+UCD-0159379,PI3K/Akt/mTOR,0.8754297144543968,0.9334286270085804,0.5553751803751803,0.0951865036372078,1.021424625264752,False
+UCD-0159470,MAPK,0.2532297917335183,1.0132613860059076,0.4451388888888889,0.2100876651438449,0.6775994455341782,False
+UCD-0159462,GPCR & G Protein,0.1066284791036975,1.2264219393124394,0.5324494949494949,0.1024660957771325,0.9894198112195514,False
+UCD-0159455,TGF-beta/Smad,0.1162755305184334,0.9904307307580802,0.56627886002886,0.0780735509977387,1.1074960672902194,False
+UCD-0159447,Ubiquitin,0.0245696985817246,0.986317061561231,0.5747113997113997,0.0775671215231654,1.1103223247063967,False
+UCD-0159517,Proteases,0.3556983528548707,0.8859351277083105,0.6000945691735166,0.0677517970751053,1.1690791809044627,False
+UCD-0159509,Others,0.1105064651677384,0.5567522949495277,0.4379477004477004,0.2190516665654787,0.659453438320374,False
+UCD-0159501,Cell Cycle,0.0831581691666162,0.3564801205021873,0.5572180134680135,0.0897892143443745,1.0467758285002375,False
+UCD-0159493,Epigenetics,0.2501230845517162,0.6001035487118456,0.55,0.0997861199203481,1.0009298641357447,False
+UCD-0159485,Transmembrane Transporters,,,,,,False
+UCD-0159477,Metabolism,0.3505181801038599,0.1806461298908906,0.5926346801346801,0.0677517970751053,1.1690791809044627,False
+UCD-0159469,Protein Tyrosine Kinase,,,,,,False
+UCD-0159461,Angiogenesis,0.0137465545010212,0.0334928229665071,,,,False
+UCD-0159454,TGF-beta/Smad,,,,,,False
+UCD-0159446,Transmembrane Transporters,,,,,,False
+UCD-0159516,Proteases,0.2157215272494935,0.4979494366066144,0.5954365079365079,0.0677517970751053,1.1690791809044627,False
+UCD-0159434,Angiogenesis,0.7739503151986251,0.6365562476510445,0.7544642857142857,0.0394811374192817,1.4036103441533103,False
+UCD-0159409,Others,0.9694219925740604,0.7108481087701133,0.5829365079365079,0.0731519403106119,1.1357741499895764,False
+UCD-0159421,Apoptosis,0.9957429034524,0.5390400032501812,0.9458333333333332,0.0027641070585855,2.5584451400053156,False
+UCD-0159368,Angiogenesis,,,,,,False
+UCD-0000936,Neuronal Signaling,0.8197004228050055,0.8003798653656887,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001886,Cell Cycle,0.1310034045210222,0.3390756645287494,0.9791666666666666,0.0027641070585855,2.5584451400053156,False
+UCD-0159344,Endocrinology & Hormones,0.1515753714886064,0.988451713057792,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159350,Neuronal Signaling,0.8582978862472107,1.1182024272667703,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159355,PI3K/Akt/mTOR,0.2107990195917259,1.1429130695278813,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159361,Others,0.7799901547992171,1.109449202443305,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159375,NF-κB,0.2133249190454664,1.0724480139618882,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159413,NF-κB,0.9645868585284776,0.6086134928180569,0.5314484126984127,0.1047789419882443,0.9797259911809444,False
+UCD-0159383,NF-κB,0.0987475838067604,1.1624824120788508,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159333,Transmembrane Transporters,0.3224956414525506,1.1393725875876564,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001616,Metabolism,0.1443003860398355,0.9967789073251798,0.9791666666666666,0.0027641070585855,2.5584451400053156,False
+UCD-0159339,Neuronal Signaling,0.2258984666599824,0.6333202520186492,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159345,Neuronal Signaling,0.0575396072987055,0.9733589734483806,0.7756944444444445,0.0259478741154133,1.5858982176908607,False
+UCD-0159351,Others,0.1445887024207281,1.0824400266845071,0.6296717171717171,0.0525641025641025,1.279310745970745,False
+UCD-0159332,Apoptosis,0.707096083296747,0.8280850804786614,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159331,Transmembrane Transporters,0.6041716288466134,0.8174052072466181,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159326,Neuronal Signaling,0.5118059704721877,0.8563672131364555,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0000972,Neuronal Signaling,0.3452326552222375,0.7586210159714999,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001078,Metabolism,0.998150771582884,0.0134743167345617,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159624,GPCR & G Protein,0.4017343464968235,0.4232646736124715,0.2882055308525896,0.3460136151551465,0.4609068119863709,False
+UCD-0159616,GPCR & G Protein,,,,,,False
+UCD-0159608,Metabolism,0.0949448061696212,0.6650996150692838,0.3588755199049316,0.2646978021978021,0.5772496646575843,False
+UCD-0159601,Protein Tyrosine Kinase,,,,,,False
+UCD-0159661,Transmembrane Transporters,0.082913382632695,0.3646683012260114,0.2388970982720982,0.4657707157707157,0.3318278201787167,False
+UCD-0159653,Protein Tyrosine Kinase,0.2301094624405381,1.0031495357879472,0.3076326451326451,0.3071928071928071,0.5125889573678831,False
+UCD-0159647,GPCR & G Protein,0.1202703804354455,0.84076024472959,0.2441405816405816,0.4536144127980863,0.3433131549545768,False
+UCD-0159639,Protein Tyrosine Kinase,0.0971662156207788,0.7965353478163362,0.4497414622414622,0.1947289998137455,0.7105693668095472,False
+UCD-0159631,Cell Cycle,0.1357352139288619,1.0123996246807343,0.5359725922225922,0.0997861199203481,1.0009298641357447,False
+UCD-0159623,DNA Damage,0.1255203618660779,0.8168911067296436,0.2576215451215451,0.4258618094234532,0.3707313049845587,False
+UCD-0159615,Angiogenesis,0.1503390828995218,1.1454545454545455,,,,False
+UCD-0001957,Epigenetics,0.4916923283957767,0.5987000447483927,0.4331196581196581,0.2283438632212474,0.6414106556355311,False
+UCD-0001790B,Protein Tyrosine Kinase,,,,,,False
+UCD-0159362,Stem Cells & Wnt,0.1074691777852134,1.0634158415904598,0.6158267400527462,0.0563186813186813,1.2493475225933015,False
+UCD-0159369,Epigenetics,0.7193869358956596,1.1901844259165066,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001861,Angiogenesis,0.3994782126343048,0.535007226099393,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159366,Metabolism,0.3067650037230786,1.0254653745292008,0.3733886483886484,0.2522159774068171,0.5982274051545786,False
+UCD-0159371,Apoptosis,0.830916340110495,0.5687393301430047,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159364,Others,0.2375226625340119,0.9208613371733364,0.6746031746031745,0.0496199835406883,1.3043433841224432,False
+UCD-0159358,Metabolism,0.3120302506939457,0.9379180591896076,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159352,Proteases,0.4195018291082867,0.566735638557051,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001885,PI3K/Akt/mTOR,0.9301004606072608,0.3378351493410846,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159342,Others,0.2845006186505917,0.8223263196764256,0.9305555555555556,0.0027641070585855,2.5584451400053156,False
+UCD-0018448,GPCR & G Protein,0.4272588677130382,0.8374216826768619,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159335,JAK/STAT,0.7371280798900858,0.1274459398155148,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159386,DNA Damage,0.207756166971036,0.9702237844191192,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159378,Protein Tyrosine Kinase,0.2244738070748783,0.7198983050933754,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159370,PI3K/Akt/mTOR,0.5563156217563376,0.8948715251206556,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0011696,JAK/STAT,0.299116416015747,0.9266479586173748,0.6649305555555556,0.0502759043531575,1.298640108656559,False
+UCD-0159357,Apoptosis,0.8905682263066627,0.0161652786412098,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159347,Metabolism,0.7947990872021455,0.8715896521289308,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159376,Transmembrane Transporters,0.0342805454119404,1.1086639340631477,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159341,Metabolism,0.1452259676928746,0.9243969382824668,0.3557359307359307,0.2646978021978021,0.5772496646575843,False
+UCD-0001186,Neuronal Signaling,0.0875336988743265,0.907091708200509,0.6030753968253968,0.0677517970751053,1.1690791809044627,False
+UCD-0000684,Others,0.2231856957924329,0.95642951326359,0.6372204184704184,0.0525641025641025,1.279310745970745,False
+UCD-0159385,Metabolism,0.1709544463629141,0.8407938940032922,0.6248704998704998,0.0547226053298927,1.2618332341413614,False
+UCD-0159377,Metabolism,0.6168237291783326,0.8098206165694446,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0018540,MAPK,0.4476487048868842,1.0051265617513916,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159363,Others,,,,,,False
+UCD-0159356,Cytoskeletal Signaling,0.1029720657719452,0.8559263784322382,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001900,PI3K/Akt/mTOR,0.1921581419983801,1.0871040366108793,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159346,Stem Cells & Wnt,0.8558897423896336,1.0691317129406877,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159340,Others,,,,,,False
+UCD-0159338,Neuronal Signaling,0.1298324978557497,1.063722358292334,0.5144841269841269,0.1126373626373626,0.9483175269293204,False
+UCD-0159334,Metabolism,0.81718253393268,0.4424773460447511,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159384,NF-κB,,,,,,False
+UCD-0000201,GPCR & G Protein,0.3112509121541552,0.6892063416036018,0.4674603174603174,0.1661318032999449,0.7795472207963838,False
+UCD-0001864,Angiogenesis,0.1057810567027074,0.0100755667506297,,,,False
+UCD-0159373,Angiogenesis,0.2468651731016476,0.331562771734781,0.5095238095238095,0.1176594106530412,0.9293733312908564,False
+UCD-0159478,Cell Cycle,0.9488829161262836,0.5638510671036133,0.5578703703703703,0.1565934065934066,0.8052265279765646,False
+UCD-0159449,Ubiquitin,0.7567513384716827,0.6645188559312732,0.4763979076479076,0.155694458446752,0.8077268447782245,False
+UCD-0159519,Stem Cells & Wnt,0.0924146473429188,0.0441806922832645,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159511,Autophagy,0.696184158663806,0.8495461227151326,0.3989357864357864,0.2466165413533834,0.6079777972554067,False
+UCD-0159503,Cytoskeletal Signaling,0.7328263309942077,0.6755749124118284,0.5402777777777777,0.0997861199203481,1.0009298641357447,False
+UCD-0159495,Apoptosis,0.7851964936049428,0.6071033425967385,0.6898989898989898,0.0489284471184923,1.3104385675748629,False
+UCD-0159479,Transmembrane Transporters,0.7824579668009641,0.7496404295264902,0.7090277777777777,0.0448414145096609,1.3483206961651315,False
+UCD-0159471,Proteases,0.7559995229342313,0.9754479634606494,0.7013888888888888,0.0448414145096609,1.3483206961651315,False
+UCD-0159463,GPCR & G Protein,,,,,,False
+UCD-0159456,Apoptosis,0.8001577500686391,0.9733710602453284,0.6833333333333332,0.0489284471184923,1.3104385675748629,False
+UCD-0159448,Ubiquitin,,,,,,False
+UCD-0159518,Proteases,0.999850919616445,0.5464637196496336,0.7458333333333333,0.040347711989503,1.394181087918541,False
+UCD-0159510,Autophagy,0.9996290987429646,0.6920272319772511,0.7458333333333333,0.040347711989503,1.394181087918541,False
+UCD-0159502,MAPK,,,,,,False
+UCD-0159494,Protein Tyrosine Kinase,,,,,,False
+UCD-0000918,Proteases,0.1711680900630381,0.8032050336365395,0.6834415584415584,0.0489284471184923,1.3104385675748629,False
+UCD-0159464,Metabolism,0.5300378457594247,1.1914385825023337,0.6201388888888889,0.0547226053298927,1.2618332341413614,False
+UCD-0159397,Others,0.2050837895679669,0.7235904551841102,0.4399801587301587,0.2189821351832525,0.6595913139626328,False
+UCD-0159441,Metabolism,0.2090019251418315,0.7959814380225703,0.5102362914862915,0.1176594106530412,0.9293733312908564,False
+UCD-0001925,Epigenetics,0.5776960040026369,0.5489326148459673,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159428,Apoptosis,0.1437124401386361,0.7218280448111476,0.7134920634920635,0.0448414145096609,1.3483206961651315,False
+UCD-0159420,Angiogenesis,0.0151880097011894,0.6222103376460448,0.6073006854256854,0.0677517970751053,1.1690791809044627,False
+UCD-0159412,Apoptosis,3.261739866158687e-07,0.0048472075869336,0.2954545454545454,0.2258919163028752,0.6460993103274033,False
+UCD-0001098,Neuronal Signaling,0.1609263281689471,0.9500420131234888,0.5814574314574315,0.0731519403106119,1.1357741499895764,False
+UCD-0159664,Epigenetics,0.1868204396922776,0.9358576293743522,0.4025974025974025,0.2466165413533834,0.6079777972554067,False
+UCD-0159405,Metabolism,0.1950792756188837,0.915507436681944,0.1893442205942206,0.6608058608058608,0.1799261138348331,False
+UCD-0159401,Angiogenesis,0.1765220483603227,0.9931039634801144,0.7638888888888888,0.0194832194832194,1.7103392770124284,False
+UCD-0159396,DNA Damage,0.4663345887968183,0.9965748682522502,0.8982142857142857,0.010298273155416,1.98723559295969,False
+UCD-0159440,Protein Tyrosine Kinase,,,,,,False
+UCD-0001966,Others,,,,,,False
+UCD-0159427,Apoptosis,,,,,,False
+UCD-0001927,Epigenetics,0.967565070822416,0.9018493186901436,0.6602272727272727,0.0502759043531575,1.298640108656559,False
+UCD-0159472,TGF-beta/Smad,0.2527974770117688,0.6883360009940249,0.9097222222222222,0.010298273155416,1.98723559295969,False
+UCD-0018183,Proteases,0.1993284698511397,0.9920321194399632,0.4518849206349206,0.1868756243756243,0.728447343295052,False
+UCD-0159522,Proteases,0.3004574739023935,0.7356603498826765,0.7652777777777778,0.0303478904515174,1.5178714923022951,False
+UCD-0159553,PI3K/Akt/mTOR,0.3032113659611647,0.1615576580286638,0.7343253968253969,0.040347711989503,1.394181087918541,False
+UCD-0159546,Ubiquitin,0.2349187798815774,1.0300586865683825,0.7775974025974026,0.0259478741154133,1.5858982176908607,False
+UCD-0159538,MAPK,0.0859138950255536,0.5136505699997693,0.9583333333333334,0.0027641070585855,2.5584451400053156,False
+UCD-0159530,Proteases,0.1401155173061275,0.8394122018467577,0.5431394993894993,0.0997861199203481,1.0009298641357447,False
+UCD-0159523,Proteases,0.2007547536520756,0.7408832128019696,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159591,Epigenetics,0.0802360709306444,1.0448494458110342,0.5343434343434343,0.0997861199203481,1.0009298641357447,False
+UCD-0159584,Cell Cycle,0.2857008523559608,1.08526622468238,0.5692460317460317,0.0780735509977387,1.1074960672902194,False
+UCD-0159576,Others,0.4822885958141235,0.969349136345644,0.6268939393939393,0.0547226053298927,1.2618332341413614,False
+UCD-0159568,Others,0.17357012635093,1.102014698616265,0.6472222222222223,0.0502759043531575,1.298640108656559,False
+UCD-0159560,DNA Damage,0.1891471661822094,0.515959383505048,0.6214285714285714,0.0547226053298927,1.2618332341413614,False
+UCD-0159552,Protein Tyrosine Kinase,0.1978008338756756,1.005948576896985,0.3021990740740741,0.3178460494374984,0.497783182119814,False
+UCD-0159545,Autophagy,0.1487361201176338,0.8646663360863673,0.3380189255189255,0.2782805429864253,0.5555171579096939,False
+UCD-0159537,Others,,,,,,False
+UCD-0159529,Cell Cycle,0.1919606529470446,0.0736886416786434,0.6620370370370371,0.0612747252747252,1.2127186272311403,False
+UCD-0159521,Others,0.1176449390961468,0.7249770083508198,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159480,Transmembrane Transporters,0.6370938354967194,0.4397214956147099,0.5,0.1060116354234001,0.9746464656516696,False
+UCD-0159513,Others,0.4182497927150356,0.8722134854632098,0.8625,0.010298273155416,1.98723559295969,False
+UCD-0159505,Others,0.2524582032066113,0.9898711405555816,0.4638888888888888,0.1725788831051989,0.7630123460017288,False
+UCD-0159497,PI3K/Akt/mTOR,0.2701731619084627,0.9535298215229296,0.6472222222222221,0.0502759043531575,1.298640108656559,False
+UCD-0159489,Epigenetics,,,,,,False
+UCD-0159481,Cell Cycle,0.5356686987200453,0.7874119648496654,0.6727272727272726,0.0496199835406883,1.3043433841224432,False
+UCD-0159473,Epigenetics,0.623138299821281,1.0099001611204628,0.9097222222222222,0.010298273155416,1.98723559295969,False
+UCD-0159465,Metabolism,0.4648778528854145,0.951824911555304,0.625,0.0547226053298927,1.2618332341413614,False
+UCD-0159457,Proteases,0.5137704467241636,1.121074774517766,0.6132034632034632,0.0579019374200097,1.2373069043771885,False
+UCD-0159450,DNA Damage,,,,,,False
+UCD-0159520,NF-κB,0.9999999209801378,0.0206185567010309,,,,False
+UCD-0159512,Others,,,,,,False
+UCD-0159504,Others,0.6713424582323,0.1285814293306444,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159496,Others,0.379781840834139,1.0243258649619829,0.4364621489621489,0.2209305845669482,0.6557441583285767,False
+UCD-0159488,Epigenetics,0.5477703348911679,0.3493711078426991,0.9305555555555556,0.0027641070585855,2.5584451400053156,False
+UCD-0159419,Protein Tyrosine Kinase,0.045820367758905,0.0231673690234639,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0011250,Endocrinology & Hormones,0.2435910012813165,1.0933428291160951,0.5510010822510822,0.0997861199203481,1.0009298641357447,False
+UCD-0159381,Neuronal Signaling,0.2268558073131864,0.6807859754731196,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159408,Neuronal Signaling,0.2231532086041569,1.0635805812187535,0.3280844155844156,0.2843273231622746,0.5461814036636747,False
+UCD-0159431,PI3K/Akt/mTOR,0.0588404533046629,0.4300192138684383,0.681060606060606,0.0489284471184923,1.3104385675748629,False
+UCD-0159423,PI3K/Akt/mTOR,0.0397510319719553,0.882781233911069,0.6643939393939393,0.0502759043531575,1.298640108656559,False
+UCD-0159415,Ubiquitin,,,,,,False
+UCD-0159410,Neuronal Signaling,0.1734062652669647,0.8176983597544698,0.7729166666666667,0.0259478741154133,1.5858982176908607,False
+UCD-0011462,Endocrinology & Hormones,,,,,,False
+UCD-0159407,NF-κB,0.1821506863231377,1.0815961372268736,0.4396762959262959,0.2190516665654787,0.659453438320374,False
+UCD-0159402,GPCR & G Protein,0.1014849335408048,0.7819939327160653,0.5940656565656566,0.0677517970751053,1.1690791809044627,False
+UCD-0159398,Protein Tyrosine Kinase,0.173032348669187,1.1251785893965254,0.693560606060606,0.0489284471184923,1.3104385675748629,False
+UCD-0159391,Protein Tyrosine Kinase,0.1197814783822208,1.2124649989716758,0.3946428571428571,0.2466165413533834,0.6079777972554067,False
+UCD-0159435,Protein Tyrosine Kinase,4.651425286072529e-05,0.011887098443808,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159430,Proteases,,,,,,False
+UCD-0159422,Apoptosis,0.4074767662326746,0.9060277510609194,0.5069444444444444,0.1208097895795056,0.9178978721048168,False
+UCD-0159414,DNA Damage,0.093274199097688,0.6560363396997744,0.4060185185185185,0.2450989010989011,0.6106586359031783,False
+UCD-0017582,Neuronal Signaling,0.2391929733379879,0.902100453303181,0.3013736263736263,0.3178460494374984,0.497783182119814,False
+UCD-0000899,Neuronal Signaling,,,,,,False
+UCD-0159392,Angiogenesis,0.9618056496327428,0.5009260704437855,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0018458,Proteases,0.2813283756268461,0.9134207996091644,0.5731150793650793,0.0775671215231654,1.1103223247063967,False
+UCD-0022022,Others,0.1250864274805549,0.9460571974492732,0.4376082251082251,0.2190516665654787,0.659453438320374,False
+UCD-0159390,Neuronal Signaling,0.3022670502884608,0.9375341295969252,0.6713383838383837,0.0496199835406883,1.3043433841224432,False
+UCD-0159389,Protein Tyrosine Kinase,0.3590864833906316,0.7456855021725108,0.5037698412698413,0.1228771228771228,0.9105289660399208,False
+UCD-0159382,Neuronal Signaling,0.3128053942337871,1.0594754631963277,0.3501230251230251,0.269655531500918,0.5691906665099344,False
+UCD-0159374,Endocrinology & Hormones,0.1781407824759115,0.8070906480284608,0.3763888888888889,0.2522159774068171,0.5982274051545786,False
+UCD-0159367,Neuronal Signaling,0.6417548682864812,0.9492801476455638,0.5871212121212122,0.072560398403095,1.1393003413863467,False
+UCD-0159360,Metabolism,0.2427118484804808,0.921996033954084,0.3622023809523809,0.2589233551258867,0.5868287740346956,False
+UCD-0001567,Transmembrane Transporters,0.1510252306930224,0.1998723301473379,0.1833333333333333,0.3234714003944773,0.490164111285638,False
+UCD-0159349,Metabolism,0.8207345891205396,0.5003480736293829,0.4796296296296296,0.147401980735314,0.8314966805432,False
+UCD-0001888,PI3K/Akt/mTOR,0.0908448856377737,0.7869841002955156,0.4232142857142857,0.238095238095238,0.6232492903979004,False
+UCD-0001870,Angiogenesis,1.9258863219054208e-19,0.0048076923076923,,,,False
+UCD-0000737,GPCR & G Protein,0.6681881279075255,1.1433540984351214,0.3893939393939394,0.2493902963876854,0.6031204486412295,False
+UCD-0159660,Epigenetics,,,,,,False
+UCD-0159436,Stem Cells & Wnt,0.4204380524126,0.8187474643784382,0.3499098124098124,0.2712262861516593,0.566668222705397,False
+UCD-0159399,Apoptosis,0.8834793005955134,0.1646186070743115,0.5944444444444444,0.0964333911702332,1.0157725605991013,False
+UCD-0011098,Neuronal Signaling,0.3290398152543918,0.8982901450923371,0.7482142857142857,0.040347711989503,1.394181087918541,False
+UCD-0159433,Protein Tyrosine Kinase,0.735386106362139,1.0021025228761418,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159404,Metabolism,0.0906359116858516,0.81766228476943,0.4472402597402597,0.2056028478563689,0.6869708741138213,False
+UCD-0018456,GPCR & G Protein,,,,,,False
+UCD-0159395,GPCR & G Protein,0.1745689019658874,1.018545259347913,0.9791666666666666,0.0027641070585855,2.5584451400053156,False
+UCD-0159439,PI3K/Akt/mTOR,0.7538769312354222,0.3431507616828252,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001924,Epigenetics,0.1492955451942889,0.4628583320326725,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159426,DNA Damage,0.2469218060153983,1.0252433057911228,0.6384920634920634,0.0525641025641025,1.279310745970745,False
+UCD-0159418,Transmembrane Transporters,0.2775686420570803,0.9535151199274984,0.5387896825396825,0.0997861199203481,1.0009298641357447,False
+UCD-0001657,GPCR & G Protein,0.6541809619718262,0.8265458606396707,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001002,Neuronal Signaling,0.6491241414618054,0.9827546996614006,0.6067550505050504,0.0677517970751053,1.1690791809044627,False
+UCD-0000377,Transmembrane Transporters,0.6119232767921465,1.214399432720041,0.9125,0.010298273155416,1.98723559295969,False
+UCD-0001653,Transmembrane Transporters,0.5347552050278691,1.0796966859164456,0.5796717171717172,0.0731519403106119,1.1357741499895764,False
+UCD-0018410,Metabolism,0.4117358611600622,1.0548714226279006,0.5282828282828282,0.1047789419882443,0.9797259911809444,False
+UCD-0159394,TGF-beta/Smad,0.4210312496973564,0.979371646823984,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159438,Cytoskeletal Signaling,0.0416538473105436,0.0706149354008822,0.4553751803751804,0.1835571835571835,0.7362286146021204,False
+UCD-0159425,Apoptosis,0.3701285121049578,0.1359792107724057,0.8194444444444443,0.0194832194832194,1.7103392770124284,False
+UCD-0159403,GPCR & G Protein,0.9792588486103536,0.7503744340481129,0.6926767676767677,0.0489284471184923,1.3104385675748629,False
+UCD-0159417,GPCR & G Protein,0.0785471092681215,0.0422408994728226,0.6027777777777777,0.0910685059621229,1.040631788248228,False
+UCD-0159411,Neuronal Signaling,0.5186944136725086,0.9049361294715798,0.8472222222222222,0.010298273155416,1.98723559295969,False
+UCD-0000463,Others,0.3662311435457167,0.683796368555226,0.9791666666666666,0.0027641070585855,2.5584451400053156,False
+UCD-0011606,Neuronal Signaling,,,,,,False
+UCD-0001059,Epigenetics,0.7673013239355088,0.9749030534908992,0.6902777777777778,0.0489284471184923,1.3104385675748629,False
+UCD-0159400,Apoptosis,,,,,,False
+UCD-0159393,GPCR & G Protein,0.4949897649555515,0.9297893110522064,0.8194444444444444,0.0125152625152625,1.9025600363686452,False
+UCD-0159437,Cytoskeletal Signaling,0.4407808016429366,0.0157059238147773,0.3214285714285714,0.2258919163028752,0.6460993103274033,False
+UCD-0159432,Angiogenesis,0.8501228265303685,1.0397265975499117,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159424,JAK/STAT,,,,,,False
+UCD-0159416,Metabolism,0.6810796832403426,0.7002038240905832,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159411B,Neuronal Signaling,0.9967335355110148,0.6292671307161024,0.7241161616161615,0.0417582417582417,1.3792577957042835,False
+UCD-0000243,Neuronal Signaling,0.9970142928908112,0.6145394792061216,0.7244075369075369,0.0417582417582417,1.3792577957042835,False
+UCD-0000904,GPCR & G Protein,0.9904920142342092,0.7288913754488651,0.726010101010101,0.040347711989503,1.394181087918541,False
+UCD-0159600,Epigenetics,0.4678388700751813,0.0562112992492739,0.4253968253968254,0.1781242013800153,0.7492770698026706,False
+UCD-0159646,GPCR & G Protein,0.0385633518606271,0.5750645884224563,0.3491395042865631,0.2721140245892721,0.5652490743732337,False
+UCD-0159652,Proteases,0.115872604394342,0.915992416407954,0.295607389686337,0.3234714003944773,0.490164111285638,False
+UCD-0159265,Neuronal Signaling,0.8853473115604323,0.0386010824956878,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001800,Others,0.9517017762986468,0.0046322335537137,0.1833333333333333,0.3234714003944773,0.490164111285638,False
+UCD-0001613,Endocrinology & Hormones,0.1947303610275635,1.0273629429831357,0.285607884833891,0.3504273504273504,0.4554020050264261,False
+UCD-0159290,PI3K/Akt/mTOR,0.9991458343659912,0.0306905370843989,,,,False
+UCD-0159293,Others,0.3610649911292491,0.9590957205468892,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001808,MAPK,0.7956648483544341,0.2072540970670695,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001801,Endocrinology & Hormones,0.8830069306838656,0.3357721240102641,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159271,Others,0.7172584984497103,0.5943158698420958,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0018516,Neuronal Signaling,0.090999361179682,1.000860698016794,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001835,Others,0.9008196497038196,0.0117295100474147,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159280,Others,0.1611929892541435,0.9535795809876662,0.3096639471639471,0.3068490262734867,0.513075250528078,False
+UCD-0159286,Others,0.9861928124716628,0.0051150895140664,,,,False
+UCD-0018131,Angiogenesis,0.3514311302432795,0.7608150825311834,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001024,Neuronal Signaling,0.1995517749134645,0.9476849321651464,0.2406740007068954,0.4597028253913129,0.3375228270130456,False
+UCD-0001829,PI3K/Akt/mTOR,0.4446428774280667,0.8684117610854121,0.9791666666666666,0.0027641070585855,2.5584451400053156,False
+UCD-0159275,Others,0.5208513752450697,0.6847856478180632,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159270,Others,0.1555441040254247,0.935323155927072,0.2976298920377868,0.3213634706525701,0.4930034909691886,False
+UCD-0001844,Others,0.341665444027787,0.3605897116520831,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0018207,Neuronal Signaling,0.959112466107337,0.4230132217800714,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001804,MAPK,0.8000518372851414,0.800418522048852,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001016,Neuronal Signaling,0.3204658166472228,1.0041043837859105,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0018179,MAPK,0.7722817436817935,1.081069868920455,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159289,Metabolism,0.3421496761178707,0.9714444535077336,0.2956114243943191,0.3234714003944773,0.490164111285638,False
+UCD-0159285,DNA Damage,0.6973106943109262,0.9640980612252382,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001792,Others,,,,,,False
+UCD-0159274,Others,0.7415506977102388,1.140760246495704,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001775,Others,0.2239777455485181,1.0428789048374103,0.2953203809434459,0.3234714003944773,0.490164111285638,False
+UCD-0159264,Neuronal Signaling,0.7590078453665071,0.0064749589887098,0.1833333333333333,0.3234714003944773,0.490164111285638,False
+UCD-0159261,Others,0.9620510413323956,0.7556821479444478,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159258,Angiogenesis,0.7088779000536966,0.786427221686658,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001014,Neuronal Signaling,0.2251117892251856,1.048722362762307,0.2461624460850467,0.4474775224775225,0.3492287751171237,False
+UCD-0000721,Neuronal Signaling,0.9791875134002924,0.0473318537103155,0.75,0.1060116354234001,0.9746464656516696,False
+UCD-0159259,PI3K/Akt/mTOR,0.3668101178577665,0.728007989182217,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001842,Neuronal Signaling,0.516152659583598,0.5822112101888113,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159266,Others,0.323264062213351,0.8993487083763417,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159260,Epigenetics,,,,,,False
+UCD-0001580,Transmembrane Transporters,0.999788669470584,0.0055401662049861,,,,False
+UCD-0000295,Metabolism,0.3147878845667231,0.4653199577519383,0.570085572107631,0.0780735509977387,1.1074960672902194,False
+UCD-0000533,GPCR & G Protein,0.482389611052921,1.1191541703199115,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001759,Angiogenesis,0.7154126099122802,0.2516673969279632,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001822,MAPK,0.8302009226338327,0.947657588738276,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0018083,JAK/STAT,0.5148235466336772,1.0500878255776307,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159295,Microbiology,0.2902485232461362,1.0432415634104195,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001101,Neuronal Signaling,0.2984836293322831,0.2736417135037377,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0000786,GPCR & G Protein,0.2020556721022602,0.995043021809751,0.9458333333333332,0.0027641070585855,2.5584451400053156,False
+UCD-0001086,Endocrinology & Hormones,0.1734139278810905,1.1885941773302804,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001733,Protein Tyrosine Kinase,0.5173475068591279,0.990318946518031,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159278,Others,0.1761620163358435,0.964671010458066,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159272,Others,,,,,,False
+UCD-0001850,Others,0.8458110514102297,1.0694063830468212,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159294,Cell Cycle,0.3171704895641372,0.7484801534287425,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159276,Others,0.8834500967818433,0.0550847457627118,,,,False
+UCD-0159292,DNA Damage,0.5814132865140365,0.3537196850944343,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0018089,Endocrinology & Hormones,0.2137083198706506,0.972823760375222,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159288,Metabolism,0.2480790089975495,1.1224870284973625,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159282,Others,0.9801621331989386,0.2329727748501487,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159277,Others,0.99999999998504,0.0042372881355932,,,,False
+UCD-0001802,Others,0.39345760062185,0.7496995075412987,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159267,Others,,,,,,False
+UCD-0001816,Metabolism,0.5713430899206853,0.9855266216601092,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0017947,Apoptosis,0.3739264964894275,1.1024100245181483,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0000421,Neuronal Signaling,0.5002855172567953,1.0650109682244255,0.7895833333333333,0.0216841981547863,1.663856632698532,False
+UCD-0159291,GPCR & G Protein,0.7018622877690617,0.5625594695961273,0.9791666666666666,0.0027641070585855,2.5584451400053156,False
+UCD-0000849,Endocrinology & Hormones,0.8026851952139509,0.8043918259305797,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159287,Endocrinology & Hormones,0.8622146451038913,0.8369244193543168,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159281,Others,,,,,,False
+UCD-0017999,Endocrinology & Hormones,0.1081541169288452,0.9744235545839768,0.6715277777777777,0.0496199835406883,1.3043433841224432,False
+UCD-0159284,Stem Cells & Wnt,0.6568851913656727,0.6787086602168512,0.9458333333333332,0.0027641070585855,2.5584451400053156,False
+UCD-0017907,Others,,,,,,False
+UCD-0159305,Transmembrane Transporters,,,,,,False
+UCD-0018483,Microbiology,0.246712396824714,1.0456860834675288,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0018240,PI3K/Akt/mTOR,0.0475917753385771,1.0849625469683453,0.3902046783625731,0.2493902963876854,0.6031204486412295,False
+UCD-0159313,Protein Tyrosine Kinase,0.6827815038255973,0.8429492317079293,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0013217,Epigenetics,0.5375321202509717,1.0650110310444834,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001790,Cell Cycle,0.600428152538604,0.4586666666666666,,,,False
+UCD-0159300,GPCR & G Protein,0.1913400670478902,0.9561959482682052,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159329,JAK/STAT,0.6211807492889702,0.009685230024213,,,,False
+UCD-0159323,Stem Cells & Wnt,0.7804986612355564,0.2214342404547578,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0018550,Neuronal Signaling,0.0703525892950604,1.1030051283833622,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001435,Others,0.0327178022537734,0.9181872419878664,0.5938341750841751,0.0677517970751053,1.1690791809044627,False
+UCD-0018439,Microbiology,0.412826334651422,0.9027150213747004,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0000490,Neuronal Signaling,0.3493816639839271,0.9975486424072508,0.8013888888888889,0.0194832194832194,1.7103392770124284,False
+UCD-0001843,DNA Damage,0.3039498642037249,1.035884211684836,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159309,Angiogenesis,0.3205160675180151,0.8933921494800174,0.9333333333333332,0.0027641070585855,2.5584451400053156,False
+UCD-0159299,Metabolism,0.099855744011836,0.6425980803318002,0.4871658312447786,0.1427206334038632,0.8455132355352715,False
+UCD-0159320,Metabolism,0.0510223226695849,0.9864005390981304,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001784,Cell Cycle,0.9927723507860328,0.0053333333333333,,,,False
+UCD-0159322,Endocrinology & Hormones,0.4840208081331043,0.8755103648952891,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001441,Neuronal Signaling,0.1169845113350006,0.9017644013732328,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0018311,Neuronal Signaling,0.1431301664038993,0.9255567443261468,0.6343344155844156,0.0525641025641025,1.279310745970745,False
+UCD-0001305,Neuronal Signaling,0.2480633002842016,0.7220468042216481,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001162,Endocrinology & Hormones,0.0665743773763126,0.8958837772397095,,,,False
+UCD-0018012,Apoptosis,0.6257003314787246,0.8347584369243601,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159308,Protein Tyrosine Kinase,0.6896773445900586,0.0769393939393939,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159304,Cell Cycle,0.8505142682539163,0.7554993975694243,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159298,Cytoskeletal Signaling,,,,,,False
+UCD-0159328,Proteases,0.9999966473643804,0.0048426150121065,,,,False
+UCD-0018569,Endocrinology & Hormones,,,,,,False
+UCD-0000730,Metabolism,0.9841768858708694,0.3896266880606756,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159319,Neuronal Signaling,0.8377864176403528,0.715468268127947,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0000101,Endocrinology & Hormones,0.2837468661937914,0.4123131306695089,0.9047619047619048,0.010298273155416,1.98723559295969,False
+UCD-0159324,Angiogenesis,0.0601550651708414,0.9253509080868582,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159273,Others,0.2880922397761926,1.0149888749370464,0.4638220439691028,0.1725788831051989,0.7630123460017288,False
+UCD-0000324,Metabolism,0.4134672480844652,0.895286955833343,0.8138888888888889,0.0125152625152625,1.9025600363686452,False
+UCD-0159269,Others,0.1596844488745538,1.0257420741430665,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159263,Others,0.6125148063987246,0.9235422988178056,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001810,DNA Damage,0.6762340910193436,0.4196401414179164,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159257,DNA Damage,0.2816213596798692,1.012341194607443,0.8506944444444444,0.010298273155416,1.98723559295969,False
+UCD-0000450,Neuronal Signaling,0.2643448879688498,1.0556281163194747,0.3403505700525824,0.2782805429864253,0.5555171579096939,False
+UCD-0018091,GPCR & G Protein,,,,,,False
+UCD-0001040,Endocrinology & Hormones,0.1233737160832696,0.8715318565357679,0.7141955266955267,0.0448414145096609,1.3483206961651315,False
+UCD-0159283,Endocrinology & Hormones,0.1408612464842747,0.9223546902877104,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159279,Others,0.955978742561846,0.0744218129964143,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001783,Others,,,,,,False
+UCD-0001915,Others,0.9822994620532044,0.0732137516247519,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159262,Others,0.9210143194395556,0.0470000052042762,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001766,Angiogenesis,0.9602947371687784,0.1300196704150546,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159256,Apoptosis,0.1990807719005992,1.0374301115958562,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0000576,Neuronal Signaling,0.2820384295989524,0.8448805449679669,0.4788558405818467,0.148334742180896,0.8287571188950228,False
+UCD-0001869,Protein Tyrosine Kinase,0.5475214936670724,1.1626191306463385,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159315,PI3K/Akt/mTOR,0.9376993035376228,0.7053375851029732,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159311,GPCR & G Protein,0.3522832318962169,0.8189625546918142,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159306,Cell Cycle,0.31495478551464,0.6372277670292835,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159301,Cell Cycle,0.8475448706407331,0.8576703720008805,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159330,Protein Tyrosine Kinase,0.0445911706059698,0.91881646141548,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159325,PI3K/Akt/mTOR,0.3997986029102429,0.0079374373272678,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159321,Neuronal Signaling,0.0571773684622088,0.8780641839575827,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0000469,Neuronal Signaling,0.1322445233541976,0.7709321937354944,0.9666666666666668,0.0027641070585855,2.5584451400053156,False
+UCD-0018527,Microbiology,0.3193425511785571,0.9191603179752156,0.5176587301587301,0.1090038993264799,0.9625579660439308,False
+UCD-0000083,Neuronal Signaling,0.1143898861207962,0.5328925626856046,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159314,PI3K/Akt/mTOR,0.0426860341417827,0.297030303030303,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159310,GPCR & G Protein,0.036673014600247,1.0082525960509905,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0018051,Metabolism,0.1135627561335531,0.9064730489552076,0.6001984126984127,0.0677517970751053,1.1690791809044627,False
+UCD-0001818,MAPK,0.7247991819526381,0.9613096956567072,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159317,Angiogenesis,0.3689340441278899,0.8240821761353613,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0018304,Endocrinology & Hormones,0.2317868777738025,0.899742950766353,0.4813291910331383,0.1464634436770659,0.834270758862523,False
+UCD-0000872,Metabolism,0.9998329973107044,0.0444405736992712,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159627,Metabolism,0.4034059482776486,1.0198715094152355,0.4624458874458875,0.1661318032999449,0.7795472207963838,False
+UCD-0159642,Proteases,0.2685070746572331,0.616151348042983,0.2005529193029193,0.6131084950684059,0.212462666362127,False
+UCD-0159648,GPCR & G Protein,0.1440825910414699,0.8752303622233913,0.4537906537906538,0.1835571835571835,0.7362286146021204,False
+UCD-0159656,GPCR & G Protein,,,,,,False
+UCD-0159604,Autophagy,0.0568460987754614,0.4451532111227929,0.4873737373737373,0.1661318032999449,0.7795472207963838,False
+UCD-0159611,Others,0.2535964338700853,0.6815463435552156,0.3585137085137085,0.2646978021978021,0.5772496646575843,False
+UCD-0159619,GPCR & G Protein,0.1135792312607198,0.5567567567567567,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159635,MAPK,0.144893831877079,0.1089743589743589,,,,False
+UCD-0159327,DNA Damage,0.268773373212108,0.8012825600599962,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159643,Metabolism,0.2060561659961003,1.131320055797242,0.2914890664890665,0.3352926143623818,0.4745760128610387,False
+UCD-0159649,Angiogenesis,0.0512070804207494,1.0020751496353524,0.3489024864024864,0.2721140245892721,0.5652490743732337,False
+UCD-0159657,Others,0.1460334579277885,1.1669060561487683,0.3190656565656565,0.2938365981844243,0.5318941125632697,False
+TGFRi,,,,,,,False
+UCD-0159253,Epigenetics,0.914026139164668,1.1341356509184188,0.1840557114454173,0.6763236763236763,0.1698454087941743,False
+UCD-0159596,Cytoskeletal Signaling,0.921113942075839,0.0823214305409881,0.393995574142633,0.2466165413533834,0.6079777972554067,False
+UCD-0159634,Cytoskeletal Signaling,0.0539543937049463,0.0209286209286209,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159626,Neuronal Signaling,0.1291394709828155,0.6281439965850613,0.4873737373737373,0.1661318032999449,0.7795472207963838,False
+UCD-0159618,MAPK,0.2014347828924801,1.0317699158161768,0.3837569837569837,0.2522159774068171,0.5982274051545786,False
+UCD-0159610,Transmembrane Transporters,0.456898440531938,1.1964238002260816,0.2626984126984127,0.3050708327816761,0.515599312408371,False
+UCD-0159603,GPCR & G Protein,0.153589175284165,0.900569903041386,0.3361319236319236,0.2839228897623058,0.5467995933961755,False
+UCD-0159663,Protein Tyrosine Kinase,0.4177476464050575,0.9621217773784314,0.4537906537906538,0.1835571835571835,0.7362286146021204,False
+UCD-0159655,Epigenetics,0.2090503671003534,0.7598678282455216,0.4537906537906538,0.1835571835571835,0.7362286146021204,False
+UCD-0001953,Epigenetics,0.162735934151441,0.8247840411338511,0.4873737373737373,0.1661318032999449,0.7795472207963838,False
+UCD-0159641,JAK/STAT,0.0948923491237888,0.7445713847805102,0.4537906537906538,0.1835571835571835,0.7362286146021204,False
+UCD-0159633,Apoptosis,0.3371135336642905,1.0212999925927682,0.2786761849261849,0.3645825438928887,0.4382041290778644,False
+UCD-0159625,Endocrinology & Hormones,0.5323658357346064,1.2085953751352991,0.3881907308377896,0.251605537319823,0.5992798051942534,False
+UCD-0159617,Endocrinology & Hormones,0.6766962732593373,1.0295898999321054,0.1982804232804232,0.4258618094234532,0.3707313049845587,False
+UCD-0159609,Others,0.3069083404545151,0.3607658184654382,0.15,0.3612700663391908,0.4421680211672878,False
+UCD-0159602,Angiogenesis,0.9612790397537416,0.8345010446246188,0.4572089947089947,0.17969891380296,0.7454545479928982,False
+UCD-0159662,GPCR & G Protein,2.7112438056065762e-09,0.0246913580246913,,,,False
+UCD-0159654,Epigenetics,0.7766656937919629,0.9296801420217136,0.4663267288267288,0.1661318032999449,0.7795472207963838,False
+UCD-0001952,Epigenetics,0.6888062764832923,0.9160866152786304,0.2616174334924335,0.4216150821655409,0.3750838617800641,False
+UCD-0159528,Transmembrane Transporters,0.947090376914064,0.8892650864321507,0.3073202819139351,0.3075349471530855,0.5121055254386104,False
+UCD-0159536,JAK/STAT,,,,,,False
+UCD-0159544,Angiogenesis,0.999877286979439,0.6045207956600362,0.6,0.1060116354234001,0.9746464656516696,False
+UCD-0159599,MAPK,,,,,,False
+UCD-0159638,Apoptosis,,,,,,False
+UCD-0159630,Transmembrane Transporters,0.0992112751168253,1.033951930404695,0.6380050505050504,0.0525641025641025,1.279310745970745,False
+UCD-0159622,JAK/STAT,0.0918539806321588,1.0345785917281651,0.4266624579124579,0.2326206402293359,0.6333517533220467,False
+UCD-0159614,Metabolism,0.1702133674787508,0.9285342761133396,0.3798611111111111,0.2522159774068171,0.5982274051545786,False
+UCD-0159607,Metabolism,0.3708866722436712,0.225135623869801,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159666,Cytoskeletal Signaling,,,,,,False
+UCD-0159659,Metabolism,0.7012046860291676,1.0799292035258792,0.6066405816405815,0.0677517970751053,1.1690791809044627,False
+UCD-0159651,Angiogenesis,0.3165223192298968,1.255182351013325,0.7118055555555555,0.0448414145096609,1.3483206961651315,False
+UCD-0159645,Transmembrane Transporters,,,,,,False
+UCD-0159637,Others,0.1568312491177824,0.9873940508272412,0.5056547619047619,0.1208097895795056,0.9178978721048168,False
+UCD-0159629,Angiogenesis,0.2732551102550593,0.2570747760348197,0.2338781588781589,0.4869983948635634,0.3124724702084579,False
+UCD-0159621,PI3K/Akt/mTOR,,,,,,False
+UCD-0159613,PI3K/Akt/mTOR,0.1589878463410923,0.1199367088607595,0.75,0.1060116354234001,0.9746464656516696,False
+UCD-0159606,Autophagy,0.0992996790675613,0.3433110081571278,0.3400144993894994,0.2782805429864253,0.5555171579096939,False
+UCD-0159658,Autophagy,0.2244235800955164,1.0086472395441335,0.4377667471417472,0.2190516665654787,0.659453438320374,False
+UCD-0159551,Epigenetics,0.9419331756231332,0.0179828489004693,0.330718954248366,0.2483343428225317,0.6049632165430582,False
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+UCD-0159644,Transmembrane Transporters,0.105230222184022,0.5510250988412513,0.3936507936507936,0.1947289998137455,0.7105693668095472,False
+UCD-0159636,Protein Tyrosine Kinase,0.232977738838271,1.1838134178475708,0.5817185007974481,0.0731519403106119,1.1357741499895764,False
+UCD-0159628,GPCR & G Protein,0.4132531088954716,1.0708023762341916,0.2193149443149443,0.5382402832067262,0.2690238017047381,False
+UCD-0159620,DNA Damage,0.510431410609915,0.9232259980283396,0.2555217658158835,0.4279706625948083,0.368586000869722,False
+UCD-0159612,Cell Cycle,0.5544477859876256,0.4581269738269172,0.4572272498743087,0.17969891380296,0.7454545479928982,False
+UCD-0159605,PI3K/Akt/mTOR,0.2255197609306882,0.1375138416267546,0.3364316239316239,0.2404549628269304,0.6189662549548729,False
+UCD-0159598,Endocrinology & Hormones,1.0449620458708487e-09,0.009090909090909,,,,False
+UCD-0159597,Epigenetics,0.7525995145270943,0.2703435804701627,0.75,0.1060116354234001,0.9746464656516696,False
+UCD-0159590,Others,0.5651263807799242,0.260243679158226,0.2184515715765715,0.5390502354788068,0.2683707599067321,False
+UCD-0159583,MAPK,,,,,,False
+UCD-0159575,Autophagy,0.8164099713709727,0.8719276446851567,0.312055292202351,0.3065035925612848,0.5135644307038106,False
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+UCD-0159640,Epigenetics,0.7018769584007278,0.9079758660873996,0.3754176379176379,0.2522159774068171,0.5982274051545786,False
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+UCD-0159582,Neuronal Signaling,0.3275492505862847,0.7437508296276631,0.4171717171717172,0.2415814748254426,0.6169363717460645,False
+UCD-0159577,Cell Cycle,,,,,,False
+UCD-0159532,PI3K/Akt/mTOR,,,,,,False
+UCD-0159525,Others,,,,,,False
+UCD-0159593,Epigenetics,0.4814178843484942,0.8742250545602192,0.5916666666666667,0.0677517970751053,1.1690791809044627,False
+UCD-0159586,Angiogenesis,0.6440841114277751,0.8714315274128501,0.6340277777777777,0.0525641025641025,1.279310745970745,False
+UCD-0159578,PI3K/Akt/mTOR,0.3327444649742764,0.5364151398264224,0.4166666666666666,0.1660635437070482,0.7797256987023287,False
+UCD-0159570,GPCR & G Protein,,,,,,False
+UCD-0159562,Epigenetics,0.6982468600418825,1.1433509490940466,0.3331582633053221,0.2839228897623058,0.5467995933961755,False
+UCD-0159554,Angiogenesis,0.5739261646175711,0.9880846922959798,0.5145833333333333,0.1126373626373626,0.9483175269293204,False
+UCD-0159547,Apoptosis,0.7381057246358582,1.2310886618203398,0.3586968586968587,0.2646978021978021,0.5772496646575843,False
+UCD-0159539,Proteases,0.7355994132672927,1.007543413927594,0.4312590187590187,0.2283438632212474,0.6414106556355311,False
+UCD-0159531,Metabolism,0.991254519155711,0.0131147540983606,,,,False
+UCD-0159524,Others,0.0048223464231861,0.6908337855617458,0.8263888888888888,0.0125152625152625,1.9025600363686452,False
+UCD-0159592,Epigenetics,0.7051599039988315,0.609418193506911,0.4568783068783069,0.1661318032999449,0.7795472207963838,False
+UCD-0159585,Protein Tyrosine Kinase,0.80273369849347,0.9636649436438808,0.7579365079365079,0.0303478904515174,1.5178714923022951,False
+UCD-0159569,Protein Tyrosine Kinase,0.5246189090249783,1.1717262892413884,0.6018939393939394,0.0677517970751053,1.1690791809044627,False
+UCD-0159548,Others,,,,,,False
+UCD-0159312,Transmembrane Transporters,0.1355472270339246,0.8640771349976797,0.9583333333333331,0.0027641070585855,2.5584451400053156,False
+UCD-0159307,GPCR & G Protein,0.8525731835595409,0.1744582561480026,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159303,Metabolism,0.1851024981582131,0.7315113797599911,1.0,0.0027641070585855,2.5584451400053156,False
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+UCD-0001847,JAK/STAT,,,,,,False
+UCD-0001463,Neuronal Signaling,0.4351066238162336,0.7189955538621151,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0001616B,Others,0.15376819659936,0.9736137708740282,0.3809638278388277,0.2522159774068171,0.5982274051545786,False
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+UCD-0000878,Transmembrane Transporters,0.2480704853036933,0.0525655880361612,1.0,0.0027641070585855,2.5584451400053156,False
+UCD-0159316,Proteases,0.2798796091839089,0.6855231990781827,0.8347222222222223,0.010298273155416,1.98723559295969,False
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+UCD-0159302,PI3K/Akt/mTOR,0.3711578777544492,0.8433704409269425,1.0,0.0027641070585855,2.5584451400053156,False
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+UCD-0159540,Others,0.5116088769462863,0.9718298462943948,0.4937499999999999,0.1291294375406524,0.888974740660675,False
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+UCD-0159574,Epigenetics,0.1994620839601825,0.4774782442345757,0.5393065268065268,0.0997861199203481,1.0009298641357447,False
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+UCD-0159566,DNA Damage,0.3156011139872329,0.8843025011159306,0.5157407407407407,0.1624708624708624,0.7892245140867148,False
+UCD-0159558,Cytoskeletal Signaling,0.2931221363729607,0.7127826590249825,0.4407675657675657,0.2189821351832525,0.6595913139626328,False
+UCD-0159550,MAPK,,,,,,False
+UCD-0159543,MAPK,0.2182752618470558,0.2353222517851635,0.7124999999999999,0.0448414145096609,1.3483206961651315,False
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+UCD-0159581,PI3K/Akt/mTOR,0.3799679235292644,0.6419743039911923,0.5484217171717172,0.0997861199203481,1.0009298641357447,False
+UCD-0159573,Protein Tyrosine Kinase,0.4102339593136315,0.6026649836612152,0.5343253968253968,0.0997861199203481,1.0009298641357447,False
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+UCD-0159580,NF-κB,0.1942291422734434,0.2032786885245901,,,,False
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+UCD-0159564,Epigenetics,,,,,,False
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+UCD-0159579,PI3K/Akt/mTOR,0.1945536138259236,0.7697680507458841,0.4996212121212121,0.128125,0.8923661216001706,False
+UCD-0159571,Others,0.3731877215083233,1.107506812707163,0.5249999999999999,0.10713390843684,0.9700730509580888,False
+UCD-0159561,JAK/STAT,0.1156010178818088,0.9554880231640706,0.6805555555555555,0.0489284471184923,1.3104385675748629,False
diff --git a/3a.train_dmso_logistic_regression_models/train_platemapwise_model/inference_results/hit_summary.csv b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/inference_results/hit_summary.csv
new file mode 100644
index 0000000..01d308b
--- /dev/null
+++ b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/inference_results/hit_summary.csv
@@ -0,0 +1,552 @@
+Metadata_treatment,Pathway,mean_score,median_score,score_std,n_scored_cells,n_score_plates,mean_cell_count,median_cell_count,mean_relative_cell_count,median_relative_cell_count,viability_std,n_treatment_wells,n_viability_plates,mean_average_precision,mean_normalized_average_precision,corrected_p_value,-log10(p-value)
+UCD-0000253,Neuronal Signaling,0.5946240388937591,0.67135522053328,0.3635584346907532,715.0,4.0,178.75,176.0,0.8598445034002572,0.8251100594320933,0.179958158827116,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0000872,Metabolism,0.9998329973107045,0.9999999998267397,0.0006975883728004919,36.0,4.0,9.0,9.0,0.04444057369927129,0.041354831609068894,0.041631884342622855,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159319,Neuronal Signaling,0.8377864176403528,0.988373441112163,0.24978526636445256,597.0,4.0,149.25,153.0,0.715468268127947,0.7470944309927361,0.11634083020969692,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0000730,Metabolism,0.9841768858708694,0.9999999992797852,0.11396853121391672,316.0,4.0,79.0,88.5,0.3896266880606756,0.412376183138895,0.2805011564864142,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0018569,Endocrinology & Hormones,,,,,,,,,,,,,,,,
+UCD-0159328,Proteases,0.9999966473643804,0.9999966473643804,,1.0,1.0,1.0,1.0,0.004842615012106538,0.004842615012106538,,1.0,1.0,,,,
+UCD-0159298,Cytoskeletal Signaling,,,,,,,,,,,,,,,,
+UCD-0159304,Cell Cycle,0.8505142682539163,0.9935453077665981,0.2589444583667887,641.0,4.0,160.25,166.0,0.7554993975694243,0.7772328548644338,0.15093577684784237,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159308,Protein Tyrosine Kinase,0.6896773445900586,0.7612804043648753,0.297516692071834,29.0,2.0,14.5,14.5,0.07693939393939395,0.07693939393939395,0.10238049092452499,2.0,2.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0018012,Apoptosis,0.6257003314787246,0.7311228371225216,0.3564516605527216,702.0,4.0,175.5,173.0,0.8347584369243601,0.8205789473684211,0.12814239576969902,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
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+UCD-0001305,Neuronal Signaling,0.24806330028420168,0.039531784872459536,0.3229439622574815,608.0,4.0,152.0,143.0,0.7220468042216481,0.7278256658595641,0.11832282813455833,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
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+UCD-0001784,Cell Cycle,0.9927723507860328,0.9927723507860328,,1.0,1.0,1.0,1.0,0.005333333333333333,0.005333333333333333,,1.0,1.0,,,,
+UCD-0159299,Metabolism,0.09985574401183604,0.0019185098899115816,0.2279180782165815,534.0,4.0,133.5,138.0,0.6425980803318002,0.662578947368421,0.45720562229250056,4.0,4.0,0.4871658312447786,0.2906002025645016,0.1427206334038632,0.8455132355352715
+UCD-0159305,Transmembrane Transporters,,,,,,,,,,,,,,,,
+UCD-0159309,Angiogenesis,0.32051606751801515,0.11249350661476527,0.3800066882360125,748.0,4.0,187.0,178.0,0.8933921494800174,0.8617017543859649,0.163611943554298,4.0,4.0,0.9333333333333332,0.9077804820536312,0.0027641070585855,2.5584451400053156
+UCD-0001843,DNA Damage,0.30394986420372494,0.18196272583203651,0.333033269289378,871.0,4.0,217.75,216.0,1.0358842116848361,1.043261562998405,0.0662400137757067,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0000490,Neuronal Signaling,0.3493816639839271,0.23830219572461334,0.35509713692456374,833.0,4.0,208.25,209.5,0.9975486424072507,0.973448162007484,0.13455007965750804,4.0,4.0,0.8013888888888889,0.7252626861181098,0.0194832194832194,1.7103392770124284
+UCD-0018439,Microbiology,0.412826334651422,0.32691129003223074,0.3759857190066712,745.0,4.0,186.25,219.0,0.9027150213747003,1.0785879374862426,0.3984277076866931,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001435,Others,0.03271780225377349,0.00011519068562756065,0.11275093539911557,773.0,4.0,193.25,198.0,0.9181872419878664,0.9068070175438596,0.03111989302931386,4.0,4.0,0.5938341750841751,0.4381537512996014,0.0677517970751053,1.1690791809044627
+UCD-0018550,Neuronal Signaling,0.07035258929506046,0.0013682985988856338,0.1683081939704589,924.0,4.0,231.0,228.5,1.1030051283833622,1.124121212121212,0.12254952426168196,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159323,Stem Cells & Wnt,0.7804986612355564,0.9861487049996776,0.32474296562168226,184.0,4.0,46.0,30.5,0.22143424045475785,0.1460877192982456,0.23325870789539932,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159329,JAK/STAT,0.6211807492889702,0.6211807492889702,0.16021691523461035,2.0,1.0,2.0,2.0,0.009685230024213076,0.009685230024213076,,1.0,1.0,,,,
+UCD-0159300,GPCR & G Protein,0.1913400670478902,0.021333788388157915,0.28974435641925345,799.0,4.0,199.75,198.5,0.9561959482682052,0.9495322474136034,0.11823805221336571,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001790,Cell Cycle,0.600428152538604,0.7054029443018064,0.36803165120555464,86.0,1.0,86.0,86.0,0.45866666666666667,0.45866666666666667,,1.0,1.0,,,,
+UCD-0013217,Epigenetics,0.5375321202509717,0.5917558783617527,0.4046789268518836,888.0,4.0,222.0,232.0,1.0650110310444834,1.0839917590586636,0.19864521370127267,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159313,Protein Tyrosine Kinase,0.6827815038255973,0.8748606789696658,0.37214010634650524,703.0,4.0,175.75,184.5,0.8429492317079293,0.8249202551834132,0.23275474938245125,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0018240,PI3K/Akt/mTOR,0.047591775338577155,8.761536609320186e-05,0.1515715029738093,909.0,4.0,227.25,227.5,1.0849625469683453,1.1149075500770418,0.09932478693992469,4.0,4.0,0.3902046783625731,0.1564745408896843,0.2493902963876854,0.6031204486412295
+UCD-0018483,Microbiology,0.24671239682471408,0.030592833768671307,0.33396701014400776,871.0,4.0,217.75,211.0,1.0456860834675288,1.0315438596491229,0.22648843135835672,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0000101,Endocrinology & Hormones,0.2837468661937914,0.12930171682706176,0.3392345969131656,357.0,4.0,89.25,60.0,0.4123131306695089,0.29675544794188863,0.29288407987039305,4.0,4.0,0.9047619047619048,0.8682578315051876,0.010298273155416,1.98723559295969
+UCD-0159320,Metabolism,0.051022322669584924,0.00021660461514702264,0.14212615007441975,827.0,4.0,206.75,204.0,0.9864005390981305,0.9801818181818183,0.09441937628838327,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159324,Angiogenesis,0.06015506517084141,8.873195722487688e-05,0.1701255546052569,772.0,4.0,193.0,192.5,0.9253509080868582,0.9192472707191708,0.14702164276637134,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001869,Protein Tyrosine Kinase,0.5475214936670724,0.5905799223332368,0.35884828016900233,973.0,4.0,243.25,249.0,1.1626191306463385,1.186329170383586,0.14239241118428667,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001818,MAPK,0.7247991819526381,0.9761294790635089,0.38559855301186646,799.0,4.0,199.75,203.0,0.9613096956567073,0.9546720228923619,0.19827642843146792,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0018051,Metabolism,0.11356275613355314,0.0004064622904304769,0.23333216850603794,757.0,4.0,189.25,197.0,0.9064730489552077,0.9071885221528397,0.15329871579056834,4.0,4.0,0.6001984126984127,0.4469573551728184,0.0677517970751053,1.1690791809044627
+UCD-0159310,GPCR & G Protein,0.03667301460024701,6.885649445248733e-06,0.12416157464845531,840.0,4.0,210.0,212.0,1.0082525960509905,0.9911112527080413,0.1789203211661185,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159314,PI3K/Akt/mTOR,0.042686034141782764,2.8509422451097125e-05,0.1157174246145455,117.0,2.0,58.5,58.5,0.29703030303030303,0.29703030303030303,0.17579103129861925,2.0,2.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0000083,Neuronal Signaling,0.11438988612079627,0.0011750253069003742,0.22647841980039254,446.0,4.0,111.5,110.0,0.5328925626856046,0.5618819776714513,0.36613826308094916,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0018527,Microbiology,0.31934255117855714,0.019299428643432627,0.40949631457900826,772.0,4.0,193.0,193.5,0.9191603179752155,0.8927575757575757,0.07907541586267411,4.0,4.0,0.5176587301587301,0.332780809143981,0.1090038993264799,0.9625579660439308
+UCD-0000469,Neuronal Signaling,0.13224452335419762,0.00044941221774976115,0.27366096992803124,651.0,4.0,162.75,150.0,0.7709321937354944,0.7631961259079904,0.22233033621903706,4.0,4.0,0.9666666666666668,0.9538902410268156,0.0027641070585855,2.5584451400053156
+UCD-0159321,Neuronal Signaling,0.05717736846220882,0.0002636747843292558,0.15904988025450634,738.0,4.0,184.5,180.0,0.8780641839575827,0.8682192770060746,0.055289437932789724,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159325,PI3K/Akt/mTOR,0.3997986029102429,0.009220777091700046,0.5402891423930444,5.0,3.0,1.6666666666666667,1.0,0.007937437327267835,0.005333333333333333,0.004941510085497663,3.0,3.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159330,Protein Tyrosine Kinase,0.044591170605969815,6.672762923602202e-05,0.14708521519623532,762.0,4.0,190.5,187.5,0.9188164614154801,0.8809487123046444,0.22059574321210143,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159301,Cell Cycle,0.8475448706407331,0.9885622113843802,0.2447281782459438,712.0,4.0,178.0,172.0,0.8576703720008805,0.7938861985472154,0.20528686692988143,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159306,Cell Cycle,0.31495478551464007,0.12633522454080073,0.37084842194903245,531.0,4.0,132.75,133.5,0.6372277670292835,0.5959728867623604,0.15050351352940033,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159311,GPCR & G Protein,0.35228323189621696,0.179551010124368,0.37619800581867296,685.0,4.0,171.25,171.5,0.8189625546918142,0.832719298245614,0.1061915626906284,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159315,PI3K/Akt/mTOR,0.9376993035376229,0.9997594514304027,0.15305995002574743,582.0,4.0,145.5,154.0,0.7053375851029732,0.7216927140655954,0.24842148100164713,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0000576,Neuronal Signaling,0.2820384295989524,0.021358371693795442,0.37182109831134014,710.0,4.0,177.5,181.0,0.8448805449679669,0.8308181818181818,0.050739076221266353,4.0,4.0,0.4788558405818467,0.2791050525683856,0.148334742180896,0.8287571188950228
+UCD-0000324,Metabolism,0.4134672480844652,0.32460169594177724,0.38363157731352326,752.0,4.0,188.0,186.5,0.895286955833343,0.9473123486682808,0.1385292634483168,4.0,4.0,0.8138888888888889,0.7425538457330539,0.0125152625152625,1.9025600363686452
+UCD-0159256,Apoptosis,0.19908077190059922,0.06375032677268425,0.26709706445781395,893.0,4.0,223.25,229.5,1.0374301115958562,1.035839042748239,0.1517534632122502,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001766,Angiogenesis,0.9602947371687784,0.999244621058888,0.10955179249533108,111.0,4.0,27.75,25.5,0.13001967041505463,0.11258322879475055,0.06597703609966805,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159262,Others,0.9210143194395555,0.9991929874983163,0.17209863806368086,40.0,4.0,10.0,10.0,0.04700000520427624,0.04350091672295667,0.022389584536355252,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001915,Others,0.9822994620532043,0.9999613335930835,0.059638120224529724,64.0,4.0,16.0,15.5,0.07321375162475191,0.07361706931587265,0.021876143934941514,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001783,Others,,,,,,,,,,,,,,,,
+UCD-0159279,Others,0.9559787425618459,0.9999776509949331,0.1297118479638283,48.0,3.0,16.0,13.0,0.0744218129964143,0.06649616368286446,0.029404429832378825,3.0,3.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159283,Endocrinology & Hormones,0.14086124648427478,0.03377428987697987,0.21603750980539346,795.0,4.0,198.75,204.0,0.9223546902877104,0.9814445352378729,0.16015747527608012,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001040,Endocrinology & Hormones,0.12337371608326962,0.037732718599967775,0.19453929611656462,747.0,4.0,186.75,183.0,0.8715318565357679,0.8404559009243242,0.09894682571543112,4.0,4.0,0.7141955266955267,0.604648738674185,0.0448414145096609,1.3483206961651315
+UCD-0018091,GPCR & G Protein,,,,,,,,,,,,,,,,
+UCD-0000450,Neuronal Signaling,0.26434488796884986,0.1506231638041226,0.2772094477576854,904.0,4.0,226.0,220.0,1.0556281163194747,1.0320972485195181,0.14244784607767802,4.0,4.0,0.3403505700525824,0.0875117133497834,0.2782805429864253,0.5555171579096939
+UCD-0159257,DNA Damage,0.2816213596798692,0.1747977569025685,0.2855123507871133,869.0,4.0,217.25,218.5,1.012341194607443,0.988690533629258,0.09340041626994605,4.0,4.0,0.8506944444444444,0.7934667045992784,0.010298273155416,1.98723559295969
+UCD-0001810,DNA Damage,0.6762340910193436,0.7719925469867039,0.3153889689710593,361.0,4.0,90.25,94.5,0.41964014141791645,0.43267608324224516,0.07590655574202056,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159263,Others,0.6125148063987246,0.6823916949769496,0.3080586706771272,795.0,4.0,198.75,183.0,0.9235422988178057,0.9105604250987014,0.1043208609339385,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159269,Others,0.15968444887455385,0.053546054255681756,0.22417100869480358,875.0,4.0,218.75,214.5,1.0257420741430665,0.9880612537275419,0.16551453707521688,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159273,Others,0.28809223977619264,0.18368139074089965,0.28815964624564794,877.0,4.0,219.25,206.0,1.0149888749370464,0.9778505305938135,0.11550648111884075,4.0,4.0,0.4638220439691028,0.2583089104204206,0.1725788831051989,0.7630123460017288
+UCD-0017907,Others,,,,,,,,,,,,,,,,
+UCD-0159284,Stem Cells & Wnt,0.6568851913656727,0.7611643193839261,0.3168415189869771,565.0,4.0,141.25,159.0,0.6787086602168512,0.7915290641372632,0.3708428888149074,4.0,4.0,0.9458333333333332,0.9250716416685754,0.0027641070585855,2.5584451400053156
+UCD-0017999,Endocrinology & Hormones,0.10815411692884529,0.02935349724548833,0.17791930982056173,837.0,4.0,209.25,212.5,0.9744235545839768,0.9744510829769397,0.16153960725101854,4.0,4.0,0.6715277777777777,0.5456267501184124,0.0496199835406883,1.3043433841224432
+UCD-0000721,Neuronal Signaling,0.9791875134002923,0.9986562537648089,0.04113638455374555,21.0,2.0,10.5,10.5,0.04733185371031554,0.04733185371031554,0.03172871304175862,2.0,2.0,0.75,0.6865885136094606,0.1060116354234001,0.9746464656516696
+UCD-0001014,Neuronal Signaling,0.22511178922518565,0.11076041188025382,0.2591922029861922,900.0,4.0,225.0,221.0,1.048722362762307,1.046778660950218,0.03577655500575474,4.0,4.0,0.2461624460850467,-0.042778037478601,0.4474775224775225,0.3492287751171237
+UCD-0159258,Angiogenesis,0.7088779000536966,0.8168720267811016,0.28841099936381887,678.0,4.0,169.5,181.0,0.786427221686658,0.8221653961208144,0.1309939684164963,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159261,Others,0.9620510413323957,0.9982576377440737,0.09948735883050232,654.0,4.0,163.5,180.0,0.7556821479444478,0.7890379233812602,0.312400872215514,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159264,Neuronal Signaling,0.7590078453665071,0.9952402233848031,0.4120170702382057,3.0,2.0,1.5,1.5,0.006474958988709833,0.006474958988709833,0.0025792373182332816,2.0,2.0,0.1833333333333333,-0.0238108555424286,0.3234714003944773,0.490164111285638
+UCD-0001775,Others,0.2239777455485181,0.11157649291329257,0.253945846704096,897.0,4.0,224.25,213.0,1.0428789048374103,1.0475256375238993,0.08707315262092338,4.0,4.0,0.2953203809434459,0.0252217783596076,0.3234714003944773,0.490164111285638
+UCD-0159274,Others,0.7415506977102388,0.8511395859331556,0.27205325641462386,974.0,4.0,243.5,245.5,1.140760246495704,1.1768393505025871,0.07742332852886595,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001792,Others,,,,,,,,,,,,,,,,
+UCD-0159285,DNA Damage,0.6973106943109262,0.81950575158987,0.3202833489829503,818.0,4.0,204.5,205.5,0.9640980612252382,0.9692707157371867,0.16671343318579993,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159289,Metabolism,0.3421496761178707,0.2171729812811688,0.3219212882445733,830.0,4.0,207.5,209.5,0.9714444535077336,0.9919764467971213,0.054234597788386886,4.0,4.0,0.2956114243943191,0.0256243766607221,0.3234714003944773,0.490164111285638
+UCD-0018179,MAPK,0.7722817436817935,0.8770604628966798,0.253547260555026,921.0,4.0,230.25,230.0,1.081069868920455,1.0999096861594038,0.1040841306750528,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001016,Neuronal Signaling,0.32046581664722285,0.2131955177896288,0.30001743358702077,860.0,4.0,215.0,225.0,1.0041043837859105,1.0158255331467723,0.15832328622444372,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001804,MAPK,0.8000518372851414,0.9252839336593502,0.2586770371448877,687.0,4.0,171.75,168.0,0.800418522048852,0.7982614585685256,0.013834561802564658,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0018207,Neuronal Signaling,0.959112466107337,0.9973849742876569,0.12080149484052868,374.0,4.0,93.5,93.5,0.42301322178007145,0.4506572295247725,0.2558675494403356,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001844,Others,0.341665444027787,0.22616232686840337,0.35087861225469374,223.0,3.0,74.33333333333333,73.0,0.3605897116520831,0.3395348837209302,0.0966381101549585,3.0,3.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159270,Others,0.1555441040254247,0.04356276451136553,0.21862807984576388,800.0,4.0,200.0,200.0,0.935323155927072,0.9315172782965563,0.045034284226398325,4.0,4.0,0.2976298920377868,0.0284165083567861,0.3213634706525701,0.4930034909691886
+UCD-0159275,Others,0.5208513752450697,0.5227651749237933,0.283977615694628,593.0,4.0,148.25,148.5,0.6847856478180632,0.724796288586213,0.18871325081349002,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001800,Others,0.9517017762986469,0.9517017762986469,0.06274854019982672,2.0,2.0,1.0,1.0,0.004632233553713746,0.004632233553713746,0.000682861447803545,2.0,2.0,0.1833333333333333,-0.0238108555424286,0.3234714003944773,0.490164111285638
+UCD-0001613,Endocrinology & Hormones,0.19473036102756355,0.07018585086863409,0.2515228204127882,883.0,4.0,220.75,219.0,1.0273629429831357,1.0590835287750315,0.08506750033488693,4.0,4.0,0.285607884833891,0.0117865527204205,0.3504273504273504,0.4554020050264261
+UCD-0001921,Epigenetics,0.96973675348078,0.996567196209216,0.08699811530846255,699.0,4.0,174.75,176.5,0.8184930530423884,0.8422292273835723,0.06037260045364889,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159290,PI3K/Akt/mTOR,0.9991458343659912,0.9998426692161004,0.0015133509998037913,6.0,1.0,6.0,6.0,0.030690537084398978,0.030690537084398978,,1.0,1.0,,,,
+UCD-0159293,Others,0.3610649911292491,0.2951056443247744,0.31409513472797235,822.0,4.0,205.5,202.0,0.9590957205468893,0.9864540949925472,0.11122495158875538,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001808,MAPK,0.7956648483544341,0.9157112619837451,0.2527261307669671,180.0,4.0,45.0,50.0,0.20725409706706951,0.2254461588043347,0.10383924772169291,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001801,Endocrinology & Hormones,0.8830069306838656,0.963349542415556,0.17798134256831188,273.0,4.0,68.25,49.0,0.33577212401026413,0.22514715815883432,0.38198691200295715,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159265,Neuronal Signaling,0.8853473115604323,0.9963551216547805,0.24983544842032263,16.0,2.0,8.0,8.0,0.03860108249568786,0.03860108249568786,0.011187200606412992,2.0,2.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159271,Others,0.7172584984497103,0.8295301864988591,0.2817989823565078,505.0,4.0,126.25,125.0,0.5943158698420958,0.5944005418830436,0.08165622505723284,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001835,Others,0.9008196497038196,0.9999335366146583,0.20705286720773555,5.0,2.0,2.5,2.5,0.011729510047414765,0.011729510047414765,0.009722917808593528,2.0,2.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159280,Others,0.16119298925414355,0.05245339232357404,0.22617531889424503,819.0,4.0,204.75,197.5,0.9535795809876662,0.9583219526730067,0.04588496034130627,4.0,4.0,0.3096639471639471,0.0450631297969041,0.3068490262734867,0.513075250528078
+UCD-0159286,Others,0.9861928124716629,0.9861928124716629,,1.0,1.0,1.0,1.0,0.005115089514066497,0.005115089514066497,,1.0,1.0,,,,
+UCD-0018131,Angiogenesis,0.3514311302432795,0.283883608250358,0.3064735275346207,654.0,4.0,163.5,158.0,0.7608150825311834,0.7832242043488873,0.09129335620033861,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001024,Neuronal Signaling,0.1995517749134645,0.09383145426764009,0.2389791653336803,811.0,4.0,202.75,202.0,0.9476849321651465,0.9636325182290617,0.10555617244453276,4.0,4.0,0.2406740007068954,-0.0503701642843222,0.4597028253913129,0.3375228270130456
+UCD-0001829,PI3K/Akt/mTOR,0.44464287742806674,0.4079793185219639,0.30719900408195217,746.0,4.0,186.5,182.0,0.8684117610854121,0.8697880739883902,0.10177094978588044,4.0,4.0,0.9791666666666666,0.9711814006417596,0.0027641070585855,2.5584451400053156
+UCD-0159259,PI3K/Akt/mTOR,0.36681011785776657,0.3064040172378214,0.311795971621013,624.0,4.0,156.0,158.0,0.728007989182217,0.7149860079127666,0.039999670165511744,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001842,Neuronal Signaling,0.516152659583598,0.5207586293834038,0.3461857314858226,504.0,4.0,126.0,146.0,0.5822112101888113,0.6671318355955047,0.2592521772109619,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159266,Others,0.323264062213351,0.22833855189515415,0.30718810295979404,726.0,4.0,181.5,172.0,0.8993487083763417,0.848166235177791,0.2038759731643953,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001812,Others,0.9779592476124797,0.9997309149019691,0.0772017642856282,660.0,4.0,165.0,167.0,0.8115532017253011,0.7997621576686833,0.15443321945639682,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159276,Others,0.8834500967818433,0.8276047279419712,0.10540136515085713,13.0,1.0,13.0,13.0,0.05508474576271186,0.05508474576271186,,1.0,1.0,,,,
+UCD-0159281,Others,,,,,,,,,,,,,,,,
+UCD-0159287,Endocrinology & Hormones,0.8622146451038913,0.9830230017520005,0.22756892197229134,668.0,4.0,167.0,164.0,0.8369244193543168,0.800805842357089,0.1646678069539092,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0000849,Endocrinology & Hormones,0.8026851952139509,0.9494007283506481,0.27415466503673236,644.0,4.0,161.0,165.5,0.8043918259305797,0.8113702521245129,0.13494226631802278,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159291,GPCR & G Protein,0.7018622877690617,0.8065751399098278,0.2987936147256038,478.0,4.0,119.5,125.0,0.5625594695961273,0.5713531151697263,0.38425451196488564,4.0,4.0,0.9791666666666666,0.9711814006417596,0.0027641070585855,2.5584451400053156
+UCD-0000421,Neuronal Signaling,0.5002855172567953,0.4792487639377252,0.353987549294741,862.0,4.0,215.5,211.0,1.0650109682244255,1.0220927336266956,0.10475047109127732,4.0,4.0,0.7895833333333333,0.7089321464817737,0.0216841981547863,1.663856632698532
+UCD-0017947,Apoptosis,0.3739264964894275,0.2843559174260788,0.33683667412330576,890.0,4.0,222.5,226.5,1.1024100245181483,1.0913145730273295,0.08904662072200946,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001816,Metabolism,0.5713430899206853,0.6519282532924959,0.36314003431200126,800.0,4.0,200.0,201.0,0.9855266216601093,1.011162495891826,0.07770844662281305,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159267,Others,,,,,,,,,,,,,,,,
+UCD-0001802,Others,0.39345760062185003,0.32313722184540994,0.3368880291338914,604.0,4.0,151.0,156.0,0.7496995075412987,0.7091780877463303,0.10920222927394589,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159277,Others,0.99999999998504,0.99999999998504,,1.0,1.0,1.0,1.0,0.00423728813559322,0.00423728813559322,,1.0,1.0,,,,
+UCD-0159282,Others,0.9801621331989386,0.9999426763358576,0.081227515440854,198.0,4.0,49.5,34.0,0.2329727748501487,0.1866900148693834,0.18178016465167948,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159288,Metabolism,0.24807900899754953,0.12684688725383717,0.27814923906624794,905.0,4.0,226.25,237.5,1.1224870284973623,1.0657489254723866,0.2186594462919646,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0018089,Endocrinology & Hormones,0.21370831987065061,0.08761017703119245,0.2734621545355834,789.0,4.0,197.25,205.5,0.9728237603752221,1.0062116104004064,0.10195176270218752,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159292,DNA Damage,0.5814132865140365,0.6386358004599184,0.33577308796961475,307.0,4.0,76.75,71.0,0.3537196850944343,0.3513487086554633,0.2783542798681156,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159294,Cell Cycle,0.31717048956413724,0.21799882978054602,0.31391752537883155,466.0,3.0,155.33333333333334,160.0,0.7484801534287425,0.8135593220338984,0.17956045752646557,3.0,3.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159260,Epigenetics,,,,,,,,,,,,,,,,
+UCD-0001850,Others,0.8458110514102297,0.9551089011645383,0.23132342259186833,857.0,4.0,214.25,206.0,1.0694063830468212,1.1183663302814617,0.13819648988416058,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159268,Others,0.9610885015018698,0.9986867557179607,0.11453461325031775,796.0,4.0,199.0,197.5,0.9960657156896622,1.0313351498637602,0.12948225059395096,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159272,Others,,,,,,,,,,,,,,,,
+UCD-0159278,Others,0.17616201633584352,0.040847995418700565,0.2593111343705076,770.0,4.0,192.5,191.5,0.9646710104580659,0.9857718458826492,0.1421124842903219,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001733,Protein Tyrosine Kinase,0.5173475068591279,0.516145888228768,0.3347850020041165,793.0,4.0,198.25,198.5,0.990318946518031,0.9753210778141526,0.1190828240278897,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001086,Endocrinology & Hormones,0.17341392788109053,0.0484903356122664,0.24755495647221157,960.0,4.0,240.0,233.5,1.1885941773302804,1.1900316806383062,0.021589731889655513,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0000786,GPCR & G Protein,0.20205567210226022,0.07569460732308256,0.2632558667089957,804.0,4.0,201.0,200.5,0.995043021809751,0.9755900578924726,0.08350397243591019,4.0,4.0,0.9458333333333332,0.9250716416685754,0.0027641070585855,2.5584451400053156
+UCD-0001101,Neuronal Signaling,0.2984836293322831,0.11385628589903402,0.34805862680060806,149.0,3.0,49.666666666666664,42.0,0.2736417135037377,0.22888283378746593,0.2938046905035445,3.0,3.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159295,Microbiology,0.2902485232461362,0.1522044149178205,0.3104544589888676,838.0,4.0,209.5,211.0,1.0432415634104195,1.0560312263913372,0.20903778407771145,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0018083,JAK/STAT,0.5148235466336772,0.5096924598660182,0.3197468711658609,840.0,4.0,210.0,204.5,1.0500878255776307,1.1021153361873584,0.16955303805250818,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001822,MAPK,0.8302009226338327,0.9395526787908359,0.22921164127890953,762.0,4.0,190.5,184.0,0.9476575887382759,0.9483421014892028,0.14349368679845417,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001759,Angiogenesis,0.7154126099122802,0.8307579352815706,0.3092552689559335,198.0,4.0,49.5,54.5,0.25166739692796325,0.28355577940065474,0.076023125257957,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0000533,GPCR & G Protein,0.48238961105292105,0.46350336267784886,0.33805214157340524,901.0,4.0,225.25,230.0,1.1191541703199115,1.082850667338202,0.09848385970510638,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0000295,Metabolism,0.31478788456672313,0.18601429992183804,0.3139168701688533,387.0,4.0,96.75,90.5,0.4653199577519383,0.4174190643328869,0.2049478840591307,4.0,4.0,0.570085572107631,0.4053024805236525,0.0780735509977387,1.1074960672902194
+UCD-0001580,Transmembrane Transporters,0.999788669470584,0.999788669470584,,1.0,1.0,1.0,1.0,0.00554016620498615,0.00554016620498615,,1.0,1.0,,,,
+UCD-0159317,Angiogenesis,0.36893404412788994,0.24563601650740507,0.346184022619783,654.0,4.0,163.5,152.5,0.8240821761353613,0.7665736672620367,0.20691858508686914,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0018516,Neuronal Signaling,0.09099936117968208,0.016182444707638462,0.17881728564861746,805.0,4.0,201.25,199.5,1.000860698016794,0.9885419289851423,0.07078837962258196,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0018304,Endocrinology & Hormones,0.23178687777380252,0.11831505836180917,0.26380379171911367,722.0,4.0,180.5,189.0,0.899742950766353,0.8659027952656762,0.17354054795792895,4.0,4.0,0.4813291910331383,0.2825264203633438,0.1464634436770659,0.834270758862523
+UCD-0159327,DNA Damage,0.268773373212108,0.13553451969260807,0.2991364216563077,642.0,4.0,160.5,163.0,0.8012825600599962,0.8064815249542232,0.11895156414836593,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159296,Metabolism,0.1761859570296725,0.0720710673227413,0.2247844095777395,728.0,4.0,182.0,175.0,0.9130973986423507,0.7914882901032485,0.29071227810511285,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159302,PI3K/Akt/mTOR,0.37115787775444925,0.28795118200530584,0.3215346955397223,673.0,4.0,168.25,165.5,0.8433704409269425,0.8733316544950894,0.17104553040033657,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001793,Cell Cycle,,,,,,,,,,,,,,,,
+UCD-0000757,Metabolism,0.8176070502298376,0.9456782080502801,0.2554558302274212,855.0,4.0,213.75,215.0,1.0632238935952585,1.0881390078065978,0.09724465567205559,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159316,Proteases,0.2798796091839089,0.15900518326508686,0.298451904747527,570.0,4.0,142.5,145.5,0.6855231990781827,0.67714058098185,0.2724042606458328,4.0,4.0,0.8347222222222223,0.7713724450912942,0.010298273155416,1.98723559295969
+UCD-0000878,Transmembrane Transporters,0.2480704853036933,0.0982085393539494,0.27309958378199783,29.0,3.0,9.666666666666666,4.0,0.05256558803616123,0.021798365122615803,0.06517052486920503,3.0,3.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159318,Angiogenesis,,,,,,,,,,,,,,,,
+UCD-0001616B,Others,0.15376819659936006,0.041623728489056266,0.2268493767491379,782.0,4.0,195.5,196.0,0.9736137708740282,0.9114336353819428,0.19272279159864758,4.0,4.0,0.3809638278388277,0.1436917391789707,0.2522159774068171,0.5982274051545786
+UCD-0001463,Neuronal Signaling,0.4351066238162336,0.3572117213322285,0.36479862460147056,572.0,4.0,143.0,142.5,0.7189955538621151,0.7037078080387991,0.13624851314857228,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001847,JAK/STAT,,,,,,,,,,,,,,,,
+UCD-0159297,Microbiology,0.23094833243261242,0.11117951132584843,0.273305068381506,624.0,4.0,156.0,156.0,0.7861676972917644,0.7718928855455458,0.168413296929516,4.0,4.0,0.9041666666666668,0.8674344429520949,0.010298273155416,1.98723559295969
+UCD-0159303,Metabolism,0.18510249815821314,0.06257914673823015,0.24853381562552607,586.0,4.0,146.5,147.5,0.7315113797599911,0.6841257562462477,0.1122949759652484,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159307,GPCR & G Protein,0.8525731835595409,0.9744940774478765,0.2538373515635509,152.0,4.0,38.0,20.0,0.17445825614800267,0.11025987455372979,0.19384883609935402,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159312,Transmembrane Transporters,0.13554722703392466,0.03593519273138378,0.2093879129974464,702.0,4.0,175.5,175.0,0.8640771349976797,0.897254565942063,0.07126458376055907,4.0,4.0,0.9583333333333331,0.9423628012835196,0.0027641070585855,2.5584451400053156
+UCD-0159569,Protein Tyrosine Kinase,0.5246189090249783,0.5226113063014249,0.28877209580099206,621.0,4.0,155.25,157.5,1.1717262892413884,1.146731267007367,0.1685554841010327,4.0,4.0,0.6018939393939394,0.4493027649907186,0.0677517970751053,1.1690791809044627
+UCD-0159577,Cell Cycle,,,,,,,,,,,,,,,,
+UCD-0159585,Protein Tyrosine Kinase,0.80273369849347,0.8971045450271242,0.23104858495556096,508.0,4.0,127.0,126.5,0.9636649436438809,0.9781495594189094,0.15903589451466768,4.0,4.0,0.7579365079365079,0.6651553217423516,0.0303478904515174,1.5178714923022951
+UCD-0159592,Epigenetics,0.7051599039988315,0.8405272384471725,0.30580825628504577,258.0,3.0,86.0,58.0,0.609418193506911,0.47540983606557374,0.3161415983018003,3.0,3.0,0.4568783068783069,0.2838828143670352,0.1661318032999449,0.7795472207963838
+UCD-0159524,Others,0.0048223464231861605,1.414268365565494e-06,0.03915891885482286,348.0,4.0,87.0,81.5,0.6908337855617458,0.6511571841851495,0.5532374531275684,4.0,4.0,0.8263888888888888,0.7598450053479981,0.0125152625152625,1.9025600363686452
+UCD-0159531,Metabolism,0.991254519155711,0.991254519155711,0.009297249741082295,2.0,1.0,2.0,2.0,0.013114754098360656,0.013114754098360656,,1.0,1.0,,,,
+UCD-0159539,Proteases,0.7355994132672927,0.8284073344321459,0.2574785657532616,534.0,4.0,133.5,134.0,1.007543413927594,1.0008437801350047,0.11167390304037184,4.0,4.0,0.4312590187590187,0.2132647131041796,0.2283438632212474,0.6414106556355311
+UCD-0159547,Apoptosis,0.7381057246358582,0.81460814385297,0.24740997301723103,658.0,4.0,164.5,179.5,1.2310886618203398,1.2834257473481196,0.22621024983030757,4.0,4.0,0.3586968586968587,0.1128900017729848,0.2646978021978021,0.5772496646575843
+UCD-0159554,Angiogenesis,0.5739261646175711,0.5780673657229214,0.2902897665056452,525.0,4.0,131.25,139.0,0.9880846922959798,0.9719382835101253,0.27846571415583576,4.0,4.0,0.5145833333333333,0.3285266349530027,0.1126373626373626,0.9483175269293204
+UCD-0159562,Epigenetics,0.6982468600418825,0.7756535763587613,0.26806179032183,609.0,4.0,152.25,154.5,1.1433509490940466,1.1100409836065572,0.08134128656060702,4.0,4.0,0.3331582633053221,0.077562647432462,0.2839228897623058,0.5467995933961755
+UCD-0159570,GPCR & G Protein,,,,,,,,,,,,,,,,
+UCD-0159578,PI3K/Akt/mTOR,0.3327444649742764,0.24866773993849015,0.3151213783196859,163.0,2.0,81.5,81.5,0.5364151398264224,0.5364151398264224,0.7066124201293053,2.0,2.0,0.4166666666666666,0.2687065317554081,0.1660635437070482,0.7797256987023287
+UCD-0159586,Angiogenesis,0.6440841114277751,0.7092852715820189,0.28267059990673155,461.0,4.0,115.25,109.0,0.8714315274128501,0.7918032786885245,0.26526976729407364,4.0,4.0,0.6340277777777777,0.49375327127358,0.0525641025641025,1.279310745970745
+UCD-0159593,Epigenetics,0.4814178843484942,0.4491571512671414,0.29501471046874045,465.0,4.0,116.25,112.5,0.8742250545602192,0.7739030858244937,0.43223983672307376,4.0,4.0,0.5916666666666667,0.4351554525784915,0.0677517970751053,1.1690791809044627
+UCD-0159525,Others,,,,,,,,,,,,,,,,
+UCD-0159532,PI3K/Akt/mTOR,,,,,,,,,,,,,,,,
+UCD-0159540,Others,0.5116088769462863,0.5039401204636216,0.30354747962928913,519.0,4.0,129.75,131.5,0.9718298462943948,0.9622950819672131,0.09318378720330489,4.0,4.0,0.4937499999999999,0.2997080355947625,0.1291294375406524,0.888974740660675
+UCD-0159548,Others,,,,,,,,,,,,,,,,
+UCD-0159555,Cytoskeletal Signaling,0.3892127584052083,0.31475294039526247,0.29083613347613907,754.0,4.0,188.5,190.5,1.4174268414806024,1.378688524590164,0.32871725177930095,4.0,4.0,0.2281084656084656,-0.0677519781270529,0.5121716848174247,0.290584434980164
+UCD-0159563,Angiogenesis,0.37399050487094077,0.31394279937818215,0.2917164433942447,382.0,4.0,95.5,89.5,0.7347193808830361,0.7006991321118612,0.28109348237515436,4.0,4.0,0.5505050505050505,0.378216886573726,0.0997861199203481,1.0009298641357447
+UCD-0159571,Others,0.37318772150832336,0.28801815817044174,0.2979843931521069,585.0,4.0,146.25,143.5,1.107506812707163,1.116321118611379,0.1591784317092594,4.0,4.0,0.5249999999999999,0.3429359346321228,0.10713390843684,0.9700730509580888
+UCD-0159579,PI3K/Akt/mTOR,0.19455361382592368,0.11308449653774354,0.22580321585180702,412.0,4.0,103.0,99.5,0.7697680507458841,0.8105462268533882,0.14115414964986672,4.0,4.0,0.4996212121212121,0.3078296408684483,0.128125,0.8923661216001706
+UCD-0159587,Angiogenesis,,,,,,,,,,,,,,,,
+UCD-0159594,Others,0.508033467845327,0.5042038409620143,0.2962496737206389,195.0,4.0,48.75,47.5,0.3656253782126111,0.3261933461909354,0.31051104179791084,4.0,4.0,0.4777777777777777,0.2776137760867783,0.1520258882222072,0.8180824505110721
+UCD-0159526,DNA Damage,0.3484107665099829,0.258991283623786,0.28660618848089536,163.0,2.0,81.5,81.5,0.5638621022179363,0.5638621022179363,0.027922876267684787,2.0,2.0,0.2291666666666666,0.0336479169625036,0.2758920092253425,0.5592608779898103
+UCD-0159533,Cytoskeletal Signaling,0.4696336299649678,0.4754017956657745,0.2967876177454407,417.0,4.0,104.25,96.0,0.7913543281187169,0.7181171648987463,0.20548339628989165,4.0,4.0,0.6333333333333333,0.4927926512949721,0.0525641025641025,1.279310745970745
+UCD-0159541,Protein Tyrosine Kinase,,,,,,,,,,,,,,,,
+UCD-0159514B,Others,0.026997099351527953,0.004473421071574566,0.07982565529385986,576.0,4.0,144.0,163.0,1.0716499146947556,1.1959016393442623,0.3127018691676896,4.0,4.0,0.5055555555555555,0.3160385752310986,0.1208097895795056,0.9178978721048168
+UCD-0159556,GPCR & G Protein,0.3570918537801786,0.25782243523800136,0.31222940175085717,538.0,4.0,134.5,134.0,1.0093372153401083,0.9861286254728878,0.11004855239851392,4.0,4.0,0.4657828282828283,0.2610212491835491,0.1722689075630251,0.7637931003367765
+UCD-0159564,Epigenetics,,,,,,,,,,,,,,,,
+UCD-0159572,Epigenetics,0.2689953569021948,0.15938216050303564,0.2646935301255121,423.0,4.0,105.75,107.5,0.7949279980791679,0.7973053693502357,0.24992902787205196,4.0,4.0,0.3796717171717171,0.1419043718361561,0.2522159774068171,0.5982274051545786
+UCD-0159580,NF-κB,0.19422914227344343,0.09633069058355606,0.2581057411298288,31.0,1.0,31.0,31.0,0.20327868852459016,0.20327868852459016,,1.0,1.0,,,,
+UCD-0159588,MAPK,0.37875362081393904,0.31457649296261253,0.3000762434736432,725.0,4.0,181.25,164.5,1.3493501575316527,1.3284495918231896,0.19784411410311045,4.0,4.0,0.6813519813519813,0.559216499885713,0.0489284471184923,1.3104385675748629
+UCD-0159595,Endocrinology & Hormones,,,,,,,,,,,,,,,,
+UCD-0159527,DNA Damage,0.3255326054626771,0.23496212546447692,0.2775455054167025,648.0,4.0,162.0,155.0,1.2293658667754617,1.20425506268081,0.2371959307386991,4.0,4.0,0.4167896917896917,0.1932493977323837,0.2415814748254426,0.6169363717460645
+UCD-0159534,Others,,,,,,,,,,,,,,,,
+UCD-0159542,MAPK,0.2592283526243589,0.149629412159247,0.27146348681283905,527.0,4.0,131.75,131.0,0.9888380430157064,0.9520010619234088,0.17098720161740502,4.0,4.0,0.422008547008547,0.2004686024201045,0.2386694386694386,0.622203188311877
+UCD-0159549,JAK/STAT,0.15354494594388018,0.06678254374564187,0.19924353861002253,251.0,4.0,62.75,59.0,0.46050474547023296,0.45491803278688525,0.12828482118693985,4.0,4.0,0.5507996632996632,0.3786244223222264,0.0997861199203481,1.0009298641357447
+UCD-0159557,Others,0.21113619493283842,0.10724429329349139,0.249564678936916,337.0,4.0,84.25,99.0,0.6260351313154185,0.7338687197185902,0.35719403931865057,4.0,4.0,0.3223443223443223,0.0626038010946036,0.2869116355799164,0.5422518387680013
+UCD-0159565,TGF-beta/Smad,0.2688747630940155,0.1453456791117655,0.28972448894324027,189.0,4.0,47.25,54.0,0.3484209501481617,0.39567266210924534,0.20281205668698762,4.0,4.0,0.5525462962962963,0.381040527116907,0.0997861199203481,1.0009298641357447
+UCD-0159573,Protein Tyrosine Kinase,0.4102339593136315,0.3235172866977345,0.36172720627304655,323.0,4.0,80.75,83.0,0.6026649836612152,0.5873071359691417,0.16958767523171084,4.0,4.0,0.5343253968253968,0.3558356886305732,0.0997861199203481,1.0009298641357447
+UCD-0159581,PI3K/Akt/mTOR,0.37996792352926445,0.3323014620748569,0.3046163253786168,336.0,4.0,84.0,78.5,0.6419743039911923,0.6077190997856633,0.18742513225842844,4.0,4.0,0.5484217171717172,0.3753350266379019,0.0997861199203481,1.0009298641357447
+UCD-0159589,DNA Damage,0.38684315191711544,0.34422005889223783,0.2976327179411429,652.0,4.0,163.0,160.5,1.223203026481715,1.2540983606557377,0.16226720356761576,4.0,4.0,0.3854014041514041,0.1498302064049109,0.2522159774068171,0.5982274051545786
+UCD-0001944,Epigenetics,0.3009691412781178,0.20155996599410553,0.27555503070265597,690.0,4.0,172.5,174.5,1.295033263189128,1.303759872569191,0.06813747692270719,4.0,4.0,0.5536706349206348,0.382595816606082,0.0951865036372078,1.021424625264752
+UCD-0159665,Others,0.2980729517818694,0.2234271707713275,0.2542664281660867,596.0,4.0,149.0,160.5,1.1388087132377342,1.2841748035246487,0.30960888535504827,4.0,4.0,0.3314493240963829,0.0751986841817416,0.2839228897623058,0.5467995933961755
+UCD-0159535,Others,0.4393382798913854,0.3945250001094692,0.2927808822215445,619.0,4.0,154.75,142.5,1.162672611355553,1.1080838919492932,0.21705039205912252,4.0,4.0,0.3881521256521256,0.1536352595669297,0.251605537319823,0.5992798051942534
+UCD-0159543,MAPK,0.21827526184705587,0.0697991750169276,0.27972507608532243,128.0,4.0,32.0,28.0,0.23532225178516353,0.2286121988451583,0.12730884046618302,4.0,4.0,0.7124999999999999,0.6023033288562849,0.0448414145096609,1.3483206961651315
+UCD-0159550,MAPK,,,,,,,,,,,,,,,,
+UCD-0159558,Cytoskeletal Signaling,0.2931221363729607,0.21152785836179305,0.263558520521694,377.0,4.0,94.25,95.5,0.7127826590249825,0.6926229508196722,0.27622890754115353,4.0,4.0,0.4407675657675657,0.2264178174266585,0.2189821351832525,0.6595913139626328
+UCD-0159566,DNA Damage,0.31560111398723295,0.18119141986713938,0.3135962926729756,358.0,3.0,119.33333333333333,109.0,0.8843025011159306,0.8014705882352942,0.23528130686096488,3.0,3.0,0.5157407407407407,0.3614941508518548,0.1624708624708624,0.7892245140867148
+UCD-0159574,Epigenetics,0.1994620839601825,0.13342440586773535,0.22101316671896612,256.0,4.0,64.0,62.0,0.47747824423457574,0.4747348119575699,0.07717694529469288,4.0,4.0,0.5393065268065268,0.3627260497158933,0.0997861199203481,1.0009298641357447
+UCD-0159582,Neuronal Signaling,0.32754925058628476,0.2524530459808981,0.27808234333588944,393.0,4.0,98.25,100.5,0.7437508296276631,0.7418032786885246,0.12105912596527242,4.0,4.0,0.4171717171717172,0.1937778506809885,0.2415814748254426,0.6169363717460645
+UCD-0159632,Angiogenesis,0.14371165727467677,0.0722557120215275,0.17512621103702397,214.0,2.0,107.0,107.0,0.7014234181534561,0.7014234181534561,0.7123461411719406,2.0,2.0,0.1454545454545454,-0.07129744438948,0.3612700663391908,0.4421680211672878
+UCD-0159640,Epigenetics,0.7018769584007278,0.7761711478211322,0.2671081793595481,443.0,4.0,110.75,116.5,0.9079758660873997,0.9277200277200277,0.22668850675316105,4.0,4.0,0.3754176379176379,0.1360197347644036,0.2522159774068171,0.5982274051545786
+UCD-0001952,Epigenetics,0.6888062764832923,0.7777641911066024,0.29366478957321623,465.0,4.0,116.25,123.5,0.9160866152786304,0.9605689339529644,0.0937088900021795,4.0,4.0,0.2616174334924335,-0.0213992651499549,0.4216150821655409,0.3750838617800641
+UCD-0159654,Epigenetics,0.7766656937919629,0.8560505399184246,0.23313999542053848,454.0,4.0,113.5,128.0,0.9296801420217136,0.9219196382694481,0.14218107061327576,4.0,4.0,0.4663267288267288,0.2617736228730882,0.1661318032999449,0.7795472207963838
+UCD-0159662,GPCR & G Protein,2.711243805606576e-09,2.711243805606576e-09,,1.0,1.0,1.0,1.0,0.024691358024691357,0.024691358024691357,,1.0,1.0,,,,
+UCD-0159602,Angiogenesis,0.9612790397537415,0.9950161744965573,0.0904878813542986,422.0,4.0,105.5,119.0,0.8345010446246188,0.815883190883191,0.26764816513198497,4.0,4.0,0.4572089947089947,0.2491611271965792,0.17969891380296,0.7454545479928982
+UCD-0159609,Others,0.30690834045451515,0.18271186127029987,0.2906330169345574,112.0,2.0,56.0,56.0,0.3607658184654382,0.3607658184654382,0.47793648603597905,2.0,2.0,0.15,-0.0655990537278338,0.3612700663391908,0.4421680211672878
+UCD-0159617,Endocrinology & Hormones,0.6766962732593373,0.7346347166795598,0.258425600527868,480.0,3.0,160.0,158.0,1.0295898999321054,0.9513513513513514,0.1490969842080578,3.0,3.0,0.1982804232804232,-0.0570838436730149,0.4258618094234532,0.3707313049845587
+UCD-0159625,Endocrinology & Hormones,0.5323658357346064,0.5383368806074953,0.29357310029484895,553.0,4.0,138.25,160.0,1.2085953751352991,1.1302037632836113,0.3579310438560561,4.0,4.0,0.3881907308377896,0.1536886618411121,0.251605537319823,0.5992798051942534
+UCD-0159633,Apoptosis,0.33711353366429053,0.28772840840115654,0.26539336797936547,473.0,4.0,118.25,117.0,1.0212999925927682,0.9172438598294111,0.3172938725821409,4.0,4.0,0.2786761849261849,0.0021979823598574,0.3645825438928887,0.4382041290778644
+UCD-0159641,JAK/STAT,0.09489234912378883,0.021727193452112197,0.15798516107694466,406.0,4.0,101.5,107.0,0.7445713847805102,0.7092282396464906,0.31973187526065383,4.0,4.0,0.4537906537906538,0.2444325609215931,0.1835571835571835,0.7362286146021204
+UCD-0001953,Epigenetics,0.16273593415144108,0.07420247270504565,0.20517925236546197,397.0,3.0,132.33333333333334,151.0,0.8247840411338511,0.8162162162162162,0.3120459172403841,3.0,3.0,0.4873737373737373,0.3240916702047272,0.1661318032999449,0.7795472207963838
+UCD-0159655,Epigenetics,0.2090503671003534,0.11196308345154321,0.23405372927358478,363.0,4.0,90.75,88.5,0.7598678282455216,0.8075388442942308,0.227417903530182,4.0,4.0,0.4537906537906538,0.2444325609215931,0.1835571835571835,0.7362286146021204
+UCD-0159663,Protein Tyrosine Kinase,0.4177476464050575,0.3771626083673415,0.3052595372422761,538.0,4.0,134.5,165.0,0.9621217773784314,1.0642238392238392,0.3220366388235147,4.0,4.0,0.4537906537906538,0.2444325609215931,0.1835571835571835,0.7362286146021204
+UCD-0159603,GPCR & G Protein,0.153589175284165,0.06848374419585815,0.19086788457460932,527.0,4.0,131.75,165.5,0.900569903041386,0.979002079002079,0.5045065888273387,4.0,4.0,0.3361319236319236,0.0816760902602935,0.2839228897623058,0.5467995933961755
+UCD-0159610,Transmembrane Transporters,0.45689844053193807,0.4320305027066277,0.2873234628987765,539.0,3.0,179.66666666666666,156.0,1.1964238002260816,1.0,0.5281835281468477,3.0,3.0,0.2626984126984127,0.0278526077159899,0.3050708327816761,0.515599312408371
+UCD-0159618,MAPK,0.20143478289248015,0.11332292036922993,0.22932361785162458,479.0,4.0,119.75,118.5,1.0317699158161768,1.0490817740817742,0.4649756640267611,4.0,4.0,0.3837569837569837,0.1475554915637916,0.2522159774068171,0.5982274051545786
+UCD-0159626,Neuronal Signaling,0.1291394709828155,0.056029850299181755,0.18180753306195008,300.0,3.0,100.0,86.0,0.6281439965850613,0.4648648648648649,0.4295430947961698,3.0,3.0,0.4873737373737373,0.3240916702047272,0.1661318032999449,0.7795472207963838
+UCD-0159634,Cytoskeletal Signaling,0.05395439370494637,0.011061274405761979,0.09560634185628458,7.0,2.0,3.5,3.5,0.02092862092862093,0.02092862092862093,0.006664346655673627,2.0,2.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159642,Proteases,0.2685070746572331,0.2010762501379143,0.24996085098629783,375.0,4.0,93.75,110.5,0.616151348042983,0.6933645183645183,0.40258739992464226,4.0,4.0,0.2005529193029193,-0.1058693660827477,0.6131084950684059,0.212462666362127
+UCD-0159648,GPCR & G Protein,0.14408259104146992,0.05009563656165115,0.20121837694001563,458.0,4.0,114.5,118.5,0.8752303622233913,0.8380040369900953,0.09109478058088165,4.0,4.0,0.4537906537906538,0.2444325609215931,0.1835571835571835,0.7362286146021204
+UCD-0159656,GPCR & G Protein,,,,,,,,,,,,,,,,
+UCD-0159604,Autophagy,0.05684609877546145,0.009795381081015724,0.11667091291620506,197.0,3.0,65.66666666666667,57.0,0.4451532111227929,0.36538461538461536,0.26188088199243514,3.0,3.0,0.4873737373737373,0.3240916702047272,0.1661318032999449,0.7795472207963838
+UCD-0159611,Others,0.2535964338700853,0.1705539036879558,0.25086411877494585,423.0,4.0,105.75,126.5,0.6815463435552156,0.8707207207207208,0.42364386191285225,4.0,4.0,0.3585137085137085,0.1126366514489564,0.2646978021978021,0.5772496646575843
+UCD-0159619,GPCR & G Protein,0.11357923126071984,0.05350746628182235,0.1523942832072629,177.0,2.0,88.5,88.5,0.5567567567567567,0.5567567567567567,0.6268406060248313,2.0,2.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159627,Metabolism,0.4034059482776486,0.3547867832330263,0.30285172929849186,481.0,3.0,160.33333333333334,151.0,1.0198715094152357,1.027027027027027,0.04874057095660759,3.0,3.0,0.4624458874458875,0.2912237844982858,0.1661318032999449,0.7795472207963838
+UCD-0159635,MAPK,0.14489383187707908,0.0980214781651189,0.16590208794629133,17.0,1.0,17.0,17.0,0.10897435897435898,0.10897435897435898,,1.0,1.0,,,,
+UCD-0159643,Metabolism,0.20605616599610033,0.10628204156093468,0.23464685374411243,488.0,4.0,122.0,120.5,1.131320055797242,0.9308558558558558,0.5517399727571713,4.0,4.0,0.2914890664890665,0.0199219487783501,0.3352926143623818,0.4745760128610387
+UCD-0159649,Angiogenesis,0.05120708042074944,0.014168773253366409,0.09612497988630829,500.0,4.0,125.0,138.0,1.0020751496353524,1.036070686070686,0.17440436178088103,4.0,4.0,0.3489024864024864,0.0993415173993706,0.2721140245892721,0.5652490743732337
+UCD-0159657,Others,0.14603345792778855,0.04040464660898018,0.20591953804583613,525.0,4.0,131.25,132.0,1.1669060561487683,0.982969507969508,0.44371100017392007,4.0,4.0,0.3190656565656565,0.0580684464303664,0.2938365981844243,0.5318941125632697
+TGFRi,,,,,,,,,,,,,,,,,
+UCD-0159253,Epigenetics,0.9140261391646681,0.9780274679802041,0.1422684677172123,629.0,4.0,157.25,170.0,1.1341356509184188,1.1413232498887855,0.32088981946584644,4.0,4.0,0.1840557114454173,-0.1286898344239459,0.6763236763236763,0.1698454087941743
+UCD-0159596,Cytoskeletal Signaling,0.921113942075839,0.9912940907997152,0.15784144726855776,50.0,4.0,12.5,8.5,0.08232143054098812,0.05883158375171155,0.08633676351563685,4.0,4.0,0.393995574142633,0.161718459611015,0.2466165413533834,0.6079777972554067
+UCD-0159528,Transmembrane Transporters,0.9470903769140641,0.9961212501865124,0.1246785118978985,496.0,4.0,124.0,120.0,0.8892650864321507,0.93697173130586,0.31486117024492827,4.0,4.0,0.3073202819139351,0.0418211546031477,0.3075349471530855,0.5121055254386104
+UCD-0159536,JAK/STAT,,,,,,,,,,,,,,,,
+UCD-0159544,Angiogenesis,0.999877286979439,0.9999996801609635,0.0006739810922892575,181.0,2.0,90.5,90.5,0.6045207956600362,0.6045207956600362,0.06700252320827318,2.0,2.0,0.6,0.498541621775137,0.1060116354234001,0.9746464656516696
+UCD-0159551,Epigenetics,0.9419331756231333,0.9978214464412785,0.1446581321850988,8.0,3.0,2.6666666666666665,3.0,0.017982848900469316,0.0189873417721519,0.011124890029938923,3.0,3.0,0.330718954248366,0.1175390986016862,0.2483343428225317,0.6049632165430582
+UCD-0159559,DNA Damage,0.858424681915235,0.9423184294408459,0.19466046496449113,511.0,4.0,127.75,125.5,0.9076829497594249,0.9402597402597402,0.12852736735546033,4.0,4.0,0.7483796296296297,0.6519353610843652,0.040347711989503,1.394181087918541
+UCD-0159567,Others,0.6225934574383981,0.712854014536564,0.33489138483544023,453.0,4.0,113.25,114.5,0.8462244265824361,0.7796112115732369,0.3783611616989942,4.0,4.0,0.7930555555555555,0.7137352463748137,0.0194832194832194,1.7103392770124284
+UCD-0159575,Autophagy,0.8164099713709727,0.9579874253203081,0.25966103345199154,463.0,4.0,115.75,125.0,0.8719276446851567,0.8391500904159133,0.4949374135401772,4.0,4.0,0.312055292202351,0.0483710600971798,0.3065035925612848,0.5135644307038106
+UCD-0159583,MAPK,,,,,,,,,,,,,,,,
+UCD-0159590,Others,0.5651263807799242,0.6039981930136618,0.3380042074309702,134.0,4.0,33.5,31.5,0.260243679158226,0.2170926517571885,0.19447562665339282,4.0,4.0,0.2184515715765715,-0.0811102898142594,0.5390502354788068,0.2683707599067321
+UCD-0159597,Epigenetics,0.7525995145270943,0.8761867773504465,0.2859107439729464,79.0,2.0,39.5,39.5,0.27034358047016277,0.27034358047016277,0.12275271427469904,2.0,2.0,0.75,0.6865885136094606,0.1060116354234001,0.9746464656516696
+UCD-0159598,Endocrinology & Hormones,1.0449620458708487e-09,1.0449620458708487e-09,,1.0,1.0,1.0,1.0,0.00909090909090909,0.00909090909090909,,1.0,1.0,,,,
+UCD-0159605,PI3K/Akt/mTOR,0.22551976093068823,0.17051589759726185,0.25585264013042813,62.0,3.0,20.666666666666668,24.0,0.13751384162675465,0.17142857142857143,0.0638190132224439,3.0,3.0,0.3364316239316239,0.1250713717327998,0.2404549628269304,0.6189662549548729
+UCD-0159612,Cell Cycle,0.5544477859876256,0.6219810148924811,0.3384781633137786,264.0,4.0,66.0,51.0,0.4581269738269172,0.3893552134766192,0.24161920023703928,4.0,4.0,0.4572272498743087,0.2491863794347585,0.17969891380296,0.7454545479928982
+UCD-0159620,DNA Damage,0.510431410609915,0.506283387258547,0.3329817439762961,524.0,4.0,131.0,108.0,0.9232259980283396,0.8243958573072496,0.2834259198180385,4.0,4.0,0.2555217658158835,-0.0298313581703454,0.4279706625948083,0.368586000869722
+UCD-0159628,GPCR & G Protein,0.4132531088954716,0.3581538625261883,0.33393082922175077,587.0,4.0,146.75,147.5,1.0708023762341916,0.9380333238969547,0.28657019816917007,4.0,4.0,0.2193149443149443,-0.0799159925481478,0.5382402832067262,0.2690238017047381
+UCD-0159636,Protein Tyrosine Kinase,0.23297773883827103,0.10792030231644098,0.27234765050326826,656.0,4.0,164.0,167.5,1.1838134178475708,1.29751356238698,0.3134389489736037,4.0,4.0,0.5817185007974481,0.4213942266648438,0.0731519403106119,1.1357741499895764
+UCD-0159644,Transmembrane Transporters,0.10523022218402205,0.004073811551737843,0.20470894305874496,242.0,3.0,80.66666666666667,90.0,0.5510250988412513,0.569620253164557,0.5228062733776262,3.0,3.0,0.3936507936507936,0.2005160304574986,0.1947289998137455,0.7105693668095472
+UCD-0159650,MAPK,0.1699758693054776,0.06778393374442343,0.22237936552054396,503.0,4.0,125.75,127.5,0.9105227920055841,1.0344155844155845,0.372142703794841,4.0,4.0,0.3548890923890924,0.1076227461726653,0.2646978021978021,0.5772496646575843
+UCD-0159658,Autophagy,0.22442358009551644,0.07620090235321646,0.2885197573129299,559.0,4.0,139.75,137.5,1.0086472395441337,1.1165584415584415,0.42566137974597923,4.0,4.0,0.4377667471417472,0.2222668067198963,0.2190516665654787,0.659453438320374
+UCD-0159599,MAPK,,,,,,,,,,,,,,,,
+UCD-0159606,Autophagy,0.09929967906756133,0.038618138670683436,0.13773095869625562,201.0,4.0,50.25,54.5,0.3433110081571278,0.3463622760545153,0.19248824593040106,4.0,4.0,0.3400144993894994,0.0870468292316019,0.2782805429864253,0.5555171579096939
+UCD-0159613,PI3K/Akt/mTOR,0.1589878463410923,0.04020074618423163,0.2234739235977036,37.0,2.0,18.5,18.5,0.1199367088607595,0.1199367088607595,0.0989054421786247,2.0,2.0,0.75,0.6865885136094606,0.1060116354234001,0.9746464656516696
+UCD-0159621,PI3K/Akt/mTOR,,,,,,,,,,,,,,,,
+UCD-0159629,Angiogenesis,0.27325511025505933,0.2495764255340108,0.23163557601218235,152.0,4.0,38.0,45.5,0.2570747760348197,0.30730260155180283,0.11116360335541706,4.0,4.0,0.2338781588781589,-0.05977080314661,0.4869983948635634,0.3124724702084579
+UCD-0159637,Others,0.15683124911778243,0.03815042078040108,0.22784614203013231,548.0,4.0,137.0,140.0,0.9873940508272413,1.0583634719710668,0.2072512686269169,4.0,4.0,0.5056547619047619,0.316175806656614,0.1208097895795056,0.9178978721048168
+UCD-0159645,Transmembrane Transporters,,,,,,,,,,,,,,,,
+UCD-0159651,Angiogenesis,0.3165223192298968,0.21570696390148453,0.3028503661623773,705.0,4.0,176.25,176.0,1.255182351013325,1.258523505248745,0.08727640640892949,4.0,4.0,0.7118055555555555,0.6013427088776768,0.0448414145096609,1.3483206961651315
+UCD-0159659,Metabolism,0.7012046860291676,0.79596780446921,0.2948590053779129,616.0,4.0,154.0,159.0,1.0799292035258792,1.0814924691921497,0.2019628925895816,4.0,4.0,0.6066405816405815,0.4558687608884568,0.0677517970751053,1.1690791809044627
+UCD-0159666,Cytoskeletal Signaling,,,,,,,,,,,,,,,,
+UCD-0159607,Metabolism,0.3708866722436712,0.32407746390643033,0.2920625920742146,66.0,2.0,33.0,33.0,0.22513562386980107,0.22513562386980107,0.08567116517088369,2.0,2.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159614,Metabolism,0.1702133674787508,0.06205809338641777,0.23095970977919064,515.0,4.0,128.75,131.5,0.9285342761133395,0.9806962025316456,0.1869986442297468,4.0,4.0,0.3798611111111111,0.1421663591030492,0.2522159774068171,0.5982274051545786
+UCD-0159622,JAK/STAT,0.09185398063215883,0.024804948168084335,0.16037295435955987,566.0,4.0,141.5,136.5,1.0345785917281651,0.9205956184390689,0.2734659798846565,4.0,4.0,0.4266624579124579,0.2069063237219647,0.2326206402293359,0.6333517533220467
+UCD-0159630,Transmembrane Transporters,0.09921127511682538,0.03278817648139269,0.1603521026711586,576.0,4.0,144.0,140.0,1.0339519304046947,1.0019439421338154,0.1310643702049001,4.0,4.0,0.6380050505050504,0.4992550038783349,0.0525641025641025,1.279310745970745
+UCD-0159638,Apoptosis,,,,,,,,,,,,,,,,
+UCD-0159646,GPCR & G Protein,0.038563351860627114,0.006816182072660805,0.08800764240073441,324.0,4.0,81.0,65.5,0.5750645884224563,0.5344155844155845,0.22319135780478117,4.0,4.0,0.3491395042865631,0.0996693825245839,0.2721140245892721,0.5652490743732337
+UCD-0159652,Proteases,0.115872604394342,0.027741540684287577,0.18978800506630444,512.0,4.0,128.0,122.0,0.9159924164079541,0.9277270652670013,0.11803074444528015,4.0,4.0,0.295607389686337,0.0256187954783447,0.3234714003944773,0.490164111285638
+UCD-0159660,Epigenetics,,,,,,,,,,,,,,,,
+UCD-0159600,Epigenetics,0.4678388700751813,0.4978808648650782,0.38622962501322955,25.0,3.0,8.333333333333334,6.0,0.056211299249273934,0.04285714285714286,0.046178204392244,3.0,3.0,0.4253968253968254,0.2423738299099856,0.1781242013800153,0.7492770698026706
+UCD-0001957,Epigenetics,0.4916923283957767,0.5013806598184866,0.34208015172837364,334.0,4.0,83.5,84.5,0.5987000447483927,0.6737012987012987,0.18393867223606636,4.0,4.0,0.4331196581196581,0.2158385220778326,0.2283438632212474,0.6414106556355311
+UCD-0159615,Angiogenesis,0.15033908289952183,0.04920330434378989,0.22333634017757836,126.0,1.0,126.0,126.0,1.1454545454545455,1.1454545454545455,,1.0,1.0,,,,
+UCD-0159623,DNA Damage,0.1255203618660779,0.06164578259992454,0.15596302426241862,483.0,4.0,120.75,147.5,0.8168911067296436,0.9380029926800664,0.40729693269334794,4.0,4.0,0.2576215451215451,-0.0269267486399172,0.4258618094234532,0.3707313049845587
+UCD-0159631,Cell Cycle,0.13573521392886195,0.03824195687716729,0.2053287357690043,574.0,4.0,143.5,145.5,1.0123996246807345,1.0877287249491723,0.19218265883370975,4.0,4.0,0.5359725922225922,0.3581142421129657,0.0997861199203481,1.0009298641357447
+UCD-0159639,Protein Tyrosine Kinase,0.09716621562077887,0.025461348461085914,0.16694568655907022,472.0,4.0,118.0,140.5,0.7965353478163362,0.9021616047235815,0.4574781999466303,4.0,4.0,0.4497414622414622,0.2388313435305087,0.1947289998137455,0.7105693668095472
+UCD-0159647,GPCR & G Protein,0.12027038043544552,0.030393569994590124,0.20224398941112925,476.0,4.0,119.0,127.5,0.84076024472959,0.8458062037448943,0.029918858970526742,4.0,4.0,0.2441405816405816,-0.045574867944923,0.4536144127980863,0.3433131549545768
+UCD-0159653,Protein Tyrosine Kinase,0.23010946244053818,0.121881963847319,0.25950448252427155,567.0,4.0,141.75,146.5,1.0031495357879472,1.0015974440894568,0.027257611344722203,4.0,4.0,0.3076326451326451,0.0422532443849519,0.3071928071928071,0.5125889573678831
+UCD-0159661,Transmembrane Transporters,0.08291338263269502,0.011121560426931276,0.15436461729020184,183.0,4.0,45.75,36.0,0.3646683012260114,0.2396473779385172,0.3725719433146072,4.0,4.0,0.2388970982720982,-0.0528281405739431,0.4657707157707157,0.3318278201787167
+UCD-0159601,Protein Tyrosine Kinase,,,,,,,,,,,,,,,,
+UCD-0159608,Metabolism,0.09494480616962125,0.02055971662594091,0.1758622590596426,378.0,4.0,94.5,97.0,0.6650996150692838,0.616562866502204,0.16955184302926704,4.0,4.0,0.3588755199049316,0.1131371425302479,0.2646978021978021,0.5772496646575843
+UCD-0159616,GPCR & G Protein,,,,,,,,,,,,,,,,
+UCD-0159624,GPCR & G Protein,0.4017343464968235,0.36106845569825075,0.3343180761960101,245.0,4.0,61.25,53.5,0.42326467361247155,0.3551783659378596,0.43725857995482414,4.0,4.0,0.2882055308525896,0.0153798576750159,0.3460136151551465,0.4609068119863709
+UCD-0001078,Metabolism,0.9981507715828841,0.9999131970634401,0.004482208493306437,8.0,3.0,2.6666666666666665,3.0,0.013474316734561705,0.015151515151515152,0.008061763564543556,3.0,3.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0000972,Neuronal Signaling,0.34523265522223756,0.27787582497399055,0.3129880107515671,622.0,4.0,155.5,156.5,0.7586210159714999,0.7333615605625283,0.07877692280149316,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159326,Neuronal Signaling,0.5118059704721877,0.5413199562297135,0.3559988722395988,704.0,4.0,176.0,171.5,0.8563672131364555,0.8432058067442916,0.04921018891765456,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159331,Transmembrane Transporters,0.6041716288466134,0.6969817553169126,0.35294451543173555,665.0,4.0,166.25,167.0,0.8174052072466181,0.8189661319073084,0.19269627072833673,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159332,Apoptosis,0.707096083296747,0.8336446179931314,0.31614306998152175,677.0,4.0,169.25,166.5,0.8280850804786614,0.8112622584023612,0.1608019790214929,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0000936,Neuronal Signaling,0.8197004228050055,0.985256393310734,0.2933981609324686,651.0,4.0,162.75,149.5,0.8003798653656887,0.7335820717890127,0.2503258674351468,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001886,Cell Cycle,0.13100340452102222,0.009421383689029502,0.23107343550468284,274.0,4.0,68.5,58.5,0.3390756645287494,0.2778787878787879,0.33065844363374547,4.0,4.0,0.9791666666666666,0.9711814006417596,0.0027641070585855,2.5584451400053156
+UCD-0159344,Endocrinology & Hormones,0.15157537148860648,0.034909721183135636,0.23150318369416573,812.0,4.0,203.0,221.5,0.9884517130577921,1.0342701525054467,0.23938242738172172,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159350,Neuronal Signaling,0.8582978862472107,0.9700622867526021,0.2085034637704065,918.0,4.0,229.5,235.0,1.1182024272667703,1.0965032679738562,0.14381121523219864,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159355,PI3K/Akt/mTOR,0.21079901959172598,0.061675611238178885,0.2845874660394801,941.0,4.0,235.25,245.0,1.1429130695278813,1.183712121212121,0.12030044150838014,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159361,Others,0.7799901547992171,0.949558261604118,0.2981140677949224,906.0,4.0,226.5,237.0,1.109449202443305,1.2083755290696165,0.27010199538953666,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159368,Angiogenesis,,,,,,,,,,,,,,,,
+UCD-0159375,NF-κB,0.21332491904546647,0.042582931228350746,0.2973228523152102,878.0,4.0,219.5,211.5,1.0724480139618882,1.0277837284585356,0.21190682684215478,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159383,NF-κB,0.09874758380676049,0.001810660556552986,0.21720911150584796,953.0,4.0,238.25,237.0,1.1624824120788508,1.1073257080610022,0.28312055977057127,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159333,Transmembrane Transporters,0.32249564145255066,0.2165298803421395,0.3177963796816247,936.0,4.0,234.0,231.5,1.1393725875876564,1.1460832619251642,0.14650880055542873,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001616,Metabolism,0.14430038603983558,0.022438700390683637,0.2333838028350127,815.0,4.0,203.75,207.0,0.9967789073251798,1.0103030303030303,0.10898096339558166,4.0,4.0,0.9791666666666666,0.9711814006417596,0.0027641070585855,2.5584451400053156
+UCD-0159339,Neuronal Signaling,0.22589846665998245,0.09313267327947723,0.2820549226970223,514.0,4.0,128.5,145.0,0.6333202520186492,0.708716577540107,0.21625961925570417,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159345,Neuronal Signaling,0.05753960729870553,0.0038002177041555063,0.14331510815236786,796.0,4.0,199.0,196.5,0.9733589734483806,0.9588510425592688,0.14470751905604384,4.0,4.0,0.7756944444444445,0.6897197469096136,0.0259478741154133,1.5858982176908607
+UCD-0159351,Others,0.1445887024207281,0.02311108569661876,0.2332156089495998,888.0,4.0,222.0,222.5,1.0824400266845071,1.0766388174807198,0.10496524901955928,4.0,4.0,0.6296717171717171,0.4877275641350388,0.0525641025641025,1.279310745970745
+UCD-0001790B,Protein Tyrosine Kinase,,,,,,,,,,,,,,,,
+UCD-0159362,Stem Cells & Wnt,0.10746917778521341,0.013322563338018772,0.20073343512571107,872.0,4.0,218.0,216.0,1.0634158415904598,1.086397933923641,0.06729790624676073,4.0,4.0,0.6158267400527462,0.4685759073966888,0.0563186813186813,1.2493475225933015
+UCD-0159369,Epigenetics,0.7193869358956596,0.8329305657619154,0.2892290983536525,980.0,4.0,245.0,250.0,1.1901844259165066,1.2185625535561266,0.17661895335180927,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159376,Transmembrane Transporters,0.03428054541194044,0.00038152960231780465,0.11722615306400648,908.0,4.0,227.0,222.5,1.1086639340631477,1.0804770065695517,0.10194982167594961,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159384,NF-κB,,,,,,,,,,,,,,,,
+UCD-0159334,Metabolism,0.81718253393268,0.9789968927745494,0.2698818029681038,362.0,4.0,90.5,91.5,0.44247734604475114,0.4448856209150327,0.22498851843275564,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159338,Neuronal Signaling,0.1298324978557497,0.01974955146254343,0.21342324320368936,870.0,4.0,217.5,211.5,1.063722358292334,1.0205169340463458,0.13827508419664666,4.0,4.0,0.5144841269841269,0.3283894035274873,0.1126373626373626,0.9483175269293204
+UCD-0159340,Others,,,,,,,,,,,,,,,,
+UCD-0159346,Stem Cells & Wnt,0.8558897423896336,0.9741823447383983,0.23157032639619549,877.0,4.0,219.25,216.0,1.0691317129406877,1.0837611408199643,0.03778487636188176,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001900,PI3K/Akt/mTOR,0.1921581419983801,0.06426580007342485,0.260747447401336,893.0,4.0,223.25,221.0,1.0871040366108793,1.0699215808470308,0.04961056172918925,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159356,Cytoskeletal Signaling,0.10297206577194529,0.007208505266855482,0.19669159086898377,701.0,4.0,175.25,175.0,0.8559263784322382,0.871301247771836,0.08186497199376802,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159363,Others,,,,,,,,,,,,,,,,
+UCD-0018540,MAPK,0.4476487048868842,0.43963671333833887,0.34780414206256827,823.0,4.0,205.75,203.0,1.0051265617513916,0.9904789107873941,0.09531409515965888,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159377,Metabolism,0.6168237291783326,0.6986844224375902,0.337880275697266,661.0,4.0,165.25,157.5,0.8098206165694446,0.7626262626262625,0.1422971975078557,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159385,Metabolism,0.17095444636291413,0.04573649548228213,0.24358466855051583,686.0,4.0,171.5,180.0,0.8407938940032922,0.8791087344028521,0.1456910238486055,4.0,4.0,0.6248704998704998,0.4810860749589285,0.0547226053298927,1.2618332341413614
+UCD-0000684,Others,0.22318569579243291,0.0964656795980531,0.273803973374643,786.0,4.0,196.5,194.5,0.95642951326359,0.9412878787878788,0.07981037600261215,4.0,4.0,0.6372204184704184,0.4981696280583492,0.0525641025641025,1.279310745970745
+UCD-0001186,Neuronal Signaling,0.08753369887432659,0.002507676337509525,0.18662881544431925,742.0,4.0,185.5,184.0,0.907091708200509,0.9055133827309845,0.10234833356308452,4.0,4.0,0.6030753968253968,0.450937066512766,0.0677517970751053,1.1690791809044627
+UCD-0159341,Metabolism,0.14522596769287463,0.011076907902571126,0.24772138073309868,758.0,4.0,189.5,181.5,0.9243969382824668,0.9254823297239749,0.16023071443376483,4.0,4.0,0.3557359307359307,0.1087941715345243,0.2646978021978021,0.5772496646575843
+UCD-0159347,Metabolism,0.7947990872021455,0.977291729458985,0.3201746307878438,713.0,4.0,178.25,183.0,0.8715896521289308,0.9016042780748663,0.10006401922080736,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001861,Angiogenesis,0.3994782126343048,0.33595507894945115,0.33327284977980565,439.0,4.0,109.75,111.0,0.535007226099393,0.5178540305010894,0.07147297000264194,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159357,Apoptosis,0.8905682263066627,0.9962931991174285,0.177296851477987,10.0,3.0,3.3333333333333335,4.0,0.016165278641209885,0.018518518518518517,0.005937596322607866,3.0,3.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0011696,JAK/STAT,0.29911641601574707,0.05037346437504042,0.383818831118766,760.0,4.0,190.0,192.0,0.9266479586173749,0.9175762527233116,0.07659911233043981,4.0,4.0,0.6649305555555556,0.5365008603216365,0.0502759043531575,1.298640108656559
+UCD-0159370,PI3K/Akt/mTOR,0.5563156217563376,0.5898554805599423,0.3769582445806872,731.0,4.0,182.75,176.5,0.8948715251206556,0.8245642701525054,0.22383016733767022,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159378,Protein Tyrosine Kinase,0.22447380707487832,0.02393225761014812,0.3209901501418284,591.0,4.0,147.75,145.5,0.7198983050933754,0.7284022746747683,0.046810098819161074,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159386,DNA Damage,0.20775616697103605,0.0753040845010512,0.26992215315439305,793.0,4.0,198.25,196.5,0.9702237844191193,0.9589958407605466,0.11659252629754197,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159335,JAK/STAT,0.7371280798900858,0.8510775845071483,0.2952788722604298,50.0,2.0,25.0,25.0,0.12744593981551489,0.12744593981551489,0.14696016332417827,2.0,2.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0018448,GPCR & G Protein,0.4272588677130382,0.3686808816729358,0.36012387183566164,683.0,4.0,170.75,170.5,0.8374216826768619,0.8330243612596554,0.14585526712791702,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159342,Others,0.28450061865059173,0.14127800352678757,0.30825147228791416,671.0,4.0,167.75,177.0,0.8223263196764256,0.8627510398098633,0.15810437045716766,4.0,4.0,0.9305555555555556,0.9039380021391992,0.0027641070585855,2.5584451400053156
+UCD-0001885,PI3K/Akt/mTOR,0.9301004606072607,0.998569920432415,0.17824520328978238,274.0,4.0,68.5,78.5,0.3378351493410846,0.38078431372549015,0.16620692707696225,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159352,Proteases,0.4195018291082867,0.34015381005631795,0.3634623549689585,464.0,4.0,116.0,114.5,0.566735638557051,0.5770588235294118,0.11161849802045753,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159358,Metabolism,0.31203025069394574,0.20489707603782603,0.3074734076368607,768.0,4.0,192.0,194.0,0.9379180591896076,0.9370791245791246,0.09411442427650975,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159364,Others,0.2375226625340119,0.07967046051687557,0.29697664911815336,756.0,4.0,189.0,188.0,0.9208613371733363,0.9212908496732026,0.08218129297280707,4.0,4.0,0.6746031746031745,0.5498809243093907,0.0496199835406883,1.3043433841224432
+UCD-0159371,Apoptosis,0.830916340110495,0.9978355085153778,0.2517047645561931,470.0,4.0,117.5,112.0,0.5687393301430047,0.5475638740344623,0.15417001922214116,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159406,Neuronal Signaling,0.9085876235742428,0.9880309125813642,0.17420910906762965,652.0,4.0,163.0,160.5,0.911870298130483,0.87807336523126,0.0727749674870243,4.0,4.0,0.8898809523809524,0.8476731176778731,0.010298273155416,1.98723559295969
+UCD-0000841,Neuronal Signaling,0.9120864131180358,0.989710649808193,0.1738488683961761,669.0,4.0,167.25,166.0,0.9448852951147371,0.8649669372614376,0.22139658971805765,4.0,4.0,0.8958333333333334,0.8559070032087989,0.010298273155416,1.98723559295969
+UCD-0159409,Others,0.9694219925740604,0.998575824139516,0.09575484114463668,507.0,4.0,126.75,122.5,0.7108481087701133,0.6931291866028708,0.1763845422464942,4.0,4.0,0.5829365079365079,0.4230790871331337,0.0731519403106119,1.1357741499895764
+UCD-0159413,NF-κB,0.9645868585284775,0.9990749717688214,0.11118097409295608,441.0,4.0,110.25,113.0,0.6086134928180569,0.6420659369469752,0.20555817811888258,4.0,4.0,0.5314484126984127,0.3518559772906257,0.1047789419882443,0.9797259911809444
+UCD-0159421,Apoptosis,0.9957429034524001,0.9999601561379322,0.024686541949903953,382.0,4.0,95.5,99.0,0.5390400032501812,0.5074704944178628,0.1057450698441131,4.0,4.0,0.9458333333333332,0.9250716416685754,0.0027641070585855,2.5584451400053156
+UCD-0159429,Stem Cells & Wnt,0.9953439093389719,0.9999649856009679,0.029117453426044305,468.0,4.0,117.0,115.5,0.6582825377140081,0.6320627923229933,0.09093524714947429,4.0,4.0,0.6076388888888888,0.4572497120864757,0.0677517970751053,1.1690791809044627
+UCD-0159434,Angiogenesis,0.7739503151986251,0.9224405905671382,0.2805382568354016,452.0,4.0,113.0,114.5,0.6365562476510445,0.6108452950558214,0.10619550725414974,4.0,4.0,0.7544642857142857,0.6603522218493116,0.0394811374192817,1.4036103441533103
+UCD-0159442,Epigenetics,0.9097251349105211,0.9907937276103206,0.1833869646626699,727.0,4.0,181.75,178.5,1.0181653087831728,1.021746142680501,0.11200361176363816,4.0,4.0,0.7683531746031746,0.6795646214214719,0.0259478741154133,1.5858982176908607
+UCD-0159443,Others,0.8877708811184992,0.9818619383325008,0.2003248339180274,602.0,4.0,150.5,150.5,0.8466576810447909,0.8013652312599682,0.13803874310677422,4.0,4.0,0.8291666666666666,0.7636874852624301,0.0125152625152625,1.9025600363686452
+UCD-0159451,Ubiquitin,0.06047635990064606,0.0006907648879350142,0.1720761080084479,44.0,2.0,22.0,22.0,0.11809132420091324,0.11809132420091324,0.1573199580207002,2.0,2.0,0.1964285714285714,-0.0073940633981622,0.3234714003944773,0.490164111285638
+UCD-0159458,Others,0.6462563925226518,0.7573917394329959,0.3359569889557439,574.0,4.0,143.5,166.0,0.7613172059046268,0.8492554163754638,0.3682602333307624,4.0,4.0,0.5949735449735449,0.4397298334290059,0.0677517970751053,1.1690791809044627
+UCD-0159466,Cell Cycle,0.7838160160404646,0.9350276728631479,0.27651654041674323,551.0,4.0,137.75,151.0,0.7384918331832211,0.7743535293801409,0.27723549965236244,4.0,4.0,0.5588624338624338,0.3897775945413895,0.0807436291307259,1.0928917345389368
+UCD-0159474,DNA Damage,0.7071047643793658,0.83130702779645,0.309684463880657,712.0,4.0,178.0,185.0,1.0066459887387604,1.0007257674318586,0.2701134810973104,4.0,4.0,0.5204545454545454,0.3366482402266886,0.1082488939631796,0.9655765323708264
+UCD-0159482,Angiogenesis,,,,,,,,,,,,,,,,
+UCD-0159490,Ubiquitin,,,,,,,,,,,,,,,,
+UCD-0159498,PI3K/Akt/mTOR,,,,,,,,,,,,,,,,
+UCD-0159506,NF-κB,0.28482248279945616,0.10936612849109414,0.3332707411762328,550.0,4.0,137.5,140.0,0.7579140975137832,0.755846459867749,0.0636384221827149,4.0,4.0,0.7822420634920635,0.6987770209936319,0.0216841981547863,1.663856632698532
+UCD-0159514,Others,0.04611524008159305,0.0017156814717400864,0.12600574875801282,682.0,4.0,170.5,186.5,0.9602461970547564,0.981097288676236,0.2909566653644352,4.0,4.0,0.5564213564213565,0.3864008697681008,0.0897892143443745,1.0467758285002375
+UCD-0159444,MAPK,0.24052864340932043,0.07770884683615686,0.30374251354160364,934.0,4.0,233.5,227.0,1.284090715896602,1.241265917602996,0.12895217479455706,4.0,4.0,0.4112193362193362,0.1855439651500628,0.2433448904037139,0.6137777683552145
+UCD-0159452,Ubiquitin,,,,,,,,,,,,,,,,
+UCD-0159459,Cytoskeletal Signaling,0.115730971056896,0.017607401186980974,0.20305526480605754,682.0,4.0,170.5,182.0,0.9565389954239707,1.0733428310305897,0.2752111299005006,4.0,4.0,0.6199074074074074,0.4742206650418838,0.0547226053298927,1.2618332341413614
+UCD-0159467,Ubiquitin,8.587363474179593e-35,8.587363474179593e-35,,1.0,1.0,1.0,1.0,0.004784688995215311,0.004784688995215311,,1.0,1.0,,,,
+UCD-0159475,Metabolism,,,,,,,,,,,,,,,,
+UCD-0159483,Endocrinology & Hormones,0.6616583153816692,0.7545973851702998,0.3208056396601332,792.0,4.0,198.0,199.5,1.1266135539430664,1.0058437001594895,0.31696505886428544,4.0,4.0,0.4667522170811645,0.2623621976988267,0.1661318032999449,0.7795472207963838
+UCD-0159491,Others,,,,,,,,,,,,,,,,
+UCD-0159499,Cell Cycle,,,,,,,,,,,,,,,,
+UCD-0159507,Cytoskeletal Signaling,,,,,,,,,,,,,,,,
+UCD-0159515,Epigenetics,0.24020093412348678,0.07538195260273566,0.3044287255789105,381.0,4.0,95.25,76.5,0.5084054073294533,0.43685547175619516,0.2595447257723342,4.0,4.0,0.569047619047619,0.4038666875609736,0.0780735509977387,1.1074960672902194
+UCD-0159445,Others,0.44226492185334276,0.3092564345428108,0.43382405872606805,3.0,1.0,3.0,3.0,0.016,0.016,,1.0,1.0,,,,
+UCD-0159453,TGF-beta/Smad,0.15265388033184746,0.03440009046985351,0.2337511215281821,706.0,4.0,176.5,185.5,0.9944440928324421,1.0345411537014138,0.22945080587437755,4.0,4.0,0.2415865384615384,-0.0491078574067066,0.4546268211426582,0.3423449462383081
+UCD-0159460,Angiogenesis,0.4971101724767224,0.4854246067825251,0.34413600146163714,826.0,4.0,206.5,206.0,1.1696653152884449,1.1279850187265916,0.21456528956293536,4.0,4.0,0.3691942195231669,0.1274109250996534,0.2538628883806549,0.5954007829762935
+UCD-0159468,Apoptosis,0.7254765055040252,0.9277802438359309,0.35097648682362953,281.0,3.0,93.66666666666667,129.0,0.4724457844486683,0.688,0.4035869304381908,3.0,3.0,0.5134259259259258,0.3584420196417776,0.1624708624708624,0.7892245140867148
+UCD-0159476,Metabolism,0.08778583968809121,0.007175331558885654,0.1826209090388542,642.0,4.0,160.5,172.5,0.8908245977348397,0.8738082191780823,0.07168720550622287,4.0,4.0,0.6265873015873016,0.4834609143599228,0.0547226053298927,1.2618332341413614
+UCD-0159484,Epigenetics,0.07337647475585315,0.003351665986884727,0.17660860453743543,726.0,4.0,181.5,173.5,1.0091534711518184,1.0321594896331738,0.09768975323013931,4.0,4.0,0.8301587301587301,0.7650597995175845,0.0125152625152625,1.9025600363686452
+UCD-0159492,Cell Cycle,0.16443569137934327,0.039251861880949,0.24473441541579677,684.0,4.0,171.0,180.0,0.9384449424455258,0.9602360446570973,0.13874171848639497,4.0,4.0,0.6777777777777778,0.5542723299258845,0.0496199835406883,1.3043433841224432
+UCD-0159500,Epigenetics,0.11149710148606055,0.010908714834554856,0.20914407765224266,623.0,4.0,155.75,189.5,0.8199504759772576,0.985444330949949,0.5448343955351409,4.0,4.0,0.4602843915343915,0.2534153013875576,0.1747028481722359,0.7577000146770427
+UCD-0159508,Epigenetics,0.0030796422187744895,0.0030796422187744895,,1.0,1.0,1.0,1.0,0.005333333333333333,0.005333333333333333,,1.0,1.0,,,,
+UCD-0159516,Proteases,0.21572152724949353,0.04615227766916359,0.2970204439078891,377.0,4.0,94.25,98.5,0.4979494366066144,0.4872025518341308,0.41390096357215317,4.0,4.0,0.5954365079365079,0.4403702467480779,0.0677517970751053,1.1690791809044627
+UCD-0159446,Transmembrane Transporters,,,,,,,,,,,,,,,,
+UCD-0159454,TGF-beta/Smad,,,,,,,,,,,,,,,,
+UCD-0159461,Angiogenesis,0.013746554501021215,0.00245469840991945,0.030919832122435972,7.0,1.0,7.0,7.0,0.03349282296650718,0.03349282296650718,,1.0,1.0,,,,
+UCD-0159469,Protein Tyrosine Kinase,,,,,,,,,,,,,,,,
+UCD-0159477,Metabolism,0.3505181801038599,0.24333762865372133,0.3306120396283269,139.0,4.0,34.75,29.0,0.18064612989089063,0.16376255707762555,0.13862050518502778,4.0,4.0,0.5926346801346801,0.4364944986092785,0.0677517970751053,1.1690791809044627
+UCD-0159485,Transmembrane Transporters,,,,,,,,,,,,,,,,
+UCD-0159493,Epigenetics,0.2501230845517162,0.08301562754792696,0.3131086015306967,442.0,4.0,110.5,103.0,0.6001035487118456,0.6076986301369862,0.188440278126826,4.0,4.0,0.55,0.3775182538620111,0.0997861199203481,1.0009298641357447
+UCD-0159501,Cell Cycle,0.0831581691666162,0.005570860694854135,0.18417611819066534,280.0,4.0,70.0,66.5,0.35648012050218736,0.3592209737827715,0.3139462970179292,4.0,4.0,0.5572180134680135,0.3875028797002701,0.0897892143443745,1.0467758285002375
+UCD-0159509,Others,0.1105064651677384,0.016450753307194477,0.20101457247313004,424.0,4.0,106.0,113.0,0.5567522949495277,0.6233033707865169,0.35620284587949935,4.0,4.0,0.4379477004477004,0.2225171181195835,0.2190516665654787,0.659453438320374
+UCD-0159517,Proteases,0.3556983528548707,0.2654726149719398,0.32955882528321145,638.0,4.0,159.5,161.0,0.8859351277083105,0.8932784951169954,0.0770204965553224,4.0,4.0,0.6000945691735166,0.4468137091757019,0.0677517970751053,1.1690791809044627
+UCD-0159447,Ubiquitin,0.02456969858172466,0.0001072557307838865,0.10618810429912949,706.0,4.0,176.5,173.0,0.986317061561231,0.9888715660448364,0.08246829277190185,4.0,4.0,0.5747113997113997,0.4117013543994909,0.0775671215231654,1.1103223247063967
+UCD-0159455,TGF-beta/Smad,0.11627553051843342,0.0071838927105771985,0.23116962436852875,711.0,4.0,177.75,169.5,0.9904307307580802,0.9869027113237641,0.0820760171257066,4.0,4.0,0.56627886002886,0.4000366832306793,0.0780735509977387,1.1074960672902194
+UCD-0159462,GPCR & G Protein,0.10662847910369759,0.006002324935729719,0.2196027267834935,866.0,4.0,216.5,218.5,1.2264219393124394,1.1292000430084403,0.26918472300071755,4.0,4.0,0.5324494949494949,0.3532407671299178,0.1024660957771325,0.9894198112195514
+UCD-0159470,MAPK,0.25322979173351834,0.10595880146538586,0.30595353647281065,715.0,4.0,178.75,180.5,1.0132613860059076,0.9944719101123596,0.19207514289670052,4.0,4.0,0.4451388888888889,0.232464637092202,0.2100876651438449,0.6775994455341782
+UCD-0159379,PI3K/Akt/mTOR,0.8754297144543968,0.9759272013987341,0.2090137948225087,756.0,4.0,189.0,171.0,0.9334286270085803,0.8413279887618679,0.199930019871556,4.0,4.0,0.5553751803751803,0.3849537020081198,0.0951865036372078,1.021424625264752
+UCD-0159387,Neuronal Signaling,0.9888601510936009,0.99954203220115,0.04430062783444363,809.0,4.0,202.25,207.5,1.0035568401297241,0.9918164774622453,0.11224617973679157,4.0,4.0,0.5594907407407407,0.3906467269029872,0.0807436291307259,1.0928917345389368
+UCD-0159336,Ubiquitin,,,,,,,,,,,,,,,,
+UCD-0001419,Transmembrane Transporters,0.9917529613532274,0.9999337803902641,0.04538110816888636,724.0,4.0,181.0,163.5,0.8965337686277699,0.8291083916083917,0.16159623769112277,4.0,4.0,0.7236531986531987,0.6177314679066559,0.0417582417582417,1.3792577957042835
+UCD-0159343,GPCR & G Protein,,,,,,,,,,,,,,,,
+UCD-0001863,Angiogenesis,0.9838102318424772,0.9997371438330593,0.0677100257809675,636.0,4.0,159.0,191.5,0.8028991346331802,1.0247230944411856,0.4870891266994304,4.0,4.0,0.5004629629629629,0.3089940287213065,0.1228771228771228,0.9105289660399208
+UCD-0159353,Proteases,0.9382881009770744,0.9941970814224109,0.13493157493301283,597.0,4.0,149.25,143.0,0.7494209995633342,0.729387193856372,0.16009615202501634,4.0,4.0,0.5996212121212121,0.4461589177880015,0.0677517970751053,1.1690791809044627
+UCD-0159359,Others,0.8584731066902558,0.9708322050172475,0.22979140471918602,723.0,4.0,180.75,174.5,0.9032274853092803,0.92096546012905,0.05906566892593798,4.0,4.0,0.600297619047619,0.4470945865983339,0.0677517970751053,1.1690791809044627
+UCD-0159365,TGF-beta/Smad,0.4976365377659735,0.505087493782663,0.34279696758830563,694.0,4.0,173.5,184.0,0.8459904998946741,0.9080846735128851,0.33345824677329144,4.0,4.0,0.7232142857142857,0.6171243228119513,0.0417582417582417,1.3792577957042835
+UCD-0159372,Protein Tyrosine Kinase,0.36927445157472183,0.24552828414970979,0.3337360660501811,61.0,1.0,61.0,61.0,0.2785388127853881,0.2785388127853881,,1.0,1.0,,,,
+UCD-0159380,GPCR & G Protein,0.8586081318562958,0.9566757296635687,0.2072205749339833,825.0,4.0,206.25,202.0,1.027931759144666,1.0581293706293706,0.18995367242726435,4.0,4.0,0.7958333333333333,0.7175777262892458,0.0194832194832194,1.7103392770124284
+UCD-0159388,NF-κB,,,,,,,,,,,,,,,,
+UCD-0159337,Neuronal Signaling,0.5776538213078914,0.6155834687603743,0.3105714528650265,800.0,4.0,200.0,206.0,1.001412702903011,1.0416018057285181,0.24004566671451272,4.0,4.0,0.39375,0.1613787586752094,0.2466165413533834,0.6079777972554067
+UCD-0001391,Metabolism,0.8553415739097523,0.9569092051929649,0.21527468337456532,765.0,4.0,191.25,201.0,0.9468343197122397,0.9380927291886196,0.21490997198405146,4.0,4.0,0.6583333333333333,0.5273749705248603,0.0502759043531575,1.298640108656559
+UCD-0001856,Angiogenesis,0.3079330289733557,0.19758195454101393,0.3097849592069099,990.0,4.0,247.5,244.0,1.2332661745509819,1.277972027972028,0.24593212593490793,4.0,4.0,0.5184523809523809,0.3338786605481044,0.1090038993264799,0.9625579660439308
+UCD-0159348,Cell Cycle,,,,,,,,,,,,,,,,
+UCD-0159354,Metabolism,0.5316557091347441,0.5549232931470386,0.3373694087221633,780.0,4.0,195.0,191.0,0.9721254758261763,0.9895104895104896,0.05911327929554458,4.0,4.0,0.6801587301587301,0.5575658841382548,0.0496199835406883,1.3043433841224432
+UCD-0000201,GPCR & G Protein,0.31125091215415523,0.15950348628300842,0.3231382947823087,424.0,3.0,141.33333333333334,98.0,0.6892063416036018,0.5568181818181818,0.33342469271082154,3.0,3.0,0.4674603174603174,0.2978354141845309,0.1661318032999449,0.7795472207963838
+UCD-0159366,Metabolism,0.3067650037230786,0.21988536008442922,0.2822689994038671,832.0,4.0,208.0,221.0,1.0254653745292008,1.0868896531679906,0.18923691182036123,4.0,4.0,0.3733886483886484,0.133213048220179,0.2522159774068171,0.5982274051545786
+UCD-0159373,Angiogenesis,0.24686517310164766,0.10615108512297466,0.28886470881495185,257.0,4.0,64.25,59.5,0.331562771734781,0.28725141759543604,0.17353905005903442,4.0,4.0,0.5095238095238095,0.3215278322517159,0.1176594106530412,0.9293733312908564
+UCD-0159381,Neuronal Signaling,0.22685580731318647,0.14506144500115326,0.25846157346387394,540.0,4.0,135.0,151.5,0.6807859754731196,0.7104956972251493,0.2517442429133906,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0001864,Angiogenesis,0.10578105670270746,0.10578105670270746,0.1495967652880162,2.0,1.0,2.0,2.0,0.010075566750629723,0.010075566750629723,,1.0,1.0,,,,
+UCD-0000737,GPCR & G Protein,0.6681881279075255,0.7540221615161338,0.2963015573810371,909.0,4.0,227.25,230.0,1.1433540984351214,1.0771535827186511,0.163405080169825,4.0,4.0,0.3893939393939394,0.1553530515366682,0.2493902963876854,0.6031204486412295
+UCD-0001870,Angiogenesis,1.9258863219054208e-19,1.9258863219054208e-19,,1.0,1.0,1.0,1.0,0.004807692307692308,0.004807692307692308,,1.0,1.0,,,,
+UCD-0001888,PI3K/Akt/mTOR,0.09084488563777376,0.012898240981322574,0.16390960615189804,635.0,4.0,158.75,165.5,0.7869841002955156,0.816508405977584,0.3739874659167992,4.0,4.0,0.4232142857142857,0.202136492053292,0.238095238095238,0.6232492903979004
+UCD-0159349,Metabolism,0.8207345891205396,0.9914721213498052,0.30411072847985754,386.0,4.0,96.5,87.5,0.5003480736293829,0.47486888111888115,0.38927622315499094,4.0,4.0,0.4796296296296296,0.2801754293630664,0.147401980735314,0.8314966805432
+UCD-0001567,Transmembrane Transporters,0.15102523069302243,0.06620116759182341,0.18840630867432626,83.0,2.0,41.5,41.5,0.1998723301473379,0.1998723301473379,0.030815909203913246,2.0,2.0,0.1833333333333333,-0.0238108555424286,0.3234714003944773,0.490164111285638
+UCD-0159360,Metabolism,0.24271184848048089,0.12131775385795956,0.2693226338153806,743.0,4.0,185.75,189.0,0.921996033954084,0.8859545135230067,0.23043691919515627,4.0,4.0,0.3622023809523809,0.1177391653613028,0.2589233551258867,0.5868287740346956
+UCD-0159367,Neuronal Signaling,0.6417548682864812,0.7217747351052474,0.32745566336169973,755.0,4.0,188.75,193.0,0.9492801476455638,0.926313791794624,0.11764520974464948,4.0,4.0,0.5871212121212122,0.4288677581730573,0.072560398403095,1.1393003413863467
+UCD-0159374,Endocrinology & Hormones,0.1781407824759115,0.0674830011360861,0.22861582877531184,651.0,4.0,162.75,174.5,0.8070906480284608,0.8180540920266948,0.14216073145050528,4.0,4.0,0.3763888888888889,0.1373632592100092,0.2522159774068171,0.5982274051545786
+UCD-0159382,Neuronal Signaling,0.31280539423378717,0.220152166923277,0.2963395953720612,849.0,4.0,212.25,219.5,1.0594754631963277,1.0496066433566433,0.11018096431083972,4.0,4.0,0.3501230251230251,0.101029879786015,0.269655531500918,0.5691906665099344
+UCD-0159389,Protein Tyrosine Kinase,0.35908648339063165,0.25904530179187557,0.34726957309801465,588.0,4.0,147.0,158.0,0.7456855021725108,0.7606017735757911,0.22899226576779158,4.0,4.0,0.5037698412698413,0.3135684095718209,0.1228771228771228,0.9105289660399208
+UCD-0159390,Neuronal Signaling,0.3022670502884608,0.18298248350482726,0.3085790148899048,746.0,4.0,186.5,179.5,0.9375341295969251,0.9403409090909091,0.13999631387799874,4.0,4.0,0.6713383838383837,0.5453647628515192,0.0496199835406883,1.3043433841224432
+UCD-0022022,Others,0.12508642748055498,0.03927804699820428,0.19220437332115498,764.0,4.0,191.0,181.0,0.9460571974492733,0.8981643356643356,0.1355633115613116,4.0,4.0,0.4376082251082251,0.2220475243371671,0.2190516665654787,0.659453438320374
+UCD-0018458,Proteases,0.2813283756268461,0.17758005547346672,0.2815968050608375,730.0,4.0,182.5,182.0,0.9134207996091643,0.916002438379168,0.04822232449784556,4.0,4.0,0.5731150793650793,0.4094931760071062,0.0775671215231654,1.1103223247063967
+UCD-0000899,Neuronal Signaling,,,,,,,,,,,,,,,,
+UCD-0159408,Neuronal Signaling,0.2231532086041569,0.1269677057225392,0.24206252675394013,844.0,4.0,211.0,219.0,1.0635805812187535,1.0275289778714436,0.22216998130500543,4.0,4.0,0.3280844155844156,0.0705440305681327,0.2843273231622746,0.5461814036636747
+UCD-0017582,Neuronal Signaling,0.2391929733379879,0.1210084265629804,0.26500273307455385,721.0,4.0,180.25,175.5,0.902100453303181,0.8605769230769231,0.12792560817188178,4.0,4.0,0.3013736263736263,0.033595188993342,0.3178460494374984,0.497783182119814
+UCD-0159414,DNA Damage,0.09327419909768801,0.019901847774557002,0.16293739281096944,530.0,4.0,132.5,130.5,0.6560363396997744,0.6250389165628891,0.23046399169766846,4.0,4.0,0.4060185185185185,0.1783497116306175,0.2450989010989011,0.6106586359031783
+UCD-0159422,Apoptosis,0.40747676623267465,0.34449181991043676,0.32838995542684074,720.0,4.0,180.0,178.0,0.9060277510609194,0.8535764811428177,0.1208027424914399,4.0,4.0,0.5069444444444444,0.3179598151883147,0.1208097895795056,0.9178978721048168
+UCD-0159430,Proteases,,,,,,,,,,,,,,,,
+UCD-0159435,Protein Tyrosine Kinase,4.651425286072529e-05,6.724109045193016e-06,7.692521459043082e-05,5.0,2.0,2.5,2.5,0.011887098443808012,0.011887098443808012,0.002561892689161433,2.0,2.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159391,Protein Tyrosine Kinase,0.11978147838222085,0.037592230255380985,0.18355572689612396,960.0,4.0,240.0,243.5,1.2124649989716758,1.1994344603758962,0.21637328356079186,4.0,4.0,0.3946428571428571,0.1626138415048483,0.2466165413533834,0.6079777972554067
+UCD-0159398,Protein Tyrosine Kinase,0.173032348669187,0.08040140797321339,0.21991405220852303,902.0,4.0,225.5,220.0,1.1251785893965254,1.087982310510648,0.10916228581980839,4.0,4.0,0.693560606060606,0.5761046021669756,0.0489284471184923,1.3104385675748629
+UCD-0159402,GPCR & G Protein,0.1014849335408048,0.031108542507878954,0.15875481503348335,633.0,4.0,158.25,176.0,0.7819939327160653,0.8252656304882333,0.16801620326823308,4.0,4.0,0.5940656565656566,0.4384739579591374,0.0677517970751053,1.1690791809044627
+UCD-0159407,NF-κB,0.18215068632313774,0.06814207404937006,0.23744556413259918,861.0,4.0,215.25,221.0,1.0815961372268736,1.0891101066215798,0.14205815564421154,4.0,4.0,0.4396762959262959,0.2249082717459887,0.2190516665654787,0.659453438320374
+UCD-0011462,Endocrinology & Hormones,,,,,,,,,,,,,,,,
+UCD-0159410,Neuronal Signaling,0.1734062652669647,0.0647894813299207,0.22436925911793396,663.0,4.0,165.75,172.0,0.8176983597544698,0.8474011819414842,0.15623093301325003,4.0,4.0,0.7729166666666667,0.6858772669951816,0.0259478741154133,1.5858982176908607
+UCD-0159415,Ubiquitin,,,,,,,,,,,,,,,,
+UCD-0159423,PI3K/Akt/mTOR,0.03975103197195535,0.004461538811179023,0.09870670572580958,707.0,4.0,176.75,173.0,0.882781233911069,0.8702781237027812,0.25606080083885097,4.0,4.0,0.6643939393939393,0.5357585630654392,0.0502759043531575,1.298640108656559
+UCD-0159431,PI3K/Akt/mTOR,0.05884045330466291,0.011343828626269321,0.12434634172702795,362.0,4.0,90.5,36.5,0.43001921386843833,0.19708180673231052,0.5158836113909729,4.0,4.0,0.681060606060606,0.5588134425520314,0.0489284471184923,1.3104385675748629
+UCD-0159436,Stem Cells & Wnt,0.4204380524126,0.3740847629695706,0.3250352915285735,660.0,4.0,165.0,179.0,0.8187474643784382,0.85074200913242,0.09538361532056484,4.0,4.0,0.3499098124098124,0.1007349441815272,0.2712262861516593,0.566668222705397
+UCD-0159392,Angiogenesis,0.9618056496327427,0.999064112672383,0.08729225923982929,220.0,2.0,110.0,110.0,0.5009260704437855,0.5009260704437855,0.05722043012629706,2.0,2.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159399,Apoptosis,0.8834793005955134,0.9866777470472686,0.191297565976823,108.0,3.0,36.0,45.0,0.16461860707431156,0.20134228187919462,0.12304086391394513,3.0,3.0,0.5944444444444444,0.4652666119944788,0.0964333911702332,1.0157725605991013
+UCD-0159403,GPCR & G Protein,0.9792588486103536,0.9990477765495368,0.06804117140804368,642.0,4.0,160.5,163.5,0.7503744340481129,0.7388526248046337,0.10839521530366047,4.0,4.0,0.6926767676767677,0.5748819949214745,0.0489284471184923,1.3104385675748629
+UCD-0000904,GPCR & G Protein,0.9904920142342093,0.9998849339992788,0.060068634101137705,625.0,4.0,156.25,163.5,0.7288913754488651,0.7387606876896203,0.13238374415634513,4.0,4.0,0.726010101010101,0.6209917538946588,0.040347711989503,1.394181087918541
+UCD-0000243,Neuronal Signaling,0.9970142928908113,0.999995658113364,0.021318617821351735,527.0,4.0,131.75,133.5,0.6145394792061216,0.6031994116024639,0.13982761415371484,4.0,4.0,0.7244075369075369,0.6187749385594095,0.0417582417582417,1.3792577957042835
+UCD-0159411B,Neuronal Signaling,0.9967335355110147,0.9999991786791289,0.04469336475272722,538.0,4.0,134.5,140.5,0.6292671307161024,0.6355765989396587,0.07868236689814465,4.0,4.0,0.7241161616161615,0.6183718812257278,0.0417582417582417,1.3792577957042835
+UCD-0159416,Metabolism,0.6810796832403426,0.7977079173891413,0.32673387079302013,597.0,4.0,149.25,151.0,0.7002038240905832,0.7197556358630184,0.08819356040580537,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159424,JAK/STAT,,,,,,,,,,,,,,,,
+UCD-0159432,Angiogenesis,0.8501228265303685,0.9612323035551458,0.22198702910955567,881.0,4.0,220.25,233.5,1.0397265975499117,1.0924285865987047,0.18569843026781704,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159437,Cytoskeletal Signaling,0.4407808016429366,0.2666556810521092,0.380234974399387,7.0,2.0,3.5,3.5,0.01570592381477736,0.01570592381477736,0.015753934293237105,2.0,2.0,0.3214285714285714,0.1493116797971074,0.2258919163028752,0.6460993103274033
+UCD-0159393,GPCR & G Protein,0.4949897649555515,0.4914764556109895,0.3394346896475253,784.0,4.0,196.0,179.0,0.9297893110522063,0.8181055537857067,0.2352775346010455,4.0,4.0,0.8194444444444444,0.750238805561918,0.0125152625152625,1.9025600363686452
+UCD-0159400,Apoptosis,,,,,,,,,,,,,,,,
+UCD-0001059,Epigenetics,0.7673013239355088,0.9017096825838035,0.2858055102643556,833.0,4.0,208.25,206.0,0.9749030534908993,0.9564102564102563,0.08714860585955288,4.0,4.0,0.6902777777777778,0.5715634895408286,0.0489284471184923,1.3104385675748629
+UCD-0011606,Neuronal Signaling,,,,,,,,,,,,,,,,
+UCD-0000463,Others,0.3662311435457167,0.28053747351692637,0.3276040664785176,582.0,4.0,145.5,138.0,0.683796368555226,0.6666666666666666,0.1317562408885307,4.0,4.0,0.9791666666666666,0.9711814006417596,0.0027641070585855,2.5584451400053156
+UCD-0159411,Neuronal Signaling,0.5186944136725086,0.4918694195556629,0.3312687731433143,772.0,4.0,193.0,189.5,0.9049361294715798,0.8565627777267015,0.1039355788433118,4.0,4.0,0.8472222222222222,0.7886636047062383,0.010298273155416,1.98723559295969
+UCD-0159417,GPCR & G Protein,0.07854710926812158,9.05387110993942e-06,0.19979118244566985,28.0,3.0,9.333333333333334,8.0,0.042240899472822606,0.0365296803652968,0.026949620606719727,3.0,3.0,0.6027777777777777,0.4762542843507566,0.0910685059621229,1.040631788248228
+UCD-0159425,Apoptosis,0.3701285121049578,0.4054618510026914,0.25132427038566213,90.0,3.0,30.0,26.0,0.1359792107724057,0.12093023255813953,0.13898788204964657,3.0,3.0,0.8194444444444443,0.7619337656139803,0.0194832194832194,1.7103392770124284
+UCD-0159433,Protein Tyrosine Kinase,0.735386106362139,0.8488302423495759,0.28547146447353716,853.0,4.0,213.25,216.0,1.0021025228761418,1.0412950874934879,0.12940460523859149,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159438,Cytoskeletal Signaling,0.041653847310543675,0.00019651846404369197,0.11061432977776471,61.0,4.0,15.25,13.5,0.07061493540088228,0.06493738819320215,0.046729115037510774,4.0,4.0,0.4553751803751804,0.2466244250885668,0.1835571835571835,0.7362286146021204
+UCD-0159394,TGF-beta/Smad,0.42103124969735645,0.45901428242492237,0.23069656862477333,845.0,4.0,211.25,219.5,0.9793716468239839,0.9892740032484447,0.4149443059245712,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0018410,Metabolism,0.4117358611600622,0.35603562753760165,0.31975193698694626,896.0,4.0,224.0,221.0,1.0548714226279006,1.0623816898984684,0.13592199831296473,4.0,4.0,0.5282828282828282,0.3474770472582697,0.1047789419882443,0.9797259911809444
+UCD-0001653,Transmembrane Transporters,0.5347552050278691,0.568465366531764,0.33699842132621205,919.0,4.0,229.75,230.0,1.0796966859164456,1.0927267051662244,0.05854492678063948,4.0,4.0,0.5796717171717172,0.4185629256752623,0.0731519403106119,1.1357741499895764
+UCD-0000377,Transmembrane Transporters,0.6119232767921465,0.681959353362649,0.3263849965500286,1025.0,4.0,256.25,242.0,1.214399432720041,1.1040008324228707,0.28438154590839243,4.0,4.0,0.9125,0.8789618826953911,0.010298273155416,1.98723559295969
+UCD-0001002,Neuronal Signaling,0.6491241414618054,0.7570552364171932,0.3357768887715625,841.0,4.0,210.25,213.0,0.9827546996614006,0.9705322303730295,0.11320400668522118,4.0,4.0,0.6067550505050504,0.4560271048409745,0.0677517970751053,1.1690791809044627
+UCD-0001657,GPCR & G Protein,0.6541809619718262,0.7211294110425017,0.29762112544460295,709.0,4.0,177.25,189.5,0.8265458606396707,0.8728317985536653,0.11714019930119884,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159418,Transmembrane Transporters,0.2775686420570803,0.16966828601768258,0.28503125924484474,805.0,4.0,201.25,194.5,0.9535151199274985,0.8861661724155872,0.2421576810517479,4.0,4.0,0.5387896825396825,0.3620111027787676,0.0997861199203481,1.0009298641357447
+UCD-0159426,DNA Damage,0.2469218060153983,0.13438523255277973,0.27209955959476184,872.0,4.0,218.0,213.5,1.0252433057911228,1.034176325957148,0.07454971010354898,4.0,4.0,0.6384920634920634,0.4999286854217744,0.0525641025641025,1.279310745970745
+UCD-0001924,Epigenetics,0.1492955451942889,0.026861888159410832,0.23456844414413527,205.0,2.0,102.5,102.5,0.46285833203267257,0.46285833203267257,0.338849130646327,2.0,2.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159439,PI3K/Akt/mTOR,0.7538769312354222,0.7812630761748637,0.25392489063294815,287.0,4.0,71.75,70.0,0.34315076168282527,0.3192133603870766,0.17149499054553052,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159395,GPCR & G Protein,0.17456890196588745,0.0590062965125695,0.24050296681300995,866.0,4.0,216.5,213.5,1.018545259347913,1.0115687536391773,0.12230170528737479,4.0,4.0,0.9791666666666666,0.9711814006417596,0.0027641070585855,2.5584451400053156
+UCD-0018456,GPCR & G Protein,,,,,,,,,,,,,,,,
+UCD-0159404,Metabolism,0.09063591168585168,0.012122887109572898,0.17394713236759007,700.0,4.0,175.0,179.5,0.81766228476943,0.8266857810342997,0.08705517433968075,4.0,4.0,0.4472402597402597,0.2353714481963015,0.2056028478563689,0.6869708741138213
+UCD-0011098,Neuronal Signaling,0.3290398152543918,0.22087134880316966,0.3127463580224391,770.0,4.0,192.5,181.0,0.8982901450923371,0.8341297653180418,0.16676965744440303,4.0,4.0,0.7482142857142857,0.6517066420418396,0.040347711989503,1.394181087918541
+UCD-0011250,Endocrinology & Hormones,0.24359100128131658,0.08004665322183034,0.30195615039326973,935.0,4.0,233.75,218.0,1.0933428291160951,1.0497912939773406,0.1767007502859676,4.0,4.0,0.5510010822510822,0.3789030437013031,0.0997861199203481,1.0009298641357447
+UCD-0018183,Proteases,0.1993284698511397,0.07511713028327074,0.2621886253701792,846.0,4.0,211.5,199.0,0.9920321194399632,0.9730471079308288,0.12181683855156883,4.0,4.0,0.4518849206349206,0.2417963740272512,0.1868756243756243,0.728447343295052
+UCD-0159419,Protein Tyrosine Kinase,0.04582036775890503,0.00012862733718903638,0.09332840152590681,10.0,2.0,5.0,5.0,0.02316736902346392,0.02316736902346392,0.026436030017202697,2.0,2.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159427,Apoptosis,,,,,,,,,,,,,,,,
+UCD-0001966,Others,,,,,,,,,,,,,,,,
+UCD-0159440,Protein Tyrosine Kinase,,,,,,,,,,,,,,,,
+UCD-0159396,DNA Damage,0.46633458879681833,0.3851931171665605,0.37615875102107044,851.0,4.0,212.75,220.5,0.9965748682522502,0.9969660752045602,0.06998014057225428,4.0,4.0,0.8982142857142857,0.8592005574211692,0.010298273155416,1.98723559295969
+UCD-0159401,Angiogenesis,0.17652204836032276,0.09160565563108694,0.20662724865542162,638.0,3.0,212.66666666666666,243.0,0.9931039634801145,1.130232558139535,0.30738150732826414,3.0,3.0,0.7638888888888888,0.688682616572128,0.0194832194832194,1.7103392770124284
+UCD-0159405,Metabolism,0.1950792756188837,0.07315245522507763,0.2521132202590741,777.0,4.0,194.25,194.5,0.915507436681944,0.8878466260860517,0.09507957114196414,4.0,4.0,0.1893442205942206,-0.1213742779585819,0.6608058608058608,0.1799261138348331
+UCD-0159664,Epigenetics,0.18682043969227763,0.05088687890589964,0.25984477220761293,797.0,4.0,199.25,194.0,0.9358576293743522,0.9483601669648181,0.08384669500196446,4.0,4.0,0.4025974025974025,0.1736173067143581,0.2466165413533834,0.6079777972554067
+UCD-0001098,Neuronal Signaling,0.16092632816894717,0.04785169970328143,0.2214689896446002,814.0,4.0,203.5,208.0,0.9500420131234887,0.9403941037663571,0.18652537534382452,4.0,4.0,0.5814574314574315,0.4210330913345401,0.0731519403106119,1.1357741499895764
+UCD-0159412,Apoptosis,3.261739866158687e-07,3.261739866158687e-07,3.0707429015658696e-07,2.0,2.0,1.0,1.0,0.004847207586933614,0.004847207586933614,0.00039739053385966894,2.0,2.0,0.2954545454545454,0.1167494474448436,0.2258919163028752,0.6460993103274033
+UCD-0159420,Angiogenesis,0.015188009701189494,5.709009504320041e-05,0.06727391758889968,527.0,4.0,131.75,107.5,0.6222103376460448,0.5218551023920152,0.35873701214910253,4.0,4.0,0.6073006854256854,0.4567818776813094,0.0677517970751053,1.1690791809044627
+UCD-0159428,Apoptosis,0.14371244013863618,0.026216580874081703,0.23008852110771366,615.0,4.0,153.75,156.0,0.7218280448111476,0.7469798657718121,0.07932424757617157,4.0,4.0,0.7134920634920635,0.6036756431114392,0.0448414145096609,1.3483206961651315
+UCD-0001925,Epigenetics,0.5776960040026369,0.635979363116961,0.33943650423272953,361.0,3.0,120.33333333333333,177.0,0.5489326148459673,0.8098434004474273,0.4635747372318779,3.0,3.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159441,Metabolism,0.20900192514183158,0.06752649257664821,0.27963480721027956,678.0,4.0,169.5,164.5,0.7959814380225703,0.7715841236389182,0.07754326228102054,4.0,4.0,0.5102362914862915,0.3225134033985993,0.1176594106530412,0.9293733312908564
+UCD-0159397,Others,0.2050837895679669,0.01987925374152859,0.3249156832335589,621.0,4.0,155.25,156.5,0.7235904551841102,0.7211957098863757,0.11964728787467277,4.0,4.0,0.4399801587301587,0.2253286029653996,0.2189821351832525,0.6595913139626328
+UCD-0000918,Proteases,0.17116809006303813,0.04133116235590268,0.25061243624696916,685.0,4.0,171.25,170.0,0.8032050336365395,0.8021074815595364,0.030224130291780053,4.0,4.0,0.6834415584415584,0.5621069967644017,0.0489284471184923,1.3104385675748629
+UCD-0159478,Cell Cycle,0.9488829161262835,0.9999884795619718,0.14870741939033694,318.0,3.0,106.0,136.0,0.5638510671036133,0.723404255319149,0.41098419471824515,3.0,3.0,0.5578703703703703,0.4170429388752595,0.1565934065934066,0.8052265279765646
+UCD-0159486,Others,0.9446007552859788,0.9994557358929892,0.1342622849189131,576.0,4.0,144.0,158.0,0.9049643166280139,0.8284985742487387,0.3268329407212868,4.0,4.0,0.7458333333333333,0.6484130878294693,0.040347711989503,1.394181087918541
+UCD-0159494,Protein Tyrosine Kinase,,,,,,,,,,,,,,,,
+UCD-0159502,MAPK,,,,,,,,,,,,,,,,
+UCD-0159510,Autophagy,0.9996290987429646,0.9999999873077212,0.0045170598521589375,442.0,4.0,110.5,118.0,0.6920272319772511,0.6507731958762887,0.20956548840329237,4.0,4.0,0.7458333333333333,0.6484130878294693,0.040347711989503,1.394181087918541
+UCD-0159518,Proteases,0.999850919616445,0.999999999421332,0.0026185442074938285,348.0,4.0,87.0,95.5,0.5464637196496336,0.5001645097609124,0.1881349541309609,4.0,4.0,0.7458333333333333,0.6484130878294693,0.040347711989503,1.394181087918541
+UCD-0159448,Ubiquitin,,,,,,,,,,,,,,,,
+UCD-0159456,Apoptosis,0.8001577500686391,0.9907747868015653,0.3062826425248639,608.0,4.0,152.0,153.5,0.9733710602453283,0.945818901533894,0.2523598565608382,4.0,4.0,0.6833333333333332,0.5619572897547486,0.0489284471184923,1.3104385675748629
+UCD-0159463,GPCR & G Protein,,,,,,,,,,,,,,,,
+UCD-0159471,Proteases,0.7559995229342313,0.9429861868003562,0.31742989116194986,614.0,4.0,153.5,147.5,0.9754479634606494,1.0174830941777997,0.16679447790555693,4.0,4.0,0.7013888888888888,0.5869334091985567,0.0448414145096609,1.3483206961651315
+UCD-0159479,Transmembrane Transporters,0.7824579668009641,0.9366036354542182,0.2754423531552595,472.0,4.0,118.0,123.0,0.7496404295264902,0.6449571169388092,0.21685189213251638,4.0,4.0,0.7090277777777777,0.5975002289632448,0.0448414145096609,1.3483206961651315
+UCD-0159487,Others,0.9594373501969189,0.9999831377850383,0.14720688435800036,505.0,4.0,126.25,130.0,0.816326295343221,0.8478088281994285,0.223621613036722,4.0,4.0,0.6805555555555555,0.5581148098403165,0.0489284471184923,1.3104385675748629
+UCD-0159495,Apoptosis,0.7851964936049428,0.9706471547925152,0.3030560018007833,397.0,4.0,99.25,108.0,0.6071033425967385,0.646550778679535,0.12405994370409451,4.0,4.0,0.6898989898989898,0.5710395150070423,0.0489284471184923,1.3104385675748629
+UCD-0159503,Cytoskeletal Signaling,0.7328263309942077,0.8863715319116742,0.31080167887332066,429.0,4.0,107.25,98.5,0.6755749124118284,0.6994922295737807,0.139794618657147,4.0,4.0,0.5402777777777777,0.364069574161499,0.0997861199203481,1.0009298641357447
+UCD-0159511,Autophagy,0.696184158663806,0.7806429456150061,0.30350393505410195,537.0,4.0,134.25,134.0,0.8495461227151326,0.8306277888905986,0.11353886934763044,4.0,4.0,0.3989357864357864,0.1685522195544251,0.2466165413533834,0.6079777972554067
+UCD-0159519,Stem Cells & Wnt,0.09241464734291888,0.0048551501992214335,0.16083652059130094,15.0,2.0,7.5,7.5,0.04418069228326453,0.04418069228326453,0.02026560400193265,2.0,2.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159449,Ubiquitin,0.7567513384716827,0.9843947637349728,0.3455489466908766,443.0,4.0,110.75,136.0,0.6645188559312732,0.7697411713094977,0.33401891690164803,4.0,4.0,0.4763979076479076,0.2757050117137001,0.155694458446752,0.8077268447782245
+UCD-0001927,Epigenetics,0.967565070822416,0.9999976128448796,0.11579332212395296,588.0,4.0,147.0,158.0,0.9018493186901437,0.9487003728887914,0.20080506222374533,4.0,4.0,0.6602272727272727,0.5299948431937912,0.0502759043531575,1.298640108656559
+UCD-0159464,Metabolism,0.5300378457594247,0.5375494042492739,0.38089224599797405,758.0,4.0,189.5,195.5,1.1914385825023335,1.1155177719649176,0.17337138163226343,4.0,4.0,0.6201388888888889,0.4745408717014198,0.0547226053298927,1.2618332341413614
+UCD-0159472,TGF-beta/Smad,0.25279747701176886,0.08098983072753768,0.3208390259374409,433.0,4.0,108.25,106.5,0.6883360009940249,0.8073585560569247,0.38533346757283515,4.0,4.0,0.9097222222222222,0.875119402780959,0.010298273155416,1.98723559295969
+UCD-0159480,Transmembrane Transporters,0.6370938354967194,0.8019283783662943,0.3659255280061374,163.0,2.0,81.5,81.5,0.43972149561470997,0.43972149561470997,0.4424449493299116,2.0,2.0,0.5,0.3731770272189212,0.1060116354234001,0.9746464656516696
+UCD-0159488,Epigenetics,0.5477703348911679,0.5522043861473203,0.3635258528985099,210.0,4.0,52.5,47.0,0.3493711078426991,0.25034620710878597,0.41245242785519187,4.0,4.0,0.9305555555555556,0.9039380021391992,0.0027641070585855,2.5584451400053156
+UCD-0159496,Others,0.379781840834139,0.27521153733923687,0.35065650410343613,659.0,4.0,164.75,167.5,1.0243258649619829,1.0047433647729767,0.0813423364686412,4.0,4.0,0.4364621489621489,0.2204621654913524,0.2209305845669482,0.6557441583285767
+UCD-0159504,Others,0.6713424582323,0.7747833226794075,0.3475387742362221,67.0,3.0,22.333333333333332,21.0,0.12858142933064443,0.11940298507462686,0.022892802216684337,3.0,3.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159512,Others,,,,,,,,,,,,,,,,
+UCD-0159520,NF-κB,0.9999999209801378,0.9999999391181118,9.433553761425501e-08,4.0,1.0,4.0,4.0,0.020618556701030927,0.020618556701030927,,1.0,1.0,,,,
+UCD-0159450,DNA Damage,,,,,,,,,,,,,,,,
+UCD-0159457,Proteases,0.5137704467241636,0.5215626523516323,0.3654041234405665,706.0,4.0,176.5,183.0,1.121074774517766,1.078508988949365,0.18518048043518373,4.0,4.0,0.6132034632034632,0.4649471474994776,0.0579019374200097,1.2373069043771885
+UCD-0159465,Metabolism,0.46487785288541456,0.44633690899846207,0.3736112405746452,598.0,4.0,149.5,148.0,0.9518249115553041,0.9376057855054625,0.16127114551466135,4.0,4.0,0.625,0.4812652115516759,0.0547226053298927,1.2618332341413614
+UCD-0159473,Epigenetics,0.623138299821281,0.7532871894309625,0.3667332517624018,644.0,4.0,161.0,174.5,1.0099001611204628,0.9118227681509102,0.6061745281716764,4.0,4.0,0.9097222222222222,0.875119402780959,0.010298273155416,1.98723559295969
+UCD-0159481,Cell Cycle,0.5356686987200453,0.5600222877642103,0.36170206612588857,492.0,4.0,123.0,124.0,0.7874119648496654,0.7407496203947894,0.18117579781221677,4.0,4.0,0.6727272727272726,0.5472860028087353,0.0496199835406883,1.3043433841224432
+UCD-0159489,Epigenetics,,,,,,,,,,,,,,,,
+UCD-0159497,PI3K/Akt/mTOR,0.27017316190846274,0.15206366032192986,0.30654533502368614,617.0,4.0,154.25,154.0,0.9535298215229295,0.940641732598098,0.03127324006923954,4.0,4.0,0.6472222222222221,0.5120050508671321,0.0502759043531575,1.298640108656559
+UCD-0159505,Others,0.2524582032066113,0.05014018973907758,0.3359696391221026,621.0,4.0,155.25,153.5,0.9898711405555815,0.9949852153760089,0.17864298634977824,4.0,4.0,0.4638888888888888,0.2584013765146181,0.1725788831051989,0.7630123460017288
+UCD-0159513,Others,0.4182497927150356,0.3326682408775804,0.3708900753109343,554.0,4.0,138.5,149.5,0.8722134854632098,0.8098267163851722,0.3151339878466261,4.0,4.0,0.8625,0.8097972442356145,0.010298273155416,1.98723559295969
+UCD-0159521,Others,0.11764493909614684,0.004268609190526492,0.21687153833735798,469.0,4.0,117.25,121.0,0.7249770083508198,0.7053630182057469,0.4134584333285916,4.0,4.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159522,Proteases,0.3004574739023935,0.1763156046247135,0.3319202195858088,497.0,4.0,124.25,145.0,0.7356603498826765,0.9018425093222198,0.41672888056077023,4.0,4.0,0.7652777777777778,0.6753104472304935,0.0303478904515174,1.5178714923022951
+UCD-0159529,Cell Cycle,0.19196065294704465,0.05893656933921334,0.25140036110946434,41.0,3.0,13.666666666666666,4.0,0.0736886416786434,0.029850746268656716,0.08823528782590681,3.0,3.0,0.6620370370370371,0.5543888433287324,0.0612747252747252,1.2127186272311403
+UCD-0159537,Others,,,,,,,,,,,,,,,,
+UCD-0159545,Autophagy,0.1487361201176338,0.017800839322859446,0.24578885407887285,554.0,4.0,138.5,120.5,0.8646663360863673,0.9032743260441976,0.2215618411779413,4.0,4.0,0.3380189255189255,0.0842863663260415,0.2782805429864253,0.5555171579096939
+UCD-0159552,Protein Tyrosine Kinase,0.1978008338756756,0.04857682580445826,0.2784539274779464,629.0,4.0,157.25,164.0,1.005948576896985,0.8598376837025663,0.3716528288780475,4.0,4.0,0.3021990740740741,0.0347370248287204,0.3178460494374984,0.497783182119814
+UCD-0159560,DNA Damage,0.1891471661822094,0.02265298250663898,0.27999345457301306,377.0,4.0,94.25,98.0,0.515959383505048,0.5432374211417141,0.4360372225708646,4.0,4.0,0.6214285714285714,0.4763248802331205,0.0547226053298927,1.2618332341413614
+UCD-0159568,Others,0.17357012635093005,0.03488340419474539,0.26058854000099174,687.0,4.0,171.75,170.0,1.1020146986162653,1.0991584630041282,0.24177449602726067,4.0,4.0,0.6472222222222223,0.5120050508671322,0.0502759043531575,1.298640108656559
+UCD-0159576,Others,0.4822885958141235,0.473290688615482,0.36667567609669927,613.0,4.0,153.25,150.0,0.9693491363456441,0.9707049857097492,0.15919380830425653,4.0,4.0,0.6268939393939393,0.4838850842206068,0.0547226053298927,1.2618332341413614
+UCD-0159584,Cell Cycle,0.2857008523559608,0.167804253499345,0.3114214241621855,679.0,4.0,169.75,172.5,1.0852662246823799,1.043704350587488,0.2450330031980062,4.0,4.0,0.5692460317460317,0.4041411504120045,0.0780735509977387,1.1074960672902194
+UCD-0159591,Epigenetics,0.08023607093064444,0.0048996903343159984,0.16756759006575647,656.0,4.0,164.0,172.0,1.0448494458110342,0.9680454112416641,0.24635883912143328,4.0,4.0,0.5343434343434343,0.3558606397988488,0.0997861199203481,1.0009298641357447
+UCD-0159523,Proteases,0.20075475365207562,0.037228020339247145,0.294684107067071,240.0,2.0,120.0,120.0,0.7408832128019696,0.7408832128019696,0.07093916315335114,2.0,2.0,1.0,1.0,0.0027641070585855,2.5584451400053156
+UCD-0159530,Proteases,0.14011551730612756,0.010666034931216765,0.24354938669354256,533.0,4.0,133.25,131.5,0.8394122018467577,0.8400263725725245,0.10854074607229917,4.0,4.0,0.5431394993894993,0.368028172974444,0.0997861199203481,1.0009298641357447
+UCD-0159538,MAPK,0.08591389502555362,0.0010771008855785013,0.1867165411439063,332.0,4.0,83.0,70.0,0.5136505699997693,0.5293300937174592,0.27415141888381905,4.0,4.0,0.9583333333333334,0.9423628012835196,0.0027641070585855,2.5584451400053156
+UCD-0159546,Ubiquitin,0.23491877988157747,0.07725562534299577,0.30902915890091465,655.0,4.0,163.75,165.5,1.0300586865683823,0.9849555414417275,0.23500814507738502,4.0,4.0,0.7775974025974026,0.6923520951626823,0.0259478741154133,1.5858982176908607
+UCD-0159553,PI3K/Akt/mTOR,0.30321136596116477,0.09503550256614227,0.3727952315631968,94.0,4.0,23.5,16.5,0.16155765802866384,0.10193398865180212,0.16918055377702856,4.0,4.0,0.7343253968253969,0.6324942424696794,0.040347711989503,1.394181087918541
+UCD-0159561,JAK/STAT,0.11560101788180888,0.0036820753963790554,0.22332347774195357,584.0,4.0,146.0,156.5,0.9554880231640706,0.9898105969791416,0.39834945830122725,4.0,4.0,0.6805555555555555,0.5581148098403165,0.0489284471184923,1.3104385675748629
diff --git a/3a.train_dmso_logistic_regression_models/train_platemapwise_model/inference_results/logit_inference_summary.csv b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/inference_results/logit_inference_summary.csv
new file mode 100644
index 0000000..944de5b
--- /dev/null
+++ b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/inference_results/logit_inference_summary.csv
@@ -0,0 +1,475 @@
+Pathway,Metadata_treatment,mean_score,median_score,score_std,n_scored_cells,n_score_plates,mean_cell_count,median_cell_count,mean_relative_cell_count,median_relative_cell_count,viability_std,n_treatment_wells,n_viability_plates
+Angiogenesis,UCD-0001759,0.7154126099122802,0.8307579352815706,0.3092552689559335,198,4,49.5,54.5,0.25166739692796325,0.28355577940065474,0.076023125257957,4,4
+Angiogenesis,UCD-0001766,0.9602947371687784,0.999244621058888,0.10955179249533108,111,4,27.75,25.5,0.13001967041505463,0.11258322879475055,0.06597703609966805,4,4
+Angiogenesis,UCD-0001856,0.3079330289733557,0.19758195454101393,0.3097849592069099,990,4,247.5,244.0,1.2332661745509819,1.277972027972028,0.24593212593490793,4,4
+Angiogenesis,UCD-0001861,0.3994782126343048,0.33595507894945115,0.33327284977980565,439,4,109.75,111.0,0.535007226099393,0.5178540305010894,0.07147297000264194,4,4
+Angiogenesis,UCD-0001863,0.9838102318424772,0.9997371438330593,0.0677100257809675,636,4,159.0,191.5,0.8028991346331802,1.0247230944411856,0.4870891266994304,4,4
+Angiogenesis,UCD-0001864,0.10578105670270746,0.10578105670270746,0.1495967652880162,2,1,2.0,2.0,0.010075566750629723,0.010075566750629723,,1,1
+Angiogenesis,UCD-0001870,1.9258863219054208e-19,1.9258863219054208e-19,,1,1,1.0,1.0,0.004807692307692308,0.004807692307692308,,1,1
+Angiogenesis,UCD-0018131,0.3514311302432795,0.283883608250358,0.3064735275346207,654,4,163.5,158.0,0.7608150825311834,0.7832242043488873,0.09129335620033861,4,4
+Angiogenesis,UCD-0159258,0.7088779000536966,0.8168720267811016,0.28841099936381887,678,4,169.5,181.0,0.786427221686658,0.8221653961208144,0.1309939684164963,4,4
+Angiogenesis,UCD-0159309,0.32051606751801515,0.11249350661476527,0.3800066882360125,748,4,187.0,178.0,0.8933921494800174,0.8617017543859649,0.163611943554298,4,4
+Angiogenesis,UCD-0159317,0.36893404412788994,0.24563601650740507,0.346184022619783,654,4,163.5,152.5,0.8240821761353613,0.7665736672620367,0.20691858508686914,4,4
+Angiogenesis,UCD-0159324,0.06015506517084141,8.873195722487688e-05,0.1701255546052569,772,4,193.0,192.5,0.9253509080868582,0.9192472707191708,0.14702164276637134,4,4
+Angiogenesis,UCD-0159373,0.24686517310164766,0.10615108512297466,0.28886470881495185,257,4,64.25,59.5,0.331562771734781,0.28725141759543604,0.17353905005903442,4,4
+Angiogenesis,UCD-0159392,0.9618056496327427,0.999064112672383,0.08729225923982929,220,2,110.0,110.0,0.5009260704437855,0.5009260704437855,0.05722043012629706,2,2
+Angiogenesis,UCD-0159401,0.17652204836032276,0.09160565563108694,0.20662724865542162,638,3,212.66666666666666,243.0,0.9931039634801145,1.130232558139535,0.30738150732826414,3,3
+Angiogenesis,UCD-0159420,0.015188009701189494,5.709009504320041e-05,0.06727391758889968,527,4,131.75,107.5,0.6222103376460448,0.5218551023920152,0.35873701214910253,4,4
+Angiogenesis,UCD-0159432,0.8501228265303685,0.9612323035551458,0.22198702910955567,881,4,220.25,233.5,1.0397265975499117,1.0924285865987047,0.18569843026781704,4,4
+Angiogenesis,UCD-0159434,0.7739503151986251,0.9224405905671382,0.2805382568354016,452,4,113.0,114.5,0.6365562476510445,0.6108452950558214,0.10619550725414974,4,4
+Angiogenesis,UCD-0159460,0.4971101724767224,0.4854246067825251,0.34413600146163714,826,4,206.5,206.0,1.1696653152884449,1.1279850187265916,0.21456528956293536,4,4
+Angiogenesis,UCD-0159461,0.013746554501021215,0.00245469840991945,0.030919832122435972,7,1,7.0,7.0,0.03349282296650718,0.03349282296650718,,1,1
+Angiogenesis,UCD-0159544,0.999877286979439,0.9999996801609635,0.0006739810922892575,181,2,90.5,90.5,0.6045207956600362,0.6045207956600362,0.06700252320827318,2,2
+Angiogenesis,UCD-0159554,0.5739261646175711,0.5780673657229214,0.2902897665056452,525,4,131.25,139.0,0.9880846922959798,0.9719382835101253,0.27846571415583576,4,4
+Angiogenesis,UCD-0159563,0.37399050487094077,0.31394279937818215,0.2917164433942447,382,4,95.5,89.5,0.7347193808830361,0.7006991321118612,0.28109348237515436,4,4
+Angiogenesis,UCD-0159586,0.6440841114277751,0.7092852715820189,0.28267059990673155,461,4,115.25,109.0,0.8714315274128501,0.7918032786885245,0.26526976729407364,4,4
+Angiogenesis,UCD-0159602,0.9612790397537415,0.9950161744965573,0.0904878813542986,422,4,105.5,119.0,0.8345010446246188,0.815883190883191,0.26764816513198497,4,4
+Angiogenesis,UCD-0159615,0.15033908289952183,0.04920330434378989,0.22333634017757836,126,1,126.0,126.0,1.1454545454545455,1.1454545454545455,,1,1
+Angiogenesis,UCD-0159629,0.27325511025505933,0.2495764255340108,0.23163557601218235,152,4,38.0,45.5,0.2570747760348197,0.30730260155180283,0.11116360335541706,4,4
+Angiogenesis,UCD-0159632,0.14371165727467677,0.0722557120215275,0.17512621103702397,214,2,107.0,107.0,0.7014234181534561,0.7014234181534561,0.7123461411719406,2,2
+Angiogenesis,UCD-0159649,0.05120708042074944,0.014168773253366409,0.09612497988630829,500,4,125.0,138.0,1.0020751496353524,1.036070686070686,0.17440436178088103,4,4
+Angiogenesis,UCD-0159651,0.3165223192298968,0.21570696390148453,0.3028503661623773,705,4,176.25,176.0,1.255182351013325,1.258523505248745,0.08727640640892949,4,4
+Apoptosis,UCD-0017947,0.3739264964894275,0.2843559174260788,0.33683667412330576,890,4,222.5,226.5,1.1024100245181483,1.0913145730273295,0.08904662072200946,4,4
+Apoptosis,UCD-0018012,0.6257003314787246,0.7311228371225216,0.3564516605527216,702,4,175.5,173.0,0.8347584369243601,0.8205789473684211,0.12814239576969902,4,4
+Apoptosis,UCD-0159256,0.19908077190059922,0.06375032677268425,0.26709706445781395,893,4,223.25,229.5,1.0374301115958562,1.035839042748239,0.1517534632122502,4,4
+Apoptosis,UCD-0159332,0.707096083296747,0.8336446179931314,0.31614306998152175,677,4,169.25,166.5,0.8280850804786614,0.8112622584023612,0.1608019790214929,4,4
+Apoptosis,UCD-0159357,0.8905682263066627,0.9962931991174285,0.177296851477987,10,3,3.3333333333333335,4.0,0.016165278641209885,0.018518518518518517,0.005937596322607866,3,3
+Apoptosis,UCD-0159371,0.830916340110495,0.9978355085153778,0.2517047645561931,470,4,117.5,112.0,0.5687393301430047,0.5475638740344623,0.15417001922214116,4,4
+Apoptosis,UCD-0159399,0.8834793005955134,0.9866777470472686,0.191297565976823,108,3,36.0,45.0,0.16461860707431156,0.20134228187919462,0.12304086391394513,3,3
+Apoptosis,UCD-0159412,3.261739866158687e-07,3.261739866158687e-07,3.0707429015658696e-07,2,2,1.0,1.0,0.004847207586933614,0.004847207586933614,0.00039739053385966894,2,2
+Apoptosis,UCD-0159421,0.9957429034524001,0.9999601561379322,0.024686541949903953,382,4,95.5,99.0,0.5390400032501812,0.5074704944178628,0.1057450698441131,4,4
+Apoptosis,UCD-0159422,0.40747676623267465,0.34449181991043676,0.32838995542684074,720,4,180.0,178.0,0.9060277510609194,0.8535764811428177,0.1208027424914399,4,4
+Apoptosis,UCD-0159425,0.3701285121049578,0.4054618510026914,0.25132427038566213,90,3,30.0,26.0,0.1359792107724057,0.12093023255813953,0.13898788204964657,3,3
+Apoptosis,UCD-0159428,0.14371244013863618,0.026216580874081703,0.23008852110771366,615,4,153.75,156.0,0.7218280448111476,0.7469798657718121,0.07932424757617157,4,4
+Apoptosis,UCD-0159456,0.8001577500686391,0.9907747868015653,0.3062826425248639,608,4,152.0,153.5,0.9733710602453283,0.945818901533894,0.2523598565608382,4,4
+Apoptosis,UCD-0159468,0.7254765055040252,0.9277802438359309,0.35097648682362953,281,3,93.66666666666667,129.0,0.4724457844486683,0.688,0.4035869304381908,3,3
+Apoptosis,UCD-0159495,0.7851964936049428,0.9706471547925152,0.3030560018007833,397,4,99.25,108.0,0.6071033425967385,0.646550778679535,0.12405994370409451,4,4
+Apoptosis,UCD-0159547,0.7381057246358582,0.81460814385297,0.24740997301723103,658,4,164.5,179.5,1.2310886618203398,1.2834257473481196,0.22621024983030757,4,4
+Apoptosis,UCD-0159633,0.33711353366429053,0.28772840840115654,0.26539336797936547,473,4,118.25,117.0,1.0212999925927682,0.9172438598294111,0.3172938725821409,4,4
+Autophagy,UCD-0159510,0.9996290987429646,0.9999999873077212,0.0045170598521589375,442,4,110.5,118.0,0.6920272319772511,0.6507731958762887,0.20956548840329237,4,4
+Autophagy,UCD-0159511,0.696184158663806,0.7806429456150061,0.30350393505410195,537,4,134.25,134.0,0.8495461227151326,0.8306277888905986,0.11353886934763044,4,4
+Autophagy,UCD-0159545,0.1487361201176338,0.017800839322859446,0.24578885407887285,554,4,138.5,120.5,0.8646663360863673,0.9032743260441976,0.2215618411779413,4,4
+Autophagy,UCD-0159575,0.8164099713709727,0.9579874253203081,0.25966103345199154,463,4,115.75,125.0,0.8719276446851567,0.8391500904159133,0.4949374135401772,4,4
+Autophagy,UCD-0159604,0.05684609877546145,0.009795381081015724,0.11667091291620506,197,3,65.66666666666667,57.0,0.4451532111227929,0.36538461538461536,0.26188088199243514,3,3
+Autophagy,UCD-0159606,0.09929967906756133,0.038618138670683436,0.13773095869625562,201,4,50.25,54.5,0.3433110081571278,0.3463622760545153,0.19248824593040106,4,4
+Autophagy,UCD-0159658,0.22442358009551644,0.07620090235321646,0.2885197573129299,559,4,139.75,137.5,1.0086472395441337,1.1165584415584415,0.42566137974597923,4,4
+Cell Cycle,UCD-0001784,0.9927723507860328,0.9927723507860328,,1,1,1.0,1.0,0.005333333333333333,0.005333333333333333,,1,1
+Cell Cycle,UCD-0001790,0.600428152538604,0.7054029443018064,0.36803165120555464,86,1,86.0,86.0,0.45866666666666667,0.45866666666666667,,1,1
+Cell Cycle,UCD-0001886,0.13100340452102222,0.009421383689029502,0.23107343550468284,274,4,68.5,58.5,0.3390756645287494,0.2778787878787879,0.33065844363374547,4,4
+Cell Cycle,UCD-0159294,0.31717048956413724,0.21799882978054602,0.31391752537883155,466,3,155.33333333333334,160.0,0.7484801534287425,0.8135593220338984,0.17956045752646557,3,3
+Cell Cycle,UCD-0159301,0.8475448706407331,0.9885622113843802,0.2447281782459438,712,4,178.0,172.0,0.8576703720008805,0.7938861985472154,0.20528686692988143,4,4
+Cell Cycle,UCD-0159304,0.8505142682539163,0.9935453077665981,0.2589444583667887,641,4,160.25,166.0,0.7554993975694243,0.7772328548644338,0.15093577684784237,4,4
+Cell Cycle,UCD-0159306,0.31495478551464007,0.12633522454080073,0.37084842194903245,531,4,132.75,133.5,0.6372277670292835,0.5959728867623604,0.15050351352940033,4,4
+Cell Cycle,UCD-0159466,0.7838160160404646,0.9350276728631479,0.27651654041674323,551,4,137.75,151.0,0.7384918331832211,0.7743535293801409,0.27723549965236244,4,4
+Cell Cycle,UCD-0159478,0.9488829161262835,0.9999884795619718,0.14870741939033694,318,3,106.0,136.0,0.5638510671036133,0.723404255319149,0.41098419471824515,3,3
+Cell Cycle,UCD-0159481,0.5356686987200453,0.5600222877642103,0.36170206612588857,492,4,123.0,124.0,0.7874119648496654,0.7407496203947894,0.18117579781221677,4,4
+Cell Cycle,UCD-0159492,0.16443569137934327,0.039251861880949,0.24473441541579677,684,4,171.0,180.0,0.9384449424455258,0.9602360446570973,0.13874171848639497,4,4
+Cell Cycle,UCD-0159501,0.0831581691666162,0.005570860694854135,0.18417611819066534,280,4,70.0,66.5,0.35648012050218736,0.3592209737827715,0.3139462970179292,4,4
+Cell Cycle,UCD-0159529,0.19196065294704465,0.05893656933921334,0.25140036110946434,41,3,13.666666666666666,4.0,0.0736886416786434,0.029850746268656716,0.08823528782590681,3,3
+Cell Cycle,UCD-0159584,0.2857008523559608,0.167804253499345,0.3114214241621855,679,4,169.75,172.5,1.0852662246823799,1.043704350587488,0.2450330031980062,4,4
+Cell Cycle,UCD-0159612,0.5544477859876256,0.6219810148924811,0.3384781633137786,264,4,66.0,51.0,0.4581269738269172,0.3893552134766192,0.24161920023703928,4,4
+Cell Cycle,UCD-0159631,0.13573521392886195,0.03824195687716729,0.2053287357690043,574,4,143.5,145.5,1.0123996246807345,1.0877287249491723,0.19218265883370975,4,4
+Cytoskeletal Signaling,UCD-0159356,0.10297206577194529,0.007208505266855482,0.19669159086898377,701,4,175.25,175.0,0.8559263784322382,0.871301247771836,0.08186497199376802,4,4
+Cytoskeletal Signaling,UCD-0159437,0.4407808016429366,0.2666556810521092,0.380234974399387,7,2,3.5,3.5,0.01570592381477736,0.01570592381477736,0.015753934293237105,2,2
+Cytoskeletal Signaling,UCD-0159438,0.041653847310543675,0.00019651846404369197,0.11061432977776471,61,4,15.25,13.5,0.07061493540088228,0.06493738819320215,0.046729115037510774,4,4
+Cytoskeletal Signaling,UCD-0159459,0.115730971056896,0.017607401186980974,0.20305526480605754,682,4,170.5,182.0,0.9565389954239707,1.0733428310305897,0.2752111299005006,4,4
+Cytoskeletal Signaling,UCD-0159503,0.7328263309942077,0.8863715319116742,0.31080167887332066,429,4,107.25,98.5,0.6755749124118284,0.6994922295737807,0.139794618657147,4,4
+Cytoskeletal Signaling,UCD-0159533,0.4696336299649678,0.4754017956657745,0.2967876177454407,417,4,104.25,96.0,0.7913543281187169,0.7181171648987463,0.20548339628989165,4,4
+Cytoskeletal Signaling,UCD-0159555,0.3892127584052083,0.31475294039526247,0.29083613347613907,754,4,188.5,190.5,1.4174268414806024,1.378688524590164,0.32871725177930095,4,4
+Cytoskeletal Signaling,UCD-0159558,0.2931221363729607,0.21152785836179305,0.263558520521694,377,4,94.25,95.5,0.7127826590249825,0.6926229508196722,0.27622890754115353,4,4
+Cytoskeletal Signaling,UCD-0159596,0.921113942075839,0.9912940907997152,0.15784144726855776,50,4,12.5,8.5,0.08232143054098812,0.05883158375171155,0.08633676351563685,4,4
+Cytoskeletal Signaling,UCD-0159634,0.05395439370494637,0.011061274405761979,0.09560634185628458,7,2,3.5,3.5,0.02092862092862093,0.02092862092862093,0.006664346655673627,2,2
+DNA Damage,UCD-0001810,0.6762340910193436,0.7719925469867039,0.3153889689710593,361,4,90.25,94.5,0.41964014141791645,0.43267608324224516,0.07590655574202056,4,4
+DNA Damage,UCD-0001843,0.30394986420372494,0.18196272583203651,0.333033269289378,871,4,217.75,216.0,1.0358842116848361,1.043261562998405,0.0662400137757067,4,4
+DNA Damage,UCD-0159257,0.2816213596798692,0.1747977569025685,0.2855123507871133,869,4,217.25,218.5,1.012341194607443,0.988690533629258,0.09340041626994605,4,4
+DNA Damage,UCD-0159285,0.6973106943109262,0.81950575158987,0.3202833489829503,818,4,204.5,205.5,0.9640980612252382,0.9692707157371867,0.16671343318579993,4,4
+DNA Damage,UCD-0159292,0.5814132865140365,0.6386358004599184,0.33577308796961475,307,4,76.75,71.0,0.3537196850944343,0.3513487086554633,0.2783542798681156,4,4
+DNA Damage,UCD-0159327,0.268773373212108,0.13553451969260807,0.2991364216563077,642,4,160.5,163.0,0.8012825600599962,0.8064815249542232,0.11895156414836593,4,4
+DNA Damage,UCD-0159386,0.20775616697103605,0.0753040845010512,0.26992215315439305,793,4,198.25,196.5,0.9702237844191193,0.9589958407605466,0.11659252629754197,4,4
+DNA Damage,UCD-0159396,0.46633458879681833,0.3851931171665605,0.37615875102107044,851,4,212.75,220.5,0.9965748682522502,0.9969660752045602,0.06998014057225428,4,4
+DNA Damage,UCD-0159414,0.09327419909768801,0.019901847774557002,0.16293739281096944,530,4,132.5,130.5,0.6560363396997744,0.6250389165628891,0.23046399169766846,4,4
+DNA Damage,UCD-0159426,0.2469218060153983,0.13438523255277973,0.27209955959476184,872,4,218.0,213.5,1.0252433057911228,1.034176325957148,0.07454971010354898,4,4
+DNA Damage,UCD-0159474,0.7071047643793658,0.83130702779645,0.309684463880657,712,4,178.0,185.0,1.0066459887387604,1.0007257674318586,0.2701134810973104,4,4
+DNA Damage,UCD-0159526,0.3484107665099829,0.258991283623786,0.28660618848089536,163,2,81.5,81.5,0.5638621022179363,0.5638621022179363,0.027922876267684787,2,2
+DNA Damage,UCD-0159527,0.3255326054626771,0.23496212546447692,0.2775455054167025,648,4,162.0,155.0,1.2293658667754617,1.20425506268081,0.2371959307386991,4,4
+DNA Damage,UCD-0159559,0.858424681915235,0.9423184294408459,0.19466046496449113,511,4,127.75,125.5,0.9076829497594249,0.9402597402597402,0.12852736735546033,4,4
+DNA Damage,UCD-0159560,0.1891471661822094,0.02265298250663898,0.27999345457301306,377,4,94.25,98.0,0.515959383505048,0.5432374211417141,0.4360372225708646,4,4
+DNA Damage,UCD-0159566,0.31560111398723295,0.18119141986713938,0.3135962926729756,358,3,119.33333333333333,109.0,0.8843025011159306,0.8014705882352942,0.23528130686096488,3,3
+DNA Damage,UCD-0159589,0.38684315191711544,0.34422005889223783,0.2976327179411429,652,4,163.0,160.5,1.223203026481715,1.2540983606557377,0.16226720356761576,4,4
+DNA Damage,UCD-0159620,0.510431410609915,0.506283387258547,0.3329817439762961,524,4,131.0,108.0,0.9232259980283396,0.8243958573072496,0.2834259198180385,4,4
+DNA Damage,UCD-0159623,0.1255203618660779,0.06164578259992454,0.15596302426241862,483,4,120.75,147.5,0.8168911067296436,0.9380029926800664,0.40729693269334794,4,4
+Endocrinology & Hormones,UCD-0000101,0.2837468661937914,0.12930171682706176,0.3392345969131656,357,4,89.25,60.0,0.4123131306695089,0.29675544794188863,0.29288407987039305,4,4
+Endocrinology & Hormones,UCD-0000849,0.8026851952139509,0.9494007283506481,0.27415466503673236,644,4,161.0,165.5,0.8043918259305797,0.8113702521245129,0.13494226631802278,4,4
+Endocrinology & Hormones,UCD-0001040,0.12337371608326962,0.037732718599967775,0.19453929611656462,747,4,186.75,183.0,0.8715318565357679,0.8404559009243242,0.09894682571543112,4,4
+Endocrinology & Hormones,UCD-0001086,0.17341392788109053,0.0484903356122664,0.24755495647221157,960,4,240.0,233.5,1.1885941773302804,1.1900316806383062,0.021589731889655513,4,4
+Endocrinology & Hormones,UCD-0001162,0.0665743773763126,0.00045464694898345406,0.16395076262327177,185,1,185.0,185.0,0.8958837772397095,0.8958837772397095,,1,1
+Endocrinology & Hormones,UCD-0001613,0.19473036102756355,0.07018585086863409,0.2515228204127882,883,4,220.75,219.0,1.0273629429831357,1.0590835287750315,0.08506750033488693,4,4
+Endocrinology & Hormones,UCD-0001801,0.8830069306838656,0.963349542415556,0.17798134256831188,273,4,68.25,49.0,0.33577212401026413,0.22514715815883432,0.38198691200295715,4,4
+Endocrinology & Hormones,UCD-0011250,0.24359100128131658,0.08004665322183034,0.30195615039326973,935,4,233.75,218.0,1.0933428291160951,1.0497912939773406,0.1767007502859676,4,4
+Endocrinology & Hormones,UCD-0017999,0.10815411692884529,0.02935349724548833,0.17791930982056173,837,4,209.25,212.5,0.9744235545839768,0.9744510829769397,0.16153960725101854,4,4
+Endocrinology & Hormones,UCD-0018089,0.21370831987065061,0.08761017703119245,0.2734621545355834,789,4,197.25,205.5,0.9728237603752221,1.0062116104004064,0.10195176270218752,4,4
+Endocrinology & Hormones,UCD-0018304,0.23178687777380252,0.11831505836180917,0.26380379171911367,722,4,180.5,189.0,0.899742950766353,0.8659027952656762,0.17354054795792895,4,4
+Endocrinology & Hormones,UCD-0159283,0.14086124648427478,0.03377428987697987,0.21603750980539346,795,4,198.75,204.0,0.9223546902877104,0.9814445352378729,0.16015747527608012,4,4
+Endocrinology & Hormones,UCD-0159287,0.8622146451038913,0.9830230017520005,0.22756892197229134,668,4,167.0,164.0,0.8369244193543168,0.800805842357089,0.1646678069539092,4,4
+Endocrinology & Hormones,UCD-0159322,0.48402080813310433,0.5073696556913667,0.4598318635956853,736,4,184.0,181.5,0.8755103648952891,0.8207543859649122,0.12373652107336712,4,4
+Endocrinology & Hormones,UCD-0159344,0.15157537148860648,0.034909721183135636,0.23150318369416573,812,4,203.0,221.5,0.9884517130577921,1.0342701525054467,0.23938242738172172,4,4
+Endocrinology & Hormones,UCD-0159374,0.1781407824759115,0.0674830011360861,0.22861582877531184,651,4,162.75,174.5,0.8070906480284608,0.8180540920266948,0.14216073145050528,4,4
+Endocrinology & Hormones,UCD-0159483,0.6616583153816692,0.7545973851702998,0.3208056396601332,792,4,198.0,199.5,1.1266135539430664,1.0058437001594895,0.31696505886428544,4,4
+Endocrinology & Hormones,UCD-0159598,1.0449620458708487e-09,1.0449620458708487e-09,,1,1,1.0,1.0,0.00909090909090909,0.00909090909090909,,1,1
+Endocrinology & Hormones,UCD-0159617,0.6766962732593373,0.7346347166795598,0.258425600527868,480,3,160.0,158.0,1.0295898999321054,0.9513513513513514,0.1490969842080578,3,3
+Endocrinology & Hormones,UCD-0159625,0.5323658357346064,0.5383368806074953,0.29357310029484895,553,4,138.25,160.0,1.2085953751352991,1.1302037632836113,0.3579310438560561,4,4
+Epigenetics,UCD-0001059,0.7673013239355088,0.9017096825838035,0.2858055102643556,833,4,208.25,206.0,0.9749030534908993,0.9564102564102563,0.08714860585955288,4,4
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+Epigenetics,UCD-0001924,0.1492955451942889,0.026861888159410832,0.23456844414413527,205,2,102.5,102.5,0.46285833203267257,0.46285833203267257,0.338849130646327,2,2
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+Epigenetics,UCD-0001927,0.967565070822416,0.9999976128448796,0.11579332212395296,588,4,147.0,158.0,0.9018493186901437,0.9487003728887914,0.20080506222374533,4,4
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+Epigenetics,UCD-0159508,0.0030796422187744895,0.0030796422187744895,,1,1,1.0,1.0,0.005333333333333333,0.005333333333333333,,1,1
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+GPCR & G Protein,UCD-0000201,0.31125091215415523,0.15950348628300842,0.3231382947823087,424,3,141.33333333333334,98.0,0.6892063416036018,0.5568181818181818,0.33342469271082154,3,3
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+GPCR & G Protein,UCD-0000786,0.20205567210226022,0.07569460732308256,0.2632558667089957,804,4,201.0,200.5,0.995043021809751,0.9755900578924726,0.08350397243591019,4,4
+GPCR & G Protein,UCD-0000904,0.9904920142342093,0.9998849339992788,0.060068634101137705,625,4,156.25,163.5,0.7288913754488651,0.7387606876896203,0.13238374415634513,4,4
+GPCR & G Protein,UCD-0001657,0.6541809619718262,0.7211294110425017,0.29762112544460295,709,4,177.25,189.5,0.8265458606396707,0.8728317985536653,0.11714019930119884,4,4
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+GPCR & G Protein,UCD-0159402,0.1014849335408048,0.031108542507878954,0.15875481503348335,633,4,158.25,176.0,0.7819939327160653,0.8252656304882333,0.16801620326823308,4,4
+GPCR & G Protein,UCD-0159403,0.9792588486103536,0.9990477765495368,0.06804117140804368,642,4,160.5,163.5,0.7503744340481129,0.7388526248046337,0.10839521530366047,4,4
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+GPCR & G Protein,UCD-0159628,0.4132531088954716,0.3581538625261883,0.33393082922175077,587,4,146.75,147.5,1.0708023762341916,0.9380333238969547,0.28657019816917007,4,4
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+GPCR & G Protein,UCD-0159662,2.711243805606576e-09,2.711243805606576e-09,,1,1,1.0,1.0,0.024691358024691357,0.024691358024691357,,1,1
+JAK/STAT,UCD-0011696,0.29911641601574707,0.05037346437504042,0.383818831118766,760,4,190.0,192.0,0.9266479586173749,0.9175762527233116,0.07659911233043981,4,4
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+JAK/STAT,UCD-0159561,0.11560101788180888,0.0036820753963790554,0.22332347774195357,584,4,146.0,156.5,0.9554880231640706,0.9898105969791416,0.39834945830122725,4,4
+JAK/STAT,UCD-0159622,0.09185398063215883,0.024804948168084335,0.16037295435955987,566,4,141.5,136.5,1.0345785917281651,0.9205956184390689,0.2734659798846565,4,4
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+MAPK,UCD-0001804,0.8000518372851414,0.9252839336593502,0.2586770371448877,687,4,171.75,168.0,0.800418522048852,0.7982614585685256,0.013834561802564658,4,4
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+Neuronal Signaling,UCD-0159265,0.8853473115604323,0.9963551216547805,0.24983544842032263,16,2,8.0,8.0,0.03860108249568786,0.03860108249568786,0.011187200606412992,2,2
+Neuronal Signaling,UCD-0159319,0.8377864176403528,0.988373441112163,0.24978526636445256,597,4,149.25,153.0,0.715468268127947,0.7470944309927361,0.11634083020969692,4,4
+Neuronal Signaling,UCD-0159321,0.05717736846220882,0.0002636747843292558,0.15904988025450634,738,4,184.5,180.0,0.8780641839575827,0.8682192770060746,0.055289437932789724,4,4
+Neuronal Signaling,UCD-0159326,0.5118059704721877,0.5413199562297135,0.3559988722395988,704,4,176.0,171.5,0.8563672131364555,0.8432058067442916,0.04921018891765456,4,4
+Neuronal Signaling,UCD-0159337,0.5776538213078914,0.6155834687603743,0.3105714528650265,800,4,200.0,206.0,1.001412702903011,1.0416018057285181,0.24004566671451272,4,4
+Neuronal Signaling,UCD-0159338,0.1298324978557497,0.01974955146254343,0.21342324320368936,870,4,217.5,211.5,1.063722358292334,1.0205169340463458,0.13827508419664666,4,4
+Neuronal Signaling,UCD-0159339,0.22589846665998245,0.09313267327947723,0.2820549226970223,514,4,128.5,145.0,0.6333202520186492,0.708716577540107,0.21625961925570417,4,4
+Neuronal Signaling,UCD-0159345,0.05753960729870553,0.0038002177041555063,0.14331510815236786,796,4,199.0,196.5,0.9733589734483806,0.9588510425592688,0.14470751905604384,4,4
+Neuronal Signaling,UCD-0159350,0.8582978862472107,0.9700622867526021,0.2085034637704065,918,4,229.5,235.0,1.1182024272667703,1.0965032679738562,0.14381121523219864,4,4
+Neuronal Signaling,UCD-0159367,0.6417548682864812,0.7217747351052474,0.32745566336169973,755,4,188.75,193.0,0.9492801476455638,0.926313791794624,0.11764520974464948,4,4
+Neuronal Signaling,UCD-0159381,0.22685580731318647,0.14506144500115326,0.25846157346387394,540,4,135.0,151.5,0.6807859754731196,0.7104956972251493,0.2517442429133906,4,4
+Neuronal Signaling,UCD-0159382,0.31280539423378717,0.220152166923277,0.2963395953720612,849,4,212.25,219.5,1.0594754631963277,1.0496066433566433,0.11018096431083972,4,4
+Neuronal Signaling,UCD-0159387,0.9888601510936009,0.99954203220115,0.04430062783444363,809,4,202.25,207.5,1.0035568401297241,0.9918164774622453,0.11224617973679157,4,4
+Neuronal Signaling,UCD-0159390,0.3022670502884608,0.18298248350482726,0.3085790148899048,746,4,186.5,179.5,0.9375341295969251,0.9403409090909091,0.13999631387799874,4,4
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+Neuronal Signaling,UCD-0159408,0.2231532086041569,0.1269677057225392,0.24206252675394013,844,4,211.0,219.0,1.0635805812187535,1.0275289778714436,0.22216998130500543,4,4
+Neuronal Signaling,UCD-0159410,0.1734062652669647,0.0647894813299207,0.22436925911793396,663,4,165.75,172.0,0.8176983597544698,0.8474011819414842,0.15623093301325003,4,4
+Neuronal Signaling,UCD-0159411,0.5186944136725086,0.4918694195556629,0.3312687731433143,772,4,193.0,189.5,0.9049361294715798,0.8565627777267015,0.1039355788433118,4,4
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+PI3K/Akt/mTOR,UCD-0018240,0.047591775338577155,8.761536609320186e-05,0.1515715029738093,909,4,227.25,227.5,1.0849625469683453,1.1149075500770418,0.09932478693992469,4,4
+PI3K/Akt/mTOR,UCD-0159259,0.36681011785776657,0.3064040172378214,0.311795971621013,624,4,156.0,158.0,0.728007989182217,0.7149860079127666,0.039999670165511744,4,4
+PI3K/Akt/mTOR,UCD-0159290,0.9991458343659912,0.9998426692161004,0.0015133509998037913,6,1,6.0,6.0,0.030690537084398978,0.030690537084398978,,1,1
+PI3K/Akt/mTOR,UCD-0159302,0.37115787775444925,0.28795118200530584,0.3215346955397223,673,4,168.25,165.5,0.8433704409269425,0.8733316544950894,0.17104553040033657,4,4
+PI3K/Akt/mTOR,UCD-0159314,0.042686034141782764,2.8509422451097125e-05,0.1157174246145455,117,2,58.5,58.5,0.29703030303030303,0.29703030303030303,0.17579103129861925,2,2
+PI3K/Akt/mTOR,UCD-0159315,0.9376993035376229,0.9997594514304027,0.15305995002574743,582,4,145.5,154.0,0.7053375851029732,0.7216927140655954,0.24842148100164713,4,4
+PI3K/Akt/mTOR,UCD-0159325,0.3997986029102429,0.009220777091700046,0.5402891423930444,5,3,1.6666666666666667,1.0,0.007937437327267835,0.005333333333333333,0.004941510085497663,3,3
+PI3K/Akt/mTOR,UCD-0159355,0.21079901959172598,0.061675611238178885,0.2845874660394801,941,4,235.25,245.0,1.1429130695278813,1.183712121212121,0.12030044150838014,4,4
+PI3K/Akt/mTOR,UCD-0159370,0.5563156217563376,0.5898554805599423,0.3769582445806872,731,4,182.75,176.5,0.8948715251206556,0.8245642701525054,0.22383016733767022,4,4
+PI3K/Akt/mTOR,UCD-0159379,0.8754297144543968,0.9759272013987341,0.2090137948225087,756,4,189.0,171.0,0.9334286270085803,0.8413279887618679,0.199930019871556,4,4
+PI3K/Akt/mTOR,UCD-0159423,0.03975103197195535,0.004461538811179023,0.09870670572580958,707,4,176.75,173.0,0.882781233911069,0.8702781237027812,0.25606080083885097,4,4
+PI3K/Akt/mTOR,UCD-0159431,0.05884045330466291,0.011343828626269321,0.12434634172702795,362,4,90.5,36.5,0.43001921386843833,0.19708180673231052,0.5158836113909729,4,4
+PI3K/Akt/mTOR,UCD-0159439,0.7538769312354222,0.7812630761748637,0.25392489063294815,287,4,71.75,70.0,0.34315076168282527,0.3192133603870766,0.17149499054553052,4,4
+PI3K/Akt/mTOR,UCD-0159497,0.27017316190846274,0.15206366032192986,0.30654533502368614,617,4,154.25,154.0,0.9535298215229295,0.940641732598098,0.03127324006923954,4,4
+PI3K/Akt/mTOR,UCD-0159553,0.30321136596116477,0.09503550256614227,0.3727952315631968,94,4,23.5,16.5,0.16155765802866384,0.10193398865180212,0.16918055377702856,4,4
+PI3K/Akt/mTOR,UCD-0159578,0.3327444649742764,0.24866773993849015,0.3151213783196859,163,2,81.5,81.5,0.5364151398264224,0.5364151398264224,0.7066124201293053,2,2
+PI3K/Akt/mTOR,UCD-0159579,0.19455361382592368,0.11308449653774354,0.22580321585180702,412,4,103.0,99.5,0.7697680507458841,0.8105462268533882,0.14115414964986672,4,4
+PI3K/Akt/mTOR,UCD-0159581,0.37996792352926445,0.3323014620748569,0.3046163253786168,336,4,84.0,78.5,0.6419743039911923,0.6077190997856633,0.18742513225842844,4,4
+PI3K/Akt/mTOR,UCD-0159605,0.22551976093068823,0.17051589759726185,0.25585264013042813,62,3,20.666666666666668,24.0,0.13751384162675465,0.17142857142857143,0.0638190132224439,3,3
+PI3K/Akt/mTOR,UCD-0159613,0.1589878463410923,0.04020074618423163,0.2234739235977036,37,2,18.5,18.5,0.1199367088607595,0.1199367088607595,0.0989054421786247,2,2
+Proteases,UCD-0000918,0.17116809006303813,0.04133116235590268,0.25061243624696916,685,4,171.25,170.0,0.8032050336365395,0.8021074815595364,0.030224130291780053,4,4
+Proteases,UCD-0018183,0.1993284698511397,0.07511713028327074,0.2621886253701792,846,4,211.5,199.0,0.9920321194399632,0.9730471079308288,0.12181683855156883,4,4
+Proteases,UCD-0018458,0.2813283756268461,0.17758005547346672,0.2815968050608375,730,4,182.5,182.0,0.9134207996091643,0.916002438379168,0.04822232449784556,4,4
+Proteases,UCD-0159316,0.2798796091839089,0.15900518326508686,0.298451904747527,570,4,142.5,145.5,0.6855231990781827,0.67714058098185,0.2724042606458328,4,4
+Proteases,UCD-0159328,0.9999966473643804,0.9999966473643804,,1,1,1.0,1.0,0.004842615012106538,0.004842615012106538,,1,1
+Proteases,UCD-0159352,0.4195018291082867,0.34015381005631795,0.3634623549689585,464,4,116.0,114.5,0.566735638557051,0.5770588235294118,0.11161849802045753,4,4
+Proteases,UCD-0159353,0.9382881009770744,0.9941970814224109,0.13493157493301283,597,4,149.25,143.0,0.7494209995633342,0.729387193856372,0.16009615202501634,4,4
+Proteases,UCD-0159457,0.5137704467241636,0.5215626523516323,0.3654041234405665,706,4,176.5,183.0,1.121074774517766,1.078508988949365,0.18518048043518373,4,4
+Proteases,UCD-0159471,0.7559995229342313,0.9429861868003562,0.31742989116194986,614,4,153.5,147.5,0.9754479634606494,1.0174830941777997,0.16679447790555693,4,4
+Proteases,UCD-0159516,0.21572152724949353,0.04615227766916359,0.2970204439078891,377,4,94.25,98.5,0.4979494366066144,0.4872025518341308,0.41390096357215317,4,4
+Proteases,UCD-0159517,0.3556983528548707,0.2654726149719398,0.32955882528321145,638,4,159.5,161.0,0.8859351277083105,0.8932784951169954,0.0770204965553224,4,4
+Proteases,UCD-0159518,0.999850919616445,0.999999999421332,0.0026185442074938285,348,4,87.0,95.5,0.5464637196496336,0.5001645097609124,0.1881349541309609,4,4
+Proteases,UCD-0159522,0.3004574739023935,0.1763156046247135,0.3319202195858088,497,4,124.25,145.0,0.7356603498826765,0.9018425093222198,0.41672888056077023,4,4
+Proteases,UCD-0159523,0.20075475365207562,0.037228020339247145,0.294684107067071,240,2,120.0,120.0,0.7408832128019696,0.7408832128019696,0.07093916315335114,2,2
+Proteases,UCD-0159530,0.14011551730612756,0.010666034931216765,0.24354938669354256,533,4,133.25,131.5,0.8394122018467577,0.8400263725725245,0.10854074607229917,4,4
+Proteases,UCD-0159539,0.7355994132672927,0.8284073344321459,0.2574785657532616,534,4,133.5,134.0,1.007543413927594,1.0008437801350047,0.11167390304037184,4,4
+Proteases,UCD-0159642,0.2685070746572331,0.2010762501379143,0.24996085098629783,375,4,93.75,110.5,0.616151348042983,0.6933645183645183,0.40258739992464226,4,4
+Proteases,UCD-0159652,0.115872604394342,0.027741540684287577,0.18978800506630444,512,4,128.0,122.0,0.9159924164079541,0.9277270652670013,0.11803074444528015,4,4
+Protein Tyrosine Kinase,UCD-0001733,0.5173475068591279,0.516145888228768,0.3347850020041165,793,4,198.25,198.5,0.990318946518031,0.9753210778141526,0.1190828240278897,4,4
+Protein Tyrosine Kinase,UCD-0001869,0.5475214936670724,0.5905799223332368,0.35884828016900233,973,4,243.25,249.0,1.1626191306463385,1.186329170383586,0.14239241118428667,4,4
+Protein Tyrosine Kinase,UCD-0159308,0.6896773445900586,0.7612804043648753,0.297516692071834,29,2,14.5,14.5,0.07693939393939395,0.07693939393939395,0.10238049092452499,2,2
+Protein Tyrosine Kinase,UCD-0159313,0.6827815038255973,0.8748606789696658,0.37214010634650524,703,4,175.75,184.5,0.8429492317079293,0.8249202551834132,0.23275474938245125,4,4
+Protein Tyrosine Kinase,UCD-0159330,0.044591170605969815,6.672762923602202e-05,0.14708521519623532,762,4,190.5,187.5,0.9188164614154801,0.8809487123046444,0.22059574321210143,4,4
+Protein Tyrosine Kinase,UCD-0159372,0.36927445157472183,0.24552828414970979,0.3337360660501811,61,1,61.0,61.0,0.2785388127853881,0.2785388127853881,,1,1
+Protein Tyrosine Kinase,UCD-0159378,0.22447380707487832,0.02393225761014812,0.3209901501418284,591,4,147.75,145.5,0.7198983050933754,0.7284022746747683,0.046810098819161074,4,4
+Protein Tyrosine Kinase,UCD-0159389,0.35908648339063165,0.25904530179187557,0.34726957309801465,588,4,147.0,158.0,0.7456855021725108,0.7606017735757911,0.22899226576779158,4,4
+Protein Tyrosine Kinase,UCD-0159391,0.11978147838222085,0.037592230255380985,0.18355572689612396,960,4,240.0,243.5,1.2124649989716758,1.1994344603758962,0.21637328356079186,4,4
+Protein Tyrosine Kinase,UCD-0159398,0.173032348669187,0.08040140797321339,0.21991405220852303,902,4,225.5,220.0,1.1251785893965254,1.087982310510648,0.10916228581980839,4,4
+Protein Tyrosine Kinase,UCD-0159419,0.04582036775890503,0.00012862733718903638,0.09332840152590681,10,2,5.0,5.0,0.02316736902346392,0.02316736902346392,0.026436030017202697,2,2
+Protein Tyrosine Kinase,UCD-0159433,0.735386106362139,0.8488302423495759,0.28547146447353716,853,4,213.25,216.0,1.0021025228761418,1.0412950874934879,0.12940460523859149,4,4
+Protein Tyrosine Kinase,UCD-0159435,4.651425286072529e-05,6.724109045193016e-06,7.692521459043082e-05,5,2,2.5,2.5,0.011887098443808012,0.011887098443808012,0.002561892689161433,2,2
+Protein Tyrosine Kinase,UCD-0159552,0.1978008338756756,0.04857682580445826,0.2784539274779464,629,4,157.25,164.0,1.005948576896985,0.8598376837025663,0.3716528288780475,4,4
+Protein Tyrosine Kinase,UCD-0159569,0.5246189090249783,0.5226113063014249,0.28877209580099206,621,4,155.25,157.5,1.1717262892413884,1.146731267007367,0.1685554841010327,4,4
+Protein Tyrosine Kinase,UCD-0159573,0.4102339593136315,0.3235172866977345,0.36172720627304655,323,4,80.75,83.0,0.6026649836612152,0.5873071359691417,0.16958767523171084,4,4
+Protein Tyrosine Kinase,UCD-0159585,0.80273369849347,0.8971045450271242,0.23104858495556096,508,4,127.0,126.5,0.9636649436438809,0.9781495594189094,0.15903589451466768,4,4
+Protein Tyrosine Kinase,UCD-0159636,0.23297773883827103,0.10792030231644098,0.27234765050326826,656,4,164.0,167.5,1.1838134178475708,1.29751356238698,0.3134389489736037,4,4
+Protein Tyrosine Kinase,UCD-0159639,0.09716621562077887,0.025461348461085914,0.16694568655907022,472,4,118.0,140.5,0.7965353478163362,0.9021616047235815,0.4574781999466303,4,4
+Protein Tyrosine Kinase,UCD-0159653,0.23010946244053818,0.121881963847319,0.25950448252427155,567,4,141.75,146.5,1.0031495357879472,1.0015974440894568,0.027257611344722203,4,4
+Protein Tyrosine Kinase,UCD-0159663,0.4177476464050575,0.3771626083673415,0.3052595372422761,538,4,134.5,165.0,0.9621217773784314,1.0642238392238392,0.3220366388235147,4,4
+Stem Cells & Wnt,UCD-0159284,0.6568851913656727,0.7611643193839261,0.3168415189869771,565,4,141.25,159.0,0.6787086602168512,0.7915290641372632,0.3708428888149074,4,4
+Stem Cells & Wnt,UCD-0159323,0.7804986612355564,0.9861487049996776,0.32474296562168226,184,4,46.0,30.5,0.22143424045475785,0.1460877192982456,0.23325870789539932,4,4
+Stem Cells & Wnt,UCD-0159346,0.8558897423896336,0.9741823447383983,0.23157032639619549,877,4,219.25,216.0,1.0691317129406877,1.0837611408199643,0.03778487636188176,4,4
+Stem Cells & Wnt,UCD-0159362,0.10746917778521341,0.013322563338018772,0.20073343512571107,872,4,218.0,216.0,1.0634158415904598,1.086397933923641,0.06729790624676073,4,4
+Stem Cells & Wnt,UCD-0159429,0.9953439093389719,0.9999649856009679,0.029117453426044305,468,4,117.0,115.5,0.6582825377140081,0.6320627923229933,0.09093524714947429,4,4
+Stem Cells & Wnt,UCD-0159436,0.4204380524126,0.3740847629695706,0.3250352915285735,660,4,165.0,179.0,0.8187474643784382,0.85074200913242,0.09538361532056484,4,4
+Stem Cells & Wnt,UCD-0159519,0.09241464734291888,0.0048551501992214335,0.16083652059130094,15,2,7.5,7.5,0.04418069228326453,0.04418069228326453,0.02026560400193265,2,2
+TGF-beta/Smad,UCD-0159365,0.4976365377659735,0.505087493782663,0.34279696758830563,694,4,173.5,184.0,0.8459904998946741,0.9080846735128851,0.33345824677329144,4,4
+TGF-beta/Smad,UCD-0159394,0.42103124969735645,0.45901428242492237,0.23069656862477333,845,4,211.25,219.5,0.9793716468239839,0.9892740032484447,0.4149443059245712,4,4
+TGF-beta/Smad,UCD-0159453,0.15265388033184746,0.03440009046985351,0.2337511215281821,706,4,176.5,185.5,0.9944440928324421,1.0345411537014138,0.22945080587437755,4,4
+TGF-beta/Smad,UCD-0159455,0.11627553051843342,0.0071838927105771985,0.23116962436852875,711,4,177.75,169.5,0.9904307307580802,0.9869027113237641,0.0820760171257066,4,4
+TGF-beta/Smad,UCD-0159472,0.25279747701176886,0.08098983072753768,0.3208390259374409,433,4,108.25,106.5,0.6883360009940249,0.8073585560569247,0.38533346757283515,4,4
+TGF-beta/Smad,UCD-0159565,0.2688747630940155,0.1453456791117655,0.28972448894324027,189,4,47.25,54.0,0.3484209501481617,0.39567266210924534,0.20281205668698762,4,4
+Transmembrane Transporters,UCD-0000377,0.6119232767921465,0.681959353362649,0.3263849965500286,1025,4,256.25,242.0,1.214399432720041,1.1040008324228707,0.28438154590839243,4,4
+Transmembrane Transporters,UCD-0000878,0.2480704853036933,0.0982085393539494,0.27309958378199783,29,3,9.666666666666666,4.0,0.05256558803616123,0.021798365122615803,0.06517052486920503,3,3
+Transmembrane Transporters,UCD-0001419,0.9917529613532274,0.9999337803902641,0.04538110816888636,724,4,181.0,163.5,0.8965337686277699,0.8291083916083917,0.16159623769112277,4,4
+Transmembrane Transporters,UCD-0001567,0.15102523069302243,0.06620116759182341,0.18840630867432626,83,2,41.5,41.5,0.1998723301473379,0.1998723301473379,0.030815909203913246,2,2
+Transmembrane Transporters,UCD-0001580,0.999788669470584,0.999788669470584,,1,1,1.0,1.0,0.00554016620498615,0.00554016620498615,,1,1
+Transmembrane Transporters,UCD-0001653,0.5347552050278691,0.568465366531764,0.33699842132621205,919,4,229.75,230.0,1.0796966859164456,1.0927267051662244,0.05854492678063948,4,4
+Transmembrane Transporters,UCD-0159312,0.13554722703392466,0.03593519273138378,0.2093879129974464,702,4,175.5,175.0,0.8640771349976797,0.897254565942063,0.07126458376055907,4,4
+Transmembrane Transporters,UCD-0159331,0.6041716288466134,0.6969817553169126,0.35294451543173555,665,4,166.25,167.0,0.8174052072466181,0.8189661319073084,0.19269627072833673,4,4
+Transmembrane Transporters,UCD-0159333,0.32249564145255066,0.2165298803421395,0.3177963796816247,936,4,234.0,231.5,1.1393725875876564,1.1460832619251642,0.14650880055542873,4,4
+Transmembrane Transporters,UCD-0159376,0.03428054541194044,0.00038152960231780465,0.11722615306400648,908,4,227.0,222.5,1.1086639340631477,1.0804770065695517,0.10194982167594961,4,4
+Transmembrane Transporters,UCD-0159418,0.2775686420570803,0.16966828601768258,0.28503125924484474,805,4,201.25,194.5,0.9535151199274985,0.8861661724155872,0.2421576810517479,4,4
+Transmembrane Transporters,UCD-0159479,0.7824579668009641,0.9366036354542182,0.2754423531552595,472,4,118.0,123.0,0.7496404295264902,0.6449571169388092,0.21685189213251638,4,4
+Transmembrane Transporters,UCD-0159480,0.6370938354967194,0.8019283783662943,0.3659255280061374,163,2,81.5,81.5,0.43972149561470997,0.43972149561470997,0.4424449493299116,2,2
+Transmembrane Transporters,UCD-0159528,0.9470903769140641,0.9961212501865124,0.1246785118978985,496,4,124.0,120.0,0.8892650864321507,0.93697173130586,0.31486117024492827,4,4
+Transmembrane Transporters,UCD-0159610,0.45689844053193807,0.4320305027066277,0.2873234628987765,539,3,179.66666666666666,156.0,1.1964238002260816,1.0,0.5281835281468477,3,3
+Transmembrane Transporters,UCD-0159630,0.09921127511682538,0.03278817648139269,0.1603521026711586,576,4,144.0,140.0,1.0339519304046947,1.0019439421338154,0.1310643702049001,4,4
+Transmembrane Transporters,UCD-0159644,0.10523022218402205,0.004073811551737843,0.20470894305874496,242,3,80.66666666666667,90.0,0.5510250988412513,0.569620253164557,0.5228062733776262,3,3
+Transmembrane Transporters,UCD-0159661,0.08291338263269502,0.011121560426931276,0.15436461729020184,183,4,45.75,36.0,0.3646683012260114,0.2396473779385172,0.3725719433146072,4,4
+Ubiquitin,UCD-0159447,0.02456969858172466,0.0001072557307838865,0.10618810429912949,706,4,176.5,173.0,0.986317061561231,0.9888715660448364,0.08246829277190185,4,4
+Ubiquitin,UCD-0159449,0.7567513384716827,0.9843947637349728,0.3455489466908766,443,4,110.75,136.0,0.6645188559312732,0.7697411713094977,0.33401891690164803,4,4
+Ubiquitin,UCD-0159451,0.06047635990064606,0.0006907648879350142,0.1720761080084479,44,2,22.0,22.0,0.11809132420091324,0.11809132420091324,0.1573199580207002,2,2
+Ubiquitin,UCD-0159467,8.587363474179593e-35,8.587363474179593e-35,,1,1,1.0,1.0,0.004784688995215311,0.004784688995215311,,1,1
+Ubiquitin,UCD-0159546,0.23491877988157747,0.07725562534299577,0.30902915890091465,655,4,163.75,165.5,1.0300586865683823,0.9849555414417275,0.23500814507738502,4,4
diff --git a/3a.train_dmso_logistic_regression_models/train_platemapwise_model/inference_results/logit_inference_summary.parquet b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/inference_results/logit_inference_summary.parquet
new file mode 100644
index 0000000..828a598
Binary files /dev/null and b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/inference_results/logit_inference_summary.parquet differ
diff --git a/3a.train_dmso_logistic_regression_models/train_platemapwise_model/inference_results/treatment_well.csv b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/inference_results/treatment_well.csv
new file mode 100644
index 0000000..8d8a119
--- /dev/null
+++ b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/inference_results/treatment_well.csv
@@ -0,0 +1,2217 @@
+,Metadata_Well,cell_type,treatment,platemap_file,Metadata_Plate,platemap_num,Pathway
+4,B03,failing,UCD-0000253,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Neuronal Signaling
+5,B03,failing,UCD-0000253,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Neuronal Signaling
+6,B03,failing,UCD-0000253,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Neuronal Signaling
+7,B03,failing,UCD-0000253,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Neuronal Signaling
+8,B04,failing,UCD-0000872,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Metabolism
+9,B04,failing,UCD-0000872,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Metabolism
+10,B04,failing,UCD-0000872,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Metabolism
+11,B04,failing,UCD-0000872,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Metabolism
+16,B06,failing,UCD-0159319,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Neuronal Signaling
+17,B06,failing,UCD-0159319,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Neuronal Signaling
+18,B06,failing,UCD-0159319,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Neuronal Signaling
+19,B06,failing,UCD-0159319,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Neuronal Signaling
+20,B07,failing,UCD-0000730,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Metabolism
+21,B07,failing,UCD-0000730,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Metabolism
+22,B07,failing,UCD-0000730,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Metabolism
+23,B07,failing,UCD-0000730,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Metabolism
+28,B09,failing,UCD-0018569,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Endocrinology & Hormones
+29,B09,failing,UCD-0018569,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Endocrinology & Hormones
+30,B09,failing,UCD-0018569,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Endocrinology & Hormones
+31,B09,failing,UCD-0018569,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Endocrinology & Hormones
+32,B10,failing,UCD-0159328,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Proteases
+33,B10,failing,UCD-0159328,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Proteases
+34,B10,failing,UCD-0159328,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Proteases
+35,B10,failing,UCD-0159328,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Proteases
+40,C02,failing,UCD-0159298,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Cytoskeletal Signaling
+41,C02,failing,UCD-0159298,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Cytoskeletal Signaling
+42,C02,failing,UCD-0159298,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Cytoskeletal Signaling
+43,C02,failing,UCD-0159298,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Cytoskeletal Signaling
+44,C03,failing,UCD-0159304,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Cell Cycle
+45,C03,failing,UCD-0159304,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Cell Cycle
+46,C03,failing,UCD-0159304,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Cell Cycle
+47,C03,failing,UCD-0159304,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Cell Cycle
+48,C04,failing,UCD-0159308,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Protein Tyrosine Kinase
+49,C04,failing,UCD-0159308,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Protein Tyrosine Kinase
+50,C04,failing,UCD-0159308,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Protein Tyrosine Kinase
+51,C04,failing,UCD-0159308,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Protein Tyrosine Kinase
+52,C05,failing,UCD-0018012,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Apoptosis
+53,C05,failing,UCD-0018012,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Apoptosis
+54,C05,failing,UCD-0018012,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Apoptosis
+55,C05,failing,UCD-0018012,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Apoptosis
+56,C06,failing,UCD-0001162,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Endocrinology & Hormones
+57,C06,failing,UCD-0001162,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Endocrinology & Hormones
+58,C06,failing,UCD-0001162,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Endocrinology & Hormones
+59,C06,failing,UCD-0001162,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Endocrinology & Hormones
+60,C07,failing,UCD-0001305,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Neuronal Signaling
+61,C07,failing,UCD-0001305,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Neuronal Signaling
+62,C07,failing,UCD-0001305,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Neuronal Signaling
+63,C07,failing,UCD-0001305,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Neuronal Signaling
+64,C08,failing,UCD-0018311,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Neuronal Signaling
+65,C08,failing,UCD-0018311,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Neuronal Signaling
+66,C08,failing,UCD-0018311,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Neuronal Signaling
+67,C08,failing,UCD-0018311,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Neuronal Signaling
+68,C09,failing,UCD-0001441,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Neuronal Signaling
+69,C09,failing,UCD-0001441,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Neuronal Signaling
+70,C09,failing,UCD-0001441,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Neuronal Signaling
+71,C09,failing,UCD-0001441,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Neuronal Signaling
+72,C10,failing,UCD-0159322,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Endocrinology & Hormones
+73,C10,failing,UCD-0159322,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Endocrinology & Hormones
+74,C10,failing,UCD-0159322,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Endocrinology & Hormones
+75,C10,failing,UCD-0159322,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Endocrinology & Hormones
+76,C11,failing,UCD-0001784,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Cell Cycle
+77,C11,failing,UCD-0001784,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Cell Cycle
+78,C11,failing,UCD-0001784,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Cell Cycle
+79,C11,failing,UCD-0001784,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Cell Cycle
+80,D02,failing,UCD-0159299,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Metabolism
+81,D02,failing,UCD-0159299,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Metabolism
+82,D02,failing,UCD-0159299,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Metabolism
+83,D02,failing,UCD-0159299,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Metabolism
+84,D03,failing,UCD-0159305,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Transmembrane Transporters
+85,D03,failing,UCD-0159305,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Transmembrane Transporters
+86,D03,failing,UCD-0159305,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Transmembrane Transporters
+87,D03,failing,UCD-0159305,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Transmembrane Transporters
+88,D04,failing,UCD-0159309,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Angiogenesis
+89,D04,failing,UCD-0159309,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Angiogenesis
+90,D04,failing,UCD-0159309,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Angiogenesis
+91,D04,failing,UCD-0159309,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Angiogenesis
+92,D05,failing,UCD-0001843,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,DNA Damage
+93,D05,failing,UCD-0001843,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,DNA Damage
+94,D05,failing,UCD-0001843,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,DNA Damage
+95,D05,failing,UCD-0001843,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,DNA Damage
+96,D06,failing,UCD-0000490,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Neuronal Signaling
+97,D06,failing,UCD-0000490,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Neuronal Signaling
+98,D06,failing,UCD-0000490,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Neuronal Signaling
+99,D06,failing,UCD-0000490,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Neuronal Signaling
+100,D07,failing,UCD-0018439,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Microbiology
+101,D07,failing,UCD-0018439,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Microbiology
+102,D07,failing,UCD-0018439,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Microbiology
+103,D07,failing,UCD-0018439,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Microbiology
+104,D08,failing,UCD-0001435,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Others
+105,D08,failing,UCD-0001435,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Others
+106,D08,failing,UCD-0001435,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Others
+107,D08,failing,UCD-0001435,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Others
+108,D09,failing,UCD-0018550,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Neuronal Signaling
+109,D09,failing,UCD-0018550,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Neuronal Signaling
+110,D09,failing,UCD-0018550,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Neuronal Signaling
+111,D09,failing,UCD-0018550,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Neuronal Signaling
+112,D10,failing,UCD-0159323,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Stem Cells & Wnt
+113,D10,failing,UCD-0159323,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Stem Cells & Wnt
+114,D10,failing,UCD-0159323,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Stem Cells & Wnt
+115,D10,failing,UCD-0159323,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Stem Cells & Wnt
+116,D11,failing,UCD-0159329,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,JAK/STAT
+117,D11,failing,UCD-0159329,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,JAK/STAT
+118,D11,failing,UCD-0159329,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,JAK/STAT
+119,D11,failing,UCD-0159329,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,JAK/STAT
+124,E03,failing,UCD-0159300,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,GPCR & G Protein
+125,E03,failing,UCD-0159300,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,GPCR & G Protein
+126,E03,failing,UCD-0159300,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,GPCR & G Protein
+127,E03,failing,UCD-0159300,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,GPCR & G Protein
+128,E04,failing,UCD-0001790,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Cell Cycle
+129,E04,failing,UCD-0001790,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Cell Cycle
+130,E04,failing,UCD-0001790,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Cell Cycle
+131,E04,failing,UCD-0001790,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Cell Cycle
+136,E06,failing,UCD-0013217,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Epigenetics
+137,E06,failing,UCD-0013217,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Epigenetics
+138,E06,failing,UCD-0013217,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Epigenetics
+139,E06,failing,UCD-0013217,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Epigenetics
+140,E07,failing,UCD-0159313,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Protein Tyrosine Kinase
+141,E07,failing,UCD-0159313,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Protein Tyrosine Kinase
+142,E07,failing,UCD-0159313,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Protein Tyrosine Kinase
+143,E07,failing,UCD-0159313,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Protein Tyrosine Kinase
+148,E09,failing,UCD-0018240,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,PI3K/Akt/mTOR
+149,E09,failing,UCD-0018240,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,PI3K/Akt/mTOR
+150,E09,failing,UCD-0018240,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,PI3K/Akt/mTOR
+151,E09,failing,UCD-0018240,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,PI3K/Akt/mTOR
+152,E10,failing,UCD-0018483,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Microbiology
+153,E10,failing,UCD-0018483,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Microbiology
+154,E10,failing,UCD-0018483,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Microbiology
+155,E10,failing,UCD-0018483,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Microbiology
+160,F02,failing,UCD-0000101,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Endocrinology & Hormones
+161,F02,failing,UCD-0000101,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Endocrinology & Hormones
+162,F02,failing,UCD-0000101,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Endocrinology & Hormones
+163,F02,failing,UCD-0000101,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Endocrinology & Hormones
+164,F03,failing,UCD-0159320,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Metabolism
+165,F03,failing,UCD-0159320,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Metabolism
+166,F03,failing,UCD-0159320,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Metabolism
+167,F03,failing,UCD-0159320,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Metabolism
+168,F04,failing,UCD-0159324,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Angiogenesis
+169,F04,failing,UCD-0159324,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Angiogenesis
+170,F04,failing,UCD-0159324,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Angiogenesis
+171,F04,failing,UCD-0159324,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Angiogenesis
+172,F05,failing,UCD-0001869,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Protein Tyrosine Kinase
+173,F05,failing,UCD-0001869,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Protein Tyrosine Kinase
+174,F05,failing,UCD-0001869,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Protein Tyrosine Kinase
+175,F05,failing,UCD-0001869,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Protein Tyrosine Kinase
+176,F06,failing,UCD-0001818,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,MAPK
+177,F06,failing,UCD-0001818,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,MAPK
+178,F06,failing,UCD-0001818,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,MAPK
+179,F06,failing,UCD-0001818,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,MAPK
+180,F07,failing,UCD-0018051,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Metabolism
+181,F07,failing,UCD-0018051,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Metabolism
+182,F07,failing,UCD-0018051,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Metabolism
+183,F07,failing,UCD-0018051,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Metabolism
+184,F08,failing,UCD-0159310,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,GPCR & G Protein
+185,F08,failing,UCD-0159310,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,GPCR & G Protein
+186,F08,failing,UCD-0159310,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,GPCR & G Protein
+187,F08,failing,UCD-0159310,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,GPCR & G Protein
+188,F09,failing,UCD-0159314,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,PI3K/Akt/mTOR
+189,F09,failing,UCD-0159314,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,PI3K/Akt/mTOR
+190,F09,failing,UCD-0159314,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,PI3K/Akt/mTOR
+191,F09,failing,UCD-0159314,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,PI3K/Akt/mTOR
+192,F10,failing,UCD-0000083,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Neuronal Signaling
+193,F10,failing,UCD-0000083,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Neuronal Signaling
+194,F10,failing,UCD-0000083,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Neuronal Signaling
+195,F10,failing,UCD-0000083,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Neuronal Signaling
+196,F11,failing,UCD-0018527,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Microbiology
+197,F11,failing,UCD-0018527,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Microbiology
+198,F11,failing,UCD-0018527,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Microbiology
+199,F11,failing,UCD-0018527,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Microbiology
+200,G02,failing,UCD-0000469,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Neuronal Signaling
+201,G02,failing,UCD-0000469,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Neuronal Signaling
+202,G02,failing,UCD-0000469,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Neuronal Signaling
+203,G02,failing,UCD-0000469,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Neuronal Signaling
+204,G03,failing,UCD-0159321,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Neuronal Signaling
+205,G03,failing,UCD-0159321,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Neuronal Signaling
+206,G03,failing,UCD-0159321,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Neuronal Signaling
+207,G03,failing,UCD-0159321,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Neuronal Signaling
+208,G04,failing,UCD-0159325,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,PI3K/Akt/mTOR
+209,G04,failing,UCD-0159325,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,PI3K/Akt/mTOR
+210,G04,failing,UCD-0159325,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,PI3K/Akt/mTOR
+211,G04,failing,UCD-0159325,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,PI3K/Akt/mTOR
+212,G05,failing,UCD-0159330,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Protein Tyrosine Kinase
+213,G05,failing,UCD-0159330,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Protein Tyrosine Kinase
+214,G05,failing,UCD-0159330,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Protein Tyrosine Kinase
+215,G05,failing,UCD-0159330,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Protein Tyrosine Kinase
+216,G06,failing,UCD-0159301,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Cell Cycle
+217,G06,failing,UCD-0159301,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Cell Cycle
+218,G06,failing,UCD-0159301,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Cell Cycle
+219,G06,failing,UCD-0159301,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Cell Cycle
+220,G07,failing,UCD-0159306,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Cell Cycle
+221,G07,failing,UCD-0159306,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Cell Cycle
+222,G07,failing,UCD-0159306,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Cell Cycle
+223,G07,failing,UCD-0159306,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Cell Cycle
+224,G08,failing,UCD-0159311,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,GPCR & G Protein
+225,G08,failing,UCD-0159311,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,GPCR & G Protein
+226,G08,failing,UCD-0159311,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,GPCR & G Protein
+227,G08,failing,UCD-0159311,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,GPCR & G Protein
+228,G09,failing,UCD-0159315,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,PI3K/Akt/mTOR
+229,G09,failing,UCD-0159315,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,PI3K/Akt/mTOR
+230,G09,failing,UCD-0159315,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,PI3K/Akt/mTOR
+231,G09,failing,UCD-0159315,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,PI3K/Akt/mTOR
+232,G10,failing,UCD-0000576,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Neuronal Signaling
+233,G10,failing,UCD-0000576,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Neuronal Signaling
+234,G10,failing,UCD-0000576,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Neuronal Signaling
+235,G10,failing,UCD-0000576,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Neuronal Signaling
+236,G11,failing,UCD-0000324,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251205100001,3,Metabolism
+237,G11,failing,UCD-0000324,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251203170001,3,Metabolism
+238,G11,failing,UCD-0000324,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251208160001,3,Metabolism
+239,G11,failing,UCD-0000324,Target_Selective_Library_Screen_Plate_3_with_pathways,CARD-CelIns-CX7_251210180001,3,Metabolism
+244,B03,failing,UCD-0159256,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Apoptosis
+245,B03,failing,UCD-0159256,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Apoptosis
+246,B03,failing,UCD-0159256,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Apoptosis
+247,B03,failing,UCD-0159256,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Apoptosis
+248,B04,failing,UCD-0001766,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Angiogenesis
+249,B04,failing,UCD-0001766,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Angiogenesis
+250,B04,failing,UCD-0001766,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Angiogenesis
+251,B04,failing,UCD-0001766,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Angiogenesis
+256,B06,failing,UCD-0159262,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+257,B06,failing,UCD-0159262,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+258,B06,failing,UCD-0159262,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+259,B06,failing,UCD-0159262,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+260,B07,failing,UCD-0001915,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+261,B07,failing,UCD-0001915,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+262,B07,failing,UCD-0001915,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+263,B07,failing,UCD-0001915,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+268,B09,failing,UCD-0001783,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+269,B09,failing,UCD-0001783,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+270,B09,failing,UCD-0001783,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+271,B09,failing,UCD-0001783,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+272,B10,failing,UCD-0159279,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+273,B10,failing,UCD-0159279,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+274,B10,failing,UCD-0159279,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+275,B10,failing,UCD-0159279,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+280,C02,failing,UCD-0159283,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Endocrinology & Hormones
+281,C02,failing,UCD-0159283,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Endocrinology & Hormones
+282,C02,failing,UCD-0159283,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Endocrinology & Hormones
+283,C02,failing,UCD-0159283,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Endocrinology & Hormones
+284,C03,failing,UCD-0001040,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Endocrinology & Hormones
+285,C03,failing,UCD-0001040,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Endocrinology & Hormones
+286,C03,failing,UCD-0001040,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Endocrinology & Hormones
+287,C03,failing,UCD-0001040,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Endocrinology & Hormones
+288,C04,failing,UCD-0018091,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,GPCR & G Protein
+289,C04,failing,UCD-0018091,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,GPCR & G Protein
+290,C04,failing,UCD-0018091,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,GPCR & G Protein
+291,C04,failing,UCD-0018091,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,GPCR & G Protein
+292,C05,failing,UCD-0000450,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Neuronal Signaling
+293,C05,failing,UCD-0000450,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Neuronal Signaling
+294,C05,failing,UCD-0000450,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Neuronal Signaling
+295,C05,failing,UCD-0000450,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Neuronal Signaling
+296,C06,failing,UCD-0159257,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,DNA Damage
+297,C06,failing,UCD-0159257,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,DNA Damage
+298,C06,failing,UCD-0159257,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,DNA Damage
+299,C06,failing,UCD-0159257,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,DNA Damage
+300,C07,failing,UCD-0001810,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,DNA Damage
+301,C07,failing,UCD-0001810,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,DNA Damage
+302,C07,failing,UCD-0001810,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,DNA Damage
+303,C07,failing,UCD-0001810,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,DNA Damage
+304,C08,failing,UCD-0159263,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+305,C08,failing,UCD-0159263,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+306,C08,failing,UCD-0159263,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+307,C08,failing,UCD-0159263,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+308,C09,failing,UCD-0159269,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+309,C09,failing,UCD-0159269,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+310,C09,failing,UCD-0159269,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+311,C09,failing,UCD-0159269,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+312,C10,failing,UCD-0159273,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+313,C10,failing,UCD-0159273,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+314,C10,failing,UCD-0159273,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+315,C10,failing,UCD-0159273,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+316,C11,failing,UCD-0017907,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+317,C11,failing,UCD-0017907,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+318,C11,failing,UCD-0017907,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+319,C11,failing,UCD-0017907,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+320,D02,failing,UCD-0159284,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Stem Cells & Wnt
+321,D02,failing,UCD-0159284,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Stem Cells & Wnt
+322,D02,failing,UCD-0159284,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Stem Cells & Wnt
+323,D02,failing,UCD-0159284,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Stem Cells & Wnt
+324,D03,failing,UCD-0017999,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Endocrinology & Hormones
+325,D03,failing,UCD-0017999,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Endocrinology & Hormones
+326,D03,failing,UCD-0017999,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Endocrinology & Hormones
+327,D03,failing,UCD-0017999,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Endocrinology & Hormones
+328,D04,failing,UCD-0000721,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Neuronal Signaling
+329,D04,failing,UCD-0000721,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Neuronal Signaling
+330,D04,failing,UCD-0000721,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Neuronal Signaling
+331,D04,failing,UCD-0000721,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Neuronal Signaling
+332,D05,failing,UCD-0001014,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Neuronal Signaling
+333,D05,failing,UCD-0001014,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Neuronal Signaling
+334,D05,failing,UCD-0001014,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Neuronal Signaling
+335,D05,failing,UCD-0001014,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Neuronal Signaling
+336,D06,failing,UCD-0159258,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Angiogenesis
+337,D06,failing,UCD-0159258,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Angiogenesis
+338,D06,failing,UCD-0159258,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Angiogenesis
+339,D06,failing,UCD-0159258,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Angiogenesis
+340,D07,failing,UCD-0159261,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+341,D07,failing,UCD-0159261,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+342,D07,failing,UCD-0159261,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+343,D07,failing,UCD-0159261,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+344,D08,failing,UCD-0159264,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Neuronal Signaling
+345,D08,failing,UCD-0159264,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Neuronal Signaling
+346,D08,failing,UCD-0159264,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Neuronal Signaling
+347,D08,failing,UCD-0159264,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Neuronal Signaling
+348,D09,failing,UCD-0001775,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+349,D09,failing,UCD-0001775,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+350,D09,failing,UCD-0001775,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+351,D09,failing,UCD-0001775,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+352,D10,failing,UCD-0159274,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+353,D10,failing,UCD-0159274,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+354,D10,failing,UCD-0159274,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+355,D10,failing,UCD-0159274,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+356,D11,failing,UCD-0001792,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+357,D11,failing,UCD-0001792,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+358,D11,failing,UCD-0001792,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+359,D11,failing,UCD-0001792,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+364,E03,failing,UCD-0159285,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,DNA Damage
+365,E03,failing,UCD-0159285,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,DNA Damage
+366,E03,failing,UCD-0159285,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,DNA Damage
+367,E03,failing,UCD-0159285,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,DNA Damage
+368,E04,failing,UCD-0159289,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Metabolism
+369,E04,failing,UCD-0159289,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Metabolism
+370,E04,failing,UCD-0159289,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Metabolism
+371,E04,failing,UCD-0159289,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Metabolism
+376,E06,failing,UCD-0018179,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,MAPK
+377,E06,failing,UCD-0018179,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,MAPK
+378,E06,failing,UCD-0018179,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,MAPK
+379,E06,failing,UCD-0018179,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,MAPK
+380,E07,failing,UCD-0001016,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Neuronal Signaling
+381,E07,failing,UCD-0001016,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Neuronal Signaling
+382,E07,failing,UCD-0001016,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Neuronal Signaling
+383,E07,failing,UCD-0001016,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Neuronal Signaling
+388,E09,failing,UCD-0001804,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,MAPK
+389,E09,failing,UCD-0001804,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,MAPK
+390,E09,failing,UCD-0001804,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,MAPK
+391,E09,failing,UCD-0001804,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,MAPK
+392,E10,failing,UCD-0018207,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Neuronal Signaling
+393,E10,failing,UCD-0018207,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Neuronal Signaling
+394,E10,failing,UCD-0018207,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Neuronal Signaling
+395,E10,failing,UCD-0018207,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Neuronal Signaling
+400,F02,failing,UCD-0001844,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+401,F02,failing,UCD-0001844,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+402,F02,failing,UCD-0001844,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+403,F02,failing,UCD-0001844,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+404,F03,failing,UCD-0159270,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+405,F03,failing,UCD-0159270,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+406,F03,failing,UCD-0159270,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+407,F03,failing,UCD-0159270,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+408,F04,failing,UCD-0159275,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+409,F04,failing,UCD-0159275,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+410,F04,failing,UCD-0159275,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+411,F04,failing,UCD-0159275,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+412,F05,failing,UCD-0001800,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+413,F05,failing,UCD-0001800,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+414,F05,failing,UCD-0001800,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+415,F05,failing,UCD-0001800,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+416,F06,failing,UCD-0001613,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Endocrinology & Hormones
+417,F06,failing,UCD-0001613,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Endocrinology & Hormones
+418,F06,failing,UCD-0001613,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Endocrinology & Hormones
+419,F06,failing,UCD-0001613,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Endocrinology & Hormones
+420,F07,failing,UCD-0001921,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Epigenetics
+421,F07,failing,UCD-0001921,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Epigenetics
+422,F07,failing,UCD-0001921,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Epigenetics
+423,F07,failing,UCD-0001921,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Epigenetics
+424,F08,failing,UCD-0159290,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,PI3K/Akt/mTOR
+425,F08,failing,UCD-0159290,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,PI3K/Akt/mTOR
+426,F08,failing,UCD-0159290,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,PI3K/Akt/mTOR
+427,F08,failing,UCD-0159290,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,PI3K/Akt/mTOR
+428,F09,failing,UCD-0159293,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+429,F09,failing,UCD-0159293,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+430,F09,failing,UCD-0159293,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+431,F09,failing,UCD-0159293,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+432,F10,failing,UCD-0001808,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,MAPK
+433,F10,failing,UCD-0001808,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,MAPK
+434,F10,failing,UCD-0001808,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,MAPK
+435,F10,failing,UCD-0001808,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,MAPK
+436,F11,failing,UCD-0001801,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Endocrinology & Hormones
+437,F11,failing,UCD-0001801,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Endocrinology & Hormones
+438,F11,failing,UCD-0001801,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Endocrinology & Hormones
+439,F11,failing,UCD-0001801,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Endocrinology & Hormones
+440,G02,failing,UCD-0159265,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Neuronal Signaling
+441,G02,failing,UCD-0159265,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Neuronal Signaling
+442,G02,failing,UCD-0159265,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Neuronal Signaling
+443,G02,failing,UCD-0159265,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Neuronal Signaling
+444,G03,failing,UCD-0159271,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+445,G03,failing,UCD-0159271,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+446,G03,failing,UCD-0159271,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+447,G03,failing,UCD-0159271,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+448,G04,failing,UCD-0001835,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+449,G04,failing,UCD-0001835,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+450,G04,failing,UCD-0001835,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+451,G04,failing,UCD-0001835,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+452,G05,failing,UCD-0159280,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+453,G05,failing,UCD-0159280,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+454,G05,failing,UCD-0159280,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+455,G05,failing,UCD-0159280,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+456,G06,failing,UCD-0159286,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Others
+457,G06,failing,UCD-0159286,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Others
+458,G06,failing,UCD-0159286,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Others
+459,G06,failing,UCD-0159286,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Others
+460,G07,failing,UCD-0018131,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Angiogenesis
+461,G07,failing,UCD-0018131,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Angiogenesis
+462,G07,failing,UCD-0018131,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Angiogenesis
+463,G07,failing,UCD-0018131,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Angiogenesis
+464,G08,failing,UCD-0001024,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Neuronal Signaling
+465,G08,failing,UCD-0001024,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Neuronal Signaling
+466,G08,failing,UCD-0001024,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Neuronal Signaling
+467,G08,failing,UCD-0001024,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Neuronal Signaling
+468,G09,failing,UCD-0001829,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,PI3K/Akt/mTOR
+469,G09,failing,UCD-0001829,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,PI3K/Akt/mTOR
+470,G09,failing,UCD-0001829,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,PI3K/Akt/mTOR
+471,G09,failing,UCD-0001829,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,PI3K/Akt/mTOR
+472,G10,failing,UCD-0159259,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,PI3K/Akt/mTOR
+473,G10,failing,UCD-0159259,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,PI3K/Akt/mTOR
+474,G10,failing,UCD-0159259,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,PI3K/Akt/mTOR
+475,G10,failing,UCD-0159259,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,PI3K/Akt/mTOR
+476,G11,failing,UCD-0001842,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251125110001,1,Neuronal Signaling
+477,G11,failing,UCD-0001842,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251023210001,1,Neuronal Signaling
+478,G11,failing,UCD-0001842,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251126130001,1,Neuronal Signaling
+479,G11,failing,UCD-0001842,Target_Selective_Library_Screen_Plate_1_with_pathways,CARD-CelIns-CX7_251124150001,1,Neuronal Signaling
+484,B03,failing,UCD-0159266,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Others
+485,B03,failing,UCD-0159266,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Others
+486,B03,failing,UCD-0159266,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Others
+487,B03,failing,UCD-0159266,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Others
+488,B04,failing,UCD-0001812,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Others
+489,B04,failing,UCD-0001812,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Others
+490,B04,failing,UCD-0001812,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Others
+491,B04,failing,UCD-0001812,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Others
+496,B06,failing,UCD-0159276,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Others
+497,B06,failing,UCD-0159276,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Others
+498,B06,failing,UCD-0159276,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Others
+499,B06,failing,UCD-0159276,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Others
+500,B07,failing,UCD-0159281,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Others
+501,B07,failing,UCD-0159281,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Others
+502,B07,failing,UCD-0159281,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Others
+503,B07,failing,UCD-0159281,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Others
+508,B09,failing,UCD-0159287,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Endocrinology & Hormones
+509,B09,failing,UCD-0159287,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Endocrinology & Hormones
+510,B09,failing,UCD-0159287,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Endocrinology & Hormones
+511,B09,failing,UCD-0159287,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Endocrinology & Hormones
+512,B10,failing,UCD-0000849,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Endocrinology & Hormones
+513,B10,failing,UCD-0000849,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Endocrinology & Hormones
+514,B10,failing,UCD-0000849,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Endocrinology & Hormones
+515,B10,failing,UCD-0000849,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Endocrinology & Hormones
+520,C02,failing,UCD-0159291,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,GPCR & G Protein
+521,C02,failing,UCD-0159291,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,GPCR & G Protein
+522,C02,failing,UCD-0159291,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,GPCR & G Protein
+523,C02,failing,UCD-0159291,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,GPCR & G Protein
+524,C03,failing,UCD-0000421,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Neuronal Signaling
+525,C03,failing,UCD-0000421,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Neuronal Signaling
+526,C03,failing,UCD-0000421,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Neuronal Signaling
+527,C03,failing,UCD-0000421,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Neuronal Signaling
+528,C04,failing,UCD-0017947,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Apoptosis
+529,C04,failing,UCD-0017947,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Apoptosis
+530,C04,failing,UCD-0017947,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Apoptosis
+531,C04,failing,UCD-0017947,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Apoptosis
+532,C05,failing,UCD-0001816,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Metabolism
+533,C05,failing,UCD-0001816,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Metabolism
+534,C05,failing,UCD-0001816,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Metabolism
+535,C05,failing,UCD-0001816,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Metabolism
+536,C06,failing,UCD-0159267,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Others
+537,C06,failing,UCD-0159267,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Others
+538,C06,failing,UCD-0159267,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Others
+539,C06,failing,UCD-0159267,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Others
+540,C07,failing,UCD-0001802,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Others
+541,C07,failing,UCD-0001802,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Others
+542,C07,failing,UCD-0001802,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Others
+543,C07,failing,UCD-0001802,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Others
+544,C08,failing,UCD-0159277,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Others
+545,C08,failing,UCD-0159277,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Others
+546,C08,failing,UCD-0159277,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Others
+547,C08,failing,UCD-0159277,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Others
+548,C09,failing,UCD-0159282,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Others
+549,C09,failing,UCD-0159282,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Others
+550,C09,failing,UCD-0159282,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Others
+551,C09,failing,UCD-0159282,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Others
+552,C10,failing,UCD-0159288,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Metabolism
+553,C10,failing,UCD-0159288,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Metabolism
+554,C10,failing,UCD-0159288,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Metabolism
+555,C10,failing,UCD-0159288,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Metabolism
+556,C11,failing,UCD-0018089,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Endocrinology & Hormones
+557,C11,failing,UCD-0018089,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Endocrinology & Hormones
+558,C11,failing,UCD-0018089,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Endocrinology & Hormones
+559,C11,failing,UCD-0018089,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Endocrinology & Hormones
+560,D02,failing,UCD-0159292,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,DNA Damage
+561,D02,failing,UCD-0159292,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,DNA Damage
+562,D02,failing,UCD-0159292,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,DNA Damage
+563,D02,failing,UCD-0159292,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,DNA Damage
+564,D03,failing,UCD-0159294,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Cell Cycle
+565,D03,failing,UCD-0159294,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Cell Cycle
+566,D03,failing,UCD-0159294,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Cell Cycle
+567,D03,failing,UCD-0159294,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Cell Cycle
+568,D04,failing,UCD-0159260,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Epigenetics
+569,D04,failing,UCD-0159260,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Epigenetics
+570,D04,failing,UCD-0159260,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Epigenetics
+571,D04,failing,UCD-0159260,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Epigenetics
+572,D05,failing,UCD-0001850,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Others
+573,D05,failing,UCD-0001850,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Others
+574,D05,failing,UCD-0001850,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Others
+575,D05,failing,UCD-0001850,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Others
+576,D06,failing,UCD-0159268,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Others
+577,D06,failing,UCD-0159268,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Others
+578,D06,failing,UCD-0159268,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Others
+579,D06,failing,UCD-0159268,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Others
+580,D07,failing,UCD-0159272,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Others
+581,D07,failing,UCD-0159272,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Others
+582,D07,failing,UCD-0159272,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Others
+583,D07,failing,UCD-0159272,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Others
+584,D08,failing,UCD-0159278,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Others
+585,D08,failing,UCD-0159278,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Others
+586,D08,failing,UCD-0159278,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Others
+587,D08,failing,UCD-0159278,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Others
+588,D09,failing,UCD-0001733,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Protein Tyrosine Kinase
+589,D09,failing,UCD-0001733,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Protein Tyrosine Kinase
+590,D09,failing,UCD-0001733,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Protein Tyrosine Kinase
+591,D09,failing,UCD-0001733,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Protein Tyrosine Kinase
+592,D10,failing,UCD-0001086,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Endocrinology & Hormones
+593,D10,failing,UCD-0001086,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Endocrinology & Hormones
+594,D10,failing,UCD-0001086,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Endocrinology & Hormones
+595,D10,failing,UCD-0001086,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Endocrinology & Hormones
+596,D11,failing,UCD-0000786,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,GPCR & G Protein
+597,D11,failing,UCD-0000786,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,GPCR & G Protein
+598,D11,failing,UCD-0000786,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,GPCR & G Protein
+599,D11,failing,UCD-0000786,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,GPCR & G Protein
+604,E03,failing,UCD-0001101,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Neuronal Signaling
+605,E03,failing,UCD-0001101,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Neuronal Signaling
+606,E03,failing,UCD-0001101,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Neuronal Signaling
+607,E03,failing,UCD-0001101,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Neuronal Signaling
+608,E04,failing,UCD-0159295,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Microbiology
+609,E04,failing,UCD-0159295,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Microbiology
+610,E04,failing,UCD-0159295,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Microbiology
+611,E04,failing,UCD-0159295,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Microbiology
+616,E06,failing,UCD-0018083,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,JAK/STAT
+617,E06,failing,UCD-0018083,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,JAK/STAT
+618,E06,failing,UCD-0018083,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,JAK/STAT
+619,E06,failing,UCD-0018083,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,JAK/STAT
+620,E07,failing,UCD-0001822,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,MAPK
+621,E07,failing,UCD-0001822,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,MAPK
+622,E07,failing,UCD-0001822,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,MAPK
+623,E07,failing,UCD-0001822,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,MAPK
+628,E09,failing,UCD-0001759,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Angiogenesis
+629,E09,failing,UCD-0001759,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Angiogenesis
+630,E09,failing,UCD-0001759,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Angiogenesis
+631,E09,failing,UCD-0001759,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Angiogenesis
+632,E10,failing,UCD-0000533,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,GPCR & G Protein
+633,E10,failing,UCD-0000533,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,GPCR & G Protein
+634,E10,failing,UCD-0000533,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,GPCR & G Protein
+635,E10,failing,UCD-0000533,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,GPCR & G Protein
+640,F02,failing,UCD-0000295,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Metabolism
+641,F02,failing,UCD-0000295,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Metabolism
+642,F02,failing,UCD-0000295,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Metabolism
+643,F02,failing,UCD-0000295,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Metabolism
+644,F03,failing,UCD-0001580,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Transmembrane Transporters
+645,F03,failing,UCD-0001580,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Transmembrane Transporters
+646,F03,failing,UCD-0001580,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Transmembrane Transporters
+647,F03,failing,UCD-0001580,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Transmembrane Transporters
+648,F04,failing,UCD-0159317,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Angiogenesis
+649,F04,failing,UCD-0159317,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Angiogenesis
+650,F04,failing,UCD-0159317,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Angiogenesis
+651,F04,failing,UCD-0159317,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Angiogenesis
+652,F05,failing,UCD-0018516,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Neuronal Signaling
+653,F05,failing,UCD-0018516,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Neuronal Signaling
+654,F05,failing,UCD-0018516,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Neuronal Signaling
+655,F05,failing,UCD-0018516,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Neuronal Signaling
+656,F06,failing,UCD-0018304,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Endocrinology & Hormones
+657,F06,failing,UCD-0018304,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Endocrinology & Hormones
+658,F06,failing,UCD-0018304,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Endocrinology & Hormones
+659,F06,failing,UCD-0018304,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Endocrinology & Hormones
+660,F07,failing,UCD-0159327,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,DNA Damage
+661,F07,failing,UCD-0159327,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,DNA Damage
+662,F07,failing,UCD-0159327,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,DNA Damage
+663,F07,failing,UCD-0159327,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,DNA Damage
+664,F08,failing,UCD-0159296,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Metabolism
+665,F08,failing,UCD-0159296,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Metabolism
+666,F08,failing,UCD-0159296,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Metabolism
+667,F08,failing,UCD-0159296,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Metabolism
+668,F09,failing,UCD-0159302,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,PI3K/Akt/mTOR
+669,F09,failing,UCD-0159302,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,PI3K/Akt/mTOR
+670,F09,failing,UCD-0159302,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,PI3K/Akt/mTOR
+671,F09,failing,UCD-0159302,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,PI3K/Akt/mTOR
+672,F10,failing,UCD-0001793,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Cell Cycle
+673,F10,failing,UCD-0001793,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Cell Cycle
+674,F10,failing,UCD-0001793,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Cell Cycle
+675,F10,failing,UCD-0001793,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Cell Cycle
+676,F11,failing,UCD-0000757,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Metabolism
+677,F11,failing,UCD-0000757,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Metabolism
+678,F11,failing,UCD-0000757,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Metabolism
+679,F11,failing,UCD-0000757,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Metabolism
+680,G02,failing,UCD-0159316,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Proteases
+681,G02,failing,UCD-0159316,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Proteases
+682,G02,failing,UCD-0159316,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Proteases
+683,G02,failing,UCD-0159316,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Proteases
+684,G03,failing,UCD-0000878,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Transmembrane Transporters
+685,G03,failing,UCD-0000878,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Transmembrane Transporters
+686,G03,failing,UCD-0000878,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Transmembrane Transporters
+687,G03,failing,UCD-0000878,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Transmembrane Transporters
+688,G04,failing,UCD-0159318,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Angiogenesis
+689,G04,failing,UCD-0159318,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Angiogenesis
+690,G04,failing,UCD-0159318,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Angiogenesis
+691,G04,failing,UCD-0159318,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Angiogenesis
+692,G05,failing,UCD-0001616B,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Others
+693,G05,failing,UCD-0001616B,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Others
+694,G05,failing,UCD-0001616B,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Others
+695,G05,failing,UCD-0001616B,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Others
+696,G06,failing,UCD-0001463,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Neuronal Signaling
+697,G06,failing,UCD-0001463,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Neuronal Signaling
+698,G06,failing,UCD-0001463,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Neuronal Signaling
+699,G06,failing,UCD-0001463,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Neuronal Signaling
+700,G07,failing,UCD-0001847,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,JAK/STAT
+701,G07,failing,UCD-0001847,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,JAK/STAT
+702,G07,failing,UCD-0001847,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,JAK/STAT
+703,G07,failing,UCD-0001847,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,JAK/STAT
+704,G08,failing,UCD-0159297,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Microbiology
+705,G08,failing,UCD-0159297,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Microbiology
+706,G08,failing,UCD-0159297,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Microbiology
+707,G08,failing,UCD-0159297,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Microbiology
+708,G09,failing,UCD-0159303,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Metabolism
+709,G09,failing,UCD-0159303,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Metabolism
+710,G09,failing,UCD-0159303,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Metabolism
+711,G09,failing,UCD-0159303,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Metabolism
+712,G10,failing,UCD-0159307,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,GPCR & G Protein
+713,G10,failing,UCD-0159307,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,GPCR & G Protein
+714,G10,failing,UCD-0159307,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,GPCR & G Protein
+715,G10,failing,UCD-0159307,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,GPCR & G Protein
+716,G11,failing,UCD-0159312,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201190001,2,Transmembrane Transporters
+717,G11,failing,UCD-0159312,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251201110001,2,Transmembrane Transporters
+718,G11,failing,UCD-0159312,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251130110002,2,Transmembrane Transporters
+719,G11,failing,UCD-0159312,Target_Selective_Library_Screen_Plate_2_with_pathways,CARD-CelIns-CX7_251202100001,2,Transmembrane Transporters
+724,B03,failing,UCD-0159569,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Protein Tyrosine Kinase
+725,B03,failing,UCD-0159569,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Protein Tyrosine Kinase
+726,B03,failing,UCD-0159569,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Protein Tyrosine Kinase
+727,B03,failing,UCD-0159569,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Protein Tyrosine Kinase
+728,B04,failing,UCD-0159577,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Cell Cycle
+729,B04,failing,UCD-0159577,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Cell Cycle
+730,B04,failing,UCD-0159577,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Cell Cycle
+731,B04,failing,UCD-0159577,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Cell Cycle
+736,B06,failing,UCD-0159585,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Protein Tyrosine Kinase
+737,B06,failing,UCD-0159585,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Protein Tyrosine Kinase
+738,B06,failing,UCD-0159585,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Protein Tyrosine Kinase
+739,B06,failing,UCD-0159585,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Protein Tyrosine Kinase
+740,B07,failing,UCD-0159592,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Epigenetics
+741,B07,failing,UCD-0159592,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Epigenetics
+742,B07,failing,UCD-0159592,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Epigenetics
+743,B07,failing,UCD-0159592,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Epigenetics
+748,B09,failing,UCD-0159524,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Others
+749,B09,failing,UCD-0159524,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Others
+750,B09,failing,UCD-0159524,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Others
+751,B09,failing,UCD-0159524,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Others
+752,B10,failing,UCD-0159531,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Metabolism
+753,B10,failing,UCD-0159531,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Metabolism
+754,B10,failing,UCD-0159531,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Metabolism
+755,B10,failing,UCD-0159531,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Metabolism
+760,C02,failing,UCD-0159539,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Proteases
+761,C02,failing,UCD-0159539,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Proteases
+762,C02,failing,UCD-0159539,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Proteases
+763,C02,failing,UCD-0159539,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Proteases
+764,C03,failing,UCD-0159547,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Apoptosis
+765,C03,failing,UCD-0159547,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Apoptosis
+766,C03,failing,UCD-0159547,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Apoptosis
+767,C03,failing,UCD-0159547,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Apoptosis
+768,C04,failing,UCD-0159554,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Angiogenesis
+769,C04,failing,UCD-0159554,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Angiogenesis
+770,C04,failing,UCD-0159554,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Angiogenesis
+771,C04,failing,UCD-0159554,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Angiogenesis
+772,C05,failing,UCD-0159562,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Epigenetics
+773,C05,failing,UCD-0159562,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Epigenetics
+774,C05,failing,UCD-0159562,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Epigenetics
+775,C05,failing,UCD-0159562,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Epigenetics
+776,C06,failing,UCD-0159570,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,GPCR & G Protein
+777,C06,failing,UCD-0159570,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,GPCR & G Protein
+778,C06,failing,UCD-0159570,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,GPCR & G Protein
+779,C06,failing,UCD-0159570,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,GPCR & G Protein
+780,C07,failing,UCD-0159578,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,PI3K/Akt/mTOR
+781,C07,failing,UCD-0159578,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,PI3K/Akt/mTOR
+782,C07,failing,UCD-0159578,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,PI3K/Akt/mTOR
+783,C07,failing,UCD-0159578,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,PI3K/Akt/mTOR
+784,C08,failing,UCD-0159586,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Angiogenesis
+785,C08,failing,UCD-0159586,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Angiogenesis
+786,C08,failing,UCD-0159586,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Angiogenesis
+787,C08,failing,UCD-0159586,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Angiogenesis
+788,C09,failing,UCD-0159593,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Epigenetics
+789,C09,failing,UCD-0159593,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Epigenetics
+790,C09,failing,UCD-0159593,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Epigenetics
+791,C09,failing,UCD-0159593,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Epigenetics
+792,C10,failing,UCD-0159525,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Others
+793,C10,failing,UCD-0159525,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Others
+794,C10,failing,UCD-0159525,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Others
+795,C10,failing,UCD-0159525,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Others
+796,C11,failing,UCD-0159532,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,PI3K/Akt/mTOR
+797,C11,failing,UCD-0159532,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,PI3K/Akt/mTOR
+798,C11,failing,UCD-0159532,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,PI3K/Akt/mTOR
+799,C11,failing,UCD-0159532,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,PI3K/Akt/mTOR
+800,D02,failing,UCD-0159540,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Others
+801,D02,failing,UCD-0159540,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Others
+802,D02,failing,UCD-0159540,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Others
+803,D02,failing,UCD-0159540,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Others
+804,D03,failing,UCD-0159548,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Others
+805,D03,failing,UCD-0159548,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Others
+806,D03,failing,UCD-0159548,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Others
+807,D03,failing,UCD-0159548,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Others
+808,D04,failing,UCD-0159555,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Cytoskeletal Signaling
+809,D04,failing,UCD-0159555,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Cytoskeletal Signaling
+810,D04,failing,UCD-0159555,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Cytoskeletal Signaling
+811,D04,failing,UCD-0159555,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Cytoskeletal Signaling
+812,D05,failing,UCD-0159563,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Angiogenesis
+813,D05,failing,UCD-0159563,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Angiogenesis
+814,D05,failing,UCD-0159563,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Angiogenesis
+815,D05,failing,UCD-0159563,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Angiogenesis
+816,D06,failing,UCD-0159571,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Others
+817,D06,failing,UCD-0159571,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Others
+818,D06,failing,UCD-0159571,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Others
+819,D06,failing,UCD-0159571,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Others
+820,D07,failing,UCD-0159579,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,PI3K/Akt/mTOR
+821,D07,failing,UCD-0159579,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,PI3K/Akt/mTOR
+822,D07,failing,UCD-0159579,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,PI3K/Akt/mTOR
+823,D07,failing,UCD-0159579,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,PI3K/Akt/mTOR
+824,D08,failing,UCD-0159587,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Angiogenesis
+825,D08,failing,UCD-0159587,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Angiogenesis
+826,D08,failing,UCD-0159587,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Angiogenesis
+827,D08,failing,UCD-0159587,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Angiogenesis
+828,D09,failing,UCD-0159594,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Others
+829,D09,failing,UCD-0159594,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Others
+830,D09,failing,UCD-0159594,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Others
+831,D09,failing,UCD-0159594,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Others
+832,D10,failing,UCD-0159526,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,DNA Damage
+833,D10,failing,UCD-0159526,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,DNA Damage
+834,D10,failing,UCD-0159526,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,DNA Damage
+835,D10,failing,UCD-0159526,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,DNA Damage
+836,D11,failing,UCD-0159533,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Cytoskeletal Signaling
+837,D11,failing,UCD-0159533,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Cytoskeletal Signaling
+838,D11,failing,UCD-0159533,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Cytoskeletal Signaling
+839,D11,failing,UCD-0159533,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Cytoskeletal Signaling
+844,E03,failing,UCD-0159541,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Protein Tyrosine Kinase
+845,E03,failing,UCD-0159541,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Protein Tyrosine Kinase
+846,E03,failing,UCD-0159541,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Protein Tyrosine Kinase
+847,E03,failing,UCD-0159541,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Protein Tyrosine Kinase
+848,E04,failing,UCD-0159514B,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Others
+849,E04,failing,UCD-0159514B,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Others
+850,E04,failing,UCD-0159514B,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Others
+851,E04,failing,UCD-0159514B,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Others
+856,E06,failing,UCD-0159556,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,GPCR & G Protein
+857,E06,failing,UCD-0159556,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,GPCR & G Protein
+858,E06,failing,UCD-0159556,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,GPCR & G Protein
+859,E06,failing,UCD-0159556,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,GPCR & G Protein
+860,E07,failing,UCD-0159564,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Epigenetics
+861,E07,failing,UCD-0159564,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Epigenetics
+862,E07,failing,UCD-0159564,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Epigenetics
+863,E07,failing,UCD-0159564,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Epigenetics
+868,E09,failing,UCD-0159572,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Epigenetics
+869,E09,failing,UCD-0159572,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Epigenetics
+870,E09,failing,UCD-0159572,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Epigenetics
+871,E09,failing,UCD-0159572,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Epigenetics
+872,E10,failing,UCD-0159580,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,NF-κB
+873,E10,failing,UCD-0159580,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,NF-κB
+874,E10,failing,UCD-0159580,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,NF-κB
+875,E10,failing,UCD-0159580,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,NF-κB
+880,F02,failing,UCD-0159588,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,MAPK
+881,F02,failing,UCD-0159588,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,MAPK
+882,F02,failing,UCD-0159588,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,MAPK
+883,F02,failing,UCD-0159588,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,MAPK
+884,F03,failing,UCD-0159595,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Endocrinology & Hormones
+885,F03,failing,UCD-0159595,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Endocrinology & Hormones
+886,F03,failing,UCD-0159595,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Endocrinology & Hormones
+887,F03,failing,UCD-0159595,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Endocrinology & Hormones
+888,F04,failing,UCD-0159527,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,DNA Damage
+889,F04,failing,UCD-0159527,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,DNA Damage
+890,F04,failing,UCD-0159527,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,DNA Damage
+891,F04,failing,UCD-0159527,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,DNA Damage
+892,F05,failing,UCD-0159534,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Others
+893,F05,failing,UCD-0159534,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Others
+894,F05,failing,UCD-0159534,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Others
+895,F05,failing,UCD-0159534,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Others
+896,F06,failing,UCD-0159542,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,MAPK
+897,F06,failing,UCD-0159542,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,MAPK
+898,F06,failing,UCD-0159542,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,MAPK
+899,F06,failing,UCD-0159542,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,MAPK
+900,F07,failing,UCD-0159549,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,JAK/STAT
+901,F07,failing,UCD-0159549,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,JAK/STAT
+902,F07,failing,UCD-0159549,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,JAK/STAT
+903,F07,failing,UCD-0159549,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,JAK/STAT
+904,F08,failing,UCD-0159557,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Others
+905,F08,failing,UCD-0159557,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Others
+906,F08,failing,UCD-0159557,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Others
+907,F08,failing,UCD-0159557,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Others
+908,F09,failing,UCD-0159565,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,TGF-beta/Smad
+909,F09,failing,UCD-0159565,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,TGF-beta/Smad
+910,F09,failing,UCD-0159565,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,TGF-beta/Smad
+911,F09,failing,UCD-0159565,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,TGF-beta/Smad
+912,F10,failing,UCD-0159573,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Protein Tyrosine Kinase
+913,F10,failing,UCD-0159573,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Protein Tyrosine Kinase
+914,F10,failing,UCD-0159573,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Protein Tyrosine Kinase
+915,F10,failing,UCD-0159573,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Protein Tyrosine Kinase
+916,F11,failing,UCD-0159581,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,PI3K/Akt/mTOR
+917,F11,failing,UCD-0159581,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,PI3K/Akt/mTOR
+918,F11,failing,UCD-0159581,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,PI3K/Akt/mTOR
+919,F11,failing,UCD-0159581,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,PI3K/Akt/mTOR
+920,G02,failing,UCD-0159589,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,DNA Damage
+921,G02,failing,UCD-0159589,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,DNA Damage
+922,G02,failing,UCD-0159589,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,DNA Damage
+923,G02,failing,UCD-0159589,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,DNA Damage
+924,G03,failing,UCD-0001944,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Epigenetics
+925,G03,failing,UCD-0001944,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Epigenetics
+926,G03,failing,UCD-0001944,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Epigenetics
+927,G03,failing,UCD-0001944,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Epigenetics
+928,G04,failing,UCD-0159665,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Others
+929,G04,failing,UCD-0159665,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Others
+930,G04,failing,UCD-0159665,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Others
+931,G04,failing,UCD-0159665,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Others
+932,G05,failing,UCD-0159535,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Others
+933,G05,failing,UCD-0159535,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Others
+934,G05,failing,UCD-0159535,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Others
+935,G05,failing,UCD-0159535,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Others
+936,G06,failing,UCD-0159543,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,MAPK
+937,G06,failing,UCD-0159543,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,MAPK
+938,G06,failing,UCD-0159543,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,MAPK
+939,G06,failing,UCD-0159543,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,MAPK
+940,G07,failing,UCD-0159550,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,MAPK
+941,G07,failing,UCD-0159550,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,MAPK
+942,G07,failing,UCD-0159550,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,MAPK
+943,G07,failing,UCD-0159550,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,MAPK
+944,G08,failing,UCD-0159558,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Cytoskeletal Signaling
+945,G08,failing,UCD-0159558,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Cytoskeletal Signaling
+946,G08,failing,UCD-0159558,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Cytoskeletal Signaling
+947,G08,failing,UCD-0159558,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Cytoskeletal Signaling
+948,G09,failing,UCD-0159566,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,DNA Damage
+949,G09,failing,UCD-0159566,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,DNA Damage
+950,G09,failing,UCD-0159566,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,DNA Damage
+951,G09,failing,UCD-0159566,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,DNA Damage
+952,G10,failing,UCD-0159574,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Epigenetics
+953,G10,failing,UCD-0159574,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Epigenetics
+954,G10,failing,UCD-0159574,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Epigenetics
+955,G10,failing,UCD-0159574,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Epigenetics
+956,G11,failing,UCD-0159582,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260107160001,9,Neuronal Signaling
+957,G11,failing,UCD-0159582,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260108130001,9,Neuronal Signaling
+958,G11,failing,UCD-0159582,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260106160001,9,Neuronal Signaling
+959,G11,failing,UCD-0159582,Target_Selective_Library_Screen_Plate_9_with_pathways,CARD-CelIns-CX7_260109070001,9,Neuronal Signaling
+964,B03,failing,UCD-0159632,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Angiogenesis
+965,B03,failing,UCD-0159632,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Angiogenesis
+966,B03,failing,UCD-0159632,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Angiogenesis
+967,B03,failing,UCD-0159632,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Angiogenesis
+968,B04,failing,UCD-0159640,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Epigenetics
+969,B04,failing,UCD-0159640,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Epigenetics
+970,B04,failing,UCD-0159640,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Epigenetics
+971,B04,failing,UCD-0159640,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Epigenetics
+976,B06,failing,UCD-0001952,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Epigenetics
+977,B06,failing,UCD-0001952,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Epigenetics
+978,B06,failing,UCD-0001952,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Epigenetics
+979,B06,failing,UCD-0001952,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Epigenetics
+980,B07,failing,UCD-0159654,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Epigenetics
+981,B07,failing,UCD-0159654,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Epigenetics
+982,B07,failing,UCD-0159654,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Epigenetics
+983,B07,failing,UCD-0159654,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Epigenetics
+988,B09,failing,UCD-0159662,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,GPCR & G Protein
+989,B09,failing,UCD-0159662,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,GPCR & G Protein
+990,B09,failing,UCD-0159662,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,GPCR & G Protein
+991,B09,failing,UCD-0159662,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,GPCR & G Protein
+992,B10,failing,UCD-0159602,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Angiogenesis
+993,B10,failing,UCD-0159602,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Angiogenesis
+994,B10,failing,UCD-0159602,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Angiogenesis
+995,B10,failing,UCD-0159602,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Angiogenesis
+1000,C02,failing,UCD-0159609,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Others
+1001,C02,failing,UCD-0159609,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Others
+1002,C02,failing,UCD-0159609,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Others
+1003,C02,failing,UCD-0159609,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Others
+1004,C03,failing,UCD-0159617,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Endocrinology & Hormones
+1005,C03,failing,UCD-0159617,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Endocrinology & Hormones
+1006,C03,failing,UCD-0159617,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Endocrinology & Hormones
+1007,C03,failing,UCD-0159617,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Endocrinology & Hormones
+1008,C04,failing,UCD-0159625,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Endocrinology & Hormones
+1009,C04,failing,UCD-0159625,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Endocrinology & Hormones
+1010,C04,failing,UCD-0159625,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Endocrinology & Hormones
+1011,C04,failing,UCD-0159625,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Endocrinology & Hormones
+1012,C05,failing,UCD-0159633,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Apoptosis
+1013,C05,failing,UCD-0159633,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Apoptosis
+1014,C05,failing,UCD-0159633,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Apoptosis
+1015,C05,failing,UCD-0159633,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Apoptosis
+1016,C06,failing,UCD-0159641,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,JAK/STAT
+1017,C06,failing,UCD-0159641,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,JAK/STAT
+1018,C06,failing,UCD-0159641,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,JAK/STAT
+1019,C06,failing,UCD-0159641,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,JAK/STAT
+1020,C07,failing,UCD-0001953,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Epigenetics
+1021,C07,failing,UCD-0001953,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Epigenetics
+1022,C07,failing,UCD-0001953,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Epigenetics
+1023,C07,failing,UCD-0001953,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Epigenetics
+1024,C08,failing,UCD-0159655,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Epigenetics
+1025,C08,failing,UCD-0159655,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Epigenetics
+1026,C08,failing,UCD-0159655,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Epigenetics
+1027,C08,failing,UCD-0159655,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Epigenetics
+1028,C09,failing,UCD-0159663,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Protein Tyrosine Kinase
+1029,C09,failing,UCD-0159663,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Protein Tyrosine Kinase
+1030,C09,failing,UCD-0159663,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Protein Tyrosine Kinase
+1031,C09,failing,UCD-0159663,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Protein Tyrosine Kinase
+1032,C10,failing,UCD-0159603,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,GPCR & G Protein
+1033,C10,failing,UCD-0159603,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,GPCR & G Protein
+1034,C10,failing,UCD-0159603,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,GPCR & G Protein
+1035,C10,failing,UCD-0159603,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,GPCR & G Protein
+1036,C11,failing,UCD-0159610,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Transmembrane Transporters
+1037,C11,failing,UCD-0159610,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Transmembrane Transporters
+1038,C11,failing,UCD-0159610,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Transmembrane Transporters
+1039,C11,failing,UCD-0159610,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Transmembrane Transporters
+1040,D02,failing,UCD-0159618,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,MAPK
+1041,D02,failing,UCD-0159618,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,MAPK
+1042,D02,failing,UCD-0159618,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,MAPK
+1043,D02,failing,UCD-0159618,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,MAPK
+1044,D03,failing,UCD-0159626,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Neuronal Signaling
+1045,D03,failing,UCD-0159626,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Neuronal Signaling
+1046,D03,failing,UCD-0159626,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Neuronal Signaling
+1047,D03,failing,UCD-0159626,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Neuronal Signaling
+1048,D04,failing,UCD-0159634,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Cytoskeletal Signaling
+1049,D04,failing,UCD-0159634,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Cytoskeletal Signaling
+1050,D04,failing,UCD-0159634,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Cytoskeletal Signaling
+1051,D04,failing,UCD-0159634,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Cytoskeletal Signaling
+1052,D05,failing,UCD-0159642,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Proteases
+1053,D05,failing,UCD-0159642,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Proteases
+1054,D05,failing,UCD-0159642,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Proteases
+1055,D05,failing,UCD-0159642,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Proteases
+1056,D06,failing,UCD-0159648,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,GPCR & G Protein
+1057,D06,failing,UCD-0159648,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,GPCR & G Protein
+1058,D06,failing,UCD-0159648,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,GPCR & G Protein
+1059,D06,failing,UCD-0159648,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,GPCR & G Protein
+1060,D07,failing,UCD-0159656,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,GPCR & G Protein
+1061,D07,failing,UCD-0159656,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,GPCR & G Protein
+1062,D07,failing,UCD-0159656,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,GPCR & G Protein
+1063,D07,failing,UCD-0159656,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,GPCR & G Protein
+1064,D08,failing,UCD-0159604,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Autophagy
+1065,D08,failing,UCD-0159604,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Autophagy
+1066,D08,failing,UCD-0159604,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Autophagy
+1067,D08,failing,UCD-0159604,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Autophagy
+1068,D09,failing,UCD-0159611,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Others
+1069,D09,failing,UCD-0159611,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Others
+1070,D09,failing,UCD-0159611,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Others
+1071,D09,failing,UCD-0159611,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Others
+1072,D10,failing,UCD-0159619,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,GPCR & G Protein
+1073,D10,failing,UCD-0159619,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,GPCR & G Protein
+1074,D10,failing,UCD-0159619,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,GPCR & G Protein
+1075,D10,failing,UCD-0159619,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,GPCR & G Protein
+1076,D11,failing,UCD-0159627,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Metabolism
+1077,D11,failing,UCD-0159627,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Metabolism
+1078,D11,failing,UCD-0159627,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Metabolism
+1079,D11,failing,UCD-0159627,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Metabolism
+1084,E03,failing,UCD-0159635,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,MAPK
+1085,E03,failing,UCD-0159635,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,MAPK
+1086,E03,failing,UCD-0159635,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,MAPK
+1087,E03,failing,UCD-0159635,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,MAPK
+1088,E04,failing,UCD-0159643,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Metabolism
+1089,E04,failing,UCD-0159643,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Metabolism
+1090,E04,failing,UCD-0159643,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Metabolism
+1091,E04,failing,UCD-0159643,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Metabolism
+1096,E06,failing,UCD-0159649,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Angiogenesis
+1097,E06,failing,UCD-0159649,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Angiogenesis
+1098,E06,failing,UCD-0159649,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Angiogenesis
+1099,E06,failing,UCD-0159649,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Angiogenesis
+1100,E07,failing,UCD-0159657,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,Others
+1101,E07,failing,UCD-0159657,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,Others
+1102,E07,failing,UCD-0159657,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,Others
+1103,E07,failing,UCD-0159657,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,Others
+1128,G02,failing,TGFRi,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,
+1129,G02,failing,TGFRi,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,
+1130,G02,failing,TGFRi,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,
+1131,G02,failing,TGFRi,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,
+1132,G05,failing,TGFRi,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,
+1133,G05,failing,TGFRi,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,
+1134,G05,failing,TGFRi,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,
+1135,G05,failing,TGFRi,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,
+1136,G08,failing,TGFRi,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,
+1137,G08,failing,TGFRi,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,
+1138,G08,failing,TGFRi,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,
+1139,G08,failing,TGFRi,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,
+1140,G11,failing,TGFRi,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114180001,11,
+1141,G11,failing,TGFRi,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260115110001,11,
+1142,G11,failing,TGFRi,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260114100001,11,
+1143,G11,failing,TGFRi,Target_Selective_Library_Screen_Plate_11_with_pathways,CARD-CelIns-CX7_260113180001,11,
+1148,B03,failing,UCD-0159253,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Epigenetics
+1149,B03,failing,UCD-0159253,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Epigenetics
+1150,B03,failing,UCD-0159253,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Epigenetics
+1151,B03,failing,UCD-0159253,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Epigenetics
+1152,B04,failing,UCD-0159596,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Cytoskeletal Signaling
+1153,B04,failing,UCD-0159596,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Cytoskeletal Signaling
+1154,B04,failing,UCD-0159596,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Cytoskeletal Signaling
+1155,B04,failing,UCD-0159596,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Cytoskeletal Signaling
+1160,B06,failing,UCD-0159528,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Transmembrane Transporters
+1161,B06,failing,UCD-0159528,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Transmembrane Transporters
+1162,B06,failing,UCD-0159528,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Transmembrane Transporters
+1163,B06,failing,UCD-0159528,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Transmembrane Transporters
+1164,B07,failing,UCD-0159536,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,JAK/STAT
+1165,B07,failing,UCD-0159536,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,JAK/STAT
+1166,B07,failing,UCD-0159536,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,JAK/STAT
+1167,B07,failing,UCD-0159536,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,JAK/STAT
+1172,B09,failing,UCD-0159544,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Angiogenesis
+1173,B09,failing,UCD-0159544,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Angiogenesis
+1174,B09,failing,UCD-0159544,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Angiogenesis
+1175,B09,failing,UCD-0159544,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Angiogenesis
+1176,B10,failing,UCD-0159551,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Epigenetics
+1177,B10,failing,UCD-0159551,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Epigenetics
+1178,B10,failing,UCD-0159551,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Epigenetics
+1179,B10,failing,UCD-0159551,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Epigenetics
+1184,C02,failing,UCD-0159559,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,DNA Damage
+1185,C02,failing,UCD-0159559,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,DNA Damage
+1186,C02,failing,UCD-0159559,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,DNA Damage
+1187,C02,failing,UCD-0159559,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,DNA Damage
+1188,C03,failing,UCD-0159567,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Others
+1189,C03,failing,UCD-0159567,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Others
+1190,C03,failing,UCD-0159567,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Others
+1191,C03,failing,UCD-0159567,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Others
+1192,C04,failing,UCD-0159575,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Autophagy
+1193,C04,failing,UCD-0159575,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Autophagy
+1194,C04,failing,UCD-0159575,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Autophagy
+1195,C04,failing,UCD-0159575,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Autophagy
+1196,C05,failing,UCD-0159583,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,MAPK
+1197,C05,failing,UCD-0159583,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,MAPK
+1198,C05,failing,UCD-0159583,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,MAPK
+1199,C05,failing,UCD-0159583,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,MAPK
+1200,C06,failing,UCD-0159590,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Others
+1201,C06,failing,UCD-0159590,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Others
+1202,C06,failing,UCD-0159590,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Others
+1203,C06,failing,UCD-0159590,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Others
+1204,C07,failing,UCD-0159597,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Epigenetics
+1205,C07,failing,UCD-0159597,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Epigenetics
+1206,C07,failing,UCD-0159597,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Epigenetics
+1207,C07,failing,UCD-0159597,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Epigenetics
+1208,C08,failing,UCD-0159598,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Endocrinology & Hormones
+1209,C08,failing,UCD-0159598,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Endocrinology & Hormones
+1210,C08,failing,UCD-0159598,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Endocrinology & Hormones
+1211,C08,failing,UCD-0159598,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Endocrinology & Hormones
+1212,C09,failing,UCD-0159605,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,PI3K/Akt/mTOR
+1213,C09,failing,UCD-0159605,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,PI3K/Akt/mTOR
+1214,C09,failing,UCD-0159605,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,PI3K/Akt/mTOR
+1215,C09,failing,UCD-0159605,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,PI3K/Akt/mTOR
+1216,C10,failing,UCD-0159612,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Cell Cycle
+1217,C10,failing,UCD-0159612,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Cell Cycle
+1218,C10,failing,UCD-0159612,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Cell Cycle
+1219,C10,failing,UCD-0159612,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Cell Cycle
+1220,C11,failing,UCD-0159620,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,DNA Damage
+1221,C11,failing,UCD-0159620,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,DNA Damage
+1222,C11,failing,UCD-0159620,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,DNA Damage
+1223,C11,failing,UCD-0159620,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,DNA Damage
+1224,D02,failing,UCD-0159628,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,GPCR & G Protein
+1225,D02,failing,UCD-0159628,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,GPCR & G Protein
+1226,D02,failing,UCD-0159628,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,GPCR & G Protein
+1227,D02,failing,UCD-0159628,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,GPCR & G Protein
+1228,D03,failing,UCD-0159636,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Protein Tyrosine Kinase
+1229,D03,failing,UCD-0159636,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Protein Tyrosine Kinase
+1230,D03,failing,UCD-0159636,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Protein Tyrosine Kinase
+1231,D03,failing,UCD-0159636,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Protein Tyrosine Kinase
+1232,D04,failing,UCD-0159644,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Transmembrane Transporters
+1233,D04,failing,UCD-0159644,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Transmembrane Transporters
+1234,D04,failing,UCD-0159644,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Transmembrane Transporters
+1235,D04,failing,UCD-0159644,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Transmembrane Transporters
+1236,D05,failing,UCD-0159650,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,MAPK
+1237,D05,failing,UCD-0159650,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,MAPK
+1238,D05,failing,UCD-0159650,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,MAPK
+1239,D05,failing,UCD-0159650,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,MAPK
+1240,D06,failing,UCD-0159658,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Autophagy
+1241,D06,failing,UCD-0159658,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Autophagy
+1242,D06,failing,UCD-0159658,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Autophagy
+1243,D06,failing,UCD-0159658,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Autophagy
+1244,D07,failing,UCD-0159599,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,MAPK
+1245,D07,failing,UCD-0159599,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,MAPK
+1246,D07,failing,UCD-0159599,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,MAPK
+1247,D07,failing,UCD-0159599,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,MAPK
+1248,D08,failing,UCD-0159606,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Autophagy
+1249,D08,failing,UCD-0159606,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Autophagy
+1250,D08,failing,UCD-0159606,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Autophagy
+1251,D08,failing,UCD-0159606,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Autophagy
+1252,D09,failing,UCD-0159613,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,PI3K/Akt/mTOR
+1253,D09,failing,UCD-0159613,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,PI3K/Akt/mTOR
+1254,D09,failing,UCD-0159613,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,PI3K/Akt/mTOR
+1255,D09,failing,UCD-0159613,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,PI3K/Akt/mTOR
+1256,D10,failing,UCD-0159621,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,PI3K/Akt/mTOR
+1257,D10,failing,UCD-0159621,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,PI3K/Akt/mTOR
+1258,D10,failing,UCD-0159621,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,PI3K/Akt/mTOR
+1259,D10,failing,UCD-0159621,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,PI3K/Akt/mTOR
+1260,D11,failing,UCD-0159629,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Angiogenesis
+1261,D11,failing,UCD-0159629,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Angiogenesis
+1262,D11,failing,UCD-0159629,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Angiogenesis
+1263,D11,failing,UCD-0159629,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Angiogenesis
+1268,E03,failing,UCD-0159637,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Others
+1269,E03,failing,UCD-0159637,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Others
+1270,E03,failing,UCD-0159637,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Others
+1271,E03,failing,UCD-0159637,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Others
+1272,E04,failing,UCD-0159645,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Transmembrane Transporters
+1273,E04,failing,UCD-0159645,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Transmembrane Transporters
+1274,E04,failing,UCD-0159645,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Transmembrane Transporters
+1275,E04,failing,UCD-0159645,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Transmembrane Transporters
+1280,E06,failing,UCD-0159651,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Angiogenesis
+1281,E06,failing,UCD-0159651,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Angiogenesis
+1282,E06,failing,UCD-0159651,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Angiogenesis
+1283,E06,failing,UCD-0159651,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Angiogenesis
+1284,E07,failing,UCD-0159659,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Metabolism
+1285,E07,failing,UCD-0159659,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Metabolism
+1286,E07,failing,UCD-0159659,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Metabolism
+1287,E07,failing,UCD-0159659,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Metabolism
+1292,E09,failing,UCD-0159666,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Cytoskeletal Signaling
+1293,E09,failing,UCD-0159666,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Cytoskeletal Signaling
+1294,E09,failing,UCD-0159666,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Cytoskeletal Signaling
+1295,E09,failing,UCD-0159666,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Cytoskeletal Signaling
+1296,E10,failing,UCD-0159607,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Metabolism
+1297,E10,failing,UCD-0159607,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Metabolism
+1298,E10,failing,UCD-0159607,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Metabolism
+1299,E10,failing,UCD-0159607,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Metabolism
+1304,F02,failing,UCD-0159614,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Metabolism
+1305,F02,failing,UCD-0159614,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Metabolism
+1306,F02,failing,UCD-0159614,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Metabolism
+1307,F02,failing,UCD-0159614,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Metabolism
+1308,F03,failing,UCD-0159622,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,JAK/STAT
+1309,F03,failing,UCD-0159622,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,JAK/STAT
+1310,F03,failing,UCD-0159622,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,JAK/STAT
+1311,F03,failing,UCD-0159622,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,JAK/STAT
+1312,F04,failing,UCD-0159630,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Transmembrane Transporters
+1313,F04,failing,UCD-0159630,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Transmembrane Transporters
+1314,F04,failing,UCD-0159630,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Transmembrane Transporters
+1315,F04,failing,UCD-0159630,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Transmembrane Transporters
+1316,F05,failing,UCD-0159638,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Apoptosis
+1317,F05,failing,UCD-0159638,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Apoptosis
+1318,F05,failing,UCD-0159638,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Apoptosis
+1319,F05,failing,UCD-0159638,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Apoptosis
+1320,F06,failing,UCD-0159646,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,GPCR & G Protein
+1321,F06,failing,UCD-0159646,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,GPCR & G Protein
+1322,F06,failing,UCD-0159646,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,GPCR & G Protein
+1323,F06,failing,UCD-0159646,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,GPCR & G Protein
+1324,F07,failing,UCD-0159652,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Proteases
+1325,F07,failing,UCD-0159652,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Proteases
+1326,F07,failing,UCD-0159652,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Proteases
+1327,F07,failing,UCD-0159652,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Proteases
+1328,F08,failing,UCD-0159660,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Epigenetics
+1329,F08,failing,UCD-0159660,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Epigenetics
+1330,F08,failing,UCD-0159660,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Epigenetics
+1331,F08,failing,UCD-0159660,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Epigenetics
+1332,F09,failing,UCD-0159600,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Epigenetics
+1333,F09,failing,UCD-0159600,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Epigenetics
+1334,F09,failing,UCD-0159600,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Epigenetics
+1335,F09,failing,UCD-0159600,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Epigenetics
+1336,F10,failing,UCD-0001957,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Epigenetics
+1337,F10,failing,UCD-0001957,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Epigenetics
+1338,F10,failing,UCD-0001957,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Epigenetics
+1339,F10,failing,UCD-0001957,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Epigenetics
+1340,F11,failing,UCD-0159615,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Angiogenesis
+1341,F11,failing,UCD-0159615,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Angiogenesis
+1342,F11,failing,UCD-0159615,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Angiogenesis
+1343,F11,failing,UCD-0159615,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Angiogenesis
+1344,G02,failing,UCD-0159623,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,DNA Damage
+1345,G02,failing,UCD-0159623,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,DNA Damage
+1346,G02,failing,UCD-0159623,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,DNA Damage
+1347,G02,failing,UCD-0159623,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,DNA Damage
+1348,G03,failing,UCD-0159631,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Cell Cycle
+1349,G03,failing,UCD-0159631,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Cell Cycle
+1350,G03,failing,UCD-0159631,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Cell Cycle
+1351,G03,failing,UCD-0159631,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Cell Cycle
+1352,G04,failing,UCD-0159639,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Protein Tyrosine Kinase
+1353,G04,failing,UCD-0159639,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Protein Tyrosine Kinase
+1354,G04,failing,UCD-0159639,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Protein Tyrosine Kinase
+1355,G04,failing,UCD-0159639,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Protein Tyrosine Kinase
+1356,G05,failing,UCD-0159647,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,GPCR & G Protein
+1357,G05,failing,UCD-0159647,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,GPCR & G Protein
+1358,G05,failing,UCD-0159647,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,GPCR & G Protein
+1359,G05,failing,UCD-0159647,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,GPCR & G Protein
+1360,G06,failing,UCD-0159653,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Protein Tyrosine Kinase
+1361,G06,failing,UCD-0159653,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Protein Tyrosine Kinase
+1362,G06,failing,UCD-0159653,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Protein Tyrosine Kinase
+1363,G06,failing,UCD-0159653,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Protein Tyrosine Kinase
+1364,G07,failing,UCD-0159661,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Transmembrane Transporters
+1365,G07,failing,UCD-0159661,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Transmembrane Transporters
+1366,G07,failing,UCD-0159661,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Transmembrane Transporters
+1367,G07,failing,UCD-0159661,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Transmembrane Transporters
+1368,G08,failing,UCD-0159601,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Protein Tyrosine Kinase
+1369,G08,failing,UCD-0159601,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Protein Tyrosine Kinase
+1370,G08,failing,UCD-0159601,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Protein Tyrosine Kinase
+1371,G08,failing,UCD-0159601,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Protein Tyrosine Kinase
+1372,G09,failing,UCD-0159608,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,Metabolism
+1373,G09,failing,UCD-0159608,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,Metabolism
+1374,G09,failing,UCD-0159608,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,Metabolism
+1375,G09,failing,UCD-0159608,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,Metabolism
+1376,G10,failing,UCD-0159616,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,GPCR & G Protein
+1377,G10,failing,UCD-0159616,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,GPCR & G Protein
+1378,G10,failing,UCD-0159616,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,GPCR & G Protein
+1379,G10,failing,UCD-0159616,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,GPCR & G Protein
+1380,G11,failing,UCD-0159624,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260112100001,10,GPCR & G Protein
+1381,G11,failing,UCD-0159624,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260113090001,10,GPCR & G Protein
+1382,G11,failing,UCD-0159624,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260110170001,10,GPCR & G Protein
+1383,G11,failing,UCD-0159624,Target_Selective_Library_Screen_Plate_10_with_pathways,CARD-CelIns-CX7_260111110001,10,GPCR & G Protein
+1388,B03,failing,UCD-0001078,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Metabolism
+1389,B03,failing,UCD-0001078,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Metabolism
+1390,B03,failing,UCD-0001078,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Metabolism
+1391,B03,failing,UCD-0001078,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Metabolism
+1392,B04,failing,UCD-0000972,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Neuronal Signaling
+1393,B04,failing,UCD-0000972,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Neuronal Signaling
+1394,B04,failing,UCD-0000972,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Neuronal Signaling
+1395,B04,failing,UCD-0000972,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Neuronal Signaling
+1400,B06,failing,UCD-0159326,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Neuronal Signaling
+1401,B06,failing,UCD-0159326,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Neuronal Signaling
+1402,B06,failing,UCD-0159326,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Neuronal Signaling
+1403,B06,failing,UCD-0159326,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Neuronal Signaling
+1404,B07,failing,UCD-0159331,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Transmembrane Transporters
+1405,B07,failing,UCD-0159331,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Transmembrane Transporters
+1406,B07,failing,UCD-0159331,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Transmembrane Transporters
+1407,B07,failing,UCD-0159331,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Transmembrane Transporters
+1412,B09,failing,UCD-0159332,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Apoptosis
+1413,B09,failing,UCD-0159332,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Apoptosis
+1414,B09,failing,UCD-0159332,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Apoptosis
+1415,B09,failing,UCD-0159332,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Apoptosis
+1416,B10,failing,UCD-0000936,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Neuronal Signaling
+1417,B10,failing,UCD-0000936,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Neuronal Signaling
+1418,B10,failing,UCD-0000936,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Neuronal Signaling
+1419,B10,failing,UCD-0000936,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Neuronal Signaling
+1424,C02,failing,UCD-0001886,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Cell Cycle
+1425,C02,failing,UCD-0001886,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Cell Cycle
+1426,C02,failing,UCD-0001886,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Cell Cycle
+1427,C02,failing,UCD-0001886,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Cell Cycle
+1428,C03,failing,UCD-0159344,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Endocrinology & Hormones
+1429,C03,failing,UCD-0159344,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Endocrinology & Hormones
+1430,C03,failing,UCD-0159344,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Endocrinology & Hormones
+1431,C03,failing,UCD-0159344,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Endocrinology & Hormones
+1432,C04,failing,UCD-0159350,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Neuronal Signaling
+1433,C04,failing,UCD-0159350,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Neuronal Signaling
+1434,C04,failing,UCD-0159350,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Neuronal Signaling
+1435,C04,failing,UCD-0159350,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Neuronal Signaling
+1436,C05,failing,UCD-0159355,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,PI3K/Akt/mTOR
+1437,C05,failing,UCD-0159355,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,PI3K/Akt/mTOR
+1438,C05,failing,UCD-0159355,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,PI3K/Akt/mTOR
+1439,C05,failing,UCD-0159355,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,PI3K/Akt/mTOR
+1440,C06,failing,UCD-0159361,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Others
+1441,C06,failing,UCD-0159361,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Others
+1442,C06,failing,UCD-0159361,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Others
+1443,C06,failing,UCD-0159361,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Others
+1444,C07,failing,UCD-0159368,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Angiogenesis
+1445,C07,failing,UCD-0159368,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Angiogenesis
+1446,C07,failing,UCD-0159368,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Angiogenesis
+1447,C07,failing,UCD-0159368,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Angiogenesis
+1448,C08,failing,UCD-0159375,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,NF-κB
+1449,C08,failing,UCD-0159375,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,NF-κB
+1450,C08,failing,UCD-0159375,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,NF-κB
+1451,C08,failing,UCD-0159375,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,NF-κB
+1452,C09,failing,UCD-0159383,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,NF-κB
+1453,C09,failing,UCD-0159383,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,NF-κB
+1454,C09,failing,UCD-0159383,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,NF-κB
+1455,C09,failing,UCD-0159383,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,NF-κB
+1456,C10,failing,UCD-0159333,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Transmembrane Transporters
+1457,C10,failing,UCD-0159333,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Transmembrane Transporters
+1458,C10,failing,UCD-0159333,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Transmembrane Transporters
+1459,C10,failing,UCD-0159333,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Transmembrane Transporters
+1460,C11,failing,UCD-0001616,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Metabolism
+1461,C11,failing,UCD-0001616,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Metabolism
+1462,C11,failing,UCD-0001616,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Metabolism
+1463,C11,failing,UCD-0001616,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Metabolism
+1464,D02,failing,UCD-0159339,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Neuronal Signaling
+1465,D02,failing,UCD-0159339,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Neuronal Signaling
+1466,D02,failing,UCD-0159339,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Neuronal Signaling
+1467,D02,failing,UCD-0159339,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Neuronal Signaling
+1468,D03,failing,UCD-0159345,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Neuronal Signaling
+1469,D03,failing,UCD-0159345,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Neuronal Signaling
+1470,D03,failing,UCD-0159345,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Neuronal Signaling
+1471,D03,failing,UCD-0159345,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Neuronal Signaling
+1472,D04,failing,UCD-0159351,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Others
+1473,D04,failing,UCD-0159351,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Others
+1474,D04,failing,UCD-0159351,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Others
+1475,D04,failing,UCD-0159351,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Others
+1476,D05,failing,UCD-0001790B,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Protein Tyrosine Kinase
+1477,D05,failing,UCD-0001790B,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Protein Tyrosine Kinase
+1478,D05,failing,UCD-0001790B,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Protein Tyrosine Kinase
+1479,D05,failing,UCD-0001790B,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Protein Tyrosine Kinase
+1480,D06,failing,UCD-0159362,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Stem Cells & Wnt
+1481,D06,failing,UCD-0159362,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Stem Cells & Wnt
+1482,D06,failing,UCD-0159362,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Stem Cells & Wnt
+1483,D06,failing,UCD-0159362,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Stem Cells & Wnt
+1484,D07,failing,UCD-0159369,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Epigenetics
+1485,D07,failing,UCD-0159369,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Epigenetics
+1486,D07,failing,UCD-0159369,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Epigenetics
+1487,D07,failing,UCD-0159369,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Epigenetics
+1488,D08,failing,UCD-0159376,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Transmembrane Transporters
+1489,D08,failing,UCD-0159376,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Transmembrane Transporters
+1490,D08,failing,UCD-0159376,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Transmembrane Transporters
+1491,D08,failing,UCD-0159376,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Transmembrane Transporters
+1492,D09,failing,UCD-0159384,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,NF-κB
+1493,D09,failing,UCD-0159384,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,NF-κB
+1494,D09,failing,UCD-0159384,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,NF-κB
+1495,D09,failing,UCD-0159384,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,NF-κB
+1496,D10,failing,UCD-0159334,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Metabolism
+1497,D10,failing,UCD-0159334,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Metabolism
+1498,D10,failing,UCD-0159334,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Metabolism
+1499,D10,failing,UCD-0159334,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Metabolism
+1500,D11,failing,UCD-0159338,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Neuronal Signaling
+1501,D11,failing,UCD-0159338,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Neuronal Signaling
+1502,D11,failing,UCD-0159338,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Neuronal Signaling
+1503,D11,failing,UCD-0159338,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Neuronal Signaling
+1508,E03,failing,UCD-0159340,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Others
+1509,E03,failing,UCD-0159340,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Others
+1510,E03,failing,UCD-0159340,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Others
+1511,E03,failing,UCD-0159340,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Others
+1512,E04,failing,UCD-0159346,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Stem Cells & Wnt
+1513,E04,failing,UCD-0159346,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Stem Cells & Wnt
+1514,E04,failing,UCD-0159346,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Stem Cells & Wnt
+1515,E04,failing,UCD-0159346,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Stem Cells & Wnt
+1520,E06,failing,UCD-0001900,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,PI3K/Akt/mTOR
+1521,E06,failing,UCD-0001900,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,PI3K/Akt/mTOR
+1522,E06,failing,UCD-0001900,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,PI3K/Akt/mTOR
+1523,E06,failing,UCD-0001900,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,PI3K/Akt/mTOR
+1524,E07,failing,UCD-0159356,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Cytoskeletal Signaling
+1525,E07,failing,UCD-0159356,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Cytoskeletal Signaling
+1526,E07,failing,UCD-0159356,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Cytoskeletal Signaling
+1527,E07,failing,UCD-0159356,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Cytoskeletal Signaling
+1532,E09,failing,UCD-0159363,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Others
+1533,E09,failing,UCD-0159363,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Others
+1534,E09,failing,UCD-0159363,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Others
+1535,E09,failing,UCD-0159363,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Others
+1536,E10,failing,UCD-0018540,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,MAPK
+1537,E10,failing,UCD-0018540,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,MAPK
+1538,E10,failing,UCD-0018540,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,MAPK
+1539,E10,failing,UCD-0018540,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,MAPK
+1544,F02,failing,UCD-0159377,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Metabolism
+1545,F02,failing,UCD-0159377,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Metabolism
+1546,F02,failing,UCD-0159377,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Metabolism
+1547,F02,failing,UCD-0159377,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Metabolism
+1548,F03,failing,UCD-0159385,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Metabolism
+1549,F03,failing,UCD-0159385,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Metabolism
+1550,F03,failing,UCD-0159385,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Metabolism
+1551,F03,failing,UCD-0159385,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Metabolism
+1552,F04,failing,UCD-0000684,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Others
+1553,F04,failing,UCD-0000684,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Others
+1554,F04,failing,UCD-0000684,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Others
+1555,F04,failing,UCD-0000684,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Others
+1556,F05,failing,UCD-0001186,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Neuronal Signaling
+1557,F05,failing,UCD-0001186,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Neuronal Signaling
+1558,F05,failing,UCD-0001186,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Neuronal Signaling
+1559,F05,failing,UCD-0001186,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Neuronal Signaling
+1560,F06,failing,UCD-0159341,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Metabolism
+1561,F06,failing,UCD-0159341,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Metabolism
+1562,F06,failing,UCD-0159341,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Metabolism
+1563,F06,failing,UCD-0159341,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Metabolism
+1564,F07,failing,UCD-0159347,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Metabolism
+1565,F07,failing,UCD-0159347,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Metabolism
+1566,F07,failing,UCD-0159347,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Metabolism
+1567,F07,failing,UCD-0159347,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Metabolism
+1568,F08,failing,UCD-0001861,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Angiogenesis
+1569,F08,failing,UCD-0001861,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Angiogenesis
+1570,F08,failing,UCD-0001861,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Angiogenesis
+1571,F08,failing,UCD-0001861,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Angiogenesis
+1572,F09,failing,UCD-0159357,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Apoptosis
+1573,F09,failing,UCD-0159357,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Apoptosis
+1574,F09,failing,UCD-0159357,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Apoptosis
+1575,F09,failing,UCD-0159357,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Apoptosis
+1576,F10,failing,UCD-0011696,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,JAK/STAT
+1577,F10,failing,UCD-0011696,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,JAK/STAT
+1578,F10,failing,UCD-0011696,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,JAK/STAT
+1579,F10,failing,UCD-0011696,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,JAK/STAT
+1580,F11,failing,UCD-0159370,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,PI3K/Akt/mTOR
+1581,F11,failing,UCD-0159370,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,PI3K/Akt/mTOR
+1582,F11,failing,UCD-0159370,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,PI3K/Akt/mTOR
+1583,F11,failing,UCD-0159370,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,PI3K/Akt/mTOR
+1584,G02,failing,UCD-0159378,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Protein Tyrosine Kinase
+1585,G02,failing,UCD-0159378,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Protein Tyrosine Kinase
+1586,G02,failing,UCD-0159378,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Protein Tyrosine Kinase
+1587,G02,failing,UCD-0159378,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Protein Tyrosine Kinase
+1588,G03,failing,UCD-0159386,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,DNA Damage
+1589,G03,failing,UCD-0159386,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,DNA Damage
+1590,G03,failing,UCD-0159386,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,DNA Damage
+1591,G03,failing,UCD-0159386,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,DNA Damage
+1592,G04,failing,UCD-0159335,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,JAK/STAT
+1593,G04,failing,UCD-0159335,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,JAK/STAT
+1594,G04,failing,UCD-0159335,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,JAK/STAT
+1595,G04,failing,UCD-0159335,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,JAK/STAT
+1596,G05,failing,UCD-0018448,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,GPCR & G Protein
+1597,G05,failing,UCD-0018448,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,GPCR & G Protein
+1598,G05,failing,UCD-0018448,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,GPCR & G Protein
+1599,G05,failing,UCD-0018448,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,GPCR & G Protein
+1600,G06,failing,UCD-0159342,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Others
+1601,G06,failing,UCD-0159342,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Others
+1602,G06,failing,UCD-0159342,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Others
+1603,G06,failing,UCD-0159342,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Others
+1604,G07,failing,UCD-0001885,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,PI3K/Akt/mTOR
+1605,G07,failing,UCD-0001885,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,PI3K/Akt/mTOR
+1606,G07,failing,UCD-0001885,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,PI3K/Akt/mTOR
+1607,G07,failing,UCD-0001885,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,PI3K/Akt/mTOR
+1608,G08,failing,UCD-0159352,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Proteases
+1609,G08,failing,UCD-0159352,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Proteases
+1610,G08,failing,UCD-0159352,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Proteases
+1611,G08,failing,UCD-0159352,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Proteases
+1612,G09,failing,UCD-0159358,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Metabolism
+1613,G09,failing,UCD-0159358,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Metabolism
+1614,G09,failing,UCD-0159358,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Metabolism
+1615,G09,failing,UCD-0159358,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Metabolism
+1616,G10,failing,UCD-0159364,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Others
+1617,G10,failing,UCD-0159364,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Others
+1618,G10,failing,UCD-0159364,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Others
+1619,G10,failing,UCD-0159364,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Others
+1620,G11,failing,UCD-0159371,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212180001,4,Apoptosis
+1621,G11,failing,UCD-0159371,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251211180001,4,Apoptosis
+1622,G11,failing,UCD-0159371,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251212100001,4,Apoptosis
+1623,G11,failing,UCD-0159371,Target_Selective_Library_Screen_Plate_4_with_pathways,CARD-CelIns-CX7_251213150001,4,Apoptosis
+1628,B03,failing,UCD-0159406,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Neuronal Signaling
+1629,B03,failing,UCD-0159406,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Neuronal Signaling
+1630,B03,failing,UCD-0159406,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Neuronal Signaling
+1631,B03,failing,UCD-0159406,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Neuronal Signaling
+1632,B04,failing,UCD-0000841,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Neuronal Signaling
+1633,B04,failing,UCD-0000841,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Neuronal Signaling
+1634,B04,failing,UCD-0000841,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Neuronal Signaling
+1635,B04,failing,UCD-0000841,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Neuronal Signaling
+1640,B06,failing,UCD-0159409,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Others
+1641,B06,failing,UCD-0159409,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Others
+1642,B06,failing,UCD-0159409,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Others
+1643,B06,failing,UCD-0159409,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Others
+1644,B07,failing,UCD-0159413,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,NF-κB
+1645,B07,failing,UCD-0159413,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,NF-κB
+1646,B07,failing,UCD-0159413,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,NF-κB
+1647,B07,failing,UCD-0159413,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,NF-κB
+1652,B09,failing,UCD-0159421,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Apoptosis
+1653,B09,failing,UCD-0159421,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Apoptosis
+1654,B09,failing,UCD-0159421,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Apoptosis
+1655,B09,failing,UCD-0159421,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Apoptosis
+1656,B10,failing,UCD-0159429,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Stem Cells & Wnt
+1657,B10,failing,UCD-0159429,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Stem Cells & Wnt
+1658,B10,failing,UCD-0159429,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Stem Cells & Wnt
+1659,B10,failing,UCD-0159429,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Stem Cells & Wnt
+1664,C02,failing,UCD-0159434,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Angiogenesis
+1665,C02,failing,UCD-0159434,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Angiogenesis
+1666,C02,failing,UCD-0159434,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Angiogenesis
+1667,C02,failing,UCD-0159434,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Angiogenesis
+1668,C03,failing,UCD-0159442,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Epigenetics
+1669,C03,failing,UCD-0159442,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Epigenetics
+1670,C03,failing,UCD-0159442,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Epigenetics
+1671,C03,failing,UCD-0159442,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Epigenetics
+1672,C04,failing,UCD-0159443,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Others
+1673,C04,failing,UCD-0159443,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Others
+1674,C04,failing,UCD-0159443,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Others
+1675,C04,failing,UCD-0159443,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Others
+1676,C05,failing,UCD-0159451,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Ubiquitin
+1677,C05,failing,UCD-0159451,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Ubiquitin
+1678,C05,failing,UCD-0159451,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Ubiquitin
+1679,C05,failing,UCD-0159451,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Ubiquitin
+1680,C06,failing,UCD-0159458,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Others
+1681,C06,failing,UCD-0159458,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Others
+1682,C06,failing,UCD-0159458,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Others
+1683,C06,failing,UCD-0159458,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Others
+1684,C07,failing,UCD-0159466,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Cell Cycle
+1685,C07,failing,UCD-0159466,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Cell Cycle
+1686,C07,failing,UCD-0159466,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Cell Cycle
+1687,C07,failing,UCD-0159466,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Cell Cycle
+1688,C08,failing,UCD-0159474,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,DNA Damage
+1689,C08,failing,UCD-0159474,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,DNA Damage
+1690,C08,failing,UCD-0159474,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,DNA Damage
+1691,C08,failing,UCD-0159474,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,DNA Damage
+1692,C09,failing,UCD-0159482,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Angiogenesis
+1693,C09,failing,UCD-0159482,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Angiogenesis
+1694,C09,failing,UCD-0159482,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Angiogenesis
+1695,C09,failing,UCD-0159482,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Angiogenesis
+1696,C10,failing,UCD-0159490,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Ubiquitin
+1697,C10,failing,UCD-0159490,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Ubiquitin
+1698,C10,failing,UCD-0159490,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Ubiquitin
+1699,C10,failing,UCD-0159490,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Ubiquitin
+1700,C11,failing,UCD-0159498,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,PI3K/Akt/mTOR
+1701,C11,failing,UCD-0159498,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,PI3K/Akt/mTOR
+1702,C11,failing,UCD-0159498,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,PI3K/Akt/mTOR
+1703,C11,failing,UCD-0159498,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,PI3K/Akt/mTOR
+1704,D02,failing,UCD-0159506,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,NF-κB
+1705,D02,failing,UCD-0159506,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,NF-κB
+1706,D02,failing,UCD-0159506,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,NF-κB
+1707,D02,failing,UCD-0159506,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,NF-κB
+1708,D03,failing,UCD-0159514,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Others
+1709,D03,failing,UCD-0159514,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Others
+1710,D03,failing,UCD-0159514,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Others
+1711,D03,failing,UCD-0159514,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Others
+1712,D04,failing,UCD-0159444,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,MAPK
+1713,D04,failing,UCD-0159444,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,MAPK
+1714,D04,failing,UCD-0159444,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,MAPK
+1715,D04,failing,UCD-0159444,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,MAPK
+1716,D05,failing,UCD-0159452,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Ubiquitin
+1717,D05,failing,UCD-0159452,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Ubiquitin
+1718,D05,failing,UCD-0159452,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Ubiquitin
+1719,D05,failing,UCD-0159452,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Ubiquitin
+1720,D06,failing,UCD-0159459,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Cytoskeletal Signaling
+1721,D06,failing,UCD-0159459,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Cytoskeletal Signaling
+1722,D06,failing,UCD-0159459,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Cytoskeletal Signaling
+1723,D06,failing,UCD-0159459,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Cytoskeletal Signaling
+1724,D07,failing,UCD-0159467,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Ubiquitin
+1725,D07,failing,UCD-0159467,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Ubiquitin
+1726,D07,failing,UCD-0159467,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Ubiquitin
+1727,D07,failing,UCD-0159467,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Ubiquitin
+1728,D08,failing,UCD-0159475,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Metabolism
+1729,D08,failing,UCD-0159475,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Metabolism
+1730,D08,failing,UCD-0159475,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Metabolism
+1731,D08,failing,UCD-0159475,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Metabolism
+1732,D09,failing,UCD-0159483,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Endocrinology & Hormones
+1733,D09,failing,UCD-0159483,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Endocrinology & Hormones
+1734,D09,failing,UCD-0159483,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Endocrinology & Hormones
+1735,D09,failing,UCD-0159483,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Endocrinology & Hormones
+1736,D10,failing,UCD-0159491,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Others
+1737,D10,failing,UCD-0159491,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Others
+1738,D10,failing,UCD-0159491,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Others
+1739,D10,failing,UCD-0159491,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Others
+1740,D11,failing,UCD-0159499,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Cell Cycle
+1741,D11,failing,UCD-0159499,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Cell Cycle
+1742,D11,failing,UCD-0159499,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Cell Cycle
+1743,D11,failing,UCD-0159499,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Cell Cycle
+1748,E03,failing,UCD-0159507,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Cytoskeletal Signaling
+1749,E03,failing,UCD-0159507,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Cytoskeletal Signaling
+1750,E03,failing,UCD-0159507,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Cytoskeletal Signaling
+1751,E03,failing,UCD-0159507,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Cytoskeletal Signaling
+1752,E04,failing,UCD-0159515,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Epigenetics
+1753,E04,failing,UCD-0159515,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Epigenetics
+1754,E04,failing,UCD-0159515,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Epigenetics
+1755,E04,failing,UCD-0159515,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Epigenetics
+1760,E06,failing,UCD-0159445,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Others
+1761,E06,failing,UCD-0159445,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Others
+1762,E06,failing,UCD-0159445,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Others
+1763,E06,failing,UCD-0159445,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Others
+1764,E07,failing,UCD-0159453,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,TGF-beta/Smad
+1765,E07,failing,UCD-0159453,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,TGF-beta/Smad
+1766,E07,failing,UCD-0159453,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,TGF-beta/Smad
+1767,E07,failing,UCD-0159453,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,TGF-beta/Smad
+1772,E09,failing,UCD-0159460,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Angiogenesis
+1773,E09,failing,UCD-0159460,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Angiogenesis
+1774,E09,failing,UCD-0159460,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Angiogenesis
+1775,E09,failing,UCD-0159460,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Angiogenesis
+1776,E10,failing,UCD-0159468,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Apoptosis
+1777,E10,failing,UCD-0159468,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Apoptosis
+1778,E10,failing,UCD-0159468,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Apoptosis
+1779,E10,failing,UCD-0159468,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Apoptosis
+1784,F02,failing,UCD-0159476,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Metabolism
+1785,F02,failing,UCD-0159476,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Metabolism
+1786,F02,failing,UCD-0159476,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Metabolism
+1787,F02,failing,UCD-0159476,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Metabolism
+1788,F03,failing,UCD-0159484,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Epigenetics
+1789,F03,failing,UCD-0159484,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Epigenetics
+1790,F03,failing,UCD-0159484,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Epigenetics
+1791,F03,failing,UCD-0159484,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Epigenetics
+1792,F04,failing,UCD-0159492,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Cell Cycle
+1793,F04,failing,UCD-0159492,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Cell Cycle
+1794,F04,failing,UCD-0159492,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Cell Cycle
+1795,F04,failing,UCD-0159492,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Cell Cycle
+1796,F05,failing,UCD-0159500,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Epigenetics
+1797,F05,failing,UCD-0159500,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Epigenetics
+1798,F05,failing,UCD-0159500,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Epigenetics
+1799,F05,failing,UCD-0159500,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Epigenetics
+1800,F06,failing,UCD-0159508,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Epigenetics
+1801,F06,failing,UCD-0159508,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Epigenetics
+1802,F06,failing,UCD-0159508,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Epigenetics
+1803,F06,failing,UCD-0159508,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Epigenetics
+1804,F07,failing,UCD-0159516,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Proteases
+1805,F07,failing,UCD-0159516,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Proteases
+1806,F07,failing,UCD-0159516,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Proteases
+1807,F07,failing,UCD-0159516,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Proteases
+1808,F08,failing,UCD-0159446,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Transmembrane Transporters
+1809,F08,failing,UCD-0159446,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Transmembrane Transporters
+1810,F08,failing,UCD-0159446,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Transmembrane Transporters
+1811,F08,failing,UCD-0159446,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Transmembrane Transporters
+1812,F09,failing,UCD-0159454,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,TGF-beta/Smad
+1813,F09,failing,UCD-0159454,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,TGF-beta/Smad
+1814,F09,failing,UCD-0159454,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,TGF-beta/Smad
+1815,F09,failing,UCD-0159454,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,TGF-beta/Smad
+1816,F10,failing,UCD-0159461,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Angiogenesis
+1817,F10,failing,UCD-0159461,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Angiogenesis
+1818,F10,failing,UCD-0159461,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Angiogenesis
+1819,F10,failing,UCD-0159461,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Angiogenesis
+1820,F11,failing,UCD-0159469,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Protein Tyrosine Kinase
+1821,F11,failing,UCD-0159469,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Protein Tyrosine Kinase
+1822,F11,failing,UCD-0159469,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Protein Tyrosine Kinase
+1823,F11,failing,UCD-0159469,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Protein Tyrosine Kinase
+1824,G02,failing,UCD-0159477,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Metabolism
+1825,G02,failing,UCD-0159477,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Metabolism
+1826,G02,failing,UCD-0159477,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Metabolism
+1827,G02,failing,UCD-0159477,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Metabolism
+1828,G03,failing,UCD-0159485,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Transmembrane Transporters
+1829,G03,failing,UCD-0159485,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Transmembrane Transporters
+1830,G03,failing,UCD-0159485,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Transmembrane Transporters
+1831,G03,failing,UCD-0159485,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Transmembrane Transporters
+1832,G04,failing,UCD-0159493,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Epigenetics
+1833,G04,failing,UCD-0159493,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Epigenetics
+1834,G04,failing,UCD-0159493,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Epigenetics
+1835,G04,failing,UCD-0159493,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Epigenetics
+1836,G05,failing,UCD-0159501,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Cell Cycle
+1837,G05,failing,UCD-0159501,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Cell Cycle
+1838,G05,failing,UCD-0159501,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Cell Cycle
+1839,G05,failing,UCD-0159501,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Cell Cycle
+1840,G06,failing,UCD-0159509,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Others
+1841,G06,failing,UCD-0159509,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Others
+1842,G06,failing,UCD-0159509,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Others
+1843,G06,failing,UCD-0159509,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Others
+1844,G07,failing,UCD-0159517,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Proteases
+1845,G07,failing,UCD-0159517,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Proteases
+1846,G07,failing,UCD-0159517,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Proteases
+1847,G07,failing,UCD-0159517,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Proteases
+1848,G08,failing,UCD-0159447,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,Ubiquitin
+1849,G08,failing,UCD-0159447,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,Ubiquitin
+1850,G08,failing,UCD-0159447,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,Ubiquitin
+1851,G08,failing,UCD-0159447,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,Ubiquitin
+1852,G09,failing,UCD-0159455,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,TGF-beta/Smad
+1853,G09,failing,UCD-0159455,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,TGF-beta/Smad
+1854,G09,failing,UCD-0159455,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,TGF-beta/Smad
+1855,G09,failing,UCD-0159455,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,TGF-beta/Smad
+1856,G10,failing,UCD-0159462,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,GPCR & G Protein
+1857,G10,failing,UCD-0159462,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,GPCR & G Protein
+1858,G10,failing,UCD-0159462,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,GPCR & G Protein
+1859,G10,failing,UCD-0159462,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,GPCR & G Protein
+1860,G11,failing,UCD-0159470,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_260102130001,7,MAPK
+1861,G11,failing,UCD-0159470,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230180001,7,MAPK
+1862,G11,failing,UCD-0159470,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251231100001,7,MAPK
+1863,G11,failing,UCD-0159470,Target_Selective_Library_Screen_Plate_7_with_pathways,CARD-CelIns-CX7_251230110001,7,MAPK
+1868,B03,failing,UCD-0159379,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,PI3K/Akt/mTOR
+1869,B03,failing,UCD-0159379,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,PI3K/Akt/mTOR
+1870,B03,failing,UCD-0159379,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,PI3K/Akt/mTOR
+1871,B03,failing,UCD-0159379,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,PI3K/Akt/mTOR
+1872,B04,failing,UCD-0159387,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Neuronal Signaling
+1873,B04,failing,UCD-0159387,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Neuronal Signaling
+1874,B04,failing,UCD-0159387,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Neuronal Signaling
+1875,B04,failing,UCD-0159387,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Neuronal Signaling
+1880,B06,failing,UCD-0159336,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Ubiquitin
+1881,B06,failing,UCD-0159336,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Ubiquitin
+1882,B06,failing,UCD-0159336,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Ubiquitin
+1883,B06,failing,UCD-0159336,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Ubiquitin
+1884,B07,failing,UCD-0001419,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Transmembrane Transporters
+1885,B07,failing,UCD-0001419,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Transmembrane Transporters
+1886,B07,failing,UCD-0001419,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Transmembrane Transporters
+1887,B07,failing,UCD-0001419,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Transmembrane Transporters
+1892,B09,failing,UCD-0159343,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,GPCR & G Protein
+1893,B09,failing,UCD-0159343,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,GPCR & G Protein
+1894,B09,failing,UCD-0159343,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,GPCR & G Protein
+1895,B09,failing,UCD-0159343,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,GPCR & G Protein
+1896,B10,failing,UCD-0001863,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Angiogenesis
+1897,B10,failing,UCD-0001863,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Angiogenesis
+1898,B10,failing,UCD-0001863,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Angiogenesis
+1899,B10,failing,UCD-0001863,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Angiogenesis
+1904,C02,failing,UCD-0159353,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Proteases
+1905,C02,failing,UCD-0159353,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Proteases
+1906,C02,failing,UCD-0159353,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Proteases
+1907,C02,failing,UCD-0159353,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Proteases
+1908,C03,failing,UCD-0159359,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Others
+1909,C03,failing,UCD-0159359,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Others
+1910,C03,failing,UCD-0159359,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Others
+1911,C03,failing,UCD-0159359,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Others
+1912,C04,failing,UCD-0159365,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,TGF-beta/Smad
+1913,C04,failing,UCD-0159365,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,TGF-beta/Smad
+1914,C04,failing,UCD-0159365,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,TGF-beta/Smad
+1915,C04,failing,UCD-0159365,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,TGF-beta/Smad
+1916,C05,failing,UCD-0159372,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Protein Tyrosine Kinase
+1917,C05,failing,UCD-0159372,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Protein Tyrosine Kinase
+1918,C05,failing,UCD-0159372,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Protein Tyrosine Kinase
+1919,C05,failing,UCD-0159372,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Protein Tyrosine Kinase
+1920,C06,failing,UCD-0159380,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,GPCR & G Protein
+1921,C06,failing,UCD-0159380,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,GPCR & G Protein
+1922,C06,failing,UCD-0159380,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,GPCR & G Protein
+1923,C06,failing,UCD-0159380,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,GPCR & G Protein
+1924,C07,failing,UCD-0159388,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,NF-κB
+1925,C07,failing,UCD-0159388,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,NF-κB
+1926,C07,failing,UCD-0159388,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,NF-κB
+1927,C07,failing,UCD-0159388,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,NF-κB
+1928,C08,failing,UCD-0159337,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Neuronal Signaling
+1929,C08,failing,UCD-0159337,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Neuronal Signaling
+1930,C08,failing,UCD-0159337,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Neuronal Signaling
+1931,C08,failing,UCD-0159337,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Neuronal Signaling
+1932,C09,failing,UCD-0001391,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Metabolism
+1933,C09,failing,UCD-0001391,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Metabolism
+1934,C09,failing,UCD-0001391,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Metabolism
+1935,C09,failing,UCD-0001391,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Metabolism
+1936,C10,failing,UCD-0001856,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Angiogenesis
+1937,C10,failing,UCD-0001856,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Angiogenesis
+1938,C10,failing,UCD-0001856,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Angiogenesis
+1939,C10,failing,UCD-0001856,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Angiogenesis
+1940,C11,failing,UCD-0159348,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Cell Cycle
+1941,C11,failing,UCD-0159348,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Cell Cycle
+1942,C11,failing,UCD-0159348,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Cell Cycle
+1943,C11,failing,UCD-0159348,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Cell Cycle
+1944,D02,failing,UCD-0159354,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Metabolism
+1945,D02,failing,UCD-0159354,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Metabolism
+1946,D02,failing,UCD-0159354,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Metabolism
+1947,D02,failing,UCD-0159354,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Metabolism
+1948,D03,failing,UCD-0000201,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,GPCR & G Protein
+1949,D03,failing,UCD-0000201,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,GPCR & G Protein
+1950,D03,failing,UCD-0000201,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,GPCR & G Protein
+1951,D03,failing,UCD-0000201,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,GPCR & G Protein
+1952,D04,failing,UCD-0159366,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Metabolism
+1953,D04,failing,UCD-0159366,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Metabolism
+1954,D04,failing,UCD-0159366,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Metabolism
+1955,D04,failing,UCD-0159366,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Metabolism
+1956,D05,failing,UCD-0159373,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Angiogenesis
+1957,D05,failing,UCD-0159373,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Angiogenesis
+1958,D05,failing,UCD-0159373,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Angiogenesis
+1959,D05,failing,UCD-0159373,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Angiogenesis
+1960,D06,failing,UCD-0159381,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Neuronal Signaling
+1961,D06,failing,UCD-0159381,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Neuronal Signaling
+1962,D06,failing,UCD-0159381,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Neuronal Signaling
+1963,D06,failing,UCD-0159381,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Neuronal Signaling
+1964,D07,failing,UCD-0001864,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Angiogenesis
+1965,D07,failing,UCD-0001864,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Angiogenesis
+1966,D07,failing,UCD-0001864,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Angiogenesis
+1967,D07,failing,UCD-0001864,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Angiogenesis
+1968,D08,failing,UCD-0000737,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,GPCR & G Protein
+1969,D08,failing,UCD-0000737,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,GPCR & G Protein
+1970,D08,failing,UCD-0000737,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,GPCR & G Protein
+1971,D08,failing,UCD-0000737,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,GPCR & G Protein
+1972,D09,failing,UCD-0001870,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Angiogenesis
+1973,D09,failing,UCD-0001870,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Angiogenesis
+1974,D09,failing,UCD-0001870,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Angiogenesis
+1975,D09,failing,UCD-0001870,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Angiogenesis
+1976,D10,failing,UCD-0001888,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,PI3K/Akt/mTOR
+1977,D10,failing,UCD-0001888,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,PI3K/Akt/mTOR
+1978,D10,failing,UCD-0001888,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,PI3K/Akt/mTOR
+1979,D10,failing,UCD-0001888,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,PI3K/Akt/mTOR
+1980,D11,failing,UCD-0159349,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Metabolism
+1981,D11,failing,UCD-0159349,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Metabolism
+1982,D11,failing,UCD-0159349,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Metabolism
+1983,D11,failing,UCD-0159349,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Metabolism
+1988,E03,failing,UCD-0001567,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Transmembrane Transporters
+1989,E03,failing,UCD-0001567,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Transmembrane Transporters
+1990,E03,failing,UCD-0001567,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Transmembrane Transporters
+1991,E03,failing,UCD-0001567,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Transmembrane Transporters
+1992,E04,failing,UCD-0159360,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Metabolism
+1993,E04,failing,UCD-0159360,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Metabolism
+1994,E04,failing,UCD-0159360,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Metabolism
+1995,E04,failing,UCD-0159360,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Metabolism
+2000,E06,failing,UCD-0159367,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Neuronal Signaling
+2001,E06,failing,UCD-0159367,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Neuronal Signaling
+2002,E06,failing,UCD-0159367,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Neuronal Signaling
+2003,E06,failing,UCD-0159367,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Neuronal Signaling
+2004,E07,failing,UCD-0159374,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Endocrinology & Hormones
+2005,E07,failing,UCD-0159374,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Endocrinology & Hormones
+2006,E07,failing,UCD-0159374,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Endocrinology & Hormones
+2007,E07,failing,UCD-0159374,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Endocrinology & Hormones
+2012,E09,failing,UCD-0159382,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Neuronal Signaling
+2013,E09,failing,UCD-0159382,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Neuronal Signaling
+2014,E09,failing,UCD-0159382,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Neuronal Signaling
+2015,E09,failing,UCD-0159382,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Neuronal Signaling
+2016,E10,failing,UCD-0159389,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Protein Tyrosine Kinase
+2017,E10,failing,UCD-0159389,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Protein Tyrosine Kinase
+2018,E10,failing,UCD-0159389,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Protein Tyrosine Kinase
+2019,E10,failing,UCD-0159389,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Protein Tyrosine Kinase
+2024,F02,failing,UCD-0159390,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Neuronal Signaling
+2025,F02,failing,UCD-0159390,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Neuronal Signaling
+2026,F02,failing,UCD-0159390,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Neuronal Signaling
+2027,F02,failing,UCD-0159390,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Neuronal Signaling
+2028,F03,failing,UCD-0022022,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Others
+2029,F03,failing,UCD-0022022,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Others
+2030,F03,failing,UCD-0022022,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Others
+2031,F03,failing,UCD-0022022,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Others
+2032,F04,failing,UCD-0018458,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Proteases
+2033,F04,failing,UCD-0018458,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Proteases
+2034,F04,failing,UCD-0018458,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Proteases
+2035,F04,failing,UCD-0018458,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Proteases
+2036,F05,failing,UCD-0000899,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Neuronal Signaling
+2037,F05,failing,UCD-0000899,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Neuronal Signaling
+2038,F05,failing,UCD-0000899,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Neuronal Signaling
+2039,F05,failing,UCD-0000899,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Neuronal Signaling
+2040,F06,failing,UCD-0159408,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Neuronal Signaling
+2041,F06,failing,UCD-0159408,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Neuronal Signaling
+2042,F06,failing,UCD-0159408,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Neuronal Signaling
+2043,F06,failing,UCD-0159408,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Neuronal Signaling
+2044,F07,failing,UCD-0017582,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Neuronal Signaling
+2045,F07,failing,UCD-0017582,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Neuronal Signaling
+2046,F07,failing,UCD-0017582,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Neuronal Signaling
+2047,F07,failing,UCD-0017582,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Neuronal Signaling
+2048,F08,failing,UCD-0159414,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,DNA Damage
+2049,F08,failing,UCD-0159414,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,DNA Damage
+2050,F08,failing,UCD-0159414,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,DNA Damage
+2051,F08,failing,UCD-0159414,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,DNA Damage
+2052,F09,failing,UCD-0159422,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Apoptosis
+2053,F09,failing,UCD-0159422,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Apoptosis
+2054,F09,failing,UCD-0159422,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Apoptosis
+2055,F09,failing,UCD-0159422,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Apoptosis
+2056,F10,failing,UCD-0159430,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Proteases
+2057,F10,failing,UCD-0159430,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Proteases
+2058,F10,failing,UCD-0159430,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Proteases
+2059,F10,failing,UCD-0159430,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Proteases
+2060,F11,failing,UCD-0159435,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Protein Tyrosine Kinase
+2061,F11,failing,UCD-0159435,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Protein Tyrosine Kinase
+2062,F11,failing,UCD-0159435,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Protein Tyrosine Kinase
+2063,F11,failing,UCD-0159435,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Protein Tyrosine Kinase
+2064,G02,failing,UCD-0159391,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Protein Tyrosine Kinase
+2065,G02,failing,UCD-0159391,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Protein Tyrosine Kinase
+2066,G02,failing,UCD-0159391,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Protein Tyrosine Kinase
+2067,G02,failing,UCD-0159391,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Protein Tyrosine Kinase
+2068,G03,failing,UCD-0159398,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Protein Tyrosine Kinase
+2069,G03,failing,UCD-0159398,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Protein Tyrosine Kinase
+2070,G03,failing,UCD-0159398,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Protein Tyrosine Kinase
+2071,G03,failing,UCD-0159398,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Protein Tyrosine Kinase
+2072,G04,failing,UCD-0159402,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,GPCR & G Protein
+2073,G04,failing,UCD-0159402,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,GPCR & G Protein
+2074,G04,failing,UCD-0159402,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,GPCR & G Protein
+2075,G04,failing,UCD-0159402,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,GPCR & G Protein
+2076,G05,failing,UCD-0159407,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,NF-κB
+2077,G05,failing,UCD-0159407,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,NF-κB
+2078,G05,failing,UCD-0159407,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,NF-κB
+2079,G05,failing,UCD-0159407,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,NF-κB
+2080,G06,failing,UCD-0011462,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Endocrinology & Hormones
+2081,G06,failing,UCD-0011462,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Endocrinology & Hormones
+2082,G06,failing,UCD-0011462,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Endocrinology & Hormones
+2083,G06,failing,UCD-0011462,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Endocrinology & Hormones
+2084,G07,failing,UCD-0159410,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Neuronal Signaling
+2085,G07,failing,UCD-0159410,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Neuronal Signaling
+2086,G07,failing,UCD-0159410,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Neuronal Signaling
+2087,G07,failing,UCD-0159410,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Neuronal Signaling
+2088,G08,failing,UCD-0159415,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Ubiquitin
+2089,G08,failing,UCD-0159415,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Ubiquitin
+2090,G08,failing,UCD-0159415,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Ubiquitin
+2091,G08,failing,UCD-0159415,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Ubiquitin
+2092,G09,failing,UCD-0159423,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,PI3K/Akt/mTOR
+2093,G09,failing,UCD-0159423,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,PI3K/Akt/mTOR
+2094,G09,failing,UCD-0159423,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,PI3K/Akt/mTOR
+2095,G09,failing,UCD-0159423,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,PI3K/Akt/mTOR
+2096,G10,failing,UCD-0159431,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,PI3K/Akt/mTOR
+2097,G10,failing,UCD-0159431,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,PI3K/Akt/mTOR
+2098,G10,failing,UCD-0159431,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,PI3K/Akt/mTOR
+2099,G10,failing,UCD-0159431,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,PI3K/Akt/mTOR
+2100,G11,failing,UCD-0159436,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216180001,5,Stem Cells & Wnt
+2101,G11,failing,UCD-0159436,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251215140002,5,Stem Cells & Wnt
+2102,G11,failing,UCD-0159436,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251217120001,5,Stem Cells & Wnt
+2103,G11,failing,UCD-0159436,Target_Selective_Library_Screen_Plate_5_with_pathways,CARD-CelIns-CX7_251216110001,5,Stem Cells & Wnt
+2108,B03,failing,UCD-0159392,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Angiogenesis
+2109,B03,failing,UCD-0159392,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Angiogenesis
+2110,B03,failing,UCD-0159392,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Angiogenesis
+2111,B03,failing,UCD-0159392,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Angiogenesis
+2112,B04,failing,UCD-0159399,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Apoptosis
+2113,B04,failing,UCD-0159399,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Apoptosis
+2114,B04,failing,UCD-0159399,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Apoptosis
+2115,B04,failing,UCD-0159399,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Apoptosis
+2120,B06,failing,UCD-0159403,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,GPCR & G Protein
+2121,B06,failing,UCD-0159403,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,GPCR & G Protein
+2122,B06,failing,UCD-0159403,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,GPCR & G Protein
+2123,B06,failing,UCD-0159403,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,GPCR & G Protein
+2124,B07,failing,UCD-0000904,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,GPCR & G Protein
+2125,B07,failing,UCD-0000904,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,GPCR & G Protein
+2126,B07,failing,UCD-0000904,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,GPCR & G Protein
+2127,B07,failing,UCD-0000904,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,GPCR & G Protein
+2132,B09,failing,UCD-0000243,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Neuronal Signaling
+2133,B09,failing,UCD-0000243,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Neuronal Signaling
+2134,B09,failing,UCD-0000243,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Neuronal Signaling
+2135,B09,failing,UCD-0000243,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Neuronal Signaling
+2136,B10,failing,UCD-0159411B,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Neuronal Signaling
+2137,B10,failing,UCD-0159411B,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Neuronal Signaling
+2138,B10,failing,UCD-0159411B,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Neuronal Signaling
+2139,B10,failing,UCD-0159411B,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Neuronal Signaling
+2144,C02,failing,UCD-0159416,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Metabolism
+2145,C02,failing,UCD-0159416,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Metabolism
+2146,C02,failing,UCD-0159416,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Metabolism
+2147,C02,failing,UCD-0159416,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Metabolism
+2148,C03,failing,UCD-0159424,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,JAK/STAT
+2149,C03,failing,UCD-0159424,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,JAK/STAT
+2150,C03,failing,UCD-0159424,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,JAK/STAT
+2151,C03,failing,UCD-0159424,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,JAK/STAT
+2152,C04,failing,UCD-0159432,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Angiogenesis
+2153,C04,failing,UCD-0159432,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Angiogenesis
+2154,C04,failing,UCD-0159432,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Angiogenesis
+2155,C04,failing,UCD-0159432,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Angiogenesis
+2156,C05,failing,UCD-0159437,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Cytoskeletal Signaling
+2157,C05,failing,UCD-0159437,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Cytoskeletal Signaling
+2158,C05,failing,UCD-0159437,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Cytoskeletal Signaling
+2159,C05,failing,UCD-0159437,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Cytoskeletal Signaling
+2160,C06,failing,UCD-0159393,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,GPCR & G Protein
+2161,C06,failing,UCD-0159393,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,GPCR & G Protein
+2162,C06,failing,UCD-0159393,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,GPCR & G Protein
+2163,C06,failing,UCD-0159393,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,GPCR & G Protein
+2164,C07,failing,UCD-0159400,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Apoptosis
+2165,C07,failing,UCD-0159400,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Apoptosis
+2166,C07,failing,UCD-0159400,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Apoptosis
+2167,C07,failing,UCD-0159400,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Apoptosis
+2168,C08,failing,UCD-0001059,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Epigenetics
+2169,C08,failing,UCD-0001059,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Epigenetics
+2170,C08,failing,UCD-0001059,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Epigenetics
+2171,C08,failing,UCD-0001059,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Epigenetics
+2172,C09,failing,UCD-0011606,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Neuronal Signaling
+2173,C09,failing,UCD-0011606,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Neuronal Signaling
+2174,C09,failing,UCD-0011606,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Neuronal Signaling
+2175,C09,failing,UCD-0011606,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Neuronal Signaling
+2176,C10,failing,UCD-0000463,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Others
+2177,C10,failing,UCD-0000463,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Others
+2178,C10,failing,UCD-0000463,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Others
+2179,C10,failing,UCD-0000463,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Others
+2180,C11,failing,UCD-0159411,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Neuronal Signaling
+2181,C11,failing,UCD-0159411,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Neuronal Signaling
+2182,C11,failing,UCD-0159411,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Neuronal Signaling
+2183,C11,failing,UCD-0159411,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Neuronal Signaling
+2184,D02,failing,UCD-0159417,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,GPCR & G Protein
+2185,D02,failing,UCD-0159417,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,GPCR & G Protein
+2186,D02,failing,UCD-0159417,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,GPCR & G Protein
+2187,D02,failing,UCD-0159417,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,GPCR & G Protein
+2188,D03,failing,UCD-0159425,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Apoptosis
+2189,D03,failing,UCD-0159425,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Apoptosis
+2190,D03,failing,UCD-0159425,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Apoptosis
+2191,D03,failing,UCD-0159425,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Apoptosis
+2192,D04,failing,UCD-0159433,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Protein Tyrosine Kinase
+2193,D04,failing,UCD-0159433,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Protein Tyrosine Kinase
+2194,D04,failing,UCD-0159433,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Protein Tyrosine Kinase
+2195,D04,failing,UCD-0159433,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Protein Tyrosine Kinase
+2196,D05,failing,UCD-0159438,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Cytoskeletal Signaling
+2197,D05,failing,UCD-0159438,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Cytoskeletal Signaling
+2198,D05,failing,UCD-0159438,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Cytoskeletal Signaling
+2199,D05,failing,UCD-0159438,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Cytoskeletal Signaling
+2200,D06,failing,UCD-0159394,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,TGF-beta/Smad
+2201,D06,failing,UCD-0159394,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,TGF-beta/Smad
+2202,D06,failing,UCD-0159394,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,TGF-beta/Smad
+2203,D06,failing,UCD-0159394,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,TGF-beta/Smad
+2204,D07,failing,UCD-0018410,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Metabolism
+2205,D07,failing,UCD-0018410,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Metabolism
+2206,D07,failing,UCD-0018410,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Metabolism
+2207,D07,failing,UCD-0018410,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Metabolism
+2208,D08,failing,UCD-0001653,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Transmembrane Transporters
+2209,D08,failing,UCD-0001653,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Transmembrane Transporters
+2210,D08,failing,UCD-0001653,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Transmembrane Transporters
+2211,D08,failing,UCD-0001653,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Transmembrane Transporters
+2212,D09,failing,UCD-0000377,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Transmembrane Transporters
+2213,D09,failing,UCD-0000377,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Transmembrane Transporters
+2214,D09,failing,UCD-0000377,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Transmembrane Transporters
+2215,D09,failing,UCD-0000377,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Transmembrane Transporters
+2216,D10,failing,UCD-0001002,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Neuronal Signaling
+2217,D10,failing,UCD-0001002,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Neuronal Signaling
+2218,D10,failing,UCD-0001002,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Neuronal Signaling
+2219,D10,failing,UCD-0001002,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Neuronal Signaling
+2220,D11,failing,UCD-0001657,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,GPCR & G Protein
+2221,D11,failing,UCD-0001657,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,GPCR & G Protein
+2222,D11,failing,UCD-0001657,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,GPCR & G Protein
+2223,D11,failing,UCD-0001657,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,GPCR & G Protein
+2228,E03,failing,UCD-0159418,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Transmembrane Transporters
+2229,E03,failing,UCD-0159418,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Transmembrane Transporters
+2230,E03,failing,UCD-0159418,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Transmembrane Transporters
+2231,E03,failing,UCD-0159418,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Transmembrane Transporters
+2232,E04,failing,UCD-0159426,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,DNA Damage
+2233,E04,failing,UCD-0159426,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,DNA Damage
+2234,E04,failing,UCD-0159426,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,DNA Damage
+2235,E04,failing,UCD-0159426,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,DNA Damage
+2240,E06,failing,UCD-0001924,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Epigenetics
+2241,E06,failing,UCD-0001924,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Epigenetics
+2242,E06,failing,UCD-0001924,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Epigenetics
+2243,E06,failing,UCD-0001924,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Epigenetics
+2244,E07,failing,UCD-0159439,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,PI3K/Akt/mTOR
+2245,E07,failing,UCD-0159439,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,PI3K/Akt/mTOR
+2246,E07,failing,UCD-0159439,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,PI3K/Akt/mTOR
+2247,E07,failing,UCD-0159439,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,PI3K/Akt/mTOR
+2252,E09,failing,UCD-0159395,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,GPCR & G Protein
+2253,E09,failing,UCD-0159395,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,GPCR & G Protein
+2254,E09,failing,UCD-0159395,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,GPCR & G Protein
+2255,E09,failing,UCD-0159395,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,GPCR & G Protein
+2256,E10,failing,UCD-0018456,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,GPCR & G Protein
+2257,E10,failing,UCD-0018456,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,GPCR & G Protein
+2258,E10,failing,UCD-0018456,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,GPCR & G Protein
+2259,E10,failing,UCD-0018456,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,GPCR & G Protein
+2264,F02,failing,UCD-0159404,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Metabolism
+2265,F02,failing,UCD-0159404,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Metabolism
+2266,F02,failing,UCD-0159404,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Metabolism
+2267,F02,failing,UCD-0159404,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Metabolism
+2268,F03,failing,UCD-0011098,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Neuronal Signaling
+2269,F03,failing,UCD-0011098,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Neuronal Signaling
+2270,F03,failing,UCD-0011098,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Neuronal Signaling
+2271,F03,failing,UCD-0011098,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Neuronal Signaling
+2272,F04,failing,UCD-0011250,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Endocrinology & Hormones
+2273,F04,failing,UCD-0011250,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Endocrinology & Hormones
+2274,F04,failing,UCD-0011250,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Endocrinology & Hormones
+2275,F04,failing,UCD-0011250,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Endocrinology & Hormones
+2276,F05,failing,UCD-0018183,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Proteases
+2277,F05,failing,UCD-0018183,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Proteases
+2278,F05,failing,UCD-0018183,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Proteases
+2279,F05,failing,UCD-0018183,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Proteases
+2280,F06,failing,UCD-0159419,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Protein Tyrosine Kinase
+2281,F06,failing,UCD-0159419,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Protein Tyrosine Kinase
+2282,F06,failing,UCD-0159419,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Protein Tyrosine Kinase
+2283,F06,failing,UCD-0159419,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Protein Tyrosine Kinase
+2284,F07,failing,UCD-0159427,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Apoptosis
+2285,F07,failing,UCD-0159427,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Apoptosis
+2286,F07,failing,UCD-0159427,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Apoptosis
+2287,F07,failing,UCD-0159427,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Apoptosis
+2288,F08,failing,UCD-0001966,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Others
+2289,F08,failing,UCD-0001966,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Others
+2290,F08,failing,UCD-0001966,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Others
+2291,F08,failing,UCD-0001966,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Others
+2292,F09,failing,UCD-0159440,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Protein Tyrosine Kinase
+2293,F09,failing,UCD-0159440,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Protein Tyrosine Kinase
+2294,F09,failing,UCD-0159440,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Protein Tyrosine Kinase
+2295,F09,failing,UCD-0159440,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Protein Tyrosine Kinase
+2296,F10,failing,UCD-0159396,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,DNA Damage
+2297,F10,failing,UCD-0159396,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,DNA Damage
+2298,F10,failing,UCD-0159396,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,DNA Damage
+2299,F10,failing,UCD-0159396,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,DNA Damage
+2300,F11,failing,UCD-0159401,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Angiogenesis
+2301,F11,failing,UCD-0159401,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Angiogenesis
+2302,F11,failing,UCD-0159401,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Angiogenesis
+2303,F11,failing,UCD-0159401,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Angiogenesis
+2304,G02,failing,UCD-0159405,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Metabolism
+2305,G02,failing,UCD-0159405,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Metabolism
+2306,G02,failing,UCD-0159405,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Metabolism
+2307,G02,failing,UCD-0159405,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Metabolism
+2308,G03,failing,UCD-0159664,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Epigenetics
+2309,G03,failing,UCD-0159664,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Epigenetics
+2310,G03,failing,UCD-0159664,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Epigenetics
+2311,G03,failing,UCD-0159664,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Epigenetics
+2312,G04,failing,UCD-0001098,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Neuronal Signaling
+2313,G04,failing,UCD-0001098,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Neuronal Signaling
+2314,G04,failing,UCD-0001098,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Neuronal Signaling
+2315,G04,failing,UCD-0001098,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Neuronal Signaling
+2316,G05,failing,UCD-0159412,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Apoptosis
+2317,G05,failing,UCD-0159412,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Apoptosis
+2318,G05,failing,UCD-0159412,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Apoptosis
+2319,G05,failing,UCD-0159412,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Apoptosis
+2320,G06,failing,UCD-0159420,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Angiogenesis
+2321,G06,failing,UCD-0159420,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Angiogenesis
+2322,G06,failing,UCD-0159420,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Angiogenesis
+2323,G06,failing,UCD-0159420,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Angiogenesis
+2324,G07,failing,UCD-0159428,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Apoptosis
+2325,G07,failing,UCD-0159428,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Apoptosis
+2326,G07,failing,UCD-0159428,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Apoptosis
+2327,G07,failing,UCD-0159428,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Apoptosis
+2328,G08,failing,UCD-0001925,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Epigenetics
+2329,G08,failing,UCD-0001925,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Epigenetics
+2330,G08,failing,UCD-0001925,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Epigenetics
+2331,G08,failing,UCD-0001925,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Epigenetics
+2332,G09,failing,UCD-0159441,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Metabolism
+2333,G09,failing,UCD-0159441,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Metabolism
+2334,G09,failing,UCD-0159441,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Metabolism
+2335,G09,failing,UCD-0159441,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Metabolism
+2336,G10,failing,UCD-0159397,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Others
+2337,G10,failing,UCD-0159397,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Others
+2338,G10,failing,UCD-0159397,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Others
+2339,G10,failing,UCD-0159397,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Others
+2340,G11,failing,UCD-0000918,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251220120001,6,Proteases
+2341,G11,failing,UCD-0000918,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251219170001,6,Proteases
+2342,G11,failing,UCD-0000918,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251229110001,6,Proteases
+2343,G11,failing,UCD-0000918,Target_Selective_Library_Screen_Plate_6_with_pathways,CARD-CelIns-CX7_251217190001,6,Proteases
+2348,B03,failing,UCD-0159478,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Cell Cycle
+2349,B03,failing,UCD-0159478,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Cell Cycle
+2350,B03,failing,UCD-0159478,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Cell Cycle
+2351,B03,failing,UCD-0159478,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Cell Cycle
+2352,B04,failing,UCD-0159486,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Others
+2353,B04,failing,UCD-0159486,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Others
+2354,B04,failing,UCD-0159486,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Others
+2355,B04,failing,UCD-0159486,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Others
+2360,B06,failing,UCD-0159494,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Protein Tyrosine Kinase
+2361,B06,failing,UCD-0159494,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Protein Tyrosine Kinase
+2362,B06,failing,UCD-0159494,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Protein Tyrosine Kinase
+2363,B06,failing,UCD-0159494,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Protein Tyrosine Kinase
+2364,B07,failing,UCD-0159502,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,MAPK
+2365,B07,failing,UCD-0159502,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,MAPK
+2366,B07,failing,UCD-0159502,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,MAPK
+2367,B07,failing,UCD-0159502,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,MAPK
+2372,B09,failing,UCD-0159510,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Autophagy
+2373,B09,failing,UCD-0159510,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Autophagy
+2374,B09,failing,UCD-0159510,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Autophagy
+2375,B09,failing,UCD-0159510,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Autophagy
+2376,B10,failing,UCD-0159518,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Proteases
+2377,B10,failing,UCD-0159518,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Proteases
+2378,B10,failing,UCD-0159518,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Proteases
+2379,B10,failing,UCD-0159518,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Proteases
+2384,C02,failing,UCD-0159448,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Ubiquitin
+2385,C02,failing,UCD-0159448,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Ubiquitin
+2386,C02,failing,UCD-0159448,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Ubiquitin
+2387,C02,failing,UCD-0159448,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Ubiquitin
+2388,C03,failing,UCD-0159456,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Apoptosis
+2389,C03,failing,UCD-0159456,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Apoptosis
+2390,C03,failing,UCD-0159456,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Apoptosis
+2391,C03,failing,UCD-0159456,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Apoptosis
+2392,C04,failing,UCD-0159463,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,GPCR & G Protein
+2393,C04,failing,UCD-0159463,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,GPCR & G Protein
+2394,C04,failing,UCD-0159463,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,GPCR & G Protein
+2395,C04,failing,UCD-0159463,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,GPCR & G Protein
+2396,C05,failing,UCD-0159471,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Proteases
+2397,C05,failing,UCD-0159471,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Proteases
+2398,C05,failing,UCD-0159471,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Proteases
+2399,C05,failing,UCD-0159471,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Proteases
+2400,C06,failing,UCD-0159479,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Transmembrane Transporters
+2401,C06,failing,UCD-0159479,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Transmembrane Transporters
+2402,C06,failing,UCD-0159479,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Transmembrane Transporters
+2403,C06,failing,UCD-0159479,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Transmembrane Transporters
+2404,C07,failing,UCD-0159487,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Others
+2405,C07,failing,UCD-0159487,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Others
+2406,C07,failing,UCD-0159487,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Others
+2407,C07,failing,UCD-0159487,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Others
+2408,C08,failing,UCD-0159495,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Apoptosis
+2409,C08,failing,UCD-0159495,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Apoptosis
+2410,C08,failing,UCD-0159495,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Apoptosis
+2411,C08,failing,UCD-0159495,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Apoptosis
+2412,C09,failing,UCD-0159503,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Cytoskeletal Signaling
+2413,C09,failing,UCD-0159503,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Cytoskeletal Signaling
+2414,C09,failing,UCD-0159503,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Cytoskeletal Signaling
+2415,C09,failing,UCD-0159503,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Cytoskeletal Signaling
+2416,C10,failing,UCD-0159511,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Autophagy
+2417,C10,failing,UCD-0159511,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Autophagy
+2418,C10,failing,UCD-0159511,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Autophagy
+2419,C10,failing,UCD-0159511,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Autophagy
+2420,C11,failing,UCD-0159519,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Stem Cells & Wnt
+2421,C11,failing,UCD-0159519,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Stem Cells & Wnt
+2422,C11,failing,UCD-0159519,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Stem Cells & Wnt
+2423,C11,failing,UCD-0159519,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Stem Cells & Wnt
+2424,D02,failing,UCD-0159449,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Ubiquitin
+2425,D02,failing,UCD-0159449,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Ubiquitin
+2426,D02,failing,UCD-0159449,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Ubiquitin
+2427,D02,failing,UCD-0159449,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Ubiquitin
+2428,D03,failing,UCD-0001927,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Epigenetics
+2429,D03,failing,UCD-0001927,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Epigenetics
+2430,D03,failing,UCD-0001927,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Epigenetics
+2431,D03,failing,UCD-0001927,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Epigenetics
+2432,D04,failing,UCD-0159464,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Metabolism
+2433,D04,failing,UCD-0159464,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Metabolism
+2434,D04,failing,UCD-0159464,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Metabolism
+2435,D04,failing,UCD-0159464,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Metabolism
+2436,D05,failing,UCD-0159472,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,TGF-beta/Smad
+2437,D05,failing,UCD-0159472,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,TGF-beta/Smad
+2438,D05,failing,UCD-0159472,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,TGF-beta/Smad
+2439,D05,failing,UCD-0159472,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,TGF-beta/Smad
+2440,D06,failing,UCD-0159480,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Transmembrane Transporters
+2441,D06,failing,UCD-0159480,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Transmembrane Transporters
+2442,D06,failing,UCD-0159480,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Transmembrane Transporters
+2443,D06,failing,UCD-0159480,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Transmembrane Transporters
+2444,D07,failing,UCD-0159488,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Epigenetics
+2445,D07,failing,UCD-0159488,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Epigenetics
+2446,D07,failing,UCD-0159488,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Epigenetics
+2447,D07,failing,UCD-0159488,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Epigenetics
+2448,D08,failing,UCD-0159496,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Others
+2449,D08,failing,UCD-0159496,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Others
+2450,D08,failing,UCD-0159496,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Others
+2451,D08,failing,UCD-0159496,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Others
+2452,D09,failing,UCD-0159504,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Others
+2453,D09,failing,UCD-0159504,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Others
+2454,D09,failing,UCD-0159504,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Others
+2455,D09,failing,UCD-0159504,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Others
+2456,D10,failing,UCD-0159512,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Others
+2457,D10,failing,UCD-0159512,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Others
+2458,D10,failing,UCD-0159512,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Others
+2459,D10,failing,UCD-0159512,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Others
+2460,D11,failing,UCD-0159520,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,NF-κB
+2461,D11,failing,UCD-0159520,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,NF-κB
+2462,D11,failing,UCD-0159520,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,NF-κB
+2463,D11,failing,UCD-0159520,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,NF-κB
+2468,E03,failing,UCD-0159450,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,DNA Damage
+2469,E03,failing,UCD-0159450,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,DNA Damage
+2470,E03,failing,UCD-0159450,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,DNA Damage
+2471,E03,failing,UCD-0159450,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,DNA Damage
+2472,E04,failing,UCD-0159457,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Proteases
+2473,E04,failing,UCD-0159457,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Proteases
+2474,E04,failing,UCD-0159457,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Proteases
+2475,E04,failing,UCD-0159457,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Proteases
+2480,E06,failing,UCD-0159465,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Metabolism
+2481,E06,failing,UCD-0159465,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Metabolism
+2482,E06,failing,UCD-0159465,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Metabolism
+2483,E06,failing,UCD-0159465,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Metabolism
+2484,E07,failing,UCD-0159473,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Epigenetics
+2485,E07,failing,UCD-0159473,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Epigenetics
+2486,E07,failing,UCD-0159473,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Epigenetics
+2487,E07,failing,UCD-0159473,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Epigenetics
+2492,E09,failing,UCD-0159481,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Cell Cycle
+2493,E09,failing,UCD-0159481,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Cell Cycle
+2494,E09,failing,UCD-0159481,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Cell Cycle
+2495,E09,failing,UCD-0159481,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Cell Cycle
+2496,E10,failing,UCD-0159489,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Epigenetics
+2497,E10,failing,UCD-0159489,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Epigenetics
+2498,E10,failing,UCD-0159489,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Epigenetics
+2499,E10,failing,UCD-0159489,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Epigenetics
+2504,F02,failing,UCD-0159497,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,PI3K/Akt/mTOR
+2505,F02,failing,UCD-0159497,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,PI3K/Akt/mTOR
+2506,F02,failing,UCD-0159497,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,PI3K/Akt/mTOR
+2507,F02,failing,UCD-0159497,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,PI3K/Akt/mTOR
+2508,F03,failing,UCD-0159505,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Others
+2509,F03,failing,UCD-0159505,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Others
+2510,F03,failing,UCD-0159505,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Others
+2511,F03,failing,UCD-0159505,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Others
+2512,F04,failing,UCD-0159513,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Others
+2513,F04,failing,UCD-0159513,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Others
+2514,F04,failing,UCD-0159513,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Others
+2515,F04,failing,UCD-0159513,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Others
+2516,F05,failing,UCD-0159521,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Others
+2517,F05,failing,UCD-0159521,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Others
+2518,F05,failing,UCD-0159521,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Others
+2519,F05,failing,UCD-0159521,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Others
+2520,F06,failing,UCD-0159522,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Proteases
+2521,F06,failing,UCD-0159522,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Proteases
+2522,F06,failing,UCD-0159522,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Proteases
+2523,F06,failing,UCD-0159522,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Proteases
+2524,F07,failing,UCD-0159529,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Cell Cycle
+2525,F07,failing,UCD-0159529,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Cell Cycle
+2526,F07,failing,UCD-0159529,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Cell Cycle
+2527,F07,failing,UCD-0159529,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Cell Cycle
+2528,F08,failing,UCD-0159537,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Others
+2529,F08,failing,UCD-0159537,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Others
+2530,F08,failing,UCD-0159537,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Others
+2531,F08,failing,UCD-0159537,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Others
+2532,F09,failing,UCD-0159545,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Autophagy
+2533,F09,failing,UCD-0159545,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Autophagy
+2534,F09,failing,UCD-0159545,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Autophagy
+2535,F09,failing,UCD-0159545,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Autophagy
+2536,F10,failing,UCD-0159552,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Protein Tyrosine Kinase
+2537,F10,failing,UCD-0159552,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Protein Tyrosine Kinase
+2538,F10,failing,UCD-0159552,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Protein Tyrosine Kinase
+2539,F10,failing,UCD-0159552,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Protein Tyrosine Kinase
+2540,F11,failing,UCD-0159560,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,DNA Damage
+2541,F11,failing,UCD-0159560,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,DNA Damage
+2542,F11,failing,UCD-0159560,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,DNA Damage
+2543,F11,failing,UCD-0159560,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,DNA Damage
+2544,G02,failing,UCD-0159568,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Others
+2545,G02,failing,UCD-0159568,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Others
+2546,G02,failing,UCD-0159568,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Others
+2547,G02,failing,UCD-0159568,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Others
+2548,G03,failing,UCD-0159576,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Others
+2549,G03,failing,UCD-0159576,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Others
+2550,G03,failing,UCD-0159576,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Others
+2551,G03,failing,UCD-0159576,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Others
+2552,G04,failing,UCD-0159584,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Cell Cycle
+2553,G04,failing,UCD-0159584,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Cell Cycle
+2554,G04,failing,UCD-0159584,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Cell Cycle
+2555,G04,failing,UCD-0159584,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Cell Cycle
+2556,G05,failing,UCD-0159591,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Epigenetics
+2557,G05,failing,UCD-0159591,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Epigenetics
+2558,G05,failing,UCD-0159591,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Epigenetics
+2559,G05,failing,UCD-0159591,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Epigenetics
+2560,G06,failing,UCD-0159523,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Proteases
+2561,G06,failing,UCD-0159523,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Proteases
+2562,G06,failing,UCD-0159523,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Proteases
+2563,G06,failing,UCD-0159523,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Proteases
+2564,G07,failing,UCD-0159530,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Proteases
+2565,G07,failing,UCD-0159530,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Proteases
+2566,G07,failing,UCD-0159530,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Proteases
+2567,G07,failing,UCD-0159530,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Proteases
+2568,G08,failing,UCD-0159538,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,MAPK
+2569,G08,failing,UCD-0159538,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,MAPK
+2570,G08,failing,UCD-0159538,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,MAPK
+2571,G08,failing,UCD-0159538,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,MAPK
+2572,G09,failing,UCD-0159546,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,Ubiquitin
+2573,G09,failing,UCD-0159546,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,Ubiquitin
+2574,G09,failing,UCD-0159546,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,Ubiquitin
+2575,G09,failing,UCD-0159546,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,Ubiquitin
+2576,G10,failing,UCD-0159553,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,PI3K/Akt/mTOR
+2577,G10,failing,UCD-0159553,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,PI3K/Akt/mTOR
+2578,G10,failing,UCD-0159553,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,PI3K/Akt/mTOR
+2579,G10,failing,UCD-0159553,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,PI3K/Akt/mTOR
+2580,G11,failing,UCD-0159561,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103180001,8,JAK/STAT
+2581,G11,failing,UCD-0159561,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260105100001,8,JAK/STAT
+2582,G11,failing,UCD-0159561,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260103110001,8,JAK/STAT
+2583,G11,failing,UCD-0159561,Target_Selective_Library_Screen_Plate_8_with_pathways,CARD-CelIns-CX7_260104140001,8,JAK/STAT
diff --git a/3a.train_dmso_logistic_regression_models/train_platemapwise_model/map_scores.csv b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/map_scores.csv
new file mode 100644
index 0000000..8da3df3
--- /dev/null
+++ b/3a.train_dmso_logistic_regression_models/train_platemapwise_model/map_scores.csv
@@ -0,0 +1,452 @@
+Metadata_treatment,Metadata_Pathway,mean_average_precision,mean_normalized_average_precision,corrected_p_value,-log10(p-value)
+UCD-0159634,Cytoskeletal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0000972,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0000936,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0000878,Transmembrane Transporters,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0000872,Metabolism,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0000849,Endocrinology & Hormones,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0000757,Metabolism,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0000730,Metabolism,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159619,GPCR & G Protein,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159607,Metabolism,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159521,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159523,Proteases,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159504,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159519,Stem Cells & Wnt,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001101,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001305,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001463,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001441,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001086,Endocrinology & Hormones,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159435,Protein Tyrosine Kinase,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001016,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001078,Metabolism,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001804,MAPK,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001808,MAPK,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001812,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159439,PI3K/Akt/mTOR,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159433,Protein Tyrosine Kinase,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159432,Angiogenesis,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159416,Metabolism,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159419,Protein Tyrosine Kinase,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001802,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001810,DNA Damage,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159394,TGF-beta/Smad,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001657,GPCR & G Protein,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001733,Protein Tyrosine Kinase,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001759,Angiogenesis,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001766,Angiogenesis,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001801,Endocrinology & Hormones,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0000083,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159301,Cell Cycle,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159300,GPCR & G Protein,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159296,Metabolism,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159295,Microbiology,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159265,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159386,DNA Damage,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159392,Angiogenesis,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001885,PI3K/Akt/mTOR,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001869,Protein Tyrosine Kinase,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159383,NF-κB,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159381,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001861,Angiogenesis,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001843,DNA Damage,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001844,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001850,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159375,NF-κB,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159378,Protein Tyrosine Kinase,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159377,Metabolism,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001842,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001835,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001822,MAPK,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001818,MAPK,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001816,Metabolism,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159370,PI3K/Akt/mTOR,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159371,Apoptosis,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159369,Epigenetics,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159376,Transmembrane Transporters,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001924,Epigenetics,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001925,Epigenetics,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0013217,Epigenetics,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0017947,Apoptosis,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0018448,GPCR & G Protein,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0018439,Microbiology,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0018207,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0018179,MAPK,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001921,Epigenetics,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001915,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0001900,PI3K/Akt/mTOR,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159361,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159358,Metabolism,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159357,Apoptosis,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159356,Cytoskeletal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159355,PI3K/Akt/mTOR,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159352,Proteases,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159350,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159347,Metabolism,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159346,Stem Cells & Wnt,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159344,Endocrinology & Hormones,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159339,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159335,JAK/STAT,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159334,Metabolism,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0018131,Angiogenesis,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0018089,Endocrinology & Hormones,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0018083,JAK/STAT,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0018012,Apoptosis,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159332,Apoptosis,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159333,Transmembrane Transporters,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159331,Transmembrane Transporters,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159324,Angiogenesis,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159325,PI3K/Akt/mTOR,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159326,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159327,DNA Damage,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159330,Protein Tyrosine Kinase,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0000253,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159314,PI3K/Akt/mTOR,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159313,Protein Tyrosine Kinase,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159311,GPCR & G Protein,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159308,Protein Tyrosine Kinase,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159294,Cell Cycle,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159263,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159262,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159261,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159259,PI3K/Akt/mTOR,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159258,Angiogenesis,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159256,Apoptosis,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0018550,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0018540,MAPK,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0018516,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0018483,Microbiology,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159287,Endocrinology & Hormones,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159285,DNA Damage,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159283,Endocrinology & Hormones,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159282,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159278,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159279,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159275,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159274,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159266,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159271,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159269,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159268,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159304,Cell Cycle,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159306,Cell Cycle,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159303,Metabolism,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159302,PI3K/Akt/mTOR,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0000533,GPCR & G Protein,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159310,GPCR & G Protein,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159307,GPCR & G Protein,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159321,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159319,Neuronal Signaling,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159320,Metabolism,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159293,Others,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159292,DNA Damage,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159288,Metabolism,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159323,Stem Cells & Wnt,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159322,Endocrinology & Hormones,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159315,PI3K/Akt/mTOR,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159317,Angiogenesis,1.0,1.0,0.0027641070585855865,2.5584451400053156
+UCD-0159395,GPCR & G Protein,0.9791666666666666,0.9711814006417597,0.0027641070585855865,2.5584451400053156
+UCD-0001886,Cell Cycle,0.9791666666666666,0.9711814006417597,0.0027641070585855865,2.5584451400053156
+UCD-0000463,Others,0.9791666666666666,0.9711814006417597,0.0027641070585855865,2.5584451400053156
+UCD-0159291,GPCR & G Protein,0.9791666666666666,0.9711814006417597,0.0027641070585855865,2.5584451400053156
+UCD-0001829,PI3K/Akt/mTOR,0.9791666666666666,0.9711814006417597,0.0027641070585855865,2.5584451400053156
+UCD-0001616,Metabolism,0.9791666666666666,0.9711814006417597,0.0027641070585855865,2.5584451400053156
+UCD-0000469,Neuronal Signaling,0.9666666666666667,0.9538902410268156,0.0027641070585855865,2.5584451400053156
+UCD-0159538,MAPK,0.9583333333333334,0.9423628012835196,0.0027641070585855865,2.5584451400053156
+UCD-0159312,Transmembrane Transporters,0.9583333333333333,0.9423628012835195,0.0027641070585855865,2.5584451400053156
+UCD-0000786,GPCR & G Protein,0.9458333333333333,0.9250716416685754,0.0027641070585855865,2.5584451400053156
+UCD-0159421,Apoptosis,0.9458333333333333,0.9250716416685754,0.0027641070585855865,2.5584451400053156
+UCD-0159284,Stem Cells & Wnt,0.9458333333333333,0.9250716416685754,0.0027641070585855865,2.5584451400053156
+UCD-0159309,Angiogenesis,0.9333333333333333,0.9077804820536313,0.0027641070585855865,2.5584451400053156
+UCD-0159488,Epigenetics,0.9305555555555555,0.9039380021391992,0.0027641070585855865,2.5584451400053156
+UCD-0159342,Others,0.9305555555555555,0.9039380021391992,0.0027641070585855865,2.5584451400053156
+UCD-0000377,Transmembrane Transporters,0.9125,0.8789618826953911,0.010298273155416014,1.98723559295969
+UCD-0159473,Epigenetics,0.9097222222222222,0.875119402780959,0.010298273155416014,1.98723559295969
+UCD-0159472,TGF-beta/Smad,0.9097222222222222,0.875119402780959,0.010298273155416014,1.98723559295969
+UCD-0000101,Endocrinology & Hormones,0.9047619047619049,0.8682578315051876,0.010298273155416014,1.98723559295969
+UCD-0159297,Microbiology,0.9041666666666667,0.8674344429520949,0.010298273155416014,1.98723559295969
+UCD-0159396,DNA Damage,0.8982142857142857,0.8592005574211692,0.010298273155416014,1.98723559295969
+UCD-0000841,Neuronal Signaling,0.8958333333333334,0.8559070032087989,0.010298273155416014,1.98723559295969
+UCD-0159406,Neuronal Signaling,0.8898809523809524,0.8476731176778731,0.010298273155416014,1.98723559295969
+DMSO_healthy,,0.8868449920449882,0.7561563553652189,0.0027641070585855865,2.5584451400053156
+UCD-0159513,Others,0.8625,0.8097972442356145,0.010298273155416014,1.98723559295969
+UCD-0159257,DNA Damage,0.8506944444444444,0.7934667045992784,0.010298273155416014,1.98723559295969
+UCD-0159411,Neuronal Signaling,0.8472222222222222,0.7886636047062383,0.010298273155416014,1.98723559295969
+UCD-0159316,Proteases,0.8347222222222223,0.7713724450912942,0.010298273155416014,1.98723559295969
+UCD-0159484,Epigenetics,0.8301587301587301,0.7650597995175845,0.012515262515262516,1.9025600363686452
+UCD-0159443,Others,0.8291666666666666,0.7636874852624301,0.012515262515262516,1.9025600363686452
+UCD-0159524,Others,0.8263888888888888,0.7598450053479981,0.012515262515262516,1.9025600363686452
+UCD-0159393,GPCR & G Protein,0.8194444444444444,0.750238805561918,0.012515262515262516,1.9025600363686452
+UCD-0159425,Apoptosis,0.8194444444444443,0.7619337656139803,0.01948321948321948,1.7103392770124284
+UCD-0000324,Metabolism,0.8138888888888889,0.7425538457330539,0.012515262515262516,1.9025600363686452
+UCD-0000490,Neuronal Signaling,0.8013888888888889,0.7252626861181098,0.01948321948321948,1.7103392770124284
+UCD-0159380,GPCR & G Protein,0.7958333333333333,0.7175777262892458,0.01948321948321948,1.7103392770124284
+UCD-0159567,Others,0.7930555555555555,0.7137352463748137,0.01948321948321948,1.7103392770124284
+UCD-0000421,Neuronal Signaling,0.7895833333333333,0.7089321464817737,0.021684198154786394,1.663856632698532
+UCD-0159506,NF-κB,0.7822420634920635,0.6987770209936319,0.021684198154786394,1.663856632698532
+UCD-0159546,Ubiquitin,0.7775974025974026,0.6923520951626823,0.025947874115413383,1.5858982176908607
+UCD-0159345,Neuronal Signaling,0.7756944444444445,0.6897197469096136,0.025947874115413383,1.5858982176908607
+UCD-0159410,Neuronal Signaling,0.7729166666666667,0.6858772669951816,0.025947874115413383,1.5858982176908607
+UCD-0159442,Epigenetics,0.7683531746031746,0.6795646214214719,0.025947874115413383,1.5858982176908607
+UCD-0159522,Proteases,0.7652777777777778,0.6753104472304935,0.0303478904515174,1.5178714923022951
+UCD-0159401,Angiogenesis,0.7638888888888888,0.688682616572128,0.01948321948321948,1.7103392770124284
+UCD-0159585,Protein Tyrosine Kinase,0.7579365079365079,0.6651553217423516,0.0303478904515174,1.5178714923022951
+UCD-0159434,Angiogenesis,0.7544642857142857,0.6603522218493116,0.03948113741928175,1.4036103441533103
+UCD-0000721,Neuronal Signaling,0.75,0.6865885136094606,0.10601163542340014,0.9746464656516696
+UCD-0159597,Epigenetics,0.75,0.6865885136094606,0.10601163542340014,0.9746464656516696
+UCD-0159613,PI3K/Akt/mTOR,0.75,0.6865885136094606,0.10601163542340014,0.9746464656516696
+UCD-0159559,DNA Damage,0.7483796296296297,0.6519353610843652,0.040347711989503036,1.394181087918541
+UCD-0011098,Neuronal Signaling,0.7482142857142857,0.6517066420418396,0.040347711989503036,1.394181087918541
+UCD-0159518,Proteases,0.7458333333333333,0.6484130878294693,0.040347711989503036,1.394181087918541
+UCD-0159510,Autophagy,0.7458333333333333,0.6484130878294693,0.040347711989503036,1.394181087918541
+UCD-0159486,Others,0.7458333333333333,0.6484130878294693,0.040347711989503036,1.394181087918541
+UCD-0159553,PI3K/Akt/mTOR,0.7343253968253969,0.6324942424696794,0.040347711989503036,1.394181087918541
+UCD-0000904,GPCR & G Protein,0.726010101010101,0.6209917538946588,0.040347711989503036,1.394181087918541
+UCD-0000243,Neuronal Signaling,0.7244075369075369,0.6187749385594095,0.041758241758241756,1.3792577957042835
+UCD-0159411B,Neuronal Signaling,0.7241161616161615,0.6183718812257278,0.041758241758241756,1.3792577957042835
+UCD-0001419,Transmembrane Transporters,0.7236531986531987,0.6177314679066559,0.041758241758241756,1.3792577957042835
+UCD-0159365,TGF-beta/Smad,0.7232142857142857,0.6171243228119513,0.041758241758241756,1.3792577957042835
+UCD-0001040,Endocrinology & Hormones,0.7141955266955267,0.604648738674185,0.044841414509660955,1.3483206961651315
+UCD-0159428,Apoptosis,0.7134920634920635,0.6036756431114392,0.044841414509660955,1.3483206961651315
+UCD-0159543,MAPK,0.7124999999999999,0.6023033288562849,0.044841414509660955,1.3483206961651315
+UCD-0159651,Angiogenesis,0.7118055555555555,0.6013427088776768,0.044841414509660955,1.3483206961651315
+UCD-0159479,Transmembrane Transporters,0.7090277777777777,0.5975002289632448,0.044841414509660955,1.3483206961651315
+UCD-0159471,Proteases,0.7013888888888888,0.5869334091985567,0.044841414509660955,1.3483206961651315
+UCD-0159398,Protein Tyrosine Kinase,0.693560606060606,0.5761046021669756,0.04892844711849236,1.3104385675748629
+UCD-0159403,GPCR & G Protein,0.6926767676767677,0.5748819949214745,0.04892844711849236,1.3104385675748629
+UCD-0001059,Epigenetics,0.6902777777777778,0.5715634895408286,0.04892844711849236,1.3104385675748629
+UCD-0159495,Apoptosis,0.6898989898989898,0.5710395150070423,0.04892844711849236,1.3104385675748629
+UCD-0000918,Proteases,0.6834415584415584,0.5621069967644017,0.04892844711849236,1.3104385675748629
+UCD-0159456,Apoptosis,0.6833333333333332,0.5619572897547486,0.04892844711849236,1.3104385675748629
+UCD-0159588,MAPK,0.6813519813519813,0.559216499885713,0.04892844711849236,1.3104385675748629
+UCD-0159431,PI3K/Akt/mTOR,0.681060606060606,0.5588134425520314,0.04892844711849236,1.3104385675748629
+UCD-0159561,JAK/STAT,0.6805555555555555,0.5581148098403165,0.04892844711849236,1.3104385675748629
+UCD-0159487,Others,0.6805555555555555,0.5581148098403165,0.04892844711849236,1.3104385675748629
+UCD-0159354,Metabolism,0.6801587301587301,0.5575658841382548,0.04961998354068839,1.3043433841224432
+UCD-0159492,Cell Cycle,0.6777777777777778,0.5542723299258845,0.04961998354068839,1.3043433841224432
+UCD-0159364,Others,0.6746031746031745,0.5498809243093907,0.04961998354068839,1.3043433841224432
+UCD-0159481,Cell Cycle,0.6727272727272726,0.5472860028087353,0.04961998354068839,1.3043433841224432
+UCD-0017999,Endocrinology & Hormones,0.6715277777777777,0.5456267501184124,0.04961998354068839,1.3043433841224432
+UCD-0159390,Neuronal Signaling,0.6713383838383837,0.5453647628515192,0.04961998354068839,1.3043433841224432
+UCD-0011696,JAK/STAT,0.6649305555555556,0.5365008603216365,0.050275904353157576,1.298640108656559
+UCD-0159423,PI3K/Akt/mTOR,0.6643939393939393,0.5357585630654392,0.050275904353157576,1.298640108656559
+UCD-0159529,Cell Cycle,0.6620370370370371,0.5543888433287324,0.06127472527472528,1.2127186272311405
+UCD-0001927,Epigenetics,0.6602272727272727,0.5299948431937912,0.050275904353157576,1.298640108656559
+UCD-0001391,Metabolism,0.6583333333333333,0.5273749705248603,0.050275904353157576,1.298640108656559
+UCD-0159568,Others,0.6472222222222223,0.5120050508671322,0.050275904353157576,1.298640108656559
+UCD-0159497,PI3K/Akt/mTOR,0.6472222222222221,0.5120050508671321,0.050275904353157576,1.298640108656559
+UCD-0159426,DNA Damage,0.6384920634920634,0.4999286854217744,0.05256410256410257,1.279310745970745
+UCD-0159630,Transmembrane Transporters,0.6380050505050504,0.49925500387833494,0.05256410256410257,1.279310745970745
+UCD-0000684,Others,0.6372204184704184,0.4981696280583492,0.05256410256410257,1.279310745970745
+UCD-0018311,Neuronal Signaling,0.6343344155844156,0.4941774411342641,0.05256410256410257,1.279310745970745
+UCD-0159586,Angiogenesis,0.6340277777777777,0.49375327127358004,0.05256410256410257,1.279310745970745
+UCD-0159533,Cytoskeletal Signaling,0.6333333333333333,0.4927926512949721,0.05256410256410257,1.279310745970745
+UCD-0159351,Others,0.6296717171717171,0.48772756413503887,0.05256410256410257,1.279310745970745
+UCD-0159576,Others,0.6268939393939393,0.48388508422060683,0.05472260532989278,1.2618332341413614
+UCD-0159476,Metabolism,0.6265873015873016,0.4834609143599228,0.05472260532989278,1.2618332341413614
+UCD-0159465,Metabolism,0.625,0.4812652115516759,0.05472260532989278,1.2618332341413614
+UCD-0159385,Metabolism,0.6248704998704998,0.4810860749589285,0.05472260532989278,1.2618332341413614
+UCD-0159560,DNA Damage,0.6214285714285714,0.4763248802331205,0.05472260532989278,1.2618332341413614
+UCD-0159464,Metabolism,0.6201388888888889,0.47454087170141984,0.05472260532989278,1.2618332341413614
+UCD-0159459,Cytoskeletal Signaling,0.6199074074074074,0.47422066504188387,0.05472260532989278,1.2618332341413614
+UCD-0159362,Stem Cells & Wnt,0.6158267400527462,0.46857590739668886,0.05631868131868131,1.2493475225933017
+UCD-0159457,Proteases,0.6132034632034632,0.46494714749947763,0.05790193742000971,1.2373069043771885
+UCD-0159429,Stem Cells & Wnt,0.6076388888888888,0.45724971208647575,0.06775179707510534,1.1690791809044627
+UCD-0159420,Angiogenesis,0.6073006854256854,0.45678187768130946,0.06775179707510534,1.1690791809044627
+UCD-0001002,Neuronal Signaling,0.6067550505050504,0.45602710484097453,0.06775179707510534,1.1690791809044627
+UCD-0159659,Metabolism,0.6066405816405815,0.4558687608884568,0.06775179707510534,1.1690791809044627
+UCD-0001186,Neuronal Signaling,0.6030753968253968,0.45093706651276605,0.06775179707510534,1.1690791809044627
+UCD-0159417,GPCR & G Protein,0.6027777777777777,0.4762542843507566,0.09106850596212299,1.0406317882482279
+UCD-0159569,Protein Tyrosine Kinase,0.6018939393939394,0.4493027649907186,0.06775179707510534,1.1690791809044627
+UCD-0159359,Others,0.600297619047619,0.4470945865983339,0.06775179707510534,1.1690791809044627
+UCD-0018051,Metabolism,0.6001984126984127,0.4469573551728184,0.06775179707510534,1.1690791809044627
+UCD-0159517,Proteases,0.6000945691735166,0.44681370917570196,0.06775179707510534,1.1690791809044627
+UCD-0159544,Angiogenesis,0.6,0.498541621775137,0.10601163542340014,0.9746464656516696
+UCD-0159353,Proteases,0.5996212121212121,0.4461589177880015,0.06775179707510534,1.1690791809044627
+UCD-0159516,Proteases,0.5954365079365079,0.4403702467480779,0.06775179707510534,1.1690791809044627
+UCD-0159458,Others,0.5949735449735449,0.43972983342900596,0.06775179707510534,1.1690791809044627
+UCD-0159399,Apoptosis,0.5944444444444444,0.4652666119944788,0.09643339117023327,1.0157725605991013
+UCD-0159402,GPCR & G Protein,0.5940656565656566,0.4384739579591374,0.06775179707510534,1.1690791809044627
+UCD-0001435,Others,0.5938341750841751,0.43815375129960143,0.06775179707510534,1.1690791809044627
+UCD-0159477,Metabolism,0.5926346801346801,0.4364944986092785,0.06775179707510534,1.1690791809044627
+UCD-0159593,Epigenetics,0.5916666666666667,0.43515545257849153,0.06775179707510534,1.1690791809044627
+UCD-0159367,Neuronal Signaling,0.5871212121212122,0.42886775817305733,0.07256039840309503,1.1393003413863467
+UCD-0159409,Others,0.5829365079365079,0.42307908713313375,0.07315194031061191,1.1357741499895764
+UCD-0159636,Protein Tyrosine Kinase,0.5817185007974481,0.4213942266648438,0.07315194031061191,1.1357741499895764
+UCD-0001098,Neuronal Signaling,0.5814574314574315,0.4210330913345401,0.07315194031061191,1.1357741499895764
+UCD-0001653,Transmembrane Transporters,0.5796717171717172,0.4185629256752623,0.07315194031061191,1.1357741499895764
+UCD-0159447,Ubiquitin,0.5747113997113997,0.4117013543994909,0.07756712152316547,1.1103223247063967
+UCD-0018458,Proteases,0.5731150793650793,0.4094931760071062,0.07756712152316547,1.1103223247063967
+UCD-0000295,Metabolism,0.570085572107631,0.4053024805236525,0.07807355099773873,1.1074960672902194
+UCD-0159584,Cell Cycle,0.5692460317460317,0.4041411504120045,0.07807355099773873,1.1074960672902194
+UCD-0159515,Epigenetics,0.569047619047619,0.40386668756097366,0.07807355099773873,1.1074960672902194
+UCD-0159455,TGF-beta/Smad,0.56627886002886,0.4000366832306793,0.07807355099773873,1.1074960672902194
+UCD-0159387,Neuronal Signaling,0.5594907407407407,0.3906467269029872,0.0807436291307259,1.0928917345389368
+UCD-0159466,Cell Cycle,0.5588624338624338,0.3897775945413895,0.0807436291307259,1.0928917345389368
+UCD-0159478,Cell Cycle,0.5578703703703703,0.4170429388752595,0.1565934065934066,0.8052265279765646
+UCD-0159501,Cell Cycle,0.5572180134680135,0.3875028797002701,0.0897892143443745,1.0467758285002375
+UCD-0159514,Others,0.5564213564213565,0.3864008697681008,0.0897892143443745,1.0467758285002375
+UCD-0159379,PI3K/Akt/mTOR,0.5553751803751803,0.38495370200811985,0.09518650363720786,1.021424625264752
+UCD-0001944,Epigenetics,0.5536706349206348,0.382595816606082,0.09518650363720786,1.021424625264752
+UCD-0159565,TGF-beta/Smad,0.5525462962962963,0.38104052711690706,0.09978611992034811,1.0009298641357447
+UCD-0011250,Endocrinology & Hormones,0.5510010822510822,0.3789030437013031,0.09978611992034811,1.0009298641357447
+UCD-0159549,JAK/STAT,0.5507996632996632,0.3786244223222264,0.09978611992034811,1.0009298641357447
+UCD-0159563,Angiogenesis,0.5505050505050505,0.37821688657372604,0.09978611992034811,1.0009298641357447
+UCD-0159493,Epigenetics,0.55,0.3775182538620111,0.09978611992034811,1.0009298641357447
+UCD-0159581,PI3K/Akt/mTOR,0.5484217171717172,0.37533502663790197,0.09978611992034811,1.0009298641357447
+UCD-0159530,Proteases,0.5431394993894993,0.36802817297444407,0.09978611992034811,1.0009298641357447
+UCD-0159503,Cytoskeletal Signaling,0.5402777777777777,0.364069574161499,0.09978611992034811,1.0009298641357447
+UCD-0159574,Epigenetics,0.5393065268065268,0.36272604971589334,0.09978611992034811,1.0009298641357447
+UCD-0159418,Transmembrane Transporters,0.5387896825396825,0.3620111027787676,0.09978611992034811,1.0009298641357447
+UCD-0159631,Cell Cycle,0.5359725922225922,0.3581142421129657,0.09978611992034811,1.0009298641357447
+UCD-0159591,Epigenetics,0.5343434343434343,0.3558606397988488,0.09978611992034811,1.0009298641357447
+UCD-0159573,Protein Tyrosine Kinase,0.5343253968253968,0.35583568863057324,0.09978611992034811,1.0009298641357447
+UCD-0159462,GPCR & G Protein,0.5324494949494949,0.35324076712991787,0.10246609577713257,0.9894198112195514
+UCD-0159413,NF-κB,0.5314484126984127,0.35185597729062573,0.10477894198824433,0.9797259911809445
+UCD-0018410,Metabolism,0.5282828282828282,0.3474770472582697,0.10477894198824433,0.9797259911809445
+UCD-0159571,Others,0.5249999999999999,0.3429359346321228,0.10713390843684004,0.9700730509580887
+UCD-0159474,DNA Damage,0.5204545454545454,0.3366482402266886,0.10824889396317966,0.9655765323708263
+UCD-0001856,Angiogenesis,0.5184523809523809,0.33387866054810444,0.10900389932647997,0.9625579660439307
+UCD-0018527,Microbiology,0.5176587301587301,0.33278080914398106,0.10900389932647997,0.9625579660439307
+UCD-0159566,DNA Damage,0.5157407407407407,0.3614941508518548,0.16247086247086248,0.7892245140867148
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+UCD-0159338,Neuronal Signaling,0.5144841269841269,0.32838940352748736,0.11263736263736264,0.9483175269293205
+UCD-0159468,Apoptosis,0.5134259259259258,0.35844201964177763,0.16247086247086248,0.7892245140867148
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+UCD-0159373,Angiogenesis,0.5095238095238095,0.3215278322517159,0.11765941065304122,0.9293733312908563
+UCD-0159422,Apoptosis,0.5069444444444444,0.3179598151883147,0.12080978957950567,0.9178978721048169
+UCD-0159637,Others,0.5056547619047619,0.31617580665661404,0.12080978957950567,0.9178978721048169
+UCD-0159514B,Others,0.5055555555555555,0.31603857523109863,0.12080978957950567,0.9178978721048169
+UCD-0159389,Protein Tyrosine Kinase,0.5037698412698413,0.31356840957182097,0.12287712287712288,0.9105289660399207
+UCD-0001863,Angiogenesis,0.5004629629629629,0.30899402872130655,0.12287712287712288,0.9105289660399207
+UCD-0159480,Transmembrane Transporters,0.5,0.37317702721892126,0.10601163542340014,0.9746464656516696
+UCD-0159579,PI3K/Akt/mTOR,0.4996212121212121,0.30782964086844833,0.128125,0.8923661216001706
+UCD-0159540,Others,0.49374999999999997,0.2997080355947625,0.12912943754065248,0.888974740660675
+UCD-0159604,Autophagy,0.4873737373737373,0.32409167020472723,0.1661318032999449,0.7795472207963838
+UCD-0001953,Epigenetics,0.4873737373737373,0.32409167020472723,0.1661318032999449,0.7795472207963838
+UCD-0159626,Neuronal Signaling,0.4873737373737373,0.32409167020472723,0.1661318032999449,0.7795472207963838
+UCD-0159299,Metabolism,0.48716583124477864,0.29060020256450164,0.14272063340386323,0.8455132355352715
+UCD-0018304,Endocrinology & Hormones,0.4813291910331383,0.2825264203633438,0.14646344367706596,0.834270758862523
+UCD-0159349,Metabolism,0.47962962962962963,0.2801754293630664,0.14740198073531408,0.8314966805432
+UCD-0000576,Neuronal Signaling,0.47885584058184677,0.27910505256838564,0.14833474218089604,0.8287571188950228
+UCD-0159594,Others,0.47777777777777775,0.27761377608677834,0.15202588822220725,0.8180824505110721
+UCD-0159449,Ubiquitin,0.4763979076479076,0.2757050117137001,0.155694458446752,0.8077268447782245
+UCD-0000201,GPCR & G Protein,0.4674603174603174,0.2978354141845309,0.1661318032999449,0.7795472207963838
+UCD-0159483,Endocrinology & Hormones,0.4667522170811645,0.26236219769882674,0.1661318032999449,0.7795472207963838
+UCD-0159654,Epigenetics,0.4663267288267288,0.26177362287308825,0.1661318032999449,0.7795472207963838
+UCD-0159556,GPCR & G Protein,0.4657828282828283,0.26102124918354913,0.17226890756302518,0.7637931003367765
+UCD-0159505,Others,0.46388888888888885,0.25840137651461814,0.17257888310519892,0.7630123460017288
+UCD-0159273,Others,0.4638220439691028,0.25830891042042065,0.17257888310519892,0.7630123460017288
+UCD-0159627,Metabolism,0.4624458874458875,0.29122378449828584,0.1661318032999449,0.7795472207963838
+UCD-0159500,Epigenetics,0.4602843915343915,0.2534153013875576,0.17470284817223594,0.7577000146770427
+UCD-0159612,Cell Cycle,0.4572272498743087,0.24918637943475858,0.17969891380296002,0.7454545479928982
+UCD-0159602,Angiogenesis,0.4572089947089947,0.24916112719657924,0.17969891380296002,0.7454545479928982
+UCD-0159592,Epigenetics,0.4568783068783069,0.2838828143670352,0.1661318032999449,0.7795472207963838
+UCD-0159438,Cytoskeletal Signaling,0.4553751803751804,0.2466244250885668,0.18355718355718353,0.7362286146021204
+UCD-0159648,GPCR & G Protein,0.4537906537906538,0.2444325609215931,0.18355718355718353,0.7362286146021204
+UCD-0159663,Protein Tyrosine Kinase,0.4537906537906538,0.2444325609215931,0.18355718355718353,0.7362286146021204
+UCD-0159641,JAK/STAT,0.4537906537906538,0.2444325609215931,0.18355718355718353,0.7362286146021204
+UCD-0159655,Epigenetics,0.4537906537906538,0.2444325609215931,0.18355718355718353,0.7362286146021204
+UCD-0018183,Proteases,0.45188492063492064,0.24179637402725124,0.18687562437562438,0.728447343295052
+UCD-0159639,Protein Tyrosine Kinase,0.4497414622414622,0.23883134353050875,0.19472899981374556,0.7105693668095472
+UCD-0159404,Metabolism,0.4472402597402597,0.23537144819630154,0.20560284785636898,0.6869708741138213
+UCD-0159470,MAPK,0.4451388888888889,0.232464637092202,0.21008766514384491,0.6775994455341782
+UCD-0159558,Cytoskeletal Signaling,0.44076756576756576,0.2264178174266585,0.2189821351832525,0.6595913139626328
+UCD-0159397,Others,0.4399801587301587,0.2253286029653996,0.2189821351832525,0.6595913139626328
+UCD-0159407,NF-κB,0.4396762959262959,0.22490827174598876,0.21905166656547875,0.659453438320374
+UCD-0159509,Others,0.4379477004477004,0.22251711811958355,0.21905166656547875,0.659453438320374
+UCD-0159658,Autophagy,0.4377667471417472,0.22226680671989638,0.21905166656547875,0.659453438320374
+UCD-0022022,Others,0.4376082251082251,0.22204752433716712,0.21905166656547875,0.659453438320374
+UCD-0159496,Others,0.43646214896214897,0.22046216549135247,0.2209305845669482,0.6557441583285767
+UCD-0001957,Epigenetics,0.43311965811965814,0.21583852207783263,0.22834386322124745,0.6414106556355311
+UCD-0159539,Proteases,0.43125901875901873,0.2132647131041796,0.22834386322124745,0.6414106556355311
+UCD-0159622,JAK/STAT,0.4266624579124579,0.2069063237219647,0.2326206402293359,0.6333517533220467
+UCD-0159600,Epigenetics,0.4253968253968254,0.24237382990998566,0.17812420138001536,0.7492770698026706
+UCD-0001888,PI3K/Akt/mTOR,0.4232142857142857,0.20213649205329204,0.23809523809523808,0.6232492903979004
+UCD-0159542,MAPK,0.42200854700854706,0.20046860242010456,0.23866943866943865,0.622203188311877
+UCD-0159582,Neuronal Signaling,0.4171717171717172,0.19377785068098857,0.24158147482544268,0.6169363717460645
+UCD-0159527,DNA Damage,0.4167896917896917,0.1932493977323837,0.24158147482544268,0.6169363717460645
+UCD-0159578,PI3K/Akt/mTOR,0.41666666666666663,0.26870653175540815,0.16606354370704823,0.7797256987023287
+UCD-0159444,MAPK,0.4112193362193362,0.18554396515006283,0.24334489040371393,0.6137777683552145
+UCD-0159414,DNA Damage,0.4060185185185185,0.17834971163061755,0.2450989010989011,0.6106586359031783
+UCD-0159664,Epigenetics,0.40259740259740256,0.17361730671435818,0.24661654135338348,0.6079777972554067
+UCD-0159511,Autophagy,0.3989357864357864,0.1685522195544251,0.24661654135338348,0.6079777972554067
+UCD-0159391,Protein Tyrosine Kinase,0.39464285714285713,0.1626138415048483,0.24661654135338348,0.6079777972554067
+UCD-0159596,Cytoskeletal Signaling,0.393995574142633,0.16171845961101505,0.24661654135338348,0.6079777972554067
+UCD-0159337,Neuronal Signaling,0.39375,0.16137875867520943,0.24661654135338348,0.6079777972554067
+UCD-0159644,Transmembrane Transporters,0.39365079365079364,0.20051603045749866,0.19472899981374556,0.7105693668095472
+UCD-0018240,PI3K/Akt/mTOR,0.3902046783625731,0.15647454088968438,0.2493902963876854,0.6031204486412295
+UCD-0000737,GPCR & G Protein,0.3893939393939394,0.15535305153666829,0.2493902963876854,0.6031204486412295
+UCD-0159625,Endocrinology & Hormones,0.38819073083778965,0.15368866184111216,0.25160553731982305,0.5992798051942534
+UCD-0159535,Others,0.38815212565212565,0.15363525956692972,0.25160553731982305,0.5992798051942534
+UCD-0159589,DNA Damage,0.3854014041514041,0.14983020640491096,0.2522159774068171,0.5982274051545786
+UCD-0159618,MAPK,0.38375698375698375,0.1475554915637916,0.2522159774068171,0.5982274051545786
+UCD-0001616B,Others,0.38096382783882776,0.14369173917897077,0.2522159774068171,0.5982274051545786
+UCD-0159614,Metabolism,0.3798611111111111,0.14216635910304928,0.2522159774068171,0.5982274051545786
+UCD-0159572,Epigenetics,0.37967171717171716,0.14190437183615617,0.2522159774068171,0.5982274051545786
+UCD-0159374,Endocrinology & Hormones,0.3763888888888889,0.13736325921000928,0.2522159774068171,0.5982274051545786
+UCD-0159640,Epigenetics,0.37541763791763794,0.13601973476440368,0.2522159774068171,0.5982274051545786
+UCD-0159366,Metabolism,0.3733886483886484,0.133213048220179,0.2522159774068171,0.5982274051545786
+UCD-0159460,Angiogenesis,0.3691942195231669,0.1274109250996534,0.2538628883806549,0.5954007829762935
+UCD-0159360,Metabolism,0.36220238095238094,0.1177391653613028,0.25892335512588677,0.5868287740346956
+UCD-0159608,Metabolism,0.35887551990493166,0.11313714253024792,0.26469780219780215,0.5772496646575843
+UCD-0159547,Apoptosis,0.3586968586968587,0.11289000177298485,0.26469780219780215,0.5772496646575843
+UCD-0159611,Others,0.3585137085137085,0.1126366514489564,0.26469780219780215,0.5772496646575843
+UCD-0159341,Metabolism,0.35573593073593074,0.10879417153452436,0.26469780219780215,0.5772496646575843
+UCD-0159650,MAPK,0.3548890923890924,0.10762274617266536,0.26469780219780215,0.5772496646575843
+UCD-0159382,Neuronal Signaling,0.3501230251230251,0.101029879786015,0.26965553150091803,0.5691906665099344
+UCD-0159436,Stem Cells & Wnt,0.3499098124098124,0.10073494418152726,0.2712262861516593,0.566668222705397
+UCD-0159646,GPCR & G Protein,0.3491395042865631,0.09966938252458397,0.2721140245892721,0.5652490743732337
+UCD-0159649,Angiogenesis,0.3489024864024864,0.09934151739937061,0.2721140245892721,0.5652490743732337
+UCD-0000450,Neuronal Signaling,0.34035057005258246,0.08751171334978347,0.27828054298642535,0.5555171579096939
+UCD-0159606,Autophagy,0.3400144993894994,0.0870468292316019,0.27828054298642535,0.5555171579096939
+UCD-0159545,Autophagy,0.33801892551892554,0.08428636632604156,0.27828054298642535,0.5555171579096939
+UCD-0159605,PI3K/Akt/mTOR,0.3364316239316239,0.1250713717327998,0.24045496282693046,0.6189662549548729
+UCD-0159603,GPCR & G Protein,0.3361319236319236,0.08167609026029352,0.28392288976230584,0.5467995933961755
+UCD-0159562,Epigenetics,0.3331582633053221,0.07756264743246202,0.28392288976230584,0.5467995933961755
+UCD-0159665,Others,0.3314493240963829,0.07519868418174167,0.28392288976230584,0.5467995933961755
+UCD-0159551,Epigenetics,0.330718954248366,0.11753909860168628,0.24833434282253178,0.6049632165430582
+UCD-0159408,Neuronal Signaling,0.3280844155844156,0.07054403056813277,0.2843273231622746,0.5461814036636747
+UCD-0159557,Others,0.32234432234432236,0.06260380109460369,0.28691163557991645,0.5422518387680013
+UCD-0159437,Cytoskeletal Signaling,0.3214285714285714,0.1493116797971074,0.2258919163028752,0.6460993103274033
+UCD-0159657,Others,0.31906565656565655,0.05806844643036644,0.2938365981844243,0.5318941125632697
+UCD-0159575,Autophagy,0.312055292202351,0.048371060097179855,0.30650359256128484,0.5135644307038106
+UCD-0159280,Others,0.30966394716394713,0.04506312979690418,0.3068490262734867,0.513075250528078
+UCD-0159653,Protein Tyrosine Kinase,0.3076326451326451,0.0422532443849519,0.30719280719280717,0.5125889573678831
+UCD-0159528,Transmembrane Transporters,0.3073202819139351,0.041821154603147746,0.30753494715308555,0.5121055254386104
+UCD-0159552,Protein Tyrosine Kinase,0.3021990740740741,0.034737024828720466,0.3178460494374984,0.49778318211981404
+UCD-0017582,Neuronal Signaling,0.30137362637362636,0.03359518899334205,0.3178460494374984,0.49778318211981404
+UCD-0159270,Others,0.2976298920377868,0.028416508356786115,0.32136347065257015,0.49300349096918866
+UCD-0159289,Metabolism,0.2956114243943191,0.02562437666072215,0.32347140039447736,0.490164111285638
+UCD-0159652,Proteases,0.29560738968633704,0.02561879547834475,0.32347140039447736,0.490164111285638
+UCD-0159412,Apoptosis,0.29545454545454547,0.1167494474448436,0.2258919163028752,0.6460993103274033
+UCD-0001775,Others,0.2953203809434459,0.025221778359607608,0.32347140039447736,0.490164111285638
+UCD-0159643,Metabolism,0.2914890664890665,0.019921948778350157,0.3352926143623818,0.4745760128610387
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+UCD-0001613,Endocrinology & Hormones,0.28560788483389105,0.01178655272042057,0.35042735042735046,0.45540200502642614
+UCD-0159633,Apoptosis,0.27867618492618496,0.002197982359857409,0.36458254389288874,0.4382041290778644
+UCD-0159610,Transmembrane Transporters,0.2626984126984127,0.02785260771598992,0.3050708327816761,0.515599312408371
+UCD-0001952,Epigenetics,0.2616174334924335,-0.021399265149954914,0.4216150821655409,0.37508386178006414
+UCD-0159623,DNA Damage,0.2576215451215451,-0.026926748639917286,0.42586180942345325,0.3707313049845587
+UCD-0159620,DNA Damage,0.2555217658158835,-0.02983135817034544,0.42797066259480837,0.368586000869722
+UCD-0001014,Neuronal Signaling,0.24616244608504673,-0.042778037478601,0.4474775224775225,0.3492287751171237
+UCD-0159647,GPCR & G Protein,0.24414058164058164,-0.045574867944923075,0.4536144127980863,0.3433131549545768
+UCD-0159453,TGF-beta/Smad,0.24158653846153846,-0.04910785740670666,0.45462682114265823,0.34234494623830813
+UCD-0001024,Neuronal Signaling,0.24067400070689546,-0.050370164284322255,0.4597028253913129,0.3375228270130456
+UCD-0159661,Transmembrane Transporters,0.23889709827209826,-0.052828140573943125,0.46577071577071577,0.3318278201787167
+UCD-0159629,Angiogenesis,0.2338781588781589,-0.05977080314661007,0.4869983948635634,0.3124724702084579
+UCD-0159526,DNA Damage,0.22916666666666666,0.03364791696250361,0.27589200922534257,0.5592608779898103
+UCD-0159555,Cytoskeletal Signaling,0.22810846560846562,-0.06775197812705291,0.5121716848174247,0.290584434980164
+UCD-0159628,GPCR & G Protein,0.2193149443149443,-0.07991599254814785,0.5382402832067262,0.2690238017047381
+UCD-0159590,Others,0.21845157157657158,-0.08111028981425941,0.5390502354788068,0.26837075990673215
+UCD-0159642,Proteases,0.2005529193029193,-0.10586936608274776,0.6131084950684059,0.21246266636212707
+UCD-0159617,Endocrinology & Hormones,0.19828042328042328,-0.05708384367301491,0.42586180942345325,0.3707313049845587
+UCD-0159451,Ubiquitin,0.19642857142857142,-0.007394063398162256,0.32347140039447736,0.490164111285638
+UCD-0159405,Metabolism,0.1893442205942206,-0.12137427795858194,0.6608058608058608,0.17992611383483312
+UCD-0159253,Epigenetics,0.18405571144541732,-0.12868983442394594,0.6763236763236763,0.16984540879417434
+UCD-0001567,Transmembrane Transporters,0.18333333333333335,-0.0238108555424286,0.32347140039447736,0.490164111285638
+UCD-0159264,Neuronal Signaling,0.18333333333333335,-0.0238108555424286,0.32347140039447736,0.490164111285638
+UCD-0001800,Others,0.18333333333333335,-0.0238108555424286,0.32347140039447736,0.490164111285638
+UCD-0159609,Others,0.15000000000000002,-0.06559905372783385,0.3612700663391908,0.4421680211672878
+UCD-0159632,Angiogenesis,0.14545454545454545,-0.07129744438948002,0.3612700663391908,0.4421680211672878
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+## Plate-specific logistic regression model training
+
+Plate-specific logistic regression models were trained to classify single-cell profiles as either failing or healthy. Here, “healthy” refers to cells from healthy patient heart samples, whereas “failing” refers to cells from patients with dilated cardiomyopathy. Models were trained independently for each plate and fold split.
+
+For each plate-specific training split, features exhibiting complete or quasi-complete separation were first identified and removed when they were perfectly predictive of class label. Recursive feature elimination was then performed iteratively to reduce the feature set until the number of retained features satisfied the prespecified event-per-variable rule, using either a 1-in-10 or 1-in-20 feature-to-minority-class-sample-size criterion depending on the split and plate. A final logistic regression model was then fit using the post-RFE feature set. Model performance was evaluated on the held-out test split, and receiver operating characteristic and precision-recall curve visualizations were generated for each fitted model.
+
+For every non-shuffled model, a corresponding shuffled-label control model was trained using the same plate, fold split, preprocessing procedure, feature selection workflow, and model-fitting procedure, except that class labels were randomly shuffled prior to training.
+
+## Inference on treated single-cell profiles
+
+For treatment response inference, all non-shuffled, fold-specific plate-level logistic regression models were applied to treated single-cell profiles. The inference dataset consisted of treated, non-DMSO, failing single-cell profiles that were not used during model training.
+
+Each trained model was applied across the treated single-cell profile dataset to produce fold-specific prediction scores for each cell. These scores estimate whether each treated failing-cell profile more closely resembled cells from healthy individuals or cells from patients with dilated cardiomyopathy. The resulting k-fold prediction scores were used as single-cell morphology rescue scores in downstream treatment-response and hit-calling analyses.
+
+## Relative cell count computation
+
+Relative cell abundance was computed by normalizing each treated well to its plate-matched DMSO control baseline. For each plate, the median DMSO cell count was computed from control wells and used as the normalization denominator. Each treated well’s relative cell count was then calculated as:
+
+$\text{relative cell count}_{i} = \frac{\text{treated well cell count}_i}{\text{median DMSO control cell count on the same plate}}$
+
+This plate-level normalization accounts for plate-to-plate differences in baseline cell abundance. A value of 1 indicates that the treated well had the same cell count as the median DMSO control well on that plate, whereas values below or above 1 indicate lower or higher cell abundance relative to plate-matched controls.
+
+For each treatment, the final relative count was computed by averaging the plate-normalized relative cell counts across all treated wells assigned to that treatment. Therefore, the reported treatment-level relative count is the mean of within-plate normalized treated-well counts, rather than a ratio computed from pooled raw counts across plates.
+
+$\text{relative cell count}_{\text{treatment}} = \frac{1}{n} \sum_n \text{relative cell count}_{i}$
+
+## Mean average precision computation
+
+[To be added.]
+
+## Hit calling
+
+Hits were identified by applying fixed thresholds to the treatment-level summary metrics. A treatment was called as a hit if it satisfied all three criteria: mean relative cell count ≥ 0.8, mean logit score ≥ 0.9, and mean average precision ≤ 0.6. These criteria retained treatments with sufficient cell abundance, strong healthy-like morphology rescue signal, and reduced residual disease-like classification performance. The final hit set consisted only of treatments passing all three filters simultaneously.